MNIST_class.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.signal import convolve\n",
    "from matplotlib.gridspec import GridSpec\n",
    "import matplotlib as mpl\n",
    "rng = np.random.default_rng()"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "def k_cap(input, cap_size):\n",
    "        \"\"\"\n",
    "\n",
    "        Given a vector input it returns the highest cap_size \n",
    "        entries from cap_size\n",
    "        \"\"\"\n",
    "        output = np.zeros_like(input)\n",
    "        if len(input.shape) == 1:\n",
    "            idx = np.argsort(input)[-cap_size:]\n",
    "            output[idx] = 1\n",
    "        else:\n",
    "            idx = np.argsort(input, axis=-1)[:, -cap_size:]\n",
    "            np.put_along_axis(output, idx, 1, axis=-1)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 28,
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   "execution_count": 3,
>>>>>>> f59f2221923dde26a4c25b5cc25e81e65d0314b3
   "metadata": {},
   "outputs": [],
   "source": [
    "class assembly_network: \n",
    "    \"\"\"\n",
    "    This class is meant to implement the assembly calculus structure\n",
    "    This generalizes for multiple inputs and brain regions\n",
    "    \n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, number_of_inputs: int , sparsity:int, layers: list, beta: float) -> None:\n",
    "\n",
    "        \"\"\"\n",
    "        Initializes the structure of the Brain Region. It takes the number of inputs and then a list for layers that should contain tuples of the form (neurons, cap_size).\n",
    "\n",
    "        \n",
    "        \"\"\"\n",
    "        self.n_in = number_of_inputs  # Vector of 28X28 pixels \n",
    "         # List with pairs of tuples (n, c) where n is the number of neurons and c is the size of the cap \n",
    "        self.create_layers(layers) # Creates all the structure for the brain regions \n",
    "        self.sparsity = sparsity\n",
    "        self.beta =beta  \n",
    "    \n",
    "\n",
    "    def create_layers(self, layers)-> None: \n",
    "        \"\"\" \n",
    "        Creates brain regions according to the list from layers \n",
    "\n",
    "        The layers list should contain tuples of the form (number of neurons, cap size)\n",
    "        \n",
    "        \"\"\"\n",
    "        \n",
    "        self.layers=[]\n",
    "        temp=self.n_in+0\n",
    "        for k, (neurons, cap_size) in enumerate(layers):\n",
    "            self.layers.append(brain_region(neurons, temp, cap_size, k))\n",
    "            temp=neurons+0\n",
    "    \n",
    "        \n",
    "    def next(self, input: np.array, initial=False, final=False ):\n",
    "        \"\"\"\n",
    "        During the training process, it puts the input \n",
    "        through the network and it runs it through all the layers \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "        \"\"\"\n",
    "    \n",
    "        temp=input\n",
    "        print(self.layers)\n",
    "        for k , brain_region_k in enumerate(self.layers): \n",
    "            new_temp=brain_region_k.next(temp, initial=initial, final=final)\n",
    "            temp=new_temp\n",
    "            \n",
    "        return temp \n",
    "\n",
    "    def classify(self,input, initial=False ):\n",
    "        temp=input\n",
    "        for brain_region in self.layers: \n",
    "            print(\"temp shape\"+str(temp.shape))\n",
    "            temp=brain_region.classify(temp, initial)\n",
    "        return temp \n",
    "\n",
    "        \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "class brain_region: \n",
    "    \"\"\"\n",
    "    Creates a brain region from assembly calculus \n",
    "    \n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, n_neurons , n_in, cap_size, id: int ) -> None:\n",
    "        \"\"\"\n",
    "        Creates a brain region that takes \n",
    "        \"\"\"\n",
    "        self.id=id\n",
    "        self.n_neurons=n_neurons\n",
    "        self._n_in=n_in\n",
    "        self.cap_size=cap_size\n",
    "        mask = np.zeros((self.n_neurons, self.n_neurons), dtype=bool) # NXN array of zeros \n",
    "        W = np.zeros((self.n_neurons, self.n_neurons)) \n",
    "        mask_a = np.zeros((self._n_in, self.n_neurons), dtype=bool) # image to N matrix \n",
    "        A = np.zeros((self._n_in, self.n_neurons))\n",
    "        mask = (rng.random((self.n_neurons, self.n_neurons)) < sparsity) & np.logical_not(np.eye(n_neurons, dtype=bool)) # Creating matrix from N to B  with no entries in the diagonal \n",
    "        W = np.ones((self.n_neurons, self.n_neurons)) * mask \n",
    "        W /= W.sum(axis=0) # Transition probabiliy matrix \n",
    "        mask_a = rng.random((self._n_in, self.n_neurons)) < sparsity \n",
    "        A = np.ones((self._n_in, self.n_neurons)) * mask_a\n",
    "        A /= A.sum(axis=0)\n",
    "        W = np.ones_like(W) * mask\n",
    "        A = np.ones_like(A) * mask_a\n",
    "        W /= W.sum(axis=0, keepdims=True)\n",
    "        A /= A.sum(axis=0, keepdims=True)\n",
    "        self._W=W\n",
    "        self._A=A\n",
    "        self.mask=mask\n",
    "        self.mask_a=mask_a\n",
    "        self.act_h = np.zeros(self.n_neurons)\n",
    "        self.bias = np.zeros(self.n_neurons)\n",
    "        self.b = -1\n",
    "          \n",
    "        self.classify_act_h=np.zeros(self.n_neurons)\n",
    "\n",
    "\n",
    "    def next(self, input, initial=False, final=False ):\n",
    "        \"\"\"\n",
    "        It computes the activation output of the input going \n",
    "        through this brain region. \n",
    "        \n",
    "        \"\"\"\n",
    "        if initial:\n",
    "            self.act_h = np.zeros(self.n_neurons)        \n",
    "\n",
    "        act_h_new = k_cap(self.act_h @ self._W + input @ self._A + self.bias, self.cap_size)   # output a NXN array for the neurons that are activated. The first part is from self activiation and second from inoput\n",
    "        self._A[(input > 0)[:, np.newaxis] & (act_h_new > 0)[np.newaxis, :]] *= 1 + beta\n",
    "        self._W[(self.act_h > 0)[:, np.newaxis] & (act_h_new > 0)[np.newaxis, :]] *= 1 + beta\n",
    "        self.act_h = act_h_new\n",
    "        if final: \n",
    "            self.reinforce_bias()\n",
    "        print(\"Shape of act_h:\"+str(self.act_h.shape))\n",
    "        return self.act_h\n",
    "\n",
    "    def reinforce_bias(self):\n",
    "        \"\"\"\n",
    "        This function is meant to be called at the end of each round to renormalize the transition matrices \n",
    "        \n",
    "        \"\"\"\n",
    "        self.bias[self.act_h>0]+=-1 # after all rounds the activated neurons have a smaller bias so they more likely to fire \n",
    "        self._A /= self._A.sum(axis=0, keepdims=True)\n",
    "        self._W /= self._W.sum(axis=0, keepdims=True)\n",
    "\n",
    "\n",
    "\n",
    "    def classify(self,input  , initial=False, ):\n",
    "        if initial:\n",
    "            self.classify_act_h=np.zeros((input.shape[0] , self.n_neurons))\n",
    "        \n",
    "        self.classify_act_h=k_cap(self.classify_act_h @ self._W + input @ self._A + self.bias, self.cap_size)\n",
    "        return self.classify_act_h\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class classification_mnist:\n",
    "\n",
    "    def __init__(self, kernels: list ,train_path: str, test_path: str, number_of_inputs: int , sparsity:int, layers: list, beta: float  ) :\n",
    "        \"\"\"\n",
    "\n",
    "        Creates a MNIST recognition architecture based on assembly calculus\n",
    "        \"\"\"\n",
    "        \n",
    "        self.cap_size=layers[-1][1]\n",
    "        self.n_neurons= layers[-1][0]\n",
    "        self.n_examples=n_examples\n",
    "        self.n_in=number_of_inputs\n",
    "        self.assembly_network=assembly_network(number_of_inputs, sparsity, layers, beta , )\n",
    "        self.get_files( train_path, test_path)\n",
    "        self.create_training_data(kernels)\n",
    "        self.create_testing_data(kernels)\n",
    "        \n",
    "\n",
    "    def create_training_data(self ,kernels= [np.ones((1, 3, 3))] ):\n",
    "        \"\"\"\n",
    "\n",
    "        Creates a data set with n_examples from the files obtained \n",
    "        by get_files \n",
    "\n",
    "        \"\"\"\n",
    "        self.train_examples = [] \n",
    "        for kernel in kernels:\n",
    "            self.train_examples.append(np.zeros((10, self.n_examples, 784)))\n",
    "            for i in range(10):\n",
    "            #Does the convulution between a all 1's 3X3 kernel and each of the images \n",
    "                self.train_examples[-1][i] = k_cap(convolve(self.train_imgs[self.train_labels == i][:self.n_examples].reshape(-1, 28, 28), kernel, mode='same').reshape(-1, 28 * 28), self.cap_size)\n",
    "    \n",
    "    def create_testing_data(self ,kernels= [np.ones((1, 3, 3))] ):\n",
    "        \"\"\"\n",
    "\n",
    "        Creates a data set with n_examples from the files obtained \n",
    "        by get_files \n",
    "\n",
    "        \"\"\"\n",
    "        self.test_examples = [] \n",
    "        for kernel in kernels:\n",
    "            self.test_examples.append(  np.zeros((10, self.n_examples, 784) ))\n",
    "            for i in range(10):\n",
    "            #Does the convulution between a all 1's 3X3 kernel and each of the images \n",
    "                self.test_examples[-1][i] = k_cap(convolve(self.test_imgs[self.test_labels == i][:self.n_examples].reshape(-1, 28, 28), kernel, mode='same').reshape(-1, 28 * 28), self.cap_size)\n",
    "    \n",
    "    \n",
    "\n",
    "\n",
    "    def get_files(self, train_path: str, test_path: str)-> None:\n",
    "        \"\"\"\n",
    "        Given two paths it retrieves the data structure encoded in those paths. traun_path should be the path of the training data \n",
    "        and test_path should be the path for test data. \n",
    "        Assumes a csv format on nthe data on the paths \n",
    "        \"\"\"\n",
    "        test_data = np.loadtxt(test_path, delimiter=',')\n",
    "\n",
    "        train_data = np.loadtxt(train_path,  delimiter=',')\n",
    "        self.train_imgs = train_data[:, 1:]\n",
    "        self.train_imgs.shape\n",
    "        self.test_imgs = test_data[:, 1:]\n",
    "        self.train_labels = train_data[:, 0]\n",
    "        self.test_labels = test_data[:, 0]\n",
    "\n",
    "    def train_model(self, n_rounds)-> np.array:\n",
    "        \"\"\"\n",
    "        Given the number of rounds (images that will be shown to the model)\n",
    "\n",
    "        The program runs and trains the edge weights for the network.\n",
    "        \"\"\"\n",
    "        self.activations = np.zeros((10, n_rounds, self.n_neurons))\n",
    "        for i in range(10): # iterations for each of the labels \n",
    "            for j in range(n_rounds):  # for each of the rounds \n",
    "                input = self.train_examples[0][i, j] # image inputs\n",
    "                act_h= self.assembly_network.next(input, initial=(j==0), final= (j==n_rounds-1) ) # output a NXN array for the neurons that are activated. The first part is from self activiation and second from inoput\n",
    "                self.activations[i, j] = act_h\n",
    "        return self.activations\n",
    "\n",
    "    def classify(self, n_rounds,  test=True )-> dict:\n",
    "        \"\"\"\n",
    "        When called, this function runs one batch of data through \n",
    "        the whole network and then returns a dictionary with succes rates\n",
    "\n",
    "        \"\"\"\n",
    "        if test:\n",
    "            examples=self.test_examples[0]\n",
    "            \n",
    "        else:\n",
    "            examples=self.train_examples[0]\n",
    "        self.n_examples=examples.shape[1]\n",
    "        #### RUNS THROUGH NETWORK    \n",
    "                    \n",
    "        outputs = np.zeros((10, n_rounds+1, self.n_examples, self.n_neurons))\n",
    "        for i in np.arange(10):\n",
    "            for j in range(n_rounds):\n",
    "                outputs[i, j+1] = self.assembly_network.classify(examples[i], initial= (j==0)) # run each one network for n_rounds and save the neurons active at each step\n",
    "        #### STARTS CLASSIFICATION \n",
    "        c = np.zeros((10, self.n_neurons))\n",
    "        for i in range(10):\n",
    "            c[i, outputs[i, 1].sum(axis=0).argsort()[-self.cap_size:]] = 1\n",
    "\n",
    "        predictions = (outputs[:, 1] @ c.T).argmax(axis=-1)\n",
    "        acc = (predictions == np.arange(10)[:, np.newaxis]).sum(axis=-1) /examples.shape[1]\n",
    "        return acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CLASSIFICATION WITH TWO CONSECUTIVE BRAIN REGIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<__main__.brain_region object at 0x0000026709B6A920>]\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026709B6A920>]\n",
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      "[<__main__.brain_region object at 0x0000026709B6A920>]\n",
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      "Shape of act_h:(2000,)\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.9275 , 0.96   , 0.66375, 0.78125, 0.72875, 0.445  , 0.87625,\n",
       "       0.7725 , 0.575  , 0.66375])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_in = 784  # Vector of 28X28 pixels \n",
    "cap_size = 200 # Size of the cap \n",
    "sparsity = 0.1\n",
    "n_rounds = 10\n",
    "n_examples=800\n",
    "beta = 1e0\n",
    "train_path=\"./data/mnist/mnist_train.csv\"\n",
    "layers=[ (2000,200)]# number of neurons in network  with respective cap_size\n",
    "test_path=\"./data/mnist/mnist_test.csv\"\n",
    "kernels=[np.ones((1, 3, 3))]\n",
    "classify_two=classification_mnist(kernels,train_path,test_path, n_in , sparsity, layers, beta)\n",
    "classify_two.train_model( 5)\n",
    "classify_two.classify( 5, test=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array([0.885  , 0.97375, 0.56   , 0.63625, 0.7975 , 0.2325 , 0.71625,\n",
    "       0.715  , 0.1725 , 0.11875])\n",
    "       \n",
    "array([0.9275 , 0.96   , 0.66375, 0.78125, 0.72875, 0.445  , 0.87625,\n",
    "       0.7725 , 0.575  , 0.66375])\n",
    "\n",
    "array([0.81875, 0.99   , 0.565  , 0.72375, 0.73875, 0.40625, 0.54   ,\n",
    "       0.73125, 0.1575 , 0.46875])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.885  , 0.97375, 0.56   , 0.63625, 0.7975 , 0.2325 , 0.71625,\n",
       "       0.715  , 0.1725 , 0.11875])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_in = 784  # Vector of 28X28 pixels \n",
    "cap_size = 200 # Size of the cap \n",
    "sparsity = 0.1\n",
    "n_rounds = 10\n",
    "n_examples=800\n",
    "beta = 1e0\n",
    "train_path=\"./data/mnist/mnist_train.csv\"\n",
    "layers=[(2000,400), (2000,200)]# number of neurons in network  with respective cap_size\n",
    "test_path=\"./data/mnist/mnist_test.csv\"\n",
    "kernels=[np.ones((1, 3, 3))]\n",
    "classify_two=classification_mnist(kernels,train_path,test_path, n_in , sparsity, layers, beta)\n",
    "classify_two.train_model( 20)\n",
    "classify_two.classify( 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
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      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026705340EE0>, <__main__.brain_region object at 0x000002678982FFD0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.81875, 0.99   , 0.565  , 0.72375, 0.73875, 0.40625, 0.54   ,\n",
       "       0.73125, 0.1575 , 0.46875])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_in = 784  # Vector of 28X28 pixels \n",
    "cap_size = 200 # Size of the cap \n",
    "sparsity = 0.1\n",
    "n_rounds = 10\n",
    "n_examples=800\n",
    "beta = 1e0\n",
    "train_path=\"./data/mnist/mnist_train.csv\"\n",
    "layers=[(2000,200), (2000,200)]# number of neurons in network  with respective cap_size\n",
    "test_path=\"./data/mnist/mnist_test.csv\"\n",
    "kernels=[np.ones((1, 3, 3))]\n",
    "classify_two=classification_mnist(kernels,train_path,test_path, n_in , sparsity, layers, beta)\n",
    "classify_two.train_model( 20)\n",
    "classify_two.classify( 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026680CAE950>, <__main__.brain_region object at 0x00000266F6E58880>, <__main__.brain_region object at 0x00000266F9CDED10>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
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      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
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      "temp shape(800, 2000)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.81   , 0.98875, 0.55375, 0.75625, 0.7425 , 0.3825 , 0.535  ,\n",
       "       0.73125, 0.     , 0.51625])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_in = 784  # Vector of 28X28 pixels \n",
    "cap_size = 200 # Size of the cap \n",
    "sparsity = 0.1\n",
    "n_rounds = 10\n",
    "n_examples=800\n",
    "beta = 1e0\n",
    "train_path=\"./data/mnist/mnist_train.csv\"\n",
    "layers=[(2000,200), (2000,200),(2000,200)]# number of neurons in network  with respective cap_size\n",
    "test_path=\"./data/mnist/mnist_test.csv\"\n",
    "kernels=[np.ones((1, 3, 3))]\n",
    "classify_two=classification_mnist(kernels,train_path,test_path, n_in , sparsity, layers, beta)\n",
    "classify_two.train_model( 20)\n",
    "classify_two.classify( 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
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      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x0000026708D915A0>, <__main__.brain_region object at 0x0000026680CC50C0>, <__main__.brain_region object at 0x00000266E746ABC0>, <__main__.brain_region object at 0x00000266E746AC20>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
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      "temp shape(800, 2000)\n",
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      "temp shape(800, 2000)\n",
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      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
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      "temp shape(800, 2000)\n",
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      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
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      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
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      "temp shape(800, 2000)\n",
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      "temp shape(800, 2000)\n",
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      "temp shape(800, 2000)\n",
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      "temp shape(800, 784)\n",
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      "temp shape(800, 2000)\n",
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      "temp shape(800, 2000)\n",
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      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
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      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.78375, 0.9925 , 0.55875, 0.74625, 0.73125, 0.38875, 0.5225 ,\n",
       "       0.71875, 0.     , 0.4775 ])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_in = 784  # Vector of 28X28 pixels \n",
    "cap_size = 200 # Size of the cap \n",
    "sparsity = 0.1\n",
    "n_rounds = 10\n",
    "n_examples=800\n",
    "beta = 1e0\n",
    "train_path=\"./data/mnist/mnist_train.csv\"\n",
    "layers=[(2000,200), (2000,200),(2000,200),(2000,200)]# number of neurons in network  with respective cap_size\n",
    "test_path=\"./data/mnist/mnist_test.csv\"\n",
    "kernels=[np.ones((1, 3, 3))]\n",
    "classify_two=classification_mnist(kernels,train_path,test_path, n_in , sparsity, layers, beta)\n",
    "classify_two.train_model( 20)\n",
    "classify_two.classify( 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
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      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "[<__main__.brain_region object at 0x00000267053420E0>, <__main__.brain_region object at 0x0000026705342E00>, <__main__.brain_region object at 0x000002672D74FF10>, <__main__.brain_region object at 0x000002672D74FCA0>]\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "Shape of act_h:(1000,)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
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      "temp shape(800, 2000)\n",
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      "temp shape(800, 2000)\n",
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      "temp shape(800, 2000)\n",
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      "temp shape(800, 2000)\n",
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      "temp shape(800, 2000)\n",
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      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
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      "temp shape(800, 784)\n",
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      "temp shape(800, 2000)\n",
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      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
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      "temp shape(800, 2000)\n",
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      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
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      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
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      "temp shape(800, 2000)\n",
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      "temp shape(800, 784)\n",
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      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 2000)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 2000)\n"
     ]
    }
   ],
   "source": [
    "n_in = 784  # Vector of 28X28 pixels \n",
    "cap_size = 200 # Size of the cap \n",
    "sparsity = 0.1\n",
    "n_rounds = 10\n",
    "n_examples=800\n",
    "beta = 1e0\n",
    "train_path=\"./data/mnist/mnist_train.csv\"\n",
    "layers=[(2000,200), (2000,200),(2000,200),(1000,100)]# number of neurons in network  with respective cap_size\n",
    "test_path=\"./data/mnist/mnist_test.csv\"\n",
    "kernels=[np.ones((1, 3, 3))]\n",
    "classify_two=classification_mnist(kernels,train_path,test_path, n_in , sparsity, layers, beta)\n",
    "classify_two.train_model( 20)\n",
    "classify_two.classify( 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_in = 784  # Vector of 28X28 pixels \n",
    "cap_size = 200 # Size of the cap \n",
    "sparsity = 0.1\n",
    "n_rounds = 10\n",
    "n_examples=800\n",
    "beta = 1e0\n",
    "train_path=\"./data/mnist/mnist_train.csv\"\n",
    "layers=[(2000,200), (2000,200)]# number of neurons in network  with respective cap_size\n",
    "test_path=\"./data/mnist/mnist_test.csv\"\n",
    "kernels=[np.ones((1, 3, 3))]\n",
    "classify_two=classification_mnist(kernels,train_path,test_path, n_in , sparsity, layers, beta)\n",
    "classify_two.train_model( 5)\n",
    "classify_two.classify( 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_in = 784  # Vector of 28X28 pixels \n",
    "cap_size = 200 # Size of the cap \n",
    "sparsity = 0.1\n",
    "n_rounds = 10\n",
    "n_examples=800\n",
    "beta = 1e0\n",
    "train_path=\"./data/mnist/mnist_train.csv\"\n",
    "layers=[(2000,200), (1000,100)]# number of neurons in network  with respective cap_size\n",
    "test_path=\"./data/mnist/mnist_test.csv\"\n",
    "kernels=[np.ones((1, 3, 3))]\n",
    "classify_two=classification_mnist(kernels,train_path,test_path, n_in , sparsity, layers, beta)\n",
    "classify_two.train_model( 5)\n",
    "classify_two.classify( 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
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      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "[<__main__.brain_region object at 0x000001BEF086A230>]\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_in = 784  # Vector of 28X28 pixels \n",
    "cap_size = 200 # Size of the cap \n",
    "sparsity = 0.1\n",
    "n_rounds = 10\n",
    "n_examples=800\n",
    "beta = 1e0\n",
    "train_path=\"./data/mnist/mnist_train.csv\"\n",
    "layers=[(2000,200)]# number of neurons in network  with respective cap_size\n",
    "test_path=\"./data/mnist/mnist_test.csv\"\n",
    "kernels=[np.ones((1, 3, 3))]\n",
    "classify_two=classification_mnist(kernels,train_path,test_path, n_in , sparsity, layers, beta)\n",
    "classify_two.train_model( 20)\n",
    "classify_two.classify( 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer0\n",
      "input:784\n",
      "Neurons:2000\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
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      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
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      "(2000,)\n",
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      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
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      "(2000,)\n",
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      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
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      "(2000,)\n",
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      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
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      "ID:0\n",
      "A shape (784, 2000)\n",
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      "(2000,)\n",
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      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
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      "ID:0\n",
      "A shape (784, 2000)\n",
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      "(2000,)\n",
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      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
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      "ID:0\n",
      "A shape (784, 2000)\n",
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      "(2000,)\n",
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      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
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      "(2000,)\n",
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      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
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      "A shape (784, 2000)\n",
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      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
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      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
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      "layer 0\n",
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      "layer 0\n",
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      "layer 0\n",
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      "layer 0\n",
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      "layer 0\n",
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      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
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      "layer 0\n",
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      "layer 0\n",
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      "layer 0\n",
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      "layer 0\n",
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      "layer 0\n",
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      "layer 0\n",
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      "layer 0\n",
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      "layer 0\n",
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      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BEF08E9AE0>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "10\n",
      "(10, 800, 784)\n",
      "temp shape(800, 784)\n",
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      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n",
      "temp shape(800, 784)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_in = 784  # Vector of 28X28 pixels \n",
    "cap_size = 200 # Size of the cap \n",
    "sparsity = 0.1\n",
    "n_rounds = 10\n",
    "n_examples=800\n",
    "beta = 1e0\n",
    "train_path=\"./data/mnist/mnist_train.csv\"\n",
    "layers=[(2000,200)]# number of neurons in network  with respective cap_size\n",
    "test_path=\"./data/mnist/mnist_test.csv\"\n",
    "kernels=[np.ones((1, 3, 3))]\n",
    "classify_two=classification_mnist(kernels,train_path,test_path, n_in , sparsity, layers, beta)\n",
    "classify_two.train_model( 20)\n",
    "classify_two.classify( 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer0\n",
      "input:784\n",
      "Neurons:2000\n",
      "layer1\n",
      "input:2000\n",
      "Neurons:2000\n"
     ]
    }
   ],
   "source": [
    "\n",
    "classify_two=classification_mnist(kernels,train_path,test_path, n_in , sparsity, layers, beta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[(2000, 200), (2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BE1C22FE80>, <__main__.brain_region object at 0x000001BE1C22ED40>]\n",
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      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
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      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200), (2000, 200)]\n",
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      "Shape of act_h:(2000,)\n",
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      "Shape of act_h:(2000,)\n",
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      "layer 1\n",
      "1\n",
      "ID:1\n",
      "A shape (2000, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200), (2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BE1C22FE80>, <__main__.brain_region object at 0x000001BE1C22ED40>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "layer 1\n",
      "1\n",
      "ID:1\n",
      "A shape (2000, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200), (2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BE1C22FE80>, <__main__.brain_region object at 0x000001BE1C22ED40>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "layer 1\n",
      "1\n",
      "ID:1\n",
      "A shape (2000, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200), (2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BE1C22FE80>, <__main__.brain_region object at 0x000001BE1C22ED40>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "layer 1\n",
      "1\n",
      "ID:1\n",
      "A shape (2000, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200), (2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BE1C22FE80>, <__main__.brain_region object at 0x000001BE1C22ED40>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "layer 1\n",
      "1\n",
      "ID:1\n",
      "A shape (2000, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200), (2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BE1C22FE80>, <__main__.brain_region object at 0x000001BE1C22ED40>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "layer 1\n",
      "1\n",
      "ID:1\n",
      "A shape (2000, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200), (2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BE1C22FE80>, <__main__.brain_region object at 0x000001BE1C22ED40>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "layer 1\n",
      "1\n",
      "ID:1\n",
      "A shape (2000, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200), (2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BE1C22FE80>, <__main__.brain_region object at 0x000001BE1C22ED40>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "layer 1\n",
      "1\n",
      "ID:1\n",
      "A shape (2000, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200), (2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BE1C22FE80>, <__main__.brain_region object at 0x000001BE1C22ED40>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "layer 1\n",
      "1\n",
      "ID:1\n",
      "A shape (2000, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "Shape of input:(784,)\n",
      "[(2000, 200), (2000, 200)]\n",
      "[<__main__.brain_region object at 0x000001BE1C22FE80>, <__main__.brain_region object at 0x000001BE1C22ED40>]\n",
      "layer 0\n",
      "0\n",
      "ID:0\n",
      "A shape (784, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(784,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n",
      "layer 1\n",
      "1\n",
      "ID:1\n",
      "A shape (2000, 2000)\n",
      "W shape (2000, 2000)\n",
      "input shape(2000,)\n",
      "Shape of act_h:(2000,)\n",
      "(2000,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[nan, nan, nan, ..., nan, nan, nan],\n",
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       "        ...,\n",
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       "\n",
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       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
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       "\n",
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       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]])"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classify_two.train_model( 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classify_two.classify( 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Note -- place your own MNIST files in the appropriate directory\n",
    "\n",
    "test_data = np.loadtxt(\"./data/mnist/mnist_test.csv\", delimiter=',')\n",
    "train_data = np.loadtxt(\"./data/mnist/mnist_train.csv\",  delimiter=',')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Every row of the CSV file must be an image. The first entry is the label of that image and the rest is the image.\n",
    "\n",
    "train_imgs = train_data[:, 1:]\n",
    "test_imgs = test_data[:, 1:]\n",
    "train_labels = train_data[:, 0]\n",
    "test_labels = test_data[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Definitoin of parameters\n",
    "n_in = 784  # Vector of 28X28 pixels \n",
    "n_neurons = 2000 # number of neurons in network \n",
    "cap_size = 200 # Size of the cap \n",
    "sparsity = 0.1\n",
    "n_rounds = 5\n",
    "beta = 1e0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = np.zeros((n_neurons, n_neurons), dtype=bool) # NXN array of zeros \n",
    "W = np.zeros((n_neurons, n_neurons)) \n",
    "mask_a = np.zeros((n_in, n_neurons), dtype=bool) # image to N matrix \n",
    "A = np.zeros((n_in, n_neurons))\n",
    "mask = (rng.random((n_neurons, n_neurons)) < sparsity) & np.logical_not(np.eye(n_neurons, dtype=bool)) # Creating matrix from N to B  with no entries in the diagonal \n",
    "W = np.ones((n_neurons, n_neurons)) * mask \n",
    "W /= W.sum(axis=0) # Transition probabiliy matrix \n",
    "mask_a = rng.random((n_in, n_neurons)) < sparsity \n",
    "A = np.ones((n_in, n_neurons)) * mask_a\n",
    "A /= A.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[False False False ... False False False]\n",
      " [False False False ... False False False]\n",
      " [False False False ... False False False]\n",
      " ...\n",
      " [False False False ... False  True False]\n",
      " [False False False ...  True False False]\n",
      " [False False False ... False False False]]\n",
      "[False False False ... False False False]\n",
      "(2000, 2000)\n",
      "[[0.         0.         0.         ... 0.         0.         0.        ]\n",
      " [0.         0.         0.         ... 0.         0.         0.        ]\n",
      " [0.         0.         0.         ... 0.         0.         0.        ]\n",
      " ...\n",
      " [0.         0.         0.         ... 0.         0.00471698 0.        ]\n",
      " [0.         0.         0.         ... 0.00492611 0.         0.        ]\n",
      " [0.         0.         0.         ... 0.         0.         0.        ]]\n",
      "(2000, 2000)\n",
      "[1. 1. 1. ... 1. 1. 1.]\n",
      "[[0.         0.         0.         ... 0.         0.         0.01388889]\n",
      " [0.         0.         0.         ... 0.         0.         0.        ]\n",
      " [0.         0.         0.         ... 0.         0.         0.        ]\n",
      " ...\n",
      " [0.         0.         0.         ... 0.         0.01       0.        ]\n",
      " [0.01282051 0.         0.         ... 0.         0.         0.        ]\n",
      " [0.         0.         0.         ... 0.         0.         0.        ]]\n",
      "(784, 2000)\n",
      "[1. 1. 1. ... 1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "print(mask)\n",
    "print(mask[0])\n",
    "print(mask.shape)\n",
    "print(W)\n",
    "print(W.shape)\n",
    "print(np.sum(W,axis=0))\n",
    "print(A)\n",
    "print(A.shape)\n",
    "print(np.sum(A, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.signal import convolve\n",
    "\n",
    "### \n",
    "n_examples = 5000\n",
    "examples = np.zeros((10, n_examples, 784))\n",
    "for i in range(10):\n",
    "    #Does the convulution between a all 1's 3X3 kernel and each of the images \n",
    "    examples[i] = k_cap( convolve(train_imgs[train_labels == i][:n_examples].reshape(-1, 28, 28), np.ones((1, 3, 3)), mode='same').reshape(-1, 28 * 28), cap_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000, 2000)\n"
     ]
    }
   ],
   "source": [
    "print(np.ones_like(W).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "W = np.ones_like(W) * mask\n",
    "A = np.ones_like(A) * mask_a\n",
    "W /= W.sum(axis=0, keepdims=True)\n",
    "A /= A.sum(axis=0, keepdims=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1999.9999999999995\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(W[W>0], axis=0) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bias = np.zeros(n_neurons)\n",
    "b = -1\n",
    "activations = np.zeros((10, n_rounds, n_neurons))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training with the number of rounds where the weights are updated at each step \n",
    "#\n",
    "for i in range(10): # iterations for each of the labels \n",
    "    act_h = np.zeros(n_neurons) # the plasticity of the neurons\n",
    "    for j in range(n_rounds):  # for each of the rounds \n",
    "        input = examples[i, j] # image inputs \n",
    "        act_h_new = k_cap(act_h @ W + input @ A + bias, cap_size)   # output a NXN array for the neurons that are activated. The first part is from self activiation and second from inoput\n",
    "        activations[i, j] = act_h_new.copy() \n",
    "        A[(input > 0)[:, np.newaxis] & (act_h_new > 0)[np.newaxis, :]] *= 1 + beta\n",
    "        W[(act_h > 0)[:, np.newaxis] & (act_h_new > 0)[np.newaxis, :]] *= 1 + beta\n",
    "        act_h = act_h_new\n",
    "    bias[act_h > 0] += b # after all rounds the activated neurons have a smaller bias so they more likely to fire \n",
    "    A /= A.sum(axis=0, keepdims=True)\n",
    "    W /= W.sum(axis=0, keepdims=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Testing on the training data set \n",
    "\n",
    "outputs = np.zeros((10, n_rounds+1, n_examples, n_neurons))\n",
    "for i in np.arange(10):\n",
    "    for j in range(n_rounds):\n",
    "        outputs[i, j+1] = k_cap(outputs[i, j] @ W + examples[i] @ A, cap_size) # run each one network for n_rounds and save the neurons active at each step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 6, 5000, 2000)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 1., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 1., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 1., 0., 0.],\n",
       "        [0., 0., 0., ..., 1., 0., 0.],\n",
       "        [0., 0., 0., ..., 1., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 1., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 1., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 1., 0., 0.],\n",
       "        [0., 0., 0., ..., 1., 0., 0.],\n",
       "        [0., 0., 0., ..., 1., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 1., 0., 0.],\n",
       "        [0., 0., 0., ..., 1., 0., 0.],\n",
       "        [0., 0., 0., ..., 1., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 1., 0., 0.],\n",
       "        [0., 0., 0., ..., 1., 0., 0.],\n",
       "        [0., 0., 0., ..., 1., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 1., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 1., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 1., 0., 0.],\n",
       "        [0., 0., 0., ..., 1., 0., 0.],\n",
       "        [0., 0., 0., ..., 1., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 1., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 1., 0., 0.]]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(outputs.shape)\n",
    "outputs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = np.full(n_neurons, -1, dtype=int) ## array with each of the neurons set to -1 \n",
    "# act = outputs[:, -1].sum(axis=1)\n",
    "act = activations[:, -1].copy()  # the last state of the neurons for each of the labels \n",
    "for i, j in enumerate(range(10)): ## for each of the labels \n",
    "    idx[i*cap_size:(i+1)*cap_size] = act[j].argsort()[-cap_size:][::-1] # put the activation values for the top cap_size neurons in the ith par\n",
    "    #partition of idx \n",
    "    act[:, idx[i*cap_size:(i+1)*cap_size]] = -1 # set the neurons picked already to -1 in each of the layers \n",
    "    #This gives priority to lower numbers \n",
    "r = np.arange(n_neurons) # creates array wwith number from 0 to n_neurons\n",
    "\n",
    "# idx[idx>-1] gets the entries \n",
    "temp=idx \n",
    "r[idx[idx > -1]] = -1 \n",
    "idx[(i+1)*cap_size:] = np.unique(r)[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 485 1693  282 ... 1045 1988 1989] (2000,)\n"
     ]
    }
   ],
   "source": [
    "print(temp, temp.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   0    1    2 ... 1997 1998 1999] (2000,)\n"
     ]
    }
   ],
   "source": [
    "r_example=np.arange(n_neurons)\n",
    "print(r_example, r_example.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "print(idx[cap_size*10:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n    if (typeof WebSocket !== 'undefined') {\n        return WebSocket;\n    } else if (typeof MozWebSocket !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert(\n            'Your browser does not have WebSocket support. ' +\n                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n                'Firefox 4 and 5 are also supported but you ' +\n                'have to enable WebSockets in about:config.'\n        );\n    }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = this.ws.binaryType !== undefined;\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById('mpl-warnings');\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent =\n                'This browser does not support binary websocket messages. ' +\n                'Performance may be slow.';\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = document.createElement('div');\n    this.root.setAttribute('style', 'display: inline-block');\n    this._root_extra_style(this.root);\n\n    parent_element.appendChild(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen = function () {\n        fig.send_message('supports_binary', { value: fig.supports_binary });\n        fig.send_message('send_image_mode', {});\n        if (fig.ratio !== 1) {\n            fig.send_message('set_device_pixel_ratio', {\n                device_pixel_ratio: fig.ratio,\n            });\n        }\n        fig.send_message('refresh', {});\n    };\n\n    this.imageObj.onload = function () {\n        if (fig.image_mode === 'full') {\n            // Full images could contain transparency (where diff images\n            // almost always do), so we need to clear the canvas so that\n            // there is no ghosting.\n            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n        }\n        fig.context.drawImage(fig.imageObj, 0, 0);\n    };\n\n    this.imageObj.onunload = function () {\n        fig.ws.close();\n    };\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n    var titlebar = document.createElement('div');\n    titlebar.classList =\n        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n    var titletext = document.createElement('div');\n    titletext.classList = 'ui-dialog-title';\n    titletext.setAttribute(\n        'style',\n        'width: 100%; text-align: center; padding: 3px;'\n    );\n    titlebar.appendChild(titletext);\n    this.root.appendChild(titlebar);\n    this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n    var fig = this;\n\n    var canvas_div = (this.canvas_div = document.createElement('div'));\n    canvas_div.setAttribute(\n        'style',\n        'border: 1px solid #ddd;' +\n            'box-sizing: content-box;' +\n            'clear: both;' +\n            'min-height: 1px;' +\n            'min-width: 1px;' +\n            'outline: 0;' +\n            'overflow: hidden;' +\n            'position: relative;' +\n            'resize: both;'\n    );\n\n    function on_keyboard_event_closure(name) {\n        return function (event) {\n            return fig.key_event(event, name);\n        };\n    }\n\n    canvas_div.addEventListener(\n        'keydown',\n        on_keyboard_event_closure('key_press')\n    );\n    canvas_div.addEventListener(\n        'keyup',\n        on_keyboard_event_closure('key_release')\n    );\n\n    this._canvas_extra_style(canvas_div);\n    this.root.appendChild(canvas_div);\n\n    var canvas = (this.canvas = document.createElement('canvas'));\n    canvas.classList.add('mpl-canvas');\n    canvas.setAttribute('style', 'box-sizing: content-box;');\n\n    this.context = canvas.getContext('2d');\n\n    var backingStore =\n        this.context.backingStorePixelRatio ||\n        this.context.webkitBackingStorePixelRatio ||\n        this.context.mozBackingStorePixelRatio ||\n        this.context.msBackingStorePixelRatio ||\n        this.context.oBackingStorePixelRatio ||\n        this.context.backingStorePixelRatio ||\n        1;\n\n    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n        'canvas'\n    ));\n    rubberband_canvas.setAttribute(\n        'style',\n        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n    );\n\n    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n    if (this.ResizeObserver === undefined) {\n        if (window.ResizeObserver !== undefined) {\n            this.ResizeObserver = window.ResizeObserver;\n        } else {\n            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n            this.ResizeObserver = obs.ResizeObserver;\n        }\n    }\n\n    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n        var nentries = entries.length;\n        for (var i = 0; i < nentries; i++) {\n            var entry = entries[i];\n            var width, height;\n            if (entry.contentBoxSize) {\n                if (entry.contentBoxSize instanceof Array) {\n                    // Chrome 84 implements new version of spec.\n                    width = entry.contentBoxSize[0].inlineSize;\n                    height = entry.contentBoxSize[0].blockSize;\n                } else {\n                    // Firefox implements old version of spec.\n                    width = entry.contentBoxSize.inlineSize;\n                    height = entry.contentBoxSize.blockSize;\n                }\n            } else {\n                // Chrome <84 implements even older version of spec.\n                width = entry.contentRect.width;\n                height = entry.contentRect.height;\n            }\n\n            // Keep the size of the canvas and rubber band canvas in sync with\n            // the canvas container.\n            if (entry.devicePixelContentBoxSize) {\n                // Chrome 84 implements new version of spec.\n                canvas.setAttribute(\n                    'width',\n                    entry.devicePixelContentBoxSize[0].inlineSize\n                );\n                canvas.setAttribute(\n                    'height',\n                    entry.devicePixelContentBoxSize[0].blockSize\n                );\n            } else {\n                canvas.setAttribute('width', width * fig.ratio);\n                canvas.setAttribute('height', height * fig.ratio);\n            }\n            canvas.setAttribute(\n                'style',\n                'width: ' + width + 'px; height: ' + height + 'px;'\n            );\n\n            rubberband_canvas.setAttribute('width', width);\n            rubberband_canvas.setAttribute('height', height);\n\n            // And update the size in Python. We ignore the initial 0/0 size\n            // that occurs as the element is placed into the DOM, which should\n            // otherwise not happen due to the minimum size styling.\n            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n                fig.request_resize(width, height);\n            }\n        }\n    });\n    this.resizeObserverInstance.observe(canvas_div);\n\n    function on_mouse_event_closure(name) {\n        return function (event) {\n            return fig.mouse_event(event, name);\n        };\n    }\n\n    rubberband_canvas.addEventListener(\n        'mousedown',\n        on_mouse_event_closure('button_press')\n    );\n    rubberband_canvas.addEventListener(\n        'mouseup',\n        on_mouse_event_closure('button_release')\n    );\n    rubberband_canvas.addEventListener(\n        'dblclick',\n        on_mouse_event_closure('dblclick')\n    );\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband_canvas.addEventListener(\n        'mousemove',\n        on_mouse_event_closure('motion_notify')\n    );\n\n    rubberband_canvas.addEventListener(\n        'mouseenter',\n        on_mouse_event_closure('figure_enter')\n    );\n    rubberband_canvas.addEventListener(\n        'mouseleave',\n        on_mouse_event_closure('figure_leave')\n    );\n\n    canvas_div.addEventListener('wheel', function (event) {\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        on_mouse_event_closure('scroll')(event);\n    });\n\n    canvas_div.appendChild(canvas);\n    canvas_div.appendChild(rubberband_canvas);\n\n    this.rubberband_context = rubberband_canvas.getContext('2d');\n    this.rubberband_context.strokeStyle = '#000000';\n\n    this._resize_canvas = function (width, height, forward) {\n        if (forward) {\n            canvas_div.style.width = width + 'px';\n            canvas_div.style.height = height + 'px';\n        }\n    };\n\n    // Disable right mouse context menu.\n    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n        event.preventDefault();\n        return false;\n    });\n\n    function set_focus() {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'mpl-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'mpl-button-group';\n    for (var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'mpl-button-group';\n            continue;\n        }\n\n        var button = (fig.buttons[name] = document.createElement('button'));\n        button.classList = 'mpl-widget';\n        button.setAttribute('role', 'button');\n        button.setAttribute('aria-disabled', 'false');\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n        var icon_img = document.createElement('img');\n        icon_img.src = '_images/' + image + '.png';\n        icon_img.srcset = '_images/' + image + '_large.png 2x';\n        icon_img.alt = tooltip;\n        button.appendChild(icon_img);\n\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    var fmt_picker = document.createElement('select');\n    fmt_picker.classList = 'mpl-widget';\n    toolbar.appendChild(fmt_picker);\n    this.format_dropdown = fmt_picker;\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = document.createElement('option');\n        option.selected = fmt === mpl.default_extension;\n        option.innerHTML = fmt;\n        fmt_picker.appendChild(option);\n    }\n\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n    var size = msg['size'];\n    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1], msg['forward']);\n        fig.send_message('refresh', {});\n    }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n    var x0 = msg['x0'] / fig.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n    var x1 = msg['x1'] / fig.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0,\n        0,\n        fig.canvas.width / fig.ratio,\n        fig.canvas.height / fig.ratio\n    );\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n    fig.rubberband_canvas.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n    fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n    fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n    for (var key in msg) {\n        if (!(key in fig.buttons)) {\n            continue;\n        }\n        fig.buttons[key].disabled = !msg[key];\n        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n    }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n    if (msg['mode'] === 'PAN') {\n        fig.buttons['Pan'].classList.add('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    } else if (msg['mode'] === 'ZOOM') {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.add('active');\n    } else {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n    // Called whenever the canvas gets updated.\n    this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            var img = evt.data;\n            if (img.type !== 'image/png') {\n                /* FIXME: We get \"Resource interpreted as Image but\n                 * transferred with MIME type text/plain:\" errors on\n                 * Chrome.  But how to set the MIME type?  It doesn't seem\n                 * to be part of the websocket stream */\n                img.type = 'image/png';\n            }\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src\n                );\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                img\n            );\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        } else if (\n            typeof evt.data === 'string' &&\n            evt.data.slice(0, 21) === 'data:image/png;base64'\n        ) {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig['handle_' + msg_type];\n        } catch (e) {\n            console.log(\n                \"No handler for the '\" + msg_type + \"' message type: \",\n                msg\n            );\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\n                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n                    e,\n                    e.stack,\n                    msg\n                );\n            }\n        }\n    };\n};\n\n// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e) {\n        e = window.event;\n    }\n    if (e.target) {\n        targ = e.target;\n    } else if (e.srcElement) {\n        targ = e.srcElement;\n    }\n    if (targ.nodeType === 3) {\n        // defeat Safari bug\n        targ = targ.parentNode;\n    }\n\n    // pageX,Y are the mouse positions relative to the document\n    var boundingRect = targ.getBoundingClientRect();\n    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n    return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n    return Object.keys(original).reduce(function (obj, key) {\n        if (typeof original[key] !== 'object') {\n            obj[key] = original[key];\n        }\n        return obj;\n    }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n    var canvas_pos = mpl.findpos(event);\n\n    if (name === 'button_press') {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * this.ratio;\n    var y = canvas_pos.y * this.ratio;\n\n    this.send_message(name, {\n        x: x,\n        y: y,\n        button: event.button,\n        step: event.step,\n        guiEvent: simpleKeys(event),\n    });\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n    // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n    // Prevent repeat events\n    if (name === 'key_press') {\n        if (event.key === this._key) {\n            return;\n        } else {\n            this._key = event.key;\n        }\n    }\n    if (name === 'key_release') {\n        this._key = null;\n    }\n\n    var value = '';\n    if (event.ctrlKey && event.key !== 'Control') {\n        value += 'ctrl+';\n    }\n    else if (event.altKey && event.key !== 'Alt') {\n        value += 'alt+';\n    }\n    else if (event.shiftKey && event.key !== 'Shift') {\n        value += 'shift+';\n    }\n\n    value += 'k' + event.key;\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n    return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n    if (name === 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message('toolbar_button', { name: name });\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n    this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.binaryType = comm.kernel.ws.binaryType;\n    ws.readyState = comm.kernel.ws.readyState;\n    function updateReadyState(_event) {\n        if (comm.kernel.ws) {\n            ws.readyState = comm.kernel.ws.readyState;\n        } else {\n            ws.readyState = 3; // Closed state.\n        }\n    }\n    comm.kernel.ws.addEventListener('open', updateReadyState);\n    comm.kernel.ws.addEventListener('close', updateReadyState);\n    comm.kernel.ws.addEventListener('error', updateReadyState);\n\n    ws.close = function () {\n        comm.close();\n    };\n    ws.send = function (m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function (msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        var data = msg['content']['data'];\n        if (data['blob'] !== undefined) {\n            data = {\n                data: new Blob(msg['buffers'], { type: data['blob'] }),\n            };\n        }\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(data);\n    });\n    return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = document.getElementById(id);\n    var ws_proxy = comm_websocket_adapter(comm);\n\n    function ondownload(figure, _format) {\n        window.open(figure.canvas.toDataURL());\n    }\n\n    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element;\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error('Failed to find cell for figure', id, fig);\n        return;\n    }\n    fig.cell_info[0].output_area.element.on(\n        'cleared',\n        { fig: fig },\n        fig._remove_fig_handler\n    );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n    var width = fig.canvas.width / fig.ratio;\n    fig.cell_info[0].output_area.element.off(\n        'cleared',\n        fig._remove_fig_handler\n    );\n    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable();\n    fig.parent_element.innerHTML =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n    fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width / this.ratio;\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message('ack', {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () {\n        fig.push_to_output();\n    }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'btn-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'btn-group';\n    var button;\n    for (var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'btn-group';\n            continue;\n        }\n\n        button = fig.buttons[name] = document.createElement('button');\n        button.classList = 'btn btn-default';\n        button.href = '#';\n        button.title = name;\n        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    // Add the status bar.\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message pull-right';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n\n    // Add the close button to the window.\n    var buttongrp = document.createElement('div');\n    buttongrp.classList = 'btn-group inline pull-right';\n    button = document.createElement('button');\n    button.classList = 'btn btn-mini btn-primary';\n    button.href = '#';\n    button.title = 'Stop Interaction';\n    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n    button.addEventListener('click', function (_evt) {\n        fig.handle_close(fig, {});\n    });\n    button.addEventListener(\n        'mouseover',\n        on_mouseover_closure('Stop Interaction')\n    );\n    buttongrp.appendChild(button);\n    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n    var fig = event.data.fig;\n    if (event.target !== this) {\n        // Ignore bubbled events from children.\n        return;\n    }\n    fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n    // this is important to make the div 'focusable\n    el.setAttribute('tabindex', 0);\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    } else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n    // Check for shift+enter\n    if (event.shiftKey && event.which === 13) {\n        this.canvas_div.blur();\n        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i = 0; i < ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code') {\n            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] === html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n    IPython.notebook.kernel.comm_manager.register_target(\n        'matplotlib',\n        mpl.mpl_figure_comm\n    );\n}\n",
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div id='ba280ba0-09c3-42f2-b785-41fa562315da'></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(10, n_rounds, figsize=(10, 2 * 10), sharex=True, sharey=True)\n",
    "for ax, output in zip(axes, outputs):\n",
    "    for i in range(n_rounds):\n",
    "        ax[i].imshow((output[i+1] > 0)[:n_neurons, idx])\n",
    "        ax[i].set_axis_off()\n",
    "fig.text(0.5, 0.04, 'Neurons', ha='center', va='center')\n",
    "fig.text(0.04, 0.5, 'Samples', ha='center', va='center', rotation='vertical')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n    if (typeof WebSocket !== 'undefined') {\n        return WebSocket;\n    } else if (typeof MozWebSocket !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert(\n            'Your browser does not have WebSocket support. ' +\n                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n                'Firefox 4 and 5 are also supported but you ' +\n                'have to enable WebSockets in about:config.'\n        );\n    }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = this.ws.binaryType !== undefined;\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById('mpl-warnings');\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent =\n                'This browser does not support binary websocket messages. ' +\n                'Performance may be slow.';\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = document.createElement('div');\n    this.root.setAttribute('style', 'display: inline-block');\n    this._root_extra_style(this.root);\n\n    parent_element.appendChild(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen = function () {\n        fig.send_message('supports_binary', { value: fig.supports_binary });\n        fig.send_message('send_image_mode', {});\n        if (fig.ratio !== 1) {\n            fig.send_message('set_device_pixel_ratio', {\n                device_pixel_ratio: fig.ratio,\n            });\n        }\n        fig.send_message('refresh', {});\n    };\n\n    this.imageObj.onload = function () {\n        if (fig.image_mode === 'full') {\n            // Full images could contain transparency (where diff images\n            // almost always do), so we need to clear the canvas so that\n            // there is no ghosting.\n            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n        }\n        fig.context.drawImage(fig.imageObj, 0, 0);\n    };\n\n    this.imageObj.onunload = function () {\n        fig.ws.close();\n    };\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n    var titlebar = document.createElement('div');\n    titlebar.classList =\n        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n    var titletext = document.createElement('div');\n    titletext.classList = 'ui-dialog-title';\n    titletext.setAttribute(\n        'style',\n        'width: 100%; text-align: center; padding: 3px;'\n    );\n    titlebar.appendChild(titletext);\n    this.root.appendChild(titlebar);\n    this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n    var fig = this;\n\n    var canvas_div = (this.canvas_div = document.createElement('div'));\n    canvas_div.setAttribute(\n        'style',\n        'border: 1px solid #ddd;' +\n            'box-sizing: content-box;' +\n            'clear: both;' +\n            'min-height: 1px;' +\n            'min-width: 1px;' +\n            'outline: 0;' +\n            'overflow: hidden;' +\n            'position: relative;' +\n            'resize: both;'\n    );\n\n    function on_keyboard_event_closure(name) {\n        return function (event) {\n            return fig.key_event(event, name);\n        };\n    }\n\n    canvas_div.addEventListener(\n        'keydown',\n        on_keyboard_event_closure('key_press')\n    );\n    canvas_div.addEventListener(\n        'keyup',\n        on_keyboard_event_closure('key_release')\n    );\n\n    this._canvas_extra_style(canvas_div);\n    this.root.appendChild(canvas_div);\n\n    var canvas = (this.canvas = document.createElement('canvas'));\n    canvas.classList.add('mpl-canvas');\n    canvas.setAttribute('style', 'box-sizing: content-box;');\n\n    this.context = canvas.getContext('2d');\n\n    var backingStore =\n        this.context.backingStorePixelRatio ||\n        this.context.webkitBackingStorePixelRatio ||\n        this.context.mozBackingStorePixelRatio ||\n        this.context.msBackingStorePixelRatio ||\n        this.context.oBackingStorePixelRatio ||\n        this.context.backingStorePixelRatio ||\n        1;\n\n    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n        'canvas'\n    ));\n    rubberband_canvas.setAttribute(\n        'style',\n        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n    );\n\n    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n    if (this.ResizeObserver === undefined) {\n        if (window.ResizeObserver !== undefined) {\n            this.ResizeObserver = window.ResizeObserver;\n        } else {\n            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n            this.ResizeObserver = obs.ResizeObserver;\n        }\n    }\n\n    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n        var nentries = entries.length;\n        for (var i = 0; i < nentries; i++) {\n            var entry = entries[i];\n            var width, height;\n            if (entry.contentBoxSize) {\n                if (entry.contentBoxSize instanceof Array) {\n                    // Chrome 84 implements new version of spec.\n                    width = entry.contentBoxSize[0].inlineSize;\n                    height = entry.contentBoxSize[0].blockSize;\n                } else {\n                    // Firefox implements old version of spec.\n                    width = entry.contentBoxSize.inlineSize;\n                    height = entry.contentBoxSize.blockSize;\n                }\n            } else {\n                // Chrome <84 implements even older version of spec.\n                width = entry.contentRect.width;\n                height = entry.contentRect.height;\n            }\n\n            // Keep the size of the canvas and rubber band canvas in sync with\n            // the canvas container.\n            if (entry.devicePixelContentBoxSize) {\n                // Chrome 84 implements new version of spec.\n                canvas.setAttribute(\n                    'width',\n                    entry.devicePixelContentBoxSize[0].inlineSize\n                );\n                canvas.setAttribute(\n                    'height',\n                    entry.devicePixelContentBoxSize[0].blockSize\n                );\n            } else {\n                canvas.setAttribute('width', width * fig.ratio);\n                canvas.setAttribute('height', height * fig.ratio);\n            }\n            canvas.setAttribute(\n                'style',\n                'width: ' + width + 'px; height: ' + height + 'px;'\n            );\n\n            rubberband_canvas.setAttribute('width', width);\n            rubberband_canvas.setAttribute('height', height);\n\n            // And update the size in Python. We ignore the initial 0/0 size\n            // that occurs as the element is placed into the DOM, which should\n            // otherwise not happen due to the minimum size styling.\n            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n                fig.request_resize(width, height);\n            }\n        }\n    });\n    this.resizeObserverInstance.observe(canvas_div);\n\n    function on_mouse_event_closure(name) {\n        return function (event) {\n            return fig.mouse_event(event, name);\n        };\n    }\n\n    rubberband_canvas.addEventListener(\n        'mousedown',\n        on_mouse_event_closure('button_press')\n    );\n    rubberband_canvas.addEventListener(\n        'mouseup',\n        on_mouse_event_closure('button_release')\n    );\n    rubberband_canvas.addEventListener(\n        'dblclick',\n        on_mouse_event_closure('dblclick')\n    );\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband_canvas.addEventListener(\n        'mousemove',\n        on_mouse_event_closure('motion_notify')\n    );\n\n    rubberband_canvas.addEventListener(\n        'mouseenter',\n        on_mouse_event_closure('figure_enter')\n    );\n    rubberband_canvas.addEventListener(\n        'mouseleave',\n        on_mouse_event_closure('figure_leave')\n    );\n\n    canvas_div.addEventListener('wheel', function (event) {\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        on_mouse_event_closure('scroll')(event);\n    });\n\n    canvas_div.appendChild(canvas);\n    canvas_div.appendChild(rubberband_canvas);\n\n    this.rubberband_context = rubberband_canvas.getContext('2d');\n    this.rubberband_context.strokeStyle = '#000000';\n\n    this._resize_canvas = function (width, height, forward) {\n        if (forward) {\n            canvas_div.style.width = width + 'px';\n            canvas_div.style.height = height + 'px';\n        }\n    };\n\n    // Disable right mouse context menu.\n    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n        event.preventDefault();\n        return false;\n    });\n\n    function set_focus() {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'mpl-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'mpl-button-group';\n    for (var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'mpl-button-group';\n            continue;\n        }\n\n        var button = (fig.buttons[name] = document.createElement('button'));\n        button.classList = 'mpl-widget';\n        button.setAttribute('role', 'button');\n        button.setAttribute('aria-disabled', 'false');\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n        var icon_img = document.createElement('img');\n        icon_img.src = '_images/' + image + '.png';\n        icon_img.srcset = '_images/' + image + '_large.png 2x';\n        icon_img.alt = tooltip;\n        button.appendChild(icon_img);\n\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    var fmt_picker = document.createElement('select');\n    fmt_picker.classList = 'mpl-widget';\n    toolbar.appendChild(fmt_picker);\n    this.format_dropdown = fmt_picker;\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = document.createElement('option');\n        option.selected = fmt === mpl.default_extension;\n        option.innerHTML = fmt;\n        fmt_picker.appendChild(option);\n    }\n\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n    var size = msg['size'];\n    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1], msg['forward']);\n        fig.send_message('refresh', {});\n    }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n    var x0 = msg['x0'] / fig.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n    var x1 = msg['x1'] / fig.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0,\n        0,\n        fig.canvas.width / fig.ratio,\n        fig.canvas.height / fig.ratio\n    );\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n    fig.rubberband_canvas.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n    fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n    fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n    for (var key in msg) {\n        if (!(key in fig.buttons)) {\n            continue;\n        }\n        fig.buttons[key].disabled = !msg[key];\n        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n    }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n    if (msg['mode'] === 'PAN') {\n        fig.buttons['Pan'].classList.add('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    } else if (msg['mode'] === 'ZOOM') {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.add('active');\n    } else {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n    // Called whenever the canvas gets updated.\n    this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            var img = evt.data;\n            if (img.type !== 'image/png') {\n                /* FIXME: We get \"Resource interpreted as Image but\n                 * transferred with MIME type text/plain:\" errors on\n                 * Chrome.  But how to set the MIME type?  It doesn't seem\n                 * to be part of the websocket stream */\n                img.type = 'image/png';\n            }\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src\n                );\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                img\n            );\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        } else if (\n            typeof evt.data === 'string' &&\n            evt.data.slice(0, 21) === 'data:image/png;base64'\n        ) {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig['handle_' + msg_type];\n        } catch (e) {\n            console.log(\n                \"No handler for the '\" + msg_type + \"' message type: \",\n                msg\n            );\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\n                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n                    e,\n                    e.stack,\n                    msg\n                );\n            }\n        }\n    };\n};\n\n// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e) {\n        e = window.event;\n    }\n    if (e.target) {\n        targ = e.target;\n    } else if (e.srcElement) {\n        targ = e.srcElement;\n    }\n    if (targ.nodeType === 3) {\n        // defeat Safari bug\n        targ = targ.parentNode;\n    }\n\n    // pageX,Y are the mouse positions relative to the document\n    var boundingRect = targ.getBoundingClientRect();\n    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n    return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n    return Object.keys(original).reduce(function (obj, key) {\n        if (typeof original[key] !== 'object') {\n            obj[key] = original[key];\n        }\n        return obj;\n    }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n    var canvas_pos = mpl.findpos(event);\n\n    if (name === 'button_press') {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * this.ratio;\n    var y = canvas_pos.y * this.ratio;\n\n    this.send_message(name, {\n        x: x,\n        y: y,\n        button: event.button,\n        step: event.step,\n        guiEvent: simpleKeys(event),\n    });\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n    // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n    // Prevent repeat events\n    if (name === 'key_press') {\n        if (event.key === this._key) {\n            return;\n        } else {\n            this._key = event.key;\n        }\n    }\n    if (name === 'key_release') {\n        this._key = null;\n    }\n\n    var value = '';\n    if (event.ctrlKey && event.key !== 'Control') {\n        value += 'ctrl+';\n    }\n    else if (event.altKey && event.key !== 'Alt') {\n        value += 'alt+';\n    }\n    else if (event.shiftKey && event.key !== 'Shift') {\n        value += 'shift+';\n    }\n\n    value += 'k' + event.key;\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n    return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n    if (name === 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message('toolbar_button', { name: name });\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n    this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.binaryType = comm.kernel.ws.binaryType;\n    ws.readyState = comm.kernel.ws.readyState;\n    function updateReadyState(_event) {\n        if (comm.kernel.ws) {\n            ws.readyState = comm.kernel.ws.readyState;\n        } else {\n            ws.readyState = 3; // Closed state.\n        }\n    }\n    comm.kernel.ws.addEventListener('open', updateReadyState);\n    comm.kernel.ws.addEventListener('close', updateReadyState);\n    comm.kernel.ws.addEventListener('error', updateReadyState);\n\n    ws.close = function () {\n        comm.close();\n    };\n    ws.send = function (m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function (msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        var data = msg['content']['data'];\n        if (data['blob'] !== undefined) {\n            data = {\n                data: new Blob(msg['buffers'], { type: data['blob'] }),\n            };\n        }\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(data);\n    });\n    return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = document.getElementById(id);\n    var ws_proxy = comm_websocket_adapter(comm);\n\n    function ondownload(figure, _format) {\n        window.open(figure.canvas.toDataURL());\n    }\n\n    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element;\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error('Failed to find cell for figure', id, fig);\n        return;\n    }\n    fig.cell_info[0].output_area.element.on(\n        'cleared',\n        { fig: fig },\n        fig._remove_fig_handler\n    );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n    var width = fig.canvas.width / fig.ratio;\n    fig.cell_info[0].output_area.element.off(\n        'cleared',\n        fig._remove_fig_handler\n    );\n    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable();\n    fig.parent_element.innerHTML =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n    fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width / this.ratio;\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message('ack', {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () {\n        fig.push_to_output();\n    }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'btn-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'btn-group';\n    var button;\n    for (var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'btn-group';\n            continue;\n        }\n\n        button = fig.buttons[name] = document.createElement('button');\n        button.classList = 'btn btn-default';\n        button.href = '#';\n        button.title = name;\n        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    // Add the status bar.\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message pull-right';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n\n    // Add the close button to the window.\n    var buttongrp = document.createElement('div');\n    buttongrp.classList = 'btn-group inline pull-right';\n    button = document.createElement('button');\n    button.classList = 'btn btn-mini btn-primary';\n    button.href = '#';\n    button.title = 'Stop Interaction';\n    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n    button.addEventListener('click', function (_evt) {\n        fig.handle_close(fig, {});\n    });\n    button.addEventListener(\n        'mouseover',\n        on_mouseover_closure('Stop Interaction')\n    );\n    buttongrp.appendChild(button);\n    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n    var fig = event.data.fig;\n    if (event.target !== this) {\n        // Ignore bubbled events from children.\n        return;\n    }\n    fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n    // this is important to make the div 'focusable\n    el.setAttribute('tabindex', 0);\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    } else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n    // Check for shift+enter\n    if (event.shiftKey && event.which === 13) {\n        this.canvas_div.blur();\n        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i = 0; i < ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code') {\n            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] === html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n    IPython.notebook.kernel.comm_manager.register_target(\n        'matplotlib',\n        mpl.mpl_figure_comm\n    );\n}\n",
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div id='605bc9a4-a277-486b-b3c5-3ec688ccb8ca'></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def softmax(x):\n",
    "    return np.exp(x) / np.exp(x).sum(axis=-1, keepdims=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "v = 0.1 * rng.standard_normal((10, n_neurons))\n",
    "targets = np.zeros((100, 10))\n",
    "for i in range(10):\n",
    "    targets[i*10:(i+1)*10, i] = 1\n",
    "update = np.zeros_like(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7608/3067451752.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_examples\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m//\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m         \u001b[0mbatch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpermutation\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_neurons\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m         \u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msoftmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnewaxis\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnewaxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m         \u001b[0mupdate\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mupdate\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1e-3\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnewaxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mscores\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mtargets\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnewaxis\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m         \u001b[0mv\u001b[0m \u001b[1;33m-=\u001b[0m \u001b[0mupdate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.conda\\envs\\assemblies\\lib\\site-packages\\numpy\\core\\_methods.py\u001b[0m in \u001b[0;36m_sum\u001b[1;34m(a, axis, dtype, out, keepdims, initial, where)\u001b[0m\n\u001b[0;32m     44\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mumr_minimum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minitial\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwhere\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 46\u001b[1;33m def _sum(a, axis=None, dtype=None, out=None, keepdims=False,\n\u001b[0m\u001b[0;32m     47\u001b[0m          initial=_NoValue, where=True):\n\u001b[0;32m     48\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minitial\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwhere\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for _ in range(100):\n",
    "    permutation = rng.permutation(n_examples - 1000)\n",
    "    for j in range((n_examples - 1000) // 10):\n",
    "        batch = outputs[:, 1, permutation[j*10:(j+1)*10]].reshape(10 * 10, n_neurons)\n",
    "        scores = softmax((batch[:, :, np.newaxis] * v.T[np.newaxis, :, :]).sum(axis=1))\n",
    "        update = 0.5 * update + 1e-3 * (batch[:, np.newaxis, :] * (scores - targets)[:, :, np.newaxis]).sum(axis=0)\n",
    "        v -= update\n",
    "#             v = v.clip(min=0.0, max=1.0)\n",
    "#             v[v < 1e-5] = 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.998375"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((outputs[:, 1, :-1000] @ v.T).argmax(axis=-1) == np.arange(10)[:, np.newaxis]).sum() / 40000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9259"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((outputs[:, 1, -1000:] @ v.T).argmax(axis=-1) == np.arange(10)[:, np.newaxis]).sum() / 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div id='1c17a395-9455-4cec-9f76-98aa661db1ac'></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Firing Probability')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 4))\n",
    "for i in range(10):\n",
    "    ax.bar(np.arange(n_neurons), outputs[i, -1].mean(axis=0)[idx], label=i)\n",
    "ax.legend(loc='upper right', ncol=2)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.set_ylim([0, 1.1])\n",
    "ax.set_xticklabels([])\n",
    "ax.set_xlabel('Neurons')\n",
    "ax.set_ylabel('Firing Probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = np.zeros((10, n_neurons))\n",
    "for i in range(10):\n",
    "    c[i, outputs[i, 1].sum(axis=0).argsort()[-cap_size:]] = 1\n",
    "#     c[i, idx[i*cap_size:(i+1)*cap_size]] = 1\n",
    "predictions = (outputs[:, 1] @ c.T).argmax(axis=-1)\n",
    "acc = (predictions == np.arange(10)[:, np.newaxis]).sum(axis=-1) / n_examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.948 , 0.9502, 0.6392, 0.78  , 0.6814, 0.462 , 0.8062, 0.7636,\n",
       "       0.6048, 0.6792])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div id='f4558953-d451-4479-a0d5-66dcb0b19c52'></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 10, figsize=(10, 2))\n",
    "for i in range(10):\n",
    "    axes[i].imshow((A * c[i][np.newaxis, :]).sum(axis=1).reshape(28, 28))\n",
    "    axes[i].set_axis_off()\n",
    "fig.tight_layout()"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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