diff --git a/iris.data.csv b/iris.data.csv new file mode 100644 index 0000000..9fd53a3 --- /dev/null +++ b/iris.data.csv @@ -0,0 +1,150 @@ +5.1,3.5,1.4,0.2,Iris-setosa +4.9,3.0,1.4,0.2,Iris-setosa +4.7,3.2,1.3,0.2,Iris-setosa +4.6,3.1,1.5,0.2,Iris-setosa +5.0,3.6,1.4,0.2,Iris-setosa +5.4,3.9,1.7,0.4,Iris-setosa +4.6,3.4,1.4,0.3,Iris-setosa +5.0,3.4,1.5,0.2,Iris-setosa +4.4,2.9,1.4,0.2,Iris-setosa +4.9,3.1,1.5,0.1,Iris-setosa +5.4,3.7,1.5,0.2,Iris-setosa +4.8,3.4,1.6,0.2,Iris-setosa +4.8,3.0,1.4,0.1,Iris-setosa +4.3,3.0,1.1,0.1,Iris-setosa +5.8,4.0,1.2,0.2,Iris-setosa +5.7,4.4,1.5,0.4,Iris-setosa +5.4,3.9,1.3,0.4,Iris-setosa +5.1,3.5,1.4,0.3,Iris-setosa +5.7,3.8,1.7,0.3,Iris-setosa +5.1,3.8,1.5,0.3,Iris-setosa +5.4,3.4,1.7,0.2,Iris-setosa +5.1,3.7,1.5,0.4,Iris-setosa +4.6,3.6,1.0,0.2,Iris-setosa +5.1,3.3,1.7,0.5,Iris-setosa +4.8,3.4,1.9,0.2,Iris-setosa +5.0,3.0,1.6,0.2,Iris-setosa +5.0,3.4,1.6,0.4,Iris-setosa +5.2,3.5,1.5,0.2,Iris-setosa +5.2,3.4,1.4,0.2,Iris-setosa 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__init__(self, eta = 0.01, n_iter = 10):\n", + " self.eta = eta\n", + " self.n_iter = n_iter\n", + " pass\n", + " \n", + " def fit(self, X, y):\n", + " '''\n", + " 输入训练数据， 培训神经元， X输入样本向量， y对应样本分类\n", + " \n", + " X:shape[n_samples, n_features]\n", + " X:[[1, 2, 3], [4, 5, 6]]\n", + " n_samples:2\n", + " n_features:3\n", + " \n", + " y:[1, -1]\n", + " '''\n", + " \"\"\"\n", + " 初始化权重向量为0\n", + " 加1是因为前面算法提到的w0, 也就是步调函数阈值，其实也就是偏置\n", + " \"\"\"\n", + " self.w_ = np.zeros(1 + X.shape[1])\n", + " self.errors_ = []\n", + " \n", + " for _ in range(self.n_iter):\n", + " errors = 0\n", + " for xi, target in zip(X, y):\n", + " \"\"\"\n", + " update = n * (y - y')\n", + " \"\"\"\n", + " update = self.eta * (target - self.predict(xi))\n", + " \"\"\"\n", + " xi 是一个向量\n", + " \"\"\"\n", + " self.w_[1:] += update * xi\n", + " self.w_[0] += update\n", + " \n", + " errors += int(update != 0.0)\n", + " self.errors_.append(errors)\n", + " pass\n", + " pass\n", + " \n", + " def net_input(self, X):\n", + " \"\"\"\n", + " z = W0*1 + W1*X1 + W2*X2 + ... Wn*Xn\n", + " \"\"\"\n", + " return np.dot(X, self.w_[1:]) + self.w_[0]\n", + " pass\n", + " \n", + " def predict(self, X):\n", + " return np.where(self.net_input(X) >= 0.0, 1, -1)\n", + " pass\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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01234
05.13.51.40.2Iris-setosa
14.93.01.40.2Iris-setosa
24.73.21.30.2Iris-setosa
34.63.11.50.2Iris-setosa
45.03.61.40.2Iris-setosa
55.43.91.70.4Iris-setosa
64.63.41.40.3Iris-setosa
75.03.41.50.2Iris-setosa
84.42.91.40.2Iris-setosa
94.93.11.50.1Iris-setosa
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 4\n", + "0 5.1 3.5 1.4 0.2 Iris-setosa\n", + "1 4.9 3.0 1.4 0.2 Iris-setosa\n", + "2 4.7 3.2 1.3 0.2 Iris-setosa\n", + "3 4.6 3.1 1.5 0.2 Iris-setosa\n", + "4 5.0 3.6 1.4 0.2 Iris-setosa\n", + "5 5.4 3.9 1.7 0.4 Iris-setosa\n", + "6 4.6 3.4 1.4 0.3 Iris-setosa\n", + "7 5.0 3.4 1.5 0.2 Iris-setosa\n", + "8 4.4 2.9 1.4 0.2 Iris-setosa\n", + "9 4.9 3.1 1.5 0.1 Iris-setosa" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('iris.data.csv', header=None)\n", + "df.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "y = df.loc[0:100, 4].values\n", + "y = np.where( y == 'Iris-setosa', -1, 1)\n", + "\n", + "X = df.loc[0:100, [0, 2]].values" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plt.scatter(X[:50, 0], X[:50, 1], color='red', marker='o', label='setosa')\n", + "plt.scatter(X[50:100, 0], X[50:100, 1], color='blue', marker='x', label='versicolor')\n", + "\n", + "plt.xlabel('花瓣的长度')\n", + "plt.ylabel('花径的长度')\n", + "plt.legend(loc='upper left')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "image/png": 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1Fi7ULQ8RaR8FDyIlqLIy/8V+1qyWR3CMMqS1iEg+nRI8mNlM4PvuvqMzti8i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\ncuB7rQQOGW5mvYAPAd/OKh8C1KZvt/R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0b3+q+YYQSZpeV9cJoRhwekNEPBVYU057R2YmsBrYGBG/UGUD68RKiKyESFJ1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\nPmaS5X8L+HDljZckaXicmpm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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ppn = Perceptron(eta=0.1, n_iter=10)\n", + "ppn.fit(X, y)\n", + "plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_, marker='o')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('错误分类次数')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from matplotlib.colors import ListedColormap" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def plot_decision_regions(X, y, classifier, resolution=0.02):\n", + " markers = ('s', 'x', 'o', 'v')\n", + " colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n", + " cmap = ListedColormap(colors[:len(np.unique(y))])\n", + " \n", + " x1_min, x1_max = X[:, 0].min(), X[:, 0].max()\n", + " x2_min, x2_max = X[:, 1].min(), X[:, 1].max()\n", + " \n", + " xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n", + " np.arange(x2_min, x2_max, resolution))\n", + " \n", + " Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n", + " \n", + " Z = Z.reshape(xx1.shape)\n", + " \n", + " plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)\n", + " plt.xlim(xx1.min(), xx1.max())\n", + " plt.ylim(xx2.min(), xx2.max())\n", + " \n", + " for inx, cl in enumerate(np.unique(y)):\n", + " plt.scatter(x=X[y==cl, 0], y=X[y==cl, 1], alpha=0.8, c=cmap(inx),\n", + " marker=markers[inx], label=cl)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Ysdn6ZGxKCkREmlTJ4r64oG+wer9SQV/GZuuTsSkpEBHJmcFv+uVsqTBb33Tq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\n5Wa2R33Dy6TXEp1vD5lZb3x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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_decision_regions(X, y, ppn, resolution=0.02)\n", + "\n", + "plt.xlabel('花径长度')\n", + "plt.ylabel('花瓣长度')\n", + "plt.legend(loc='upper left')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
自适应线性神经元
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class AdalineGD(object):\n", + " \"\"\"\n", + " eta:float\n", + " 学习效率，处于0和1\n", + " \n", + " n_iter:int\n", + " 对训练数据进行学习改进次数\n", + " \n", + " w_:一维向量\n", + " 存储权重数值\n", + " \n", + " error_:\n", + " 存储每次迭代改进时，网络对数据进行错误判断的次数\n", + " \"\"\"\n", + " \n", + " def __init__(self, eta=0.01, n_iter=50):\n", + " self.eta = eta\n", + " self.n_iter = n_iter\n", + " \n", + " def fit(self, X, y):\n", + " \"\"\"\n", + " X:二维数组[n_samples, n_features]\n", + " n_samples 表示X中含有训练数据条目数\n", + " n_features 含有4个数据的一维向量，用于表示一条训练数目\n", + " \n", + " y:一维向量\n", + " 用于存储每一训练条目对应的正确分类\n", + " \"\"\"\n", + " self.w_ = np.zeros(1 + X.shape[1])\n", + " self.cost_ = []\n", + " \n", + " for i in range(self.n_iter):\n", + " output = self.net_input(X)\n", + " errors = (y - output)\n", + " self.w_[1:] += self.eta * X.T.dot(errors)\n", + " self.w_[0] += self.eta * errors.sum()\n", + " cost = (errors ** 2).sum() / 2.0\n", + " self.cost_.append(cost)\n", + " return self\n", + " \n", + " def net_input(self, X):\n", + " return np.dot(X, self.w_[1:]) + self.w_[0]\n", + " \n", + " def activation(self, X):\n", + " return self.net_input(X)\n", + " \n", + " def predict(self, X):\n", + " return np.where(self.activation(X) >= 0, 1, -1 )" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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7zDtiJcyeDZxT1XJV2nxwDXAwsA64G/gsYUjjtxCaFk4BxgFfAv43vWKKiIg0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\nREgqLLYpWj+UjtGj2eDO6SU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9cRfgMkJOwc+AdWZ2eUrnQ0SKaJhjEUldVHuwxd03D7lx\n3z6jgN3c/blBtmkn/N1KfFwRSU5BgYiIiABqPhAREZGIggIREREBFBSIiIhIREGBiIiIAAoKRERE\nJKKgQERERAAFBSIiIhJRUCAiIiIA/H9dWIy4fF1lSwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ada = AdalineGD(eta=0.0001, n_iter=50)\n", + "ada.fit(X, y)\n", + "plot_decision_regions(X, y, classifier=ada, resolution=0.02)\n", + "plt.title('Adaline-Gradient descent')\n", + "plt.xlabel('花径长度')\n", + "plt.ylabel('花瓣的长度')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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