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stim_comparison.py
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import scipy
from scipy import linalg
import pickle
from contrib.modelfit import *
import numpy
import contrib.dd
import pylab
def CompareNaturalVSHartley():
f = open("modelfitDatabase1.dat",'rb')
dd = pickle.load(f)
node = dd.children[26]
rfs = node.children[0].data["ReversCorrelationRFs"][0:102]
#params = fitGabor(rfs)
#numpy.savetxt("params.txt", params)
#return
#b = numpy.reshape(numpy.array(rfs),(102,41*41)).T
#numpy.savetxt("RFs.txt", b)
pred_act = numpy.array(node.children[0].data["ReversCorrelationPredictedActivities"][:,0:102])
pred_val_act = numpy.array(node.children[0].data["ReversCorrelationPredictedValidationActivities"][:,0:102])
pred_act_t = numpy.array(node.children[0].data["ReversCorrelationPredictedActivities+TF"][:,0:102])
pred_val_act_t = numpy.array(node.children[0].data["ReversCorrelationPredictedValidationActivities+TF"][:,0:102])
training_set = node.data["training_set"][:,0:102]
validation_set = node.data["validation_set"][:,0:102]
training_inputs = node.data["training_inputs"]
validation_inputs = node.data["validation_inputs"]
raw_validation_set = node.data["raw_validation_set"]
f = file("/home/antolikjan/topographica/topographica/Mice/2010_04_22/Hartley/imcutout.dat", "r")
hartley_inputs = [line.split() for line in f]
f.close()
(a,b) = numpy.shape(hartley_inputs)
for i in xrange(0,a):
for j in xrange(0,b):
hartley_inputs[i][j] = float(hartley_inputs[i][j])
hartley_in = numpy.array(numpy.mat(hartley_inputs).T)
hartley_inputs = []
for i in xrange(0,b):
z = numpy.reshape(hartley_in[i,:],(numpy.sqrt(a),numpy.sqrt(a)))
hartley_inputs.append(z.T)
hartley_inputs_all = numpy.reshape(numpy.array(hartley_inputs),(900,41*41))
hartley_inputs = numpy.array(hartley_inputs_all)[0:800,:]
hartley_val_inputs = numpy.array(hartley_inputs_all)[801:850,:]
f = file("/home/antolikjan/topographica/topographica/Mice/2010_04_22/Hartley/RFsubspace.dat", "r")
hartley_RFs = [line.split() for line in f]
f.close()
(a,b) = numpy.shape(hartley_RFs)
for i in xrange(0,a):
for j in xrange(0,b):
hartley_RFs[i][j] = float(hartley_RFs[i][j])
hartley_RFs = numpy.array(hartley_RFs)
hartley_rfs = []
for i in xrange(0,b):
z = numpy.reshape(hartley_RFs[:,i],(numpy.sqrt(a),numpy.sqrt(a)))/800
hartley_rfs.append(z.T)
#params = fitGabor(hartley_rfs)
#numpy.savetxt("Hart_params.txt", params)
#return
f = file("/home/antolikjan/topographica/topographica/Mice/2010_04_22/Hartley/responses.dat", "r")
hartley_set = [line.split() for line in f]
f.close()
(a,b) = numpy.shape(hartley_set)
for i in xrange(0,a):
for j in xrange(0,b):
hartley_set[i][j] = float(hartley_set[i][j])
hartley_set_all = hartley_set
hartley_set = numpy.array(hartley_set_all)[0:800]
hartley_val_set = numpy.array(hartley_set_all)[801:850]
print numpy.shape(hartley_inputs)
print numpy.shape(hartley_RFs)
print numpy.shape(validation_inputs)
hartley_pred_act = numpy.mat(training_inputs) * numpy.mat(hartley_RFs)
hartley_pred_val_act = numpy.mat(validation_inputs) * numpy.reshape(numpy.array(hartley_rfs),(102,41*41)).T
gratings_hartley_pred_act = numpy.mat(hartley_inputs) * numpy.reshape(numpy.array(hartley_rfs),(102,41*41)).T
gratings_hartley_pred_val_act = numpy.mat(hartley_val_inputs) * numpy.reshape(numpy.array(hartley_rfs),(102,41*41)).T
gratings_pred_act = numpy.mat(hartley_inputs) * numpy.reshape(numpy.array(rfs),(102,41*41)).T
gratings_pred_val_act = numpy.mat(hartley_val_inputs) * numpy.reshape(numpy.array(rfs),(102,41*41)).T
print numpy.shape(hartley_set)
print numpy.var(hartley_set[:,1])
print numpy.var(training_set[:,1])
pylab.figure()
pylab.subplot(2,1,1)
pylab.plot(training_set[:,1])
pylab.subplot(2,1,2)
pylab.plot(hartley_set[:,1])
rf_mag = [numpy.sum(numpy.power(r,2)) for r in rfs]
#discard ugly RFs
pylab.figure()
pylab.hist(rf_mag)
#to_delete = numpy.nonzero((numpy.array(rf_mag) < 0.000000)*1.0)[0]
#print to_delete
#rfs = numpy.delete(rfs,to_delete,axis=0)
#pred_act = numpy.delete(pred_act,to_delete,axis=1)
#pred_val_act = numpy.delete(pred_val_act,to_delete,axis=1)
#training_set = numpy.delete(training_set,to_delete,axis=1)
#validation_set = numpy.delete(validation_set,to_delete,axis=1)
#for i in xrange(0,len(raw_validation_set)):
# raw_validation_set[i] = numpy.delete(raw_validation_set[i],to_delete,axis=1)
#(sx,sy) = numpy.shape(rfs[0])
ofs = run_nonlinearity_detection(numpy.mat(hartley_set),numpy.mat(gratings_pred_act))
pred_act_t = apply_output_function(numpy.mat(pred_act),ofs)
pred_val_act_t= apply_output_function(numpy.mat(pred_val_act),ofs)
gratings_pred_act_t = apply_output_function(numpy.mat(gratings_pred_act),ofs)
gratings_pred_val_act_t= apply_output_function(numpy.mat(gratings_pred_val_act),ofs)
pylab.figure()
pylab.plot(hartley_set[:,1],gratings_hartley_pred_act[:,1],'o')
pylab.figure()
w = numpy.array(hartley_rfs[1])
pylab.show._needmain=False
pylab.imshow(w,interpolation='nearest',cmap=pylab.cm.RdBu)
pylab.colorbar()
pylab.figure()
w = numpy.array(hartley_inputs[1,:])
pylab.show._needmain=False
pylab.imshow(numpy.reshape(w,(41,41)),interpolation='nearest',cmap=pylab.cm.RdBu)
pylab.colorbar()
ofs = run_nonlinearity_detection(numpy.mat(hartley_set),numpy.mat(gratings_hartley_pred_act))
hartley_pred_act_t = apply_output_function(numpy.mat(hartley_pred_act),ofs)
hartley_pred_val_act_t = apply_output_function(numpy.mat(hartley_pred_val_act),ofs)
gratings_hartley_pred_act_t = apply_output_function(numpy.mat(gratings_hartley_pred_act),ofs)
gratings_hartley_pred_val_act_t = apply_output_function(numpy.mat(gratings_hartley_pred_val_act),ofs)
pylab.figure()
m = numpy.max([numpy.abs(numpy.min(rfs)),numpy.abs(numpy.max(rfs))])
for k in xrange(0,len(rfs)):
pylab.subplot(15,15,k+1)
w = numpy.array(rfs[k])
pylab.show._needmain=False
pylab.imshow(w,vmin=-m,vmax=m,interpolation='nearest',cmap=pylab.cm.RdBu)
pylab.axis('off')
pylab.figure()
m = numpy.max([numpy.abs(numpy.min(hartley_rfs)),numpy.abs(numpy.max(hartley_rfs))])
for k in xrange(0,len(rfs)):
pylab.subplot(15,15,k+1)
w = numpy.array(hartley_rfs[k])
pylab.show._needmain=False
pylab.imshow(w,vmin=-m,vmax=m,interpolation='nearest',cmap=pylab.cm.RdBu)
pylab.axis('off')
pylab.figure()
pylab.plot(validation_set[:,1],pred_val_act[:,1],'o')
pylab.figure()
pylab.plot(validation_set[:,1],hartley_pred_val_act_t[:,1],'o')
pylab.figure()
pylab.plot(validation_set[:,1],hartley_pred_val_act[:,1],'o')
raw_validation_data_set=numpy.rollaxis(numpy.array(raw_validation_set),2)
print 'NATURAL RF -> NATURAL'
(ranks,correct,pred) = performIdentification(validation_set,pred_val_act)
print "Natural:", correct , "Mean rank:", numpy.mean(ranks) , "MSE", numpy.mean(numpy.power(validation_set - pred_val_act,2))
(ranks,correct,pred) = performIdentification(validation_set,pred_val_act_t)
print "Natural+TF:", correct , "Mean rank:", numpy.mean(ranks) , "MSE", numpy.mean(numpy.power(validation_set - pred_val_act_t,2))
signal_power,noise_power,normalized_noise_power,training_prediction_power,validation_prediction_power = signal_power_test(raw_validation_data_set, numpy.array(training_set), numpy.array(validation_set), pred_act, pred_val_act)
signal_power,noise_power,normalized_noise_power,training_prediction_power_t,validation_prediction_power_t = signal_power_test(raw_validation_data_set, numpy.array(training_set), numpy.array(validation_set), pred_act_t, pred_val_act_t)
print "Prediction power on training set / validation set: ", numpy.mean(training_prediction_power) , " / " , numpy.mean(validation_prediction_power)
print "Prediction power after TF on training set / validation set: ", numpy.mean(training_prediction_power_t) , " / " , numpy.mean(validation_prediction_power_t)
print 'HARTLEY RF -> NATURAL'
(ranks,correct,pred) = performIdentification(validation_set,hartley_pred_val_act)
print "Natural:", correct , "Mean rank:", numpy.mean(ranks) , "MSE", numpy.mean(numpy.power(validation_set - hartley_pred_val_act,2))
(ranks,correct,pred) = performIdentification(validation_set,hartley_pred_val_act_t)
print "Natural+TF:", correct , "Mean rank:", numpy.mean(ranks) , "MSE", numpy.mean(numpy.power(validation_set - hartley_pred_val_act_t,2))
signal_power,noise_power,normalized_noise_power,training_prediction_power,validation_prediction_power = signal_power_test(raw_validation_data_set, numpy.array(hartley_set), numpy.array(validation_set), numpy.array(gratings_hartley_pred_act), numpy.array(hartley_pred_val_act))
signal_power,noise_power,normalized_noise_power,training_prediction_power_t,validation_prediction_power_t = signal_power_test(raw_validation_data_set, numpy.array(hartley_set), numpy.array(validation_set), numpy.array(gratings_hartley_pred_act_t), numpy.array(hartley_pred_val_act_t))
print "Prediction power on training set / validation set: ", numpy.mean(training_prediction_power) , " / " , numpy.mean(validation_prediction_power)
print "Prediction power after TF on training set / validation set: ", numpy.mean(training_prediction_power_t) , " / " , numpy.mean(validation_prediction_power_t)
print 'NATURAL RF -> HARTLEY'
(ranks,correct,pred) = performIdentification(hartley_val_set,gratings_pred_val_act)
print "Natural:", correct , "Mean rank:", numpy.mean(ranks) , "MSE", numpy.mean(numpy.power(hartley_val_set - gratings_pred_val_act,2))
(ranks,correct,pred) = performIdentification(hartley_val_set,gratings_pred_val_act_t)
print "Natural+TF:", correct , "Mean rank:", numpy.mean(ranks) , "MSE", numpy.mean(numpy.power(hartley_val_set - gratings_pred_val_act_t,2))
print 'HARTLEY RF -> HARTLEY'
(ranks,correct,pred) = performIdentification(hartley_val_set,gratings_hartley_pred_val_act)
print "Natural:", correct , "Mean rank:", numpy.mean(ranks) , "MSE", numpy.mean(numpy.power(hartley_val_set - gratings_hartley_pred_val_act,2))
(ranks,correct,pred) = performIdentification(hartley_val_set,gratings_hartley_pred_val_act_t)
print "Natural+TF:", correct , "Mean rank:", numpy.mean(ranks) , "MSE", numpy.mean(numpy.power(hartley_val_set - gratings_hartley_pred_val_act_t,2))