def analyse():
from noiseEstimation import signal_power_test
(pred_act,pred_val_act,training_set,validation_set,training_inputs,validation_inputs,raw_validation_set)= fetch_data()
size =numpy.sqrt(numpy.shape(training_inputs)[1])
print numpy.shape(training_set)
print numpy.shape(validation_set)
print numpy.shape(pred_act)
print numpy.shape(pred_val_act)
printCorrelationAnalysis(training_set,validation_set,pred_act,pred_val_act)
raw_validation_data_set=numpy.rollaxis(numpy.array(raw_validation_set),2)
#print numpy.shape(numpy.array(ts[i]))
#print numpy.shape(numpy.array(vs[i]))
#print numpy.shape(pred_act[i])
#print numpy.shape(pred_val_act[i])
signal_power,noise_power,normalized_noise_power,training_prediction_power,validation_prediction_power,signal_power_variance = signal_power_test(raw_validation_data_set, numpy.array(training_set), numpy.array(validation_set), pred_act, pred_val_act)
to_delete = numpy.array(numpy.nonzero((numpy.array(normalized_noise_power) > 70) * 1.0))[0]
print 'Validation prediction power:', numpy.mean(numpy.delete(validation_prediction_power, to_delete))
#compareModelPerformanceWithRPI(training_set,validation_set,training_inputs,validation_inputs,numpy.mat(pred_act),numpy.mat(pred_val_act),raw_validation_set,size,size,modelname='LSCSM')
def runLSCSMAnalysis(rpi_pred_act,rpi_pred_val_act,glm_pred_act,glm_pred_val_act,training_set,validation_set,num_neurons,raw_validation_data_set):
ofs = run_nonlinearity_detection(numpy.mat(training_set),numpy.mat(rpi_pred_act),display=True)
rpi_pred_act_t = apply_output_function(numpy.mat(rpi_pred_act),ofs)
rpi_pred_val_act_t = apply_output_function(numpy.mat(rpi_pred_val_act),ofs)
ofs = run_nonlinearity_detection(numpy.mat(training_set),numpy.mat(glm_pred_act),display=True)
glm_pred_act_t = apply_output_function(numpy.mat(glm_pred_act),ofs)
glm_pred_val_act_t = apply_output_function(numpy.mat(glm_pred_val_act),ofs)
pylab.figure()
for i in xrange(0,num_neurons):
pylab.subplot(11,11,i+1)
pylab.plot(rpi_pred_val_act[:,i],validation_set[:,i],'o')
pylab.savefig(normalize_path('RPI_val_relationship.png'))
pylab.figure()
for i in xrange(0,num_neurons):
pylab.subplot(11,11,i+1)
pylab.plot(glm_pred_val_act[:,i],validation_set[:,i],'o')
pylab.savefig(normalize_path('GLM_val_relationship.png'))
pylab.figure()
for i in xrange(0,num_neurons):
pylab.subplot(11,11,i+1)
pylab.plot(rpi_pred_val_act_t[:,i],validation_set[:,i],'o')
pylab.savefig(normalize_path('RPI_t_val_relationship.png'))
pylab.figure()
for i in xrange(0,num_neurons):
pylab.subplot(11,11,i+1)
pylab.plot(glm_pred_val_act_t[:,i],validation_set[:,i],'o')
pylab.title('RPI')
pylab.savefig(normalize_path('GLM_t_val_relationship.png'))
print numpy.shape(validation_set)
print numpy.shape(rpi_pred_val_act_t)
print numpy.shape(glm_pred_val_act)
pylab.figure()
pylab.plot(numpy.mean(numpy.power(validation_set - rpi_pred_val_act_t,2),0),numpy.mean(numpy.power(validation_set - glm_pred_val_act,2),0),'o')
pylab.hold(True)
pylab.plot([0.0,1.0],[0.0,1.0])
pylab.xlabel('RPI')
pylab.ylabel('GLM')
pylab.savefig(normalize_path('GLM_vs_RPI_MSE.png'))
print 'RPI \n'
(ranks,correct,pred) = performIdentification(validation_set,rpi_pred_val_act)
print "Natural:", correct , "Mean rank:", numpy.mean(ranks) , "MSE", numpy.mean(numpy.power(validation_set - rpi_pred_val_act,2))
(ranks,correct,pred) = performIdentification(validation_set,rpi_pred_val_act_t)
print "Natural+TF:", correct , "Mean rank:", numpy.mean(ranks) , "MSE", numpy.mean(numpy.power(validation_set - rpi_pred_val_act_t,2))
signal_power,noise_power,normalized_noise_power,training_prediction_power,validation_prediction_power,signal_power_variance = signal_power_test(raw_validation_data_set, numpy.array(training_set), numpy.array(validation_set), numpy.array(rpi_pred_act), numpy.array(rpi_pred_val_act))
signal_power,noise_power,normalized_noise_power,training_prediction_power_t,rpi_validation_prediction_power_t,signal_power_variance = signal_power_test(raw_validation_data_set, numpy.array(training_set), numpy.array(validation_set), numpy.array(rpi_pred_act_t), numpy.array(rpi_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(rpi_validation_prediction_power_t)
print '\n \n GLM \n'
(ranks,correct,pred) = performIdentification(validation_set,glm_pred_val_act)
print "Natural:", correct , "Mean rank:", numpy.mean(ranks) , "MSE", numpy.mean(numpy.power(validation_set - glm_pred_val_act,2))
(ranks,correct,pred) = performIdentification(validation_set,glm_pred_val_act_t)
print "Natural+TF:", correct , "Mean rank:", numpy.mean(ranks) , "MSE", numpy.mean(numpy.power(validation_set - glm_pred_val_act_t,2))
glm_signal_power,glm_noise_power,glm_normalized_noise_power,glm_training_prediction_power,glm_validation_prediction_power,glm_signal_power_variance = signal_power_test(raw_validation_data_set, numpy.array(training_set), numpy.array(validation_set), numpy.array(glm_pred_act), numpy.array(glm_pred_val_act))
glm_signal_power_t,glm_noise_power_t,glm_normalized_noise_power_t,glm_training_prediction_power_t,glm_validation_prediction_power_t,glm_signal_power_variances_t = signal_power_test(raw_validation_data_set, numpy.array(training_set), numpy.array(validation_set), numpy.array(glm_pred_act_t), numpy.array(glm_pred_val_act_t))
print "Prediction power on training set / validation set: ", numpy.mean(glm_training_prediction_power) , " / " , numpy.mean(glm_validation_prediction_power)
print "Prediction power after TF on training set / validation set: ", numpy.mean(glm_training_prediction_power_t) , " / " , numpy.mean(glm_validation_prediction_power_t)
pylab.figure()
pylab.plot(rpi_validation_prediction_power_t[:num_neurons],glm_validation_prediction_power[:num_neurons],'o')
pylab.hold(True)
pylab.plot([0.0,1.0],[0.0,1.0])
pylab.xlabel('RPI_t')
pylab.ylabel('GLM')
pylab.savefig(normalize_path('GLM_vs_RPI_prediction_power.png'))
pylab.figure()
pylab.plot(rpi_validation_prediction_power_t[:num_neurons],glm_validation_prediction_power_t[:num_neurons],'o')
pylab.hold(True)
pylab.plot([0.0,1.0],[0.0,1.0])
pylab.xlabel('RPI_t')
pylab.ylabel('GLM_t')
pylab.savefig('GLMt_vs_RPIt_prediction_power.png')
print 'WithoutTF'
printCorrelationAnalysis(training_set,validation_set,glm_pred_act,glm_pred_val_act)
print 'WithTF'
printCorrelationAnalysis(training_set,validation_set,glm_pred_act_t,glm_pred_val_act_t)
#db_node.add_data("Kernels",K,force=True)
#db_node.add_data("GLM",glm,force=True)
#db_node.add_data("ReversCorrelationPredictedActivities",glm_pred_act,force=True)
#db_node.add_data("ReversCorrelationPredictedActivities+TF",glm_pred_act_t,force=True)
#db_node.add_data("ReversCorrelationPredictedValidationActivities",glm_pred_val_act,force=True)
#db_node.add_data("ReversCorrelationPredictedValidationActivities+TF",glm_pred_val_act_t,force=True)
#return [K,validation_inputs, validation_set]
def runLSCSMAnalysis(rpi_pred_act,rpi_pred_val_act,glm_pred_act,glm_pred_val_act,training_set,validation_set,num_neurons,raw_validation_data_set):
ofs = run_nonlinearity_detection(numpy.mat(training_set),numpy.mat(rpi_pred_act),display=True)
rpi_pred_act_t = apply_output_function(numpy.mat(rpi_pred_act),ofs)
rpi_pred_val_act_t = apply_output_function(numpy.mat(rpi_pred_val_act),ofs)
ofs = run_nonlinearity_detection(numpy.mat(training_set),numpy.mat(glm_pred_act),display=True)
glm_pred_act_t = apply_output_function(numpy.mat(glm_pred_act),ofs)
glm_pred_val_act_t = apply_output_function(numpy.mat(glm_pred_val_act),ofs)
pylab.figure()
for i in xrange(0,num_neurons):
pylab.subplot(11,11,i+1)
pylab.plot(rpi_pred_val_act[:,i],validation_set[:,i],'o')
pylab.savefig(normalize_path('RPI_val_relationship.png'))
pylab.figure()
for i in xrange(0,num_neurons):
pylab.subplot(11,11,i+1)
pylab.plot(glm_pred_val_act[:,i],validation_set[:,i],'o')
pylab.savefig(normalize_path('GLM_val_relationship.png'))
pylab.figure()
for i in xrange(0,num_neurons):
pylab.subplot(11,11,i+1)
pylab.plot(rpi_pred_val_act_t[:,i],validation_set[:,i],'o')
pylab.savefig(normalize_path('RPI_t_val_relationship.png'))
pylab.figure()
for i in xrange(0,num_neurons):
pylab.subplot(11,11,i+1)
pylab.plot(glm_pred_val_act_t[:,i],validation_set[:,i],'o')
pylab.title('RPI')
pylab.savefig(normalize_path('GLM_t_val_relationship.png'))
print numpy.shape(validation_set)
print numpy.shape(rpi_pred_val_act_t)
print numpy.shape(glm_pred_val_act)
pylab.figure()
pylab.plot(numpy.mean(numpy.power(validation_set - rpi_pred_val_act_t,2),0),numpy.mean(numpy.power(validation_set - glm_pred_val_act,2),0),'o')
pylab.hold(True)
pylab.plot([0.0,1.0],[0.0,1.0])
pylab.xlabel('RPI')
pylab.ylabel('GLM')
pylab.savefig(normalize_path('GLM_vs_RPI_MSE.png'))
print 'RPI \n'
(ranks,correct,pred) = performIdentification(validation_set,rpi_pred_val_act)
print "Natural:", correct , "Mean rank:", numpy.mean(ranks) , "MSE", numpy.mean(numpy.power(validation_set - rpi_pred_val_act,2))
(ranks,correct,pred) = performIdentification(validation_set,rpi_pred_val_act_t)
print "Natural+TF:", correct , "Mean rank:", numpy.mean(ranks) , "MSE", numpy.mean(numpy.power(validation_set - rpi_pred_val_act_t,2))
signal_power,noise_power,normalized_noise_power,training_prediction_power,validation_prediction_power,signal_power_variance = signal_power_test(raw_validation_data_set, numpy.array(training_set), numpy.array(validation_set), numpy.array(rpi_pred_act), numpy.array(rpi_pred_val_act))
signal_power,noise_power,normalized_noise_power,training_prediction_power_t,rpi_validation_prediction_power_t,signal_power_variance = signal_power_test(raw_validation_data_set, numpy.array(training_set), numpy.array(validation_set), numpy.array(rpi_pred_act_t), numpy.array(rpi_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(rpi_validation_prediction_power_t)
print '\n \n GLM \n'
(ranks,correct,pred) = performIdentification(validation_set,glm_pred_val_act)
print "Natural:", correct , "Mean rank:", numpy.mean(ranks) , "MSE", numpy.mean(numpy.power(validation_set - glm_pred_val_act,2))
(ranks,correct,pred) = performIdentification(validation_set,glm_pred_val_act_t)
print "Natural+TF:", correct , "Mean rank:", numpy.mean(ranks) , "MSE", numpy.mean(numpy.power(validation_set - glm_pred_val_act_t,2))
glm_signal_power,glm_noise_power,glm_normalized_noise_power,glm_training_prediction_power,glm_validation_prediction_power,glm_signal_power_variance = signal_power_test(raw_validation_data_set, numpy.array(training_set), numpy.array(validation_set), numpy.array(glm_pred_act), numpy.array(glm_pred_val_act))
glm_signal_power_t,glm_noise_power_t,glm_normalized_noise_power_t,glm_training_prediction_power_t,glm_validation_prediction_power_t,glm_signal_power_variances_t = signal_power_test(raw_validation_data_set, numpy.array(training_set), numpy.array(validation_set), numpy.array(glm_pred_act_t), numpy.array(glm_pred_val_act_t))
print "Prediction power on training set / validation set: ", numpy.mean(glm_training_prediction_power) , " / " , numpy.mean(glm_validation_prediction_power)
print "Prediction power after TF on training set / validation set: ", numpy.mean(glm_training_prediction_power_t) , " / " , numpy.mean(glm_validation_prediction_power_t)
pylab.figure()
pylab.plot(rpi_validation_prediction_power_t[:num_neurons],glm_validation_prediction_power[:num_neurons],'o')
pylab.hold(True)
pylab.plot([0.0,1.0],[0.0,1.0])
pylab.xlabel('RPI_t')
pylab.ylabel('GLM')
pylab.savefig(normalize_path('GLM_vs_RPI_prediction_power.png'))
pylab.figure()
pylab.plot(rpi_validation_prediction_power_t[:num_neurons],glm_validation_prediction_power_t[:num_neurons],'o')
pylab.hold(True)
pylab.plot([0.0,1.0],[0.0,1.0])
pylab.xlabel('RPI_t')
pylab.ylabel('GLM_t')
pylab.savefig('GLMt_vs_RPIt_prediction_power.png')
print 'WithoutTF'
printCorrelationAnalysis(training_set,validation_set,glm_pred_act,glm_pred_val_act)
print 'WithTF'
printCorrelationAnalysis(training_set,validation_set,glm_pred_act_t,glm_pred_val_act_t)
def runLSCTM():
import noiseEstimation
res = contrib.dd.DB(None)
(sizex, sizey, training_inputs, training_set, validation_inputs,
validation_set, ff, db_node) = contrib.JanA.dataimport.sortOutLoading(res)
raw_validation_set = db_node.data["raw_validation_set"]
training_inputs1 = training_inputs[0:-4, :]
validation_inputs1 = validation_inputs[0:-4, :]
training_inputs2 = training_inputs[1:-3, :]
validation_inputs2 = validation_inputs[1:-3, :]
training_inputs3 = training_inputs[2:-2, :]
validation_inputs3 = validation_inputs[2:-2, :]
training_set = training_set[4:, :]
validation_set = validation_set[4:, :]
for i in xrange(0, len(raw_validation_set)):
raw_validation_set[i] = raw_validation_set[i][4:, :]
num_pres, num_neurons = numpy.shape(training_set)
num_pres, kernel_size = numpy.shape(training_inputs)
size = int(numpy.sqrt(kernel_size))
raw_validation_data_set = numpy.rollaxis(numpy.array(raw_validation_set),
2)
params = {}
params["LSCTM"] = True
db_node = db_node.get_child(params)
params = {}
params["LaplacaBias"] = __main__.__dict__.get('LaplaceBias', 0.0004)
params["LGN_NUM"] = __main__.__dict__.get('LgnNum', 6)
params["num_neurons"] = __main__.__dict__.get('NumNeurons', 103)
params["V1OF"] = __main__.__dict__.get('V1OF', 'Exp')
params["LGNOF"] = __main__.__dict__.get('LGNOF', 'Exp')
params["LGNTreshold"] = __main__.__dict__.get('LGNTreshold', False)
params["HiddenLayerSize"] = __main__.__dict__.get('HiddenLayerSize', 1.0)
params["LogLossCoef"] = __main__.__dict__.get('LogLossCoef', 1.0)
params["NegativeLgn"] = __main__.__dict__.get('NegativeLgn', True)
params["MaxW"] = __main__.__dict__.get('MaxW', 5000)
params["GenerationSize"] = __main__.__dict__.get('GenerationSize', 100)
params["PopulationSize"] = __main__.__dict__.get('PopulationSize', 100)
params["MutationRate"] = __main__.__dict__.get('MutationRate', 0.05)
params["CrossoverRate"] = __main__.__dict__.get('CrossoverRate', 0.9)
db_node1 = db_node
db_node = db_node.get_child(params)
num_neurons = params["num_neurons"]
raw_validation_data_set = numpy.rollaxis(numpy.array(raw_validation_set),
2)
[K, lsctm, rfs] = fitLSCTMEvo(numpy.mat(training_inputs1),
numpy.mat(training_inputs2),
numpy.mat(training_inputs3),
numpy.mat(training_set), params["LGN_NUM"],
num_neurons)
(rfs1, rfs2, rfs3) = rfs
pylab.figure()
m = numpy.max(numpy.abs(rfs1))
for i in xrange(
0,
int(num_neurons * __main__.__dict__.get('HiddenLayerSize', 1.0))):
pylab.subplot(11, 11, i + 1)
pylab.imshow(numpy.reshape(rfs1[i, 0:kernel_size], (size, size)),
vmin=-m,
vmax=m,
cmap=pylab.cm.RdBu,
interpolation='nearest')
pylab.savefig('GLM_rfs1.png')
pylab.figure()
m = numpy.max(numpy.abs(rfs2))
for i in xrange(
0,
int(num_neurons * __main__.__dict__.get('HiddenLayerSize', 1.0))):
pylab.subplot(11, 11, i + 1)
pylab.imshow(numpy.reshape(rfs2[i, 0:kernel_size], (size, size)),
vmin=-m,
vmax=m,
cmap=pylab.cm.RdBu,
interpolation='nearest')
pylab.savefig('GLM_rfs2.png')
pylab.figure()
m = numpy.max(numpy.abs(rfs3))
for i in xrange(
0,
int(num_neurons * __main__.__dict__.get('HiddenLayerSize', 1.0))):
pylab.subplot(11, 11, i + 1)
pylab.imshow(numpy.reshape(rfs3[i, 0:kernel_size], (size, size)),
vmin=-m,
vmax=m,
cmap=pylab.cm.RdBu,
interpolation='nearest')
pylab.savefig('GLM_rfs3.png')
lsctm_pred_act = lsctm.response(training_inputs1, training_inputs2,
training_inputs3, K)
lsctm_pred_val_act = lsctm.response(validation_inputs1, validation_inputs2,
validation_inputs3, K)
ofs = run_nonlinearity_detection(numpy.mat(training_set),
numpy.mat(lsctm_pred_act),
num_bins=10,
display=False,
name='RPI_piece_wise_nonlinearity.png')
lsctm_pred_act_t = numpy.mat(
apply_output_function(numpy.mat(lsctm_pred_act), ofs))
lsctm_pred_val_act_t = numpy.mat(
apply_output_function(numpy.mat(lsctm_pred_val_act), ofs))
if len(raw_validation_set) != 1:
print 'Without TF'
(signal_power, noise_power, normalized_noise_power,
training_prediction_power, rpi_validation_prediction_power,
signal_power_variance) = performance_analysis(training_set,
validation_set,
lsctm_pred_act,
lsctm_pred_val_act,
raw_validation_set, 85)
print 'With TF'
(signal_power_t, noise_power_t, normalized_noise_power_t,
training_prediction_power_t, rpi_validation_prediction_power_t,
signal_power_variance_t) = performance_analysis(
training_set, validation_set, lsctm_pred_act_t,
lsctm_pred_val_act_t, raw_validation_set, 85)
significant = numpy.array(
numpy.nonzero((numpy.array(normalized_noise_power) < 85) * 1.0))[0]
perf.append(numpy.mean(rpi_validation_prediction_power[significant]))
perf_t.append(
numpy.mean(rpi_validation_prediction_power_t[significant]))
(train_c, val_c) = printCorrelationAnalysis(training_set, validation_set,
lsctm_pred_act,
lsctm_pred_val_act)
(t_train_c, t_val_c) = printCorrelationAnalysis(training_set,
validation_set,
lsctm_pred_act_t,
lsctm_pred_val_act_t)
54.9755813888,
]
for i in xrange(0, num_neurons):
corrs = []
for j in xrange(0, 40):
pa = numpy.mat(training_inputs * rpis[j][:, i])
pta = numpy.mat(testing_inputs * rpis[j][:, i])
corrs.append(scipy.stats.pearsonr(numpy.array(pta).flatten(), numpy.array(testing_set)[:, i].flatten())[0])
if i < 10:
pylab.plot(corrs)
print numpy.shape(best_rpis[:, i])
print numpy.shape(rpis[numpy.argmax(corrs)][:, i])
best_rpis[:, i] = rpis[numpy.argmax(corrs)][:, i]
best_lambda[0, i] = a[numpy.argmax(corrs)]
print best_lambda
rpa = numpy.mat(training_inputs * best_rpis)
rpva = numpy.mat(validation_inputs * best_rpis)
ofs = run_nonlinearity_detection(numpy.mat(training_set), numpy.mat(rpa), display=False)
rpi_pred_act_t = apply_output_function(numpy.mat(rpa), ofs)
rpi_pred_val_act_t = apply_output_function(numpy.mat(rpva), ofs)
printCorrelationAnalysis(training_set, validation_set, rpi_pred_act_t, rpi_pred_val_act_t)
numpy.savetxt(animal_names[animal_num] + "STA_20091011.txt", best_rpis)
a = [1e-10,2e-10,1e-10,8e-10,1.6e-09,3.2e-09,6.4e-09,1.28e-08,2.56e-08,5.12e-08,1.024e-07,2.048e-07,4.096e-07,8.192e-07,1.6384e-06,3.2768e-06,6.5536e-06,1.31072e-05,2.62144e-05,5.24288e-05,0.0001048576,0.0002097152,0.0004194304,0.0008388608,0.0016777216,0.0033554432,0.0067108864,0.0134217728,0.0268435456,0.0536870912,0.1073741824,0.2147483648,0.4294967296,0.8589934592,1.7179869184,3.4359738368,6.8719476736,13.7438953472,27.4877906944,54.9755813888]
for i in xrange(0,num_neurons):
corrs=[]
for j in xrange(0,40):
pa = numpy.mat(training_inputs * rpis[j][:,i])
pta = numpy.mat(testing_inputs * rpis[j][:,i])
corrs.append(scipy.stats.pearsonr(numpy.array(pta).flatten(),numpy.array(testing_set)[:,i].flatten())[0])
if i < 10:
pylab.plot(corrs)
print numpy.shape(best_rpis[:,i])
print numpy.shape(rpis[numpy.argmax(corrs)][:,i])
best_rpis[:,i] = rpis[numpy.argmax(corrs)][:,i]
best_lambda[0,i] = a[numpy.argmax(corrs)]
print best_lambda
rpa = numpy.mat(training_inputs * best_rpis)
rpva = numpy.mat(validation_inputs * best_rpis)
ofs = run_nonlinearity_detection(numpy.mat(training_set),numpy.mat(rpa),display=False)
rpi_pred_act_t = apply_output_function(numpy.mat(rpa),ofs)
rpi_pred_val_act_t = apply_output_function(numpy.mat(rpva),ofs)
printCorrelationAnalysis(training_set,validation_set,rpi_pred_act_t,rpi_pred_val_act_t)
numpy.savetxt(animal_names[animal_num]+'STA_20091011.txt',best_rpis)
def runLSCTM():
import noiseEstimation
res = contrib.dd.DB(None)
(sizex,sizey,training_inputs,training_set,validation_inputs,validation_set,ff,db_node) = contrib.JanA.dataimport.sortOutLoading(res)
raw_validation_set = db_node.data["raw_validation_set"]
training_inputs1 = training_inputs[0:-4,:]
validation_inputs1 = validation_inputs[0:-4,:]
training_inputs2 = training_inputs[1:-3,:]
validation_inputs2 = validation_inputs[1:-3,:]
training_inputs3 = training_inputs[2:-2,:]
validation_inputs3 = validation_inputs[2:-2,:]
training_set = training_set[4:,:]
validation_set = validation_set[4:,:]
for i in xrange(0,len(raw_validation_set)):
raw_validation_set[i] = raw_validation_set[i][4:,:]
num_pres,num_neurons = numpy.shape(training_set)
num_pres,kernel_size = numpy.shape(training_inputs)
size = int(numpy.sqrt(kernel_size))
raw_validation_data_set=numpy.rollaxis(numpy.array(raw_validation_set),2)
params={}
params["LSCTM"]=True
db_node = db_node.get_child(params)
params={}
params["LaplacaBias"] = __main__.__dict__.get('LaplaceBias',0.0004)
params["LGN_NUM"] = __main__.__dict__.get('LgnNum',6)
params["num_neurons"] = __main__.__dict__.get('NumNeurons',103)
params["V1OF"] = __main__.__dict__.get('V1OF','Exp')
params["LGNOF"] = __main__.__dict__.get('LGNOF','Exp')
params["LGNTreshold"] = __main__.__dict__.get('LGNTreshold',False)
params["HiddenLayerSize"] = __main__.__dict__.get('HiddenLayerSize',1.0)
params["LogLossCoef"] = __main__.__dict__.get('LogLossCoef',1.0)
params["NegativeLgn"] = __main__.__dict__.get('NegativeLgn',True)
params["MaxW"] = __main__.__dict__.get('MaxW',5000)
params["GenerationSize"] = __main__.__dict__.get('GenerationSize',100)
params["PopulationSize"] = __main__.__dict__.get('PopulationSize',100)
params["MutationRate"] = __main__.__dict__.get('MutationRate',0.05)
params["CrossoverRate"] = __main__.__dict__.get('CrossoverRate',0.9)
db_node1 = db_node
db_node = db_node.get_child(params)
num_neurons=params["num_neurons"]
raw_validation_data_set=numpy.rollaxis(numpy.array(raw_validation_set),2)
[K,lsctm,rfs]= fitLSCTMEvo(numpy.mat(training_inputs1),numpy.mat(training_inputs2),numpy.mat(training_inputs3),numpy.mat(training_set),params["LGN_NUM"],num_neurons)
(rfs1,rfs2,rfs3) = rfs
pylab.figure()
m = numpy.max(numpy.abs(rfs1))
for i in xrange(0,int(num_neurons*__main__.__dict__.get('HiddenLayerSize',1.0))):
pylab.subplot(11,11,i+1)
pylab.imshow(numpy.reshape(rfs1[i,0:kernel_size],(size,size)),vmin=-m,vmax=m,cmap=pylab.cm.RdBu,interpolation='nearest')
pylab.savefig('GLM_rfs1.png')
pylab.figure()
m = numpy.max(numpy.abs(rfs2))
for i in xrange(0,int(num_neurons*__main__.__dict__.get('HiddenLayerSize',1.0))):
pylab.subplot(11,11,i+1)
pylab.imshow(numpy.reshape(rfs2[i,0:kernel_size],(size,size)),vmin=-m,vmax=m,cmap=pylab.cm.RdBu,interpolation='nearest')
pylab.savefig('GLM_rfs2.png')
pylab.figure()
m = numpy.max(numpy.abs(rfs3))
for i in xrange(0,int(num_neurons*__main__.__dict__.get('HiddenLayerSize',1.0))):
pylab.subplot(11,11,i+1)
pylab.imshow(numpy.reshape(rfs3[i,0:kernel_size],(size,size)),vmin=-m,vmax=m,cmap=pylab.cm.RdBu,interpolation='nearest')
pylab.savefig('GLM_rfs3.png')
lsctm_pred_act = lsctm.response(training_inputs1,training_inputs2,training_inputs3,K)
lsctm_pred_val_act = lsctm.response(validation_inputs1,validation_inputs2,validation_inputs3,K)
ofs = run_nonlinearity_detection(numpy.mat(training_set),numpy.mat(lsctm_pred_act),num_bins=10,display=False,name='RPI_piece_wise_nonlinearity.png')
lsctm_pred_act_t = numpy.mat(apply_output_function(numpy.mat(lsctm_pred_act),ofs))
lsctm_pred_val_act_t = numpy.mat(apply_output_function(numpy.mat(lsctm_pred_val_act),ofs))
if len(raw_validation_set) != 1:
print 'Without TF'
(signal_power,noise_power,normalized_noise_power,training_prediction_power,rpi_validation_prediction_power,signal_power_variance) =performance_analysis(training_set,validation_set,lsctm_pred_act,lsctm_pred_val_act,raw_validation_set,85)
print 'With TF'
(signal_power_t,noise_power_t,normalized_noise_power_t,training_prediction_power_t,rpi_validation_prediction_power_t,signal_power_variance_t) =performance_analysis(training_set,validation_set,lsctm_pred_act_t,lsctm_pred_val_act_t,raw_validation_set,85)
significant = numpy.array(numpy.nonzero((numpy.array(normalized_noise_power) < 85) * 1.0))[0]
perf.append(numpy.mean(rpi_validation_prediction_power[significant]))
perf_t.append(numpy.mean(rpi_validation_prediction_power_t[significant]))
(train_c,val_c) = printCorrelationAnalysis(training_set,validation_set,lsctm_pred_act,lsctm_pred_val_act)
(t_train_c,t_val_c) = printCorrelationAnalysis(training_set,validation_set,lsctm_pred_act_t,lsctm_pred_val_act_t)
