def fitGLM(X, Y, H, l, hl, sp, norm, of, lateral, num_neurons_to_estimate):
num_pres, num_neurons = numpy.shape(Y)
num_pres, kernel_size = numpy.shape(X)
if H != None:
(trash, hist_size) = numpy.shape(H)
else:
hist_size = 0
Ks = numpy.zeros((num_neurons, kernel_size + 2 + hist_size + lateral * (num_neurons - 1)))
laplace = laplaceBias(numpy.sqrt(kernel_size), numpy.sqrt(kernel_size))
rpi = numpy.linalg.pinv(X.T * X + __main__.__dict__.get("RPILaplaceBias", 0.0001) * laplace) * X.T * Y
for i in xrange(0, num_neurons_to_estimate):
print i
k0 = (
rpi[:, i].getA1().tolist()
+ [0, 0]
+ numpy.zeros((1, hist_size)).flatten().tolist()
+ numpy.zeros((1, lateral * (num_neurons - 1))).flatten().tolist()
)
if lateral and H != None:
HH = numpy.hstack((H, Y[:, :i], Y[:, i + 1 :]))
elif lateral:
HH = numpy.hstack((Y[:, :i], Y[:, i + 1 :]))
else:
HH = H
glm = GLM(numpy.mat(X), numpy.mat(Y[:, i]), l * laplace, HH, hl, sp, norm, of=of)
K = fmin_ncg(glm.func(), numpy.array(k0), glm.der(), fhess=glm.hess(), avextol=0.0000001, maxiter=200)
Ks[i, :] = K
return [Ks, rpi, glm]
def fitGLM(X,Y,H,l,hl,sp,norm,of,lateral,num_neurons_to_estimate):
num_pres,num_neurons = numpy.shape(Y)
num_pres,kernel_size = numpy.shape(X)
if H != None:
(trash,hist_size) = numpy.shape(H)
else:
hist_size = 0
Ks = numpy.zeros((num_neurons,kernel_size+2+hist_size+lateral*(num_neurons-1)))
laplace = laplaceBias(numpy.sqrt(kernel_size),numpy.sqrt(kernel_size))
rpi = numpy.linalg.pinv(X.T*X + __main__.__dict__.get('RPILaplaceBias',0.0001)*laplace) * X.T * Y
for i in xrange(0,num_neurons_to_estimate):
print i
k0 = rpi[:,i].getA1().tolist()+[0,0] + numpy.zeros((1,hist_size)).flatten().tolist() + numpy.zeros((1,lateral*(num_neurons-1))).flatten().tolist()
if lateral and H != None:
HH = numpy.hstack((H,Y[:,:i],Y[:,i+1:]))
elif lateral:
HH = numpy.hstack((Y[:,:i],Y[:,i+1:]))
else:
HH = H
glm = GLM(numpy.mat(X),numpy.mat(Y[:,i]),l*laplace,HH,hl,sp,norm,of=of)
K = fmin_ncg(glm.func(),numpy.array(k0),glm.der(),fhess = glm.hess(),avextol=0.0000001,maxiter=200)
Ks[i,:] = K
return [Ks,rpi,glm]
def sequentialFilterFinding():
d = contrib.dd.loadResults("newest_dataset.dat")
(sizex, sizey, training_inputs, training_set, validation_inputs,
validation_set, ff, db_node) = sortOutLoading(d)
raw_validation_set = db_node.data["raw_validation_set"]
contrib.modelfit.save_fig_directory = '/home/antolikjan/Doc/reports/Sparsness/SequentialFilterFitting/'
params = {}
params["SequentialFilterFitting"] = True
db_node = db_node.get_child(params)
params = {}
params["alpha"] = __main__.__dict__.get('Alpha', 0.02)
params["num_neurons"] = __main__.__dict__.get('NumNeurons', 10)
params["OF"] = __main__.__dict__.get('OF', 'Square')
db_node = db_node.get_child(params)
num_neurons_to_run = params["num_neurons"]
training_set = numpy.mat(training_set)[:, 0:num_neurons_to_run]
validation_set = numpy.mat(validation_set)[:, 0:num_neurons_to_run]
training_inputs = numpy.mat(training_inputs)
validation_inputs = numpy.mat(validation_inputs)
raw_validation_set = numpy.array(raw_validation_set)[:, :,
0:num_neurons_to_run]
num_pres, kernel_size = numpy.shape(training_inputs)
laplace = laplaceBias(numpy.sqrt(kernel_size), numpy.sqrt(kernel_size))
rpi = numpy.linalg.pinv(training_inputs.T * training_inputs +
__main__.__dict__.get('RPILaplaceBias', 0.0001) *
laplace) * training_inputs.T * training_set
new_training_inputs = numpy.zeros(numpy.shape(training_inputs))
new_validation_inputs = numpy.zeros(numpy.shape(validation_inputs))
Ks = numpy.zeros((num_neurons_to_run, kernel_size * 2 + 4))
second_pred_act = []
second_pred_val_act = []
for i in xrange(0, num_neurons_to_run):
print i
# project out STA
a = rpi[:, i] / numpy.sqrt(numpy.power(rpi[:, i], 2))
for j in xrange(0, numpy.shape(training_inputs)[0]):
new_training_inputs[j, :] = training_inputs[j, :] - a.T * (
(training_inputs[j, :] * a)[0, 0])
for j in xrange(0, numpy.shape(validation_inputs)[0]):
new_validation_inputs[j, :] = validation_inputs[j, :] - a.T * (
(validation_inputs[j, :] * a)[0, 0])
k0 = numpy.zeros(
(1, kernel_size * 2)).flatten().tolist() + [0, 0, 0, 0]
scm = SimpleContextualModel(numpy.mat(new_training_inputs),
numpy.mat(training_set[:, i]),
laplace,
of1=params["OF"],
of2='Zero')
#K = fmin_ncg(scm.func(),numpy.array(k0),scm.der(),fhess = scm.hess(),avextol=0.0000001,maxiter=20)
(K, success, c) = fmin_tnc(scm.func(),
numpy.array(k0)[:],
fprime=scm.der(),
maxfun=1000,
messages=0)
Ks[i, :] = K
second_pred_act.append(
scm.response(new_training_inputs, numpy.array([K])))
second_pred_val_act.append(
scm.response(new_validation_inputs, numpy.array([K])))
second_pred_act = numpy.hstack(second_pred_act)
second_pred_val_act = numpy.hstack(second_pred_val_act)
showRFS(
numpy.reshape(Ks[:, 0:kernel_size],
(-1, numpy.sqrt(kernel_size), numpy.sqrt(kernel_size))))
release_fig('K1.png')
showRFS(
numpy.reshape(Ks[:, kernel_size:2 * kernel_size],
(-1, numpy.sqrt(kernel_size), numpy.sqrt(kernel_size))))
release_fig('K2.png')
rpi_pred_act = training_inputs * rpi
rpi_pred_val_act = validation_inputs * rpi
#ofs = run_nonlinearity_detection(numpy.mat(training_set),numpy.mat(rpi_pred_act),num_bins=10,display=True)
#rpi_pred_act_t = numpy.mat(apply_output_function(numpy.mat(rpi_pred_act),ofs))
#rpi_pred_val_act_t = numpy.mat(apply_output_function(numpy.mat(rpi_pred_val_act),ofs))
#ofs = run_nonlinearity_detection(numpy.mat(training_set),numpy.mat(second_pred_act),num_bins=10,display=True)
#second_pred_act_t = numpy.mat(apply_output_function(numpy.mat(second_pred_act),ofs))
#second_pred_val_act_t = numpy.mat(apply_output_function(numpy.mat(second_pred_val_act),ofs))
visualize2DOF(rpi_pred_act, numpy.mat(second_pred_act), training_set)
visualize2DOF(rpi_pred_val_act, numpy.mat(second_pred_val_act),
validation_set)
#pred_act = second_pred_act_t + rpi_pred_act_t #numpy.multiply(second_pred_act_t,rpi_pred_act_t)
#pred_val_act = second_pred_val_act_t + rpi_pred_val_act_t #numpy.multiply(second_pred_val_act_t,rpi_pred_val_act_t)
of = fit2DOF(rpi_pred_act, numpy.mat(second_pred_act), training_set)
pred_act = apply2DOF(rpi_pred_act, numpy.mat(second_pred_act), of)
pred_val_act = apply2DOF(rpi_pred_val_act, numpy.mat(second_pred_val_act),
of)
compareModelPerformanceWithRPI(training_set, validation_set,
training_inputs, validation_inputs,
numpy.mat(pred_act),
numpy.mat(pred_val_act),
numpy.array(raw_validation_set),
'BilinearModel')
def runSCM():
res = contrib.dd.loadResults("newest_dataset.dat")
(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"]
params = {}
params["SCM"] = True
db_node = db_node.get_child(params)
params = {}
params["CLaplacaBias"] = __main__.__dict__.get('CLaplaceBias', 0.0004)
params["SLaplacaBias"] = __main__.__dict__.get('SLaplaceBias', 0.0004)
params["OF1"] = __main__.__dict__.get('OF1', 'Exp')
params["OF2"] = __main__.__dict__.get('OF2', 'Square')
params["num_neurons"] = __main__.__dict__.get('NumNeurons', 103)
# creat history
training_set = numpy.mat(training_set)
validation_set = numpy.mat(validation_set)
training_inputs = numpy.mat(training_inputs)
validation_inputs = numpy.mat(validation_inputs)
db_node1 = db_node
db_node = db_node.get_child(params)
num_pres, num_neurons = numpy.shape(training_set)
num_pres, kernel_size = numpy.shape(training_inputs)
num_neurons_to_run = params["num_neurons"]
Ks = numpy.zeros((num_neurons, kernel_size * 2 + 4))
print 'Kernel size', kernel_size
laplace = laplaceBias(numpy.sqrt(kernel_size), numpy.sqrt(kernel_size))
rpi = numpy.linalg.pinv(training_inputs.T * training_inputs +
__main__.__dict__.get('RPILaplaceBias', 0.0001) *
laplace) * training_inputs.T * training_set
for i in xrange(0, num_neurons_to_run):
print i
#k0 = rpi[:,i].getA1().tolist() + numpy.zeros((1,kernel_size)).flatten().tolist() + [0,0]
k0 = numpy.zeros(
(1, kernel_size * 2)).flatten().tolist() + [0, 0, 0, 0]
scm = SimpleContextualModel(numpy.mat(training_inputs),
numpy.mat(training_set[:, i]),
laplace,
of1=params["OF1"],
of2=params["OF2"])
#K = fmin_ncg(scm.func(),numpy.array(k0),scm.der(),fhess = scm.hess(),avextol=0.0000001,maxiter=20)
(K, success, c) = fmin_tnc(scm.func(),
numpy.array(k0)[:],
fprime=scm.der(),
maxfun=10000,
messages=0)
#print success
#print c
Ks[i, :] = K
pred_act = scm.response(training_inputs, Ks)
pred_val_act = scm.response(validation_inputs, Ks)
print Ks[0, :]
showRFS(
numpy.reshape(Ks[:, 0:kernel_size],
(-1, numpy.sqrt(kernel_size), numpy.sqrt(kernel_size))))
release_fig('K1.png')
showRFS(
numpy.reshape(Ks[:, kernel_size:2 * kernel_size],
(-1, numpy.sqrt(kernel_size), numpy.sqrt(kernel_size))))
release_fig('K2.png')
compareModelPerformanceWithRPI(
training_set[:, :num_neurons_to_run],
validation_set[:, :num_neurons_to_run], training_inputs,
validation_inputs,
numpy.mat(pred_act)[:, :num_neurons_to_run],
numpy.mat(pred_val_act)[:, :num_neurons_to_run],
numpy.array(raw_validation_set)[:, :, :num_neurons_to_run], 'SCM')
db_node.add_data("Kernels", Ks, force=True)
db_node.add_data("GLM", scm, force=True)
def fitLSCSMEvo(X,Y,num_lgn,num_neurons_to_estimate):
num_pres,num_neurons = numpy.shape(Y)
num_pres,kernel_size = numpy.shape(X)
Ks = numpy.ones((num_neurons,num_lgn*4+1))
laplace = laplaceBias(numpy.sqrt(kernel_size),numpy.sqrt(kernel_size))
setOfAlleles = GAllele.GAlleles()
bounds = []
for j in xrange(0,num_lgn):
setOfAlleles.add(GAllele.GAlleleRange(6,(numpy.sqrt(kernel_size)-6),real=True))
bounds.append((6,(numpy.sqrt(kernel_size)-6)))
setOfAlleles.add(GAllele.GAlleleRange(6,(numpy.sqrt(kernel_size)-6),real=True))
bounds.append((6,(numpy.sqrt(kernel_size)-6)))
for j in xrange(0,num_lgn):
setOfAlleles.add(GAllele.GAlleleRange(1.0,25,real=True))
bounds.append((1.0,25))
setOfAlleles.add(GAllele.GAlleleRange(1.0,25,real=True))
bounds.append((1.0,25))
if not __main__.__dict__.get('BalancedLGN',True):
for j in xrange(0,num_lgn):
setOfAlleles.add(GAllele.GAlleleRange(0.0,1.0,real=True))
bounds.append((0.0,1.0))
setOfAlleles.add(GAllele.GAlleleRange(0.0,1.0,real=True))
bounds.append((0.0,1.0))
if __main__.__dict__.get('LGNTreshold',False):
for j in xrange(0,num_lgn):
setOfAlleles.add(GAllele.GAlleleRange(0,20,real=True))
bounds.append((0,20))
if __main__.__dict__.get('NegativeLgn',True):
minw = -__main__.__dict__.get('MaxW',5000)
else:
minw = 0
maxw = __main__.__dict__.get('MaxW',5000)
print __main__.__dict__.get('MaxW',5000)
if __main__.__dict__.get('SecondLayer',False):
for j in xrange(0,num_lgn):
for k in xrange(0,int(num_neurons_to_estimate*__main__.__dict__.get('HiddenLayerSize',1.0))):
setOfAlleles.add(GAllele.GAlleleRange(minw,maxw,real=True))
bounds.append((minw,maxw))
for j in xrange(0,int(num_neurons_to_estimate*__main__.__dict__.get('HiddenLayerSize',1.0))):
for k in xrange(0,num_neurons_to_estimate):
setOfAlleles.add(GAllele.GAlleleRange(-4,4,real=True))
bounds.append((-4,4))
else:
for j in xrange(0,num_lgn):
for k in xrange(0,num_neurons_to_estimate):
setOfAlleles.add(GAllele.GAlleleRange(minw,maxw,real=True))
bounds.append((minw,maxw))
for k in xrange(0,num_neurons_to_estimate):
setOfAlleles.add(GAllele.GAlleleRange(0,20,real=True))
bounds.append((0,20))
if __main__.__dict__.get('SecondLayer',False):
for k in xrange(0,int(num_neurons_to_estimate*__main__.__dict__.get('HiddenLayerSize',1.0))):
setOfAlleles.add(GAllele.GAlleleRange(0,20,real=True))
bounds.append((0,20))
if __main__.__dict__.get('PyBrain',False):
ggevo = GGEvoPyBrain(X,Y,num_lgn,num_neurons_to_estimate,bounds)
else:
ggevo = GGEvo(X,Y,num_lgn,num_neurons_to_estimate,bounds)
if __main__.__dict__.get('SecondLayer',False):
genome_size = num_lgn*4+int(num_neurons_to_estimate*__main__.__dict__.get('HiddenLayerSize',1.0))*num_lgn+num_neurons_to_estimate
genome_size += num_neurons_to_estimate*int(num_neurons_to_estimate*__main__.__dict__.get('HiddenLayerSize',1.0)) + int(num_neurons_to_estimate*__main__.__dict__.get('HiddenLayerSize',1.0))
else:
genome_size = num_lgn*4+num_neurons_to_estimate*num_lgn+num_neurons_to_estimate
if not __main__.__dict__.get('BalancedLGN',True):
genome_size += num_lgn*2
if __main__.__dict__.get('LGNTreshold',False):
genome_size += num_lgn
print genome_size
print len(bounds)
genome = G1DList.G1DList(genome_size)
genome.setParams(allele=setOfAlleles)
genome.evaluator.set(ggevo.perform_gradient_descent)
genome.mutator.set(Mutators.G1DListMutatorAllele)
genome.initializator.set(Initializators.G1DListInitializatorAllele)
genome.crossover.set(Crossovers.G1DListCrossoverUniform)
ga = GSimpleGA.GSimpleGA(genome,__main__.__dict__.get('Seed',1023))
ga.minimax = Consts.minimaxType["minimize"]
#ga.selector.set(Selectors.GRouletteWheel)
ga.setElitism(True)
ga.setGenerations(__main__.__dict__.get('GenerationSize',100))
ga.setPopulationSize(__main__.__dict__.get('PopulationSize',100))
ga.setMutationRate(__main__.__dict__.get('MutationRate',0.05))
ga.setCrossoverRate(__main__.__dict__.get('CrossoverRate',0.9))
pop = ga.getPopulation()
#pop.scaleMethod.set(Scaling.SigmaTruncScaling)
ga.evolve(freq_stats=1)
best = ga.bestIndividual()
#profmode.print_summary()
#print best
inp = [v for v in best]
(new_K,success,c)=fmin_tnc(ggevo.func,inp[:],fprime=ggevo.der,bounds=bounds,maxfun = __main__.__dict__.get('FinalNumEval',10000),messages=0)
#inp[:-1] = numpy.reshape(inp[:-1],(num_lgn,4)).T.flatten()
print 'Final likelyhood'
print ggevo.func(new_K)
Ks = new_K
#rf= ggevo.lscsm.returnRFs(numpy.array([Ks[i,:]]))
#pylab.figure()
#m = numpy.max(numpy.abs(rf[0,0:kernel_size]))
#pylab.imshow(numpy.reshape(rf[0],(numpy.sqrt(kernel_size),numpy.sqrt(kernel_size))),vmin=-m,vmax=m,cmap=pylab.cm.RdBu,interpolation='nearest')
#pylab.colorbar()
#pylab.show()
rfs = ggevo.lscsm.returnRFs(Ks)
rpi = numpy.linalg.pinv(X.T*X + __main__.__dict__.get('RPILaplaceBias',0.0001)*laplace) * X.T * Y
return [Ks,rpi,ggevo.lscsm,rfs]
def compareModelPerformanceWithRPI(training_set,validation_set,training_inputs,validation_inputs,pred_act,pred_val_act,raw_validation_set,sizex,sizey,modelname='Model'):
from contrib.JanA.regression import laplaceBias
num_neurons = numpy.shape(pred_act)[1]
kernel_size= numpy.shape(validation_inputs)[1]
laplace = laplaceBias(sizex,sizey)
X = numpy.mat(training_inputs)
rpi = numpy.linalg.pinv(X.T*X + __main__.__dict__.get('RPILaplaceBias',0.0001)*laplace) * X.T * training_set
rpi_pred_act = training_inputs * rpi
rpi_pred_val_act = validation_inputs * rpi
showRFS(numpy.reshape(numpy.array(rpi.T),(-1,sizex,sizey)))
print numpy.shape(numpy.mat(training_set))
print numpy.shape(numpy.mat(pred_act))
ofs = contrib.JanA.ofestimation.run_nonlinearity_detection(numpy.mat(training_set),numpy.mat(pred_act),num_bins=10,display=True,name=(modelname+'_piece_wise_nonlinearity.png'))
pred_act_t = numpy.mat(contrib.JanA.ofestimation.apply_output_function(numpy.mat(pred_act),ofs))
pred_val_act_t = numpy.mat(contrib.JanA.ofestimation.apply_output_function(numpy.mat(pred_val_act),ofs))
ofs = contrib.JanA.ofestimation.run_nonlinearity_detection(numpy.mat(training_set),numpy.mat(rpi_pred_act),num_bins=10,display=True,name='RPI_piece_wise_nonlinearity.png')
rpi_pred_act_t = numpy.mat(contrib.JanA.ofestimation.apply_output_function(numpy.mat(rpi_pred_act),ofs))
rpi_pred_val_act_t = numpy.mat(contrib.JanA.ofestimation.apply_output_function(numpy.mat(rpi_pred_val_act),ofs))
pylab.figure()
pylab.title('RPI')
for i in xrange(0,num_neurons):
pylab.subplot(11,11,i+1)
pylab.plot(rpi_pred_val_act[:,i],validation_set[:,i],'o')
contrib.modelfit.release_fig('RPI_val_relationship.png')
pylab.figure()
pylab.title(modelname)
for i in xrange(0,num_neurons):
pylab.subplot(11,11,i+1)
pylab.plot(pred_val_act[:,i],validation_set[:,i],'o')
contrib.modelfit.release_fig('GLM_val_relationship.png')
pylab.figure()
pylab.title('RPI')
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')
contrib.modelfit.release_fig('RPI_t_val_relationship.png')
pylab.figure()
pylab.title(modelname)
for i in xrange(0,num_neurons):
pylab.subplot(11,11,i+1)
pylab.plot(pred_val_act_t[:,i],validation_set[:,i],'o')
contrib.modelfit.release_fig('GLM_t_val_relationship.png')
pylab.figure()
print numpy.shape(numpy.mean(numpy.power(validation_set - rpi_pred_val_act_t,2)[:,:num_neurons],0))
print numpy.shape(numpy.mean(numpy.power(validation_set - pred_val_act,2)[:,:num_neurons],0))
pylab.plot(numpy.mean(numpy.power(validation_set - rpi_pred_val_act_t,2)[:,:num_neurons],0),numpy.mean(numpy.power(validation_set - pred_val_act,2)[:,:num_neurons],0),'o')
pylab.hold(True)
pylab.plot([0.0,1.0],[0.0,1.0])
pylab.xlabel('RPI')
pylab.ylabel(modelname)
contrib.modelfit.release_fig('GLM_vs_RPI_MSE.png')
print '\n \n RPI \n'
print 'Without TF'
contrib.modelfit.performance_analysis(training_set,validation_set,rpi_pred_act,rpi_pred_val_act,raw_validation_set,85)
print 'With TF'
(signal_power,noise_power,normalized_noise_power,training_prediction_power,rpi_validation_prediction_power,signal_power_variance) = contrib.modelfit.performance_analysis(training_set,validation_set,rpi_pred_act_t,rpi_pred_val_act_t,raw_validation_set,85)
print '\n \n', modelname, '\n'
print 'Without TF'
(signal_power,noise_power,normalized_noise_power,training_prediction_power,validation_prediction_power,signal_power_variance) = contrib.modelfit.performance_analysis(training_set,validation_set,pred_act,pred_val_act,raw_validation_set,85)
print 'With TF'
(signal_power_t,noise_power_t,normalized_noise_power_t,training_prediction_power_t,validation_prediction_power_t,signal_power_variance_t) = contrib.modelfit.performance_analysis(training_set,validation_set,pred_act_t,pred_val_act_t,raw_validation_set,85)
significant = numpy.array(numpy.nonzero((numpy.array(normalized_noise_power) < 85) * 1.0))[0]
print significant
pylab.figure()
pylab.plot(rpi_validation_prediction_power[significant],validation_prediction_power[significant],'o')
pylab.hold(True)
pylab.plot([0.0,1.0],[0.0,1.0])
pylab.xlabel('RPI')
pylab.ylabel(modelname)
contrib.modelfit.release_fig('GLM_vs_RPI_prediction_power.png')
pylab.figure()
pylab.plot(rpi_validation_prediction_power[significant],validation_prediction_power_t[significant ],'o')
pylab.hold(True)
pylab.plot([0.0,1.0],[0.0,1.0])
pylab.xlabel('RPI')
pylab.ylabel(modelname+'+TF')
contrib.modelfit.release_fig('GLM_vs_RPI_prediction_power.png')
def runCNM():
res = contrib.dd.loadResults("newest_dataset.dat")
(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"]
params={}
params["SCM"]=True
db_node = db_node.get_child(params)
params={}
params["LaplacaBias"] = __main__.__dict__.get('LaplaceBias',0.0004)
params["OFAff"] = __main__.__dict__.get('OFAff','Exp')
params["OFSurr"] = __main__.__dict__.get('OFSurr','Linear')
params["num_neurons"] = __main__.__dict__.get('NumNeurons',103)
significant = [0,1,6,7,8,9,12,13,15,16,17,19,20,21,22,23,24,25,26,27,28,30,31,32,33,34,38,39,40,42,43,45,46,47,48,49,50,51,52,53,56,58,59,61,62,63,64,65,66,69,71,72,75,77,78,79,80,83,85,89,91,92,93,94,96,100,101,102]
# creat history
training_set = numpy.mat(training_set)[:,significant]
validation_set = numpy.mat(validation_set)[:,significant]
training_inputs= numpy.mat(training_inputs)
validation_inputs= numpy.mat(validation_inputs)
for i in xrange(0,len(raw_validation_set)):
raw_validation_set[i] = numpy.mat(raw_validation_set[i])[:,significant]
db_node1 = db_node
db_node = db_node.get_child(params)
num_pres,num_neurons = numpy.shape(training_set)
num_pres,kernel_size = numpy.shape(training_inputs)
num_neurons_to_run=params["num_neurons"]
Ks = numpy.zeros((num_neurons,kernel_size+7))
print 'Kernel size',kernel_size
laplace = laplaceBias(sizex,sizey)
rpi = numpy.linalg.pinv(training_inputs.T*training_inputs + __main__.__dict__.get('RPILaplaceBias',0.0004)*laplace) * training_inputs.T * training_set
bounds = []
for i in xrange(0,kernel_size):
bounds.append((-1000000000,10000000000))
bounds = [(6,26),(6,25),(1,1000000),(0.0,500000),(0.001,100000),(-100,100),(-100,100)] + bounds
for i in xrange(0,num_neurons_to_run):
print i
k0 = [15,15,20,100000,1,0,0] + rpi[:,i].getA1().tolist()
print numpy.shape(k0)
print sizex,sizey
scm = ContrastNormalizationModel(training_inputs,numpy.mat(training_set[:,i]),laplace,sizex,sizey,of_aff=params["OFAff"],of_surr=params["OFSurr"])
#K = fmin_ncg(scm.func(),numpy.array(k0),scm.der(),fhess = scm.hess(),avextol=0.0000001,maxiter=20)
(K,success,c)=fmin_tnc(scm.func(),numpy.array(k0)[:],fprime=scm.der(),bounds = bounds,maxfun = 100000,messages=0)
print scm.func()(K)
Ks[i,:] = K
pred_act = scm.response(training_inputs,Ks)
pred_val_act = scm.response(validation_inputs,Ks)
from contrib.JanA.sparsness_analysis import TrevesRollsSparsness
showRFS(numpy.reshape(numpy.array(rpi.T),(-1,sizex,sizey)))
showRFS(numpy.reshape(Ks[:,7:kernel_size+7],(-1,sizex,sizey)))
print Ks[:,:7]
pylab.figure()
pylab.hist(TrevesRollsSparsness(numpy.mat(pred_val_act)).flatten())
pylab.figure()
pylab.hist(TrevesRollsSparsness(numpy.mat(pred_val_act.T)).flatten())
pylab.figure()
pylab.hist(TrevesRollsSparsness(numpy.mat(validation_set)).flatten())
pylab.figure()
pylab.hist(TrevesRollsSparsness(numpy.mat(validation_set.T)).flatten())
compareModelPerformanceWithRPI(training_set[:,:num_neurons_to_run],validation_set[:,:num_neurons_to_run],training_inputs,validation_inputs,numpy.mat(pred_act)[:,:num_neurons_to_run],numpy.mat(pred_val_act)[:,:num_neurons_to_run],numpy.array(raw_validation_set)[:,:,:num_neurons_to_run],sizex,sizey,'SCM')
db_node.add_data("Kernels",Ks,force=True)
db_node.add_data("GLM",scm,force=True)
def sequentialFilterFinding():
d = contrib.dd.loadResults("newest_dataset.dat")
(sizex,sizey,training_inputs,training_set,validation_inputs,validation_set,ff,db_node) = sortOutLoading(d)
raw_validation_set = db_node.data["raw_validation_set"]
contrib.modelfit.save_fig_directory='/home/antolikjan/Doc/reports/Sparsness/SequentialFilterFitting/'
params={}
params["SequentialFilterFitting"]=True
db_node = db_node.get_child(params)
params={}
params["alpha"] = __main__.__dict__.get('Alpha',0.02)
params["num_neurons"]= __main__.__dict__.get('NumNeurons',10)
params["OF"] = __main__.__dict__.get('OF','Square')
db_node = db_node.get_child(params)
num_neurons_to_run=params["num_neurons"]
training_set = numpy.mat(training_set)[:,0:num_neurons_to_run]
validation_set = numpy.mat(validation_set)[:,0:num_neurons_to_run]
training_inputs= numpy.mat(training_inputs)
validation_inputs= numpy.mat(validation_inputs)
raw_validation_set = numpy.array(raw_validation_set)[:,:,0:num_neurons_to_run]
num_pres,kernel_size = numpy.shape(training_inputs)
laplace = laplaceBias(numpy.sqrt(kernel_size),numpy.sqrt(kernel_size))
rpi = numpy.linalg.pinv(training_inputs.T*training_inputs + __main__.__dict__.get('RPILaplaceBias',0.0001)*laplace) * training_inputs.T * training_set
new_training_inputs = numpy.zeros(numpy.shape(training_inputs))
new_validation_inputs = numpy.zeros(numpy.shape(validation_inputs))
Ks = numpy.zeros((num_neurons_to_run,kernel_size*2+4))
second_pred_act=[]
second_pred_val_act=[]
for i in xrange(0,num_neurons_to_run):
print i
# project out STA
a = rpi[:,i]/numpy.sqrt(numpy.power(rpi[:,i],2))
for j in xrange(0,numpy.shape(training_inputs)[0]):
new_training_inputs[j,:] = training_inputs[j,:] - a.T * ((training_inputs[j,:]*a)[0,0])
for j in xrange(0,numpy.shape(validation_inputs)[0]):
new_validation_inputs[j,:] = validation_inputs[j,:] - a.T * ((validation_inputs[j,:]*a)[0,0])
k0 = numpy.zeros((1,kernel_size*2)).flatten().tolist() + [0,0,0,0]
scm = SimpleContextualModel(numpy.mat(new_training_inputs),numpy.mat(training_set[:,i]),laplace,of1=params["OF"],of2='Zero')
#K = fmin_ncg(scm.func(),numpy.array(k0),scm.der(),fhess = scm.hess(),avextol=0.0000001,maxiter=20)
(K,success,c)=fmin_tnc(scm.func(),numpy.array(k0)[:],fprime=scm.der(),maxfun = 1000,messages=0)
Ks[i,:] = K
second_pred_act.append(scm.response(new_training_inputs,numpy.array([K])))
second_pred_val_act.append(scm.response(new_validation_inputs,numpy.array([K])))
second_pred_act = numpy.hstack(second_pred_act)
second_pred_val_act = numpy.hstack(second_pred_val_act)
showRFS(numpy.reshape(Ks[:,0:kernel_size],(-1,numpy.sqrt(kernel_size),numpy.sqrt(kernel_size))))
release_fig('K1.png')
showRFS(numpy.reshape(Ks[:,kernel_size:2*kernel_size],(-1,numpy.sqrt(kernel_size),numpy.sqrt(kernel_size))))
release_fig('K2.png')
rpi_pred_act = training_inputs * rpi
rpi_pred_val_act = validation_inputs * rpi
#ofs = run_nonlinearity_detection(numpy.mat(training_set),numpy.mat(rpi_pred_act),num_bins=10,display=True)
#rpi_pred_act_t = numpy.mat(apply_output_function(numpy.mat(rpi_pred_act),ofs))
#rpi_pred_val_act_t = numpy.mat(apply_output_function(numpy.mat(rpi_pred_val_act),ofs))
#ofs = run_nonlinearity_detection(numpy.mat(training_set),numpy.mat(second_pred_act),num_bins=10,display=True)
#second_pred_act_t = numpy.mat(apply_output_function(numpy.mat(second_pred_act),ofs))
#second_pred_val_act_t = numpy.mat(apply_output_function(numpy.mat(second_pred_val_act),ofs))
visualize2DOF(rpi_pred_act,numpy.mat(second_pred_act),training_set)
visualize2DOF(rpi_pred_val_act,numpy.mat(second_pred_val_act),validation_set)
#pred_act = second_pred_act_t + rpi_pred_act_t #numpy.multiply(second_pred_act_t,rpi_pred_act_t)
#pred_val_act = second_pred_val_act_t + rpi_pred_val_act_t #numpy.multiply(second_pred_val_act_t,rpi_pred_val_act_t)
of = fit2DOF(rpi_pred_act,numpy.mat(second_pred_act),training_set)
pred_act = apply2DOF(rpi_pred_act,numpy.mat(second_pred_act),of)
pred_val_act = apply2DOF(rpi_pred_val_act,numpy.mat(second_pred_val_act),of)
compareModelPerformanceWithRPI(training_set,validation_set,training_inputs,validation_inputs,numpy.mat(pred_act),numpy.mat(pred_val_act),numpy.array(raw_validation_set),'BilinearModel')
def compareModelPerformanceWithRPI(training_set,
validation_set,
training_inputs,
validation_inputs,
pred_act,
pred_val_act,
raw_validation_set,
sizex,
sizey,
modelname='Model'):
from contrib.JanA.regression import laplaceBias
num_neurons = numpy.shape(pred_act)[1]
kernel_size = numpy.shape(validation_inputs)[1]
laplace = laplaceBias(sizex, sizey)
X = numpy.mat(training_inputs)
rpi = numpy.linalg.pinv(X.T * X +
__main__.__dict__.get('RPILaplaceBias', 0.0001) *
laplace) * X.T * training_set
rpi_pred_act = training_inputs * rpi
rpi_pred_val_act = validation_inputs * rpi
showRFS(numpy.reshape(numpy.array(rpi.T), (-1, sizex, sizey)))
print numpy.shape(numpy.mat(training_set))
print numpy.shape(numpy.mat(pred_act))
ofs = contrib.JanA.ofestimation.run_nonlinearity_detection(
numpy.mat(training_set),
numpy.mat(pred_act),
num_bins=10,
display=True,
name=(modelname + '_piece_wise_nonlinearity.png'))
pred_act_t = numpy.mat(
contrib.JanA.ofestimation.apply_output_function(
numpy.mat(pred_act), ofs))
pred_val_act_t = numpy.mat(
contrib.JanA.ofestimation.apply_output_function(
numpy.mat(pred_val_act), ofs))
ofs = contrib.JanA.ofestimation.run_nonlinearity_detection(
numpy.mat(training_set),
numpy.mat(rpi_pred_act),
num_bins=10,
display=True,
name='RPI_piece_wise_nonlinearity.png')
rpi_pred_act_t = numpy.mat(
contrib.JanA.ofestimation.apply_output_function(
numpy.mat(rpi_pred_act), ofs))
rpi_pred_val_act_t = numpy.mat(
contrib.JanA.ofestimation.apply_output_function(
numpy.mat(rpi_pred_val_act), ofs))
pylab.figure()
pylab.title('RPI')
for i in xrange(0, num_neurons):
pylab.subplot(11, 11, i + 1)
pylab.plot(rpi_pred_val_act[:, i], validation_set[:, i], 'o')
contrib.modelfit.release_fig('RPI_val_relationship.png')
pylab.figure()
pylab.title(modelname)
for i in xrange(0, num_neurons):
pylab.subplot(11, 11, i + 1)
pylab.plot(pred_val_act[:, i], validation_set[:, i], 'o')
contrib.modelfit.release_fig('GLM_val_relationship.png')
pylab.figure()
pylab.title('RPI')
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')
contrib.modelfit.release_fig('RPI_t_val_relationship.png')
pylab.figure()
pylab.title(modelname)
for i in xrange(0, num_neurons):
pylab.subplot(11, 11, i + 1)
pylab.plot(pred_val_act_t[:, i], validation_set[:, i], 'o')
contrib.modelfit.release_fig('GLM_t_val_relationship.png')
pylab.figure()
print numpy.shape(
numpy.mean(
numpy.power(validation_set - rpi_pred_val_act_t,
2)[:, :num_neurons], 0))
print numpy.shape(
numpy.mean(
numpy.power(validation_set - pred_val_act, 2)[:, :num_neurons], 0))
pylab.plot(
numpy.mean(
numpy.power(validation_set - rpi_pred_val_act_t,
2)[:, :num_neurons], 0),
numpy.mean(
numpy.power(validation_set - pred_val_act, 2)[:, :num_neurons], 0),
'o')
pylab.hold(True)
pylab.plot([0.0, 1.0], [0.0, 1.0])
pylab.xlabel('RPI')
pylab.ylabel(modelname)
contrib.modelfit.release_fig('GLM_vs_RPI_MSE.png')
print '\n \n RPI \n'
print 'Without TF'
contrib.modelfit.performance_analysis(training_set, validation_set,
rpi_pred_act, rpi_pred_val_act,
raw_validation_set, 85)
print 'With TF'
(signal_power, noise_power, normalized_noise_power,
training_prediction_power, rpi_validation_prediction_power,
signal_power_variance) = contrib.modelfit.performance_analysis(
training_set, validation_set, rpi_pred_act_t, rpi_pred_val_act_t,
raw_validation_set, 85)
print '\n \n', modelname, '\n'
print 'Without TF'
(signal_power, noise_power, normalized_noise_power,
training_prediction_power, validation_prediction_power,
signal_power_variance) = contrib.modelfit.performance_analysis(
training_set, validation_set, pred_act, pred_val_act,
raw_validation_set, 85)
print 'With TF'
(signal_power_t, noise_power_t, normalized_noise_power_t,
training_prediction_power_t, validation_prediction_power_t,
signal_power_variance_t) = contrib.modelfit.performance_analysis(
training_set, validation_set, pred_act_t, pred_val_act_t,
raw_validation_set, 85)
significant = numpy.array(
numpy.nonzero((numpy.array(normalized_noise_power) < 85) * 1.0))[0]
print significant
pylab.figure()
pylab.plot(rpi_validation_prediction_power[significant],
validation_prediction_power[significant], 'o')
pylab.hold(True)
pylab.plot([0.0, 1.0], [0.0, 1.0])
pylab.xlabel('RPI')
pylab.ylabel(modelname)
contrib.modelfit.release_fig('GLM_vs_RPI_prediction_power.png')
pylab.figure()
pylab.plot(rpi_validation_prediction_power[significant],
validation_prediction_power_t[significant], 'o')
pylab.hold(True)
pylab.plot([0.0, 1.0], [0.0, 1.0])
pylab.xlabel('RPI')
pylab.ylabel(modelname + '+TF')
contrib.modelfit.release_fig('GLM_vs_RPI_prediction_power.png')
def analyseStoredGLM():
from copy import deepcopy
dataset = contrib.JanA.dataimport.loadSimpleDataSet('Mice/2009_11_04/Raw/region3/val/spiking_13-15.dat',50,103,num_rep=10,num_frames=1,offset=0,transpose=False)
rr=[]
(index,raw_val_set) = dataset
for i in xrange(0,10):
rr.append(contrib.JanA.dataimport.generateTrainingSet(contrib.JanA.dataimport.averageRepetitions((index,deepcopy(raw_val_set)),reps=[i])))
raw_history_validation_set=rr
res = contrib.dd.loadResults("results.dat")
node = res.children[0].children[3]
training_set = node.data["training_set"][1:,:]
validation_set = node.data["validation_set"][1:,:]
training_inputs = node.data["training_inputs"][1:,:]
validation_inputs = node.data["validation_inputs"][1:,:]
raw_validation_set = node.data["raw_validation_set"]
for i in xrange(0,len(raw_validation_set)):
raw_history_validation_set[i] = raw_history_validation_set[i][0:-1,:]
K = node.children[10].data["Kernels"]
glm = node.children[10].data["GLM"]
history_set = node.children[10].data["HistorySet"]
history_validation_set = node.children[10].data["HistoryValidationSet"]
print training_inputs
print training_set
rpi = numpy.linalg.pinv(numpy.mat(training_inputs).T*numpy.mat(training_inputs) + __main__.__dict__.get('RPILaplaceBias',0.0001)*laplaceBias(numpy.sqrt(numpy.shape(training_inputs)[1]),numpy.sqrt(numpy.shape(training_inputs)[1]))) * numpy.mat(training_inputs).T * numpy.mat(training_set)
analyseGLM(K,rpi,glm,validation_inputs,training_inputs,validation_set,training_set,raw_validation_set,history_set,history_validation_set,raw_history_validation_set,contrib.dd.DB(None),numpy.shape(training_set)[1])
num_pres,num_neurons = numpy.shape(training_set)
num_pres,kernel_size = numpy.shape(training_inputs)
size = numpy.sqrt(kernel_size)
raw_validation_data_set=numpy.rollaxis(numpy.array(raw_validation_set),2)
(num_pres,num_neurons) = numpy.shape(training_set)
testing_set = training_set[-0.1*num_pres:,:]
training_set = training_set[:-0.1*num_pres,:]
testing_inputs = training_inputs[-0.1*num_pres:,:]
training_inputs = training_inputs[:-0.1*num_pres,:]
kernel_size = numpy.shape(numpy.mat(training_inputs))[1]
laplace = laplaceBias(numpy.sqrt(kernel_size),numpy.sqrt(kernel_size))
a = 0.0000000001
ITER = 40
rpis = []
for i in xrange(0,ITER):
print a
a = a*2
rpis.append(numpy.linalg.pinv(numpy.mat(training_inputs).T*numpy.mat(training_inputs) + a*laplace) * numpy.mat(training_inputs).T * numpy.mat(training_set))
# these will hold the best lambda and RFs for each neuron
best_rpis = numpy.mat(numpy.zeros((kernel_size,num_neurons)))
best_lambda = numpy.mat(numpy.zeros((1,num_neurons)))
raw_validation_set = dataset_node.data["raw_validation_set"]
training_inputs= dataset_node.data["training_inputs"]
validation_inputs= dataset_node.data["validation_inputs"]
(num_pres,num_neurons) = numpy.shape(training_set)
testing_set = training_set[-0.1*num_pres:,:]
training_set = training_set[:-0.1*num_pres,:]
testing_inputs = training_inputs[-0.1*num_pres:,:]
training_inputs = training_inputs[:-0.1*num_pres,:]
kernel_size = numpy.shape(numpy.mat(training_inputs))[1]
laplace = laplaceBias(int(numpy.sqrt(kernel_size)),int(numpy.sqrt(kernel_size)))
a = 0.0000000001
rpis = []
for i in xrange(0,40):
print a
a = a*2
rpis.append(numpy.linalg.pinv(numpy.mat(training_inputs).T*numpy.mat(training_inputs) + a*laplace) * numpy.mat(training_inputs).T * numpy.mat(training_set))
best_rpis = numpy.mat(numpy.zeros((kernel_size,num_neurons)))
best_lambda = numpy.mat(numpy.zeros((1,num_neurons)))
import pylab
pylab.figure()
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]
def runCNM():
res = contrib.dd.loadResults("newest_dataset.dat")
(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"]
params = {}
params["SCM"] = True
db_node = db_node.get_child(params)
params = {}
params["LaplacaBias"] = __main__.__dict__.get('LaplaceBias', 0.0004)
params["OFAff"] = __main__.__dict__.get('OFAff', 'Exp')
params["OFSurr"] = __main__.__dict__.get('OFSurr', 'Linear')
params["num_neurons"] = __main__.__dict__.get('NumNeurons', 103)
significant = [
0, 1, 6, 7, 8, 9, 12, 13, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26,
27, 28, 30, 31, 32, 33, 34, 38, 39, 40, 42, 43, 45, 46, 47, 48, 49, 50,
51, 52, 53, 56, 58, 59, 61, 62, 63, 64, 65, 66, 69, 71, 72, 75, 77, 78,
79, 80, 83, 85, 89, 91, 92, 93, 94, 96, 100, 101, 102
]
# creat history
training_set = numpy.mat(training_set)[:, significant]
validation_set = numpy.mat(validation_set)[:, significant]
training_inputs = numpy.mat(training_inputs)
validation_inputs = numpy.mat(validation_inputs)
for i in xrange(0, len(raw_validation_set)):
raw_validation_set[i] = numpy.mat(raw_validation_set[i])[:,
significant]
db_node1 = db_node
db_node = db_node.get_child(params)
num_pres, num_neurons = numpy.shape(training_set)
num_pres, kernel_size = numpy.shape(training_inputs)
num_neurons_to_run = params["num_neurons"]
Ks = numpy.zeros((num_neurons, kernel_size + 7))
print 'Kernel size', kernel_size
laplace = laplaceBias(sizex, sizey)
rpi = numpy.linalg.pinv(training_inputs.T * training_inputs +
__main__.__dict__.get('RPILaplaceBias', 0.0004) *
laplace) * training_inputs.T * training_set
bounds = []
for i in xrange(0, kernel_size):
bounds.append((-1000000000, 10000000000))
bounds = [(6, 26), (6, 25), (1, 1000000), (0.0, 500000), (0.001, 100000),
(-100, 100), (-100, 100)] + bounds
for i in xrange(0, num_neurons_to_run):
print i
k0 = [15, 15, 20, 100000, 1, 0, 0] + rpi[:, i].getA1().tolist()
print numpy.shape(k0)
print sizex, sizey
scm = ContrastNormalizationModel(training_inputs,
numpy.mat(training_set[:, i]),
laplace,
sizex,
sizey,
of_aff=params["OFAff"],
of_surr=params["OFSurr"])
#K = fmin_ncg(scm.func(),numpy.array(k0),scm.der(),fhess = scm.hess(),avextol=0.0000001,maxiter=20)
(K, success, c) = fmin_tnc(scm.func(),
numpy.array(k0)[:],
fprime=scm.der(),
bounds=bounds,
maxfun=100000,
messages=0)
print scm.func()(K)
Ks[i, :] = K
pred_act = scm.response(training_inputs, Ks)
pred_val_act = scm.response(validation_inputs, Ks)
from contrib.JanA.sparsness_analysis import TrevesRollsSparsness
showRFS(numpy.reshape(numpy.array(rpi.T), (-1, sizex, sizey)))
showRFS(numpy.reshape(Ks[:, 7:kernel_size + 7], (-1, sizex, sizey)))
print Ks[:, :7]
pylab.figure()
pylab.hist(TrevesRollsSparsness(numpy.mat(pred_val_act)).flatten())
pylab.figure()
pylab.hist(TrevesRollsSparsness(numpy.mat(pred_val_act.T)).flatten())
pylab.figure()
pylab.hist(TrevesRollsSparsness(numpy.mat(validation_set)).flatten())
pylab.figure()
pylab.hist(TrevesRollsSparsness(numpy.mat(validation_set.T)).flatten())
compareModelPerformanceWithRPI(
training_set[:, :num_neurons_to_run],
validation_set[:, :num_neurons_to_run], training_inputs,
validation_inputs,
numpy.mat(pred_act)[:, :num_neurons_to_run],
numpy.mat(pred_val_act)[:, :num_neurons_to_run],
numpy.array(raw_validation_set)[:, :, :num_neurons_to_run], sizex,
sizey, 'SCM')
db_node.add_data("Kernels", Ks, force=True)
db_node.add_data("GLM", scm, force=True)
def runSCM():
res = contrib.dd.loadResults("newest_dataset.dat")
(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"]
params={}
params["SCM"]=True
db_node = db_node.get_child(params)
params={}
params["CLaplacaBias"] = __main__.__dict__.get('CLaplaceBias',0.0004)
params["SLaplacaBias"] = __main__.__dict__.get('SLaplaceBias',0.0004)
params["OF1"] = __main__.__dict__.get('OF1','Exp')
params["OF2"] = __main__.__dict__.get('OF2','Square')
params["num_neurons"] = __main__.__dict__.get('NumNeurons',103)
# creat history
training_set = numpy.mat(training_set)
validation_set = numpy.mat(validation_set)
training_inputs= numpy.mat(training_inputs)
validation_inputs= numpy.mat(validation_inputs)
db_node1 = db_node
db_node = db_node.get_child(params)
num_pres,num_neurons = numpy.shape(training_set)
num_pres,kernel_size = numpy.shape(training_inputs)
num_neurons_to_run=params["num_neurons"]
Ks = numpy.zeros((num_neurons,kernel_size*2+4))
print 'Kernel size',kernel_size
laplace = laplaceBias(numpy.sqrt(kernel_size),numpy.sqrt(kernel_size))
rpi = numpy.linalg.pinv(training_inputs.T*training_inputs + __main__.__dict__.get('RPILaplaceBias',0.0001)*laplace) * training_inputs.T * training_set
for i in xrange(0,num_neurons_to_run):
print i
#k0 = rpi[:,i].getA1().tolist() + numpy.zeros((1,kernel_size)).flatten().tolist() + [0,0]
k0 = numpy.zeros((1,kernel_size*2)).flatten().tolist() + [0,0,0,0]
scm = SimpleContextualModel(numpy.mat(training_inputs),numpy.mat(training_set[:,i]),laplace,of1=params["OF1"],of2=params["OF2"])
#K = fmin_ncg(scm.func(),numpy.array(k0),scm.der(),fhess = scm.hess(),avextol=0.0000001,maxiter=20)
(K,success,c)=fmin_tnc(scm.func(),numpy.array(k0)[:],fprime=scm.der(),maxfun = 10000,messages=0)
#print success
#print c
Ks[i,:] = K
pred_act = scm.response(training_inputs,Ks)
pred_val_act = scm.response(validation_inputs,Ks)
print Ks[0,:]
showRFS(numpy.reshape(Ks[:,0:kernel_size],(-1,numpy.sqrt(kernel_size),numpy.sqrt(kernel_size))))
release_fig('K1.png')
showRFS(numpy.reshape(Ks[:,kernel_size:2*kernel_size],(-1,numpy.sqrt(kernel_size),numpy.sqrt(kernel_size))))
release_fig('K2.png')
compareModelPerformanceWithRPI(training_set[:,:num_neurons_to_run],validation_set[:,:num_neurons_to_run],training_inputs,validation_inputs,numpy.mat(pred_act)[:,:num_neurons_to_run],numpy.mat(pred_val_act)[:,:num_neurons_to_run],numpy.array(raw_validation_set)[:,:,:num_neurons_to_run],'SCM')
db_node.add_data("Kernels",Ks,force=True)
db_node.add_data("GLM",scm,force=True)
def analyseStoredGLM():
from copy import deepcopy
dataset = contrib.JanA.dataimport.loadSimpleDataSet(
"Mice/2009_11_04/Raw/region3/val/spiking_13-15.dat",
50,
103,
num_rep=10,
num_frames=1,
offset=0,
transpose=False,
)
rr = []
(index, raw_val_set) = dataset
for i in xrange(0, 10):
rr.append(
contrib.JanA.dataimport.generateTrainingSet(
contrib.JanA.dataimport.averageRepetitions((index, deepcopy(raw_val_set)), reps=[i])
)
)
raw_history_validation_set = rr
res = contrib.dd.loadResults("results.dat")
node = res.children[0].children[3]
training_set = node.data["training_set"][1:, :]
validation_set = node.data["validation_set"][1:, :]
training_inputs = node.data["training_inputs"][1:, :]
validation_inputs = node.data["validation_inputs"][1:, :]
raw_validation_set = node.data["raw_validation_set"]
for i in xrange(0, len(raw_validation_set)):
raw_history_validation_set[i] = raw_history_validation_set[i][0:-1, :]
K = node.children[10].data["Kernels"]
glm = node.children[10].data["GLM"]
history_set = node.children[10].data["HistorySet"]
history_validation_set = node.children[10].data["HistoryValidationSet"]
print training_inputs
print training_set
rpi = (
numpy.linalg.pinv(
numpy.mat(training_inputs).T * numpy.mat(training_inputs)
+ __main__.__dict__.get("RPILaplaceBias", 0.0001)
* laplaceBias(numpy.sqrt(numpy.shape(training_inputs)[1]), numpy.sqrt(numpy.shape(training_inputs)[1]))
)
* numpy.mat(training_inputs).T
* numpy.mat(training_set)
)
analyseGLM(
K,
rpi,
glm,
validation_inputs,
training_inputs,
validation_set,
training_set,
raw_validation_set,
history_set,
history_validation_set,
raw_history_validation_set,
contrib.dd.DB(None),
numpy.shape(training_set)[1],
)
raw_validation_set = dataset_node.data["raw_validation_set"]
training_inputs = dataset_node.data["training_inputs"]
validation_inputs = dataset_node.data["validation_inputs"]
(num_pres, num_neurons) = numpy.shape(training_set)
testing_set = training_set[-0.1 * num_pres :, :]
training_set = training_set[: -0.1 * num_pres, :]
testing_inputs = training_inputs[-0.1 * num_pres :, :]
training_inputs = training_inputs[: -0.1 * num_pres, :]
kernel_size = numpy.shape(numpy.mat(training_inputs))[1]
laplace = laplaceBias(int(numpy.sqrt(kernel_size)), int(numpy.sqrt(kernel_size)))
a = 0.0000000001
rpis = []
for i in xrange(0, 40):
print a
a = a * 2
rpis.append(
numpy.linalg.pinv(numpy.mat(training_inputs).T * numpy.mat(training_inputs) + a * laplace)
* numpy.mat(training_inputs).T
* numpy.mat(training_set)
)
best_rpis = numpy.mat(numpy.zeros((kernel_size, num_neurons)))
best_lambda = numpy.mat(numpy.zeros((1, num_neurons)))
def fetch_data():
dirr= "/home/antolikjan/eddie_data/LSCSM_SEQ_LGN5_LLTrue_SL=True_LinearLGN/"
reg=re.compile(".*FromWhichNeuron\=([^,]*)")
import contrib.JanA.LSCSM
import contrib
(sizex,sizey,training_inputs,training_set,validation_inputs,validation_set,ff,db_node) = contrib.JanA.dataimport.sortOutLoading(contrib.dd.DB(None))
raw_validation_set = db_node.data["raw_validation_set"]
num_lgn=16
pred_act=[]
pred_val_act=[]
idxs = []
to_delete = []
rfs = []
Ks = []
lscsm_new = contrib.JanA.LSCSM.LSCSM1(numpy.mat(training_inputs),numpy.mat(training_set)[:,0],num_lgn,1)
for a in os.listdir(dirr):
num = int(reg.search(a).group(1))
print num
idxs.append(num)
if os.path.exists(dirr+a+'/res.dat'):
res = contrib.dd.loadResults(dirr+a+'/res.dat')
dataset_node = res.children[0].children[0]
print res.children[0].children[0].children[0].children[0].data.keys()
K = res.children[0].children[0].children[0].children[0].data["Kernels"]
lscsm = res.children[0].children[0].children[0].children[0].data["LSCSM"][0]
Ks.append(K[0,:])
#rfs.append(lscsm_new.returnRFs(K[0,:]))
#rfs=lscsm.returnRFs(numpy.array(K)[0])
#print numpy.shape(numpy.array(rfs))
#print sizex,sizey
#showRFS(numpy.reshape(numpy.array(rfs),(-1,sizex,sizey)))
#print 'A'
#print numpy.shape(numpy.array(K)[0])
#print numpy.array(K)[0]
#lscsm.X.value = numpy.mat(training_inputs)
#lscsm.Y.value = numpy.mat(training_set[:,int(num)]).T
#lscsm1.X.value = numpy.mat(training_inputs)
#lscsm1.Y.value = numpy.mat(training_set[:,int(num)]).T
#fun = lscsm1.func()
#print lscsm.func()(numpy.array(K)[0])
#print fun(numpy.array(K)[0])
pred_act.append(lscsm.response(training_inputs,numpy.array(K)[0]))
pred_val_act.append(lscsm.response(validation_inputs,numpy.array(K)[0]))
else:
pred_act.append(numpy.zeros((1800,1)))
pred_val_act.append(numpy.zeros((50,1)))
rfs.append(numpy.zeros((1,sizex*sizey)))
to_delete.append(num)
print 'Number of missed neurons:', len(to_delete)
from contrib.JanA.regression import laplaceBias
pred_act = numpy.hstack(pred_act)
pred_val_act = numpy.hstack(pred_val_act)
#print numpy.shape(rfs)
#rfs = numpy.vstack(rfs)
pred_act_new = pred_act[:,numpy.argsort(idxs)]
pred_val_act_new = pred_val_act[:,numpy.argsort(idxs)]
#rfs = rfs[numpy.argsort(idxs),:]
print 'Y'
print numpy.shape(pred_act_new)
print numpy.shape(training_set)
training_set = numpy.delete(training_set, to_delete, axis = 1)
validation_set = numpy.delete(validation_set, to_delete, axis = 1)
pred_act_new = numpy.delete(pred_act_new, to_delete, axis = 1)
pred_val_act_new = numpy.delete(pred_val_act_new, to_delete, axis = 1)
#rfs = numpy.delete(rfs, to_delete, axis = 0)
for i in xrange(0,10):
raw_validation_set[i] = numpy.delete(raw_validation_set[i], to_delete, axis = 1)
print 'X'
print numpy.shape(pred_act_new)
print numpy.shape(training_set)
laplace = laplaceBias(sizex,sizey)
LSCSM_rpi = numpy.linalg.pinv(numpy.mat(training_inputs).T*numpy.mat(training_inputs) + __main__.__dict__.get('RPILaplaceBias',0.0001)*laplace) * numpy.mat(training_inputs).T * numpy.mat(pred_act_new)
print 'A'
print numpy.shape(LSCSM_rpi.T)
print numpy.shape(numpy.array(LSCSM_rpi.T))
print numpy.shape(numpy.reshape(numpy.array(LSCSM_rpi.T),(-1,sizex,sizey)))
showRFS(numpy.reshape(numpy.array(LSCSM_rpi.T),(-1,sizex,sizey)))
#print numpy.shape(rfs)
#rfs = numpy.vstack(rfs)
Ks = numpy.vstack(Ks)
#print numpy.shape(rfs)
#showRFS(numpy.reshape(rfs,(-1,sizex,sizey)))
#a = Ks[:,7*num_lgn:7*num_lgn+1*num_lgn]
#t = Ks[:,6*num_lgn:6*num_lgn+1*num_lgn]
#print 'T'
#print t
#m = numpy.mat(numpy.tile(numpy.mean(abs(a),axis=1),(num_lgn,1))).T
#print 'A'
#print a
#a = (abs(a) >= 0.3*m)*1.0
#a = (t < 0.001)*1.0
#Ks[:,0:num_lgn] = numpy.multiply(Ks[:,0:num_lgn],a)
#Ks[:,num_lgn:2*num_lgn] = numpy.multiply(Ks[:,num_lgn:2*num_lgn],a)
pylab.figure()
pylab.plot(Ks[:,0:num_lgn].flatten(),Ks[:,num_lgn:2*num_lgn].flatten(),'bo')
from contrib.JanA.sparsness_analysis import TrevesRollsSparsness
pylab.figure()
pylab.subplot(221)
pylab.hist(TrevesRollsSparsness(numpy.mat(validation_set)).flatten())
pylab.axis(xmin=0.0,xmax=1.0)
pylab.title('Lifetime sparesness')
pylab.subplot(222)
pylab.hist(TrevesRollsSparsness(numpy.mat(validation_set.T)).flatten())
pylab.axis(xmin=0.0,xmax=1.0)
pylab.title('Population sparesness')
pylab.subplot(223)
pylab.hist(TrevesRollsSparsness(numpy.mat(pred_val_act)).flatten())
pylab.axis(xmin=0.0,xmax=1.0)
pylab.subplot(224)
pylab.hist(TrevesRollsSparsness(numpy.mat(pred_val_act.T)).flatten())
pylab.axis(xmin=0.0,xmax=1.0)
print 'Life time sparsness measured:',numpy.mean(TrevesRollsSparsness(numpy.mat(validation_set)).flatten())
print 'Life time sparsness predicted:',numpy.mean(TrevesRollsSparsness(numpy.mat(pred_val_act)).flatten())
print 'Population sparsness measured:',numpy.mean(TrevesRollsSparsness(numpy.mat(validation_set.T)).flatten())
print 'Population sparsness predicted:',numpy.mean(TrevesRollsSparsness(numpy.mat(pred_val_act.T)).flatten())
#pred_act_new = numpy.mat(training_inputs) * numpy.mat(rfs).T
#pred_val_act_new = numpy.mat(validation_inputs) * numpy.mat(rfs).T
print 'b'
print numpy.shape(pred_act_new)
print numpy.shape(training_set)
return (pred_act_new,pred_val_act_new,training_set,validation_set,training_inputs,validation_inputs,numpy.array(raw_validation_set))
num_pres, num_neurons = numpy.shape(training_set)
num_pres, kernel_size = numpy.shape(training_inputs)
size = numpy.sqrt(kernel_size)
raw_validation_data_set = numpy.rollaxis(numpy.array(raw_validation_set), 2)
(num_pres, num_neurons) = numpy.shape(training_set)
testing_set = training_set[-0.1 * num_pres:, :]
training_set = training_set[:-0.1 * num_pres, :]
testing_inputs = training_inputs[-0.1 * num_pres:, :]
training_inputs = training_inputs[:-0.1 * num_pres, :]
kernel_size = numpy.shape(numpy.mat(training_inputs))[1]
laplace = laplaceBias(numpy.sqrt(kernel_size), numpy.sqrt(kernel_size))
a = 0.0000000001
ITER = 40
rpis = []
for i in xrange(0, ITER):
print a
a = a * 2
rpis.append(
numpy.linalg.pinv(
numpy.mat(training_inputs).T * numpy.mat(training_inputs) +
a * laplace) * numpy.mat(training_inputs).T *
numpy.mat(training_set))
# these will hold the best lambda and RFs for each neuron
