def bilinearRegression(training_inputs,training_set,sizex,sizey,num_steps,alpha,num_neurons):
laplace = laplaceBias(sizex,sizey)
(num_pres,kernel_size) = numpy.shape(training_inputs)
X = numpy.mat(training_inputs)
Y = numpy.mat(training_set)
print numpy.shape(alpha*laplace)
print numpy.shape(X.T*X)
K1 = []
K2 = []
kk1 = numpy.linalg.pinv(X.T*X + alpha*laplace) * X.T * Y
M = numpy.zeros((num_pres,kernel_size,kernel_size))
for xx in xrange(0,sizex):
for yy in xrange(0,sizey):
for x in xrange(0,sizex):
for y in xrange(0,sizey):
if( ((xx + (x-sizex/2)) < 0) or ((xx + (x-sizex/2)) >= sizex) or ((yy + (y-sizey/2)) < 0) or ((yy + (y-sizey/2)) >= sizey)):
M[:,yy*sizex+xx, y*sizex+x] = 0
else:
M[:,yy*sizex+xx, y*sizex+x] = training_inputs[:,(yy + (y-sizey/2))*sizex + (xx + (x-sizex/2))]
for n in xrange(0,num_neurons):
print 'Neuron: ', n
k1 = kk1[:,n]
for i in xrange(0,num_steps):
print 'Step: ', i
A = numpy.hstack([numpy.mat(M[:,:,i]) * k1 for i in xrange(0,sizex*sizey)])
k2 = numpy.linalg.pinv(A.T*A + alpha*laplace) * A.T * Y[:,n]
B = numpy.hstack([numpy.mat(M[:,i,:]) * k2 for i in xrange(0,sizex*sizey)])
k1 = numpy.linalg.pinv(B.T*B + alpha*laplace) * B.T * Y[:,n]
K1.append(k1)
K2.append(k2)
K1 = numpy.hstack(K1).T
K2 = numpy.hstack(K2).T
print sizex
print sizey
print shape(K1)
numpy.reshape(numpy.array(K1),(-1,sizex,sizey))
print numpy.shape(numpy.reshape(numpy.array(K1),(-1,sizex,sizey)))
showRFS(numpy.reshape(numpy.array(K1),(-1,sizex,sizey)))
release_fig('K1.png')
showRFS(numpy.reshape(numpy.array(K2),(-1,sizex,sizey)))
release_fig('K2.png')
return (K1,K2)
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))
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 bilinearRegression(training_inputs, training_set, sizex, sizey, num_steps,
alpha, num_neurons):
laplace = laplaceBias(sizex, sizey)
(num_pres, kernel_size) = numpy.shape(training_inputs)
X = numpy.mat(training_inputs)
Y = numpy.mat(training_set)
print numpy.shape(alpha * laplace)
print numpy.shape(X.T * X)
K1 = []
K2 = []
kk1 = numpy.linalg.pinv(X.T * X + alpha * laplace) * X.T * Y
M = numpy.zeros((num_pres, kernel_size, kernel_size))
for xx in xrange(0, sizex):
for yy in xrange(0, sizey):
for x in xrange(0, sizex):
for y in xrange(0, sizey):
if (((xx + (x - sizex / 2)) < 0)
or ((xx + (x - sizex / 2)) >= sizex)
or ((yy + (y - sizey / 2)) < 0)
or ((yy + (y - sizey / 2)) >= sizey)):
M[:, yy * sizex + xx, y * sizex + x] = 0
else:
M[:, yy * sizex + xx, y * sizex +
x] = training_inputs[:,
(yy + (y - sizey / 2)) * sizex +
(xx + (x - sizex / 2))]
for n in xrange(0, num_neurons):
print 'Neuron: ', n
k1 = kk1[:, n]
for i in xrange(0, num_steps):
print 'Step: ', i
A = numpy.hstack(
[numpy.mat(M[:, :, i]) * k1 for i in xrange(0, sizex * sizey)])
k2 = numpy.linalg.pinv(A.T * A + alpha * laplace) * A.T * Y[:, n]
B = numpy.hstack(
[numpy.mat(M[:, i, :]) * k2 for i in xrange(0, sizex * sizey)])
k1 = numpy.linalg.pinv(B.T * B + alpha * laplace) * B.T * Y[:, n]
K1.append(k1)
K2.append(k2)
K1 = numpy.hstack(K1).T
K2 = numpy.hstack(K2).T
print sizex
print sizey
print shape(K1)
numpy.reshape(numpy.array(K1), (-1, sizex, sizey))
print numpy.shape(numpy.reshape(numpy.array(K1), (-1, sizex, sizey)))
showRFS(numpy.reshape(numpy.array(K1), (-1, sizex, sizey)))
release_fig('K1.png')
showRFS(numpy.reshape(numpy.array(K2), (-1, sizex, sizey)))
release_fig('K2.png')
return (K1, K2)
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)
best_rpis = numpy.mat(numpy.zeros((kernel_size,num_neurons)))
best_lambda = numpy.mat(numpy.zeros((1,num_neurons)))
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]
for i in xrange(0,num_neurons):
corrs=[]
for j in xrange(0,ITER):
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)
best_rpis[:,i] = rpis[numpy.argmax(corrs)][:,i]
best_lambda[0,i] = a[numpy.argmax(corrs)]
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)
showRFS(numpy.reshape(numpy.array(best_rpis),(num_neurons,size,size)))
numpy.savetxt('STA_20091011.txt',best_rpis)
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 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')
for i in xrange(0, num_neurons):
corrs = []
for j in xrange(0, ITER):
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)
best_rpis[:, i] = rpis[numpy.argmax(corrs)][:, i]
best_lambda[0, i] = a[numpy.argmax(corrs)]
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)
showRFS(numpy.reshape(numpy.array(best_rpis), (num_neurons, size, size)))
numpy.savetxt('STA_20091011.txt', best_rpis)
