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CNM.py
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CNM.py
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from scipy.optimize import fmin_ncg, fmin_tnc
import scipy
import __main__
import numpy
import pylab
import sys
sys.path.append('/home/antolikjan/topographica/Theano/')
import theano
from theano import tensor as T
from topo.misc.filepath import normalize_path, application_path
from contrib.JanA.ofestimation import *
from contrib.modelfit import *
import contrib.dd
import contrib.JanA.dataimport
from contrib.JanA.regression import laplaceBias
from contrib.JanA.visualization import compareModelPerformanceWithRPI, showRFS, visualize2DOF
class ContrastNormalizationModel(object):
def __init__(self,XX,YY,ZZ,sizex,sizey,of_aff='Exp',of_surr='Linear'):
(self.num_pres,self.kernel_size) = numpy.shape(XX)
self.Y = theano.shared(YY)
self.X = theano.shared(XX)
self.Z = theano.shared(ZZ)
self.xx = theano.shared(numpy.repeat([numpy.arange(0,sizex,1)],sizey,axis=0).T.flatten())
self.yy = theano.shared(numpy.repeat([numpy.arange(0,sizey,1)],sizex,axis=0).flatten())
self.K = T.dvector('K')
self.x = self.K[0]
self.y = self.K[1]
self.surr_size = self.K[2]
self.surr_gain = self.K[3]
self.surr_c50 = self.K[4]
self.n1 = self.K[5]
self.n2 = self.K[6]
self.k = self.K[7:sizex*sizey+7]
self.of_aff = of_aff
self.of_surr = of_surr
#self.a = T.reshape(self.K[5:sizex*sizey],(sizex,sizey))
def model_output(self):
#surr = self.surr_gain *T.var(self.X,axis=1)**2
#surr = self.surr_gain *T.mean(self.X,axis=1)**2
surr = self.surr_gain * T.dot(self.X,T.exp(-T.div_proxy(((self.xx - self.x)**2 + (self.yy - self.y)**2),self.surr_size)).T/T.sqrt(self.surr_size*numpy.pi))
aff = T.dot(self.X,self.k.T)
#lgn_output = theano.printing.Print(message='lgn output:')(lgn_output)
lin = self.construct_of(aff / (self.surr_c50 + self.construct_of(surr,self.of_surr)) - self.n1,self.of_aff)
return lin
def log_likelyhood(self):
mo = self.model_output()
ll = T.sum(mo) - T.sum(T.dot(self.Y.T, T.log(mo)))
ll = ll + T.sum(T.dot(self.k ,T.dot(__main__.__dict__.get('LaplaceBias',0.0004)*self.Z,self.k.T)))
return ll
def func(self):
return theano.function(inputs=[self.K], outputs=self.log_likelyhood(),mode='FAST_RUN')
def der(self):
g_K = T.grad(self.log_likelyhood(), self.K)
return theano.function(inputs=[self.K], outputs=g_K,mode='FAST_RUN')
def hess(self):
g_K = T.grad(self.log_likelyhood(), self.K,consider_constant=[self.Y,self.X])
H, updates = theano.scan(lambda i,v: T.grad(g_K[i],v), sequences= T.arange(g_K.shape[0]), non_sequences=self.K)
f = theano.function(inputs=[self.K], outputs=H,mode='FAST_RUN')
return f
def construct_of(self,inn,of):
if of == 'Exp':
return T.exp(inn)
elif of == 'Sigmoid':
return 1 / (1 + T.exp(-inn))
elif of == 'Square':
return T.sqr(inn)
elif of == 'ExpExp':
return T.exp(T.exp(inn))
elif of == 'Linear':
return inn
elif of == 'ExpSquare':
return T.exp(T.sqr(inn))
elif of == 'LogisticLoss':
return 1*T.log(1+T.exp(1*inn))
elif of == 'Zero':
return inn*0
elif of == 'Linear':
return inn
def response(self,X,kernels):
self.X.value = X
resp = theano.function(inputs=[self.K], outputs=self.model_output())
(a,b) = numpy.shape(kernels)
(c,d) = numpy.shape(X)
responses = numpy.zeros((c,a))
for i in xrange(0,a):
responses[:,i] = resp(kernels[i,:]).T
return responses
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)
#contrib.dd.saveResults(res,"newest_dataset.dat")