def fit(self, corpus, n_iter=1, offset=10, eta=1e-2, timevarying=True, **kwargs):
""" online learning solution for the following optimization problem.
max_w {softmax(dot(L2_w,xt)+L2_b)-0.5*nnz*mean(\|L2_w\|_F^2+\|L2_b\|^2)} + R
"""
vocab_size = corpus.shape[1]
if self.t == 0:
mu, sigma = corpus.data.mean(), 2.56*corpus.data.var()**0.5
self.L2_w = mu + (mu if mu < sigma else sigma)*\
self.rng.uniform(low=-1,high=1,size=(vocab_size,self.topic_num))
self.L2_b = numpy.zeros(self.topic_num)
L2_w = numpy.empty((vocab_size, self.topic_num))
L2_b = numpy.empty(self.topic_num)
P_z = numpy.empty(self.topic_num)
for t in xrange(n_iter):
L2_w[:] = self.L2_w
L2_b[:] = self.L2_b
for s in xrange(corpus.shape[0]):
#update parameter
#1.learning rate
alpha = eta/(s+1.) if timevarying else eta
#2.difference between xt*L2_w*P_z - nnz*mean(L2_w**2)
euclid2prob(corpus[s].dot(L2_w)+L2_b, P_z, offset)
idx, xt = corpus[s].nonzero()[1], corpus[s].data
L2_w *= (1-alpha*xt.size/L2_w.size)
L2_w[idx] += (alpha*xt)[:,numpy.newaxis]*P_z
L2_b += alpha*(1-L2_b)*P_z
##
print "."
self.t += 1; beta = 1./self.t
self.L2_w *=(1-beta); self.L2_w += beta*L2_w
self.L2_b *=(1-beta); self.L2_b += beta*L2_b
def fit(self, corpus, n_iter=1, offset=10, eta=1e-2, timevarying=False, **kwargs):
""" online learning solution for the following optimization problem.
max_w softmax[dot(Pasterik_w,xt)+L2_b)-0.5*(nnz*mean(L2_w**2)+L2_b**2)]+R
"""
vocab_size = corpus.shape[1]
if self.t == 0:
mu, sigma = corpus.data.mean(), 2.56*corpus.data.var()**0.5
self.L2_w = mu + (mu if mu < sigma else sigma)*\
self.rng.uniform(low=-1,high=1,size=(vocab_size, self.topic_num))
self.L2_b = numpy.zeros(self.topic_num)
L2_w = numpy.empty((vocab_size, self.topic_num))
L2_b = numpy.empty(self.topic_num)
P_topic = numpy.empty(self.topic_num)
for t in xrange(n_iter):
L2_w[:] = self.L2_w
L2_b[:] = self.L2_b
for s in xrange(corpus.shape[0]):
#update parameter
#1.learning rate
alpha = eta/(s+1.) if timevarying else eta
#2.difference between xt*prob(topic) - eta*L2_w
idx, xt = corpus[s].nonzero()[1], corpus[s].data
P_w, Pasterik_w = euclid2sign(L2_w[idx])
euclid2prob(numpy.dot(xt, Pasterik_w)+L2_b, P_topic, offset)
scale = 1-float(xt.size)/L2_w.size*alpha
L2_w *= scale; L2_b *= scale
L2_w[idx] += (alpha*xt)[:,numpy.newaxis]*P_w*P_topic
L2_b += alpha*P_topic
##
print "."
self.t += 1
beta = 1./self.t
self.L2_w *= beta; self.L2_w += (1-beta)*L2_w
self.Pasterik_w = euclid2sign(self.L2_w, offset)[1]
self.L2_b *= beta; self.L2_b += (1-beta)*L2_b
def fit(self, corpus, n_iter=1, offset=10, eta=1e-2, timevarying=False, **kwargs):
""" online learning solution for the following optimization problem.
max_w logphi(dot(xt,w)+b-0.5*(w[I]**2+b**2)) - eta/2*\|w\|_F^2
log_phi(.) := (.) - log_sum_exp(.)
"""
vocab_size = corpus.shape[0]
if self.t == 0:
mu, sigma = corpus.data.mean(), 2.56*corpus.data.var()**0.5
self.L2_w = mu + (mu if mu < sigma else sigma)*\
self.rng.uniform(low=-1,high=1,size=(vocab_size, self.topic_num))
self.L2_b = numpy.zeros(self.topic_num)
L2_w = numpy.empty((vocab_size, self.topic_num))
L2_b = numpy.empty(self.topic_num)
P_z = numpy.empty(self.topic_num)
for t in xrange(n_iter):
L2_w[:] = self.L2_w
L2_b[:] = self.L2_b
for s in xrange(corpus.shape[0]):
#update parameter
#1.learning rate
alpha = eta/(s+1.) if timevarying else eta
#2.probability of topic
idx, xt = corpus[s].nonzero()[1], corpus[s].data
L2_w_idx = L2_w[idx]
euclid2prob(xt.dot(L2_w_idx)+L2_b-0.5*((L2_w_idx*L2_w_idx).sum(0)+L2_b*L2_b), P_z, offset)
#w+= alpha*(xt-w)*P_z-> w-= alpha*w*Pz; w += alpha*xt'*P_z
L2_w[idx] *= (1-alpha*P_z)
L2_w[idx] += (alpha*xt)[:,numpy.newaxis]*P_z
L2_b += alpha*(1-L2_b)*P_z
##
print "."
self.t += 1; beta = 1./self.t
self.L2_w *=(1-beta); self.L2_w += beta*L2_w
self.L2_b *=(1-beta); self.L2_b += beta*L2_b
self.L2_w2 = self.L2_w*self.L2_w
self.L2_b_b2 = self.L2_b-0.5*self.L2_b*self.L2_b
