def NMijF_jp(tt_matrix, tw_matrix, k_topic, alpha):
print "Entering NMijF_jp process..."
tt_shape = tt_matrix.shape
tw_shape = tw_matrix.shape
W = np.random.rand(tt_shape[0], k_topic)
Y = np.random.rand(k_topic, tt_shape[1])
H = np.random.rand(k_topic, tw_shape[1])
observation = 0
for i in range(30):
print "Fatoring..." + str(float(i) / 30.0) + "%"
W = NMF.update_a(W, Y, tt_matrix)
Y = NMF.update_x(W, Y, tt_matrix)
H = NMF.update_x(W, H, tw_matrix)
di = NMF.divergence_function(tt_matrix, np.dot(W, Y))
dj = NMF.divergence_function(tw_matrix, np.dot(W, H))
if (object_function(di, dj, alpha) - observation) < 0.001:
break
observation = object_function(di, dj, alpha)
# print "tweet - topic matrix \n" + str(W)
# print "topic - tweet matrix \n" + str(Y)
# print "topic - word matrix \n" + str(H)
print "Done!"
return [W, H]
