from gensim.models import Doc2Vec
from GeneraVectores import GeneraVectores
import numpy as np
from sklearn import svm
from NNet import NeuralNet
if __name__ == '__main__':
model_dbow = Doc2Vec.load('./imdb_dbow.d2v')
model_dm = Doc2Vec.load('./imdb_dm.d2v')
dim = 200
#print model["TRAIN_POS_8029"]
#exit()
train_arrays = np.zeros((25000, dim))
train_labels = np.zeros(25000)
generador = GeneraVectores(model_dbow)
dbowVecs_Pos = generador.getVecsFromFile("data/trainpos.txt")
print "generados vectores dbowVecs_Pos"
generador.setModel(model_dm)
dmVecs_Pos = generador.getVecsFromFile("data/trainpos.txt")
print "generados vectores dmVecs_Pos"
generador.setModel(model_dbow)
dbowVecs_Neg = generador.getVecsFromFile("data/trainneg.txt")
print "generados vectores dbowVecs_Neg"
generador.setModel(model_dm)
dmVecs_Neg = generador.getVecsFromFile("data/trainneg.txt")
print "generados vectores dmVecs_Neg"
for i in range(12500):
train_arrays[i] = np.concatenate((dbowVecs_Pos[i], dmVecs_Pos[i]))
train_arrays[12500 + i] = np.concatenate(
from gensim.models import Doc2Vec
from GeneraVectores import GeneraVectores
import numpy as np
from sklearn import svm
from NNet import NeuralNet
if __name__ == '__main__':
model_dbow = Doc2Vec.load('./imdb_dbow.d2v')
model_dm = Doc2Vec.load('./imdb_dm.d2v')
dim = 200
#print model["TRAIN_POS_8029"]
#exit()
train_arrays = np.zeros((25000, dim))
train_labels = np.zeros(25000)
generador = GeneraVectores(model_dbow)
dbowVecs_Pos = generador.getVecsFromFile("data/trainpos.txt")
print "generados vectores dbowVecs_Pos"
generador.setModel(model_dm)
dmVecs_Pos = generador.getVecsFromFile("data/trainpos.txt")
print "generados vectores dmVecs_Pos"
generador.setModel(model_dbow)
dbowVecs_Neg = generador.getVecsFromFile("data/trainneg.txt")
print "generados vectores dbowVecs_Neg"
generador.setModel(model_dm)
dmVecs_Neg = generador.getVecsFromFile("data/trainneg.txt")
print "generados vectores dmVecs_Neg"
for i in range(12500):
train_arrays[i] = np.concatenate((dbowVecs_Pos[i],dmVecs_Pos[i]))
from gensim.models import Doc2Vec
import numpy
from GeneraVectores import GeneraVectores
from sklearn import svm
from NNet import NeuralNet
if __name__ == '__main__':
model = Doc2Vec.load('./imdb_dbow.d2v')
#print model["TRAIN_POS_8029"]
#exit()
dim = 100
train_arrays = numpy.zeros((25000, dim))
train_labels = numpy.zeros(25000)
generador = GeneraVectores(model)
Pos = generador.getVecsFromFile("data/trainpos.txt")
print "generados vectores Pos"
Neg = generador.getVecsFromFile("data/trainneg.txt")
print "generados vectores Neg"
for i in range(12500):
train_arrays[i] = Pos[i]
train_arrays[12500 + i] = Neg[i]
train_labels[i] = 1
train_labels[12500 + i] = 0
test_arrays = numpy.zeros((25000, dim))
test_labels = numpy.zeros(25000)
Pos = generador.getVecsFromFile("data/testpos.txt")
from gensim.models import Doc2Vec
import numpy
from GeneraVectores import GeneraVectores
from sklearn import svm
from NNet import NeuralNet
if __name__ == '__main__':
model = Doc2Vec.load('./imdb_dbow.d2v')
#print model["TRAIN_POS_8029"]
#exit()
dim = 100
train_arrays = numpy.zeros((25000, dim))
train_labels = numpy.zeros(25000)
generador = GeneraVectores(model)
Pos = generador.getVecsFromFile("data/trainpos.txt")
print "generados vectores Pos"
Neg = generador.getVecsFromFile("data/trainneg.txt")
print "generados vectores Neg"
for i in range(12500):
train_arrays[i] = Pos[i]
train_arrays[12500 + i] = Neg[i]
train_labels[i] = 1
train_labels[12500 + i] = 0
test_arrays = numpy.zeros((25000, dim))
test_labels = numpy.zeros(25000)
Pos = generador.getVecsFromFile("data/testpos.txt")
