def loadData(path, k=100):
x = cPickle.load(open(path, "rb"))
revs, W, W2, word_idx_map, vocab = x[0], x[1], x[2], x[3], x[4]
max_l = np.max(pd.DataFrame(revs)["num_words"])
print(len(word_idx_map))
print(len(vocab))
print(len(revs))
datasets = make_idx_data_cv(revs,
word_idx_map,
1,
max_l=max_l,
k=k,
filter_h=5)
img_h = len(datasets[0][0]) - 1
print('img_h', img_h)
print('max len', max_l)
print(datasets[0].shape)
test_set_x = datasets[1][:, :img_h]
test_set_y = np.asarray(datasets[1][:, -1], "int32")
train_set_x = datasets[0][:, :img_h]
train_set_y = np.asarray(datasets[0][:, -1], "int32")
print(np.shape(train_set_x))
print('load data...')
print(np.shape(W))
print(type(W))
return (train_set_x, train_set_y), (test_set_x, test_set_y), W
def loadData(path):
x = cPickle.load(open(path,"rb"))
revs, W, W2, word_idx_map, vocab = x[0], x[1], x[2], x[3], x[4]
print(len(word_idx_map))
print(len(vocab))
datasets = make_idx_data_cv(revs, word_idx_map, 1, max_l=10,k=100, filter_h=5)
img_h = len(datasets[0][0])-1
test_set_x = datasets[1][:,:img_h]
test_set_y = np.asarray(datasets[1][:,-1],"int32")
train_set_x =datasets[0][:,:img_h]
train_set_y =np.asarray(datasets[0][:,-1],"int32")
print (np.shape(train_set_x))
print('load data...')
print(np.shape(W))
print(type(W))
return (train_set_x,train_set_y),(test_set_x,test_set_y),W