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import os
import sys
import re # for regex
import math
import json
import pickle
import importlib

from PIL import Image
import numpy as np

from sklearn.datasets import fetch_mldata
# import matplotlib.pyplot as plt
# %matplotlib inline

# import pycuda.autoinit

from chainer import cuda, Function, FunctionSet, gradient_check, Variable, optimizers
import chainer.functions as F

import SuperClass
import dA
import SdA
# import CdA

data = np.random.ranf((100,100)).astype(np.float32)

# importlib.reload(SuperClass)
# importlib.reload(dA)
# importlib.reload(SdA)

n_in = data.shape[1]
n_hiddens = (6, 3, 2)
sda = SdA.StackedDenoisingAutoencoder(n_in, n_hiddens, n_epoch=5, use_cuda=False)

sda.train(data)

sda.predict(data, bAllLayer=True)