# Path: utils.py
# def zero_mean_unit_variance(Data):
#     """ Scales all values in the ndarray ndar to be between 0 and 1 """
#     Mean = numpy.mean(Data, axis=0)
#     Data  -=  Mean
# 
#     Std = numpy.std(Data, axis = 0)
#     index = (numpy.abs(Std<10**-5))
#     Std[index] = 1
#     Data /= Std
#     return [Data, Mean, Std]
# 
# Path: utils.py
# def normalize(Data, Mean, Std):
#     Data -= Mean
#     Data /= Std
#     return Data


import cPickle
import gzip
import os
import sys
import time
import numpy
import theano
import theano.tensor as T
import warnings
                import warnings
        import urllib
from theano.tensor.shared_randomstreams import RandomStreams
from logistic_sgd import LogisticRegression, load_data
from mlp import HiddenLayer
from rbm import RBM
from grbm import GBRBM
from utils import zero_mean_unit_variance
from utils import normalize
    from sklearn import preprocessing
        data_x, data_y = data_xy
        shared_x = theano.shared(numpy.asarray(data_x,
                                               dtype=theano.config.floatX),
                                 borrow=borrow)
        shared_y = theano.shared(numpy.asarray(data_y,
                                               dtype=theano.config.floatX),
                                 borrow=borrow)
        # When storing data on the GPU it has to be stored as floats
        # therefore we will store the labels as ``floatX`` as well
        # (``shared_y`` does exactly that). But during our computations
        # we need them as ints (we use labels as index, and if they are
        # floats it doesn't make sense) therefore instead of returning
        # ``shared_y`` we will have to cast it to int. This little hack
        # lets ous get around this issue
        return shared_x, T.cast(shared_y, 'int32')


    # Wudi made it a small set:
    train_set_feature = train_set[0][0:1000,:]
    [train_set_feature1, Mean1, Std1]  = preprocessing.scale(train_set_feature)
    # Wudi added normalized data for GRBM
    #[train_set_feature2, Mean2, Var2] = zero_mean_unit_variance(train_set_feature)
    train_set_new_target = train_set[1][0:1000]
    train_set_x, train_set_y = shared_dataset( (train_set_feature1,train_set_new_target))

    valid_set_feature = valid_set[0]
    valid_set_feature = normalize(valid_set_feature, Mean1, Std1)
    valid_set_x, valid_set_y = shared_dataset((valid_set_feature,valid_set[1]))
    # test feature set
    test_set_feature = test_set[0]

# Path: utils.py
# def zero_mean_unit_variance(Data):
#     """ Scales all values in the ndarray ndar to be between 0 and 1 """
#     Mean = numpy.mean(Data, axis=0)
#     Data  -=  Mean
# 
#     Std = numpy.std(Data, axis = 0)
#     index = (numpy.abs(Std<10**-5))
#     Std[index] = 1
#     Data /= Std
#     return [Data, Mean, Std]
# 
# Path: utils.py
# def normalize(Data, Mean, Std):
#     Data -= Mean
#     Data /= Std
#     return Data


# Path: TheanoDL/GRBM_DBN - 副本.py
import cPickle
import gzip
import os
import sys
import time
import numpy
import theano
import theano.tensor as T
import warnings
                import warnings
        import urllib
from theano.tensor.shared_randomstreams import RandomStreams
from logistic_sgd import LogisticRegression, load_data
from mlp import HiddenLayer
from rbm import RBM
from grbm import GBRBM
from utils import zero_mean_unit_variance
from utils import normalize
    from sklearn import preprocessing

        data_x, data_y = data_xy
        shared_x = theano.shared(numpy.asarray(data_x,
                                               dtype=theano.config.floatX),
                                 borrow=borrow)
        shared_y = theano.shared(numpy.asarray(data_y,
                                               dtype=theano.config.floatX),
                                 borrow=borrow)
        # When storing data on the GPU it has to be stored as floats
        # therefore we will store the labels as ``floatX`` as well
        # (``shared_y`` does exactly that). But during our computations
        # we need them as ints (we use labels as index, and if they are
        # floats it doesn't make sense) therefore instead of returning
        # ``shared_y`` we will have to cast it to int. This little hack
        # lets ous get around this issue
        return shared_x, T.cast(shared_y, 'int32')


    # Wudi made it a small set:
    train_set_feature = train_set[0][0:1000,:]
    [train_set_feature1, Mean1, Std1]  = preprocessing.scale(train_set_feature)
    # Wudi added normalized data for GRBM
    #[train_set_feature2, Mean2, Var2] = zero_mean_unit_variance(train_set_feature)
    train_set_new_target = train_set[1][0:1000]
    train_set_x, train_set_y = shared_dataset( (train_set_feature1,train_set_new_target))

    valid_set_feature = valid_set[0]
    valid_set_feature = normalize(valid_set_feature, Mean1, Std1)
    valid_set_x, valid_set_y = shared_dataset((valid_set_feature,valid_set[1]))
    # test feature set
    test_set_feature = test_set[0]

    test_set_feature = normalize(test_set_feature, Mean1, Std1)