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In [5]:



    


# this part imports libs and load data from csv file
import sys
sys.path.append('../../../libs/')
import csv
from dateutil import parser
from datetime import timedelta
from sklearn import svm
import numpy as np
import pandas as pd
import pickle
from sklearn.cross_validation import train_test_split
from sklearn import preprocessing
import sklearn
import scipy.stats as ss
import cPickle
import gzip
import os
import time
import numpy
import theano
import theano.tensor as T
from theano.tensor.shared_randomstreams import RandomStreams
import os.path
import IO_class
from IO_class import FileOperator
from sklearn import cross_validation
import sklearn
import numpy as np
import csv
from dateutil import parser
from datetime import timedelta
from sklearn import svm
import numpy as np
import pandas as pd
import pdb, PIL
import pickle
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.cross_validation import KFold
from sklearn import preprocessing
import sklearn
import scipy.stats as ss
from sklearn.svm import LinearSVC
import random
from DL_libs import *
from itertools import izip #new
import math
from sklearn.svm import SVC



    











In [7]:



    


class NetworkDisplayer:
    def __init__(self, StackedNNobject, input_dimension_x =28, input_dimension_y  = 28):
        self.StackedNNobject = StackedNNobject
        self.input_dimension_x = input_dimension_x
        self.input_dimension_y = input_dimension_y
        self.weights_map_to_input_space = self.map_to_input_space()
    
    def map_to_input_space(self):
        weights_map_to_input_space = []
        if isinstance(self.StackedNNobject, SdA) or isinstance(self.StackedNNobject, MultipleAEs):
            weights_product = self.StackedNNobject.dA_layers[0].W.get_value(borrow=True)
            weights_map_to_input_space.append(weights_product)
            for i_layer in range(1, len(self.StackedNNobject.dA_layers)):
                i_weigths = self.StackedNNobject.dA_layers[i_layer].W.get_value(borrow=True)
                weights_product = np.dot(weights_product, i_weigths)
                weights_map_to_input_space.append(weights_product)
        return weights_map_to_input_space
    def save_to_file(self, file_prefix):
        weights_map_to_input_space = []
        if isinstance(self.StackedNNobject, SdA) or isinstance(self.StackedNNobject, MultipleAEs):
            weights_product = self.StackedNNobject.dA_layers[0].W.get_value(borrow=True)
            image = PIL.Image.fromarray(tile_raster_images(
                X=weights_product.T,
                img_shape=(self.input_dimension_x, self.input_dimension_y), tile_shape=(10, 10),
                tile_spacing=(1, 1)))
            sample_image_path = file_prefix + 'hidden_0_layer_weights.png'
            image.save(sample_image_path)
            weights_map_to_input_space.append(weights_product)
            for i_layer in range(1, len(self.StackedNNobject.dA_layers)):
                i_weigths = self.StackedNNobject.dA_layers[i_layer].W.get_value(borrow=True)
                weights_product = np.dot(weights_product, i_weigths)
                weights_map_to_input_space.append(weights_product)
                image = PIL.Image.fromarray(tile_raster_images(
                X=weights_product.T,
                img_shape=(self.input_dimension_x, self.input_dimension_y), tile_shape=(10, 10),
                tile_spacing=(1, 1)))
                sample_image_path = file_prefix + 'hidden_'+ str(i_layer)+ '_layer_weights.png'
                image.save(sample_image_path)



    











In [ ]:



    


# save objects
sda, a_MAE_original, a_MAE_A, a_MAE_B, analysis_scr
with open('_'.join(map(str, settings['hidden_layers_sizes'])) +'_'.join(map(str, settings['corruption_levels']))+ '_' + current_date +'sda.pickle', 'wb') as handle:
  pickle.dump(sda, handle)
with open('_'.join(map(str, settings['hidden_layers_sizes'])) +'_'.join(map(str, settings['corruption_levels']))+ '_' + current_date + 'a_MAE_original.pickle', 'wb') as handle:
  pickle.dump(a_MAE_original, handle)
with open('_'.join(map(str, settings['hidden_layers_sizes'])) +'_'.join(map(str, settings['corruption_levels']))+ '_' + current_date + 'a_MAE_A.pickle', 'wb') as handle:
  pickle.dump(a_MAE_A, handle)
with open('_'.join(map(str, settings['hidden_layers_sizes'])) +'_'.join(map(str, settings['corruption_levels']))+ '_' + current_date + 'a_MAE_B.pickle', 'wb') as handle:
  pickle.dump(a_MAE_B, handle)



    











In [14]:



    


a_MAE_A = pickle.load(open('200_200_200_200_2000_0_0_0_0_01_09_2015a_MAE_original.pickle', 'r'))



    











In [15]:



    


a_network_displayer = NetworkDisplayer(a_MAE_A, 28*2, 28)



    











In [3]:



    


a = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0]



    











In [14]:



    


a= np.array(a)



    











In [19]:



    


from scipy.misc import toimage
toimage(a[:784].reshape(28,28)).show()



    











In [22]:



    


import scipy
scipy.misc.imsave('clusteringB.jpg', a[784:].reshape(28,28))



    











In [16]:



    


a_network_displayer.save_to_file('200_200_200_200_2000_0_0_0_0_01_09_2015a_MAE_original')



    


















    
Out[16]:








[array([[-0.00180203, -0.23674197,  0.07596708, ...,  0.12929686,
         -0.20206118,  0.02346152],
        [-0.22701688, -0.16748825,  0.12354064, ...,  0.09457577,
          0.00725654,  0.11224164],
        [-0.26869998,  0.06011932, -0.22648984, ...,  0.02103613,
         -0.23438133, -0.1367956 ],
        ..., 
        [ 0.01468106,  0.12848775, -0.13793831, ...,  0.04847079,
          0.07931987,  0.10724036],
        [ 0.14664115,  0.04559146,  0.06085094, ..., -0.21585745,
         -0.12516544, -0.30335294],
        [ 0.06935384, -0.0646003 , -0.18972963, ...,  0.13061687,
         -0.23277142, -0.03621377]]),
 array([[-0.71654556,  0.67587138,  0.46326179, ..., -0.42290064,
          0.51260979, -0.22242207],
        [-0.61675276,  0.34081306,  0.09010535, ..., -0.11756547,
          1.01835901,  0.15331363],
        [ 0.79785108,  1.08504812, -0.27115732, ..., -0.3441766 ,
          0.05359748,  1.28391285],
        ..., 
        [ 1.81597605, -0.29634281,  0.76197401, ..., -0.16985752,
         -0.71881914, -0.34142907],
        [-0.38803706, -0.20387243, -0.02294153, ...,  0.46357759,
          0.17827442,  0.86902087],
        [-0.84701103,  0.81619142, -0.66421646, ..., -0.25439183,
         -0.13612463, -0.57735069]]),
 array([[-1.40058412, -2.35752943,  1.06120752, ...,  3.52969413,
         -0.31212787, -4.87874486],
        [ 0.40157377, -2.63664805,  6.80183716, ..., -9.17230986,
         -0.53682593, -5.75991949],
        [-0.86233239, -0.66096286, -1.09705117, ...,  2.27643893,
          1.62306525, -0.45916656],
        ..., 
        [-0.41084586,  7.23833816, -0.46330102, ...,  1.06311225,
         -3.85769959,  1.13858023],
        [-2.5703622 , -2.75175121,  0.82838906, ..., -0.52956603,
         -6.15544862, -1.23000044],
        [-2.24128358, -2.49820117,  1.35031306, ...,  0.63099488,
          1.13671097,  1.50671667]]),
 array([[ 11.44452253,   6.50622895,  -7.57971015, ...,  36.47830463,
         -15.23433121, -19.68225733],
        [ 10.13781948,  16.78813042, -35.54602823, ...,   1.443216  ,
         -18.58461855,  -1.83285192],
        [ 16.75645581, -27.09345245,  19.46964253, ...,   4.2713101 ,
           0.84190277, -15.8549687 ],
        ..., 
        [ 26.57434633, -27.34460714, -22.28193131, ...,  20.24928928,
          -3.75231423,   1.04091015],
        [ 47.16121999,   7.21223609,  26.86751748, ..., -22.98804617,
         -19.20590029,   3.64721297],
        [-10.25106203,   3.86179284,  -9.02847964, ...,  22.16082033,
         -31.3802746 ,  12.70905338]]),
 array([[ -33.11642346,  -42.88522   ,   92.14035349, ...,   72.10557585,
           55.93733451,   30.70550353],
        [-104.3664712 ,   67.67478657,    7.67182868, ...,   66.85942848,
         -145.07496085, -109.09023576],
        [ -36.73584039,  -21.49300056,   54.18562271, ..., -102.69765237,
          -23.19257033,   24.98723016],
        ..., 
        [ 184.95469798,   77.9300401 ,  122.14504309, ..., -150.83999093,
           26.5143904 ,  -27.91517801],
        [ -55.19481707,   53.94342444,   21.24216858, ...,  -35.10886313,
           46.23616351,   59.20158219],
        [ -70.72371315,  -55.07105791,  -72.88455848, ...,   86.12929679,
           42.60111247,  -84.25425803]])]

Content source: magic2du/contact_matrix

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