In [1]:

    

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors as pyplotcolors
from sklearn.preprocessing import normalize
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
import sktensor as skt
from scipy.sparse.linalg import svds
from scipy.misc import face
%matplotlib inline


    

In [2]:

    

import tools
reload(tools)
from tools import *


    

In [5]:

    

racc_og = face()

# Normalize across each dim
SS = StandardScaler()
racc = racc_og.copy()
print racc.shape


    

    




(768, 1024, 3)

In [6]:

    

# T = np.zeros((3, 4, 2))
# T[:, :, 0] = [[ 1,  4,  7, 10], [ 2,  5,  8, 11], [3,  6,  9, 12]]
# T[:, :, 1] = [[13, 16, 19, 22], [14, 17, 20, 23], [15, 18, 21, 24]]

T = skt.dtensor(racc)

Y = skt.tucker_hooi(T, [768//2, 1024//2, 3], init='nvecs')


    

In [8]:

    

Trec = np.array(skt.ttm(Y[0],Y[1]))

core_S = Y[0]
core_S = np.array(core_S)

U1 = Y[1][0]
U2 = Y[1][1]
U3 = Y[1][2]


    

In [9]:

    

core_S.shape


    

    
Out[9]:





(384, 512, 3)

In [46]:

    

Trec[:,:,0].shape


    

    
Out[46]:





(768, 1024)

In [49]:

    

plt.plot(Trec[:,:,0].flatten())


    

    
Out[49]:





[<matplotlib.lines.Line2D at 0x2b0175c95890>]






    

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In [20]:

    

print U1


    

    




[[ 0.54043979  0.7357025 ]
 [ 0.57659506  0.02952065]
 [ 0.61275033 -0.67666119]]

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