global dimensionality-reduction (GDR) algorithm 示例



In [1]:

    
import numpy as np
import pandas as pd
from scipy import linalg
from scipy import optimize 
import functools

import tensorly
from tensorly.decomposition import partial_tucker
from tensorly.decomposition import tucker
tensorly.set_backend('numpy')









    



Using mxnet backend.
Using numpy backend.

生成数据集



In [2]:

    
tensor_steam_length = 300

factors_tensor_list = []
for i in np.arange(tensor_steam_length):
    a = np.random.normal(size=[69], scale=0.5)
    b = np.random.normal(size=[16], scale=0.5)
    c = np.random.normal(size=[32], scale=0.5)
    x = np.zeros([1, 69, 16, 32])
    x[0,:,0,0] = a
    x[0,1,:,1] = b
    x[0,2,2,:] = c
    factors_tensor_list.append(x)
factors_tensor = np.concatenate(factors_tensor_list)

targets = np.random.normal(scale=0.01, size=[300,1])

3.3 $\sum$ 循环向量化

$U^i_k\in\mathbb{R}^{batch \times I_k \times J_k}$

$U^{iT}_k\in\mathbb{R}^{batch \times J_k \times I_k}$

$M^i_k = U^i_k U^{iT}_k\in \mathbb{R}^{batch \times I_k \times I_k}$

$M^i_{k(0)} \in \mathbb{R}^{batch \times I_kI_k}$

令 $N = batch$

$D_{U_k} = \sum\limits ^N_{i=1}d_{i,i}U^{i}_kU^{iT}_k \\=\sum\limits ^N_{i=1}d_{i,i}M^i_k\\= mat \{(vec(diag(D)))^T M_{k(0)}\}$
$W_{U_k} = \sum\limits^N_{i=1}\sum\limits ^N_{j=1}w_{i,j}U^i_kU^{jT}_k\\= \sum\limits^N_{i=1}(\sum\limits ^N_{j=i}w_{i,j})U^i_kU^{jT}_k\\= \sum\limits^N_{i=1}(\sum\limits ^N_{j=i}w_{i,j})U^i_kU^{jT}_k\\=\sum\limits^N_{i=1}w_iU^i_kU^{jT}_k\\=\sum\limits^N_{i=1}w_i M^i_{k}\\=mat\{w M_{k(0)} \}$

其中

$w_{i,j} = \begin{cases}1,\ if\ \ i \le j \ and \ \|y_i -y_j \| \le 5\% \\0, \ otherwise \end{cases} $
$\sum\limits ^N_{j=1}w_{i,j} = w_i= sum(w_{i,:}, axis=1)$



In [3]:

    
def get_weighting_of_geometric_structure (targets):
    W = (targets - targets.T) / targets.T # 广播
    W = np.abs(W) - 0.05 # 转换绝对值并判断相似度
    W[W>0.0]=0
    W[W<0.0]=1
    upper_traingular_matrix = np.eye(W.shape[0]).cumsum(1) # 上三角矩阵掩码
    return W * upper_traingular_matrix

def get_var_of_adjusting_geometric_structure(targets):
    W = get_weighting_of_geometric_structure(targets)
    return np.expand_dims(W.sum(0),axis=0)

D = get_var_of_adjusting_geometric_structure(targets)
W = get_weighting_of_geometric_structure(targets)

循环tucker分解

计划迁移PyTorch构建批次处理



In [4]:

    
factors_tensor = tensorly.tensor(factors_tensor)

core_list = []
mode_factors_list = []
for i in range(factors_tensor.shape[0]):
    print (i)
    core, mode_factors= tucker(factors_tensor[i])
    core = np.expand_dims(core, axis=0)
    core_list.append(core)
    mode_factors_list.append(mode_factors)

连接张量流



In [5]:

    
batch_length = tensor_steam_length
a_list = []
b_list = []
c_list = []
for i in range(batch_length):
    a = np.expand_dims(mode_factors_list[i][0], axis=0)
    b = np.expand_dims(mode_factors_list[i][1], axis=0)
    c = np.expand_dims(mode_factors_list[i][2], axis=0)
    a_list.append(a)
    b_list.append(b)
    c_list.append(c)
    
U1 = np.concatenate(a_list)
U2 = np.concatenate(b_list)
U3 = np.concatenate(c_list)

M1 = U1 * np.transpose(U1, axes=[0,2,1])
M2 = U2 * np.transpose(U2, axes=[0,2,1])
M3 = U3 * np.transpose(U3, axes=[0,2,1])

暂时没有找到较好的批次处理tucker分解的方法，这里特例处理



In [6]:

    
D_U1_core = np.matmul(D, tensorly.base.unfold(M1, mode=0))
I_k = np.int(np.sqrt(D_U1_core.shape[1]))
D_U1 = tensorly.base.fold(D_U1_core, mode=0, shape=[I_k, I_k])

D_U2_core = np.matmul(D, tensorly.base.unfold(M2, mode=0))
I_k = np.int(np.sqrt(D_U2_core.shape[1]))
D_U2 = tensorly.base.fold(D_U2_core, mode=0, shape=[I_k, I_k])

D_U3_core = np.matmul(D, tensorly.base.unfold(M3, mode=0))
I_k = np.int(np.sqrt(D_U3_core.shape[1]))
D_U3 = tensorly.base.fold(D_U3_core, mode=0, shape=[I_k, I_k])



In [7]:

    
vec_W = np.expand_dims(W.sum(axis=0), axis=0)

W_U1_core = np.matmul(vec_W, tensorly.base.unfold(M1, mode=0))
I_k = np.int(np.sqrt(W_U1_core.shape[1]))
W_U1 = tensorly.base.fold(W_U1_core, mode=0, shape=[I_k, I_k])

W_U2_core = np.matmul(vec_W, tensorly.base.unfold(M2, mode=0))
I_k = np.int(np.sqrt(W_U2_core.shape[1]))
W_U2 = tensorly.base.fold(W_U2_core, mode=0, shape=[I_k, I_k])

W_U3_core = np.matmul(vec_W, tensorly.base.unfold(M3, mode=0))
I_k = np.int(np.sqrt(W_U3_core.shape[1]))
W_U3 = tensorly.base.fold(W_U3_core, mode=0, shape=[I_k, I_k])

二次规划优化估计修正矩阵$V_k$

添加约束条件 $trace(V^TD_UV)=1$ 获得目标函数的唯一优化结果，这是获取因子矩阵对应的修正矩阵的优化问题转化为：

$min\ J(V) = trace(V^TD_UV-V^TW_UV)$
$s.t. \ trace(V^TD_UV)=1$



In [8]:

    
def objective_function(V, D_U, W_U, J_K):
    newshape = [D_U.shape[0], J_K]
    V = np.reshape(V,newshape=newshape)
    left = np.matmul(np.matmul(V.T, D_U),V)
    right = np.matmul(np.matmul(V.T, W_U),V)
    return np.trace(left + right)

def constraints(V, D_U):
    newshape = [D_U.shape[0], J_K]
    V = np.reshape(V,newshape=newshape)
    left = np.matmul(np.matmul(V.T, D_U),V)
    return np.trace(left)- 1.0

$V_1$



In [9]:

    
objective_function_1 = functools.partial(
    objective_function, D_U = D_U1, W_U = W_U1, J_K = 10)

constraints_1 = functools.partial(constraints, D_U = D_U1)

cons_1 = ({'type':'ineq', 'fun':constraints_1})
initial_1 = np.random.normal(scale=0.1, size=D_U1.shape[0]*10).reshape(D_U1.shape[0],10)

V1 = optimize.minimize(objective_function_1, initial_1).x.reshape(D_U1.shape[0],10)

$V_2$



In [10]:

    
objective_function_2 = functools.partial(
    objective_function, D_U = D_U2, W_U = W_U2, J_K = 10)

constraints_2 = functools.partial(constraints, D_U = D_U2)

cons_2 = ({'type':'ineq', 'fun':constraints_2})
initial_2 = np.random.normal(scale=0.1, size=D_U2.shape[0]*10).reshape(D_U2.shape[0],10)

V2 = optimize.minimize(objective_function_2, initial_2).x.reshape(D_U2.shape[0],10)

$V_3$



In [11]:

    
objective_function_3 = functools.partial(
    objective_function, D_U = D_U3, W_U = W_U3, J_K = 10)

constraints_3 = functools.partial(constraints, D_U = D_U3)

cons_3 = ({'type':'ineq', 'fun':constraints_3})
initial_3 = np.random.normal(scale=0.1, size=D_U3.shape[0]*10).reshape(D_U3.shape[0],10)

V3 = optimize.minimize(objective_function_3, initial_3).x.reshape(D_U3.shape[0],10)

$\bar{\mathcal{X}_i} = \mathcal{C}_i \times_1 (V^T_1U^i_1) \times_2 (V^T_2U^i_2) \times_3 (V^T_3U^i_3)$



In [12]:

    
new_U1 =  np.matmul(V1.T, U1)
new_U2 =  np.matmul(V2.T, U2)
new_U3 =  np.matmul(V3.T, U3)



In [13]:

    
unfold_mode1 = tensorly.base.partial_unfold(factors_tensor, mode=0, skip_begin=1)
times_mode1 = np.matmul(new_U1, unfold_mode1)
times1_shape = (factors_tensor.shape[0] ,new_U1.shape[1], factors_tensor.shape[2], factors_tensor.shape[3])
times1 = tensorly.base.partial_fold(times_mode1, 0, times1_shape, skip_begin=1, skip_end=0)

unfold_mode2 = tensorly.base.partial_unfold(times1, mode=1, skip_begin=1)
times_mode2 = np.matmul(new_U2, unfold_mode2)
times2_shape = (factors_tensor.shape[0] ,new_U1.shape[1] ,new_U2.shape[1], factors_tensor.shape[3])
times2 = tensorly.base.partial_fold(times_mode2, 1, times2_shape, skip_begin=1, skip_end=0)



In [14]:

    
unfold_mode3 = tensorly.base.partial_unfold(times2, mode=2, skip_begin=1)
times_mode3 = np.matmul(new_U3, unfold_mode3)
times3_shape = (factors_tensor.shape[0] ,new_U1.shape[1], new_U2.shape[1], new_U3.shape[1])
times3 = tensorly.base.partial_fold(times_mode3, 2, times3_shape, skip_begin=1, skip_end=0)



In [15]:

    
new_factors_tensor = times3

动态关系捕获和降维 $\mathcal{X}\in\mathbb{R}^{I_1 \times I_2 \times I_3} \to \bar{\mathcal{X}} \in \mathbb{R}^{J_1 \times J_2 \times J_3}$



In [16]:

    
factors_tensor.shape









    Out[16]:





(300, 69, 16, 32)



In [17]:

    
new_factors_tensor.shape









    Out[17]:





(300, 10, 10, 10)



In [18]:

    
new_factors_tensor









    Out[18]:





array([[[[  1.20088711e+18,  -9.39257236e+18,   4.17323386e+18, ...,
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       [[[  9.99455160e+17,   8.65125923e+17,  -2.68625403e+18, ...,
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        ..., 
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         [  2.82041169e+19,   1.73071238e+19,  -4.61519108e+19, ...,
            2.99913386e+19,  -9.13591910e+18,  -3.17546140e+19]],

        [[ -4.28463225e+17,  -5.20862836e+17,   1.80094208e+18, ...,
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         ..., 
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            1.93298594e+17,   1.11269164e+18,  -5.68719928e+18]],

        [[  2.41479014e+18,   1.64138491e+18,  -4.57267689e+18, ...,
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            1.62658647e+19,  -7.36805972e+18,  -5.67064590e+18],
         ..., 
         [ -1.27851878e+19,  -5.99182874e+18,   1.30953305e+19, ...,
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         [  1.21245164e+17,  -5.05135168e+17,   2.31700644e+18, ...,
            7.98344196e+18,  -4.31065162e+18,   2.60155584e+17],
         [  3.42351642e+18,   1.01764499e+18,  -1.06908931e+18, ...,
            1.76730561e+19,  -8.68047810e+18,  -3.18665336e+18]]],


       [[[ -5.52863108e+17,   6.99011993e+17,   8.07162785e+17, ...,
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         ..., 
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            1.37217289e+18,   1.22410058e+18,  -1.73961989e+18]],

        [[  1.35781328e+18,   1.34057131e+19,   1.77721631e+18, ...,
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         ..., 
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         [ -5.35807083e+18,  -5.51773995e+19,  -7.57989546e+18, ...,
            2.38135209e+18,   2.65559122e+18,  -1.61853677e+19]],

        [[ -2.86104175e+17,   4.02780239e+18,   1.32912852e+18, ...,
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         [  2.38625580e+17,  -1.52415252e+18,  -6.52330951e+17, ...,
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            2.12286684e+18,   1.78876978e+18,  -5.53843551e+16],
         ..., 
         [ -2.76356404e+18,   3.51393608e+18,   4.03975041e+18, ...,
           -3.32239128e+18,  -2.95782371e+18,   4.02970486e+18],
         [  5.41540462e+17,  -2.16395512e+18,  -1.15841682e+18, ...,
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         [ -7.63155622e+16,  -1.53236936e+19,  -3.72212820e+18, ...,
            2.10334165e+18,   2.00536582e+18,  -5.85866058e+18]],

        ..., 
        [[  1.48223150e+17,  -2.16970667e+19,  -5.56392484e+18, ...,
            3.23977525e+18,   3.06966235e+18,  -8.54287379e+18],
         [ -5.11044836e+17,   7.93000249e+18,   2.55676046e+18, ...,
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         [ -1.26053676e+19,  -4.96702075e+19,   2.09275620e+18, ...,
           -5.80315076e+18,  -4.59951176e+18,  -7.05335290e+18],
         ..., 
         [  8.87157354e+18,  -1.34894362e+19,  -1.35175239e+19, ...,
            1.09799974e+19,   9.79414034e+18,  -1.37913390e+19],
         [ -1.42882423e+18,   1.08182412e+19,   4.32642156e+18, ...,
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         [  3.48889975e+18,   8.50639484e+19,   1.71509102e+19, ...,
           -8.54490659e+18,  -8.37914260e+18,   2.95611957e+19]],

        [[  3.35838288e+17,  -2.60750361e+18,  -1.03300292e+18, ...,
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           -1.98280212e+18,  -1.70220410e+18,   8.30237592e+17],
         ..., 
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            2.89867467e+18,   2.57855434e+18,  -3.46456146e+18],
         [ -5.11453745e+17,   1.49150721e+18,   9.56787058e+17, ...,
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         [ -2.83649031e+17,   9.64819727e+18,   2.72344702e+18, ...,
           -1.66319151e+18,  -1.56050736e+18,   4.00920982e+18]],

        [[ -9.91006712e+17,  -5.24816977e+18,  -1.69409634e+17, ...,
           -2.65039911e+17,  -1.79809842e+17,  -1.07452046e+18],
         [  4.46728683e+17,   1.71201768e+18,  -8.65684324e+16, ...,
            2.12091054e+17,   1.69723221e+17,   2.31459748e+17],
         [  1.92407291e+17,  -1.79220558e+19,  -4.67688541e+18, ...,
            2.74662352e+18,   2.59882105e+18,  -7.12373103e+18],
         ..., 
         [ -2.28809327e+18,   7.46694053e+17,   2.80710075e+18, ...,
           -2.44288002e+18,  -2.15625339e+18,   2.49912593e+18],
         [  7.51361845e+17,   1.99877243e+18,  -3.64022665e+17, ...,
            4.82665127e+17,   4.04412267e+17,   4.81067534e+16],
         [  3.11361699e+18,   2.24308522e+19,   2.01005845e+18, ...,
           -1.23702208e+16,  -2.39514662e+17,   5.67600244e+18]]],


       ..., 
       [[[ -1.12646850e+17,   1.18088830e+17,   6.79233070e+16, ...,
           -2.17116228e+17,  -2.45196186e+17,   3.60756335e+17],
         [ -4.72940003e+18,   4.78217188e+18,   3.17521351e+18, ...,
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           -1.08083303e+19,  -1.33124670e+19,   8.07254416e+18],
         ..., 
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            1.44024659e+19,   1.78465565e+19,  -9.87024952e+18],
         [ -3.97169833e+18,   4.01361304e+18,   2.67099713e+18, ...,
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           -9.22357168e+18,  -1.14881698e+19,   5.81427521e+18]],

        [[  4.75482044e+17,  -9.38188135e+17,   5.34013393e+17, ...,
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            1.01419822e+19,   7.04349411e+18,  -4.84858935e+19],
         ..., 
         [  5.37150980e+18,  -5.98994809e+18,  -2.56440605e+18, ...,
            1.43827920e+19,   1.48271943e+19,  -3.20627635e+19],
         [ -2.16210297e+18,   2.50809158e+18,   8.51478150e+17, ...,
           -7.05015671e+18,  -7.00709843e+18,   1.77681494e+19],
         [ -4.79214784e+18,   6.31224391e+18,   4.81948081e+17, ...,
           -2.51290837e+19,  -2.32783733e+19,   7.57254051e+19]],

        [[ -1.40225982e+16,  -9.08348759e+16,   2.05449289e+17, ...,
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         [ -5.86184417e+18,   5.96300758e+18,   3.86870332e+18, ...,
           -8.43867192e+18,  -1.02584115e+19,   7.21446773e+18],
         [ -9.29587445e+18,   9.16714587e+18,   6.67455097e+18, ...,
           -9.73254845e+18,  -1.32919665e+19,  -2.51925391e+18],
         ..., 
         [  1.31868423e+19,  -1.34648120e+19,  -8.60926648e+18, ...,
            1.96423321e+19,   2.36212701e+19,  -1.87739685e+19],
         [ -4.98472963e+18,   5.11478626e+18,   3.20784893e+18, ...,
           -7.74911942e+18,  -9.19602148e+18,   8.34639805e+18],
         [ -8.89379417e+18,   9.33081042e+18,   5.34082267e+18, ...,
           -1.63867393e+19,  -1.84877367e+19,   2.46571628e+19]],

        ..., 
        [[ -2.59862423e+17,   8.94066254e+17,  -1.00338455e+18, ...,
           -8.31461607e+18,  -6.92737149e+18,   3.06835654e+19],
         [  2.09269608e+19,  -2.13699840e+19,  -1.36586432e+19, ...,
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         [  3.23116894e+19,  -3.12603689e+19,  -2.43255021e+19, ...,
            2.60756233e+19,   3.98392850e+19,   3.85609525e+19],
         ..., 
         [ -4.72446209e+19,   4.85509527e+19,   3.02657012e+19, ...,
           -7.43303822e+19,  -8.78670991e+19,   8.24427485e+19],
         [  1.79401858e+19,  -1.85871031e+19,  -1.12118172e+19, ...,
            3.01799709e+19,   3.49746722e+19,  -3.88375328e+19],
         [  3.26119508e+19,  -3.50046853e+19,  -1.81107384e+19, ...,
            7.01909319e+19,   7.60688940e+19,  -1.29202203e+20]],

        [[  4.14503708e+16,   2.05581302e+16,  -1.44383635e+17, ...,
           -7.31133629e+17,  -5.72038699e+17,   2.97281269e+18],
         [  5.17378188e+18,  -5.25286551e+18,  -3.43373064e+18, ...,
            7.32926897e+18,   8.96034055e+18,  -5.92392591e+18],
         [  8.29187293e+18,  -8.24565774e+18,  -5.82569754e+18, ...,
            9.55067354e+18,   1.25662049e+19,  -1.08149452e+18],
         ..., 
         [ -1.16190327e+19,   1.18271857e+19,   7.65442927e+18, ...,
           -1.68574160e+19,  -2.04506352e+19,   1.48381864e+19],
         [  4.38269617e+18,  -4.47633912e+18,  -2.85907436e+18, ...,
            6.55452177e+18,   7.87522044e+18,  -6.35179539e+18],
         [  7.76342264e+18,  -8.05176688e+18,  -4.83600864e+18, ...,
            1.31538601e+19,   1.52077305e+19,  -1.71518442e+19]],

        [[ -2.87893696e+17,   4.90336640e+17,  -1.78291780e+17, ...,
           -2.91890595e+18,  -2.54962939e+18,   9.94700994e+18],
         [ -2.56985530e+18,   2.53479197e+18,   1.84440900e+18, ...,
           -2.71776906e+18,  -3.70295335e+18,  -5.69692047e+17],
         [ -4.88070128e+18,   5.38571744e+18,   2.43712982e+18, ...,
           -1.24393095e+19,  -1.29860770e+19,   2.68244917e+19],
         ..., 
         [  5.62075920e+18,  -5.44191153e+18,  -4.22431854e+18, ...,
            4.61974770e+18,   7.00842929e+18,   6.35108762e+18],
         [ -2.04726432e+18,   1.93033544e+18,   1.63510058e+18, ...,
           -1.01365092e+18,  -2.00253971e+18,  -4.88920394e+18],
         [ -3.10530829e+18,   2.50193889e+18,   3.27467606e+18, ...,
            3.86344531e+18,   1.37382734e+18,  -2.81009174e+19]]],


       [[[ -2.34662545e+18,   3.78842621e+18,  -2.92935785e+18, ...,
           -6.74710630e+18,  -1.93072474e+18,   4.79301512e+18],
         [  1.44980435e+18,  -1.80397088e+18,   2.35650916e+18, ...,
            3.58549600e+18,   1.92569399e+18,  -3.75241807e+18],
         [  6.83837085e+18,  -9.44374184e+18,   1.01611185e+19, ...,
            1.79256892e+19,   7.80484556e+18,  -1.63188080e+19],
         ..., 
         [ -3.50254634e+18,   5.64301640e+18,  -4.38441051e+18, ...,
           -1.00585090e+19,  -2.89784656e+18,   7.17145281e+18],
         [ -5.06908417e+17,   7.52449252e+15,  -1.45807953e+18, ...,
           -5.75572658e+17,  -1.52370774e+18,   2.22984323e+18],
         [  2.05076224e+18,  -4.28150222e+18,   1.57287951e+18, ...,
            6.95333393e+18,   3.63269864e+17,  -2.75988121e+18]],

        [[  1.14093763e+19,  -2.19985118e+19,   1.05970358e+19, ...,
            3.66940388e+19,   4.49994382e+18,  -1.80276921e+19],
         [ -1.32371772e+19,   2.73691714e+19,  -1.04147716e+19, ...,
           -4.45801220e+19,  -2.70025409e+18,   1.81949298e+19],
         [ -5.16466176e+19,   1.05069595e+20,  -4.23775962e+19, ...,
           -1.72067021e+20,  -1.28735784e+19,   7.35128981e+19],
         ..., 
         [  1.71645465e+19,  -3.31354856e+19,   1.59019429e+19, ...,
            5.52480859e+19,   6.71527608e+18,  -2.70626213e+19],
         [  1.18106481e+19,  -2.55607240e+19,   8.12924807e+18, ...,
            4.10147304e+19,   8.50288913e+17,  -1.45508363e+19],
         [  1.21286315e+18,  -5.67510824e+18,  -2.27394567e+18, ...,
            7.52433498e+18,  -4.07955257e+18,   3.00469957e+18]],

        [[  1.77077763e+17,  -1.14389851e+18,  -6.51758634e+17, ...,
            1.44300205e+18,  -1.02487078e+18,   9.01623273e+17],
         [ -1.59172068e+18,   4.59219025e+18,   7.15743427e+16, ...,
           -6.77633467e+18,   1.45072561e+18,   2.71676303e+17],
         [ -4.92627620e+18,   1.48903027e+19,   9.13255894e+17, ...,
           -2.17071582e+19,   5.41669363e+18,  -1.60146338e+17],
         ..., 
         [  2.96007692e+17,  -1.79115772e+18,  -9.65922575e+17, ...,
            2.28103970e+18,  -1.54766302e+18,   1.32836841e+18],
         [  2.27850458e+18,  -6.11766904e+18,   3.60062249e+17, ...,
            9.20234799e+18,  -1.45585553e+18,  -1.05848904e+18],
         [  2.52776646e+18,  -6.02796549e+18,   1.16985275e+18, ...,
            9.38098710e+18,  -5.81237337e+17,  -2.28954711e+18]],

        ..., 
        [[ -7.43874516e+18,   1.70861834e+19,  -4.11195992e+18, ...,
           -2.69020364e+19,   8.14894199e+17,   7.70589655e+18],
         [  1.33766510e+19,  -3.21063687e+19,   5.98975805e+18, ...,
            4.98804396e+19,  -3.34929418e+18,  -1.18240631e+19],
         [  4.77767670e+19,  -1.13855498e+20,   2.22338110e+19, ...,
            1.77277277e+20,  -1.08390015e+19,  -4.34469504e+19],
         ..., 
         [ -1.12960707e+19,   2.59743497e+19,  -6.21358493e+18, ...,
           -4.08802854e+19,   1.27773009e+18,   1.16576577e+19],
         [ -1.48651844e+19,   3.62348000e+19,  -6.09575195e+18, ...,
           -5.60417050e+19,   4.47567820e+18,   1.23279782e+19],
         [ -9.36037725e+18,   2.40071111e+19,  -2.63559960e+18, ...,
           -3.65947683e+19,   4.43478119e+18,   6.02087227e+18]],

        [[  6.43344271e+17,  -5.08021396e+17,   1.34304067e+18, ...,
            1.27249821e+18,   1.25337833e+18,  -2.09552361e+18],
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            3.04058024e+18,  -1.51513167e+18,   1.03591405e+18],
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        [[ -6.64648217e+18,   1.22640461e+19,  -6.73444323e+18, ...,
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         ..., 
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       [[[  3.14585383e+15,  -2.36689691e+16,   8.32403433e+15, ...,
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        [[ -1.59979461e+18,  -6.20476631e+18,   7.52919359e+18, ...,
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         ..., 
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        [[ -3.70501893e+17,  -1.47764705e+18,   1.77007074e+18, ...,
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         ..., 
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        ..., 
        [[  2.25770556e+18,   8.88947263e+18,  -1.07110187e+19, ...,
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         ..., 
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        [[  2.35771001e+17,   9.48792817e+17,  -1.13138152e+18, ...,
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         ..., 
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           -7.89060637e+18,   3.09129179e+18,   2.51661446e+18]],

        [[  7.00711214e+17,   2.68785193e+18,  -3.27804583e+18, ...,
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           -7.21315790e+17,   1.89341651e+18,  -1.04075197e+19],
         ..., 
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           -3.64321918e+18,   4.39622520e+15,   1.05828010e+19],
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            4.21115346e+18,  -1.08944256e+18,  -5.11431478e+18]]]])



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