# Le-Net 1 based architecture

```python
#We start with 41X41 (I) after first convolution (9x9)we have 33X33 (L1). The next pooling layer reduces dimension with 3 to an output image of 11X11 with 4x4 pooling kernels (L2). Then we apply different types of convolution 4x4 kernels on the L2 layer resulting in 8x8 (L3) . Then followed by pooling 2X2 resulting in 4x4 output map (L4). So we have 16 connection for each element in layer L4 (which depend on the amount of different Covolutions in L3)

\begin{equation}
f(x)=\frac{1}{1+e^{-x}} \\
F_{k}= f( \sum_{i} \mathbf{W^{k}_{i} \cdot y_{i}}-b_{k})
\end{equation}

\begin{equation}
E=\sum_{k} \frac{1}{2}|t_k-F_{k}|^{2} \\
\Delta W_{ij}= - \eta \frac{dE}{d W_{ij}}
\end{equation}

\begin{equation}
\Delta W_{ij}= \sum_{k} - \eta \frac{dE}{d F_{k}} \frac{dF_{k}}{dx_{k}} \frac{dx_{k}}{dW_{ij}}=\sum_{k} \eta (t_{k}-F_{k})\frac{e^{-x_{k}}}{(1+e^{-x_{k}})^{2}} \frac{dx_{k}}{dW_{ij}} \\
= \eta (t_{k}-F_{k})\frac{e^{-x_{k}}}{(1+e^{-x_{k}})^{2}} y_{ij}
\end{equation}

\begin{equation}
\Delta b_{k}= - \eta \frac{dE}{d F_{k}} \frac{dF_{k}}{dx_{k}} \frac{dx_{k}}{b_{k}}=\eta (t_{k}-F_{k})\frac{e^{-x_{k}}}{(1+e^{-x_{k}})^{2}} \cdot-1
\end{equation}

Since $\frac{e^{-x_{k}}}{(1+e^{-x_{k}})^{2}}$ is always positive we can neglect this term in our programme

\begin{equation}
x_{k}=\sum_{ij} W^{k}[i,j] \; y^{4rb}[i,j] - b_{k}
\end{equation}

\begin{equation}
y^{4rb}[i,j]= \sum_{u,v} W^{3rb}[u,v] \; y^{3rb} [2i+u,2j+v]
\end{equation}

\begin{equation}
y^{3rb} [2i+u,2j+v]= f\left (x^{3rb}[2i+u,2j+v] \right)
\end{equation}

\begin{equation}
x^{3rb}[2i+u,2j+v]=\sum_{nm} W^{2rb}[n,m] \; y^{2rb}[n+(2i+u),m+(2j+v)] -b^{3rb}[2i+u,2j+v]
\end{equation}

\begin{equation}
\begin{split}
\Delta W^{2rb}[n,m] =\sum_{k} - \eta  \frac{dE}{dF_{k}}
\frac{dF_{k}}{dx_{k}}
\sum_{ij} \frac{dx_{k}}{dy^{4rb}[i,j]}
 \sum_{uv}\frac{dy^{4rb}[i,j]}{d y^{3rb} [2i+u,2j+v]}
\frac{d y^{3rb} [2i+u,2j+v]}{d x^{3rb}[2i+u,2j+v]}
\sum_{nm}\frac{d x^{3rb}[2i+u,2j+v]}{d W^{2rb}[n,m]}
\end{split}
\end{equation}

\begin{equation}
\begin{split}
\Delta b^{3rb}[2i+u,2j+v] =\sum_{k} - \eta  \frac{dE}{dF_{k}}
\frac{dF_{k}}{dx_{k}}
\sum_{ij} \frac{dx_{k}}{dy^{4rb}[i,j]}
 \sum_{uv}\frac{dy^{4rb}[i,j]}{d y^{3rb} [2i+u,2j+v]}
\frac{d y^{3rb} [2i+u,2j+v]}{d x^{3rb}[2i+u,2j+v]}
\frac{d x^{3rb}[2i+u,2j+v]}{d b^{3rb}[2i+u,2j+v]}
\end{split}
\end{equation}

\begin{equation}
    \frac{dx_{k}}{dy^{4rb}[i,j]} = W^{4rbk}[i,j]\\
\end{equation}

\begin{equation}
    \frac{dy^{4rb}[i,j]}{d y^{3rb} [2i+u,2j+v]} = W^{3rb}[u,v] \\
 \end{equation}

\begin{equation}
\frac{d y^{3rb} [2i+u,2j+v]}{d x^{3rb}[2i+u,2j+v]}=\frac{e^{-x^{3rb}[2i+u,2j+v]}}{(1+e^{-x^{3rb}[2i+u,2j+v]})^2}
\end{equation}
This term is first not included since it is always positive. If the training will not converge it might be possible to include this term

 \begin{equation}
    \frac{d y^{3rb} [2i+u,2j+v]}{d W^{2rb}[n,m]}= y^{2rb} [n+(2i+u),m+(2j+v)] \\
\end{equation}

\begin{equation}
\frac{d x^{3rb}[2i+u,2j+v]}{d b^{3rb}[2i+u,2j+v]}=-1
\end{equation}
```

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from numpy import linalg as lin
import scipy.signal as sig
from PIL import Image
import glob
import matplotlib.cm as cm
import itertools

```python
########### Load Input ############################################################################################################################
# In this script I used the brightness to determine structures, instead of one RGB color:
# this is determined by: 0.2126*R + 0.7152*G + 0.0722*B
# Source: https://en.wikipedia.org/wiki/Relative_luminance

patchSize=41 # patchsize this must be 48 since our network can only handle this value

# Open forest
Amount_data= len(glob.glob('Forest/F*'))
dataPatchedF=[]
for k in range (0, Amount_data):
    name="Forest/F%d.png" % (k+1)
    img = Image.open(name)
    data=img.convert('RGB')
    data= np.asarray( data, dtype="int32" )
    data=0.2126*data[:,:,0]+0.7152*data[:,:,1]+0.0722*data[:,:,2]
    Yamount=data.shape[0]/patchSize # Counts how many times the windowsize fits in the picture
    Xamount=data.shape[1]/patchSize # Counts how many times the windowsize fits in the picture
    dataPatchedF.append(np.array([[data[j*patchSize:(j+1)*patchSize,i*patchSize:(i+1)*patchSize] for i in range(0,Xamount)] for j in range(0,Yamount)]))

# Open city
Amount_data= len(glob.glob('City/C*'))
dataPatchedC=[]
for k in range (0, Amount_data):
    name="City/C%d.png" % (k+1)
    img = Image.open(name)
    data=img.convert('RGB')
    data = np.asarray( data, dtype="int32" )
    data=0.2126*data[:,:,0]+0.7152*data[:,:,1]+0.0722*data[:,:,2]
    Yamount=data.shape[0]/patchSize # Counts how many times the windowsize fits in the picture
    Xamount=data.shape[1]/patchSize # Counts how many times the windowsize fits in the picture
    dataPatchedC.append(np.array([[data[j*patchSize:(j+1)*patchSize,i*patchSize:(i+1)*patchSize] for i in range(0,Xamount)] for j in range(0,Yamount)]))

# Open water
dataPatchedW=[]
Amount_data= len(glob.glob('Water/W*'))
for k in range (0, Amount_data):
    name="Water/W%d.png" % (k+1)
    img = Image.open(name)
    data=img.convert('RGB')
    data = np.asarray( data, dtype="int32" )
    data=0.2126*data[:,:,0]+0.7152*data[:,:,1]+0.0722*data[:,:,2]
    Yamount=data.shape[0]/patchSize # Counts how many times the windowsize fits in the picture
    Xamount=data.shape[1]/patchSize # Counts how many times the windowsize fits in the picture
    dataPatchedW.append(np.array([[data[j*patchSize:(j+1)*patchSize,i*patchSize:(i+1)*patchSize] for i in range(0,Xamount)] for j in range(0,Yamount)]))

```

```python
########### Functions ############################################################################################################################

# Define Activitation functions, pooling and convolution functions (the rules)

def Sigmoid(x):
    return (1/(1+np.exp(-x)))

def TanH(x):
    return (1-np.exp(-x))/(1+np.exp(-x))

def Pool(I,W):
    PoolImg=np.zeros((len(I)/len(W),len(I)/len(W))) # W must fit an integer times into I.
    for i in range(0,len(PoolImg)):
        for j in range(0,len(PoolImg)):
            SelAr=I[i*len(W):(i+1)*len(W),j*len(W):(j+1)*len(W)]
            PoolImg[i,j]=np.inner(SelAr.flatten(),W.flatten()) # Now this is just an inner product since we have vectors
    return PoolImg

# To automatically make Gaussian kernels
def makeGaussian(size, fwhm = 3, center=None):
    x = np.arange(0, size, 1, float)
    y = x[:,np.newaxis]

    if center is None:
        x0 = y0 = size // 2
    else:
        x0 = center[0]
        y0 = center[1]

    return np.exp(-4*np.log(2) * ((x-x0)**2 + (y-y0)**2) / fwhm**2)

# To automatically define pooling nodes
def Pool_node(N):
    s=(N,N)
    a=float(N)*float(N)
    return (1.0/a)*np.ones(s)

```

```python
#################### Define pooling layers ###########################################################################
P12=Pool_node(3)*(1.0/100.0) #factor 1000 added to lower values more
P34=Pool_node(2)

#################### Define Convolution layers #######################################################################

######### First C layer #########
C1=[]

## First Kernel

# Inspiration: http://en.wikipedia.org/wiki/Sobel_operator
# http://stackoverflow.com/questions/9567882/sobel-filter-kernel-of-large-size

Kernel=np.array([[4,3,2,1,0,-1,-2,-3,-4],
                 [5,4,3,2,0,-2,-3,-4,-5],
                 [6,5,4,3,0,-3,-4,-5,-6],
                 [7,6,5,4,0,-4,-5,-6,-7],
                 [8,7,6,5,0,-5,-6,-7,-8],
                 [7,6,5,4,0,-4,-5,-6,-7],
                 [6,5,4,3,0,-3,-4,-5,-6],
                 [5,4,3,2,0,-2,-3,-4,-5],
                 [4,3,2,1,0,-1,-2,-3,-4]])

C1.append(Kernel)

## Second Kernel
Kernel=np.matrix.transpose(Kernel)
#Kernel= [[np.sin(x) for x in np.linspace(0,15*np.pi,9)] for i in range(0,9)]
C1.append(Kernel)

## Third Kernel
Kernel=makeGaussian(9,5)
Kernel=(1/np.sum(Kernel))*Kernel
C1.append(Kernel)

## Fourth Kernel
#Kernel=np.zeros((9,9))
#Kernel[4:5,:]=1
#Kernel[5:9,:]=2
#Kernel=(1/np.sum(Kernel))*Kernel
#C1.append(Kernel)

## Fifth kernel
#Kernel=np.zeros((9,9))
#for i in range(0,9):
#    for j in range(0,9):
#        if(i==j):Kernel[i,j]=1
#        if(i>j): Kernel[i,j]=2
#Kernel=(1/np.sum(Kernel))*Kernel
#C1.append(Kernel)

#Sixth Kernel
#Kernel=makeGaussian(9,2)
#Kernel=(1/np.sum(Kernel))*Kernel
#C1.append(Kernel)

# ## Seventh Kernel
# Kernel= np.transpose([[np.sin(x) for x in np.linspace(0,5*np.pi,9)] for i in range(0,9)])+[[np.sin(x) for x in np.linspace(0,5*np.pi,9)] for i in range(0,9)]
# Kernel=(1/np.sum(Kernel))*Kernel
# C1.append(Kernel)

# ## Eigth Kernel
# Kernel= np.transpose([[np.sin(x) for x in np.linspace(0,5*np.pi,9)] for i in range(0,9)])
# Kernel=(1/np.sum(Kernel))*Kernel
# C1.append(Kernel)

# ## Ninth Kernel
# Kernel= np.fliplr(C1[4])
# Kernel=(1/np.sum(Kernel))*Kernel
# C1.append(Kernel)

######### Initialize output weights and biases #########

# Initialisation, since this layer should be trained!
C2=[] # second convolution weigths
H3_bias=[]
W=[] # end-weight from output to classifier-neurons
# Define the number of branches in one row
N_branches=2
ClassAmount=3 # Forest, City, Water

for i in range (0, len(C1)):
    C2.append(np.random.rand(N_branches,4,4)*(1.0/100.0))
    H3_bias.append(np.random.rand(N_branches,8,8)*(1.0/10.0))
    W.append(np.random.rand(N_branches,ClassAmount,4,4)*(1.0/100.0)) # W[r][b,k,:,:]

bias=np.zeros((ClassAmount))# bias on the three classes
```

```python
N_plts=len(C1)
for i in range(0,N_plts):
    plt.subplot(4,3,i+1)
    plt.imshow(C1[i])
```

```python
#################### Start training  #######################################################################

# Determines which patch is chosen when
N_F=len(dataPatchedF)
Patches_F=[]
j=0;
for i in range(0,N_F):
    N_ff=dataPatchedF[i].shape[0]*dataPatchedF[i].shape[1]
    Patches_F.append(np.reshape(dataPatchedF[i], (N_ff, patchSize, patchSize)))
    j=j+N_ff
N_F=j

N_C=len(dataPatchedC)
Patches_C=[]
j=0;
for i in range(0,N_C):
    N_cc=dataPatchedC[i].shape[0]*dataPatchedC[i].shape[1]
    j=j+N_cc
    Patches_C.append(np.reshape(dataPatchedC[i], (N_cc, patchSize, patchSize)))
N_C=j

N_W=len(dataPatchedW)
Patches_W=[]
j=0;
for i in range(0,N_W):
    N_ww=dataPatchedW[i].shape[0]*dataPatchedW[i].shape[1]
    Patches_W.append(np.reshape(dataPatchedW[i], (N_ww, patchSize, patchSize)))
    j=j+N_ww
N_W=j

N_total=N_F+N_C+N_W
Sequence = np.arange(N_total)
Sequence = np.random.permutation(Sequence)

# Make patches tractable
Patches_F = list(itertools.chain.from_iterable(Patches_F))
Patches_F=np.asarray(Patches_F)

Patches_C = list(itertools.chain.from_iterable(Patches_C))
Patches_C=np.asarray(Patches_C)

Patches_W = list(itertools.chain.from_iterable(Patches_W))
Patches_W=np.asarray(Patches_W)

```

```python
len(Sequence)
```

    15288

```python
from itertools import product
###### Chooses patch and defines label #####
for PP in range(0,len(Sequence)):
       # 1 has to be changed to the number of training data
    SS=Sequence[PP]
    if SS<N_F:
        Class_label=np.array([1,0,0])
        inputPatch=Patches_F[SS]
    elif(SS>=N_F) and (SS<(N_F+N_C)):
        Class_label=np.array([0,1,0])
        inputPatch=Patches_C[SS-N_F]
    else:
        Class_label=np.array([0,0,1])
        inputPatch=Patches_W[SS-N_F-N_C]

    ### Layer 1 ###
    H1=[]
    H2=[]
    H3=[[np.zeros((8,8)) for b in range(0,N_branches)] for r in range(0,len(C1))]
    H4=[[np.zeros((8,8)) for b in range(0,N_branches)] for r in range(0,len(C1))]
    x=np.zeros(ClassAmount)
    f=np.zeros(ClassAmount)
    #learning rates
    n_bias=1*10**-3
    n_W=2*10**-4
    n_C2=1*10**-4
    n_H3_bias=1*10**-3

    for r in range (0, len(C1)):
        H1.append(sig.convolve(inputPatch, C1[r], 'valid'))
        H2.append(Pool(H1[r], P12))
        for b in range(0,N_branches):
            H3[r][b]=Sigmoid(sig.convolve(H2[r], C2[r][b],'valid')-H3_bias[r][b])
            H4[r][b]=Pool(H3[r][b],P34)

    for k in range(0,ClassAmount):
        for r in range (0, len(C1)):
            for b in range(0,N_branches):
                x[k]=x[k]+np.inner(H4[r][b].flatten(),W[r][b,k].flatten())
        f[k]=Sigmoid(x[k]-bias[k])

    ###### Back-propagation #####
    for k in range(0, ClassAmount):
        bias[k]=bias[k]-n_bias*(Class_label[k]-f[k])
        for r in range (0, len(C1)):
            for b in range(0,N_branches):
                W[r][b,k]=W[r][b,k]+n_W*(Class_label[k]-f[k])*H4[r][b]
                for i, j, u, v, n, m in product(range(0,4), range(0,4), range(0,2), range(0,2), range(0,4), range(0,4)):
                    C2[r][b,n,m]=C2[r][b,n,m]+n_C2*(Class_label[k]-f[k])*W[r][b,k,i,j]*H2[r][n+(2*j+u),m+(2*j+v)]
                    H3_bias[r][b,2*i+u,2*j+v]=H3_bias[r][b,2*i+u,2*j+v]-n_H3_bias*(Class_label[k]-f[k])*W[r][b,k,i,j]

    print(Class_label,f)
    if (PP%100==0):print(PP)

```

    (array([1, 0, 0]), array([ 0.54880024,  0.55000824,  0.53649523]))
    0
    (array([1, 0, 0]), array([ 0.55030838,  0.54941889,  0.53613642]))
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    (array([0, 1, 0]), array([ 0.53507309,  0.53326416,  0.50452764]))
    (array([0, 1, 0]), array([ 0.5320838 ,  0.53199482,  0.503043  ]))
    (array([1, 0, 0]), array([ 0.53023774,  0.53216649,  0.50078841]))
    (array([1, 0, 0]), array([ 0.53025451,  0.53058189,  0.49950421]))
    (array([0, 1, 0]), array([ 0.53518501,  0.53233176,  0.49873468]))
    (array([1, 0, 0]), array([ 0.5309816 ,  0.53122033,  0.4975831 ]))
    (array([0, 1, 0]), array([ 0.53565946,  0.53310647,  0.49886976]))
    (array([0, 1, 0]), array([ 0.52335568,  0.52393294,  0.49108844]))
    (array([1, 0, 0]), array([ 0.52908428,  0.53146742,  0.49382691]))
    (array([0, 1, 0]), array([ 0.52626856,  0.52583629,  0.49141603]))
    (array([1, 0, 0]), array([ 0.53208231,  0.53392218,  0.49495512]))
    (array([1, 0, 0]), array([ 0.53306679,  0.53294637,  0.49346606]))
    (array([1, 0, 0]), array([ 0.53204958,  0.52971166,  0.49104888]))
    (array([0, 1, 0]), array([ 0.53486121,  0.53191234,  0.49152224]))
    (array([0, 0, 1]), array([ 0.53106945,  0.5290712 ,  0.48839403]))
    (array([0, 1, 0]), array([ 0.53890647,  0.53738546,  0.49560986]))
    (array([0, 0, 1]), array([ 0.53038337,  0.53050511,  0.48924465]))
    (array([0, 0, 1]), array([ 0.52774005,  0.5279917 ,  0.48949838]))
    (array([1, 0, 0]), array([ 0.52341776,  0.52378488,  0.4886476 ]))
    (array([0, 0, 1]), array([ 0.52800016,  0.52621225,  0.48979166]))
    (array([0, 0, 1]), array([ 0.52675328,  0.52501821,  0.49081229]))
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    (array([1, 0, 0]), array([ 0.47644599,  0.45315761,  0.40329879]))
    300
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    (array([1, 0, 0]), array([ 0.47836657,  0.45098081,  0.4016768 ]))
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    (array([1, 0, 0]), array([ 0.48239734,  0.44246391,  0.3898996 ]))
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    (array([1, 0, 0]), array([ 0.47612165,  0.43910755,  0.39008998]))
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    (array([1, 0, 0]), array([ 0.47343254,  0.43874969,  0.38859025]))
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    (array([0, 0, 1]), array([ 0.46628447,  0.43189414,  0.38683876]))
    (array([0, 0, 1]), array([ 0.46524696,  0.43098856,  0.38804519]))
    (array([0, 0, 1]), array([ 0.46428181,  0.43013937,  0.3892621 ]))
    (array([1, 0, 0]), array([ 0.46328946,  0.42940586,  0.39061923]))
    (array([0, 1, 0]), array([ 0.46538616,  0.42851474,  0.38765787]))
    (array([1, 0, 0]), array([ 0.46472613,  0.42964866,  0.3875417 ]))
    (array([0, 0, 1]), array([ 0.46463082,  0.4286531 ,  0.38805457]))
    (array([1, 0, 0]), array([ 0.46390389,  0.42807942,  0.38935565]))
    (array([0, 1, 0]), array([ 0.46626942,  0.42743392,  0.38754632]))
    (array([0, 1, 0]), array([ 0.46934473,  0.42950085,  0.38424391]))
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    (array([0, 1, 0]), array([ 0.45513515,  0.42869147,  0.38716619]))
    400
    (array([1, 0, 0]), array([ 0.45367909,  0.42919791,  0.38673846]))
    (array([1, 0, 0]), array([ 0.45443029,  0.42823575,  0.38565957]))
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    (array([0, 1, 0]), array([ 0.45004413,  0.42531261,  0.38750917]))
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    (array([0, 0, 1]), array([ 0.45084345,  0.42588917,  0.3862601 ]))
    (array([0, 1, 0]), array([ 0.45050479,  0.42518501,  0.38664059]))
    (array([0, 0, 1]), array([ 0.44924168,  0.42598468,  0.38599907]))
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    (array([0, 0, 1]), array([ 0.44939535,  0.42425477,  0.38655765]))
    (array([0, 0, 1]), array([ 0.44847113,  0.42336767,  0.3876818 ]))
    (array([0, 1, 0]), array([ 0.44906669,  0.42165841,  0.38634203]))
    (array([0, 1, 0]), array([ 0.44651357,  0.42390893,  0.38781158]))
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    (array([0, 0, 1]), array([ 0.44402051,  0.42356966,  0.38825165]))
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    (array([0, 0, 1]), array([ 0.44029145,  0.42429525,  0.38910949]))
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    (array([0, 1, 0]), array([ 0.43495972,  0.42735468,  0.38949826]))
    (array([0, 1, 0]), array([ 0.43354613,  0.42678668,  0.38732036]))
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    (array([0, 1, 0]), array([ 0.43391701,  0.4270549 ,  0.38651781]))
    (array([1, 0, 0]), array([ 0.43255529,  0.42902782,  0.38613982]))
    (array([1, 0, 0]), array([ 0.43400323,  0.42795295,  0.384014  ]))
    (array([1, 0, 0]), array([ 0.43543447,  0.42777925,  0.38451748]))
    (array([1, 0, 0]), array([ 0.43548334,  0.4265037 ,  0.3843262 ]))
    (array([1, 0, 0]), array([ 0.43734161,  0.42580095,  0.38238184]))
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    (array([1, 0, 0]), array([ 0.43998473,  0.42270146,  0.3781455 ]))
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    (array([0, 1, 0]), array([ 0.44181291,  0.420148  ,  0.37251854]))
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    (array([0, 1, 0]), array([ 0.44111566,  0.41470541,  0.37704657]))
    (array([0, 1, 0]), array([ 0.43988646,  0.41501055,  0.37428297]))
    (array([0, 0, 1]), array([ 0.43976017,  0.41683129,  0.3745898 ]))
    (array([0, 1, 0]), array([ 0.44053237,  0.41554687,  0.37278212]))
    (array([0, 0, 1]), array([ 0.43739394,  0.41769448,  0.37578828]))
    (array([1, 0, 0]), array([ 0.43805598,  0.41592383,  0.37433083]))
    (array([1, 0, 0]), array([ 0.43899819,  0.41637789,  0.37661346]))
    (array([1, 0, 0]), array([ 0.43970447,  0.41449036,  0.3755398 ]))
    (array([0, 1, 0]), array([ 0.43814444,  0.41423697,  0.37784111]))
    (array([1, 0, 0]), array([ 0.43911599,  0.41538769,  0.37462132]))
    (array([0, 1, 0]), array([ 0.44028206,  0.41304447,  0.36990309]))
    (array([0, 0, 1]), array([ 0.43982891,  0.41534111,  0.37172565]))
    (array([1, 0, 0]), array([ 0.4376783 ,  0.41365645,  0.3731392 ]))
    (array([0, 1, 0]), array([ 0.44091382,  0.41433784,  0.37449376]))
    (array([0, 1, 0]), array([ 0.43866129,  0.41554389,  0.37370296]))
    (array([0, 0, 1]), array([ 0.43812699,  0.4161679 ,  0.37172712]))
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    (array([0, 1, 0]), array([ 0.43746183,  0.41606631,  0.37089612]))
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    (array([0, 1, 0]), array([ 0.43315224,  0.4149116 ,  0.37038103]))
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    (array([1, 0, 0]), array([ 0.43151463,  0.41747181,  0.37082559]))
    (array([0, 1, 0]), array([ 0.43527545,  0.41665499,  0.36697317]))
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    (array([0, 0, 1]), array([ 0.43428677,  0.4141029 ,  0.36613154]))
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    (array([0, 0, 1]), array([ 0.43256869,  0.41457819,  0.36698219]))
    (array([1, 0, 0]), array([ 0.43144122,  0.4129343 ,  0.3659257 ]))
    (array([0, 1, 0]), array([ 0.43326661,  0.41295705,  0.36630196]))
    (array([0, 1, 0]), array([ 0.43006499,  0.41267051,  0.36498092]))
    (array([0, 1, 0]), array([ 0.42985868,  0.41510971,  0.36565673]))
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    (array([0, 1, 0]), array([ 0.42874846,  0.41716918,  0.36334761]))
    (array([0, 1, 0]), array([ 0.42926974,  0.41987685,  0.3618752 ]))
    (array([1, 0, 0]), array([ 0.42800746,  0.42027025,  0.36190009]))
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    (array([1, 0, 0]), array([ 0.35998119,  0.40232018,  0.32092894]))
    (array([1, 0, 0]), array([ 0.36353959,  0.40201639,  0.32279066]))
    (array([1, 0, 0]), array([ 0.36237785,  0.39981522,  0.31862043]))
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    (array([1, 0, 0]), array([ 0.3631236 ,  0.39829592,  0.31900364]))
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    (array([0, 1, 0]), array([ 0.36110994,  0.39476612,  0.31620017]))
    1000
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    (array([0, 0, 1]), array([ 0.3622795 ,  0.39891123,  0.31926716]))
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    (array([1, 0, 0]), array([ 0.36214359,  0.39566358,  0.32334802]))
    (array([0, 1, 0]), array([ 0.36026124,  0.39431679,  0.3201257 ]))
    (array([0, 1, 0]), array([ 0.36090804,  0.3959852 ,  0.32125063]))
    (array([1, 0, 0]), array([ 0.36191452,  0.39762499,  0.32247801]))
    (array([1, 0, 0]), array([ 0.36185336,  0.39702747,  0.31839678]))
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    (array([1, 0, 0]), array([ 0.36322088,  0.39398529,  0.32194865]))
    (array([1, 0, 0]), array([ 0.36369671,  0.39324973,  0.32123891]))
    (array([0, 1, 0]), array([ 0.36767455,  0.39464457,  0.32416074]))
    (array([1, 0, 0]), array([ 0.36209933,  0.39216896,  0.31631967]))
    (array([1, 0, 0]), array([ 0.36581498,  0.39199662,  0.31994848]))
    (array([0, 1, 0]), array([ 0.36292057,  0.39007215,  0.31248211]))
    (array([0, 0, 1]), array([ 0.36546705,  0.39295354,  0.3174003 ]))
    (array([0, 0, 1]), array([ 0.36561574,  0.39284106,  0.3203474 ]))
    (array([0, 1, 0]), array([ 0.36419847,  0.391364  ,  0.32133252]))
    (array([1, 0, 0]), array([ 0.36697568,  0.39418204,  0.32404557]))
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    (array([1, 0, 0]), array([ 0.36671262,  0.39163452,  0.32232925]))
    (array([1, 0, 0]), array([ 0.36741927,  0.39032996,  0.32115757]))
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    (array([0, 0, 1]), array([ 0.36648661,  0.38762209,  0.31891861]))
    (array([0, 0, 1]), array([ 0.36670604,  0.38740577,  0.32172625]))
    (array([0, 1, 0]), array([ 0.36324611,  0.38645381,  0.3212288 ]))
    (array([1, 0, 0]), array([ 0.36407699,  0.38672894,  0.32043395]))
    (array([1, 0, 0]), array([ 0.36651222,  0.38749671,  0.32179624]))
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    (array([0, 1, 0]), array([ 0.36872542,  0.38961471,  0.32773465]))
    (array([1, 0, 0]), array([ 0.36424083,  0.38801378,  0.32017781]))
    (array([1, 0, 0]), array([ 0.37072068,  0.38987981,  0.32659594]))
    (array([1, 0, 0]), array([ 0.36797907,  0.38650826,  0.32148801]))
    (array([0, 1, 0]), array([ 0.36907329,  0.38604211,  0.31915986]))
    (array([1, 0, 0]), array([ 0.36728979,  0.38690276,  0.31720172]))
    (array([0, 1, 0]), array([ 0.36992501,  0.38733908,  0.31864088]))
    (array([0, 1, 0]), array([ 0.36787998,  0.3863385 ,  0.31692096]))
    (array([0, 0, 1]), array([ 0.36881325,  0.38908609,  0.31810885]))
    (array([0, 0, 1]), array([ 0.36792224,  0.38812234,  0.31869295]))
    (array([0, 1, 0]), array([ 0.36565465,  0.38727687,  0.32004621]))
    (array([0, 0, 1]), array([ 0.36650651,  0.38871092,  0.31964236]))
    (array([0, 0, 1]), array([ 0.36593986,  0.38800973,  0.3212791 ]))
    (array([0, 1, 0]), array([ 0.36422602,  0.38776031,  0.32286085]))
    (array([1, 0, 0]), array([ 0.36807049,  0.38907702,  0.32689831]))
    (array([1, 0, 0]), array([ 0.36344151,  0.38577343,  0.31694208]))
    (array([1, 0, 0]), array([ 0.37092455,  0.38897382,  0.32567888]))
    (array([1, 0, 0]), array([ 0.36968689,  0.38655003,  0.32123993]))
    (array([1, 0, 0]), array([ 0.36955562,  0.38539895,  0.32023955]))
    (array([0, 1, 0]), array([ 0.37187101,  0.38450142,  0.31948599]))
    (array([1, 0, 0]), array([ 0.36990284,  0.38576217,  0.31790414]))
    (array([0, 0, 1]), array([ 0.37104079,  0.38506581,  0.31758477]))
    (array([0, 1, 0]), array([ 0.3711753 ,  0.38461477,  0.32117375]))
    (array([0, 1, 0]), array([ 0.37146242,  0.38621025,  0.32111389]))
    (array([1, 0, 0]), array([ 0.37015124,  0.3875123 ,  0.32009567]))
    (array([0, 1, 0]), array([ 0.37179954,  0.38696217,  0.32014455]))
    (array([1, 0, 0]), array([ 0.36791074,  0.38640569,  0.31530428]))
    (array([1, 0, 0]), array([ 0.36964005,  0.38553563,  0.31416593]))
    (array([1, 0, 0]), array([ 0.37231748,  0.38536748,  0.31569559]))
    (array([1, 0, 0]), array([ 0.37324224,  0.38471125,  0.31492264]))
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    (array([0, 1, 0]), array([ 0.37273705,  0.38259982,  0.3153252 ]))
    (array([0, 1, 0]), array([ 0.36959459,  0.38185831,  0.31085513]))
    (array([0, 0, 1]), array([ 0.37052266,  0.38421545,  0.31394164]))
    (array([1, 0, 0]), array([ 0.37208132,  0.38488803,  0.31876346]))
    (array([0, 0, 1]), array([ 0.37083616,  0.38257471,  0.31436274]))
    (array([0, 0, 1]), array([ 0.37147649,  0.38305736,  0.31889085]))
    (array([0, 1, 0]), array([ 0.37719911,  0.38568491,  0.32736882]))
    (array([0, 0, 1]), array([ 0.37016719,  0.38343222,  0.31903518]))
    (array([1, 0, 0]), array([ 0.36830756,  0.38178351,  0.31741805]))
    (array([1, 0, 0]), array([ 0.37007625,  0.37993464,  0.31725595]))
    (array([1, 0, 0]), array([ 0.37105469,  0.38055344,  0.31791173]))
    (array([0, 0, 1]), array([ 0.37239106,  0.37949784,  0.31691525]))
    (array([1, 0, 0]), array([ 0.37215465,  0.3789284 ,  0.32044877]))
    (array([1, 0, 0]), array([ 0.3722337 ,  0.37672838,  0.31606843]))
    (array([1, 0, 0]), array([ 0.37570542,  0.3781467 ,  0.31997252]))
    (array([0, 1, 0]), array([ 0.3751889 ,  0.37843644,  0.31636966]))
    (array([1, 0, 0]), array([ 0.3761219 ,  0.37930786,  0.31941249]))
    (array([0, 1, 0]), array([ 0.36934701,  0.37131508,  0.30287432]))
    (array([1, 0, 0]), array([ 0.37557434,  0.37891679,  0.31550119]))
    (array([0, 1, 0]), array([ 0.37422362,  0.37622117,  0.31013944]))
    (array([0, 1, 0]), array([ 0.37334899,  0.37887244,  0.30953448]))
    (array([0, 0, 1]), array([ 0.37541852,  0.38103594,  0.31446873]))
    (array([1, 0, 0]), array([ 0.37713327,  0.38236601,  0.32111872]))
    (array([1, 0, 0]), array([ 0.37569843,  0.37944763,  0.31454206]))
    (array([0, 0, 1]), array([ 0.37605914,  0.37769687,  0.31235846]))
    (array([0, 1, 0]), array([ 0.37284479,  0.37363198,  0.30702941]))
    (array([0, 1, 0]), array([ 0.37545   ,  0.37942511,  0.31480736]))
    (array([0, 0, 1]), array([ 0.37365828,  0.37942221,  0.31218554]))
    (array([0, 0, 1]), array([ 0.37384367,  0.37963002,  0.31548583]))
    (array([0, 1, 0]), array([ 0.36900677,  0.3743548 ,  0.30907586]))
    (array([1, 0, 0]), array([ 0.3722965 ,  0.38048456,  0.31617548]))
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    (array([0, 0, 1]), array([ 0.37277534,  0.37857182,  0.31661282]))
    (array([0, 1, 0]), array([ 0.36736862,  0.37293773,  0.3092483 ]))
    1100
    (array([0, 1, 0]), array([ 0.36910582,  0.37759223,  0.31299729]))
    (array([0, 0, 1]), array([ 0.36974781,  0.37980538,  0.31532602]))
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    (array([0, 0, 1]), array([ 0.36970571,  0.37980562,  0.32065857]))
    (array([1, 0, 0]), array([ 0.37126137,  0.38010044,  0.32461564]))
    (array([0, 1, 0]), array([ 0.36667088,  0.3743966 ,  0.31581866]))
    (array([1, 0, 0]), array([ 0.36801559,  0.37776675,  0.31597849]))
    (array([0, 1, 0]), array([ 0.36736777,  0.37649083,  0.31497692]))
    (array([1, 0, 0]), array([ 0.36930143,  0.37980919,  0.31931256]))
    (array([0, 0, 1]), array([ 0.37056178,  0.37865494,  0.31770453]))
    (array([0, 1, 0]), array([ 0.36492254,  0.37532671,  0.31066216]))
    (array([0, 1, 0]), array([ 0.36833344,  0.37928461,  0.31763773]))
    (array([0, 0, 1]), array([ 0.36717505,  0.37930823,  0.31555991]))
    (array([1, 0, 0]), array([ 0.36546093,  0.37868911,  0.31688995]))
    (array([0, 0, 1]), array([ 0.3680826 ,  0.37823334,  0.31707256]))
    (array([0, 0, 1]), array([ 0.36785488,  0.37797785,  0.31916901]))
    (array([0, 1, 0]), array([ 0.36841482,  0.37647094,  0.31892783]))
    (array([1, 0, 0]), array([ 0.36741503,  0.37854601,  0.32226284]))
    (array([1, 0, 0]), array([ 0.36714946,  0.37737201,  0.31861279]))
    (array([0, 1, 0]), array([ 0.36849823,  0.37824391,  0.32040245]))
    (array([0, 1, 0]), array([ 0.36845641,  0.37735075,  0.3169966 ]))
    (array([1, 0, 0]), array([ 0.36691633,  0.37922516,  0.3175464 ]))
    (array([0, 1, 0]), array([ 0.36790428,  0.37818353,  0.31562655]))
    (array([1, 0, 0]), array([ 0.36717782,  0.37861202,  0.3151945 ]))
    (array([1, 0, 0]), array([ 0.36672913,  0.37863946,  0.31364929]))
    (array([0, 0, 1]), array([ 0.37060615,  0.37851244,  0.31533671]))
    (array([0, 1, 0]), array([ 0.37015184,  0.37645723,  0.31607101]))
    (array([0, 1, 0]), array([ 0.36849235,  0.37788492,  0.3146629 ]))
    (array([1, 0, 0]), array([ 0.36772549,  0.37980224,  0.31461514]))
    (array([0, 0, 1]), array([ 0.37004679,  0.37962545,  0.31542934]))
    (array([1, 0, 0]), array([ 0.3697874 ,  0.37872377,  0.31695453]))
    (array([0, 1, 0]), array([ 0.37235469,  0.38088341,  0.32140744]))
    (array([1, 0, 0]), array([ 0.36924249,  0.37870549,  0.31451709]))
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    (array([0, 1, 0]), array([ 0.37283325,  0.38090255,  0.32007999]))
    (array([0, 0, 1]), array([ 0.36988549,  0.37949388,  0.31417054]))
    (array([0, 1, 0]), array([ 0.36791   ,  0.37708621,  0.31120145]))
    (array([0, 1, 0]), array([ 0.36711675,  0.37770413,  0.31050021]))
    (array([1, 0, 0]), array([ 0.36743197,  0.38137934,  0.31381064]))
    (array([0, 0, 1]), array([ 0.36911755,  0.38073904,  0.31402885]))
    (array([1, 0, 0]), array([ 0.36849067,  0.38011964,  0.31514477]))
    (array([0, 1, 0]), array([ 0.36582498,  0.37747211,  0.30969239]))
    (array([0, 0, 1]), array([ 0.36865638,  0.3801226 ,  0.31343611]))
    (array([0, 0, 1]), array([ 0.36794931,  0.37957671,  0.31514212]))
    (array([0, 0, 1]), array([ 0.3672882 ,  0.37880682,  0.31629583]))
    (array([1, 0, 0]), array([ 0.36501404,  0.37624806,  0.31492557]))
    (array([0, 1, 0]), array([ 0.37239348,  0.37834488,  0.3223828 ]))
    (array([0, 1, 0]), array([ 0.36537662,  0.37651855,  0.31121325]))
    (array([0, 1, 0]), array([ 0.36590423,  0.37947249,  0.31499601]))
    (array([0, 1, 0]), array([ 0.36526804,  0.3802537 ,  0.31249185]))
    (array([0, 0, 1]), array([ 0.36480571,  0.38210171,  0.31435754]))
    (array([1, 0, 0]), array([ 0.36423911,  0.38179893,  0.31622787]))
    (array([1, 0, 0]), array([ 0.36509496,  0.38036587,  0.31469348]))
    (array([1, 0, 0]), array([ 0.36676315,  0.37973189,  0.31461816]))
    (array([1, 0, 0]), array([ 0.36725652,  0.37839677,  0.31340532]))
    (array([0, 1, 0]), array([ 0.37064173,  0.37946439,  0.31522333]))
    (array([0, 1, 0]), array([ 0.37038522,  0.38212598,  0.31659317]))
    (array([0, 1, 0]), array([ 0.3669175 ,  0.3801055 ,  0.31088786]))
    (array([0, 0, 1]), array([ 0.3673546 ,  0.38239271,  0.31246919]))
    (array([1, 0, 0]), array([ 0.36328471,  0.3794298 ,  0.3084463 ]))
    (array([1, 0, 0]), array([ 0.36698195,  0.37984732,  0.31118067]))
    (array([0, 1, 0]), array([ 0.36843211,  0.37899601,  0.31188098]))
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    (array([0, 0, 1]), array([ 0.36635548,  0.37950226,  0.31083747]))
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    (array([0, 1, 0]), array([ 0.36545381,  0.37940618,  0.31283076]))
    (array([1, 0, 0]), array([ 0.36540119,  0.38020131,  0.31140884]))
    (array([1, 0, 0]), array([ 0.36736643,  0.37968192,  0.3112115 ]))
    (array([0, 0, 1]), array([ 0.36796944,  0.37868489,  0.30903246]))
    (array([0, 1, 0]), array([ 0.36561469,  0.37680439,  0.30790311]))
    (array([0, 1, 0]), array([ 0.36195493,  0.3749996 ,  0.30306428]))
    (array([0, 1, 0]), array([ 0.36550663,  0.37937585,  0.30575734]))
    (array([1, 0, 0]), array([ 0.36906124,  0.38371515,  0.31590616]))
    (array([1, 0, 0]), array([ 0.36680259,  0.38154284,  0.30949453]))
    (array([1, 0, 0]), array([ 0.36914775,  0.38163344,  0.31092656]))
    (array([0, 1, 0]), array([ 0.37254104,  0.38278028,  0.31411719]))
    (array([1, 0, 0]), array([ 0.36870053,  0.38153105,  0.30817197]))
    (array([0, 0, 1]), array([ 0.37053225,  0.38113349,  0.30809684]))
    (array([0, 1, 0]), array([ 0.3675047 ,  0.37960344,  0.30698279]))
    (array([1, 0, 0]), array([ 0.36997584,  0.38232893,  0.31000525]))
    (array([1, 0, 0]), array([ 0.37010186,  0.38130395,  0.30975405]))
    (array([0, 0, 1]), array([ 0.37090423,  0.37974446,  0.30683262]))
    (array([1, 0, 0]), array([ 0.3704274 ,  0.37954382,  0.30980148]))
    (array([1, 0, 0]), array([ 0.37210276,  0.378021  ,  0.30867609]))
    (array([0, 0, 1]), array([ 0.37244324,  0.37710233,  0.30648253]))
    (array([1, 0, 0]), array([ 0.37207373,  0.37749381,  0.30968885]))
    (array([0, 1, 0]), array([ 0.37331178,  0.37673136,  0.3093406 ]))
    (array([0, 0, 1]), array([ 0.37280374,  0.37767457,  0.30805419]))
    (array([0, 1, 0]), array([ 0.37105986,  0.37857598,  0.30952379]))
    (array([0, 1, 0]), array([ 0.37212848,  0.37899115,  0.31012024]))
    (array([1, 0, 0]), array([ 0.37036729,  0.37960069,  0.30838716]))
    (array([1, 0, 0]), array([ 0.37006495,  0.37689738,  0.30418913]))
    (array([0, 1, 0]), array([ 0.36892304,  0.3749487 ,  0.30136873]))
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    (array([0, 1, 0]), array([ 0.37193107,  0.37738217,  0.30527297]))
    1200
    (array([1, 0, 0]), array([ 0.37235564,  0.37860889,  0.305817  ]))
    (array([0, 1, 0]), array([ 0.37562318,  0.37847402,  0.30769225]))
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    (array([0, 0, 1]), array([ 0.37387621,  0.37834997,  0.30249573]))
    (array([1, 0, 0]), array([ 0.37568917,  0.37898685,  0.30807484]))
    (array([0, 1, 0]), array([ 0.37492426,  0.37703885,  0.30105813]))
    (array([1, 0, 0]), array([ 0.37304896,  0.37552087,  0.30015669]))
    (array([1, 0, 0]), array([ 0.37637166,  0.37772665,  0.30365485]))
    (array([1, 0, 0]), array([ 0.37775328,  0.3768582 ,  0.30376925]))
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    (array([0, 1, 0]), array([ 0.37465753,  0.37515381,  0.2995418 ]))
    (array([0, 1, 0]), array([ 0.3729003 ,  0.37313114,  0.2949575 ]))
    (array([1, 0, 0]), array([ 0.37464885,  0.37789456,  0.30177529]))
    (array([1, 0, 0]), array([ 0.37504835,  0.37615217,  0.29828292]))
    (array([1, 0, 0]), array([ 0.37788191,  0.37607749,  0.30057585]))
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    (array([1, 0, 0]), array([ 0.3668141 ,  0.37402011,  0.310017  ]))
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    (array([0, 1, 0]), array([ 0.36411429,  0.37495592,  0.30658683]))
    (array([1, 0, 0]), array([ 0.36499468,  0.37572865,  0.30534715]))
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    (array([1, 0, 0]), array([ 0.36380129,  0.37570758,  0.29882356]))
    (array([0, 1, 0]), array([ 0.36260831,  0.37442735,  0.2936232 ]))
    (array([0, 1, 0]), array([ 0.36905336,  0.37848511,  0.30300442]))
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    (array([0, 0, 1]), array([ 0.36373383,  0.38368712,  0.30065778]))
    1300
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    (array([1, 0, 0]), array([ 0.36260621,  0.3826597 ,  0.30415316]))
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    (array([1, 0, 0]), array([ 0.36195196,  0.381043  ,  0.30394111]))
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    (array([0, 0, 1]), array([ 0.36541699,  0.37639148,  0.30038285]))
    (array([0, 1, 0]), array([ 0.36447649,  0.37518221,  0.30213674]))
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    (array([0, 1, 0]), array([ 0.36375157,  0.37563547,  0.30123635]))
    (array([0, 1, 0]), array([ 0.36414366,  0.37484844,  0.29819581]))
    (array([0, 1, 0]), array([ 0.36429943,  0.37958533,  0.30043826]))
    (array([0, 1, 0]), array([ 0.36166642,  0.37893613,  0.29576758]))
    (array([0, 1, 0]), array([ 0.36270901,  0.37917624,  0.29676767]))
    (array([0, 0, 1]), array([ 0.36058675,  0.38162588,  0.29599246]))
    (array([0, 1, 0]), array([ 0.35758856,  0.38110401,  0.29599728]))
    (array([0, 0, 1]), array([ 0.36063858,  0.38313095,  0.29926959]))
    (array([1, 0, 0]), array([ 0.3584462 ,  0.38180868,  0.29897282]))
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    (array([0, 0, 1]), array([ 0.35265987,  0.3823875 ,  0.30079375]))
    (array([0, 1, 0]), array([ 0.35196341,  0.37982682,  0.29839933]))
    (array([0, 1, 0]), array([ 0.34712315,  0.38193693,  0.29811997]))
    (array([0, 0, 1]), array([ 0.35222769,  0.38544791,  0.30370967]))
    (array([0, 0, 1]), array([ 0.35157115,  0.38470933,  0.30496092]))
    (array([1, 0, 0]), array([ 0.35118451,  0.38470827,  0.3070465 ]))
    (array([0, 1, 0]), array([ 0.35121256,  0.38101216,  0.30277195]))
    (array([0, 0, 1]), array([ 0.3503259 ,  0.38356358,  0.30319127]))
    (array([1, 0, 0]), array([ 0.3503278 ,  0.38353471,  0.30567099]))
    (array([0, 1, 0]), array([ 0.35246848,  0.38188702,  0.30517149]))
    (array([0, 1, 0]), array([ 0.35093989,  0.3841531 ,  0.30448454]))
    (array([1, 0, 0]), array([ 0.34993003,  0.38492105,  0.30347984]))
    (array([1, 0, 0]), array([ 0.35093858,  0.3834532 ,  0.30321838]))
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    (array([0, 1, 0]), array([ 0.35318485,  0.38218576,  0.30455019]))
    (array([1, 0, 0]), array([ 0.35465449,  0.38590658,  0.30774538]))
    (array([0, 0, 1]), array([ 0.35283869,  0.38352075,  0.3035537 ]))
    (array([1, 0, 0]), array([ 0.35134814,  0.38224636,  0.30349312]))
    (array([0, 0, 1]), array([ 0.35302429,  0.38169415,  0.30357837]))
    (array([1, 0, 0]), array([ 0.35350864,  0.38100628,  0.30648946]))
    (array([1, 0, 0]), array([ 0.35291899,  0.37999072,  0.30520788]))
    (array([1, 0, 0]), array([ 0.35444963,  0.37853509,  0.30314882]))
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    (array([0, 1, 0]), array([ 0.357225  ,  0.37618092,  0.30229268]))
    1400
    (array([0, 1, 0]), array([ 0.35616427,  0.37756238,  0.30065679]))
    (array([1, 0, 0]), array([ 0.35650288,  0.38064827,  0.30191547]))
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    (array([0, 1, 0]), array([ 0.35956816,  0.37833936,  0.30072877]))
    (array([1, 0, 0]), array([ 0.35982987,  0.37944843,  0.30145979]))
    (array([0, 0, 1]), array([ 0.36061042,  0.37872592,  0.29993811]))
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    (array([0, 1, 0]), array([ 0.35724016,  0.37300668,  0.29128613]))
    (array([0, 1, 0]), array([ 0.35538617,  0.37547868,  0.29194078]))
    (array([0, 1, 0]), array([ 0.36214299,  0.38011381,  0.30259898]))
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    (array([0, 0, 1]), array([ 0.35822611,  0.38226826,  0.29839852]))
    (array([1, 0, 0]), array([ 0.3565697 ,  0.3786379 ,  0.29711772]))
    (array([1, 0, 0]), array([ 0.3575705 ,  0.37962907,  0.2967516 ]))
    (array([0, 0, 1]), array([ 0.36084474,  0.38038167,  0.29936203]))
    (array([0, 1, 0]), array([ 0.35669611,  0.37710251,  0.29567759]))
    (array([0, 0, 1]), array([ 0.35886124,  0.38070678,  0.29957482]))
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    (array([0, 0, 1]), array([ 0.35662693,  0.38024031,  0.30123868]))
    (array([1, 0, 0]), array([ 0.35485939,  0.37807186,  0.29918564]))
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    (array([1, 0, 0]), array([ 0.3517553 ,  0.37670631,  0.29871699]))
    (array([1, 0, 0]), array([ 0.35629453,  0.37727633,  0.30238614]))
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    (array([1, 0, 0]), array([ 0.3609018 ,  0.37407988,  0.29865143]))
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    (array([0, 0, 1]), array([ 0.35351012,  0.33893143,  0.31704938]))
    (array([1, 0, 0]), array([ 0.35275452,  0.3394833 ,  0.31888743]))
    (array([1, 0, 0]), array([ 0.35343042,  0.33710513,  0.31673431]))
    (array([1, 0, 0]), array([ 0.35383754,  0.33583034,  0.3158058 ]))
    (array([0, 1, 0]), array([ 0.35424006,  0.33467043,  0.31351007]))
    (array([0, 0, 1]), array([ 0.35580814,  0.33736179,  0.31573066]))
    (array([0, 0, 1]), array([ 0.35522444,  0.33694949,  0.31723141]))
    (array([0, 1, 0]), array([ 0.35975331,  0.34184904,  0.32628612]))
    (array([0, 1, 0]), array([ 0.35045694,  0.33376827,  0.31326315]))
    (array([0, 0, 1]), array([ 0.35338287,  0.33905779,  0.31759502]))
    (array([0, 1, 0]), array([ 0.35176776,  0.33534251,  0.31733565]))
    (array([0, 1, 0]), array([ 0.34848144,  0.33582158,  0.31436866]))
    (array([1, 0, 0]), array([ 0.35111528,  0.34094525,  0.31760962]))
    (array([0, 0, 1]), array([ 0.3535016 ,  0.34147804,  0.31834138]))
    (array([0, 1, 0]), array([ 0.35067947,  0.33803402,  0.31572993]))
    (array([1, 0, 0]), array([ 0.35081369,  0.34087018,  0.31755206]))
    (array([0, 1, 0]), array([ 0.35440314,  0.33855051,  0.31616984]))
    (array([0, 0, 1]), array([ 0.35133489,  0.34121632,  0.31602715]))
    (array([0, 0, 1]), array([ 0.35017342,  0.33993378,  0.31648685]))
    (array([1, 0, 0]), array([ 0.35169678,  0.34218727,  0.32094537]))
    (array([1, 0, 0]), array([ 0.35108082,  0.34062918,  0.31884349]))
    (array([0, 0, 1]), array([ 0.35162544,  0.33745125,  0.31607982]))
    (array([0, 0, 1]), array([ 0.35177993,  0.33789941,  0.3186339 ]))
    (array([0, 1, 0]), array([ 0.35576913,  0.34213697,  0.32617008]))
    (array([0, 0, 1]), array([ 0.3503075 ,  0.33833207,  0.31905724]))
    (array([1, 0, 0]), array([ 0.34817947,  0.33610854,  0.31776846]))
    (array([1, 0, 0]), array([ 0.34965551,  0.33700263,  0.31907313]))
    (array([1, 0, 0]), array([ 0.35266368,  0.3369772 ,  0.31979743]))
    (array([0, 1, 0]), array([ 0.34784294,  0.32862644,  0.31052988]))
    (array([0, 1, 0]), array([ 0.34677808,  0.33138623,  0.31072931]))
    (array([0, 1, 0]), array([ 0.34983301,  0.33467442,  0.31391356]))
    (array([0, 1, 0]), array([ 0.35172977,  0.34052939,  0.31684696]))
    (array([0, 1, 0]), array([ 0.35266085,  0.3427935 ,  0.31710615]))
    (array([0, 0, 1]), array([ 0.35035901,  0.34282493,  0.31619834]))
    (array([0, 0, 1]), array([ 0.3484875 ,  0.34082102,  0.31583273]))
    (array([1, 0, 0]), array([ 0.35130429,  0.34327702,  0.32196299]))
    (array([0, 1, 0]), array([ 0.34546815,  0.33510421,  0.3112708 ]))
    (array([0, 0, 1]), array([ 0.34925591,  0.34198803,  0.31722849]))
    (array([0, 1, 0]), array([ 0.34840371,  0.34060063,  0.31818756]))
    (array([1, 0, 0]), array([ 0.34463467,  0.3404887 ,  0.31337512]))
    (array([0, 1, 0]), array([ 0.35042114,  0.34129638,  0.31713535]))
    (array([0, 1, 0]), array([ 0.34824394,  0.34187095,  0.31583209]))
    (array([0, 1, 0]), array([ 0.34494251,  0.34353872,  0.31233081]))
    (array([0, 1, 0]), array([ 0.34411594,  0.34329143,  0.31018809]))
    (array([0, 0, 1]), array([ 0.34603795,  0.34637593,  0.31420491]))
    (array([0, 1, 0]), array([ 0.35105502,  0.35002432,  0.32283868]))
    (array([0, 1, 0]), array([ 0.34274133,  0.34342572,  0.31045067]))
    (array([1, 0, 0]), array([ 0.3457981 ,  0.35096047,  0.31718297]))
    (array([1, 0, 0]), array([ 0.3464676 ,  0.3491484 ,  0.31515891]))
    (array([0, 0, 1]), array([ 0.34751667,  0.34815201,  0.31461106]))
    (array([0, 0, 1]), array([ 0.34566889,  0.34632134,  0.3142891 ]))
    (array([0, 1, 0]), array([ 0.34176569,  0.34104473,  0.31202201]))
    (array([0, 0, 1]), array([ 0.34536284,  0.34810363,  0.31643644]))
    (array([1, 0, 0]), array([ 0.34241902,  0.34389882,  0.31360954]))
    (array([1, 0, 0]), array([ 0.34403235,  0.34492323,  0.31457886]))
    (array([0, 1, 0]), array([ 0.34809133,  0.34670873,  0.31552207]))
    (array([1, 0, 0]), array([ 0.34303357,  0.34262606,  0.3099557 ]))
    (array([0, 1, 0]), array([ 0.34804276,  0.34565137,  0.31548452]))
    (array([0, 1, 0]), array([ 0.34712449,  0.34697898,  0.31580999]))
    (array([1, 0, 0]), array([ 0.34620643,  0.34830109,  0.31342471]))
    (array([0, 1, 0]), array([ 0.34315151,  0.34345543,  0.30631168]))
    2200
    (array([1, 0, 0]), array([ 0.34974382,  0.35250758,  0.3160553 ]))
    (array([0, 0, 1]), array([ 0.34827905,  0.34904647,  0.31234819]))
    (array([0, 0, 1]), array([ 0.34647945,  0.34710305,  0.31192979]))
    (array([0, 1, 0]), array([ 0.3466336 ,  0.34660342,  0.31459714]))
    (array([0, 0, 1]), array([ 0.34428356,  0.34698693,  0.31146085]))
    (array([0, 1, 0]), array([ 0.34185424,  0.34388051,  0.30959267]))
    (array([1, 0, 0]), array([ 0.34257191,  0.34733075,  0.31222382]))
    (array([0, 0, 1]), array([ 0.34469733,  0.34729016,  0.31222043]))
    (array([1, 0, 0]), array([ 0.34570179,  0.3472435 ,  0.31580432]))
    (array([1, 0, 0]), array([ 0.34668707,  0.34984404,  0.316703  ]))
    (array([0, 1, 0]), array([ 0.34869815,  0.34757597,  0.31502001]))
    (array([1, 0, 0]), array([ 0.34401397,  0.34373071,  0.30925994]))
    (array([0, 1, 0]), array([ 0.3491534 ,  0.34832951,  0.31410992]))
    (array([1, 0, 0]), array([ 0.34768831,  0.34821904,  0.31219273]))
    (array([0, 0, 1]), array([ 0.34832229,  0.34699617,  0.31078994]))
    (array([1, 0, 0]), array([ 0.34637868,  0.34459196,  0.31036706]))
    (array([0, 0, 1]), array([ 0.35016699,  0.34696732,  0.31321832]))
    (array([0, 0, 1]), array([ 0.35058807,  0.34710372,  0.31574346]))
    (array([0, 0, 1]), array([ 0.3478743 ,  0.34448853,  0.31438621]))
    (array([0, 1, 0]), array([ 0.35296072,  0.3495963 ,  0.32412804]))
    (array([1, 0, 0]), array([ 0.34543391,  0.34491624,  0.31444971]))
    (array([0, 0, 1]), array([ 0.34861887,  0.3451913 ,  0.31556981]))
    (array([0, 0, 1]), array([ 0.34633546,  0.34281592,  0.31463428]))
    (array([0, 1, 0]), array([ 0.34947085,  0.34548663,  0.32032476]))
    (array([0, 0, 1]), array([ 0.3457011 ,  0.34413833,  0.31634895]))
    (array([0, 1, 0]), array([ 0.34281177,  0.34067663,  0.31528581]))
    (array([0, 1, 0]), array([ 0.34229645,  0.34238607,  0.31388768]))
    (array([1, 0, 0]), array([ 0.34435638,  0.34717277,  0.31747241]))
    (array([0, 1, 0]), array([ 0.34307365,  0.34370127,  0.31355689]))
    (array([0, 0, 1]), array([ 0.34465087,  0.34718899,  0.31577543]))
    (array([0, 0, 1]), array([ 0.34372846,  0.34616157,  0.31665107]))
    (array([1, 0, 0]), array([ 0.34233231,  0.34435963,  0.31708624]))
    (array([0, 0, 1]), array([ 0.34545644,  0.34602934,  0.3188737 ]))
    (array([1, 0, 0]), array([ 0.34549389,  0.3460479 ,  0.32114782]))
    (array([0, 0, 1]), array([ 0.34600459,  0.34472124,  0.3196916 ]))
    (array([0, 1, 0]), array([ 0.34178714,  0.33909244,  0.31548792]))
    (array([0, 1, 0]), array([ 0.34238789,  0.34150399,  0.31788171]))
    (array([0, 1, 0]), array([ 0.34415676,  0.34687298,  0.31849136]))
    (array([0, 1, 0]), array([ 0.34410026,  0.34761203,  0.31942532]))
    (array([0, 0, 1]), array([ 0.34242302,  0.34883489,  0.31792448]))
    (array([0, 1, 0]), array([ 0.33875216,  0.34485201,  0.31497732]))
    (array([0, 0, 1]), array([ 0.34093654,  0.34939039,  0.31849441]))
    (array([0, 0, 1]), array([ 0.34012379,  0.34849908,  0.31943411]))
    (array([0, 0, 1]), array([ 0.33997346,  0.34834164,  0.3214625 ]))
    (array([0, 1, 0]), array([ 0.33510707,  0.34204356,  0.31693774]))
    (array([1, 0, 0]), array([ 0.33863912,  0.34818386,  0.32195097]))
    (array([0, 1, 0]), array([ 0.33909302,  0.34704721,  0.32037295]))
    (array([0, 1, 0]), array([ 0.33685117,  0.34679354,  0.31732665]))
    (array([0, 1, 0]), array([ 0.33813178,  0.34902868,  0.31880282]))
    (array([1, 0, 0]), array([ 0.3400143 ,  0.35378614,  0.32152722]))
    (array([0, 1, 0]), array([ 0.33760109,  0.35004675,  0.31945756]))
    (array([0, 0, 1]), array([ 0.33616182,  0.35058309,  0.31547557]))
    (array([0, 1, 0]), array([ 0.33300942,  0.34730009,  0.31463975]))
    (array([1, 0, 0]), array([ 0.33586961,  0.35160486,  0.31713044]))
    (array([1, 0, 0]), array([ 0.33667048,  0.35134723,  0.31689258]))
    (array([0, 0, 1]), array([ 0.33936573,  0.35169418,  0.31752075]))
    (array([0, 1, 0]), array([ 0.34117447,  0.35210988,  0.32059952]))
    (array([1, 0, 0]), array([ 0.33719568,  0.35159181,  0.3168489 ]))
    (array([0, 1, 0]), array([ 0.33835462,  0.35171955,  0.31787667]))
    (array([0, 0, 1]), array([ 0.3389935 ,  0.35302187,  0.31735354]))
    (array([0, 1, 0]), array([ 0.33285997,  0.34799597,  0.312773  ]))
    (array([0, 1, 0]), array([ 0.33846127,  0.35474392,  0.31895399]))
    (array([0, 0, 1]), array([ 0.33696382,  0.35482201,  0.31725574]))
    (array([0, 0, 1]), array([ 0.33649325,  0.35430571,  0.318765  ]))
    (array([1, 0, 0]), array([ 0.33503992,  0.35403145,  0.31992412]))
    (array([0, 1, 0]), array([ 0.33093533,  0.34733626,  0.31126517]))
    (array([1, 0, 0]), array([ 0.33540278,  0.35326144,  0.31746468]))
    (array([1, 0, 0]), array([ 0.33501102,  0.35131757,  0.31542766]))
    (array([0, 1, 0]), array([ 0.33153669,  0.34754353,  0.30891414]))
    (array([1, 0, 0]), array([ 0.33301283,  0.34878115,  0.30977607]))
    (array([0, 0, 1]), array([ 0.33898367,  0.35292782,  0.31564653]))
    (array([0, 0, 1]), array([ 0.33976501,  0.35332141,  0.31839098]))
    (array([1, 0, 0]), array([ 0.33987795,  0.35317803,  0.32018848]))
    (array([0, 1, 0]), array([ 0.3355439 ,  0.34810729,  0.31371058]))
    (array([0, 0, 1]), array([ 0.33854796,  0.35269477,  0.31750055]))
    (array([0, 1, 0]), array([ 0.33912047,  0.35205533,  0.31979488]))
    (array([0, 0, 1]), array([ 0.33701328,  0.35284668,  0.31764568]))
    (array([1, 0, 0]), array([ 0.33707528,  0.35270063,  0.31990885]))
    (array([1, 0, 0]), array([ 0.33943232,  0.35198966,  0.31981445]))
    (array([1, 0, 0]), array([ 0.33767132,  0.35094113,  0.31664928]))
    (array([0, 0, 1]), array([ 0.34044797,  0.35033166,  0.31755815]))
    (array([1, 0, 0]), array([ 0.34005707,  0.34977483,  0.31908752]))
    (array([0, 1, 0]), array([ 0.33944658,  0.34764421,  0.31700064]))
    (array([0, 0, 1]), array([ 0.33836066,  0.34827969,  0.31491156]))
    (array([0, 0, 1]), array([ 0.34050178,  0.35025181,  0.31977623]))
    (array([0, 0, 1]), array([ 0.33784789,  0.34765407,  0.3184739 ]))
    (array([0, 1, 0]), array([ 0.34193603,  0.35144577,  0.32453777]))
    (array([1, 0, 0]), array([ 0.33804516,  0.34973325,  0.32072154]))
    (array([1, 0, 0]), array([ 0.34023376,  0.34934419,  0.32098442]))
    (array([0, 1, 0]), array([ 0.33855557,  0.3455029 ,  0.31695135]))
    (array([1, 0, 0]), array([ 0.34187538,  0.35066295,  0.32057015]))
    (array([0, 0, 1]), array([ 0.33892799,  0.34682628,  0.31596412]))
    (array([0, 1, 0]), array([ 0.33798975,  0.34477403,  0.31646769]))
    (array([0, 1, 0]), array([ 0.33802236,  0.34902486,  0.3168174 ]))
    (array([0, 0, 1]), array([ 0.33933954,  0.35087724,  0.31889384]))
    (array([0, 1, 0]), array([ 0.32946365,  0.34282614,  0.3091393 ]))
    (array([0, 0, 1]), array([ 0.33814006,  0.35182128,  0.31983683]))
    (array([1, 0, 0]), array([ 0.33629103,  0.34889404,  0.31881947]))
    (array([0, 0, 1]), array([ 0.33887879,  0.3506674 ,  0.32067812]))
    (array([1, 0, 0]), array([ 0.3346428 ,  0.34689694,  0.31966719]))
    2300
    (array([0, 0, 1]), array([ 0.33722118,  0.34692327,  0.3183308 ]))
    (array([0, 0, 1]), array([ 0.33603471,  0.34589575,  0.31914562]))
    (array([1, 0, 0]), array([ 0.33676218,  0.34641165,  0.32193861]))
    (array([1, 0, 0]), array([ 0.33836861,  0.34658504,  0.32192257]))
    (array([0, 1, 0]), array([ 0.33510619,  0.34142117,  0.31693723]))
    (array([0, 1, 0]), array([ 0.3369638 ,  0.34525229,  0.31947384]))
    (array([0, 0, 1]), array([ 0.33885406,  0.34864553,  0.32073359]))
    (array([1, 0, 0]), array([ 0.33723607,  0.34740501,  0.32111645]))
    (array([0, 0, 1]), array([ 0.33944866,  0.3472197 ,  0.32131501]))
    (array([1, 0, 0]), array([ 0.33934668,  0.34704383,  0.32310687]))
    (array([0, 0, 1]), array([ 0.34064465,  0.34647686,  0.32270984]))
    (array([1, 0, 0]), array([ 0.33799785,  0.34334294,  0.32013181]))
    (array([1, 0, 0]), array([ 0.33888784,  0.34371781,  0.32147289]))
    (array([1, 0, 0]), array([ 0.34189727,  0.343722  ,  0.32172177]))
    (array([0, 0, 1]), array([ 0.34319549,  0.34313249,  0.321541  ]))
    (array([1, 0, 0]), array([ 0.34236009,  0.34201431,  0.32211135]))
    (array([0, 0, 1]), array([ 0.34450023,  0.34255684,  0.32285179]))
    (array([1, 0, 0]), array([ 0.34223549,  0.34081958,  0.32215909]))
    (array([1, 0, 0]), array([ 0.34054524,  0.33641651,  0.31815816]))
    (array([1, 0, 0]), array([ 0.3459975 ,  0.34048269,  0.32302142]))
    (array([0, 1, 0]), array([ 0.34256333,  0.33618631,  0.31661275]))
    (array([0, 1, 0]), array([ 0.34281484,  0.33665502,  0.31508993]))
    (array([0, 0, 1]), array([ 0.34349947,  0.33955951,  0.31739823]))
    (array([0, 1, 0]), array([ 0.33841193,  0.33354118,  0.31298049]))
    (array([0, 1, 0]), array([ 0.33906282,  0.33773564,  0.31532538]))
    (array([1, 0, 0]), array([ 0.34366203,  0.34455271,  0.32065208]))
    (array([0, 0, 1]), array([ 0.34514177,  0.34364953,  0.32010087]))
    (array([0, 0, 1]), array([ 0.34296674,  0.34137201,  0.31936757]))
    (array([0, 1, 0]), array([ 0.34183828,  0.33942139,  0.31830044]))
    (array([1, 0, 0]), array([ 0.34317403,  0.3437636 ,  0.32193268]))
    (array([0, 0, 1]), array([ 0.34429189,  0.34288742,  0.32135312]))
    (array([1, 0, 0]), array([ 0.34565908,  0.34408519,  0.32487896]))
    (array([0, 1, 0]), array([ 0.34375263,  0.33949433,  0.32104901]))
    (array([0, 1, 0]), array([ 0.33788187,  0.33794047,  0.31484578]))
    (array([0, 1, 0]), array([ 0.33939117,  0.3389695 ,  0.31403141]))
    (array([0, 0, 1]), array([ 0.34074587,  0.34340154,  0.31747546]))
    (array([0, 0, 1]), array([ 0.34157599,  0.34422338,  0.32058975]))
    (array([0, 1, 0]), array([ 0.33966946,  0.33979121,  0.31827803]))
    (array([0, 0, 1]), array([ 0.34038827,  0.34500003,  0.3214268 ]))
    (array([0, 0, 1]), array([ 0.33913915,  0.3439365 ,  0.32212393]))
    (array([1, 0, 0]), array([ 0.338506  ,  0.34336747,  0.32353605]))
    (array([1, 0, 0]), array([ 0.34012483,  0.34310123,  0.32318467]))
    (array([1, 0, 0]), array([ 0.34200984,  0.34322996,  0.32380284]))
    (array([0, 1, 0]), array([ 0.34284682,  0.34178248,  0.32240396]))
    (array([1, 0, 0]), array([ 0.3449253 ,  0.34519382,  0.32406022]))
    (array([0, 1, 0]), array([ 0.34302158,  0.34142046,  0.32149609]))
    (array([0, 1, 0]), array([ 0.34265094,  0.34579949,  0.32119082]))
    (array([0, 1, 0]), array([ 0.33817236,  0.34266849,  0.31785183]))
    (array([1, 0, 0]), array([ 0.34093852,  0.34652972,  0.31823688]))
    (array([0, 0, 1]), array([ 0.34203904,  0.34470314,  0.31766443]))
    (array([0, 1, 0]), array([ 0.33541381,  0.33710847,  0.31017184]))
    (array([1, 0, 0]), array([ 0.34075097,  0.34509032,  0.31855129]))
    (array([0, 0, 1]), array([ 0.34270684,  0.345582  ,  0.31877745]))
    (array([0, 1, 0]), array([ 0.34317313,  0.34371928,  0.31916265]))
    (array([0, 0, 1]), array([ 0.34011991,  0.34497741,  0.31787629]))
    (array([1, 0, 0]), array([ 0.34087228,  0.34571922,  0.32060385]))

```python
sig.convolve(H2[2], C2[2][0],'valid')-H3_bias[2][0]
```

    array([[-0.02721981, -0.02712551,  0.00788712, -0.0435859 ,  0.02112727,
             0.03848705,  0.02630255,  0.00178263],
           [ 0.03844995, -0.0292354 , -0.05182575, -0.03364323, -0.01535562,
            -0.01552874, -0.01317172, -0.03252631],
           [ 0.00432032,  0.02501352, -0.03594971,  0.02494986,  0.02148822,
             0.03144228,  0.01833665, -0.02199267],
           [-0.0150795 ,  0.02077842, -0.05819155, -0.01498211, -0.04608708,
             0.00417621, -0.04189598, -0.04749   ],
           [ 0.01288567, -0.0252931 , -0.02020549, -0.04393179, -0.04194323,
            -0.03737209,  0.02264269,  0.00510131],
           [-0.01681428, -0.00063995, -0.0136121 ,  0.03258166,  0.00830394,
             0.01139103, -0.00185968, -0.00394129],
           [ 0.00872384, -0.04413418, -0.02473482,  0.02498438,  0.01533118,
            -0.027075  , -0.03571208, -0.02613056],
           [-0.0064417 ,  0.02946428,  0.01598912, -0.02634804,  0.01656108,
             0.03735876, -0.0375563 , -0.01799422]])

```python
x
```

    array([ 0.4683264 ,  0.44451467,  0.40694903])

```python
bias
```

    array([ 0.0104727 , -0.00161589,  0.00916062])

```python
W
```

    [array([[[[-0.09451344,  0.01764522,  0.11949737,  0.05031588],
              [-0.01004807,  0.04527503,  0.04744747, -0.03971535],
              [ 0.03838153,  0.01504355, -0.01052378,  0.02718381],
              [-0.06472421,  0.02633871,  0.07561101,  0.10256035]],

             [[ 0.0925761 , -0.06997852, -0.20426371, -0.13304848],
              [ 0.0159448 , -0.13523851, -0.10588554, -0.02101891],
              [-0.04890296, -0.05134114,  0.01119788, -0.07687243],
              [ 0.04130907, -0.08961312, -0.08614837, -0.13360783]],

             [[-0.01342397,  0.04167937,  0.06528107,  0.0829884 ],
              [-0.01932079,  0.08675308,  0.0557435 ,  0.0578841 ],
              [ 0.0004018 ,  0.03261402, -0.00740806,  0.04229913],
              [ 0.00575538,  0.05788358, -0.00819038,  0.01844118]]],

            [[[-0.12770824,  0.02129023,  0.16041599,  0.03354945],
              [-0.0322927 ,  0.06073887,  0.02692608, -0.05585178],
              [ 0.03283028,  0.02612535, -0.03732709,  0.04066974],
              [-0.08803913,  0.03126276,  0.07075652,  0.12170169]],

             [[ 0.1021058 , -0.07622915, -0.27054583, -0.10141596],
              [ 0.02273793, -0.14892472, -0.11073684,  0.01431501],
              [-0.0410166 , -0.0902408 ,  0.05105482, -0.12269707],
              [ 0.05265558, -0.09315412, -0.05679834, -0.1798109 ]],

             [[-0.00237541,  0.03659404,  0.09769915,  0.06018537],
              [ 0.00292207,  0.08215126,  0.06911516,  0.03064045],
              [ 0.00088982,  0.06282902, -0.01923765,  0.06313309],
              [ 0.01569652,  0.066199  , -0.035213  ,  0.04712716]]]]),
     array([[[[ 0.00507463,  0.00780472,  0.01423729,  0.00708448],
              [ 0.01118629,  0.008867  ,  0.0096952 ,  0.01370163],
              [ 0.01454006,  0.0130855 ,  0.00646826,  0.0101494 ],
              [ 0.0155926 ,  0.01411818,  0.00930188,  0.01068359]],

             [[-0.1082048 , -0.1102268 , -0.11065844, -0.10943519],
              [-0.10328994, -0.10692809, -0.10784327, -0.10921619],
              [-0.11081796, -0.10883589, -0.11030501, -0.10478726],
              [-0.11154781, -0.11226336, -0.10497134, -0.10697832]],

             [[ 0.10185704,  0.10010298,  0.10523647,  0.09903463],
              [ 0.10708423,  0.10682162,  0.098414  ,  0.09756669],
              [ 0.10270087,  0.09718347,  0.10754883,  0.10100794],
              [ 0.10416391,  0.10506627,  0.09907078,  0.10051775]]],

            [[[ 0.01076353,  0.01270998,  0.01340022,  0.01804303],
              [ 0.01584464,  0.0103628 ,  0.01861409,  0.01956825],
              [ 0.02275934,  0.01355188,  0.01901942,  0.01263357],
              [ 0.02023453,  0.02031081,  0.01618124,  0.00851546]],

             [[-0.10712697, -0.11646827, -0.11255464, -0.11445601],
              [-0.10481143, -0.11187318, -0.11669296, -0.11546197],
              [-0.11741785, -0.11178083, -0.11803485, -0.11917166],
              [-0.12932737, -0.12064556, -0.1202086 , -0.1107643 ]],

             [[ 0.09479954,  0.0985417 ,  0.10408309,  0.09876059],
              [ 0.10163988,  0.10557031,  0.10305073,  0.10632401],
              [ 0.10715528,  0.1067924 ,  0.10803594,  0.1043488 ],
              [ 0.10221259,  0.10800618,  0.09960432,  0.10292631]]]]),
     array([[[[-0.0054972 , -0.0082966 , -0.01328345, -0.00879509],
              [-0.00880757, -0.00767575, -0.0136435 , -0.00784989],
              [-0.01431509, -0.00919104, -0.00637415, -0.00662353],
              [-0.00873981, -0.00890231, -0.00641546, -0.00985337]],

             [[-0.00022715,  0.00658654,  0.00576399,  0.00480096],
              [ 0.00334558, -0.00164606,  0.00197955,  0.00421732],
              [ 0.0047014 ,  0.0049387 ,  0.00364979,  0.00658337],
              [-0.00117798,  0.0070661 ,  0.00311406,  0.00674847]],

             [[-0.022072  , -0.02104761, -0.02501629, -0.01880138],
              [-0.0179703 , -0.02406015, -0.02173449, -0.0189958 ],
              [-0.01839924, -0.02411311, -0.0249694 , -0.02610005],
              [-0.02354458, -0.02176279, -0.022072  , -0.02282002]]],

            [[[-0.0126394 , -0.01063864, -0.00881648, -0.0102493 ],
              [-0.01492824, -0.01209899, -0.01096503, -0.01090598],
              [-0.01486324, -0.01367285, -0.01184989, -0.01070078],
              [-0.01493207, -0.01651045, -0.0147819 , -0.01323788]],

             [[ 0.00497507,  0.00484086,  0.00858956,  0.00506814],
              [ 0.00530387,  0.00550936,  0.00541287,  0.00467607],
              [ 0.00470221,  0.00602529,  0.00404152,  0.01223231],
              [ 0.00850403,  0.00747606,  0.00354806,  0.00260821]],

             [[-0.02649017, -0.02777472, -0.02104019, -0.02641525],
              [-0.02625522, -0.0288568 , -0.01987536, -0.02293694],
              [-0.02412214, -0.0195542 , -0.02103418, -0.02466444],
              [-0.02296524, -0.02824865, -0.02391112, -0.02136729]]]]),
     array([[[[-0.02600758, -0.02098845, -0.02720445, -0.02563542],
              [-0.02624214, -0.02783755, -0.02335442, -0.01932625],
              [-0.02074395, -0.0182136 , -0.02529795, -0.02308949],
              [-0.02286851, -0.0249688 , -0.02439315, -0.01987909]],

             [[ 0.0271061 ,  0.0230231 ,  0.02898912,  0.02418719],
              [ 0.03013817,  0.02272867,  0.03080398,  0.02395689],
              [ 0.02977066,  0.02537782,  0.0264635 ,  0.02835363],
              [ 0.02558571,  0.02233914,  0.03215606,  0.03234368]],

             [[-0.03214974, -0.03642393, -0.03469447, -0.02947703],
              [-0.02984901, -0.03812227, -0.03559948, -0.03617257],
              [-0.0320278 , -0.03370177, -0.02928969, -0.03072791],
              [-0.03294015, -0.03559171, -0.03166382, -0.02887461]]],

            [[[-0.01713669, -0.02195003, -0.01483689, -0.01385306],
              [-0.01897211, -0.02115993, -0.01235729, -0.02104276],
              [-0.01631379, -0.01453867, -0.0155817 , -0.01441539],
              [-0.01971403, -0.01645545, -0.02245664, -0.01729963]],

             [[ 0.00951442,  0.01466517,  0.01272022,  0.0126009 ],
              [ 0.01480938,  0.01153742,  0.01317437,  0.0176338 ],
              [ 0.01758471,  0.01363715,  0.011302  ,  0.0127394 ],
              [ 0.0143223 ,  0.01192003,  0.01592481,  0.01162502]],

             [[-0.02746733, -0.03234926, -0.02419078, -0.02462123],
              [-0.02654163, -0.03058019, -0.02535117, -0.02376415],
              [-0.02910458, -0.03249412, -0.03008852, -0.02456848],
              [-0.03175768, -0.02989268, -0.02883216, -0.02567991]]]]),
     array([[[[-0.01327824, -0.01845856, -0.01458134, -0.01451495],
              [-0.01187649, -0.01638512, -0.01278855, -0.01828005],
              [-0.01576957, -0.01824181, -0.0181902 , -0.01297498],
              [-0.00987788, -0.01201314, -0.0108885 , -0.01121735]],

             [[ 0.00878301,  0.00971753,  0.01014207,  0.00955929],
              [ 0.00880295,  0.01309003,  0.0106729 ,  0.00877988],
              [ 0.00577223,  0.00848328,  0.00553509,  0.00797128],
              [ 0.01046513,  0.0130631 ,  0.01488298,  0.01075419]],

             [[-0.02915698, -0.02014944, -0.02256722, -0.01908289],
              [-0.02487542, -0.02383461, -0.02648781, -0.02469826],
              [-0.03017265, -0.02874712, -0.02205944, -0.02473355],
              [-0.02383857, -0.02710588, -0.02248241, -0.02338207]]],

            [[[-0.01674851, -0.00881909, -0.01534091, -0.01493336],
              [-0.01257245, -0.01474794, -0.00807357, -0.00853992],
              [-0.00977093, -0.00901168, -0.01075006, -0.01693607],
              [-0.0107176 , -0.01289755, -0.01433068, -0.01517628]],

             [[ 0.00168033,  0.00527259,  0.00537219,  0.00452417],
              [ 0.01030732,  0.00166708,  0.00708773,  0.00976649],
              [ 0.00728993,  0.00442624,  0.00504767,  0.0108352 ],
              [ 0.0083675 ,  0.01038495,  0.00286869,  0.00932652]],

             [[-0.01842837, -0.01939398, -0.02077519, -0.02331738],
              [-0.02125706, -0.01701772, -0.01958394, -0.02443172],
              [-0.0255015 , -0.02154481, -0.02047066, -0.02095854],
              [-0.01857916, -0.02421521, -0.01677902, -0.01927327]]]]),
     array([[[[ -1.05763017e-02,  -1.64628020e-02,  -1.32598434e-02,
                -1.74181420e-02],
              [ -1.27182740e-02,  -1.69228173e-02,  -9.26356257e-03,
                -1.45864132e-02],
              [ -1.58234460e-02,  -1.31484322e-02,  -1.47196395e-02,
                -1.63473519e-02],
              [ -1.15115060e-02,  -1.63223376e-02,  -1.67912125e-02,
                -1.79354610e-02]],

             [[  1.07817765e-02,   1.01040666e-02,   9.79064697e-03,
                 1.14720375e-02],
              [  8.17980355e-03,   1.13028624e-02,   7.14133631e-03,
                 5.48142933e-03],
              [  9.20566183e-03,   1.07531523e-02,   1.56971359e-02,
                 8.26775261e-03],
              [  1.58930636e-02,   7.92911363e-03,   7.92536106e-03,
                 8.60416137e-03]],

             [[ -1.75539835e-02,  -1.84257317e-02,  -2.25025562e-02,
                -2.43005574e-02],
              [ -2.80276161e-02,  -1.85360524e-02,  -2.61736189e-02,
                -2.40671527e-02],
              [ -2.49452205e-02,  -2.48344833e-02,  -2.44247051e-02,
                -2.17676484e-02],
              [ -2.10527332e-02,  -2.03732528e-02,  -1.79319762e-02,
                -2.37514638e-02]]],

            [[[ -1.49802478e-02,  -1.57968853e-02,  -1.51850306e-02,
                -1.20864573e-02],
              [ -9.23611049e-03,  -1.39008568e-02,  -7.59908523e-03,
                -1.39823158e-02],
              [ -1.46107061e-02,  -9.53562755e-03,  -1.25245284e-02,
                -9.09859078e-03],
              [ -6.20554810e-03,  -8.90730422e-03,  -6.10895828e-03,
                -8.02258717e-03]],

             [[  6.07125858e-03,  -7.69681219e-04,  -1.43731041e-03,
                 4.69043216e-03],
              [  3.44628735e-03,  -1.94786649e-03,   3.33231696e-03,
                 5.20270260e-03],
              [  4.67308383e-03,   2.07053690e-03,   4.58131918e-06,
                 5.26006707e-03],
              [  2.22734384e-03,   4.80120548e-03,   9.32709985e-04,
                 3.07333771e-03]],

             [[ -1.54463229e-02,  -2.20104455e-02,  -2.29134842e-02,
                -2.11574039e-02],
              [ -1.73094297e-02,  -2.29961269e-02,  -1.49056918e-02,
                -1.82977576e-02],
              [ -1.57194376e-02,  -2.45553227e-02,  -2.02019997e-02,
                -1.72671494e-02],
              [ -2.25375815e-02,  -1.85155066e-02,  -2.28037177e-02,
                -1.67973113e-02]]]])]

```python
plt.figure(figsize=(10,10))
for i in range(0,len(C1)):
    for j in range(0,N_branches):
        plt.subplot(len(C1),N_branches,(i*N_branches)+j)
        plt.imshow(H4[i][j])
```

```python
plt.figure(figsize=(10,10))
for i in range(0,len(C1)):
    for j in range(0,N_branches):
        plt.subplot(len(C1),N_branches,(i*N_branches)+j)
        plt.imshow(C2[i][j])
```

# save training parameters

```python
with open("C2.txt", 'w') as f:
    f.write(str(C2))
with open("W.txt", 'w') as f:
    f.write(str(W))
with open("H3_bias.txt", 'w') as f:
    f.write(str(H3_bias))
with open("Bias.txt", 'w') as f:
    f.write(str(bias))

```