In [1]:

    

from __future__ import division, print_function, absolute_import
#import tensorflow as tf

# Latest version curve generation helper functions
import numpy as np
import scipy as sp
import math
from scipy.interpolate import splprep, splev
from multiprocessing import Process, Queue, Pool
import json


    

In [2]:

    

n_planar_curves_list = []
planar_curves_list = []


def add_noise(inputs, mean, std_dev):
    result = map(lambda x: x + np.random.normal(mean,std_dev,x.shape), inputs)
    return list(result)

#Step autoencoder
_debug_verbose = False 

class AutoEncoder(object):
    def __init__(self, arch):        
        self.num_layers = len(arch)
        self.input_layer_size = arch[0]
        self.output_layer_size = arch[-1]
        self.num_hidden_layers = len(arch)-2
        self.costs = []
        self.weights = [np.random.randn(y, x)
                        for x, y in zip(arch[:-1], arch[1:])]        
        self.biases = [np.random.randn(y, 1) for y in arch[1:]]

    def getParams(self):
        #Get weights and biases unrolled into vector:
        params = [(x.ravel(), y.ravel()) for x, y in zip(self.weights, self.biases)]
        return params
    
    def forward(self, X):
        for b, w in zip(self.biases, self.weights):
            if (_debug_verbose): print("weights: ", w)
            if (_debug_verbose): print("biases: ", b)
            if (_debug_verbose): print("inputs :", X)
            if (_debug_verbose): print("dot product :", np.dot(w, X))
            #print("matrix dot product :", w.dot(X))
            X = self.unit_step(np.dot(w, X) + b)
            if (_debug_verbose): print("result :", X)
        return X.reshape(3,1)
    
    def unit_step(self, z):
        #return (lambda x: 0 if (x) < 0 else 1, z)[1]
        return z
    
    def unit_step_prime(self, z):
        return (1)

    def cost_function(self, X):
        self.yHat = self.forward(X)
        if (_debug_verbose): print ("squared error of X:{0} - Xhat:{1} is {2} & sum is {3}\n".format(X, self.yHat, ((X-self.yHat)**2), sum((X-self.yHat)**2)))
        J = 0.5*sum((X-self.yHat)**2)
        #self.costs.append(J) 
        return J
    
    def cost_derivative(self, output_activations, y):
        return (output_activations-y)   
    
    def cost_function_by_epoch(self, test_data, n_test):
        y_hat = [(self.forward(y)) for (y) in test_data[0:n_test]]
        y = [(y) for (y) in test_data[0:n_test]]
        #print([float(a[0][0]) for a in y])
        np.seterr( over='ignore' )
        #costs = []
        costs = [0.5*((a - b)**2) for a, b in zip(y, y_hat)]
        #costs.append([max(math.sqrt(0.5*(round(a[0][0],2) - round(b[0][0],2))**2),1000) for a, b in zip(y, y_hat)])
        #costs.append([0.5*math.sqrt((float(a[1][0]) - float(b[1][0]))**2) for a, b in zip(y, y_hat)])
        #costs.append([0.5*math.sqrt((float(a[2][0]) - float(b[2][0]))**2) for a, b in zip(y, y_hat)])
        self.costs.append(sum(costs)) #/n_test)
        #self.costs.append(sum(costs[:][:]))
        #self.costs.append([sum(costs[0]),sum(costs[1]),sum(costs[2])])
        if (_debug_verbose): print ("Total Cost {1} for Epoch {0} complete".format(len(self.costs), sum(self.costs[-1])))
        if (_debug_verbose): print ("Axis-wise Cost is {0} ".format((self.costs[-1])))
        return self.costs[-1]

 
    def GD(self, training_data, epochs, learning_rate, test_data=None):
        """Train the neural network using batch-wise 
        gradient descent. If ``test_data`` is provided then the
        network will be evaluated against the test data after each
        epoch, and partial progress printed out."""
        if test_data: n_test = len(test_data)
        n = len(training_data)
        for j in range(epochs):
            np.random.shuffle(training_data)
            self.process_batch(training_data, learning_rate)
            if test_data:
                result = self.evaluate(test_data, n_test)
                if (_debug_verbose): print ("Epoch {0}: Score {1} / {2}".format(j, result, n_test))
            else:
                if (_debug_verbose): print ("Epoch {0} complete".format(j))

                        
    def process_batch(self, batch, learning_rate):
        """Update the network's weights by applying
        gradient descent using backpropagation to a single batch.
        """
        base_w = [np.zeros(w.shape) for w in self.weights]
        base_b = [np.zeros(b.shape) for b in self.biases]
        count=0
        for x in batch:
            delta_error_b , delta_error_w = self.backprop(x)
            updated_b = [nb+dnb for nb, dnb in zip(base_b, delta_error_b)]
            updated_w = [nw+dnw for nw, dnw in zip(base_w, delta_error_w)]
            count=count+1
        #print ("Process {0} inputs backprop ".format(count))    
            eta=learning_rate   
            self.weights = [w-(eta/len(batch))*nw
                        for w, nw in zip(self.weights, updated_w)]
            self.biases = [b-(eta/len(batch))*nb
                       for b, nb in zip(self.biases, updated_b)]
        
    def backprop(self, x):
        """Return ``( delta_w)`` representing the
        gradient for the cost function C_x. """
        
        if (_debug_verbose): print ("input: ", x)
        
        delta_w = [np.zeros(w.shape) for w in self.weights]
        delta_b = [np.zeros(b.shape) for b in self.biases]
        
        activation = x
        activations = [x] # list to store all the activations, layer by layer
        zs = [] # list to store all the activation (z) vectors, layer by layer
        
        for b, w in zip(self.biases, self.weights):
            z = np.dot(w, activation) + b
            zs.append(z)
            activation = self.unit_step(z)
            activations.append(activation)
        
        if (_debug_verbose): print ("activations: ", activations)
        
        # backward pass
        delta = self.cost_derivative(activations[-1], x) *             self.unit_step_prime(zs[-1]) 
        delta_b[-1] = delta    
        delta_w[-1] = np.dot(delta, activations[-2].transpose())
        
        if (_debug_verbose): print ("cost derivative: ", self.cost_derivative(activations[-1], x))
        if (_debug_verbose): print ("unit step: ", self.unit_step_prime(zs[-1]))
        if (_debug_verbose): print("delta: ",delta)
        
        for l in range(2, self.num_layers):
            z = zs[-l]
            step1 = np.dot(self.weights[-l+1].transpose(), delta)
            delta = step1 * z
            delta_b[-l] = delta
            delta_w[-l] = np.dot(delta, activations[-l-1].transpose())
            if (_debug_verbose): print ("delta b updated: ", delta_b)
            if (_debug_verbose): print ("delta w updated:", delta_w)
            
        #print ("delta b: ", delta_b) 
        #print ("delta w:", delta_w)    
            
        return (delta_b, delta_w)

    def evaluate(self, test_data, n_test):
        """Return the number of test inputs for which the neural
        network outputs the correct result. Note that the neural
        network's output is assumed to be the index of whichever
        neuron in the final layer has the highest activation."""
        self.cost_function_by_epoch(test_data, n_test)
        test_results = [self.forward(x)
                        for (x) in test_data]
        return sum(((x) - (x_hat))**2 for (x, x_hat) in zip(test_data, test_results))/n_test      
    
    def reconstruct(self, inputs):
        return [self.forward(x) for (x) in inputs]


    

In [8]:

    

import time
start = time.time()
def process_curves(curves_array):
    errors = []
    start = time.time()
    for i in range(len(curves_array[:])):
        print("Analysing curve [", i ,"]")
        start = time.time()
        Planar_Data = list(np.asarray(zip(curves_array[i][0], curves_array[i][1], curves_array[i][2])).reshape(300,3,1)) 
        ae = AutoEncoder([3,2,3])
        #print(Planar_Data)
        #print("AE params:", ae.getParams())
        ae.GD(Planar_Data, 500, 0.1, Planar_Data)
        #print("costs: ", ae.costs)
        print("error for this curve: ", errors)
        #scale errors
        scale=[curves_array[i][j].max() - curves_array[i][j].min() for j in [0,1,2]]
        #scaled_error = [ a[0]/b for a,b in zip(ae.costs[-1],scale)]
        #print("scaled error for this curve: ", scale,  scaled_error)
        #errors.append(scaled_error)
        errors.append(ae.costs[-1])
        planarity_errors = [sum(i[:][:]) for i in errors]
        print("\nTotal Sum Error: ", planarity_errors)
        end = time.time()
        print("Time spent in this curve: ", end - start)
    print(end - start)
    print(errors)
    return planarity_errors

data = {}
data['planar_curves_error'] = []
data['non_planar_curves_error'] = []


    

In [ ]:

    

#p_curve_data_1488329066.88168.json
#np_curve_data_1488329083.428903.json
data['planar_curves_error'] = []
with open('p_curve_data_cube_method_1489173944.8.json') as infile:
    c = json.load(infile)
    planar_curves_array = np.asarray(c['planar_curves'])
print("Planar curves read array shape: ", planar_curves_array.shape)
planar_errors = process_curves(planar_curves_array)
data['planar_curves_error'].append(np.asarray(planar_errors).tolist())

import os
os.system('say "your program has finished"')


    

    




Planar curves read array shape:  (100, 3, 300)
Analysing curve [ 0 ]
input:  [[-0.86733502]
 [-0.09135316]
 [ 1.02132887]]
activations:  [array([[-0.86733502],
       [-0.09135316],
       [ 1.02132887]]), array([[ 0.2718419 ],
       [ 0.24988635]]), array([[ 0.61081816],
       [ 0.63460069],
       [ 0.04982504]])]
cost derivative:  [[ 1.47815318]
 [ 0.72595385]
 [-0.97150382]]
unit step:  1
delta:  [[ 1.47815318]
 [ 0.72595385]
 [-0.97150382]]
delta b updated:  [array([[ 0.3482355 ],
       [-0.33421657]]), array([[ 1.47815318],
       [ 0.72595385],
       [-0.97150382]])]
delta w updated: [array([[-0.30203685, -0.03181241,  0.35566297],
       [ 0.28987773,  0.03053174, -0.34134503]]), array([[ 0.40182397,  0.36937031],
       [ 0.19734468,  0.18140596],
       [-0.26409545, -0.24276555]])]
input:  [[-0.75549339]
 [-0.01066178]
 [ 1.0795801 ]]
activations:  [array([[-0.75549339],
       [-0.01066178],
       [ 1.0795801 ]]), array([[ 0.31574717],
       [ 0.23894797]]), array([[ 0.68675125],
       [ 0.62188451],
       [ 0.08434051]])]
cost derivative:  [[ 1.44224464]
 [ 0.63254629]
 [-0.99523959]]
unit step:  1
delta:  [[ 1.44224464]
 [ 0.63254629]
 [-0.99523959]]
delta b updated:  [array([[ 0.39654827],
       [-0.29111018]]), array([[ 1.44224464],
       [ 0.63254629],
       [-0.99523959]])]
delta w updated: [array([[-0.2995896 , -0.00422791,  0.42810562],
       [ 0.21993182,  0.00310375, -0.31427675]]), array([[ 0.45538466,  0.34462143],
       [ 0.1997247 ,  0.15114566],
       [-0.31424408, -0.23781048]])]
input:  [[-0.80786871]
 [-0.86671599]
 [ 0.47927961]]
activations:  [array([[-0.80786871],
       [-0.86671599],
       [ 0.47927961]]), array([[ 0.17091487],
       [ 0.01565293]]), array([[ 0.68840464],
       [ 0.85289382],
       [ 0.14421316]])]
cost derivative:  [[ 1.49627334]
 [ 1.71960981]
 [-0.33506645]]
unit step:  1
delta:  [[ 1.49627334]
 [ 1.71960981]
 [-0.33506645]]
delta b updated:  [array([[ 0.21092773],
       [-0.03924027]]), array([[ 1.49627334],
       [ 1.71960981],
       [-0.33506645]])]
delta w updated: [array([[-0.17040192, -0.18281444,  0.10109336],
       [ 0.03170098,  0.03401017, -0.01880706]]), array([[ 0.25573537,  0.02342106],
       [ 0.29390689,  0.02691693],
       [-0.05726784, -0.00524477]])]
input:  [[ 0.8841156 ]
 [-0.79645963]
 [ 0.55486465]]
activations:  [array([[ 0.8841156 ],
       [-0.79645963],
       [ 0.55486465]]), array([[ 0.66601246],
       [-0.4763024 ]]), array([[ 1.9120734 ],
       [ 0.98224495],
       [ 0.77408257]])]
cost derivative:  [[ 1.0279578 ]
 [ 1.77870458]
 [ 0.21921792]]
unit step:  1
delta:  [[ 1.0279578 ]
 [ 1.77870458]
 [ 0.21921792]]
delta b updated:  [array([[ 0.56240551],
       [ 1.17102558]]), array([[ 1.0279578 ],
       [ 1.77870458],
       [ 0.21921792]])]
delta w updated: [array([[ 0.49723148, -0.44793328,  0.31205894],
       [ 1.03532198, -0.93267459,  0.6497607 ]]), array([[ 0.6846327 , -0.48961877],
       [ 1.18463941, -0.84720126],
       [ 0.14600187, -0.10441402]])]
input:  [[-0.85040337]
 [-0.65782519]
 [ 0.62489984]]
activations:  [array([[-0.85040337],
       [-0.65782519],
       [ 0.62489984]]), array([[ 0.18993707],
       [ 0.08652804]]), array([[ 0.64645704],
       [ 0.79081769],
       [ 0.10886156]])]
cost derivative:  [[ 1.4968604 ]
 [ 1.44864287]
 [-0.51603828]]
unit step:  1
delta:  [[ 1.4968604 ]
 [ 1.44864287]
 [-0.51603828]]
delta b updated:  [array([[ 0.23728436],
       [-0.18932778]]), array([[ 1.4968604 ],
       [ 1.44864287],
       [-0.51603828]])]
delta w updated: [array([[-0.20178742, -0.15609163,  0.14827896],
       [ 0.16100498,  0.12454458, -0.1183109 ]]), array([[ 0.28430928,  0.12952039],
       [ 0.27515099,  0.12534822],
       [-0.0980148 , -0.04465178]])]
input:  [[-0.81402889]
 [-0.83872816]
 [ 0.49878306]]
activations:  [array([[-0.81402889],
       [-0.83872816],
       [ 0.49878306]]), array([[ 0.17276735],
       [ 0.0250599 ]]), array([[ 0.68054   ],
       [ 0.84338794],
       [ 0.13931697]])]
cost derivative:  [[ 1.49456888]
 [ 1.6821161 ]
 [-0.35946609]]
unit step:  1
delta:  [[ 1.49456888]
 [ 1.6821161 ]
 [-0.35946609]]
delta b updated:  [array([[ 0.21293479],
       [-0.06167234]]), array([[ 1.49456888],
       [ 1.6821161 ],
       [-0.35946609]])]
delta w updated: [array([[-0.17333507, -0.17859441,  0.10620827],
       [ 0.05020307,  0.05172633, -0.03076112]]), array([[ 0.2582127 ,  0.03745375],
       [ 0.29061473,  0.04215366],
       [-0.062104  , -0.00900818]])]
input:  [[ 0.63635401]
 [-1.09331501]
 [ 0.34311697]]
activations:  [array([[ 0.63635401],
       [-1.09331501],
       [ 0.34311697]]), array([[ 0.54942182],
       [-0.48530044]]), array([[ 1.74468633],
       [ 1.04113226],
       [ 0.70883723]])]
cost derivative:  [[ 1.10833232]
 [ 2.13444727]
 [ 0.36572027]]
unit step:  1
delta:  [[ 1.10833232]
 [ 2.13444727]
 [ 0.36572027]]
delta b updated:  [array([[ 0.4873303 ],
       [ 1.40491036]]), array([[ 1.10833232],
       [ 2.13444727],
       [ 0.36572027]])]
delta w updated: [array([[ 0.31011459, -0.53280554,  0.16721129],
       [ 0.89402034, -1.53600959,  0.48204858]]), array([[ 0.60894196, -0.53787417],
       [ 1.17271191, -1.03584821],
       [ 0.2009347 , -0.17748421]])]
input:  [[ 0.69887632]
 [-1.1126556 ]
 [ 0.330548  ]]
activations:  [array([[ 0.69887632],
       [-1.1126556 ],
       [ 0.330548  ]]), array([[ 0.56394904],
       [-0.51094311]]), array([[ 1.79102016],
       [ 1.05204118],
       [ 0.7345627 ]])]
cost derivative:  [[ 1.09214384]
 [ 2.16469678]
 [ 0.40401471]]
unit step:  1
delta:  [[ 1.09214384]
 [ 2.16469678]
 [ 0.40401471]]
delta b updated:  [array([[ 0.49119499],
       [ 1.49484949]]), array([[ 1.09214384],
       [ 2.16469678],
       [ 0.40401471]])]
delta w updated: [array([[ 0.34328455, -0.54653086,  0.16236352],
       [ 1.04471491, -1.66325266,  0.4941195 ]]), array([[ 0.61591347, -0.55802337],
       [ 1.22077867, -1.10603692],
       [ 0.22784371, -0.20642853]])]
input:  [[-0.89329364]
 [-0.24810329]
 [ 0.91115396]]
activations:  [array([[-0.89329364],
       [-0.24810329],
       [ 0.91115396]]), array([[ 0.23898464],
       [ 0.21341071]]), array([[ 0.59360376],
       [ 0.67234995],
       [ 0.05474091]])]
cost derivative:  [[ 1.4868974 ]
 [ 0.92045325]
 [-0.85641305]]
unit step:  1
delta:  [[ 1.4868974 ]
 [ 0.92045325]
 [-0.85641305]]
delta b updated:  [array([[ 0.30366435],
       [-0.33383042]]), array([[ 1.4868974 ],
       [ 0.92045325],
       [-0.85641305]])]
delta w updated: [array([[-0.27126143, -0.07534012,  0.27668497],
       [ 0.29820859,  0.08282443, -0.30417091]]), array([[ 0.35534564,  0.31731984],
       [ 0.21997419,  0.19643458],
       [-0.20466956, -0.18276772]])]
input:  [[ 0.3062507 ]
 [-0.86339003]
 [ 0.49898272]]
activations:  [array([[ 0.3062507 ],
       [-0.86339003],
       [ 0.49898272]]), array([[ 0.4887938],
       [-0.3231067]]), array([[ 1.49320595],
       [ 0.94815453],
       [ 0.56399642]])]
cost derivative:  [[ 1.18695525]
 [ 1.81154456]
 [ 0.06501371]]
unit step:  1
delta:  [[ 1.18695525]
 [ 1.81154456]
 [ 0.06501371]]
delta b updated:  [array([[ 0.47365622],
       [ 0.81884971]]), array([[ 1.18695525],
       [ 1.81154456],
       [ 0.06501371]])]
delta w updated: [array([[ 0.14505755, -0.40895005,  0.23634626],
       [ 0.2507733 , -0.70698668,  0.40859185]]), array([[ 0.58017637, -0.3835132 ],
       [ 0.88547175, -0.58532219],
       [ 0.0317783 , -0.02100636]])]
input:  [[-0.21431142]
 [-0.37798482]
 [ 0.8307879 ]]
activations:  [array([[-0.21431142],
       [-0.37798482],
       [ 0.8307879 ]]), array([[ 0.4131216 ],
       [-0.02930914]]), array([[ 1.09128246],
       [ 0.76762144],
       [ 0.32270817]])]
cost derivative:  [[ 1.30559388]
 [ 1.14560626]
 [-0.50807972]]
unit step:  1
delta:  [[ 1.30559388]
 [ 1.14560626]
 [-0.50807972]]
delta b updated:  [array([[ 0.45709944],
       [ 0.05223803]]), array([[ 1.30559388],
       [ 1.14560626],
       [-0.50807972]])]
delta w updated: [array([[-0.09796163, -0.17277665,  0.37975268],
       [-0.01119521, -0.01974518,  0.04339872]]), array([[ 0.53936903, -0.03826583],
       [ 0.47327469, -0.03357673],
       [-0.20989871,  0.01489138]])]
input:  [[ 0.27353785]
 [-0.83480259]
 [ 0.51849199]]
activations:  [array([[ 0.27353785],
       [-0.83480259],
       [ 0.51849199]]), array([[ 0.4831816 ],
       [-0.30574565]]), array([[ 1.46672268],
       [ 0.9367955 ],
       [ 0.54921701]])]
cost derivative:  [[ 1.19318483]
 [ 1.77159809]
 [ 0.03072501]]
unit step:  1
delta:  [[ 1.19318483]
 [ 1.77159809]
 [ 0.03072501]]
delta b updated:  [array([[ 0.47100058],
       [ 0.76065589]]), array([[ 1.19318483],
       [ 1.77159809],
       [ 0.03072501]])]
delta w updated: [array([[ 0.12883649, -0.3931925 ,  0.24421003],
       [ 0.20806818, -0.63499751,  0.39439399]]), array([[ 0.57652495, -0.36481107],
       [ 0.85600359, -0.54165841],
       [ 0.01484576, -0.00939404]])]
input:  [[ 0.18458517]
 [-0.75451282]
 [ 0.57333073]]
activations:  [array([[ 0.18458517],
       [-0.75451282],
       [ 0.57333073]]), array([[ 0.4695886 ],
       [-0.25669233]]), array([[ 1.39775458],
       [ 0.90646898],
       [ 0.50833406]])]
cost derivative:  [[ 1.21316941]
 [ 1.66098179]
 [-0.06499667]]
unit step:  1
delta:  [[ 1.21316941]
 [ 1.66098179]
 [-0.06499667]]
delta b updated:  [array([[ 0.46824416],
       [ 0.6064412 ]]), array([[ 1.21316941],
       [ 1.66098179],
       [-0.06499667]])]
delta w updated: [array([[ 0.08643093, -0.35329622,  0.26845876],
       [ 0.11194005, -0.45756766,  0.34769138]]), array([[ 0.56969052, -0.31141129],
       [ 0.77997811, -0.42636129],
       [-0.0305217 ,  0.01668415]])]
input:  [[-0.78790495]
 [-0.95398039]
 [ 0.41848079]]
activations:  [array([[-0.78790495],
       [-0.95398039],
       [ 0.41848079]]), array([[ 0.16070049],
       [-0.01773895]]), array([[ 0.70087508],
       [ 0.87562742],
       [ 0.16063494]])]
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result : [[ 0.53910809]
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weights:  [[ 1.45140399 -0.96368958]
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 [ 0.61723482 -0.65300792]]
biases:  [[ 0.33030415]
 [ 0.80817505]
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inputs : [[ 0.53910809]
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dot product : [[ 1.44949426]
 [ 0.19525547]
 [ 0.78474445]]
result : [[ 1.77979841]
 [ 1.00343052]
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weights:  [[ 0.28379622  0.27976811 -0.17866676]
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biases:  [[ 0.33030415]
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inputs : [[ 0.2702312 ]
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dot product : [[ 0.22172857]
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result : [[ 0.55203272]
 [ 0.54322   ]
 [ 0.12723915]]
weights:  [[ 0.28379622  0.27976811 -0.17866676]
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biases:  [[ 0.66803362]
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inputs : [[-0.88785181]
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dot product : [[-0.47279606]
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result : [[ 0.19523756]
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weights:  [[ 1.45140399 -0.96368958]
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biases:  [[ 0.33030415]
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dot product : [[ 0.10024378]
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result : [[ 0.43054793]
 [ 0.57366232]
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weights:  [[ 0.28379622  0.27976811 -0.17866676]
 [-0.3393786   0.67566089 -0.50934367]]
biases:  [[ 0.66803362]
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inputs : [[-0.6015206 ]
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dot product : [[-0.37553494]
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result : [[ 0.29249868]
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weights:  [[ 1.45140399 -0.96368958]
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biases:  [[ 0.33030415]
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inputs : [[ 0.29249868]
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dot product : [[ 0.30385583]
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result : [[ 0.63415998]
 [ 0.56728095]
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weights:  [[ 0.28379622  0.27976811 -0.17866676]
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biases:  [[ 0.66803362]
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inputs : [[ 0.80545352]
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dot product : [[-0.13914111]
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result : [[ 0.52889251]
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weights:  [[ 1.45140399 -0.96368958]
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dot product : [[ 1.43388564]
 [ 0.20007785]
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result : [[ 1.76418979]
 [ 1.0082529 ]
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Total Cost [ 553.27727253] for Epoch 1 complete
Axis-wise Cost is [[ 192.66517576]
 [ 310.24903499]
 [  50.36306179]] 
weights:  [[ 0.28379622  0.27976811 -0.17866676]
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inputs : [[-0.86733502]
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dot product : [[-0.45418163]
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result : [[ 0.21385199]
 [ 0.20940052]]
weights:  [[ 1.45140399 -0.96368958]
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biases:  [[ 0.33030415]
 [ 0.80817505]
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inputs : [[ 0.21385199]
 [ 0.20940052]]
dot product : [[ 0.10858854]
 [-0.25774335]
 [-0.0047433 ]]
result : [[ 0.43889269]
 [ 0.5504317 ]
 [ 0.07122321]]
weights:  [[ 0.28379622  0.27976811 -0.17866676]
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biases:  [[ 0.66803362]
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inputs : [[-0.75549339]
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dot product : [[-0.41027408]
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result : [[ 0.25775954]
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biases:  [[ 0.33030415]
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inputs : [[ 0.25775954]
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dot product : [[ 0.18494677]
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result : [[ 0.51525092]
 [ 0.53636766]
 [ 0.10688315]]
weights:  [[ 0.28379622  0.27976811 -0.17866676]
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result : [[ 0.1106527 ]
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dot product : [[ 0.21704898]
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result : [[ 0.54735313]
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inputs : [[ 0.59698241]
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dot product : [[ 1.46763785]
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result : [[ 1.797942  ]
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weights:  [[ 0.28379622  0.27976811 -0.17866676]
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biases:  [[ 0.66803362]
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dot product : [[-0.5370286 ]
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result : [[ 0.13100502]
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weights:  [[ 1.45140399 -0.96368958]
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result : [[ 0.54045768]
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result : [[ 0.48145021]
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result : [[ 1.63858077]
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Epoch 0: Score [[ 1.28443451]
 [ 2.0683269 ]
 [ 0.33575375]] / 300
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unit step:  1
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delta b updated:  [array([[ 0.33130482],
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delta w updated: [array([[ 0.28636519, -0.33039061,  0.13714436],
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delta b updated:  [array([[ 0.27376686],
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delta w updated: [array([[-0.15234777, -0.02573097,  0.28042782],
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cost derivative:  [[ 0.88559437]
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delta b updated:  [array([[ 0.42898297],
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delta w updated: [array([[ 0.34734163, -0.10471736,  0.40346279],
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cost derivative:  [[ 0.88874812]
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delta b updated:  [array([[ 0.41380186],
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delta w updated: [array([[ 0.34391379, -0.14649394,  0.3574723 ],
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delta b updated:  [array([[ 0.09662389],
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delta w updated: [array([[-0.07744042, -0.08650268,  0.04438868],
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cost derivative:  [[ 1.30970057]
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unit step:  1
delta:  [[ 1.30970057]
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delta b updated:  [array([[ 0.19551953],
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delta w updated: [array([[-0.17215625, -0.02611696,  0.19386827],
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input:  [[ 0.85862676]
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activations:  [array([[ 0.85862676],
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cost derivative:  [[ 0.93538534]
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unit step:  1
delta:  [[ 0.93538534]
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delta b updated:  [array([[ 0.32572446],
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delta w updated: [array([[ 0.27967574, -0.33059189,  0.13076724],
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input:  [[ 0.05949974]
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cost derivative:  [[ 1.10961747]
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unit step:  1
delta:  [[ 1.10961747]
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delta b updated:  [array([[ 0.29461408],
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delta w updated: [array([[ 0.01752946, -0.18778996,  0.19249662],
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input:  [[-0.53784878]
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activations:  [array([[-0.53784878],
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cost derivative:  [[ 1.21818765]
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unit step:  1
delta:  [[ 1.21818765]
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delta b updated:  [array([[ 0.27286213],
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delta w updated: [array([[-0.14675856, -0.0290955 ,  0.27716469],
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input:  [[ 0.53959763]
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delta w updated: [array([[-0.13350922, -0.0385408 ,  0.27044036],
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cost derivative:  [[ 0.99869413]
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delta w updated: [array([[ 0.18151608, -0.31813154,  0.10193563],
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delta w updated: [array([[ 0.26628112, -0.325843  ,  0.12070159],
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cost derivative:  [[ 1.10152453]
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delta w updated: [array([[-0.08742102, -0.0479909 ,  0.07443006],
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delta w updated: [array([[ 0.17800093, -0.26811821,  0.07903193],
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delta w updated: [array([[ 0.24458953, -0.24911665,  0.13429727],
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delta w updated: [array([[-0.09816897, -0.03505181,  0.20839223],
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delta w updated: [array([[-0.09032234, -0.04240233,  0.20552945],
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delta w updated: [array([[-0.07747587, -0.04445196,  0.06491395],
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dot product : [[ 1.43851933]
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result : [[ 1.66044328]
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weights:  [[ 0.28003415  0.29361948 -0.19522318]
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dot product : [[-0.08722927]
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biases:  [[ 0.64419214]
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weights:  [[ 1.41438055 -0.93577526]
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dot product : [[-0.40300802]
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Total Cost [ 510.79911772] for Epoch 2 complete
Axis-wise Cost is [[ 170.39250908]
 [ 290.65966707]
 [  49.74694157]] 
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inputs : [[ 0.08575923]
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dot product : [[ 0.20934922]
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result : [[ 0.43127316]
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result : [[ 0.2378194]
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result : [[ 0.47618504]
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Epoch 1: Score [[ 1.13595006]
 [ 1.93773111]
 [ 0.33164628]] / 300
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delta w updated: [array([[ 0.01322671, -0.14169542,  0.14524679],
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cost derivative:  [[ 1.12050963]
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unit step:  1
delta:  [[ 1.12050963]
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delta b updated:  [array([[ 0.20560288],
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delta w updated: [array([[-0.09868263, -0.03060599,  0.20295029],
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input:  [[-0.88785181]
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activations:  [array([[-0.88785181],
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cost derivative:  [[ 1.21909238]
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unit step:  1
delta:  [[ 1.21909238]
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delta b updated:  [array([[ 0.12547244],
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delta w updated: [array([[-0.11140093, -0.02188711,  0.12080812],
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input:  [[ 0.54885873]
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activations:  [array([[ 0.54885873],
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cost derivative:  [[ 0.98449452]
 [ 1.97443339]
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unit step:  1
delta:  [[ 0.98449452]
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delta b updated:  [array([[ 0.22640697],
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       [ 0.47525836]])]
delta w updated: [array([[ 0.12426544, -0.23722135,  0.08459707],
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input:  [[-0.79480844]
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cost derivative:  [[ 1.28668189]
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unit step:  1
delta:  [[ 1.28668189]
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delta b updated:  [array([[ 0.04681252],
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delta w updated: [array([[-0.03720698, -0.0432706 ,  0.02055683],
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input:  [[ 0.10523791]
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activations:  [array([[ 0.10523791],
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delta w updated: [array([[ 0.12048617, -0.22008972,  0.07406395],
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delta w updated: [array([[-0.02130899, -0.08973033,  0.14499139],
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Total Cost [ 486.96158834] for Epoch 3 complete
Axis-wise Cost is [[ 163.45908677]
 [ 272.64695297]
 [  50.8555486 ]] 
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inputs : [[ 0.31076666]
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result : [[ 1.09561231]
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result : [[ 0.24137006]
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inputs : [[ 0.24137006]
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dot product : [[ 0.47969214]
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result : [[ 0.59709148]
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weights:  [[ 1.38247814 -0.89191079]
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biases:  [[ 0.11739934]
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dot product : [[ 0.22295979]
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weights:  [[ 0.27597061  0.30504743 -0.20841451]
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biases:  [[ 0.62496182]
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biases:  [[ 0.62496182]
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inputs : [[ 0.84514637]
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dot product : [[-0.16489995]
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result : [[ 0.46006188]
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weights:  [[ 1.38247814 -0.89191079]
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biases:  [[ 0.11739934]
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dot product : [[ 1.65740536]
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result : [[ 1.77480471]
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biases:  [[ 0.62496182]
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inputs : [[-0.26986321]
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result : [[ 0.27033508]
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biases:  [[ 0.11739934]
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biases:  [[ 0.62496182]
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biases:  [[ 0.11739934]
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inputs : [[ 0.15680231]
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biases:  [[ 0.62496182]
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biases:  [[ 0.11739934]
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dot product : [[ 0.17999061]
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weights:  [[ 0.27597061  0.30504743 -0.20841451]
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biases:  [[ 0.62496182]
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dot product : [[-0.42135999]
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result : [[ 0.20360183]
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weights:  [[ 1.38247814 -0.89191079]
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biases:  [[ 0.11739934]
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inputs : [[ 0.20360183]
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dot product : [[ 0.29411191]
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result : [[ 0.41151126]
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weights:  [[ 0.27597061  0.30504743 -0.20841451]
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biases:  [[ 0.62496182]
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inputs : [[-0.85040337]
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dot product : [[-0.56559241]
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result : [[ 0.05936941]
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weights:  [[ 1.38247814 -0.89191079]
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biases:  [[ 0.11739934]
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inputs : [[ 0.05936941]
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dot product : [[ 0.26780012]
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result : [[ 0.38519946]
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weights:  [[ 0.27597061  0.30504743 -0.20841451]
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biases:  [[ 0.62496182]
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inputs : [[ 0.88371996]
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dot product : [[-0.11213299]
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result : [[ 0.51282884]
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weights:  [[ 1.38247814 -0.89191079]
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biases:  [[ 0.11739934]
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inputs : [[ 0.51282884]
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dot product : [[ 1.64664125]
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result : [[ 1.76404059]
 [ 0.81926451]
 [ 1.07773484]]
Epoch 2: Score [[ 1.08972725]
 [ 1.81764635]
 [ 0.33903699]] / 300
input:  [[-0.88628836]
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 [ 0.97042779]]
activations:  [array([[-0.88628836],
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       [ 0.46387621],
       [ 0.17693664]])]
cost derivative:  [[ 1.16701314]
 [ 0.62749331]
 [-0.79349115]]
unit step:  1
delta:  [[ 1.16701314]
 [ 0.62749331]
 [-0.79349115]]
delta b updated:  [array([[ 0.09404329],
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       [ 0.62749331],
       [-0.79349115]])]
delta w updated: [array([[-0.08334947, -0.01538709,  0.09126222],
       [ 0.01241126,  0.00229123, -0.01358952]]), array([[ 0.14962285,  0.01821637],
       [ 0.08045097,  0.00979479],
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input:  [[-0.87806208]
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activations:  [array([[-0.87806208],
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       [-0.12179388]]), array([[ 0.33584054],
       [ 0.57057056],
       [ 0.23286129]])]
cost derivative:  [[ 1.21390263]
 [ 1.05259412]
 [-0.51471885]]
unit step:  1
delta:  [[ 1.21390263]
 [ 1.05259412]
 [-0.51471885]]
delta b updated:  [array([[ 0.05617825],
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       [ 1.05259412],
       [-0.51471885]])]
delta w updated: [array([[-0.04932799, -0.02707924,  0.04199775],
       [-0.14433349, -0.07923374,  0.12288522]]), array([[ 0.09676618, -0.14784591],
       [ 0.08390748, -0.12819952],
       [-0.04103078,  0.0626896 ]])]
input:  [[-0.87822135]
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 [ 0.99804596]]
activations:  [array([[-0.87822135],
       [-0.1243584 ],
       [ 0.99804596]]), array([[ 0.13652107],
       [ 0.0282781 ]]), array([[ 0.28011156],
       [ 0.4510584 ],
       [ 0.17461097]])]
cost derivative:  [[ 1.15833291]
 [ 0.5754168 ]
 [-0.82343499]]
unit step:  1
delta:  [[ 1.15833291]
 [ 0.5754168 ]
 [-0.82343499]]
delta b updated:  [array([[ 0.10039566],
       [-0.02379912]]), array([[ 1.15833291],
       [ 0.5754168 ],
       [-0.82343499]])]
delta w updated: [array([[-0.08816961, -0.01248504,  0.10019948],
       [ 0.0209009 ,  0.00295962, -0.02375262]]), array([[ 0.15813685,  0.03275546],
       [ 0.07855652,  0.01627169],
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input:  [[-0.48980896]
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 [ 0.99217199]]
activations:  [array([[-0.48980896],
       [-0.14139571],
       [ 0.99217199]]), array([[ 0.23967411],
       [-0.1561629 ]]), array([[ 0.58680983],
       [ 0.48942086],
       [ 0.3531974 ]])]
cost derivative:  [[ 1.07661879]
 [ 0.63081657]
 [-0.63897459]]
unit step:  1
delta:  [[ 1.07661879]
 [ 0.63081657]
 [-0.63897459]]
delta b updated:  [array([[ 0.16809488],
       [ 0.1428051 ]]), array([[ 1.07661879],
       [ 0.63081657],
       [-0.63897459]])]
delta w updated: [array([[-0.08233438, -0.02376789,  0.16677903],
       [-0.06994722, -0.02019203,  0.14168722]]), array([[ 0.25803765, -0.16812791],
       [ 0.1511904 , -0.09851014],
       [-0.15314567,  0.09978413]])]
input:  [[-0.82850036]
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 [ 1.06168277]]
activations:  [array([[-0.82850036],
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       [ 1.06168277]]), array([[ 0.16413158],
       [ 0.04316695]]), array([[ 0.30423553],
       [ 0.42511767],
       [ 0.18284538]])]
cost derivative:  [[ 1.1327359 ]
 [ 0.45971081]
 [-0.87883739]]
unit step:  1
delta:  [[ 1.1327359 ]
 [ 0.45971081]
 [-0.87883739]]
delta b updated:  [array([[ 0.12109395],
       [-0.03106562]]), array([[ 1.1327359 ],
       [ 0.45971081],
       [-0.87883739]])]
delta w updated: [array([[-0.10032638, -0.00418902,  0.12856336],
       [ 0.02573788,  0.00107466, -0.03298183]]), array([[ 0.18591773,  0.04889676],
       [ 0.07545306,  0.01984432],
       [-0.14424497, -0.03793673]])]
input:  [[-0.8624718 ]
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 [ 0.67352033]]
activations:  [array([[-0.8624718 ],
       [-0.58812709],
       [ 0.67352033]]), array([[ 0.06673598],
       [-0.17364925]]), array([[ 0.36259243],
       [ 0.60667113],
       [ 0.25811066]])]
cost derivative:  [[ 1.22506423]
 [ 1.19479821]
 [-0.41540967]]
unit step:  1
delta:  [[ 1.22506423]
 [ 1.19479821]
 [-0.41540967]]
delta b updated:  [array([[ 0.04618934],
       [ 0.2605695 ]]), array([[ 1.22506423],
       [ 1.19479821],
       [-0.41540967]])]
delta w updated: [array([[-0.039837  , -0.0271652 ,  0.03110946],
       [-0.22473385, -0.15324798,  0.17549886]]), array([[ 0.08175586, -0.21273149],
       [ 0.07973603, -0.20747581],
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input:  [[-0.35658302]
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activations:  [array([[-0.35658302],
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cost derivative:  [[ 1.06331837]
 [ 0.78627514]
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unit step:  1
delta:  [[ 1.06331837]
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delta b updated:  [array([[ 0.17569065],
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       [ 0.78627514],
       [-0.4864002 ]])]
delta w updated: [array([[-0.0626483 , -0.04390973,  0.1613274 ],
       [-0.10158749, -0.07120191,  0.26160079]]), array([[ 0.27487995, -0.27943736],
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input:  [[ 0.85550694]
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 [ 0.39562474]]
activations:  [array([[ 0.85550694],
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       [-1.14074265]]), array([[ 1.77623835],
       [ 0.89611776],
       [ 1.10643912]])]
cost derivative:  [[ 0.92073141]
 [ 1.91933264]
 [ 0.71081438]]
unit step:  1
delta:  [[ 0.92073141]
 [ 1.91933264]
 [ 0.71081438]]
delta b updated:  [array([[ 0.23908712],
       [ 2.66239241]]), array([[ 0.92073141],
       [ 1.91933264],
       [ 0.71081438]])]
delta w updated: [array([[ 0.20454069, -0.24463749,  0.09458878],
       [ 2.27769518, -2.72419953,  1.05330829]]), array([[ 0.42926004, -1.05031758],
       [ 0.89482427, -2.1894646 ],
       [ 0.33139329, -0.81085628]])]
input:  [[-0.62740077]
 [-0.05140675]
 [ 1.05304444]]
activations:  [array([[-0.62740077],
       [-0.05140675],
       [ 1.05304444]]), array([[ 0.21597658],
       [-0.05689606]]), array([[ 0.46359153],
       [ 0.44700598],
       [ 0.27739281]])]
cost derivative:  [[ 1.0909923 ]
 [ 0.49841273]
 [-0.77565164]]
unit step:  1
delta:  [[ 1.0909923 ]
 [ 0.49841273]
 [-0.77565164]]
delta b updated:  [array([[ 0.15527251],
       [ 0.04366448]]), array([[ 1.0909923 ],
       [ 0.49841273],
       [-0.77565164]])]
delta w updated: [array([[-0.09741809, -0.00798206,  0.16350886],
       [-0.02739513, -0.00224465,  0.04598064]]), array([[ 0.23562879, -0.06207316],
       [ 0.10764548, -0.02835772],
       [-0.16752259,  0.04413152]])]
input:  [[ 0.207027  ]
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 [ 0.55928504]]
activations:  [array([[ 0.207027  ],
       [-0.77506961],
       [ 0.55928504]]), array([[ 0.32845364],
       [-0.74259962]]), array([[ 1.22980695],
       [ 0.7585484 ],
       [ 0.77357838]])]
cost derivative:  [[ 1.02277994]
 [ 1.53361801]
 [ 0.21429335]]
unit step:  1
delta:  [[ 1.02277994]
 [ 1.53361801]
 [ 0.21429335]]
delta b updated:  [array([[ 0.19311666],
       [ 1.41030841]]), array([[ 1.02277994],
       [ 1.53361801],
       [ 0.21429335]])]
delta w updated: [array([[ 0.03998036, -0.14967886,  0.10800726],
       [ 0.29197192, -1.09308719,  0.78876439]]), array([[ 0.33593579, -0.759516  ],
       [ 0.50372241, -1.13886415],
       [ 0.07038543, -0.15913416]])]
input:  [[ 0.39129169]
 [-0.9345011 ]
 [ 0.45051071]]
activations:  [array([[ 0.39129169],
       [-0.9345011 ],
       [ 0.45051071]]), array([[ 0.35324722],
       [-0.89482407]]), array([[ 1.3991393 ],
       [ 0.82694842],
       [ 0.88343447]])]
cost derivative:  [[ 1.00784761]
 [ 1.76144952]
 [ 0.43292376]]
unit step:  1
delta:  [[ 1.00784761]
 [ 1.76144952]
 [ 0.43292376]]
delta b updated:  [array([[ 0.19757963],
       [ 1.92192677]]), array([[ 1.00784761],
       [ 1.76144952],
       [ 0.43292376]])]
delta w updated: [array([[ 0.07731127, -0.18463838,  0.08901174],
       [ 0.75203398, -1.79604269,  0.8658486 ]]), array([[ 0.35601937, -0.9018463 ],
       [ 0.62222715, -1.57618743],
       [ 0.15292911, -0.3873906 ]])]
input:  [[-0.22549134]
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 [ 0.83786346]]
activations:  [array([[-0.22549134],
       [-0.36763205],
       [ 0.83786346]]), array([[ 0.27500864],
       [-0.37544601]]), array([[ 0.82776005],
       [ 0.58586892],
       [ 0.51191644]])]
cost derivative:  [[ 1.0532514 ]
 [ 0.95350097]
 [-0.32594702]]
unit step:  1
delta:  [[ 1.0532514 ]
 [ 0.95350097]
 [-0.32594702]]
delta b updated:  [array([[ 0.18120821],
       [ 0.47518897]]), array([[ 1.0532514 ],
       [ 0.95350097],
       [-0.32594702]])]
delta w updated: [array([[-0.04086088, -0.06661795,  0.15182774],
       [-0.107151  , -0.1746947 ,  0.39814348]]), array([[ 0.28965324, -0.39543903],
       [ 0.26222101, -0.35798813],
       [-0.08963825,  0.12237551]])]
input:  [[-0.66848697]
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 [ 1.06606158]]
activations:  [array([[-0.66848697],
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       [ 1.06606158]]), array([[ 0.20743588],
       [-0.03177399]]), array([[ 0.42789871],
       [ 0.43662557],
       [ 0.25665272]])]
cost derivative:  [[ 1.09638567]
 [ 0.46852911]
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unit step:  1
delta:  [[ 1.09638567]
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delta b updated:  [array([[ 0.15009317],
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       [-0.80940886]])]
delta w updated: [array([[-0.10033533, -0.0047885 ,  0.16000856],
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input:  [[ 0.16203029]
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activations:  [array([[ 0.16203029],
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cost derivative:  [[ 1.02565501]
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 [ 0.16022897]]
unit step:  1
delta:  [[ 1.02565501]
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delta b updated:  [array([[ 0.19171861],
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       [ 0.16022897]])]
delta w updated: [array([[ 0.03106422, -0.14066089,  0.11264716],
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input:  [[-0.24775025]
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activations:  [array([[-0.24775025],
       [-0.34713934],
       [ 0.85186713]]), array([[ 0.27186749],
       [-0.3576935 ]]), array([[ 0.80633409],
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       [ 0.49928766]])]
cost derivative:  [[ 1.05408434]
 [ 0.92388876]
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unit step:  1
delta:  [[ 1.05408434]
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delta b updated:  [array([[ 0.17986398],
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       [ 0.92388876],
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delta w updated: [array([[-0.04456135, -0.06243786,  0.15322021],
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result : [[ 1.76400621]
 [ 0.8322743 ]
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weights:  [[ 0.27128095  0.31576503 -0.22024837]
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dot product : [[-0.61309412]
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biases:  [[ 0.0138718 ]
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dot product : [[ 0.48753068]
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weights:  [[ 0.27128095  0.31576503 -0.22024837]
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biases:  [[ 0.60742848]
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inputs : [[ 0.70617593]
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weights:  [[ 1.35405737 -0.82721596]
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biases:  [[ 0.0138718 ]
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inputs : [[ 0.37463482]
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dot product : [[ 1.68192994]
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result : [[ 1.69580173]
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weights:  [[ 0.27128095  0.31576503 -0.22024837]
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result : [[ 1.73578124]
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Total Cost [ 463.92784848] for Epoch 4 complete
Axis-wise Cost is [[ 162.0887817 ]
 [ 247.30641308]
 [  54.5326537 ]] 
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inputs : [[ 0.10159563]
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dot product : [[ 0.24313248]
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result : [[ 0.25700428]
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result : [[ 0.05236747]
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result : [[ 0.32136378]
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Epoch 3: Score [[ 1.08059188]
 [ 1.64870942]
 [ 0.36355102]] / 300
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unit step:  1
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unit step:  1
delta:  [[ 1.03200799]
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delta b updated:  [array([[ 0.18145565],
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delta w updated: [array([[ 0.02734804, -0.13122462,  0.10792009],
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cost derivative:  [[ 1.02163726]
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unit step:  1
delta:  [[ 1.02163726]
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delta b updated:  [array([[ 0.19094258],
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result : [[ 0.40188405]
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result : [[ 0.32682125]
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Total Cost [ 417.93473129] for Epoch 5 complete
Axis-wise Cost is [[ 154.02151713]
 [ 204.76254178]
 [  59.15067238]] 
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inputs : [[ 0.10386393]
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dot product : [[ 0.33148908]
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result : [[ 0.24299027]
 [ 0.33522945]
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weights:  [[ 0.26593491  0.32626143 -0.23138231]
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inputs : [[ 0.15071471]
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dot product : [[-0.33347836]
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result : [[ 0.25720403]
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biases:  [[-0.08849881]
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inputs : [[ 0.25720403]
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dot product : [[ 1.24499529]
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result : [[ 1.15649649]
 [ 0.54579958]
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inputs : [[ 0.28425662]
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result : [[ 1.37611011]
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result : [[ 0.32682125]
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Epoch 4: Score [[ 1.02681011]
 [ 1.36508361]
 [ 0.39433782]] / 300
input:  [[ 0.77227571]
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activations:  [array([[ 0.77227571],
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cost derivative:  [[ 0.94681152]
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unit step:  1
delta:  [[ 0.94681152]
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delta b updated:  [array([[ 0.21926743],
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delta w updated: [array([[ 0.16933491, -0.24351158,  0.0730498 ],
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cost derivative:  [[ 0.96860743]
 [ 1.7696908 ]
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unit step:  1
delta:  [[ 0.96860743]
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delta b updated:  [array([[ 0.20393303],
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delta w updated: [array([[ 0.13633111, -0.2253517 ,  0.0684024 ],
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input:  [[-0.15797529]
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activations:  [array([[-0.15797529],
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cost derivative:  [[ 1.01663158]
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unit step:  1
delta:  [[ 1.01663158]
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delta b updated:  [array([[ 0.15553818],
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delta w updated: [array([[-0.02457119, -0.06698405,  0.12361845],
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input:  [[-0.85738571]
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activations:  [array([[-0.85738571],
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cost derivative:  [[ 1.09684063]
 [ 0.40802073]
 [-0.72734533]]
unit step:  1
delta:  [[ 1.09684063]
 [ 0.40802073]
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delta b updated:  [array([[ 0.07344225],
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delta w updated: [array([[-0.06296834, -0.00518178,  0.07608971],
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input:  [[-0.88956859]
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cost derivative:  [[ 1.16053272]
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unit step:  1
delta:  [[ 1.16053272]
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delta b updated:  [array([[ 0.02956701],
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delta w updated: [array([[-0.02630188, -0.01096077,  0.0244032 ],
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input:  [[-0.42968263]
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cost derivative:  [[ 1.02734466]
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unit step:  1
delta:  [[ 1.02734466]
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delta b updated:  [array([[ 0.13861844],
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delta w updated: [array([[-0.05956193, -0.02613304,  0.13308834],
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input:  [[-0.88454188]
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cost derivative:  [[ 1.11745081]
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unit step:  1
delta:  [[ 1.11745081]
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delta b updated:  [array([[ 0.05734518],
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delta w updated: [array([[-0.05072421, -0.00878544,  0.05606913],
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input:  [[-0.8608687 ]
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activations:  [array([[-0.8608687 ],
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cost derivative:  [[ 1.09755656]
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unit step:  1
delta:  [[ 1.09755656]
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delta b updated:  [array([[ 0.07162904],
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       [-0.7218074 ]])]
delta w updated: [array([[-0.0616632 , -0.00552397,  0.07387802],
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input:  [[-0.81380157]
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 [ 1.06910845]]
activations:  [array([[-0.81380157],
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cost derivative:  [[ 1.07705877]
 [ 0.34820803]
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unit step:  1
delta:  [[ 1.07705877]
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delta b updated:  [array([[ 0.08757409],
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       [ 0.34820803],
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delta w updated: [array([[-0.07126793, -0.00212951,  0.0936262 ],
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input:  [[ 0.79752477]
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activations:  [array([[ 0.79752477],
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cost derivative:  [[ 0.76263238]
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 [ 0.16060501]]
unit step:  1
delta:  [[ 0.76263238]
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delta b updated:  [array([[ 0.36451695],
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       [ 0.57684398],
       [ 0.16060501]])]
delta w updated: [array([[ 0.29071129, -0.06775139,  0.3576294 ],
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input:  [[ 0.46254887]
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activations:  [array([[ 0.46254887],
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cost derivative:  [[ 0.98655456]
 [ 1.61382893]
 [ 0.73345875]]
unit step:  1
delta:  [[ 0.98655456]
 [ 1.61382893]
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delta b updated:  [array([[ 0.18509641],
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delta w updated: [array([[ 0.08561613, -0.18313573,  0.07647662],
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input:  [[ 0.88430752]
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activations:  [array([[ 0.88430752],
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cost derivative:  [[ 0.86561196]
 [ 1.40798645]
 [ 0.78557099]]
unit step:  1
delta:  [[ 0.86561196]
 [ 1.40798645]
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delta b updated:  [array([[ 0.27706432],
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delta w updated: [array([[ 0.24501006, -0.22929684,  0.14769315],
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input:  [[ 0.47245184]
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activations:  [array([[ 0.47245184],
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cost derivative:  [[ 0.98613488]
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unit step:  1
delta:  [[ 0.98613488]
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delta b updated:  [array([[ 0.18582876],
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delta w updated: [array([[ 0.08779514, -0.18519918,  0.07587032],
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input:  [[-0.80326333]
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activations:  [array([[-0.80326333],
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cost derivative:  [[ 1.07346472]
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unit step:  1
delta:  [[ 1.07346472]
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delta b updated:  [array([[ 0.08999011],
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delta w updated: [array([[-0.07228575, -0.00171893,  0.09655264],
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input:  [[-0.78638149]
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activations:  [array([[-0.78638149],
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cost derivative:  [[ 1.0684032 ]
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unit step:  1
delta:  [[ 1.0684032 ]
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delta b updated:  [array([[ 0.09386467],
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biases:  [[-0.18543218]
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inputs : [[ 0.11774629]
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result : [[ 0.18301722]
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weights:  [[ 0.26045735  0.33589088 -0.24143143]
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dot product : [[ 1.74106351]
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weights:  [[ 0.26045735  0.33589088 -0.24143143]
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result : [[ 1.45005134]
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dot product : [[-0.46432574]
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result : [[ 0.11115219]
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Total Cost [ 334.40368892] for Epoch 6 complete
Axis-wise Cost is [[ 128.78206969]
 [ 145.2580314 ]
 [  60.36358784]] 
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result : [[ 0.32315902]
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inputs : [[ 0.32315902]
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dot product : [[ 1.77583366]
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result : [[ 1.59040148]
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 [ 1.3563103 ]]
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inputs : [[ 0.66850921]
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result : [[ 0.29744729]
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dot product : [[ 1.69699493]
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result : [[ 1.51156275]
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Epoch 5: Score [[ 0.85854713]
 [ 0.96838688]
 [ 0.40242392]] / 300
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activations:  [array([[-0.77443049],
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cost derivative:  [[ 1.01532479]
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unit step:  1
delta:  [[ 1.01532479]
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delta b updated:  [array([[ 0.08087272],
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delta w updated: [array([[-0.0626303 , -0.00093525,  0.08723357],
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input:  [[ 0.87164646]
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activations:  [array([[ 0.87164646],
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cost derivative:  [[ 0.76816176]
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unit step:  1
delta:  [[ 0.76816176]
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delta b updated:  [array([[ 0.22453216],
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       [ 0.93971654]])]
delta w updated: [array([[ 0.19571266, -0.21728638,  0.09761135],
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result : [[ 0.47246119]
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inputs : [[ 0.86272391]
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dot product : [[-0.14966737]
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result : [[ 0.41344169]
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Total Cost [ 234.66776832] for Epoch 7 complete
Axis-wise Cost is [[ 91.58264408]
 [ 87.15012869]
 [ 55.93499555]] 
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result : [[ 0.09180689]
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biases:  [[-0.26998327]
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inputs : [[ 0.09180689]
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dot product : [[ 0.42172889]
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result : [[ 0.15174562]
 [ 0.11583679]
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weights:  [[ 0.25571627  0.34365819 -0.24965728]
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biases:  [[ 0.56310906]
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inputs : [[ 0.87164646]
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dot product : [[-0.21820801]
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result : [[ 0.34490105]
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weights:  [[ 1.29310373 -0.52044633]
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 [ 0.58267066 -0.44204383]]
biases:  [[-0.26998327]
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inputs : [[ 0.34490105]
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dot product : [[ 1.68738708]
 [-0.1053736 ]
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Epoch 6: Score [[ 0.61055096]
 [ 0.58100086]
 [ 0.37289997]] / 300
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delta w updated: [array([[ 0.04262463, -0.10559339,  0.05318239],
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unit step:  1
delta:  [[ 0.67525202]
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delta b updated:  [array([[ 0.11669489],
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delta w updated: [array([[ 0.052813  , -0.11459849,  0.04879934],
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cost derivative:  [[ 0.87958619]
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unit step:  1
delta:  [[ 0.87958619]
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delta b updated:  [array([[ 0.08617404],
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delta w updated: [array([[-0.05183546, -0.00567116,  0.08991065],
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cost derivative:  [[ 0.59425142]
 [ 1.18894754]
 [ 0.97468366]]
unit step:  1
delta:  [[ 0.59425142]
 [ 1.18894754]
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delta b updated:  [array([[ 0.13485994],
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delta w updated: [array([[ 0.10333718, -0.14999737,  0.04475811],
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input:  [[-0.66848697]
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cost derivative:  [[ 0.89500832]
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unit step:  1
delta:  [[ 0.89500832]
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delta b updated:  [array([[ 0.07935305],
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delta w updated: [array([[-0.05304648, -0.00253164,  0.08459524],
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input:  [[ 0.88431089]
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activations:  [array([[ 0.88431089],
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cost derivative:  [[ 0.51791666]
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unit step:  1
delta:  [[ 0.51791666]
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delta b updated:  [array([[ 0.18906336],
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delta w updated: [array([[ 0.16719079, -0.15356712,  0.10281425],
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input:  [[-0.32441767]
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cost derivative:  [[ 0.82017768]
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unit step:  1
delta:  [[ 0.82017768]
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delta b updated:  [array([[ 0.10172897],
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delta w updated: [array([[-0.03300267, -0.02828584,  0.09145976],
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input:  [[-0.84591795]
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activations:  [array([[-0.84591795],
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cost derivative:  [[ 1.03207238]
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unit step:  1
delta:  [[ 1.03207238]
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delta b updated:  [array([[-0.02330751],
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delta w updated: [array([[ 0.01971624,  0.01589775, -0.01417049],
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input:  [[-0.88454188]
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activations:  [array([[-0.88454188],
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cost derivative:  [[ 0.97562715]
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unit step:  1
delta:  [[ 0.97562715]
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delta b updated:  [array([[ 0.02692009],
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delta w updated: [array([[-0.02381195, -0.00412423,  0.02632107],
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input:  [[-0.891427  ]
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activations:  [array([[-0.891427  ],
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cost derivative:  [[ 0.98455177]
 [ 0.37088489]
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unit step:  1
delta:  [[ 0.98455177]
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delta b updated:  [array([[ 0.01903623],
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       [-0.5700562 ]])]
delta w updated: [array([[-0.01696941, -0.00398569,  0.01786178],
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input:  [[ 0.07095282]
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activations:  [array([[ 0.07095282],
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cost derivative:  [[ 0.74360759]
 [ 0.74288507]
 [ 0.25013476]]
unit step:  1
delta:  [[ 0.74360759]
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delta b updated:  [array([[ 0.1099033],
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delta w updated: [array([[ 0.00779795, -0.07124611,  0.07099368],
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input:  [[ 0.207027  ]
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 [ 0.55928504]]
activations:  [array([[ 0.207027  ],
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cost derivative:  [[ 0.71711182]
 [ 0.86998713]
 [ 0.42400986]]
unit step:  1
delta:  [[ 0.71711182]
 [ 0.86998713]
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delta b updated:  [array([[ 0.11141554],
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Total Cost [ 155.9485368] for Epoch 8 complete
Axis-wise Cost is [[ 59.68002724]
 [ 47.41501823]
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Epoch 7: Score [[ 0.39786685]
 [ 0.31610012]
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 [ 0.33211869]
 [-0.49860973]]
delta b updated:  [array([[-0.00604471],
       [ 0.09017823]]), array([[ 0.82446846],
       [ 0.33211869],
       [-0.49860973]])]
delta w updated: [array([[ 0.00539017,  0.0020392 , -0.00513001],
       [-0.08041348, -0.03042194,  0.07653226]]), array([[-0.00959161, -0.69688257],
       [-0.00386376, -0.28072357],
       [ 0.00580067,  0.42145025]])]
input:  [[-0.77334271]
 [-1.01494568]
 [ 0.37601468]]
activations:  [array([[-0.77334271],
       [-1.01494568],
       [ 0.37601468]]), array([[-0.09770094],
       [-1.41774871]]), array([[ 0.03998068],
       [ 0.00537842],
       [ 0.50316246]])]
cost derivative:  [[ 0.81332339]
 [ 1.0203241 ]
 [ 0.12714778]]
unit step:  1
delta:  [[ 0.81332339]
 [ 1.0203241 ]
 [ 0.12714778]]
delta b updated:  [array([[-0.03363132],
       [ 0.36570397]]), array([[ 0.81332339],
       [ 1.0203241 ],
       [ 0.12714778]])]
delta w updated: [array([[ 0.02600854,  0.03413396, -0.01264587],
       [-0.2828145 , -0.37116967,  0.13751006]]), array([[-0.07946246, -1.15308819],
       [-0.09968662, -1.44656317],
       [-0.01242246, -0.1802636 ]])]
input:  [[ 0.26254751]
 [-0.82507121]
 [ 0.52513537]]
activations:  [array([[ 0.26254751],
       [-0.82507121],
       [ 0.52513537]]), array([[ 0.19055669],
       [-2.05920914]]), array([[ 0.65164848],
       [-0.27366292],
       [ 0.89391199]])]
cost derivative:  [[ 0.38910097]
 [ 0.5514083 ]
 [ 0.36877662]]
unit step:  1
delta:  [[ 0.38910097]
 [ 0.5514083 ]
 [ 0.36877662]]
delta b updated:  [array([[ 0.05581591],
       [ 0.46461917]]), array([[ 0.38910097],
       [ 0.5514083 ],
       [ 0.36877662]])]
delta w updated: [array([[ 0.01465433, -0.0460521 ,  0.02931091],
       [ 0.1219846 , -0.3833439 ,  0.24398796]]), array([[ 0.07414579, -0.80124028],
       [ 0.10507454, -1.13546501],
       [ 0.07027285, -0.75938819]])]
input:  [[ 0.15071471]
 [-0.72317733]
 [ 0.59474638]]
activations:  [array([[ 0.15071471],
       [-0.72317733],
       [ 0.59474638]]), array([[ 0.18023264],
       [-1.9029234 ]]), array([[ 0.57840706],
       [-0.25196849],
       [ 0.83228286]])]
cost derivative:  [[ 0.42769236]
 [ 0.47120884]
 [ 0.23753648]]
unit step:  1
delta:  [[ 0.42769236]
 [ 0.47120884]
 [ 0.23753648]]
delta b updated:  [array([[ 0.05906322],
       [ 0.38298544]]), array([[ 0.42769236],
       [ 0.47120884],
       [ 0.23753648]])]
delta w updated: [array([[ 0.0089017 , -0.04271318,  0.03512763],
       [ 0.05772154, -0.27696639,  0.2277792 ]]), array([[ 0.07708412, -0.81386579],
       [ 0.08492721, -0.89667433],
       [ 0.04281183, -0.45201373]])]
input:  [[ 0.5761078 ]
 [-1.06372307]
 [ 0.36290032]]
activations:  [array([[ 0.5761078 ],
       [-1.06372307],
       [ 0.36290032]]), array([[ 0.2273253 ],
       [-2.46528059]]), array([[ 0.85138832],
       [-0.34191255],
       [ 1.05693169]])]
cost derivative:  [[ 0.27528052]
 [ 0.72181053]
 [ 0.69403137]]
unit step:  1
delta:  [[ 0.27528052]
 [ 0.72181053]
 [ 0.69403137]]
delta b updated:  [array([[ 0.04651504],
       [ 0.69030968]]), array([[ 0.27528052],
       [ 0.72181053],
       [ 0.69403137]])]
delta w updated: [array([[ 0.02679768, -0.04947912,  0.01688032],
       [ 0.39769279, -0.73429833,  0.2505136 ]]), array([[ 0.06257823, -0.67864373],
       [ 0.16408579, -1.77946548],
       [ 0.15777089, -1.71098207]])]
input:  [[ 0.87372259]
 [-0.95708859]
 [ 0.44221619]]
activations:  [array([[ 0.87372259],
       [-0.95708859],
       [ 0.44221619]]), array([[ 0.31895473],
       [-2.61292053]]), array([[ 1.02392605],
       [-0.42686181],
       [ 1.159535  ]])]
cost derivative:  [[ 0.15020346]
 [ 0.53022678]
 [ 0.71731881]]
unit step:  1
delta:  [[ 0.15020346]
 [ 0.53022678]
 [ 0.71731881]]
delta b updated:  [array([[ 0.0644953 ],
       [ 0.67491563]]), array([[ 0.15020346],
       [ 0.53022678],
       [ 0.71731881]])]
delta w updated: [array([[ 0.056351  , -0.06172772,  0.02852087],
       [ 0.58968903, -0.64595405,  0.29845861]]), array([[ 0.0479081 , -0.3924697 ],
       [ 0.16911834, -1.38544045],
       [ 0.22879223, -1.87429705]])]
input:  [[ 0.5017083 ]
 [-1.01718375]
 [ 0.39433099]]
activations:  [array([[ 0.5017083 ],
       [-1.01718375],
       [ 0.39433099]]), array([[ 0.21677182],
       [-2.37742602]]), array([[ 0.80355102],
       [-0.32851651],
       [ 1.01661325]])]
cost derivative:  [[ 0.30184272]
 [ 0.68866724]
 [ 0.62228226]]
unit step:  1
delta:  [[ 0.30184272]
 [ 0.68866724]
 [ 0.62228226]]
delta b updated:  [array([[ 0.0482149 ],
       [ 0.63335508]]), array([[ 0.30184272],
       [ 0.68866724],
       [ 0.62228226]])]
delta w updated: [array([[ 0.02418982, -0.04904341,  0.01901263],
       [ 0.3177595 , -0.6442385 ,  0.24975154]]), array([[ 0.06543099, -0.71760873],
       [ 0.14928365, -1.63725542],
       [ 0.13489326, -1.47943004]])]
input:  [[ 0.61112977]
 [-1.08199651]
 [ 0.35064981]]
activations:  [array([[ 0.61112977],
       [-1.08199651],
       [ 0.35064981]]), array([[ 0.23272681],
       [-2.50634345]]), array([[ 0.87212974],
       [-0.35460192],
       [ 1.06953356]])]
cost derivative:  [[ 0.26099997]
 [ 0.72739459]
 [ 0.71888375]]
unit step:  1
delta:  [[ 0.26099997]
 [ 0.72739459]
 [ 0.71888375]]
delta b updated:  [array([[ 0.04563338],
       [ 0.7024825 ]]), array([[ 0.26099997],
       [ 0.72739459],
       [ 0.71888375]])]
delta w updated: [array([[ 0.02788792, -0.04937516,  0.01600134],
       [ 0.42930797, -0.76008362,  0.24632536]]), array([[ 0.06074169, -0.65415556],
       [ 0.16928422, -1.82310067],
       [ 0.16730352, -1.80176957]])]
input:  [[ 0.41197964]
 [-0.95094907]
 [ 0.43931503]]
activations:  [array([[ 0.41197964],
       [-0.95094907],
       [ 0.43931503]]), array([[ 0.20580754],
       [-2.2638686 ]]), array([[ 0.74524238],
       [-0.31238967],
       [ 0.96753773]])]
cost derivative:  [[ 0.33326275]
 [ 0.6385594 ]
 [ 0.52822271]]
unit step:  1
delta:  [[ 0.33326275]
 [ 0.6385594 ]
 [ 0.52822271]]
delta b updated:  [array([[ 0.05073754],
       [ 0.56294356]]), array([[ 0.33326275],
       [ 0.6385594 ],
       [ 0.52822271]])]
delta w updated: [array([[ 0.02090283, -0.04824882,  0.02228976],
       [ 0.23192128, -0.53533065,  0.24730956]]), array([[ 0.06858799, -0.75446307],
       [ 0.13142034, -1.44561457],
       [ 0.10871222, -1.19582681]])]
input:  [[ 0.44252695]
 [-0.97449941]
 [ 0.42329939]]

In [6]:

    

#p_curve_data_1488329066.88168.json
#np_curve_data_1488329083.428903.json
#np_curve_data_1488343211.03.json
#np_curve_data_1488818487.27.json
data['non_planar_curves_error'] = []
with open('np_curve_data_cube_method_1489680586.46.json') as infile:
    c = json.load(infile)
    n_planar_curves_array = np.asarray(c['non_planar_curves'])
print("Non-Planar curves read array shape: ", n_planar_curves_array.shape)
non_planar_errors = process_curves(n_planar_curves_array)
data['non_planar_curves_error'].append(np.asarray(non_planar_errors).tolist())

import os
os.system('say "your program has finished"')


    

    




Non-Planar curves read array shape:  (100, 3, 300)
Analysing curve [ 0 ]






    




---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-6-03ca1a188c6d> in <module>()
      8     n_planar_curves_array = np.asarray(c['non_planar_curves'])
      9 print("Non-Planar curves read array shape: ", n_planar_curves_array.shape)
---> 10 non_planar_errors = process_curves(n_planar_curves_array)
     11 data['non_planar_curves_error'].append(np.asarray(non_planar_errors).tolist())
     12 

<ipython-input-4-1fd84f11b5cc> in process_curves(curves_array)
      7         print("Analysing curve [", i ,"]")
      8         start = time.time()
----> 9         Planar_Data = list(np.asarray(zip(curves_array[i][0], curves_array[i][1], curves_array[i][2])).reshape(300,3,1))
     10         ae = AutoEncoder([3,2,3])
     11         #print(Planar_Data)

ValueError: total size of new array must be unchanged

In [7]:

    

planar_curves_array.shape


    

    
Out[7]:





(100, 3, 300)

In [8]:

    

n_planar_curves_array.shape


    

    
Out[8]:





(100, 3, 300)

In [9]:

    

data


    

    
Out[9]:





{'non_planar_curves_error': [], 'planar_curves_error': []}

In [126]:

    

import numpy.linalg as linalg

def get_eig(curves_array, dim=0):
    eig_vals = []
    eig_val_all = np.asarray(eig_vals)
    for i in  range(len(curves_array[:])): #range(5):# 
        #print("Analysing curve [", i ,"]")
        Planar_Data = np.stack([curves_array[i][0], curves_array[i][1], curves_array[i][2]])
        X = np.asmatrix(Planar_Data)
        cov = np.dot(X, X.T)
        #print(cov)
        eigvals, eigvecs = np.linalg.eig(cov)
        #print(eigvals) 
        #print(eigvecs)
        #print(curves_array[i][0].max() - curves_array[i][0].min(), curves_array[i][1].max() - curves_array[i][1].min(), 
        #      curves_array[i][2].max() - curves_array[i][2].min())
        eig_vals.append(eigvals)
        #print(eig_vals)
        eig_val_all = np.asarray(eig_vals)
        #print(eig_val_all)
        #print(eig_val_all.mean(axis=0))
        #print(eig_val_all[0].min(), eig_val_all[1].min(), eig_val_all[2].min())
    print("\nresult for this curve type:")    
    print("max x: ", eig_val_all[:,0].max()) #x-axis eig vals
    print("max y: ", eig_val_all[:,1].max()) #y-axis eig vals
    print("max z: ", eig_val_all[:,2].max()) #z-axis eig vals
    print("\nmin x: ", eig_val_all[:,0].min()) #x-axis eig vals
    print("min y: ", eig_val_all[:,1].min()) #y-axis eig vals
    print("min z: ", eig_val_all[:,2].min()) #z-axis eig vals
    
    id_x = np.argmax(eig_val_all[:,0])
    id_y = np.argmax(eig_val_all[:,1])
    id_z = np.argmax(eig_val_all[:,2])
    print("max x eig index: ", id_x, eig_val_all[id_x])
    print("max y eig index: ", id_y, eig_val_all[id_y])
    print("max z eig index: ", id_z, eig_val_all[id_z])

    return curves_array[np.argmax(eig_val_all[:,dim])]

np_curve_max_z = get_eig(n_planar_curves_array, 2)
p_curve_max_y = get_eig(planar_curves_array, 1)


    

    




result for this curve type:
max x:  279.289546994
max y:  222.282116465
max z:  177.681701965

min x:  17.3627592599
min y:  17.1771626207
min z:  12.7433243095
max x eig index:  75 [ 279.28954699   44.53676383   59.55753166]
max y eig index:  61 [  36.96145275  222.28211647  137.17591214]
max z eig index:  1 [  48.74830601  114.90389028  177.68170197]

result for this curve type:
max x:  279.525893792
max y:  288.655389538
max z:  288.54342177

min x:  20.6470824695
min y:  29.7560077383
min z:  19.6316009094
max x eig index:  75 [ 279.52589379  144.98003206   26.57703368]
max y eig index:  35 [  32.04798316  288.65538954  216.74396297]
max z eig index:  94 [  28.58243725  197.14672515  288.54342177]

In [ ]:

    

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

def plot_curve(x_new, y_new, z_new):
    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')
    #ax.plot(pts[0], pts[1], pts[2], 'ro')
    ax.plot(x_new, y_new, z_new, 'b-')
    ax.set_xlabel('X axis')
    ax.set_ylabel('Y axis')
    ax.set_zlabel('Z axis')
    plt.axis('equal')
    ax.set_autoscale_on(False)
    ax.set_xlim([-3,3])
    ax.set_ylim([-3,3])
    ax.set_zlim([-3,3])
    plt.show()  
plot_curve(n_planar_curves_array[1][0], n_planar_curves_array[1][1], n_planar_curves_array[1][2])
plot_curve(planar_curves_array[1][0], planar_curves_array[1][1], planar_curves_array[1][2])


    

In [9]:

    

start = time.time()
with open('planarity_errors_' + str(start) + '.json', 'w') as outfile:
    json.dump(data, outfile)
    
print("Error file written: " + str(start))


    

    




Error file written: 1489714415.76

In [10]:

    

with open('planarity_errors_1489714415.76.json') as infile:
    c = json.load(infile)
    np_errors = np.asarray(c['non_planar_curves_error'])
    p_errors = np.asarray(c['planar_curves_error'])


    

In [10]:

    

np_errors.shape


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-10-fa4718aed7bb> in <module>()
----> 1 np_errors.shape

NameError: name 'np_errors' is not defined

In [12]:

    

p_errors.shape


    

    
Out[12]:





(1, 100, 1)

In [13]:

    

NPE = np.insert(np_errors, 1, 1, axis=2)


    

In [14]:

    

PE = np.insert(p_errors, 1, 0, axis=2)


    

In [15]:

    

from sklearn.naive_bayes import GaussianNB


    

In [16]:

    

#New
X = np.concatenate((NPE, PE), axis=0)
X.shape


    

    
Out[16]:





(2, 100, 2)

In [17]:

    

X = X.reshape(200,2)
X


    

    
Out[17]:





array([[  1.32415033e+02,   1.00000000e+00],
       [  6.65332355e+01,   1.00000000e+00],
       [  6.03597358e+01,   1.00000000e+00],
       [  4.88972181e+01,   1.00000000e+00],
       [  1.91792930e+01,   1.00000000e+00],
       [  5.01188380e+01,   1.00000000e+00],
       [  4.03235786e+01,   1.00000000e+00],
       [  6.79566541e+01,   1.00000000e+00],
       [  2.88405140e+01,   1.00000000e+00],
       [  5.04334465e+01,   1.00000000e+00],
       [  2.58981557e+01,   1.00000000e+00],
       [  3.92187794e+01,   1.00000000e+00],
       [  6.44533493e+01,   1.00000000e+00],
       [  1.65418401e+02,   1.00000000e+00],
       [  3.38484516e+01,   1.00000000e+00],
       [  1.34711175e+02,   1.00000000e+00],
       [  4.26513832e+01,   1.00000000e+00],
       [  9.82172586e+00,   1.00000000e+00],
       [  2.54846770e+01,   1.00000000e+00],
       [  8.59944043e+01,   1.00000000e+00],
       [  7.14799466e+01,   1.00000000e+00],
       [  2.77116041e+01,   1.00000000e+00],
       [  2.32389569e+01,   1.00000000e+00],
       [  2.41089646e+01,   1.00000000e+00],
       [  3.01737355e+01,   1.00000000e+00],
       [  4.17966925e+01,   1.00000000e+00],
       [  8.09004832e+01,   1.00000000e+00],
       [  6.79529978e+01,   1.00000000e+00],
       [  1.17576178e+02,   1.00000000e+00],
       [  2.20025545e+01,   1.00000000e+00],
       [  4.60816019e+01,   1.00000000e+00],
       [  1.28981885e+02,   1.00000000e+00],
       [  1.36569083e+01,   1.00000000e+00],
       [  5.30692855e+01,   1.00000000e+00],
       [  4.07073759e+01,   1.00000000e+00],
       [  4.96489893e+01,   1.00000000e+00],
       [  9.95035289e+01,   1.00000000e+00],
       [  5.42454791e+01,   1.00000000e+00],
       [  2.54377749e+01,   1.00000000e+00],
       [  6.90178580e+01,   1.00000000e+00],
       [  9.27986877e+01,   1.00000000e+00],
       [  9.05849912e+01,   1.00000000e+00],
       [  1.45131421e+01,   1.00000000e+00],
       [  9.49381727e+01,   1.00000000e+00],
       [  7.03873042e+01,   1.00000000e+00],
       [  3.08984731e+01,   1.00000000e+00],
       [  3.73300090e+01,   1.00000000e+00],
       [  3.65297297e+01,   1.00000000e+00],
       [  6.90775251e+01,   1.00000000e+00],
       [  3.91810829e+01,   1.00000000e+00],
       [  2.17209092e+01,   1.00000000e+00],
       [  4.20975961e+01,   1.00000000e+00],
       [  3.36933605e+01,   1.00000000e+00],
       [  2.06200583e+01,   1.00000000e+00],
       [  1.46413537e+02,   1.00000000e+00],
       [  6.57020200e+01,   1.00000000e+00],
       [  6.10539771e+01,   1.00000000e+00],
       [  4.78467033e+01,   1.00000000e+00],
       [  3.76707143e+01,   1.00000000e+00],
       [  9.20195631e+01,   1.00000000e+00],
       [  2.29627990e+01,   1.00000000e+00],
       [  1.83192788e+01,   1.00000000e+00],
       [  9.20345788e+01,   1.00000000e+00],
       [  1.04465520e+02,   1.00000000e+00],
       [  2.53351591e+01,   1.00000000e+00],
       [  7.42408093e+01,   1.00000000e+00],
       [  1.13217438e+02,   1.00000000e+00],
       [  1.86762083e+01,   1.00000000e+00],
       [  3.70645582e+01,   1.00000000e+00],
       [  7.45209809e+01,   1.00000000e+00],
       [  7.94209952e+01,   1.00000000e+00],
       [  3.70192810e+01,   1.00000000e+00],
       [  2.15508433e+01,   1.00000000e+00],
       [  5.05853275e+01,   1.00000000e+00],
       [  5.59948401e+01,   1.00000000e+00],
       [  2.60377018e+01,   1.00000000e+00],
       [  6.08968183e+01,   1.00000000e+00],
       [  3.71234451e+01,   1.00000000e+00],
       [  4.25442839e+01,   1.00000000e+00],
       [  1.12783869e+02,   1.00000000e+00],
       [  1.06497125e+02,   1.00000000e+00],
       [  4.85069321e+01,   1.00000000e+00],
       [  9.36825459e+01,   1.00000000e+00],
       [  1.54416666e+01,   1.00000000e+00],
       [  5.93079723e+01,   1.00000000e+00],
       [  4.52906061e+01,   1.00000000e+00],
       [  1.12383247e+02,   1.00000000e+00],
       [  6.88568251e+01,   1.00000000e+00],
       [  4.00533218e+01,   1.00000000e+00],
       [  8.67637774e+01,   1.00000000e+00],
       [  9.48672298e+01,   1.00000000e+00],
       [  6.32274049e+01,   1.00000000e+00],
       [  1.15745957e+02,   1.00000000e+00],
       [  4.14806751e+01,   1.00000000e+00],
       [  3.15316969e+01,   1.00000000e+00],
       [  4.60820907e+01,   1.00000000e+00],
       [  1.52746856e+02,   1.00000000e+00],
       [  9.75483961e+01,   1.00000000e+00],
       [  5.40452882e+01,   1.00000000e+00],
       [  2.58521032e+01,   1.00000000e+00],
       [  9.81002826e+01,   0.00000000e+00],
       [  1.96732361e+01,   0.00000000e+00],
       [  1.14004747e+01,   0.00000000e+00],
       [  1.52983354e+01,   0.00000000e+00],
       [  7.41681943e+01,   0.00000000e+00],
       [  2.33752227e+01,   0.00000000e+00],
       [  7.69421492e+00,   0.00000000e+00],
       [  5.54822753e-02,   0.00000000e+00],
       [  3.48475522e+01,   0.00000000e+00],
       [  2.36416434e+00,   0.00000000e+00],
       [  7.76947504e+01,   0.00000000e+00],
       [  7.50632986e+01,   0.00000000e+00],
       [  1.20845794e+02,   0.00000000e+00],
       [  1.28496272e+01,   0.00000000e+00],
       [  1.72836509e+00,   0.00000000e+00],
       [  8.60138620e+00,   0.00000000e+00],
       [  1.73975295e+01,   0.00000000e+00],
       [  1.03079954e+02,   0.00000000e+00],
       [  1.06751296e+02,   0.00000000e+00],
       [  4.00550457e+00,   0.00000000e+00],
       [  9.69360503e+00,   0.00000000e+00],
       [  1.14709341e+01,   0.00000000e+00],
       [  5.15518216e-05,   0.00000000e+00],
       [  1.00149682e+00,   0.00000000e+00],
       [  2.77507777e-03,   0.00000000e+00],
       [  2.94370180e+01,   0.00000000e+00],
       [  6.75913354e+00,   0.00000000e+00],
       [  5.94163627e-02,   0.00000000e+00],
       [  1.23068605e+00,   0.00000000e+00],
       [  2.17337744e+01,   0.00000000e+00],
       [  7.17490349e+01,   0.00000000e+00],
       [  3.53976396e+00,   0.00000000e+00],
       [  1.27426944e+01,   0.00000000e+00],
       [  1.25325032e-01,   0.00000000e+00],
       [  8.32803996e+01,   0.00000000e+00],
       [  1.69523011e+01,   0.00000000e+00],
       [  1.37637213e+01,   0.00000000e+00],
       [  1.06539231e+02,   0.00000000e+00],
       [  1.28274559e+01,   0.00000000e+00],
       [  5.24192387e-03,   0.00000000e+00],
       [  9.24854862e+01,   0.00000000e+00],
       [  4.58251547e+01,   0.00000000e+00],
       [  2.33142431e+00,   0.00000000e+00],
       [  6.03925916e+01,   0.00000000e+00],
       [  1.16236779e+02,   0.00000000e+00],
       [  2.60069406e+01,   0.00000000e+00],
       [  2.74973191e+01,   0.00000000e+00],
       [  3.06539919e+01,   0.00000000e+00],
       [  1.72439178e+00,   0.00000000e+00],
       [  1.26011757e+01,   0.00000000e+00],
       [  5.14739713e-01,   0.00000000e+00],
       [  1.32088577e+02,   0.00000000e+00],
       [  1.35782590e+02,   0.00000000e+00],
       [  1.51462446e+01,   0.00000000e+00],
       [  7.13130086e-01,   0.00000000e+00],
       [  1.17476107e+01,   0.00000000e+00],
       [  3.39421896e-01,   0.00000000e+00],
       [  6.85832721e+01,   0.00000000e+00],
       [  7.62864257e-06,   0.00000000e+00],
       [  6.93659603e+00,   0.00000000e+00],
       [  1.11697734e+01,   0.00000000e+00],
       [  1.55123655e+01,   0.00000000e+00],
       [  6.16518261e-07,   0.00000000e+00],
       [  1.59194065e+00,   0.00000000e+00],
       [  2.11523933e+00,   0.00000000e+00],
       [  8.98135763e+01,   0.00000000e+00],
       [  1.66321203e+02,   0.00000000e+00],
       [  2.10526249e+00,   0.00000000e+00],
       [  2.77766492e+01,   0.00000000e+00],
       [  7.34258380e+00,   0.00000000e+00],
       [  8.09341209e+00,   0.00000000e+00],
       [  1.11718941e+02,   0.00000000e+00],
       [  5.45263197e+01,   0.00000000e+00],
       [  1.95542400e+01,   0.00000000e+00],
       [  5.64981693e-20,   0.00000000e+00],
       [  5.13961707e-02,   0.00000000e+00],
       [  1.46391122e+01,   0.00000000e+00],
       [  1.78271544e+01,   0.00000000e+00],
       [  1.71266322e+01,   0.00000000e+00],
       [  1.27138101e+00,   0.00000000e+00],
       [  1.81696026e+01,   0.00000000e+00],
       [  9.69417597e-02,   0.00000000e+00],
       [  1.92377429e+01,   0.00000000e+00],
       [  2.41222991e+01,   0.00000000e+00],
       [  1.25788276e+00,   0.00000000e+00],
       [  6.66130702e+00,   0.00000000e+00],
       [  2.07030040e+01,   0.00000000e+00],
       [  1.91835222e+01,   0.00000000e+00],
       [  1.46697422e+01,   0.00000000e+00],
       [  7.79036561e+01,   0.00000000e+00],
       [  1.05966987e+01,   0.00000000e+00],
       [  8.67685905e+01,   0.00000000e+00],
       [  5.08383255e+00,   0.00000000e+00],
       [  9.30495398e+01,   0.00000000e+00],
       [  1.10450010e+02,   0.00000000e+00],
       [  8.55238257e+01,   0.00000000e+00],
       [  1.06799747e+01,   0.00000000e+00],
       [  1.27135961e-02,   0.00000000e+00],
       [  1.37473477e+01,   0.00000000e+00],
       [  1.18470790e+02,   0.00000000e+00]])

In [18]:

    

nan_idx = [i for i, x in enumerate(X) if (math.isnan(x[0]) == True)]
nan_idx


    

    
Out[18]:





[]

In [19]:

    

X_cleaned = np.delete(X, nan_idx, axis=0)
X_cleaned


    

    
Out[19]:





array([[  1.32415033e+02,   1.00000000e+00],
       [  6.65332355e+01,   1.00000000e+00],
       [  6.03597358e+01,   1.00000000e+00],
       [  4.88972181e+01,   1.00000000e+00],
       [  1.91792930e+01,   1.00000000e+00],
       [  5.01188380e+01,   1.00000000e+00],
       [  4.03235786e+01,   1.00000000e+00],
       [  6.79566541e+01,   1.00000000e+00],
       [  2.88405140e+01,   1.00000000e+00],
       [  5.04334465e+01,   1.00000000e+00],
       [  2.58981557e+01,   1.00000000e+00],
       [  3.92187794e+01,   1.00000000e+00],
       [  6.44533493e+01,   1.00000000e+00],
       [  1.65418401e+02,   1.00000000e+00],
       [  3.38484516e+01,   1.00000000e+00],
       [  1.34711175e+02,   1.00000000e+00],
       [  4.26513832e+01,   1.00000000e+00],
       [  9.82172586e+00,   1.00000000e+00],
       [  2.54846770e+01,   1.00000000e+00],
       [  8.59944043e+01,   1.00000000e+00],
       [  7.14799466e+01,   1.00000000e+00],
       [  2.77116041e+01,   1.00000000e+00],
       [  2.32389569e+01,   1.00000000e+00],
       [  2.41089646e+01,   1.00000000e+00],
       [  3.01737355e+01,   1.00000000e+00],
       [  4.17966925e+01,   1.00000000e+00],
       [  8.09004832e+01,   1.00000000e+00],
       [  6.79529978e+01,   1.00000000e+00],
       [  1.17576178e+02,   1.00000000e+00],
       [  2.20025545e+01,   1.00000000e+00],
       [  4.60816019e+01,   1.00000000e+00],
       [  1.28981885e+02,   1.00000000e+00],
       [  1.36569083e+01,   1.00000000e+00],
       [  5.30692855e+01,   1.00000000e+00],
       [  4.07073759e+01,   1.00000000e+00],
       [  4.96489893e+01,   1.00000000e+00],
       [  9.95035289e+01,   1.00000000e+00],
       [  5.42454791e+01,   1.00000000e+00],
       [  2.54377749e+01,   1.00000000e+00],
       [  6.90178580e+01,   1.00000000e+00],
       [  9.27986877e+01,   1.00000000e+00],
       [  9.05849912e+01,   1.00000000e+00],
       [  1.45131421e+01,   1.00000000e+00],
       [  9.49381727e+01,   1.00000000e+00],
       [  7.03873042e+01,   1.00000000e+00],
       [  3.08984731e+01,   1.00000000e+00],
       [  3.73300090e+01,   1.00000000e+00],
       [  3.65297297e+01,   1.00000000e+00],
       [  6.90775251e+01,   1.00000000e+00],
       [  3.91810829e+01,   1.00000000e+00],
       [  2.17209092e+01,   1.00000000e+00],
       [  4.20975961e+01,   1.00000000e+00],
       [  3.36933605e+01,   1.00000000e+00],
       [  2.06200583e+01,   1.00000000e+00],
       [  1.46413537e+02,   1.00000000e+00],
       [  6.57020200e+01,   1.00000000e+00],
       [  6.10539771e+01,   1.00000000e+00],
       [  4.78467033e+01,   1.00000000e+00],
       [  3.76707143e+01,   1.00000000e+00],
       [  9.20195631e+01,   1.00000000e+00],
       [  2.29627990e+01,   1.00000000e+00],
       [  1.83192788e+01,   1.00000000e+00],
       [  9.20345788e+01,   1.00000000e+00],
       [  1.04465520e+02,   1.00000000e+00],
       [  2.53351591e+01,   1.00000000e+00],
       [  7.42408093e+01,   1.00000000e+00],
       [  1.13217438e+02,   1.00000000e+00],
       [  1.86762083e+01,   1.00000000e+00],
       [  3.70645582e+01,   1.00000000e+00],
       [  7.45209809e+01,   1.00000000e+00],
       [  7.94209952e+01,   1.00000000e+00],
       [  3.70192810e+01,   1.00000000e+00],
       [  2.15508433e+01,   1.00000000e+00],
       [  5.05853275e+01,   1.00000000e+00],
       [  5.59948401e+01,   1.00000000e+00],
       [  2.60377018e+01,   1.00000000e+00],
       [  6.08968183e+01,   1.00000000e+00],
       [  3.71234451e+01,   1.00000000e+00],
       [  4.25442839e+01,   1.00000000e+00],
       [  1.12783869e+02,   1.00000000e+00],
       [  1.06497125e+02,   1.00000000e+00],
       [  4.85069321e+01,   1.00000000e+00],
       [  9.36825459e+01,   1.00000000e+00],
       [  1.54416666e+01,   1.00000000e+00],
       [  5.93079723e+01,   1.00000000e+00],
       [  4.52906061e+01,   1.00000000e+00],
       [  1.12383247e+02,   1.00000000e+00],
       [  6.88568251e+01,   1.00000000e+00],
       [  4.00533218e+01,   1.00000000e+00],
       [  8.67637774e+01,   1.00000000e+00],
       [  9.48672298e+01,   1.00000000e+00],
       [  6.32274049e+01,   1.00000000e+00],
       [  1.15745957e+02,   1.00000000e+00],
       [  4.14806751e+01,   1.00000000e+00],
       [  3.15316969e+01,   1.00000000e+00],
       [  4.60820907e+01,   1.00000000e+00],
       [  1.52746856e+02,   1.00000000e+00],
       [  9.75483961e+01,   1.00000000e+00],
       [  5.40452882e+01,   1.00000000e+00],
       [  2.58521032e+01,   1.00000000e+00],
       [  9.81002826e+01,   0.00000000e+00],
       [  1.96732361e+01,   0.00000000e+00],
       [  1.14004747e+01,   0.00000000e+00],
       [  1.52983354e+01,   0.00000000e+00],
       [  7.41681943e+01,   0.00000000e+00],
       [  2.33752227e+01,   0.00000000e+00],
       [  7.69421492e+00,   0.00000000e+00],
       [  5.54822753e-02,   0.00000000e+00],
       [  3.48475522e+01,   0.00000000e+00],
       [  2.36416434e+00,   0.00000000e+00],
       [  7.76947504e+01,   0.00000000e+00],
       [  7.50632986e+01,   0.00000000e+00],
       [  1.20845794e+02,   0.00000000e+00],
       [  1.28496272e+01,   0.00000000e+00],
       [  1.72836509e+00,   0.00000000e+00],
       [  8.60138620e+00,   0.00000000e+00],
       [  1.73975295e+01,   0.00000000e+00],
       [  1.03079954e+02,   0.00000000e+00],
       [  1.06751296e+02,   0.00000000e+00],
       [  4.00550457e+00,   0.00000000e+00],
       [  9.69360503e+00,   0.00000000e+00],
       [  1.14709341e+01,   0.00000000e+00],
       [  5.15518216e-05,   0.00000000e+00],
       [  1.00149682e+00,   0.00000000e+00],
       [  2.77507777e-03,   0.00000000e+00],
       [  2.94370180e+01,   0.00000000e+00],
       [  6.75913354e+00,   0.00000000e+00],
       [  5.94163627e-02,   0.00000000e+00],
       [  1.23068605e+00,   0.00000000e+00],
       [  2.17337744e+01,   0.00000000e+00],
       [  7.17490349e+01,   0.00000000e+00],
       [  3.53976396e+00,   0.00000000e+00],
       [  1.27426944e+01,   0.00000000e+00],
       [  1.25325032e-01,   0.00000000e+00],
       [  8.32803996e+01,   0.00000000e+00],
       [  1.69523011e+01,   0.00000000e+00],
       [  1.37637213e+01,   0.00000000e+00],
       [  1.06539231e+02,   0.00000000e+00],
       [  1.28274559e+01,   0.00000000e+00],
       [  5.24192387e-03,   0.00000000e+00],
       [  9.24854862e+01,   0.00000000e+00],
       [  4.58251547e+01,   0.00000000e+00],
       [  2.33142431e+00,   0.00000000e+00],
       [  6.03925916e+01,   0.00000000e+00],
       [  1.16236779e+02,   0.00000000e+00],
       [  2.60069406e+01,   0.00000000e+00],
       [  2.74973191e+01,   0.00000000e+00],
       [  3.06539919e+01,   0.00000000e+00],
       [  1.72439178e+00,   0.00000000e+00],
       [  1.26011757e+01,   0.00000000e+00],
       [  5.14739713e-01,   0.00000000e+00],
       [  1.32088577e+02,   0.00000000e+00],
       [  1.35782590e+02,   0.00000000e+00],
       [  1.51462446e+01,   0.00000000e+00],
       [  7.13130086e-01,   0.00000000e+00],
       [  1.17476107e+01,   0.00000000e+00],
       [  3.39421896e-01,   0.00000000e+00],
       [  6.85832721e+01,   0.00000000e+00],
       [  7.62864257e-06,   0.00000000e+00],
       [  6.93659603e+00,   0.00000000e+00],
       [  1.11697734e+01,   0.00000000e+00],
       [  1.55123655e+01,   0.00000000e+00],
       [  6.16518261e-07,   0.00000000e+00],
       [  1.59194065e+00,   0.00000000e+00],
       [  2.11523933e+00,   0.00000000e+00],
       [  8.98135763e+01,   0.00000000e+00],
       [  1.66321203e+02,   0.00000000e+00],
       [  2.10526249e+00,   0.00000000e+00],
       [  2.77766492e+01,   0.00000000e+00],
       [  7.34258380e+00,   0.00000000e+00],
       [  8.09341209e+00,   0.00000000e+00],
       [  1.11718941e+02,   0.00000000e+00],
       [  5.45263197e+01,   0.00000000e+00],
       [  1.95542400e+01,   0.00000000e+00],
       [  5.64981693e-20,   0.00000000e+00],
       [  5.13961707e-02,   0.00000000e+00],
       [  1.46391122e+01,   0.00000000e+00],
       [  1.78271544e+01,   0.00000000e+00],
       [  1.71266322e+01,   0.00000000e+00],
       [  1.27138101e+00,   0.00000000e+00],
       [  1.81696026e+01,   0.00000000e+00],
       [  9.69417597e-02,   0.00000000e+00],
       [  1.92377429e+01,   0.00000000e+00],
       [  2.41222991e+01,   0.00000000e+00],
       [  1.25788276e+00,   0.00000000e+00],
       [  6.66130702e+00,   0.00000000e+00],
       [  2.07030040e+01,   0.00000000e+00],
       [  1.91835222e+01,   0.00000000e+00],
       [  1.46697422e+01,   0.00000000e+00],
       [  7.79036561e+01,   0.00000000e+00],
       [  1.05966987e+01,   0.00000000e+00],
       [  8.67685905e+01,   0.00000000e+00],
       [  5.08383255e+00,   0.00000000e+00],
       [  9.30495398e+01,   0.00000000e+00],
       [  1.10450010e+02,   0.00000000e+00],
       [  8.55238257e+01,   0.00000000e+00],
       [  1.06799747e+01,   0.00000000e+00],
       [  1.27135961e-02,   0.00000000e+00],
       [  1.37473477e+01,   0.00000000e+00],
       [  1.18470790e+02,   0.00000000e+00]])

In [20]:

    

nan_idx = [i for i, x in enumerate(X_cleaned) if (math.isnan(x[0]) == True)]
nan_idx


    

    
Out[20]:





[]

In [21]:

    

import matplotlib.pyplot as plt
#import plotly.plotly as py
plt.plot(X[0:190], 'k-', lw=2)
plt.show()


    

In [22]:

    

import matplotlib.pyplot as plt
import numpy as np
get_ipython().magic('matplotlib inline')

hist, bins = np.histogram(X_cleaned[0:100,0], bins=50)
width = 0.7 * (bins[1] - bins[0])
center = (bins[:-1] + bins[1:]) / 2
plt.bar(center, hist, align='center', width=width)
plt.show()


    

In [23]:

    

import matplotlib.pyplot as plt
import numpy as np
get_ipython().magic('matplotlib inline')

hist, bins = np.histogram(X_cleaned[100:200,0], bins=50)
width = 0.7 * (bins[1] - bins[0])
center = (bins[:-1] + bins[1:]) / 2
plt.bar(center, hist, align='center', width=width)
plt.show()


    

In [25]:

    

import random
import numpy
import matplotlib.pyplot as plt

bins = numpy.linspace(0, 100, 100)

plt.hist(X_cleaned[0:100,0], bins, alpha=0.25, label='NPE')
plt.hist(X_cleaned[100:198,0], bins, alpha=0.25, label='PE')
plt.legend(loc='upper right')
plt.show()


    

In [26]:

    

model = GaussianNB()


    

In [27]:

    

X_cleaned[:,0].shape


    

    
Out[27]:





(200,)

In [28]:

    

model.fit(X_cleaned[:,0].reshape(200,1), X_cleaned[:,1])


    

    
Out[28]:





GaussianNB(priors=None)

In [29]:

    

model.get_params()


    

    
Out[29]:





{'priors': None}

In [30]:

    

predicted= model.predict_proba([[1.20092645e+02],[4.95902904e+02]])


    

In [31]:

    

predicted


    

    
Out[31]:





array([[  2.88557388e-01,   7.11442612e-01],
       [  9.99999490e-01,   5.10052422e-07]])

In [32]:

    

X_cleaned.shape


    

    
Out[32]:





(200, 2)

In [33]:

    

#new
np.random.shuffle(X_cleaned)
total = X_cleaned.shape[0]
num_batches = 5
batch_size = int(total/num_batches)
X_usable = X_cleaned[0:int(num_batches*batch_size)]
X_all = X_usable.reshape(num_batches, batch_size, 2)
X_all.shape


    

    
Out[33]:





(5, 40, 2)

In [34]:

    

#new
from sklearn.linear_model import LogisticRegression
import random

for v_idx, v_data in enumerate(X_all):
    print("batch index: ", v_idx)
    indices = [0,1,2,3,4]
    indices.remove(v_idx)
    print("test set: ", indices)
    n = X_all.shape[1]
    print("n=", n)
    X_test = np.asarray([X_all[i,:,:] for i in indices]).reshape(4*n,2)
    print("X test shape: ", X_test.shape)
    model = GaussianNB()
    model.fit(X_test[:,0].reshape(4*n,1), X_test[:,1].reshape(4*n))

    #print(v_data.shape)
    #print(v_data)
    predicted= model.predict_proba(v_data[:,0].reshape(n,1))
    #print(predicted)
    
    for i in range(n):
        print("validation data: ", v_data[i][1], "predicted: ", predicted[i].argmax())
    
    score = sum([v_data[i][1] == predicted[i].argmax() for i in range(n)])
    print("num correct: ",  score, "\npercent correct: ", (score/n)*100)
    scores = model.score(v_data[:,0].reshape(n,1), v_data[:,1], sample_weight=None)
    print("self reported score: ", scores)
    
    model1 = LogisticRegression()
    mdl = model1.fit(X_test[:,0].reshape(4*n,1), X_test[:,1].reshape(4*n))
    w = model1.coef_[0]
    print("w:", w)
    
    #xx = np.linspace(0,5000)
    #yy = a * xx - (model1.intercept_[0])/w[1]
    #plt.plot(xx,yy,'k-')
    
    
    #xx, yy = np.mgrid[1:5000:100, 1:5000:100]
    #grid = np.c_[xx.ravel(), yy.ravel()]
    #probs = model.predict_proba(grid)[:, 1].reshape(xx.shape)


    #correct = [predicted[i]==v_data[1][i] for i in range(n)]
    #print(sum(correct)/n)
    #from matplotlib import pyplot as plt
    #ax = plt.gca()
    #cm_bright = ListedColormap(['#FF0000', '#0000FF'])
    #ax.scatter(, df.income, c=(df.default == 'Yes'), cmap=cm_bright)


    

    




batch index:  0
test set:  [1, 2, 3, 4]
n= 40
X test shape:  (160, 2)
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  1
validation data:  0.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  1
validation data:  0.0 predicted:  1
validation data:  0.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  0
validation data:  1.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
num correct:  26 
percent correct:  65.0
self reported score:  0.65
w: [ 0.01908942]
batch index:  1
test set:  [0, 2, 3, 4]
n= 40
X test shape:  (160, 2)
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  1
validation data:  0.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  1
validation data:  1.0 predicted:  0
validation data:  0.0 predicted:  1
validation data:  1.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  0
validation data:  0.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
num correct:  28 
percent correct:  70.0
self reported score:  0.7
w: [ 0.02077936]
batch index:  2
test set:  [0, 1, 3, 4]
n= 40
X test shape:  (160, 2)
validation data:  1.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  0
validation data:  0.0 predicted:  1
validation data:  1.0 predicted:  0
validation data:  0.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  1
validation data:  1.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
num correct:  29 
percent correct:  72.5
self reported score:  0.725
w: [ 0.01528897]
batch index:  3
test set:  [0, 1, 2, 4]
n= 40
X test shape:  (160, 2)
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  0
validation data:  0.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  1
validation data:  0.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  0
validation data:  1.0 predicted:  0
validation data:  1.0 predicted:  1
num correct:  31 
percent correct:  77.5
self reported score:  0.775
w: [ 0.01484667]
batch index:  4
test set:  [0, 1, 2, 3]
n= 40
X test shape:  (160, 2)
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  1
validation data:  1.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  0
validation data:  0.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  1
validation data:  1.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  0.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  1
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  0
validation data:  0.0 predicted:  0
validation data:  1.0 predicted:  0
validation data:  1.0 predicted:  0
num correct:  25 
percent correct:  62.5
self reported score:  0.625
w: [ 0.01858976]

In [12]:

    

(65+70+72.57+77.5+62.5)/5


    

    
Out[12]:





69.514

In [86]:

    

%matplotlib qt 
%matplotlib notebook


    

    




Warning: Cannot change to a different GUI toolkit: notebook. Using qt instead.

In [11]:

    

import numpy.linalg as linalg
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
#ax1 = fig.add_subplot(221, projection='3d')   
#ax2 = fig.add_subplot(222, projection='3d')   
#ax3 = fig.add_subplot(223, projection='3d')   


#planar_curves_array - from file
#n_planar_curves_array - from file

def get_eig(curves_array, dim=0):
    eig_vals = []
    eig_val_all = np.asarray(eig_vals)
    eig_vecs = []
    eig_vec_all = np.asarray(eig_vecs)
    for i in  range(1):# range(len(curves_array[:])): # 
        fig = plt.figure()
        #ax = fig.add_subplot(111, projection='3d')
        ax1 = fig.add_subplot(221, projection='3d')   
        ax2 = fig.add_subplot(222, projection='3d')   
        ax3 = fig.add_subplot(223, projection='3d')   
    
        print("\nAnalysing curve [", i ,"]")
        Planarity_Data = np.stack([curves_array[i][0], curves_array[i][1], curves_array[i][2]])
        ax1.plot(curves_array[i][0], curves_array[i][1], curves_array[i][2], 'ro')
        #plt.show()
        
        X = np.asmatrix(Planarity_Data)
        mu = np.mean(X, axis=0)
        #print("mu: ",mu)
        print("Shape of X matrix containing all points: ", X.shape)
        cov = np.dot(X, X.T)
        #print("Cov\n", cov)
        eigvals, eigvecs = np.linalg.eig(cov)
        eig_pairs = [(np.abs(eigvals[i]), eigvecs[:,i]) for i in range(len(eigvals))]
        eig_pairs.sort(key=lambda x: x[0], reverse=True)
        
        #for i in eig_pairs:
        #    print("eig vals: ", i[0])
        
        for i in [0,1,2]:
            #print("pairs: ", eigvals[i], eigvecs[:,i])
            v = eigvecs[:,i].reshape(1,3).T
            #print(v)
            lhs = np.dot(cov,v)
            #print(lhs)
            rhs = np.dot(eigvals[i],v)
            #print(rhs)
            
        matrix_w = np.hstack((eig_pairs[0][1].reshape(3,1), eig_pairs[1][1].reshape(3,1)))
        print('Matrix W:\n', matrix_w)
        print('Shape of transformation matrix W: ', matrix_w.shape)

        transformed = matrix_w.T.dot(X)
        print("Transformed matrix shape: ", transformed.shape)
        ax2.plot(transformed[0], transformed[1], 'ro') 
        #plt.show()
        #plt.clf()
        
        #reconstruct from eig vectors
        recoverd = matrix_w.dot(transformed)
        
        print("Recovered matrix shape: ", recoverd.shape)
        recoverd += mu
        ax3.plot(recoverd[0], recoverd[1], recoverd[2], 'ro') 
        
        plt.show()
        
        plt.clf()
        
        eig_vals.append(eigvals)
        eig_vecs.append(eigvecs)
        eig_val_all = np.asarray(eig_vals)
        eig_vec_all = np.asarray(eig_vecs)
        
    
    return 
np_curve_max_z = get_eig(n_planar_curves_array)
p_curve_max_y = get_eig(planar_curves_array)


    

    




Analysing curve [ 0 ]
Shape of X matrix containing all points:  (3, 300)
Matrix W:
 [[ 0.62467872  0.01821941]
 [ 0.11749009  0.98615433]
 [-0.7719926   0.16482624]]
Shape of transformation matrix W:  (3, 2)
Transformed matrix shape:  (2, 300)
Recovered matrix shape:  (3, 300)






    




---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
/Users/pmishra/anaconda3/lib/python3.5/site-packages/matplotlib/backends/backend_macosx.py in close(self)
    398             self.canvas.draw_idle()
    399 
--> 400     def close(self):
    401         Gcf.destroy(self.num)
    402 

KeyboardInterrupt: 





    




Analysing curve [ 0 ]
Shape of X matrix containing all points:  (3, 300)
Matrix W:
 [[ 0.07260732 -0.86084384]
 [-0.657978    0.33816634]
 [ 0.74952861  0.38025177]]
Shape of transformation matrix W:  (3, 2)
Transformed matrix shape:  (2, 300)
Recovered matrix shape:  (3, 300)

In [15]:

    

import numpy as np
import sklearn.datasets, sklearn.decomposition
import numpy.linalg as linalg
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

def pca_err(curves_array):
    errors=[]
    for i in range(len(curves_array[:])):  
        #fig = plt.figure()
        #ax1 = fig.add_subplot(221, projection='3d')   
        #ax2 = fig.add_subplot(222, projection='3d')   
        #ax3 = fig.add_subplot(223, projection='3d')   

        X = curves_array[i].T #sklearn.datasets.load_iris().data
        mu = np.mean(X, axis=0)
        print("X: ", X.shape)
        #print("mu: ", mu)
        pca = sklearn.decomposition.PCA()
        pca.fit(X)

        #ax1.plot(curves_array[i][0], curves_array[i][1], curves_array[i][2], 'ro') 

        nComp = 2
        print("Transfomed: ", pca.transform(X)[:,:nComp].shape)
        print("EV: ", pca.components_[:,:][:,:nComp])

        transformed = pca.transform(X)[:,:nComp].T
        #ax2.plot(transformed[0], transformed[1], 'ro') 

        Xhat = np.dot(pca.transform(X)[:,:nComp], pca.components_[:nComp,:])
        Xhat += mu

        reconstructed_curve = Xhat.T
        #ax3.plot(reconstructed_curve[0], reconstructed_curve[1], reconstructed_curve[2], 'ro') 

        
        #print(Xhat.shape)
        #plt.show()
        
        err = 0.5*sum((X-Xhat)**2)
        errors.append(sum(err))
        
        print("Err: ", err)
        
    return np.asarray(errors)

np_pca_err = pca_err(n_planar_curves_array)
p_pca_err = pca_err(planar_curves_array)


    

    




X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.62260335 -0.07118286]
 [-0.04511575 -0.99093258]
 [ 0.78123597 -0.1139545 ]]
Err:  [ 8.19987967  0.17446406  5.0608218 ]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.11679272  0.80448543]
 [ 0.80302158  0.42153752]
 [-0.58439353  0.41846064]]
Err:  [  7.08411661   3.63232025  10.02672242]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.22782555  0.48373054]
 [ 0.12924294  0.87521179]
 [-0.96508641  0.003014  ]]
Err:  [  2.01891927e+01   1.96912536e-04   1.48698024e+00]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.74492891 -0.44301582]
 [-0.63872372  0.25769023]
 [ 0.19264717 -0.85868081]]
Err:  [  1.57132615  31.21796383   9.54975516]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.84041088  0.11203411]
 [ 0.5018057  -0.20868062]
 [ 0.20469633  0.97154555]]
Err:  [  0.76846306  17.31128212   0.26040547]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.60650149 -0.67754428]
 [ 0.3432346  -0.24887787]
 [-0.71717917 -0.6920936 ]]
Err:  [ 6.84525373  6.37476067  0.0886422 ]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.19703456  0.89468936]
 [ 0.97090833  0.23482003]
 [-0.13606762  0.37998751]]
Err:  [  0.47582633   3.71088546  21.51363015]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.8978251   0.10739781]
 [ 0.36362158  0.72781744]
 [-0.2483736   0.677309  ]]
Err:  [  1.58853211  11.81296282  12.34897376]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.74284671 -0.4837997 ]
 [ 0.50524735 -0.85861725]
 [ 0.43920825  0.16945282]]
Err:  [  4.27741571   0.63670478  17.25969802]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.93972545  0.03065544]
 [ 0.19718499 -0.76509276]
 [-0.27934595 -0.64318995]]
Err:  [  1.68303063   8.92248394  10.96235628]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.75959289 -0.37929386]
 [-0.64856162  0.50273708]
 [ 0.04885156  0.77678285]]
Err:  [ 0.03712154  9.38573505  6.13210816]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.91599153 -0.39457363]
 [ 0.23200944 -0.66860163]
 [ 0.32730892 -0.63030429]]
Err:  [ 1.30340739  4.8335368   6.02952321]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.51022475  0.30089023]
 [ 0.49563522  0.86846779]
 [-0.70286302  0.39399083]]
Err:  [ 3.70932421  1.16553584  2.63364373]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.47597339 -0.83469559]
 [-0.45140509  0.50218824]
 [ 0.75477333 -0.22603151]]
Err:  [ 5.46399812  0.49002183  3.63727952]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.92393729  0.0855147 ]
 [ 0.29241675 -0.47054775]
 [ 0.2466421   0.87822096]]
Err:  [  1.07247643  13.59755804   2.96000677]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.4568569  -0.50802111]
 [ 0.3591309  -0.85634122]
 [ 0.81382232  0.09270531]]
Err:  [ 25.57944309   0.33192584  12.71037626]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.54670134 -0.19556   ]
 [-0.37390312  0.92703283]
 [-0.74920898 -0.31994752]]
Err:  [ 12.27495959   2.23857482   7.35472015]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.89259093  0.20598767]
 [ 0.0331584   0.91711568]
 [-0.44964648 -0.34127395]]
Err:  [ 1.90776611  1.09897804  6.42914277]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.47876627  0.39597002]
 [-0.03284938  0.88380919]
 [-0.87732763 -0.24917678]]
Err:  [ 13.38533996   1.07974434   2.92516425]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.54082842  0.80378155]
 [-0.21579946  0.41741548]
 [-0.81297922  0.42390981]]
Err:  [ 5.91730531  1.60883691  1.42678563]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.71998312  0.04987603]
 [ 0.66695453 -0.2259621 ]
 [ 0.19182272  0.97285842]]
Err:  [  0.74054622  19.04808756   0.33711705]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.5660966   0.45905279]
 [ 0.05680709  0.85034673]
 [-0.82237922 -0.25725662]]
Err:  [ 6.55292121  0.64124473  2.49509575]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.8338768   0.04873386]
 [ 0.50179991  0.48180235]
 [-0.22988329  0.87492372]]
Err:  [  1.17613359  17.03657236   4.04302407]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.93472682 -0.2806465 ]
 [-0.34499347  0.56950146]
 [ 0.08523661 -0.77259668]]
Err:  [  0.15209691  12.49607763   8.28658792]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.48615278  0.28711275]
 [ 0.55605905  0.62696791]
 [ 0.67413189 -0.7242082 ]]
Err:  [ 13.27452453  15.31990615   0.6154129 ]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.70173651  0.21125244]
 [ 0.09131222  0.97381894]
 [-0.70656065 -0.08395879]]
Err:  [ 16.52302884   0.23330448  16.34082969]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.85838124 -0.50745433]
 [-0.5125277   0.85465884]
 [-0.02229353 -0.10976506]]
Err:  [  1.64198542e-02   3.98051870e-01   3.26233543e+01]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.07237902 -0.44234082]
 [ 0.16642241 -0.88906343]
 [-0.98339456 -0.11790169]]
Err:  [ 39.24778615   0.56415653   0.77249766]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.77590433  0.36435773]
 [ 0.1558443  -0.68033538]
 [-0.61129782 -0.63591447]]
Err:  [  9.29815921  10.0621016    5.52208268]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.88915086  0.20939553]
 [ 0.26887139 -0.48045383]
 [ 0.37029572  0.85165582]]
Err:  [  2.77436697  14.67556278   2.78336128]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.57855369 -0.31028319]
 [ 0.22236639 -0.94978635]
 [ 0.78474761  0.04037614]]
Err:  [ 12.1972762    0.03228884   7.5767122 ]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.75287589  0.13394584]
 [ 0.46979451  0.79506446]
 [-0.46094579  0.59154967]]
Err:  [  6.08390184  10.01993553  12.53019404]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.96016353 -0.05491534]
 [-0.11884644 -0.96765309]
 [-0.25290616  0.24623527]]
Err:  [ 0.54828953  0.51974658  7.50414074]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.63326685  0.73708242]
 [ 0.22078097 -0.46428052]
 [-0.74177413  0.49107342]]
Err:  [ 7.8226487   3.4284934   2.96593967]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.74321663  0.19935615]
 [ 0.21985417  0.97433682]
 [ 0.6318965  -0.10452219]]
Err:  [  9.22447027   0.25238673  13.62514057]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.81983731  0.22555593]
 [ 0.0563896  -0.88287358]
 [ 0.56981312  0.41189654]]
Err:  [  6.46139197   3.3762724   10.06270767]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.9105326  -0.36118486]
 [ 0.24047732 -0.066846  ]
 [-0.33630498  0.93009521]]
Err:  [  3.51640041  26.89592938   0.67845876]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.82053455 -0.26540604]
 [-0.50665314 -0.07231728]
 [ 0.26462359 -0.96142074]]
Err:  [  3.28753636  43.39507455   0.26499639]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.97777054 -0.18308075]
 [ 0.14632064 -0.9449098 ]
 [-0.15018334  0.27134278]]
Err:  [  0.56198428   1.8344978   22.51965221]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.16767682 -0.63307168]
 [ 0.97052747  0.24059571]
 [ 0.17309222 -0.735754  ]]
Err:  [  0.80794499  14.59795289  11.56073322]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.33681317 -0.77187393]
 [ 0.73631248 -0.5728617 ]
 [-0.58685672 -0.2757537 ]]
Err:  [  9.56513593   2.11188216  16.09624945]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.72283401  0.51693118]
 [ 0.37961869  0.85156793]
 [-0.57740855 -0.08725942]]
Err:  [  7.43923762   0.16989736  14.70407248]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.6914438   0.45869519]
 [ 0.52590741 -0.21008495]
 [-0.49530482 -0.86340201]]
Err:  [  3.54762944  10.78001216   0.13318567]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.96979981 -0.01460181]
 [ 0.13059578  0.81196894]
 [-0.20599288  0.58351798]]
Err:  [ 0.62453816  5.01144559  9.08221085]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.6755061   0.64983586]
 [ 0.31479487 -0.17315448]
 [ 0.6667801   0.74008843]]
Err:  [ 6.97789269  8.59659085  0.12043376]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.05927282  0.95531529]
 [ 0.85265368  0.19930562]
 [-0.5191035   0.21828873]]
Err:  [  4.56103151   0.80652493  11.55847578]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.96562142  0.25609155]
 [ 0.18430466  0.79554309]
 [-0.18332229 -0.54911594]]
Err:  [  1.14381911  10.26254062  22.62872109]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.95296285  0.22475061]
 [ 0.12974777 -0.30381581]
 [-0.27391116 -0.92584184]]
Err:  [  1.4870421   16.98937592   1.34358971]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.49904252 -0.57787245]
 [ 0.06120745  0.76684068]
 [ 0.86441322  0.27931846]]
Err:  [ 9.87634955  1.03122379  2.31005862]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.51181886  0.81635374]
 [ 0.82470176  0.37963483]
 [-0.24064178 -0.43525162]]
Err:  [  1.32394603   4.33120768  17.20758273]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.69151798  0.01385256]
 [ 0.16210462 -0.97133969]
 [ 0.70393535  0.2372916 ]]
Err:  [ 7.58223979  0.86158219  6.85760621]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.44545295 -0.14605784]
 [ 0.74549501  0.48583875]
 [-0.49579114  0.86175856]]
Err:  [  7.11990897  21.51038199   0.3349344 ]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.56095352  0.09578753]
 [ 0.79481973  0.34008061]
 [ 0.23150105 -0.93550517]]
Err:  [  1.10484409  18.04211557   1.4685974 ]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.44026054  0.86248542]
 [ 0.52475517  0.47271969]
 [-0.72856206 -0.18070691]]
Err:  [ 9.22081385  0.56726458  7.5833749 ]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.79216618 -0.59883249]
 [ 0.44823225 -0.43988534]
 [-0.41419875  0.66925372]]
Err:  [  6.09587592  15.91476168  13.52127252]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.22855371 -0.36830834]
 [-0.10614285  0.92958311]
 [-0.9677277  -0.01497359]]
Err:  [  2.48933436e+01   5.95976288e-03   1.68203788e+00]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.88438331 -0.46431151]
 [-0.43213505  0.77580365]
 [-0.17642411  0.42725112]]
Err:  [  0.6312578    3.7021779   15.94763441]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.81220718 -0.38887637]
 [-0.5065515   0.83985345]
 [-0.28935283 -0.37871012]]
Err:  [  2.17277401   3.72197023  20.05655344]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.51390051  0.85527216]
 [ 0.6414637   0.33168522]
 [-0.56958809 -0.39811362]]
Err:  [  7.48463368   3.6564769   11.92895115]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.31910496  0.46717422]
 [ 0.8914856  -0.14727763]
 [ 0.32159828  0.8718128 ]]
Err:  [  1.4070117   10.33990958   1.85719275]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.39229822  0.88116783]
 [ 0.86451434  0.25520452]
 [ 0.3141927   0.39801244]]
Err:  [ 0.62881853  1.00908194  4.73200717]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.86486366 -0.30662839]
 [ 0.37988816 -0.91735812]
 [ 0.32817044  0.25383678]]
Err:  [  1.96960138   1.17838864  15.14056662]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.11610586  0.24538846]
 [-0.18525264 -0.95734677]
 [-0.97580781  0.15255051]]
Err:  [ 33.40567294   0.81643      0.86048657]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.94902763  0.0413552 ]
 [ 0.1575483  -0.79638609]
 [ 0.27299283  0.60337297]]
Err:  [  1.99245336   9.73323847  15.00964549]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.46555723 -0.74862275]
 [-0.43736625 -0.2690688 ]
 [ 0.76939407 -0.60594221]]
Err:  [ 6.87196996  4.26231945  0.4744105 ]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.77800339 -0.39876906]
 [ 0.2903428   0.45706402]
 [ 0.55714611 -0.79503189]]
Err:  [  7.91579873  16.11853775   1.46662394]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.40040134  0.40138404]
 [ 0.44297585  0.87172606]
 [-0.80215407  0.28104188]]
Err:  [ 16.46660423   2.0212989    7.10316782]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.69369149 -0.31257094]
 [ 0.07819226 -0.92828679]
 [ 0.71601542 -0.20145232]]
Err:  [ 9.56650235  0.75727387  8.33608595]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.02367427 -0.84046667]
 [-0.85459565 -0.26396905]
 [-0.51875409  0.47321889]]
Err:  [  8.11862892   6.75590854  15.29437232]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.75113011 -0.06237076]
 [ 0.52801476  0.54077321]
 [-0.39623727  0.83885292]]
Err:  [  4.18992524  18.77877574   3.71804565]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.70807678  0.46468453]
 [ 0.04564841 -0.72126199]
 [ 0.70465842  0.51366276]]
Err:  [ 12.28364909   6.52719026   5.92747515]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.20308969  0.92988356]
 [-0.8118225   0.01522313]
 [ 0.54744753  0.36753886]]
Err:  [  7.06841606   3.18598431  13.33066566]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.84835659 -0.38448189]
 [ 0.30231383 -0.91616286]
 [ 0.43462333 -0.11322228]]
Err:  [  3.20867584   0.21775269  13.55990973]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.49727969  0.67375855]
 [ 0.55796748  0.73080068]
 [-0.66436827  0.1094522 ]]
Err:  [ 16.1512578    0.43836675  20.00258041]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.64874702 -0.60464906]
 [-0.76011954  0.48558799]
 [ 0.03668224 -0.63135079]]
Err:  [  0.02324448   6.88572498  10.36563934]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.7514837  -0.22785793]
 [ 0.0653479   0.90814482]
 [-0.65650734  0.35121752]]
Err:  [ 9.37572491  2.68335428  9.69424735]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.31775024  0.45551048]
 [ 0.94545879  0.21854387]
 [ 0.07171089 -0.86298829]]
Err:  [  0.08914532  12.91035264   4.33567989]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.51581717  0.44194718]
 [ 0.68362519 -0.30396465]
 [ 0.51632281  0.84397167]]
Err:  [  6.78719402  18.13440766   0.53776716]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.30118341 -0.55291974]
 [ 0.74104522 -0.37702277]
 [-0.60011711 -0.74305693]]
Err:  [ 12.99268891  19.91916833   3.16486135]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.28398457 -0.42655372]
 [ 0.93858427 -0.30673396]
 [ 0.19599064  0.85086204]]
Err:  [ 0.28090207  5.29422705  1.73768054]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ -1.68611160e-01   1.29359124e-01]
 [  2.28569761e-02   9.91597432e-01]
 [ -9.85417594e-01   8.66157459e-04]]
Err:  [  2.06136885e+01   1.59260760e-05   6.14589122e-01]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.26077953  0.67463174]
 [-0.11719382 -0.6878931 ]
 [-0.95825865  0.26772205]]
Err:  [ 24.0692204    1.87873192   0.26382838]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.70647874  0.21856432]
 [ 0.23252365  0.97000206]
 [-0.66844637  0.10642197]]
Err:  [ 11.64092      0.29506437  14.11680142]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.61849635  0.62597864]
 [ 0.40196988  0.77144136]
 [-0.6751907  -0.11414451]]
Err:  [ 5.82805358  0.16656382  6.78949659]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.94570763 -0.13721046]
 [ 0.05666949 -0.96224559]
 [ 0.32004008 -0.23506747]]
Err:  [  3.16662305   1.70833361  26.04135138]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.36465836  0.75916933]
 [ 0.11938951  0.61236533]
 [-0.9234557  -0.2206142 ]]
Err:  [ 17.1120103    0.97664301   1.9777207 ]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.86952518  0.37461484]
 [ 0.39806992  0.14583711]
 [ 0.29234621  0.91563926]]
Err:  [  2.52329609  24.75267204   2.24790115]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.68934646  0.64949413]
 [ 0.02093155 -0.4248868 ]
 [ 0.72412936  0.63057797]]
Err:  [ 5.42223108  4.11171778  0.80665046]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.8223273   0.42749806]
 [ 0.24142386 -0.33549164]
 [ 0.51525948  0.83945862]]
Err:  [  4.87319897  12.9348305    0.54729896]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.7078044   0.41752686]
 [ 0.02890204 -0.78883875]
 [ 0.70581697  0.45100416]]
Err:  [ 6.84932194  2.7965622   4.10287116]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.41204211  0.83565002]
 [ 0.89727244  0.30279131]
 [ 0.15850384  0.45826463]]
Err:  [  0.52578993   4.39506539  16.00738286]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.8236273  -0.32366792]
 [ 0.18214171 -0.92861351]
 [ 0.53708703 -0.18142774]]
Err:  [  8.15323334   0.93035332  19.18086362]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.74078754  0.01776081]
 [ 0.47503226  0.72064979]
 [-0.47495071  0.69307174]]
Err:  [ 3.65340112  7.77958904  4.76273053]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.48480672  0.32152636]
 [ 0.61652116  0.53400833]
 [ 0.62037416 -0.78195645]]
Err:  [  9.95896441  15.82238383   0.09522585]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.56142212  0.1377203 ]
 [-0.26254263 -0.96475606]
 [ 0.78477804 -0.22422948]]
Err:  [ 15.86775622   1.29541003   8.60134057]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.15669322 -0.98728452]
 [-0.25979095  0.01505322]
 [-0.9528672   0.15824881]]
Err:  [ 18.62083724   0.5135886    1.37410034]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.83827309  0.45553205]
 [ 0.39565279  0.88633139]
 [-0.37517608 -0.08310971]]
Err:  [  2.97824827   0.14614843  18.03438337]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.60525447 -0.41766793]
 [-0.71256621 -0.09521015]
 [-0.35484704  0.90359755]]
Err:  [ 1.53086664  9.92670379  0.70022906]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.25509053 -0.16769555]
 [-0.7816322  -0.54398275]
 [-0.56919234  0.82216845]]
Err:  [  3.89929581e+00   8.13560224e+00   7.11480425e-04]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.69525881  0.55283142]
 [ 0.71869541  0.52617585]
 [ 0.00959638  0.64615509]]
Err:  [  2.07230444e-03   9.39534062e+00   1.31055131e+01]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.86286511 -0.420236  ]
 [-0.50527286 -0.7031467 ]
 [-0.01277264 -0.57357338]]
Err:  [  3.82555327e-30   3.53085702e-30   2.22637502e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.77820442 -0.62285575]
 [-0.62670112 -0.76195169]
 [-0.04054128 -0.17742702]]
Err:  [  2.65184303e-30   1.37489280e-30   4.25861629e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.79485624  0.20844286]
 [ 0.58744603  0.49962198]
 [ 0.15202213 -0.84079097]]
Err:  [  4.07271741e-30   3.35111810e-30   4.67587503e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.66149672  0.45120244]
 [ 0.65919675  0.7307086 ]
 [ 0.35760555 -0.51232928]]
Err:  [  1.36960582e-29   2.80370195e-30   5.02020911e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.89907389 -0.42028708]
 [ 0.30079091 -0.79644591]
 [ 0.31810527 -0.43477889]]
Err:  [  5.30612959e-30   1.38671319e-30   4.28827561e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.10829909 -0.93055293]
 [-0.02734001  0.34892337]
 [ 0.99374234  0.11101228]]
Err:  [  2.44670140e-30   5.38983682e-30   4.26231054e-31]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.8824697  -0.42774128]
 [-0.35468419 -0.33190378]
 [ 0.30894394  0.84075994]]
Err:  [  2.50650153e-30   3.86880807e-30   2.26504333e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.87856832 -0.4171309 ]
 [ 0.4645681  -0.85941804]
 [-0.11087915 -0.29563903]]
Err:  [  2.25757508e-30   9.97494790e-31   4.09837892e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.93886227 -0.33418066]
 [-0.16308206 -0.21977067]
 [-0.30321918 -0.91652831]]
Err:  [  1.91804304e-30   3.31876248e-30   9.78643246e-31]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.85307985 -0.37787939]
 [-0.52160069 -0.63569387]
 [ 0.01369291 -0.67312738]]
Err:  [  7.39896544e-30   1.08026193e-29   3.16083622e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.32196752  0.74001663]
 [-0.0090344  -0.62130206]
 [ 0.94670761 -0.25760268]]
Err:  [  1.97523375e-30   2.68715375e-30   7.71262720e-31]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.51243985  0.78626017]
 [ 0.38787051  0.57063359]
 [ 0.76613437  0.23700687]]
Err:  [  3.12559171e-30   9.51101281e-30   5.35768357e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.47346482  0.31017967]
 [ 0.87754325  0.08554526]
 [ 0.0758216   0.9468213 ]]
Err:  [  7.71089688e-31   1.75922145e-29   4.45949079e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.95938245  0.25358357]
 [-0.28178099  0.88248789]
 [ 0.0135936   0.3961193 ]]
Err:  [  3.52146661e-30   4.74460064e-30   4.36338688e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.90704804 -0.25027066]
 [ 0.16496075 -0.95110696]
 [ 0.38736521 -0.18099765]]
Err:  [  3.19031988e-30   9.03923276e-31   7.96872774e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.51986759  0.130644  ]
 [ 0.48345112 -0.76975356]
 [-0.70428169 -0.62482925]]
Err:  [  4.60723604e-30   1.28893012e-30   1.60461261e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.83001462 -0.20324325]
 [ 0.02286211  0.9180603 ]
 [-0.55727288  0.34037843]]
Err:  [  1.58758257e-29   1.47414891e-30   8.00234126e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.91494563 -0.37976291]
 [-0.39239774 -0.9162116 ]
 [ 0.09433192 -0.12781408]]
Err:  [  5.19435381e-30   4.88219790e-30   6.77311043e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.59555856 -0.80255564]
 [-0.63943997 -0.44736271]
 [ 0.4862371   0.3946784 ]]
Err:  [  2.74154698e-30   1.14530239e-29   2.90015107e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.6325719   0.52513922]
 [-0.5353807   0.82762285]
 [-0.55966088 -0.19816462]]
Err:  [  4.47422415e-30   1.04921805e-30   4.28683117e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.81784471  0.50129762]
 [-0.57160729  0.65115866]
 [ 0.06629579 -0.56981848]]
Err:  [  1.12226827e-29   1.52382421e-29   4.81790635e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.9126073   0.4074884 ]
 [ 0.05757114  0.20844251]
 [-0.40476349 -0.88910344]]
Err:  [  1.40566404e-30   5.60060428e-30   3.15636596e-31]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.35686452  0.83089962]
 [-0.92691131  0.37176297]
 [-0.11611694 -0.41400255]]
Err:  [  2.36222530e-30   2.12771925e-30   2.52682009e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.81330426  0.56054895]
 [-0.57397562  0.81687246]
 [-0.09533187 -0.13603038]]
Err:  [  1.17575616e-30   1.21067582e-29   2.85345781e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.93309338  0.33745482]
 [ 0.3241238   0.63929111]
 [ 0.15582202  0.69096391]]
Err:  [  1.56267548e-30   1.16928985e-29   3.20643600e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.46663001  0.80981774]
 [ 0.07923158  0.43871503]
 [ 0.88089658  0.38951811]]
Err:  [  1.40176885e-29   3.97822497e-30   2.03522647e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.27680426  0.34055538]
 [ 0.85359242 -0.34230848]
 [ 0.44131552  0.87569797]]
Err:  [  4.62075291e-30   2.67473151e-30   6.39870673e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.94825825 -0.3174174 ]
 [-0.28993244 -0.8563987 ]
 [ 0.12940429  0.40721917]]
Err:  [  1.11205602e-30   2.02374010e-30   3.42815530e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.46540095  0.71386516]
 [-0.46708289 -0.30407849]
 [ 0.75182147 -0.63081915]]
Err:  [  1.15621278e-29   2.33040593e-29   1.00500802e-29]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.52219024  0.72609484]
 [-0.77192007  0.17941666]
 [-0.36256966  0.66377402]]
Err:  [  1.67132201e-30   4.67399876e-30   1.65468679e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.0582837  -0.47584652]
 [ 0.95423877 -0.23173058]
 [-0.29331106 -0.84845213]]
Err:  [  2.02473015e-30   4.33719423e-30   6.24557414e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.73838706 -0.57020895]
 [ 0.3884774  -0.79608202]
 [-0.55124392  0.20276874]]
Err:  [  1.77686681e-29   1.01036211e-29   2.79182805e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.78573408 -0.60336878]
 [ 0.60136601 -0.69353677]
 [ 0.14484781 -0.39364052]]
Err:  [  6.60256933e-31   1.87973509e-30   4.60528368e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.68897528 -0.68975242]
 [ 0.72302499 -0.63269137]
 [-0.05047696  0.35205573]]
Err:  [  3.12752516e-30   2.25538524e-29   3.77174120e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.2899418   0.49464834]
 [ 0.92262598  0.37198513]
 [-0.25435221  0.7854617 ]]
Err:  [  1.02209102e-30   5.47426327e-30   4.16393577e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.96501494 -0.25198457]
 [ 0.26187961 -0.93986848]
 [ 0.0128544   0.23054503]]
Err:  [  1.16518370e-29   4.34977898e-30   4.74549138e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.75235138  0.39646876]
 [-0.65351182  0.54981187]
 [-0.08300423 -0.73520027]]
Err:  [  6.67479170e-31   1.73834437e-30   3.06135555e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.37557484 -0.84403429]
 [ 0.91563354 -0.40182355]
 [-0.14338327 -0.3551675 ]]
Err:  [  1.23307665e-30   1.02610659e-29   5.16303299e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.47639252 -0.84447764]
 [ 0.40861946  0.0338401 ]
 [-0.77851159  0.53452069]]
Err:  [  1.04046439e-29   1.84670757e-29   3.27687350e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.97128672  0.0460593 ]
 [ 0.01821314 -0.96380671]
 [ 0.23721382  0.26259318]]
Err:  [  1.69092587e-30   1.50665048e-30   2.74868722e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.54081641  0.41386701]
 [-0.7813967  -0.07504487]
 [-0.31134676  0.90723887]]
Err:  [  1.77397407e-30   3.94892676e-30   3.35239789e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.5959533   0.79869443]
 [-0.71332224  0.57413424]
 [ 0.36879676 -0.18015848]]
Err:  [  6.28221536e-30   1.75906737e-29   3.14311767e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.91580579 -0.11107616]
 [-0.38708654  0.01207588]
 [ 0.10706901  0.99373853]]
Err:  [  5.62152469e-30   3.44510348e-30   9.97591086e-31]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.94014329 -0.14926652]
 [-0.22811305  0.39220488]
 [ 0.25316997  0.90768653]]
Err:  [  9.80551722e-30   2.84113185e-30   1.19894556e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.21539256 -0.97019557]
 [-0.49520483 -0.01052821]
 [-0.84165208 -0.24209443]]
Err:  [  1.92592994e-30   1.30291869e-29   3.28698464e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.87048336 -0.2107449 ]
 [ 0.47617094 -0.58931248]
 [ 0.12457915  0.77993422]]
Err:  [  6.78986602e-30   1.19134174e-29   1.10501518e-29]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.92640477 -0.09710974]
 [ 0.37590057 -0.29433271]
 [-0.02174785 -0.95075652]]
Err:  [  1.63388674e-30   8.09198725e-30   8.28988643e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.89957962 -0.04021278]
 [ 0.37597403 -0.57802727]
 [ 0.22226118  0.81502602]]
Err:  [  1.49899942e-30   1.90898176e-30   1.65783081e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.00711201 -0.12811161]
 [-0.97399413 -0.22548854]
 [ 0.22646158 -0.96578586]]
Err:  [  2.19426653e-30   3.51905919e-30   1.30309699e-29]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.1042617  -0.02065412]
 [ 0.27925269  0.95895633]
 [-0.95454043  0.28280056]]
Err:  [  2.48367926e-30   1.19068693e-29   1.08276459e-29]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.61217551  0.44201541]
 [-0.28461174 -0.650418  ]
 [ 0.73772441 -0.61772066]]
Err:  [  4.08952566e-30   3.70523794e-30   9.89791916e-31]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.99712995 -0.06023908]
 [ 0.02419815  0.32038286]
 [ 0.07173784 -0.94537087]]
Err:  [  3.96907229e-30   3.75941525e-30   5.40897425e-31]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.31314925 -0.94279242]
 [ 0.44487416 -0.25201738]
 [ 0.8390617  -0.21824226]]
Err:  [  2.66548704e-30   1.06809791e-29   1.46133245e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.9853284  -0.03231352]
 [-0.16751278  0.0048621 ]
 [-0.03267134 -0.99946596]]
Err:  [  1.78435002e-30   2.39739759e-30   1.51735105e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.68561173  0.13674208]
 [ 0.18527523 -0.91707833]
 [ 0.70399549  0.37452495]]
Err:  [  2.43919027e-30   8.74594956e-30   3.86093998e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.42665837 -0.08065253]
 [-0.73173669 -0.61614925]
 [-0.53152992  0.78348916]]
Err:  [  1.39783995e-30   2.02607830e-30   1.06572537e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.10203323 -0.79211455]
 [ 0.77029179  0.31988204]
 [-0.6294758   0.51983653]]
Err:  [  7.31530334e-30   1.43925948e-29   2.91147278e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.42180397  0.89041454]
 [ 0.01237737  0.19424351]
 [ 0.90660257  0.41162046]]
Err:  [  4.04137140e-30   1.85513869e-29   5.97697312e-31]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.67237882  0.43740641]
 [-0.73137668  0.26834562]
 [ 0.11399501 -0.85829264]]
Err:  [  7.63684261e-31   5.33251483e-30   5.07968838e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.1945548  -0.9211469 ]
 [ 0.84167612 -0.33325702]
 [-0.50371592 -0.20106754]]
Err:  [  2.09007823e-30   3.08384266e-30   2.21867130e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.8148489   0.09141679]
 [ 0.53474035  0.49974491]
 [-0.22377228  0.86133501]]
Err:  [  3.21324559e-30   3.19088073e-30   3.14203170e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.66060933  0.57786072]
 [ 0.42477769 -0.23863756]
 [ 0.61899856  0.78046723]]
Err:  [  7.06217591e-30   2.37284199e-30   1.32047324e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.22356522 -0.001723  ]
 [-0.95072459 -0.22076867]
 [-0.21480539  0.97532468]]
Err:  [  2.18980003e-30   2.39123462e-30   2.88719769e-29]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.42833163  0.81809214]
 [-0.39845529  0.55215126]
 [-0.81102737  0.16079253]]
Err:  [  7.97373516e-30   6.26953770e-29   4.08398260e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.13592366  0.96277265]
 [-0.84954944 -0.00806591]
 [-0.50969649  0.2701921 ]]
Err:  [  1.19494323e-30   8.38531240e-30   1.33736575e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.48705233 -0.78495573]
 [-0.85756632 -0.34678731]
 [-0.16540871 -0.51340342]]
Err:  [  1.60538298e-30   3.21245115e-30   2.19401939e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.10498247  0.98924213]
 [-0.98861922  0.09271557]
 [-0.10775304  0.11315399]]
Err:  [  1.74604809e-30   1.37841876e-29   5.72540454e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.94538802 -0.02168028]
 [ 0.12248525  0.90102553]
 [ 0.3020577  -0.4332239 ]]
Err:  [  1.16092650e-30   2.78682063e-30   5.73773049e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.85811574  0.50363547]
 [-0.46311511  0.84323345]
 [ 0.22172454 -0.18790599]]
Err:  [  5.77243936e-30   1.29133603e-30   3.38347373e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.88063493  0.12577652]
 [-0.1862224  -0.79464236]
 [-0.43566424  0.59390553]]
Err:  [  1.71176722e-29   1.32522873e-30   2.03511663e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.36221833 -0.73158434]
 [ 0.38427262 -0.68174168]
 [ 0.84919517 -0.00355465]]
Err:  [  5.53281154e-30   2.41545319e-30   4.43558969e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.36535868  0.19121583]
 [ 0.9306704   0.05493146]
 [ 0.01912153  0.98000971]]
Err:  [  9.92510694e-31   6.85322911e-30   1.83358160e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.46511755 -0.37965913]
 [ 0.6375371  -0.7704031 ]
 [ 0.61417596  0.51218943]]
Err:  [  1.85505845e-30   2.30798498e-29   1.90293434e-29]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.60748529  0.78751726]
 [-0.42145637  0.20877015]
 [-0.67330242  0.5798548 ]]
Err:  [  2.69167969e-30   3.42228723e-30   1.30963236e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.96365882  0.26658209]
 [ 0.06004136 -0.2788627 ]
 [-0.26030119  0.92258852]]
Err:  [  2.57440259e-30   5.22928499e-30   8.19290598e-31]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.81577733  0.48306376]
 [ 0.28616937  0.81500924]
 [ 0.50260764  0.3200146 ]]
Err:  [  3.01143221e-30   8.12184669e-30   6.43462824e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.19262182  0.62393817]
 [-0.90564229 -0.18409422]
 [ 0.3777683  -0.7594804 ]]
Err:  [  1.63325179e-30   6.15707766e-30   1.95808696e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.44890185  0.5698034 ]
 [-0.66583727 -0.30044222]
 [-0.59594283  0.7648912 ]]
Err:  [  1.23189701e-30   2.34193081e-30   3.57337042e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.58628959  0.36164448]
 [ 0.78941893  0.05414103]
 [-0.18188532 -0.93074272]]
Err:  [  8.07181314e-31   7.11977782e-30   3.46515883e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.9458053   0.12666589]
 [ 0.31803876  0.5473314 ]
 [-0.06560251  0.8272751 ]]
Err:  [  5.74218420e-30   4.09260113e-30   3.38604702e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.6950115  -0.71883499]
 [-0.31355644 -0.28382896]
 [ 0.64702502  0.63460017]]
Err:  [  1.01086443e-29   3.45700573e-29   1.76530739e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.9442448  -0.32922468]
 [-0.13054917 -0.38438346]
 [ 0.30225597  0.86247346]]
Err:  [  1.29637956e-30   3.51443696e-30   3.13319612e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.7090867   0.69955102]
 [ 0.70454908  0.6978613 ]
 [ 0.02840157  0.15368139]]
Err:  [  1.49404015e-30   1.13756497e-29   4.17849761e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.50382055  0.73151337]
 [-0.78070387  0.15800102]
 [-0.36968408  0.66326757]]
Err:  [  3.50153323e-30   4.60700273e-30   5.05032248e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.27289026 -0.83502551]
 [-0.74133997  0.13399014]
 [-0.61314431 -0.53364692]]
Err:  [  3.46924983e-30   1.71605128e-29   2.47821443e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.67467734  0.73624943]
 [-0.28416756  0.19355188]
 [-0.68121897  0.64844001]]
Err:  [  9.58227185e-30   1.02456513e-29   2.10267013e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.69557607  0.28920687]
 [ 0.70993951  0.13618011]
 [ 0.11027162  0.94753067]]
Err:  [  6.47930982e-31   3.45126646e-30   9.20008083e-31]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.78578502  0.53304146]
 [ 0.5548664   0.83161573]
 [ 0.2732493  -0.15582705]]
Err:  [  1.05740776e-30   1.02538617e-30   4.64688377e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.91444595 -0.18947327]
 [-0.13588977  0.97608621]
 [-0.38121199  0.10656258]]
Err:  [  1.34907497e-29   9.71792201e-30   1.45153488e-29]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.46560246 -0.73646433]
 [ 0.58662815 -0.67202652]
 [ 0.66263245  0.07746387]]
Err:  [  1.86064092e-30   8.34318870e-31   1.48884014e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.12896818 -0.09451939]
 [-0.98491141  0.10363103]
 [-0.11539815 -0.99011449]]
Err:  [  1.51514263e-30   8.52955854e-30   2.27253474e-29]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.79596234 -0.59638718]
 [-0.59405747 -0.80250668]
 [ 0.11636014 -0.01747466]]
Err:  [  1.01413693e-29   2.60193451e-29   2.25564915e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.98395045 -0.00946337]
 [ 0.17240991  0.20702579]
 [-0.04600353  0.97828971]]
Err:  [  6.11771647e-30   2.98904327e-30   2.33449191e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.12073514  0.21318219]
 [-0.83496264 -0.50642767]
 [ 0.53689888 -0.83551444]]
Err:  [  4.46483863e-30   2.40818280e-29   5.66517725e-29]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.94793402 -0.22027579]
 [ 0.25383272 -0.08643884]
 [ 0.19232799 -0.97160018]]
Err:  [  1.41833305e-30   3.08765089e-30   2.68985081e-31]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.38227417 -0.83615305]
 [-0.28443595  0.29852298]
 [-0.87918294 -0.46014357]]
Err:  [  2.31881965e-30   1.35975428e-29   1.16860097e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.29228005  0.13633868]
 [ 0.83851367  0.51247662]
 [ 0.45985563 -0.84780863]]
Err:  [  2.27401771e-30   6.82087349e-30   1.79354747e-29]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[ 0.59515931 -0.59887728]
 [-0.24045822 -0.76896598]
 [ 0.76678891  0.22369025]]
Err:  [  8.20320971e-30   4.97714769e-29   1.04194194e-29]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.74759962 -0.23195685]
 [ 0.56633674 -0.71211494]
 [-0.34692578 -0.66263741]]
Err:  [  4.99846228e-30   9.93086517e-30   4.42587014e-30]
X:  (300, 3)
Transfomed:  (300, 2)
EV:  [[-0.23535902 -0.77306963]
 [ 0.17134311  0.56356992]
 [-0.95668577  0.29112248]]
Err:  [  4.91805471e-30   2.50727190e-30   6.86603429e-31]

In [20]:

    

import random
import numpy
import matplotlib.pyplot as plt

bins = numpy.linspace(0, 100, 100)

plt.hist(np_pca_err, bins, alpha=0.25, label='NPE')
plt.hist(p_pca_err, bins, alpha=0.25, label='PE')
plt.legend(loc='upper right')
plt.show()


    

In [ ]: