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import matplotlib as mpl
mpl.use('Agg')
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import sys


    

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sys.path.append('../code')


    

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from NeuralNet import NeuralNet
from TransferFunctions import TanhTF, LinearTF


    

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from sklearn.datasets import make_classification
X, y = make_classification(n_samples=200, n_features=10, 
                           n_informative=10, n_redundant=0, n_repeated=0, 
                           n_classes=2, n_clusters_per_class=1, 
                           weights=None, flip_y=0.0001, class_sep=1.0, 
                           hypercube=True, shift=0.0, scale=1.0, 
                           shuffle=True, random_state=None)
X=X.T
print('shape of X: {}.  (Columns are rows; rows are features)'.format(X.shape))


    

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y.shape


    

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# columns are data points and rows are features
d, N = np.shape(X)
C = np.unique(y).shape[0]


    

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n = NeuralNet(X=X, y=y, hidden_nodes=20, 
              hiddenTF=TanhTF, outputTF=TanhTF,
              minibatch_size=4,
              eta0 = 0.001,
              verbose=True)


    

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n.run(epochs=20)


    

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n.results.tail()


    

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sl = n.plot_square_loss()


    

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l01 = n.plot_01_loss()


    

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p = n.plot_weight_evolution()


    

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p1 = n.plot_sum_of_weights('W1')
p2 = n.plot_sum_of_weights('W2')


    

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p1 = n.plot_norm_of_gradient('W1')
p2 = n.plot_norm_of_gradient('W2')


    

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n.results.tail(1).T


    

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