point = {0: 'ro', 1: 'bo'}
marker = {0: 's', 1: 'D'}
# O clasificador da vigesima realização
plot_dict = {
'xx': xx,
'yy': yy,
'Z': array(best_acc_clf.predict(space)),
'classes': {}
}
# utilizando o x_test e o y_test da ultima realização
for c in [0, 1]:
plot_dict['classes'].update({
c: {
'X': x_test[where(y_test == c)[0]],
'point': point[c],
'marker': marker[c]
}
})
# #FFAAAA red
# #AAAAFF blue
coloring(plot_dict,
ListedColormap(['#FFAAAA', '#AAAAFF']),
xlabel='x1',
ylabel='x2',
title='mapa de cores com knn',
path=path + 'color_map_and_knn.jpg',
save=True)
print('dataset shape %s' % Counter(base[:, 2]))
# utilizando o x_test e o y_test da ultima realização
for c in [0, 1, 2]:
plot_dict['classes'].update({
c: {
'X': x[where(y == c)[0]],
'point': point[c],
'marker': marker[c]
}
})
# #FFAAAA red
# #AAAAFF blue
coloring(plot_dict,
ListedColormap(['#87CEFA', '#228B22', "#FF00FF"]),
xlabel='x1',
ylabel='x2',
title='mapa de cores com Rede Perceptron ',
xlim=[-0.1, 1.1],
ylim=[-0.1, 1.1],
path=get_project_root() + '/run/TR-04/ARTIFICIAL/results/' +
'color_map_triangle_simple_net.jpg',
save=True)
# print('dataset shape %s' % Counter(base[:, 2]))
print(pd.DataFrame(final_result))
# del final_result['best_cf']
pd.DataFrame(final_result).to_csv(get_project_root() +
'/run/TR-04/ARTIFICIAL/results/' +
'result_simple_net.csv')
'classes': {}
}
# utilizando o x_test e o y_test da ultima realização
for c in [0, 1]:
plot_dict['classes'].update({
c: {
'X': x_test[where(y_test == c)[0]],
'point': point[c],
'marker': marker[c]
}
})
# #FFAAAA red
# #AAAAFF blue
coloring(plot_dict,
ListedColormap(['#FFAAAA', '#AAAAFF']),
xlabel='x1',
ylabel='x2',
title='mapa de cores com Perceptron sigmoid',
path=get_project_root() + '/run/TR-035/ARTIFICIAL/results/' +
'color_map_triangle_sigmoid_hyper_net.jpg',
save=True)
# print('dataset shape %s' % Counter(base[:, 2]))
print(pd.DataFrame(final_result))
# del final_result['best_cf']
pd.DataFrame(final_result).to_csv(get_project_root() +
'/run/TR-035/ARTIFICIAL/results/' +
'result_sigmoid_hyper_net.csv')
# utilizando o x_test e o y_test da ultima realização
for c in [0, 1]:
plot_dict['classes'].update({
c: {
'X': x[where(y == c)[0]],
'point': point[c],
'marker': marker[c]
}
})
# #FFAAAA red
# #AAAAFF blue
coloring(plot_dict,
ListedColormap(['#87CEFA', '#228B22']),
xlabel='x1',
ylabel='x2',
title='mapa de cores com RBF',
xlim=[-0.1, 1.1],
ylim=[-0.1, 1.1],
path=get_project_root() + '/run/TR-06/XOR/results/' +
'color_map_xor_rbf_net.jpg',
save=True)
# print('dataset shape %s' % Counter(base[:, 2]))
print(pd.DataFrame(final_result))
# del final_result['best_cf']
pd.DataFrame(final_result).to_csv(get_project_root() +
'/run/TR-06/XOR/results/' +
'result_rbf.csv')
2: '*'
}
# O clasificador da vigesima realização
plot_dict = {
'xx': xx,
'yy': yy,
'Z': out_of_c_to_label(simple_net.predict(space)),
'classes': {}
}
# utilizando o x_test e o y_test da ultima realização
for c in [0, 1, 2]:
plot_dict['classes'].update({
c: {
'X': x[where(y == c)[0]],
'point': point[c],
'marker': marker[c]
}
})
# #FFAAAA red
# #AAAAFF blue
coloring(plot_dict, ListedColormap(['#87CEFA', '#228B22', "#FF00FF"]), xlabel='SepalLengthCm', ylabel='SepalWidthCm',
title='mapa de cores com Rede Perceptron', xlim=[-0.1, 1.1], ylim=[-0.1, 1.1],
path=get_project_root() + '/run/TR-03/IRIS/results/' + 'color_map_sepal.jpg', save=True)
# print('dataset shape %s' % Counter(base[:, 2]))
}
marker = {
0: '^',
1: 'o',
}
# O clasificador da vigesima realização
plot_dict = {
'xx': xx,
'yy': yy,
'Z': classifier_perceptron.predict(space),
'classes': {}
}
# utilizando o x_test e o y_test da ultima realização
for c in [0, 1]:
plot_dict['classes'].update({
c: {
'X': x[where(y == c)[0]],
'point': point[c],
'marker': marker[c]
}
})
path = get_project_root() + '/run/TR-035/IRIS/results/' + 'color_map_' + str(combination) + str(one_versus_others) + '.jpg'
coloring(plot_dict, ListedColormap(['#87CEFA', '#228B22']), xlabel=combination[0],
ylabel=combination[1], title='mapa de cores com Rede Perceptron' + str(one_versus_others),
xlim=[-0.1, 1.1], ylim=[-0.1, 1.1],
path=path,
save=True)
}
# utilizando o x_test e o y_test da ultima realização
for c in [0, 1, 2]:
plot_dict['classes'].update({
c: {
'X': x[where(y == c)[0]],
'point': point[c],
'marker': marker[c]
}
})
# #FFAAAA red
# #AAAAFF blue
coloring(plot_dict, ListedColormap(['#87CEFA', '#228B22', "#FF00FF"]), xlabel='SepalLengthCm',
ylabel='SepalWidthCm',
title='mapa de cores com Rede Perceptron - ACC: ' + str(metric_results['ACCURACY'].round(2)), xlim=[-0.1, 1.1], ylim=[-0.1, 1.1],
path=get_project_root() + '/run/TR-04/IRIS/results/' + 'color_map_sepal_test.jpg', save=True)
# print('dataset shape %s' % Counter(base[:, 2]))
# ------------------ All points -------------------------------------------------------------------
x = array(base[:, :2])
y = array(out_of_c_to_label(base[:, 2:]))
xx, yy = generate_space(x)
space = c_[xx.ravel(), yy.ravel()]
point = {
0: 'bo',
1: 'go',
'classes': {}
}
# utilizando o x_test e o y_test da ultima realização
for c in [0, 1]:
plot_dict['classes'].update({
c: {
'X': x_test[where(y_test == c)[0]],
'point': point[c],
'marker': marker[c]
}
})
# #FFAAAA red
# #AAAAFF blue
coloring(plot_dict,
ListedColormap(['#FFAAAA', '#AAAAFF']),
xlabel='x1',
ylabel='x2',
title='mapa de cores com perceptron',
path=get_project_root() + '/run/TR-01/ARTIFICIAL/results/' +
'color_map_and_percptron.jpg',
save=True)
print('dataset shape %s' % Counter(base[:, 2]))
del final_result['best_cf']
del final_result['ErrosxEpocohs']
DataFrame(final_result).to_csv(get_project_root() +
'/run/TR-01/ARTIFICIAL/results/' +
'result_percptron.csv')