# for item in results:
# confusion_matrix(test_instances[]results[item]
acuracia = []
for i in range(len(files)):
range_data = data_x[i][TEST].shape
results = []
# for index in range(len(clfs)):
predictions = []
for inner in range(len(data_x[i][TEST])):
predictions.append(clfs[i].classify(np.squeeze(np.asarray(data_x[i][TEST][inner]))))
# print ("valor real, ", data_y[i][TEST][inner])
# print ("predicao",predictions[len(predictions)-1])
# print ("Iteracao", len(predictions))
#
# print('tampred', len(predictions))
# print('tamy', len(data_y[i][TEST]))
cm = confusion_matrix(data_y[i][TEST], predictions, Pds.TARGET_NAMES)
acuracia.append(((np.sum(np.diag(cm))*100)/range_data[0]))
print('acuracia', acuracia, ' %', str(i))
Pds.plot_confusion_matrix(cm, files[i], k)
acuracia_file = open((Pds.PATH + "/K" + str(k) + "/acuracia.txt"), 'w')
for i in range(len(acuracia)):
acuracia_file.write(str(acuracia[i]) + '\n')
acuracia_file.close()
# confusions_matrix = [confusion_matrix(data_y[index][TEST], results[index]) for index in range(len(results))]
#
# for index in range(len(confusions_matrix)):
# with open(str(files[index])+"_confusion_matrix.txt", 'w') as f:
# f.write(confusions_matrix[index])
# for item in results:
# confusion_matrix(test_instances[]results[item]
results = []
# for index in range(len(clfs)):
predictions = []
for inner in range(len(data_x[0][TEST])):
predictions.append(clfs[0].classify(np.squeeze(np.asarray(data_x[0][TEST][inner]))))
print ("valor real, ", data_y[0][TEST][inner])
print ("predicao",predictions[len(predictions)-1])
print ("Iteracao", len(predictions))
print('tampred', len(predictions))
print('tamy', len(data_y[0][TEST]))
cm = confusion_matrix(data_y[0][TEST], predictions, Pds.TARGET_NAMES)
Pds.plot_confusion_matrix(cm)
# print('pred', clfs[0].classify(np.squeeze(np.asarray(data_x[0][1][0]))))
