# 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]))))