from sklearn.metrics import confusion_matrix TRAINING = 0 TEST = 1 k = 5 # 3 files = [f for f in listdir(Pds.PATH) if isfile(join(Pds.PATH, f)) and "windowed" in f] # b1_va3_windowed windowed files.sort() print(files) # print(len(files)) data = [Pds.get_dataset(Pds.PATH+"/"+file) for file in files] data_x = [Pds.get_training_data(Pds.TRAINING_SLICE, data[index]['x'], type='x') for index in range(len(data))] print(data_x) data_y = [Pds.get_training_data(Pds.TRAINING_SLICE, data[index]['y'], type='y') for index in range(len(data))] clfs = [Knn.KNNClassifier(data_x[index][TRAINING], data_y[index][TRAINING], k) for index in range(len(data))] # clf = knn.KNNClassifier(data_x[1][1], data_y[1][1], k) # results = [] # for index in range(len(clfs)): # predictions = [] # for inner in range(len(data_x[index][1])): # predictions.append(clfs[index].classify(np.squeeze(np.asarray(data_x[index][TEST][inner]))))
x = [i for i in range(len(eigen_values))]
soma = 0
for index in range(15):
soma += var_exp[index]
print soma
plt.plot(x, y, linestyle='--', marker='o', color='b')
plt.ylabel("Porcentagem de Representacao")
plt.xlabel("Indice dos Autovalores")
plt.show()
# dataset = pds.get_dataset(pds.FILE)
dataset = pds.get_dataset("")
reduced_matrix = execute(dataset)
print ("final", reduced_matrix)
# with open(pds.PATH+"/a1_va3_reducedR.csv", 'w') as csvw:
# csvw = csv.writer(csvw, delimiter=',')
# csvw.writerows(reduced_matrix)
np.savetxt(pds.FILE_REDUCED, reduced_matrix, delimiter=',', fmt='%.8f')
print('y', dataset['y'])
outf = open(pds.FILE_REDUCED_PRED, 'w')
for index in range(len(dataset['y'])):
