def Main(): x_train = pd.read_csv('Dataset/xtrain_3spirals.txt', sep=' ', header=None) x_test = pd.read_csv('Dataset/xtest_3spirals.txt', sep=' ', header=None) d_train = pd.read_csv('Dataset/dtrain_3spirals.txt', sep=',', header=None) d_test = pd.read_csv('Dataset/dtest_3spirals.txt', sep=',', header=None) ## Aplication of MLP algorithm mlp = MLP(15000, 0.15, 0.000001, [4, 3], 0.5) mlp.train(x_train.to_numpy(), d_train.to_numpy()) new_classes = mlp.application(x_test.to_numpy()) comparative = np.concatenate((d_test.to_numpy(), new_classes), 1) print("Matrix of comparative between classes") print(comparative) print("------------------------------") hit_table = np.zeros((len(new_classes), 1)) for row in range(len(new_classes)): if all(d_test.to_numpy()[row] == new_classes[row]): hit_table[row] = 1 tax_hit = sum(hit_table) / len(new_classes) print("------------------------------") print("Matrix of hits") print(hit_table) print("------------------------------") print("Tax of hits: " + str(tax_hit))
real_classes[row,1] = 1 ''' x_train = pd.read_csv('Dataset/xtrain_3spirals.txt', sep=' ', header=None) x_test = pd.read_csv('Dataset/xtest_3spirals.txt', sep=' ', header=None) #converterClasses('Dataset/dtest_3spirals.txt') #converterClasses('Dataset/dtrain_3spirals.txt') d_train = pd.read_csv('Dataset/dtrain_3spirals.txt', sep=',', header=None) d_test = pd.read_csv('Dataset/dtest_3spirals.txt', sep=',', header=None) ## Aplication of MLP algorithm mlp = MLP(15000, 0.15, 0.000001, [4, 3], 0.5) mlp.train(x_train.to_numpy(), d_train.to_numpy()) new_classes = mlp.application(x_test.to_numpy()) comparative = np.concatenate((d_test.to_numpy(), new_classes), 1) print("Matrix of comparative between classes") print(comparative) print("------------------------------") hit_table = np.zeros((len(new_classes), 1)) for row in range(len(new_classes)): if all(d_test.to_numpy()[row] == new_classes[row]): hit_table[row] = 1 tax_hit = sum(hit_table) / len(new_classes)