# open and load csv files time_load_start = time.clock() X_train, y_train = fipr.load_csv("train_file.csv", True) X_test, y_test = fipr.load_csv("test_file.csv", True) y_train = y_train.flatten() y_test = y_test.flatten() time_load_end = time.clock() print("Loading finished, loading time: %g seconds" % (time_load_end - time_load_start)) X_test_even, y_test_even = fipr.load_csv("test_file_even.csv", True) y_test_even = y_test_even.flatten() # scale features to encourage gradient descent convergence X_train = fipr.scale_features(X_train, 0.0, 1.0) X_test = fipr.scale_features(X_test, 0.0, 1.0) X_test_even = fipr.scale_features(X_test_even, 0.0, 1.0) # create the logistic regression classifier using the training data LRC = LogisticRegressionClassifier(alpha, lmbda, maxiter) print("\nCreated a logistic regression classifier =", LRC) # start counting time for training time_train_start = time.clock() # fit the model to the loaded training data print("Fitting the training data...\n") LRC.fit(X_train, y_train)
def main(): # open and load csv files time_load_start = time.clock() X_train, y_train = fipr.load_csv("train_file.csv", True) X_test, y_test = fipr.load_csv("test_file.csv", True) y_train = y_train.flatten() y_test = y_test.flatten() time_load_end = time.clock() print("Loading finished, loading time: %g seconds" % (time_load_end - time_load_start)) X_test_even, y_test_even = fipr.load_csv("test_file_even.csv", True) y_test_even = y_test_even.flatten() # scale features to encourage gradient descent convergence X_train = fipr.scale_features(X_train, 0.0, 1.0) X_test = fipr.scale_features(X_test, 0.0, 1.0) X_test_even = fipr.scale_features(X_test_even, 0.0, 1.0) Pattern_train = [] for i, sample_train in enumerate(X_train): Pattern_train.append([sample_train, y_train[i]]) Pattern_test = [] for j, sample_test in enumerate(X_test): Pattern_test.append([sample_test, y_test[j]]) Pattern_test_even = [] for k, sample_test_even in enumerate(X_test_even): Pattern_test_even.append([sample_test_even, y_test_even[k]]) #print(Pattern_train) #print(Pattern_test) # Teach network XOR function (for test only) '''pat = [ [[0,0], [0]], [[0,1], [1]], [[1,0], [1]], [[1,1], [0]] ] print(pat) # create a network with two input, two hidden, and one output nodes n = NN(2, 2, 1) # train it with some patterns n.train(pat) # test it n.test(pat)''' # Test on Iris data #pattern = irisdemo() # create a network with two hundred inputs, two hidden, and one output nodes n = NN(200, 4, 1) # start counting time for training time_train_start = time.clock() # train it with some patterns n.train(Pattern_train) # print training time time_train_end = time.clock() print("Training finished, training time: %g seconds \n" % (time_train_end - time_train_start)) # start counting time for testing time_test_start = time.clock() # test it n.test(Pattern_test) # print testing time time_test_end = time.clock() print("Testing finished, testing time: %g seconds \n" % (time_test_end - time_test_start)) # test on EVEN data set n.test(Pattern_test_even)