def test_linreg():
'''
Helper function that tests LinearRegression.
@param:
None
@return:
None
'''
X_train, X_test, Y_train, Y_test = import_wine(WINE_FILE_PATH)
num_features = X_train.shape[1]
# Padding the inputs with a bias
X_train_b = np.append(X_train, np.ones((len(X_train), 1)), axis=1)
X_test_b = np.append(X_test, np.ones((len(X_test), 1)), axis=1)
#### Stochastic Gradient Descent ######
print('---------- LINEAR REGRESSION w/ SGD ----------')
sgd_model = LinearRegression(num_features, sgd=True)
sgd_model.train(X_train_b, Y_train)
print('Average Training Loss:', sgd_model.average_loss(X_train_b, Y_train))
print('Average Testing Loss:', sgd_model.average_loss(X_test_b, Y_test))
#### Matrix Inversion ######
print('---- LINEAR REGRESSION w/ Matrix Inversion ---')
solver_model = LinearRegression(num_features)
solver_model.train(X_train_b, Y_train)
print('Average Training Loss:',
solver_model.average_loss(X_train_b, Y_train))
print('Average Testing Loss:', solver_model.average_loss(X_test_b, Y_test))
def test_linreg():
'''
Helper function that tests LinearRegression.
@param:
None
@return:
None
'''
m = np.array([[2, 3], [1, 0]])
mm = np.array([[2, 3, 4, 5], [1, 1, 3, 0]])
for l in range(2):
print(mm[l, range(2)])
###print(m)
n = np.append(m, np.ones((len(m), 1)), axis=1)
#print(n)
#print(m.shape[1])
s = LinearRegression(m.shape[1])
#print(s.weights)
X_train, X_test, Y_train, Y_test = import_wine(WINE_FILE_PATH)
#print(X_train.shape[1])
num_features = X_train.shape[1]
# Padding the inputs with a bias
X_train_b = np.append(X_train, np.ones((len(X_train), 1)), axis=1)
X_test_b = np.append(X_test, np.ones((len(X_test), 1)), axis=1)
#print(6.7**2)
#### Matrix Inversion ######
print('---- LINEAR REGRESSION w/ Matrix Inversion ---')
solver_model = LinearRegression(num_features)
solver_model.train(X_train_b, Y_train)
print('Average Training Loss:',
solver_model.average_loss(X_train_b, Y_train))
print('Average Testing Loss:', solver_model.average_loss(X_test_b, Y_test))
def test_models(dataset, epochs, test_size=0.2):
'''
Tests LinearRegression, OneLayerNN, TwoLayerNN on a given dataset.
:param dataset The path to the dataset
:return None
'''
# Check if the file exists
if not os.path.exists(dataset):
print('The file {} does not exist'.format(dataset))
exit()
# Load in the dataset
data = np.loadtxt(dataset, skiprows=1)
X, Y = data[:, 1:], data[:, 0]
# Normalize the features
X = (X - np.mean(X, axis=0)) / np.std(X, axis=0)
X_train, X_test, Y_train, Y_test = train_test_split(X,
Y,
test_size=test_size)
print('Running models on {} dataset'.format(dataset))
#### Linear Regression ######
print('----- LINEAR REGRESSION -----')
# Add a bias
X_train_b = np.append(X_train, np.ones((len(X_train), 1)), axis=1)
X_test_b = np.append(X_test, np.ones((len(X_test), 1)), axis=1)
regmodel = LinearRegression()
regmodel.train(X_train_b, Y_train)
print('Average Training Loss:', regmodel.average_loss(X_train_b, Y_train))
print('Average Testing Loss:', regmodel.average_loss(X_test_b, Y_test))
#### 1-Layer NN ######
print('----- 1-Layer NN -----')
nnmodel = OneLayerNN()
nnmodel.train(X_train_b, Y_train, epochs=epochs, print_loss=False)
print('Average Training Loss:', nnmodel.average_loss(X_train_b, Y_train))
print('Average Testing Loss:', nnmodel.average_loss(X_test_b, Y_test))
#### 2-Layer NN ######
print('----- 2-Layer NN -----')
model = TwoLayerNN(5)
# Use X without a bias, since we learn a bias in the 2 layer NN.
model.train(X_train, Y_train, epochs=epochs, print_loss=False)
print('Average Training Loss:', model.average_loss(X_train, Y_train))
print('Average Testing Loss:', model.average_loss(X_test, Y_test))