def regression():
# Generate a random regression problem
X, y = make_regression(n_samples=10000, n_features=100,
n_informative=75, n_targets=1, noise=0.05,
random_state=1111, bias=0.5)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,
random_state=1111)
model = LinearRegression(lr=0.01, max_iters=2000, penalty='l2', C=0.03)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print('regression mse', mean_squared_error(y_test, predictions))
def regression():
X, y = make_regression(n_samples=10000,
n_features=100,
n_informative=75,
n_targets=1,
noise=0.05,
random_state=1111,
bias=0.5)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.25,
random_state=1111)
model = LinearRegression(lr=0.01, max_iters=2000, penalty='l2', C=0.03)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print('regression mse', mean_squared_error(y_test, predictions))
def test_linear():
model = LinearRegression(lr=0.01, max_iters=2000, penalty='l2', C=0.03)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
assert mean_squared_error(y_test, predictions) < 0.25
def test_linear():
model = LinearRegression(lr=0.01, max_iters=2000, penalty='l2', C=0.03)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
assert mean_squared_error(y_test, predictions) < 0.25
import sys
sys.path.append("../")
import numpy as np
from mla.linear_models import LinearRegression, LinearRegression_GD
from mla.metrics import mean_squared_error
# construct data
X = np.random.random(1000)[:, None]
Y = np.sqrt(X)
X = np.hstack([np.ones(X.shape), X, X**2, X**3, X**4])
# linear regression with close form
model1 = LinearRegression()
model1.train(X, Y)
print("Closed Form LR MSE:", mean_squared_error(Y, model1.predict(X)))
# linear regression with gradient descent
model2 = LinearRegression_GD()
model2.train(X, Y, 10000, 0.0001)
print("Gradient Descent LR MSE:", mean_squared_error(Y, model2.predict(X)))