linear_regression.py

#!/usr/bin/env python

import numpy as np
from sklearn.linear_model import LinearRegression
import sklearn.metrics

regressor = LinearRegression()

n = 4
feature_dim = 2

x = np.random.rand(n * feature_dim).reshape(n, feature_dim)
y_true = np.random.rand(n)
x[:, 1] = x[:, 0]

print(x)

regressor.fit(x, y_true)
y_pred = regressor.predict(x)
print(regressor)
print(sklearn.metrics.mean_squared_error(y_true, y_pred))