Implementation of Ordinary Least Squares in Python using NumPy

ordinary-least-squares.py

def ols(X, y, fit_intercept=True):
    """Ordinary Least Squares (OLS) Regression model with intercept term.
    Fits an OLS regression model using the closed-form OLS estimator equation.
    Intercept term is included via design matrix augmentation.

    Params:
        X - NumPy matrix, size (N, p), of numerical predictors
        y - NumPy array, length N, of numerical response
        fit_intercept - Boolean indicating whether to include an intercept term

    Returns:
        NumPy array, length p + 1, of fitted model coefficients
    """
    m, n = np.shape(X)
    if fit_intercept:
        X = np.hstack((np.ones((m, 1)), X))
    return np.linalg.solve(np.dot(X.T, X), np.dot(X.T, y))