calc_reg.py

# performs the gradient descent for linear regression
def calc_regression_simple(w1, frames, x_data, y_data):
    learn_rate = 0.001
    ys = []
    for i in range(frames):
        # get the gradient and update the parameter
        w1_gradient = 2*np.mean(x_data*(w1*x_data - y_data))
        w1 = w1 - (w1_gradient*learn_rate)
        # calculate the predictions from this new function
        x = np.linspace(0,30)
        ys.append(w1 * x)
    # returns each linear regsession y values
    # at each step, x values don't change
    return ys