optimization.py 文件源码

def test_gd():
    '''
    A gradient descent and linear regression example to solve y = mx + b equation
    using gradient descent, m is slope, b is y-intercept
    by Matt Nedrich
    Source: http://spin.atomicobject.com/2014/06/24/gradient-descent-linear-regression/
    '''
    # read data
    points = genfromtxt("data/spring.csv", delimiter=",")
    # initial y-intercept guess
    b0 = 0
    # initial slope guess
    m0 = 0
    # number of iterations to perform the GD
    n_iter = 1000
    for i in range(n_iter):
        # perform GD iterations
        b0, m0 = step_gradient(b0, m0, points, 0.0001)
    print("GD\ti=%d\tb=%f\tm=%f\te=%f\t(y=%f*x+%f)" %
          (n_iter, b0, m0, compute_error(b0, m0, points), m0, b0))