Optimization.py 文件源码

def gradient_descent(initial_point, gradient, cost_function, alpha=0.001, max_it=100):
    current_point = initial_point
    done = False
    costs = [cost_function(current_point)]
    it = 0
    while not done:
        initial_point = current_point
        #print "Cost = "+str(cost_function(current_point))
        delta = gradient(initial_point)
        #print "Delta: "+str(delta)
        #print "Alpha x Delta: "+str(np.dot(delta, alpha))
        current_point = np.subtract(initial_point, np.dot(delta, alpha))
        #print "Current Point:"+str(current_point)
        costs.append(cost_function(current_point))
        if it > max_it:
            done = True
        it += 1
    print it
    return current_point, costs