Q: 来一个从0到100，间隔是5.3的数组：
np.arange(0., 100., 5.3) #array([ 0. , 5.3, 10.6, 15.9, 21.2, 26.5, 31.8, 37.1, 42.4, 47.7, 53. ,58.3, 63.6, 68.9, 74.2, 79.5, 84.8, 90.1, 95.4])Q: 来5个全部都是1的数组：
np.full(5, 1.0) #array([1., 1., 1., 1., 1.])Q: 来5个全部都是0的数组：
np.zeros(5) #array([0., 0., 0., 0., 0.])Q: 数组变matrix怎么搞？
np.arange(0, 10, 1).reshape(2, 5) #array([[0, 1, 2, 3, 4], # [5, 6, 7, 8, 9]])Q: randn咋用？
np.random.randn(5, 2) #array([[ 0.8644362 , -0.74216502], # [ 2.26975462, -1.45436567], # [ 0.04575852, -0.18718385], # [ 1.53277921, 1.46935877], # [ 0.15494743, 0.37816252]])Q: matrix 运算
x = np.arange(0, 6, 1).reshape(2, 3) y = np.arange(1, 7, 1).reshape(3, 2) np.dot(x, y) '''array([[13, 16], [40, 52]]) ''' np.dot(y, x) '''array([[ 6, 9, 12], [12, 19, 26], [18, 29, 40]]) '''Q: gradient descent 运算写法：
theta = np.random.randn(2) x = np.arange(1.0, 10.0, 0.2) x0 = np.full(len(x), 1.0) # combine input_data = np.column_stack([x0, x]) ''' [[1. 0. ] [1. 0.2] [1. 0.4] [1. 0.6] [1. 0.8]] ''' target_data = 2 * x + 5 + np.random.randn(m) alpha = 0.001 sum_m = np.zeros(2) count = 0 while count < 10000: count += 1 for i in range(len(x)): aL_atheta = (np.dot(theta, input_data[i]) - target_data[i]) * input_data[i] #遍历后，把每个row加起来，最后就是一个1x2vector。分别是[theta0, theta1] sum_m += aL_atheta # alpha => learning rate.更新theta的值 theta = theta - alpha*sum_m