一、Meshgrid函数

import numpy as npimport matplotlib.pyplot as pltx = np.linspace(0, 1, 5)y = np.linspace(0, 1, 3)print("x = ", x)print("-" * 50)print("y = ", y)print("-" * 100)X, Y = np.meshgrid(x, y)print("\nX.shape = {0} \nX = \n{1}".format(X.shape, X))print("-" * 50)print("Y.shape = {0} \nY = \n{1}".format(Y.shape, Y))print("-" * 100)

打印结果：

x = [0. 0.25 0.5 0.75 1. ]--------------------------------------------------y = [0. 0.5 1. ]----------------------------------------------------------------------------------------------------X.shape = (3, 5) X = [[0. 0.25 0.5 0.75 1. ][0. 0.25 0.5 0.75 1. ][0. 0.25 0.5 0.75 1. ]]--------------------------------------------------Y.shape = (3, 5) Y = [[0. 0. 0. 0. 0. ][0.5 0.5 0.5 0.5 0.5][1. 1. 1. 1. 1. ]]----------------------------------------------------------------------------------------------------Process finished with exit code 0

meshgrid函数的运行过程，可以通过下面的示意图来加深理解：

再者，也可以通过在matplotlib中进行可视化，来查看函数运行后得到的网格化数据的结果

import numpy as npimport matplotlib.pyplot as pltx = np.linspace(0, 1, 5)y = np.linspace(0, 1, 3)print("x = ", x)print("-" * 50)print("y = ", y)print("-" * 100)X, Y = np.meshgrid(x, y)print("\nX.shape = {0} \nX = \n{1}".format(X.shape, X))print("-" * 50)print("Y.shape = {0} \nY = \n{1}".format(Y.shape, Y))print("-" * 100)plt.plot(X, Y, marker='.', color='blue', linestyle='none')plt.show()

当然，我们也可以获得网格平面上坐标点的数据，如下：

import numpy as npimport matplotlib.pyplot as pltx = np.linspace(0, 1, 5)y = np.linspace(0, 1, 3)print("x = ", x)print("-" * 50)print("y = ", y)print("-" * 100)X, Y = np.meshgrid(x, y)print("\nX.shape = {0} \nX = \n{1}".format(X.shape, X))print("-" * 50)print("Y.shape = {0} \nY = \n{1}".format(Y.shape, Y))print("-" * 100)z = [i for i in zip(X.flat, Y.flat)]print("z = {0}".format(z))

打印结果：

x = [0. 0.25 0.5 0.75 1. ]--------------------------------------------------y = [0. 0.5 1. ]----------------------------------------------------------------------------------------------------X.shape = (3, 5) X = [[0. 0.25 0.5 0.75 1. ][0. 0.25 0.5 0.75 1. ][0. 0.25 0.5 0.75 1. ]]--------------------------------------------------Y.shape = (3, 5) Y = [[0. 0. 0. 0. 0. ][0.5 0.5 0.5 0.5 0.5][1. 1. 1. 1. 1. ]]----------------------------------------------------------------------------------------------------z = [(0.0, 0.0), (0.25, 0.0), (0.5, 0.0), (0.75, 0.0), (1.0, 0.0), (0.0, 0.5), (0.25, 0.5), (0.5, 0.5), (0.75, 0.5), (1.0, 0.5), (0.0, 1.0), (0.25, 1.0), (0.5, 1.0), (0.75, 1.0), (1.0, 1.0)]Process finished with exit code 0

二、Meshgrid函数的一些应用场景

Meshgrid函数常用的场景有等高线绘制及机器学习中SVC超平面的绘制（二维场景下）。

1、等高线

import tensorflow as tfimport matplotlib.pyplot as pltdef func(x):""":param x: [b, 2]:return:"""z = tf.math.sin(x[..., 0]) + tf.math.sin(x[..., 1])return zx = tf.linspace(0., 2 * 3.14, 500)y = tf.linspace(0., 2 * 3.14, 500)# [50, 50]point_x, point_y = tf.meshgrid(x, y)# [50, 50, 2]points = tf.stack([point_x, point_y], axis=2)# points = tf.reshape(points, [-1, 2])print('points:', points.shape)z = func(points)print('z:', z.shape)plt.figure('plot 2d func value')plt.imshow(z, origin='lower', interpolation='none')plt.colorbar()plt.figure('plot 2d func contour')plt.contour(point_x, point_y, z)plt.colorbar()plt.show()

打印结果：

points: (500, 500, 2)z: (500, 500)

2、SVM中超平面的绘制

参考资料：

MatLab：meshgrid

Numpy中Meshgrid函数介绍及2种应用场景