python 3.7
numpy库
元素运算:numpy中数组中对应元素的运算操作如+，-，*，/。举个例子：import numpy as npx = np.array([[1,2],[3,4]], dtype=np.float64)y = np.array([[5,6],[7,8]], dtype=np.float64)# Elementwise sum; both produce the array# [[ 6.0 8.0]# [10.0 12.0]]print(x + y)print(np.add(x, y))# Elementwise difference; both produce the array# [[-4.0 -4.0]# [-4.0 -4.0]]print(x - y)print(np.subtract(x, y))# Elementwise product; both produce the array# [[ 5.0 12.0]# [21.0 32.0]]print(x * y)print(np.multiply(x, y))# Elementwise division; both produce the array# [[ 0.2 0.33333333]# [ 0.42857143 0.5 ]]print(x / y)print(np.divide(x, y))# Elementwise square root; produces the array# [[ 1. 1.41421356]# [ 1.73205081 2. ]]print(np.sqrt(x))
结果显示：[[ 6. 8.] [10. 12.]][[ 6. 8.] [10. 12.]][[-4. -4.] [-4. -4.]][[-4. -4.] [-4. -4.]][[ 5. 12.] [21. 32.]][[ 5. 12.] [21. 32.]][[0.2 0.33333333] [0.42857143 0.5 ]][[0.2 0.33333333] [0.42857143 0.5 ]][[1. 1.41421356] [1.73205081 2. ]]
numpy使用dot函数实现，向量间的内积，矩阵乘法等运算。举个例子：import numpy as npx = np.array([[1,2],[3,4]])y = np.array([[5,6],[7,8]])v = np.array([9,10])w = np.array([11, 12])# Inner product of vectors; both produce 219print(v.dot(w))print(np.dot(v, w))# Matrix / vector product; both produce the rank 1 array [29 67]print(x.dot(v))print(np.dot(x, v))# Matrix / matrix product; both produce the rank 2 array# [[19 22]# [43 50]]print(x.dot(y))print(np.dot(x, y))
运行结果：219219[29 67][29 67][[19 22] [43 50]][[19 22] [43 50]]
numpy提供了许多函数对矩阵进行计算。其中最有用的是sum函数。举个例子：import numpy as npx = np.array([[1,2],[3,4]])print(np.sum(x)) # Compute sum of all elements; prints '10'print(np.sum(x, axis=0)) # Compute sum of each column; prints '[4 6]'print(np.sum(x, axis=1)) # Compute sum of each row; prints '[3 7]'
运行结果：10[4 6][3 7]