Return the non-negative square-root of an array, element-wise. 8)numpy.square numpy.square(x, *args, **kwargs) Return the element-wise square of the input. 3.三角函数 1 )numpy.sin numpy.sin(x, *args, **kwargs) Tr
hypot(x1, x2[, out]) 求直角三角形斜边 arctan2(x1, x2[, out]) Element-wise arc tangent of x1/x2 choosing the quadrant correctly. degrees(x[, out]) 弧度求角度 radians(x[, out]) 角度求弧度 unwrap(p[, discont, axis]) Unwrap by changing deltas between values to 2*pi complement....
square() Return Value The square() function returns the array containing the element-wise squares of the input array. Example 1: Use of dtype Argument in square() import numpy as np # create an array array1 = np.array([1, 2, 3, 4]) # compute the square of array1 with different...
在数组上进行算术操作都是元素级别的(elementwise)。 注:在线性代数中,乘号*代表的矩阵乘法,但在numpy中代表元素级别乘法,矩阵乘法用np.dot(a,b)或a.dot(b) 若a+=b,a是int,b是float,则会报错,因为b比a更精错,但b+=a则不会报错 a = np.array([1,2,3,4]) b = np.arange(4) a-b, a+b,...
式中, ∘ 表示按元素积 (element-wise product), 又称为 Hadamard 积; 1→k 表示维的全1向量 (all-ones vector), 余者类推. 上式中 1→k 的作用是计算 X∘X 每行元素的和, 返回一个列向量; 1→nT 的作用类似于 NumPy 中的广播机制, 在这里是将一个列向量扩展为一个矩阵, 矩阵的每一列都是...
平方:numpy.square(x, *args, **kwargs) Return the element-wise square of the input. 示例 矩阵与数字 x = np.array([1, 2, 3, 4, 5, 6, 7, 8]) y = x + 1 print(y) print(np.add(x, 1)) # [2 3 4 5 6 7 8 9] ...
importnumpyasnp# 创建一个二维数组arr=np.array([[1,2],[3,4]])# 矩阵乘法result=np.dot(arr,arr)print("矩阵乘法:")print(result)# 转置transpose_arr=arr.Tprint("转置:")print(transpose_arr)# 元素级操作elementwise_square=np.square(arr)print("元素级操作(平方):")print(elementwise_square) ...
[ 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...
numpy.square(x, args, kwargs) Return the element-wise square of the input. 2.2.2三角函数 numpy.sin numpy.sin(x, args, kwargs) Trigonometric sine, element-wise. numpy.cos numpy.cos(x, args, kwargs) Cosine element-wise. numpy.tan numpy.tan(x, args, kwargs) Compute tangent element-wi...
numpy.square(x, *args, **kwargs) Return the element-wise square of the input. x = np.arange(1, 5) print(x) # [1 2 3 4] y = np.sqrt(x) print(y) # [1. 1.41421356 1.73205081 2. ] print(np.power(x, 0.5)) # [1. 1.41421356 1.73205081 2. ] ...