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...
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) Trigonometric sine, element-wise. 2 )numpy.cos numpy.cos(x, *args,...
v= np.array([1, 0, 1]) vv= np.tile(v, (4, 1))#Stack 4 copies of v on top of each otherprint(vv)#Prints "[[1 0 1]#[1 0 1]#[1 0 1]#[1 0 1]]"y = x + vv#Add x and vv elementwiseprint(y)#Prints "[[ 2 2 4#[ 5 5 7]#[ 8 8 10]#[11 11 13]]"...
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...
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. deg2rad(x[, out]) 角度求弧度 rad2deg(x...
numpy.square():获取矩阵元素的平方 np.exp(x):表示自然数e(2.718281828459045)的多少次方,括号里给2表示e的2次方,括号里的数可以是负数或分数。 2.三角函数 numpy.sin(x, *args, **kwargs) Trigonometric sine, element-wise. numpy.cos(x, *args, **kwargs) Cosine element-wise. ...
如果直接在Python中做,需要进行大量循环操作,写出的代码不容易读,而且执行起来还贼慢。但在Numpy中,可以简洁地将需要完成的计算以数组的形式表现出来,error = (1/n) * np.sum(np.square(predictions - labels)),表面上是逐元素计算(element-wise),实际上背后的循环操作已经交给效率更高的C和Fortran执行了。
Numpy:提供了一个在Python中做科学计算的基础库,重在数值计算,主要用于多维数组(矩阵)处理的库。用来存储和处理大型矩阵,比Python自身的嵌套列表结构要高效的多。本身是由C语言开发,是个很基础的扩展,Python其余的科学计算扩展大部分都是以此为基础。 高性能科学计算和数据分析的基础包 ...
(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; ...
>>> A*B # elementwise product array([[2, 0], [0, 4]]) >>> dot(A,B) # matrix product array([[5, 4], [3, 4]]) 有些操作符像+=和*=被用来更改已存在数组而不创建一个新的数组。 >>> a = ones((2,3), dtype=int) >>> b = random.random((2,3)) >>> a *= 3 ...