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numpy rEshApE

Numpy的主要数据类型是ndarray,即多维数组。它有以下几个属性:ndarray.ndim:数组的维数 ndarray.shape:数组每一维的大小 ndarray.size:数组中全部元素的数量 ndarray.dtype:数组中元素的类型(numpy.int32, numpy.int16, and numpy.float6...

>>> import numpy >>> numpy.reshape([1,2,3,4],(2,-1)) array([[1, 2], [3, 4]]) >>> numpy.reshape([1,2,3,4],(-1,4)) array([[1, 2, 3, 4]]) >>> numpy.reshape([1,2,3,4],(1,-1,4)) array([[[1, 2, 3, 4]]])

这三个数组的主要区别在于维数不同,三个数组分别是一维,二维矩阵和三维矩阵; 比如现在要寻址数组中第二个元素2,分别是: a1[1] a2[0][1] a3[0][0][1]

Numpy可以使用reshape()函数进行矩阵重排列,默认按行排列(C语言风格),通过修改order参数可以改为按列排列(Fortran风格)。参考例子: In [1]: import numpy as npIn [2]: a = np.array([[1,2,3],[4,5,6]])In [3]: print a[[1 2 3] [4 5 6]]...

import numpy as npobjp[:,:2] = np.mgrid[0:7,0:6].T.resharp(-1... 1 np.mgrid[0:7,0:6].T.reshape(-1,2) 是reshape,不是resharp。 ...

直接用实例说明: In [1]: import numpy In [2]: a = array([[1,2,3],[4,5,6]]) In [3]: b = array([[9,8,7],[6,5,4]]) In [4]: numpy.concatenate((a,b)) Out[4]: array([[1, 2, 3], [4, 5, 6], [9, 8, 7], [6, 5, 4]]) 或者这么写 In [1]: a =...

>>> import numpy as np>>> a = np.arange(1,11).reshape(10,1)>>> b = a * 1.1>>> c = a / 1.1>>> aarray([[ 1], [ 2]...

import torch import numpy as np np_data = np.arange(6).reshape((2, 3)) torch_data = torch.from_numpy(np_data) tensor2array = torch_data.numpy...

import torch import numpy as np np_data = np.arange(6).reshape((2, 3)) torch_data = torch.from_numpy(np_data) tensor2array = torch_data....

import numpy as npa=np.arange(9).reshape(3,3)12 a Out[31]: array([[0, 1, 2], [3, 4, 5], [6, 7, 8]])12345 矩阵的某一行 a[1]Out[32]: array([3, 4, 5])12 矩阵的某一列 a[:,1]Out[33]: array([1, 4, 7])12 b=np.eye(3,3) b Out[36]: array...

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