from NumPy import *

函数形式: tile(A,rep)

功能:重复A的各个维度

参数类型:

- A: Array类的都可以

- rep:A沿着各个维度重复的次数

这个英文单词的本意是:贴瓷砖,还挺形象的。

举例:

tile([17,29],2),如果rep参数是一个整数,则表示重复A中的元素rep次,即行数(即维度)只有1维,所以2的意思是在“列”这个维度上重复2次

输出[17,29,17,29]

tile([29,17],(3,5))

此时的(3,5)和[3,5]是相同的效果。

结果是3组,每组重复5次,也可以理解为二维表,3行,5列。先分3组(重复3次),每组重复5次。

array([[29, 17, 29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17, 29, 17]])

tile([29,17],[3,5,7])

结果是3组,每组一个二维表,每个二维表5行,7列,可以理解为三维表

array([[[29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17]],

[[29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17]],

[[29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17, 29, 17]]])

tile([29,17],[3,5,7,4])

结果是4组,怎样理解?我也不知道,这已经超过了人类空间的认知。

依次分组,先分3组重复,然后分5组重复,然后分7组,最后重复4次。

如果5维会怎样?也是继续按组重复下去。先分5组,用中括号分隔。

array([[[[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17]],

[[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17]],

[[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17]],

[[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17]],

[[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17]]],

[[[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17]],

[[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17]],

[[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17]],

[[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17]],

[[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17]]],

[[[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17]],

[[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17]],

[[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17]],

[[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17]],

[[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17],

[29, 17, 29, 17, 29, 17, 29, 17]]]])

Python模块NumPy中的tile(A,rep) 函数的更多相关文章

  1. Python:numpy中的tile函数

    在学习机器学习实教程时,实现KNN算法的代码中用到了numpy的tile函数,因此对该函数进行了一番学习: tile函数位于python模块 numpy.lib.shape_base中,他的功能是重复 ...

  2. Mathab和Python的numpy中的数组维度

    Matlab和Python的numpy在维度索引方面的不同点: 1.索引的起始点不同:Matlab起始位置的索引为1,Python为0. 2.索引的括号不同:Matlab中元素可以通过小括号表示索引, ...

  3. python和numpy中sum()函数的异同

    转载:https://blog.csdn.net/amuchena/article/details/89060798和https://www.runoob.com/python/python-func ...

  4. numpy中的tile函数

    tile()函数可以很方便的生成多维数组.它有两个参数,第一个数是原始数组;第二个表示如何来生成,第一个数字表示生成几行,第二个表示每行有多少个原始数组(如果只写一个数字,那么就默认是一行). fro ...

  5. python模块collections中namedtuple()的理解

    Python中存储系列数据,比较常见的数据类型有list,除此之外,还有tuple数据类型.相比与list,tuple中的元素不可修改,在映射中可以当键使用.tuple元组的item只能通过index ...

  6. python模块win32com中的early-bind与lazy-bind(以Autocad为例)

    1.什么是Lazy-bind模式,Early-bind模式? win32com中,Lazy-bind 模式指的是程序事先不知道对象的任何方法和属性,当对象属性,方法被调用时,程序才向对象发出一个询问( ...

  7. Python模块包中__init__.py文件的作用

    转载自:http://hi.baidu.com/tjuer/item/ba37ac4ce7482a0f6dc2f08b 模块包: 包通常总是一个目录,目录下为首的一个文件便是 __init__.py. ...

  8. python类库numpy中常见函数的用法

    1. numpy.reshape  重塑 reshape是一种函数,函数可以重新调整矩阵的行数.列数.维数. B = reshape(A,m,n) 返回一个m*n的矩阵B, B中元素是按列从A中得到的 ...

  9. 【python】Numpy中stack(),hstack(),vstack()函数详解

    转自 https://blog.csdn.net/csdn15698845876/article/details/73380803 这三个函数有些相似性,都是堆叠数组,里面最难理解的应该就是stack ...

随机推荐

  1. WCF+Nhibernate循环引用导致序列化的问题

    WCF+Nhibernate 在查询中只要涉及到表关联,都会存在一个循环引用而导致客户端不能反序列化的问题. 解决的办法: 1.多对一关联中,设置lazy="false". 2.一 ...

  2. GDAL------加载Shapefile文件

    代码: using System; using System.Collections.Generic; using System.Linq; using System.Text; using Syst ...

  3. SPREAD for Windows Forms 下箭头追加行

    ''' <summary> ''' 下矢印の動作クラス ''' </summary> ''' <remarks></remarks> Public Cl ...

  4. Android内存泄漏检測与MAT使用

    公司相关项目须要进行内存优化.所以整理了一些分析内存泄漏的知识以及工作分析过程. 本文中不会刻意的编写一个内存泄漏的程序,然后利用工具去分析它.而是通过介绍相关概念,来分析怎样寻找内存泄漏.并附上自己 ...

  5. 线程同步 –AutoResetEvent和ManualResetEvent

    上一篇介绍了通过lock关键字和Monitor类型进行线程同步,本篇中就介绍一下通过同步句柄进行线程同步. 在Windows系统中,可以使用内核对象进行线程同步,内核对象由系统创建并维护.内核对象为内 ...

  6. cocos2dx 3.0 scrollview 在android下面背景變綠色了

    在windows上面跑的是OK的,  在android下面跑的時候就變成這樣子了:

  7. IE8兼容性调试及IE 8 css hack

    做网站开发,一提到IE,就会让人头大,有一肚子的牢骚要发:微软为什么不跟着国际标准走呢,总是独树一帜,搞出那么多问题来.IE的firebug调试工具也不太好用,尤其是低版本的IE,更是让人头疼. 最近 ...

  8. PHP代码执行函数总结

    PHP中可以执行代码的函数,常用于编写一句话木马,可能导致代码执行漏洞,这里对代码执行函数做一些归纳. 常见代码执行函数,如 eval().assert().preg_replace().create ...

  9. centos系统-java -jdk 环境配置

    方法一:手动解压JDK的压缩包,然后设置环境变量 1.在/usr/目录下创建java目录 [root@localhost ~]# mkdir/usr/java[root@localhost ~]# c ...

  10. 对C#中几个循环语句的使用,请教

    今天是在云和数据学院学习的第四天,由于各种原因···今天自己预习的循环语句的用法以及写了几个程序,也遇到各种的问题了···纠结.由于还是在学习的很初初初级,所以好多简单的方法还是不知道怎么写出来,只得 ...