numpy matrix 矩阵对象
https://docs.scipy.org/doc/numpy/reference/generated/numpy.matrix.html#numpy.matrix
1.简介
Matrix类型继承于ndarray类型,因此含有ndarray的所有数据属性和方法。Matrix类型与ndarray类型有六个重要的不同点,当你当Matrix对象当arrays操作时,这些不同点会导致非预期的结果。
1)Matrix对象可以使用一个Matlab风格的字符串来创建,也就是一个以空格分隔列,以分号分隔行的字符串。
2)Matrix对象总是二维的。这包含有深远的影响,比如m.ravel()的返回值是二维的,成员选择的返回值也是二维的,因此序列的行为与array会有本质的不同。
3)Matrix类型的乘法覆盖了array的乘法,使用的是矩阵的乘法运算。当你接收矩阵的返回值的时候,确保你已经理解这些函数的含义。特别地,事实上函数asanyarray(m)会返回一个matrix,如果m是一个matrix。
4)Matrix类型的幂运算也覆盖了之前的幂运算,使用矩阵的幂。根据这个事实,再提醒一下,如果使用一个矩阵的幂作为参数调用asanarray(…)跟上面的相同。
5)矩阵默认的array_priority是10.0,因而ndarray和matrix对象混合的运算总是返回矩阵。
6)矩阵有几个特有的属性使得计算更加容易,这些属性有:
(a) .T -- 返回自身的转置
(b) .H -- 返回自身的共轭转置
(c) .I -- 返回自身的逆矩阵
(d) .A -- 返回自身数据的2维数组的一个视图(没有做任何的拷贝)
Matrix对象也可以使用其它的Matrix对象,字符串,或者其它的可以转换为一个ndarray的参数来构造。另外,在NumPy里,“mat”是“matrix”的一个别名。
1)通过字符串创建矩阵
>>> a=np.mat('1 2 3; 4 5 3')
>>> print (a*a.T).I
[[ 0.2924 -0.1345]
[-0.1345 0.0819]]
2)通过嵌套列表创建矩阵
>>> mp.mat([[1,5,10],[1.0,3,4j]])
matrix([[ 1.+0.j, 5.+0.j, 10.+0.j],
[ 1.+0.j, 3.+0.j, 0.+4.j]])
3)通过数组创建矩阵
>>> np.mat(random.rand(3,3)).T
matrix([[ 0.7699, 0.7922, 0.3294],
[ 0.2792, 0.0101, 0.9219],
[ 0.3398, 0.7571, 0.8197]])
2.属性与描述
name | descripe |
---|---|
A | 返回对应的数组对象 ndarray |
A1 | 返回扁平数组 flattened ndarray. |
H | Returns the (complex) conjugate transpose of self. |
I | Returns the (multiplicative) inverse of invertible self. |
T | Returns the transpose of the matrix. |
base | Base object if memory is from some other object. |
ctypes | An object to simplify the interaction of the array with the ctypes module. |
data | Python buffer object pointing to the start of the array’s data. |
dtype | Data-type of the array’s elements. |
flags | Information about the memory layout of the array. |
flat | A 1-D iterator over the array. |
imag | The imaginary part of the array. |
itemsize | Length of one array element in bytes. |
nbytes | Total bytes consumed by the elements of the array. |
ndim | Number of array dimensions. |
real | The real part of the array. |
shape | Tuple of array dimensions. |
size | Number of elements in the array. |
strides | Tuple of bytes to step in each dimension when traversing an array. |
3.方法与描述
name | describe |
---|---|
all([axis, out]) | Test whether all matrix elements along a given axis evaluate to True. |
any([axis, out]) | Test whether any array element along a given axis evaluates to True. |
argmax([axis, out]) | Indexes of the maximum values along an axis. |
argmin([axis, out]) | Indexes of the minimum values along an axis. |
argpartition(kth[, axis, kind, order]) | Returns the indices that would partition this array. |
argsort([axis, kind, order]) | Returns the indices that would sort this array. |
astype(dtype[, order, casting, subok, copy]) | Copy of the array, cast to a specified type. |
byteswap(inplace) | Swap the bytes of the array elements |
choose(choices[, out, mode]) | Use an index array to construct a new array from a set of choices. |
clip([min, max, out]) | Return an array whose values are limited to [min, max]. |
compress(condition[, axis, out]) | Return selected slices of this array along given axis. |
conj() | Complex-conjugate all elements. |
conjugate() | Return the complex conjugate, element-wise. |
copy([order]) | Return a copy of the array. |
cumprod([axis, dtype, out]) | Return the cumulative product of the elements along the given axis. |
cumsum([axis, dtype, out]) | Return the cumulative sum of the elements along the given axis. |
diagonal([offset, axis1, axis2]) | Return specified diagonals. |
dot(b[, out]) | Dot product of two arrays. |
dump(file) | Dump a pickle of the array to the specified file. |
dumps() | Returns the pickle of the array as a string. |
fill(value) | Fill the array with a scalar value. |
flatten([order]) | Return a flattened copy of the matrix. |
getA() | Return self as an ndarray object. |
getA1() | Return self as a flattened ndarray. |
getH() | Returns the (complex) conjugate transpose of self. |
getI() | Returns the (multiplicative) inverse of invertible self. |
getT() | Returns the transpose of the matrix. |
getfield(dtype[, offset]) | Returns a field of the given array as a certain type. |
item(*args) | Copy an element of an array to a standard Python scalar and return it. |
itemset(*args) | Insert scalar into an array (scalar is cast to array’s dtype, if possible) |
max([axis, out]) | Return the maximum value along an axis. |
mean([axis, dtype, out]) | Returns the average of the matrix elements along the given axis. |
min([axis, out]) | Return the minimum value along an axis. |
newbyteorder([new_order]) | Return the array with the same data viewed with a different byte order. |
nonzero() | Return the indices of the elements that are non-zero. |
partition(kth[, axis, kind, order]) | Rearranges the elements in the array in such a way that value of the element in kth position prod([axis, dtype, out]) Return the product of the array elements over the given axis. |
ptp([axis, out]) | Peak-to-peak (maximum - minimum) value along the given axis. |
put(indices, values[, mode]) | Set a.flat[n] = values[n] for all n in indices. |
ravel([order]) | Return a flattened matrix. |
repeat(repeats[, axis]) | Repeat elements of an array. |
reshape(shape[, order]) | Returns an array containing the same data with a new shape. |
resize(new_shape[, refcheck]) | Change shape and size of array in-place. |
round([decimals, out]) | Return a with each element rounded to the given number of decimals. |
searchsorted(v[, side, sorter]) | Find indices where elements of v should be inserted in a to maintain order. |
setfield(val, dtype[, offset]) | Put a value into a specified place in a field defined by a data-type. |
setflags([write, align, uic]) | Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively. |
sort([axis, kind, order]) | Sort an array, in-place. |
squeeze([axis]) | Return a possibly reshaped matrix. |
std([axis, dtype, out, ddof]) | Return the standard deviation of the array elements along the given axis. |
sum([axis, dtype, out]) | Returns the sum of the matrix elements, along the given axis. |
swapaxes(axis1, axis2) | Return a view of the array with axis1 and axis2 interchanged. |
take(indices[, axis, out, mode]) | Return an array formed from the elements of a at the given indices. |
tobytes([order]) | Construct Python bytes containing the raw data bytes in the array. |
tofile(fid[, sep, format]) | Write array to a file as text or binary (default). |
tolist() | Return the matrix as a (possibly nested) list. |
tostring([order]) | Construct Python bytes containing the raw data bytes in the array. |
trace([offset, axis1, axis2, dtype, out]) | Return the sum along diagonals of the array. |
transpose(*axes) | Returns a view of the array with axes transposed. |
var([axis, dtype, out, ddof]) | Returns the variance of the matrix elements, along the given axis. |
view([dtype, type]) | New view of array with the same data. |
http://blog.csdn.net/shuaishuai3409/article/details/50830196
numpy matrix 矩阵对象相关推荐
- Numpy中矩阵对象
numpy模块中的矩阵对象为numpy.matrix,包括矩阵数据的处理,矩阵的计算,以及基本的统计功能,转置,可逆性等等,包括对复数的处理,均在matrix对象中. class numpy.matr ...
- numpy.mat和numpy.matrix的区别
正文 np.mat和np.matrix的区别 官方文档 np.mat 解释 代码示例 np.matrix 解释 代码示例 np.mat和np.matrix的区别 官方文档 np.mat 解释 如果输入 ...
- numpy中的matrix矩阵处理
numpy模块中的矩阵对象为numpy.matrix,包括矩阵数据的处理,矩阵的计算,以及基本的统计功能,转置,可逆性等等,包括对复数的处理,均在matrix对象中. class numpy.matr ...
- 技术图文:Matlab VS. Numpy 常见矩阵
背景 前段时间在知识星球上立了一个Flag,至少写10篇关于 Python,Matlab 和 C# 对比的总结. 这是第 4 篇,对比 Matlab 与 Numpy 中经常用到的各种矩阵,比如零矩阵. ...
- python科学计算笔记(一)NumPy中ndarray对象、ufunc运算、矩阵运算
标准安装的Python中用列表(list)保存一组值,可以用来当作数组使用,不过由于列表的元素可以是任何对象,因此列表中所保存的是对象的指针.这样为了保存一个简单的[1,2,3],需要有3个指针和三个 ...
- python numpy逆_Python使用numpy计算矩阵特征值、特征向量与逆矩阵
原标题:Python使用numpy计算矩阵特征值.特征向量与逆矩阵 Python扩展库numpy.linalg的eig()函数可以用来计算矩阵的特征值与特征向量,而numpy.linalg.inv() ...
- Python使用numpy计算矩阵特征值、特征向量与逆矩阵
Python扩展库numpy.linalg的eig()函数可以用来计算矩阵的特征值与特征向量,而numpy.linalg.inv()函数用来计算可逆矩阵的逆矩阵. >>> impor ...
- numpy中矩阵的转置_NumPy矩阵transpose()-Python中数组的转置
numpy中矩阵的转置 The transpose of a matrix is obtained by moving the rows data to the column and columns ...
- 利用 Numpy 进行矩阵相关运算
正文共:3266 字 31 图 预计阅读时间: 9 分钟 本文目录: 1. 前言 1.1 基本介绍 1.2 运行环境 2. 函数清单 3. 案例讲解 3.1 Numpy.linalg 3.2 Nump ...
最新文章
- php页面最大执行时间 set_time_limit函数不起作用
- 5月15日直播预告:英飞凌AURIX™培训—图像处理、实车演示等热点问题
- 美国部分Android手机竟将用户隐私数据回传至上海服务器!
- [jQuery基础] jQuery事件相关
- 算法高级(24)-一致性哈希算法在分布式系统中的使用场景
- 忍不住了, 和大家聊聊怎么写简历吧, 关于简历的深度思考
- mft按钮设计_《ZEMAX光学设计超级学习手册》一一1.2 用户界面
- 【RS】BGP14条选路原则(1)
- java前后端分离开发思路
- lav点搜网metro风格分享
- 编程英语:常见代码错误 error 语句学习(12)
- MINIS FORUM U820黑苹果安装教程
- 安卓神秘事件之点击事件不响应
- 以“实景+科幻三维建模渲染”,助力“实景三维中国建设”
- 几种优化算法(求最优解)
- 2021前端面试总结及反思
- ESP8266开发之旅 阿里云物联网平台篇⑤ LED智能灯控制系统(使用HTTPS认证再连接)
- 「代码家」的学习过程和学习经验分享【转】
- zen coding缩写语法
- Latex报错:xxxx.sty文件不存在解决方案
热门文章
- Python使用Apriori算法分析导演请某演员后还会请哪个演员
- 1000道Python题库系列分享16(10道填空题)
- c语言求阶层的某位数,求10000的阶乘(c语言代码实现)
- linux awk 脚本格式,偷偷学习shell脚本之awk编辑器
- mysql hive 安装 配置_hive 安装配置部署与测试
- 数组中其余的排除_[leetcode 剑指offer系列] 面试题04. 二维数组中的查找
- C 设计语言编译生成的是中间语言IL,一、源代码-面向CLR的编译器-托管模块-(元数据IL代码)...
- nas存储服务器 文件夹加密,NAS网络存储服务器储备数据的安全性怎么样?
- r语言在线编译器w3c,R语言运算符知识点总结
- ros_readbagfile:未找到命令的解决方法