<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[danerer的专栏]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/danerer</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; danerer]]></copyright><item><title><![CDATA[服务器启动 jupyter lab，并在本地浏览器免密登陆]]></title><link>https://blog.youkuaiyun.com/danerer/article/details/127737506</link><guid>https://blog.youkuaiyun.com/danerer/article/details/127737506</guid><author>danerer</author><pubDate>Mon, 07 Nov 2022 19:32:40 +0800</pubDate><description><![CDATA[【代码】服务器启动 jupyter lab，并在本地浏览器免密登陆。]]></description><category></category></item><item><title><![CDATA[git submodules]]></title><link>https://blog.youkuaiyun.com/danerer/article/details/123262111</link><guid>https://blog.youkuaiyun.com/danerer/article/details/123262111</guid><author>danerer</author><pubDate>Thu, 03 Mar 2022 19:46:29 +0800</pubDate><description><![CDATA[Git submodules使用方法
1.构建submodule实例
Step1:下载git父仓库
cd /root/workspace
git clone https://github.com/daneren/main_project.git

Step2:添加git子仓库
cd /root/workspace/main_project
git submodule add https://github.com/daneren/sub_project.git

Step3:初始化mian_project
g]]></description><category></category></item><item><title><![CDATA[ubuntu16.04升级Python3.5到Python3.7]]></title><link>https://blog.youkuaiyun.com/danerer/article/details/85343909</link><guid>https://blog.youkuaiyun.com/danerer/article/details/85343909</guid><author>danerer</author><pubDate>Sat, 29 Dec 2018 19:03:57 +0800</pubDate><description><![CDATA[ubuntu16.04升级Python3.5到Python3.7
ubuntu16.04自带python有两个版本，一个2版本，使用的是python；另一个是3版本，使用的是python3。
简易安装python后得到的3版本的版本号是python3.5。
可以使用下面的命令查看py版本：
python3 --version

因为版本内容/接口等的不同，需要将其升级到更高的版本操作如下：
1、安...]]></description><category></category></item><item><title><![CDATA[python中os.path模块下的函数总结]]></title><link>https://blog.youkuaiyun.com/danerer/article/details/84309239</link><guid>https://blog.youkuaiyun.com/danerer/article/details/84309239</guid><author>danerer</author><pubDate>Tue, 20 Nov 2018 20:27:45 +0800</pubDate><description><![CDATA[#coding:utf-8
import  os

path = 'C:\Python27\Lib\site-packages\myLibrary'

print os.path.abspath(path) #返回绝对路径
print os.path.basename(path) #返回文件名
'os.path.commonprefix(list) #返回list(多个路径)中，所有path共有的...]]></description><category></category></item><item><title><![CDATA[torch.triu(input, diagonal=0, out=None)]]></title><link>https://blog.youkuaiyun.com/danerer/article/details/82932891</link><guid>https://blog.youkuaiyun.com/danerer/article/details/82932891</guid><author>danerer</author><pubDate>Wed, 03 Oct 2018 17:51:21 +0800</pubDate><description><![CDATA[torch.triu(input, diagonal=0, out=None) → Tensor
返回矩阵上三角部分，其余部分定义为0。
Parameters:

input (Tensor) – the input tensor
diagonal (int, optional) – the diagonal to consider
out (Tensor, optional) – the out...]]></description><category></category></item><item><title><![CDATA[numpy.tile(A, reps)]]></title><link>https://blog.youkuaiyun.com/danerer/article/details/82912647</link><guid>https://blog.youkuaiyun.com/danerer/article/details/82912647</guid><author>danerer</author><pubDate>Sun, 30 Sep 2018 18:29:28 +0800</pubDate><description><![CDATA[numpy.tile(A, reps)
Parameters:	
A : The input array.
reps : The number of repetitions of A along each axis
import torch
import numpy as np

x = torch.from_numpy(np.arange(4).reshape((1, 4)))

print('...]]></description><category></category></item><item><title><![CDATA[numpy.delete(arr, obj, axis=None)]]></title><link>https://blog.youkuaiyun.com/danerer/article/details/82912551</link><guid>https://blog.youkuaiyun.com/danerer/article/details/82912551</guid><author>danerer</author><pubDate>Sun, 30 Sep 2018 18:19:10 +0800</pubDate><description><![CDATA[numpy.delete(arr, obj, axis=None)
arr：输入矩阵
obj：在什么位置处理
axis：这是一个可选参数，axis = None，1，0
axis=None：arr会先按行展开，然后按照obj，删除第obj-1（从0开始）位置的数，返回一个行矩阵。
axis = 0：按行删除
axis = 1：按列删除
import torch
import numpy as np...]]></description><category></category></item><item><title><![CDATA[numpy.ravel(a, order='C')]]></title><link>https://blog.youkuaiyun.com/danerer/article/details/82909530</link><guid>https://blog.youkuaiyun.com/danerer/article/details/82909530</guid><author>danerer</author><pubDate>Sun, 30 Sep 2018 14:55:06 +0800</pubDate><description><![CDATA[numpy.ravel(a, order=‘C’)


a是要进行改变形状的数组


order : {‘C’,’F’, ‘A’, ‘K’}, optional
由ravel()产生的数组中元素的顺序通常是“C风格”，也就是说，最右边的索引“改变最快”，所以[0,0]之后的元素是[0,1] 。如果数组被重新塑造成其他形状，数组又被视为“C-style”。NumPy通常创建按此顺序存储的数组，因此r...]]></description><category></category></item><item><title><![CDATA[torch.clamp(input, min, max, out=None)]]></title><link>https://blog.youkuaiyun.com/danerer/article/details/82908459</link><guid>https://blog.youkuaiyun.com/danerer/article/details/82908459</guid><author>danerer</author><pubDate>Sun, 30 Sep 2018 13:37:48 +0800</pubDate><description><![CDATA[torch.clamp(input, min, max, out=None) → Tensor
将输入input张量每个元素的夹紧到区间 [min,max]，并返回结果到一个新张量。
input={min,if&amp;nbsp;input&amp;nbsp;&amp;amp;lt;=&amp;nbsp;mininput,if&amp;nbsp;min&amp;amp;lt;input&amp;nbsp;&amp;amp;lt;maxmax,if&amp;nbsp;i...]]></description><category></category></item><item><title><![CDATA[torch.tensor.view(*args)]]></title><link>https://blog.youkuaiyun.com/danerer/article/details/82908205</link><guid>https://blog.youkuaiyun.com/danerer/article/details/82908205</guid><author>danerer</author><pubDate>Sun, 30 Sep 2018 13:04:05 +0800</pubDate><description><![CDATA[view(*args) → Tensor
返回一个有相同数据但大小不同的tensor。 返回的tensor必须有与原tensor相同的数据和相同数目的元素，但可以有不同的大小。一个tensor必须是连续的contiguous()才能被查看。
import torch
x = torch.randn(4, 5)

print('tensor原型:',x)

print('tensor维度变换，由（4...]]></description><category></category></item><item><title><![CDATA[torch.sum(input, dim, out=None)]]></title><link>https://blog.youkuaiyun.com/danerer/article/details/82908081</link><guid>https://blog.youkuaiyun.com/danerer/article/details/82908081</guid><author>danerer</author><pubDate>Sun, 30 Sep 2018 12:32:31 +0800</pubDate><description><![CDATA[torch.sum(input, dim, out=None) → Tensor

input (Tensor) – 输入张量
dim (int) – 缩减的维度
out (Tensor, optional) – 结果张量

代码：
import torch
x = torch.randn(4, 5)

print(x)

print(x.sum(0)) #按列求和
print(x.sum(1))...]]></description><category></category></item><item><title><![CDATA[torch.cat(inputs, dimension=0)]]></title><link>https://blog.youkuaiyun.com/danerer/article/details/82907117</link><guid>https://blog.youkuaiyun.com/danerer/article/details/82907117</guid><author>danerer</author><pubDate>Sun, 30 Sep 2018 11:16:05 +0800</pubDate><description><![CDATA[参数:

inputs (sequence of Tensors) – 可以是任意相同Tensor 类型的python 序列
dimension (int, optional) – 沿着此维连接张量序列

代码
import torch
x = torch.randn(2, 3)
print(torch.cat((x, x, x)))    #默认按行连接张量
print(torch.cat((x...]]></description><category></category></item><item><title><![CDATA[Python：super( test, self).__init__()]]></title><link>https://blog.youkuaiyun.com/danerer/article/details/82906962</link><guid>https://blog.youkuaiyun.com/danerer/article/details/82906962</guid><author>danerer</author><pubDate>Sun, 30 Sep 2018 11:06:49 +0800</pubDate><description><![CDATA[python中的super( test, self).init()
首先找到test的父类（比如是类A），然后把类test的对象self转换为类A的对象，然后“被转换”的类A对象调用类A对象自己的__init__函数.

...]]></description><category></category></item><item><title><![CDATA[莫烦PYTHON之Git版本管理学习笔记]]></title><link>https://blog.youkuaiyun.com/danerer/article/details/82757822</link><guid>https://blog.youkuaiyun.com/danerer/article/details/82757822</guid><author>danerer</author><pubDate>Tue, 18 Sep 2018 15:37:34 +0800</pubDate><description><![CDATA[莫烦PYTHON之Git版本管理学习笔记创建/修改版本库
创建/修改版本库
首先创建一个文件夹用于你的git版本库。E:\Test_git

git 中添加用户名 和 用户:
$ git config --global user.name “xxx”
$ git config --global user.email “xxx”
初始化版本库
$ git init
新建 1.py
$ touch 1...]]></description><category></category></item><item><title><![CDATA[numpy中的axis=0与axis=1]]></title><link>https://blog.youkuaiyun.com/danerer/article/details/82726092</link><guid>https://blog.youkuaiyun.com/danerer/article/details/82726092</guid><author>danerer</author><pubDate>Sun, 16 Sep 2018 17:00:30 +0800</pubDate><description><![CDATA[axis=0表述列  
axis=1表述行]]></description><category></category></item><item><title><![CDATA[Andrew Ng机器学习课程笔记（十九）之强化学习]]></title><link>https://blog.youkuaiyun.com/danerer/article/details/80417136</link><guid>https://blog.youkuaiyun.com/danerer/article/details/80417136</guid><author>danerer</author><pubDate>Fri, 25 May 2018 22:16:35 +0800</pubDate><description><![CDATA[

Preface
Markov Decision Processes
MDP定义
MDP例子
MDP过程


Value Function
Value Iteration
Policy Iteration







Preface

Markov Decision Processes（MDP，马尔科夫决策过程） 
Value Function（价值函数） 
Value Iteration（值...]]></description><category></category></item><item><title><![CDATA[Andrew Ng机器学习课程笔记（十八）之无监督学习之独立成份分析]]></title><link>https://blog.youkuaiyun.com/danerer/article/details/80306100</link><guid>https://blog.youkuaiyun.com/danerer/article/details/80306100</guid><author>danerer</author><pubDate>Fri, 18 May 2018 23:24:10 +0800</pubDate><description><![CDATA[

Preface
Cocktail Party Problem


ICA Ambiguities
Densities Function
Linear Transformations
Independent Component Analysis







Preface

Cocktail Party Problem（鸡尾酒宴会问题） 
ICA Ambiguities（ICA不确定性） 
D...]]></description><category></category></item><item><title><![CDATA[Andrew Ng机器学习课程笔记（十七）之无监督学习之主成份分析与降维]]></title><link>https://blog.youkuaiyun.com/danerer/article/details/80304098</link><guid>https://blog.youkuaiyun.com/danerer/article/details/80304098</guid><author>danerer</author><pubDate>Sun, 13 May 2018 23:50:13 +0800</pubDate><description><![CDATA[

Preface
Principal Components Analysis
Problem
Normalize Mean and Variance
For Example
Principal Components Analysis









Preface

Principal Components Analysis（主成份分析）

Principal Components Analy...]]></description><category></category></item><item><title><![CDATA[Andrew Ng机器学习课程笔记（十六）之无监督学习之因子分析模型与EM算法]]></title><link>https://blog.youkuaiyun.com/danerer/article/details/80295989</link><guid>https://blog.youkuaiyun.com/danerer/article/details/80295989</guid><author>danerer</author><pubDate>Sun, 13 May 2018 19:53:38 +0800</pubDate><description><![CDATA[

Preface
Jensen’s Inequality
Expectation-Maximization Algorithm






Preface

Factor Analysis（因子分析模型）

EM Algorithm for Factor Analysis



Jensen’s Inequality



Expectation-Maximization Algorithm...]]></description><category></category></item><item><title><![CDATA[Andrew Ng机器学习课程笔记（十五）之无监督学习之混合贝叶斯模型与EM算法]]></title><link>https://blog.youkuaiyun.com/danerer/article/details/80282798</link><guid>https://blog.youkuaiyun.com/danerer/article/details/80282798</guid><author>danerer</author><pubDate>Fri, 11 May 2018 23:14:07 +0800</pubDate><description><![CDATA[Preface
Jensen’s Inequality
Expectation-Maximization Algorithm







Preface

Jensen’s Inequality（Jensen不等式） 
Expectation-Maximization Algorithm（EM算法）



Jensen’s Inequality

Expectation-Maximizati...]]></description><category></category></item></channel></rss>