<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[StephenFengz的博客]]></title><description><![CDATA[一个小程序猿的学习历程]]></description><link>https://blog.youkuaiyun.com/StephenFengz</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; StephenFengz]]></copyright><item><title><![CDATA[Oracle使用小技巧]]></title><link>https://blog.youkuaiyun.com/StephenFengz/article/details/62049044</link><guid>https://blog.youkuaiyun.com/StephenFengz/article/details/62049044</guid><author>StephenFengz</author><pubDate>Tue, 14 Mar 2017 19:06:43 +0800</pubDate><description><![CDATA[一、如果安装好Oracle后忘了设置sys或者system密码，可在命令行修改：
cd..进入C盘符下：
C:> sqlplus
请输入用户名：sys

输入口令：sys as sysdba //注意：在口令这里输入的密码后面必须要跟上 as sysdba 才可以。     (连接到数据库)

SQL> alter user system account unlock; 对syste]]></description><category></category></item><item><title><![CDATA[使用地图的API获取目标的经纬度以及获取两地间的距离]]></title><link>https://blog.youkuaiyun.com/StephenFengz/article/details/54426024</link><guid>https://blog.youkuaiyun.com/StephenFengz/article/details/54426024</guid><author>StephenFengz</author><pubDate>Sat, 14 Jan 2017 12:17:04 +0800</pubDate><description><![CDATA[一般百度地图、高德地图等都会提供API接口给我们，我们要使用其地图服务需要注册账号申请个密钥。
举例：
百度地图开放平台的网址：百度地图开放平台
注册后去创建个应用申请密钥：
图片中红框中的AK就是百度地图API的密钥(该AK就是我下面代码中调用百度地图API时使用的ak)
可以在这个网站找到对应功能API的用法：


最后，附上我跑的有关地图python代码：
# -- cod]]></description><category></category></item><item><title><![CDATA[pycharm中配置SVN]]></title><link>https://blog.youkuaiyun.com/StephenFengz/article/details/53454785</link><guid>https://blog.youkuaiyun.com/StephenFengz/article/details/53454785</guid><author>StephenFengz</author><pubDate>Sun, 04 Dec 2016 16:33:54 +0800</pubDate><description><![CDATA[最近因为项目去求你需要把代码从台式移植到笔记本，之前都是直接用u盘拷贝比较麻烦。所以折腾了下载pycham中使用SVN


1、首先获得个自己SVN代码托管地址，推荐Taocode:http://code.taobao.org/ 这是阿里云旗下的一个云代码托管，主要目的是注册后获得一个自己的SVN地址。
使用教程： 使用Myeclipse + SVN + TaoCode 免费实现项目版本控]]></description><category></category></item><item><title><![CDATA[Python pydot模块安装]]></title><link>https://blog.youkuaiyun.com/StephenFengz/article/details/52997387</link><guid>https://blog.youkuaiyun.com/StephenFengz/article/details/52997387</guid><author>StephenFengz</author><pubDate>Tue, 01 Nov 2016 17:08:46 +0800</pubDate><description><![CDATA[因为学习机器学习用到决策树，用pydot可以直接可视化出来决策树的模型。
遂直接pip安装了pydot模块，然而运行出错。
后来在网上搜索说使用pydot要预先安装pyparsing和GraphViz。


安装顺序：
1、下载安装GraphViz     链接: http://pan.baidu.com/s/1pKrvHUz 密码: hvmn

下载完后双击运行安装

在环境]]></description><category></category></item><item><title><![CDATA[机器学习算法之Logistic Regression算法实现]]></title><link>https://blog.youkuaiyun.com/StephenFengz/article/details/52752624</link><guid>https://blog.youkuaiyun.com/StephenFengz/article/details/52752624</guid><author>StephenFengz</author><pubDate>Fri, 07 Oct 2016 23:10:15 +0800</pubDate><description><![CDATA[该代码基本上参考了zouxy09的专栏中的逻辑回归代码，他对于ML一些经典的算法都写得很不错，读后受益匪浅。


直接上代码：
#-*- coding:utf8 -*-

'''
逻辑回归1.0
Author:Stephen
2016.10.6
'''

from numpy import *
import matplotlib.pyplot as plt
import time

def]]></description><category></category></item><item><title><![CDATA[mablab/Octave中的小知识]]></title><link>https://blog.youkuaiyun.com/StephenFengz/article/details/52446611</link><guid>https://blog.youkuaiyun.com/StephenFengz/article/details/52446611</guid><author>StephenFengz</author><pubDate>Tue, 06 Sep 2016 08:11:09 +0800</pubDate><description><![CDATA[最近刚学完Coursera中Ng开的ML课程，用到Octave，顺便记一下其编程的小技巧。


1、求矩阵A中符合某条件的元素个数：

>> sum(a(:) == k);



2、求矩阵A中符合某条件的所有元素索引：

>> find(a == k);



3、终止程序执行：

>> Ctrl + C



4、使新图像画在旧图像上面而不是覆盖旧图像]]></description><category></category></item><item><title><![CDATA[Coursera机器学习 week9 assignment]]></title><link>https://blog.youkuaiyun.com/StephenFengz/article/details/52433190</link><guid>https://blog.youkuaiyun.com/StephenFengz/article/details/52433190</guid><author>StephenFengz</author><pubDate>Sun, 04 Sep 2016 16:45:10 +0800</pubDate><description><![CDATA[代码仅供思路参考————>


estimateGaussian.m:
mu = ( sum(X, 1) )'./m;    %(n*1)

temp = bsxfun(@minus, X, mu');  %(m*n)

sigma2 = ( sum( (temp.^2), 1) )'./m;  %(n*1)


selectThreshold.m:

predictions =]]></description><category></category></item><item><title><![CDATA[Coursera机器学习 week4 assignment]]></title><link>https://blog.youkuaiyun.com/StephenFengz/article/details/52416897</link><guid>https://blog.youkuaiyun.com/StephenFengz/article/details/52416897</guid><author>StephenFengz</author><pubDate>Fri, 02 Sep 2016 21:01:38 +0800</pubDate><description><![CDATA[lrCostFunction.m:
b = sigmoid(X*theta);

J = -(1/m)*( (log(b'))*y + log(1-b')*(1-y) ) + (lambda/(2*m))*(sum(theta.^2)-theta(1)^2);


k = X'*(b-y);
grad(1) = (1/m)*k(1);

long = length(k);
k2 = (1/m) ]]></description><category></category></item><item><title><![CDATA[Coursera机器学习 week3 assignment]]></title><link>https://blog.youkuaiyun.com/StephenFengz/article/details/52416852</link><guid>https://blog.youkuaiyun.com/StephenFengz/article/details/52416852</guid><author>StephenFengz</author><pubDate>Fri, 02 Sep 2016 20:58:48 +0800</pubDate><description><![CDATA[plotData.m:
pos = find(y==1); neg = find(y==0);

plot(X(pos, 1), X(pos, 2), 'k+', 'LineWidth', 2, 'Markersize', 8)
plot(X(neg, 1), X(neg, 2), 'ko', 'MarkerFaceColor', 'y', 'Markersize', 8)




s]]></description><category></category></item><item><title><![CDATA[Coursera机器学习 week2 assignment]]></title><link>https://blog.youkuaiyun.com/StephenFengz/article/details/52416804</link><guid>https://blog.youkuaiyun.com/StephenFengz/article/details/52416804</guid><author>StephenFengz</author><pubDate>Fri, 02 Sep 2016 20:50:41 +0800</pubDate><description><![CDATA[warmUpExercise.m:
A = eye(5);


plotData.m:

plot(x,y,'rx','markersize',10);
xlabel('Population of City in 10,1000s');
ylabel('Profit in $10,1000s');




computeCost.m:
J = (1/(2*m))*sum((]]></description><category></category></item><item><title><![CDATA[Coursera机器学习 week8 assignment]]></title><link>https://blog.youkuaiyun.com/StephenFengz/article/details/52416739</link><guid>https://blog.youkuaiyun.com/StephenFengz/article/details/52416739</guid><author>StephenFengz</author><pubDate>Fri, 02 Sep 2016 20:42:59 +0800</pubDate><description><![CDATA[findClosestCentroids.m:

K_temp = zeros(K, 1);

for i = 1:size(idx, 1)  %遍历所有样本
  for j = 1:K   %遍历所有K值
    K_temp(j) = sum(( X(i,:) - centroids(j,:) ).^2);

  end
  mini = min(K_temp); %找出最小值
  %id]]></description><category></category></item><item><title><![CDATA[Coursera机器学习 week7 assignment]]></title><link>https://blog.youkuaiyun.com/StephenFengz/article/details/52382384</link><guid>https://blog.youkuaiyun.com/StephenFengz/article/details/52382384</guid><author>StephenFengz</author><pubDate>Wed, 31 Aug 2016 10:25:32 +0800</pubDate><description><![CDATA[仅供思路参考，代码最好还是自己独立完成。


gaussianKernel.m:
sim = exp(- sum((x1-x2).^2) / (2*(sigma^2)) );



dataset3Params.m:

lambda_all = [0.01 0.03 0.1 0.3 1 3 10 30]';
len_all = size(lambda_all, 1);
erro]]></description><category></category></item><item><title><![CDATA[Coursera机器学习 week6 assignment]]></title><link>https://blog.youkuaiyun.com/StephenFengz/article/details/52292495</link><guid>https://blog.youkuaiyun.com/StephenFengz/article/details/52292495</guid><author>StephenFengz</author><pubDate>Tue, 23 Aug 2016 16:56:57 +0800</pubDate><description><![CDATA[linearRegCostFunction.m:
function [J, grad] = linearRegCostFunction(X, y, theta, lambda)
%LINEARREGCOSTFUNCTION Compute cost and gradient for regularized linear 
%regression with multiple variables
%]]></description><category></category></item><item><title><![CDATA[Coursera机器学习  week5 神经网络的学习 assignment]]></title><link>https://blog.youkuaiyun.com/StephenFengz/article/details/52253142</link><guid>https://blog.youkuaiyun.com/StephenFengz/article/details/52253142</guid><author>StephenFengz</author><pubDate>Fri, 19 Aug 2016 17:49:03 +0800</pubDate><description><![CDATA[sigmoidGradient.m:

function g = sigmoidGradient(z)
%SIGMOIDGRADIENT returns the gradient of the sigmoid function
%evaluated at z
%   g = SIGMOIDGRADIENT(z) computes the gradient of the sigmoid f]]></description><category></category></item><item><title><![CDATA[糗事百科的网络爬虫]]></title><link>https://blog.youkuaiyun.com/StephenFengz/article/details/52080269</link><guid>https://blog.youkuaiyun.com/StephenFengz/article/details/52080269</guid><author>StephenFengz</author><pubDate>Sun, 31 Jul 2016 19:35:55 +0800</pubDate><description><![CDATA[最近看完《python基础教程》后，想学下数据分析和爬虫。找了一些简单的爬虫练练手。
参考了Python爬虫实战一之爬取糗事百科段子，因为糗事百科代码改版了，我修改了Re后基本实现，然后用bs4改为第二版。


工具：python2.7.11  IDE:pyhcarn
1、用Re匹配识别：
#-*- coding:utf8 -*-

"""
爬取糗事百科  ——>2.0版本 ——>使用]]></description><category></category></item><item><title><![CDATA[《python基础教程》项目3]]></title><link>https://blog.youkuaiyun.com/StephenFengz/article/details/52066543</link><guid>https://blog.youkuaiyun.com/StephenFengz/article/details/52066543</guid><author>StephenFengz</author><pubDate>Fri, 29 Jul 2016 16:52:30 +0800</pubDate><description><![CDATA[项目3 ：万能的XML


情况是：自动构建网站，根据一份XML文件，生成对应目录结构的网站


首先是：website.xml

  
  Welcome to my Home page

  Hi, there. My name is Mr.gumby,and this is my home page,here are some of my int:

  
    Shouti]]></description><category></category></item><item><title><![CDATA[python的numpy模块安装问题]]></title><link>https://blog.youkuaiyun.com/StephenFengz/article/details/52057110</link><guid>https://blog.youkuaiyun.com/StephenFengz/article/details/52057110</guid><author>StephenFengz</author><pubDate>Thu, 28 Jul 2016 17:14:37 +0800</pubDate><description><![CDATA[Numpy是Python的一个科学计算的库，提供了矩阵运算的功能，其一般与Scipy、matplotlib一起使用。


本人的安装过程：
1、首先选择了pip isntall numpy  ，可惜这种方法很依赖网速，且numpy文件挺大下载断断续续，基本下载不了报错，无奈放弃。
2、去https://sourceforge.net/projects/numpy/files/NumPy/]]></description><category></category></item><item><title><![CDATA[python的Tidy工具安装]]></title><link>https://blog.youkuaiyun.com/StephenFengz/article/details/52004314</link><guid>https://blog.youkuaiyun.com/StephenFengz/article/details/52004314</guid><author>StephenFengz</author><pubDate>Sat, 23 Jul 2016 14:51:59 +0800</pubDate><description><![CDATA[Tidy是用来修复不规范/随意的HTML文档的工具，可以修复HTML代码中普通的错误，确保文件的格式是正确的


安装步骤：
1、先去https://pypi.python.org/pypi/pytidylib    下载pytidylib
（或者直接在系统命令行下输入：按enter等待它自行下载安装）
2、再去http://www.paehl.com/open_source/?HTM]]></description><category></category></item><item><title><![CDATA[Python的chardet模块安装]]></title><link>https://blog.youkuaiyun.com/StephenFengz/article/details/51993642</link><guid>https://blog.youkuaiyun.com/StephenFengz/article/details/51993642</guid><author>StephenFengz</author><pubDate>Fri, 22 Jul 2016 12:19:29 +0800</pubDate><description><![CDATA[chardet模块可以检测字符编码，在检测txt/字符串或者其他文件时的编码时很有用


以下是安装方法：
在python的安装文件夹的scripts文件夹里面有个pip.exe文件，安装时需要用到这个
命令行下：


等待一下，系统会自动加载进度：


至此，运行下
import chardet
如果没有问题就说明模块导入成功]]></description><category></category></item><item><title><![CDATA[Python和wxpython小问题]]></title><link>https://blog.youkuaiyun.com/StephenFengz/article/details/51984697</link><guid>https://blog.youkuaiyun.com/StephenFengz/article/details/51984697</guid><author>StephenFengz</author><pubDate>Thu, 21 Jul 2016 17:08:59 +0800</pubDate><description><![CDATA[python最近开始学，然后看书看到图形用户界面一章时下载安装了wxpython来玩。


输入语句：import wx
运行报错：No module named wx


意思就是说没有这个模块，导入错误。


出错情况：我在安装wxpython时改的安装路径为python的安装路径（D:\360Install\Python）。即一开始这两者的安装路径相同。运行出错
解决：]]></description><category></category></item></channel></rss>