<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[u010814042的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/u010814042</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; u010814042]]></copyright><item><title><![CDATA[二叉树题目总结(Java版本)]]></title><link>https://blog.youkuaiyun.com/u010814042/article/details/79706788</link><guid>https://blog.youkuaiyun.com/u010814042/article/details/79706788</guid><author>u010814042</author><pubDate>Tue, 27 Mar 2018 02:30:33 +0800</pubDate><description><![CDATA[二叉树总结





1.  重建二叉树



import java.util.*;
public class Solution {
    public TreeNode reConstructBinaryTree(int [] pre,int [] in) {
        if(pre.length == 0||in.length == 0){
            return nu...]]></description><category></category></item><item><title><![CDATA[回溯法题目总结]]></title><link>https://blog.youkuaiyun.com/u010814042/article/details/79576176</link><guid>https://blog.youkuaiyun.com/u010814042/article/details/79576176</guid><author>u010814042</author><pubDate>Fri, 16 Mar 2018 02:40:49 +0800</pubDate><description><![CDATA[1. 数字键盘组合

17. Letter Combinations of a Phone Number



class Solution {
    private static final String[] keys = {&quot;&quot;, &quot;&quot;, &quot;abc&quot;, &quot;def&quot;, &quot;ghi&quot;, &quot;jkl&quot;, &quot;mno&quot;, &quot;pqrs&quot;, &quot;tuv&]]></description><category></category></item><item><title><![CDATA[链表题目总结]]></title><link>https://blog.youkuaiyun.com/u010814042/article/details/79548991</link><guid>https://blog.youkuaiyun.com/u010814042/article/details/79548991</guid><author>u010814042</author><pubDate>Wed, 14 Mar 2018 00:48:52 +0800</pubDate><description><![CDATA[链表题目总结



1. 从尾到头打印链表

思路: 使用递归



import java.util.ArrayList;
public class Solution {
    // 定义在外部，以防递归的时候被覆盖
    ArrayList&amp;lt;Integer&amp;gt; ret = new ArrayList&amp;lt;&amp;gt;();
    public ArrayList&amp;lt;Integ...]]></description><category></category></item><item><title><![CDATA[贪心法题目总结]]></title><link>https://blog.youkuaiyun.com/u010814042/article/details/79545041</link><guid>https://blog.youkuaiyun.com/u010814042/article/details/79545041</guid><author>u010814042</author><pubDate>Tue, 13 Mar 2018 19:18:18 +0800</pubDate><description><![CDATA[1. 分饼干

Leetcode : 455. Assign Cookies (Easy) 
Assume you are an awesome parent and want to give your children some cookies. But, you should give each child at most one cookie. Each child i has a gree...]]></description><category></category></item><item><title><![CDATA[双指针问题总结]]></title><link>https://blog.youkuaiyun.com/u010814042/article/details/79535212</link><guid>https://blog.youkuaiyun.com/u010814042/article/details/79535212</guid><author>u010814042</author><pubDate>Tue, 13 Mar 2018 01:12:26 +0800</pubDate><description><![CDATA[双指针题目



1. 和为S的连续正数序列

思想: 设计前后两个指针以及一个用于保存当前和的变量



import java.util.ArrayList;
public class Solution {
    public ArrayList&amp;lt;ArrayList&amp;lt;Integer&amp;gt; &amp;gt; FindContinuousSequence(int sum) {
      ...]]></description><category></category></item><item><title><![CDATA[数据结构-数组题总结]]></title><link>https://blog.youkuaiyun.com/u010814042/article/details/79531022</link><guid>https://blog.youkuaiyun.com/u010814042/article/details/79531022</guid><author>u010814042</author><pubDate>Mon, 12 Mar 2018 20:38:33 +0800</pubDate><description><![CDATA[1. 二维数组中的查找

在一个二维数组中，每一行都按照从左到右递增的顺序排序，每一列都按照从上到下递增的顺序排序。请完成一个函数，输入这样的一个二维数组和一个整数，判断数组中是否含有该整数。 
主要思路：利用二维数组的有序性，定向地搜索。



public class Solution {
    public boolean Find(int target, int [][] array) ...]]></description><category></category></item><item><title><![CDATA[稳定排序和不稳定排序]]></title><link>https://blog.youkuaiyun.com/u010814042/article/details/78714518</link><guid>https://blog.youkuaiyun.com/u010814042/article/details/78714518</guid><author>u010814042</author><pubDate>Mon, 04 Dec 2017 22:08:44 +0800</pubDate><description><![CDATA[https://www.cnblogs.com/codingmylife/archive/2012/10/21/2732980.html]]></description><category></category></item><item><title><![CDATA[精确率、召回率、F1 值、ROC、AUC 各自的优缺点]]></title><link>https://blog.youkuaiyun.com/u010814042/article/details/78149480</link><guid>https://blog.youkuaiyun.com/u010814042/article/details/78149480</guid><author>u010814042</author><pubDate>Sun, 01 Oct 2017 23:44:28 +0800</pubDate><description><![CDATA[本文主要汇总了知乎的回答[https://www.zhihu.com/question/30643044] 
ROC曲线的概念：http://blog.youkuaiyun.com/abcjennifer/article/details/7359370 
PRC曲线的概念: http://blog.youkuaiyun.com/u012089317/article/details/521565141. 总体介绍ROC：RO]]></description><category></category></item><item><title><![CDATA[非常好的t-SNE介绍]]></title><link>https://blog.youkuaiyun.com/u010814042/article/details/78135891</link><guid>https://blog.youkuaiyun.com/u010814042/article/details/78135891</guid><author>u010814042</author><pubDate>Fri, 29 Sep 2017 17:24:05 +0800</pubDate><description><![CDATA[从SNE到t-SNE再到LargeVis，转自http://bindog.github.io/blog/2016/06/04/from-sne-to-tsne-to-largevis]]></description><category></category></item><item><title><![CDATA[Quora比赛代码学习笔记]]></title><link>https://blog.youkuaiyun.com/u010814042/article/details/78129085</link><guid>https://blog.youkuaiyun.com/u010814042/article/details/78129085</guid><author>u010814042</author><pubDate>Thu, 28 Sep 2017 23:01:40 +0800</pubDate><description><![CDATA[1.sns调色板使用pal = sns.color_palette()2.打印文件大小print('# File sizes')
for f in os.listdir('../input'):
    if 'zip' not in f:
        #ljust() 返回一个原字符串左对齐,并使用空格填充至指定长度的新字符串。如果指定的长度小于原字符串的长度则返回原字符串。]]></description><category></category></item><item><title><![CDATA[python os模块 常用命令]]></title><link>https://blog.youkuaiyun.com/u010814042/article/details/77714624</link><guid>https://blog.youkuaiyun.com/u010814042/article/details/77714624</guid><author>u010814042</author><pubDate>Wed, 30 Aug 2017 13:38:36 +0800</pubDate><description><![CDATA[1. os.name()——判断现在正在实用的平台，Windows 返回 ‘nt'; Linux 返回’posix'

2. os.getcwd()——得到当前工作的目录。

3. os.listdir()——指定所有目录下所有的文件和目录名。例： 



4. os.remove()——删除指定文件

5. os.rmdir()——删除指定目录

6. os.mkdir(]]></description><category></category></item><item><title><![CDATA[Python的argparse库用法]]></title><link>https://blog.youkuaiyun.com/u010814042/article/details/77651456</link><guid>https://blog.youkuaiyun.com/u010814042/article/details/77651456</guid><author>u010814042</author><pubDate>Mon, 28 Aug 2017 17:41:49 +0800</pubDate><description><![CDATA[一、argparse介绍argparse 是 Python 内置的一个用于命令项选项与参数解析的模块，通过在程序中定义好我们需要的参数，argparse 将会从 sys.argv 中解析出这些参数，并自动生成帮助和使用信息。当然，Python 也有第三方的库可用于命令行解析，而且功能也更加强大，比如 docopt，Click。二、argparse的使用
创建 ArgumentParser() 对象]]></description><category></category></item><item><title><![CDATA[python进度条 tqdm 解析]]></title><link>https://blog.youkuaiyun.com/u010814042/article/details/77650678</link><guid>https://blog.youkuaiyun.com/u010814042/article/details/77650678</guid><author>u010814042</author><pubDate>Mon, 28 Aug 2017 16:48:25 +0800</pubDate><description><![CDATA[一、使用方式 
1. 自动控制进度更新>>> for i in tqdm(range(9)):
...     sleep(0.1)
100%|####################################################################| 9/9 [00:00<00:00,  9.95it/s]
2.手动更新方式>>> with tqdm(total=10]]></description><category></category></item><item><title><![CDATA[Sklearn库学习笔记1 Feature_Engineering之预处理篇]]></title><link>https://blog.youkuaiyun.com/u010814042/article/details/77649980</link><guid>https://blog.youkuaiyun.com/u010814042/article/details/77649980</guid><author>u010814042</author><pubDate>Mon, 28 Aug 2017 16:06:37 +0800</pubDate><description><![CDATA[一、预处理1.  Binarizer 二值化处理from sklearn.preprocessing import Binarizer
import numpy as np'''
数据二值化处理:
适用场景：泊松分布，文本数据
操作特点：返回对于数值特征的阈值判断
'''x_train = np.array([[1,2,-1],
                  [2, 3, -2],]]></description><category></category></item><item><title><![CDATA[线性回归中何时中心化和标准化训练数据]]></title><link>https://blog.youkuaiyun.com/u010814042/article/details/76842242</link><guid>https://blog.youkuaiyun.com/u010814042/article/details/76842242</guid><author>u010814042</author><pubDate>Mon, 07 Aug 2017 14:51:52 +0800</pubDate><description><![CDATA[注明: 本文主要参考https://stats.stackexchange.com/questions/29781/when-conducting-multiple-regression-when-should-you-center-your-predictor-varia中的回答一般来说，我们再做线性回归时并不需要中心化和标准化数据。大多数情况下数据中的特征会以不同的测量单位展现，无论有没有中心化]]></description><category></category></item><item><title><![CDATA[Google Cloud 磁盘满解决办法]]></title><link>https://blog.youkuaiyun.com/u010814042/article/details/76636212</link><guid>https://blog.youkuaiyun.com/u010814042/article/details/76636212</guid><author>u010814042</author><pubDate>Thu, 03 Aug 2017 16:34:56 +0800</pubDate><description><![CDATA[使用命令：df -lk
adslwang4605@instance-1:~/assignment1/data$ df -lk
Filesystem     1K-blocks    Used Available Use% Mounted on
udev            15432536       0  15432536   0% /dev
tmpfs            3088044]]></description><category></category></item><item><title><![CDATA[Pandas 中map, applymap and apply的区别]]></title><link>https://blog.youkuaiyun.com/u010814042/article/details/76401133</link><guid>https://blog.youkuaiyun.com/u010814042/article/details/76401133</guid><author>u010814042</author><pubDate>Sun, 30 Jul 2017 18:52:40 +0800</pubDate><description><![CDATA[1.apply()当想让方程作用在一维的向量上时，可以使用apply来完成，如下所示In [116]: frame = DataFrame(np.random.randn(4, 3), columns=list('bde'), index=['Utah', 'Ohio', 'Texas', 'Oregon'])In [117]: frame
Out[117]: 
               b]]></description><category></category></item><item><title><![CDATA[Python中@staticmethod和@classmethod的区别和用法]]></title><link>https://blog.youkuaiyun.com/u010814042/article/details/76346323</link><guid>https://blog.youkuaiyun.com/u010814042/article/details/76346323</guid><author>u010814042</author><pubDate>Sat, 29 Jul 2017 18:12:07 +0800</pubDate><description><![CDATA[Python其实有3类方法：

静态方法（staticmethod）类方法（classmethod）实例方法（instance method）


常规方式, @classmethod修饰方式, @staticmethod修饰方式.



def foo(x):
    print "executing foo(%s)" %(x)

class A(object):
    de]]></description><category></category></item><item><title><![CDATA[梯度爆炸的解决办法：clip gradient]]></title><link>https://blog.youkuaiyun.com/u010814042/article/details/76154391</link><guid>https://blog.youkuaiyun.com/u010814042/article/details/76154391</guid><author>u010814042</author><pubDate>Wed, 26 Jul 2017 17:27:36 +0800</pubDate><description><![CDATA[1.梯度爆炸的影响在一个只有一个隐藏节点的网络中，损失函数和权值w偏置b构成error surface，其中有一堵墙，如下所示 
  
损失函数每次迭代都是每次一小步，但是当遇到这堵墙时，在墙上的某点计算梯度，梯度会瞬间增大，指向某处不理想的位置。如果我们使用缩放，可以把误导控制在可接受范围内，如虚线箭头所示2.解决梯度爆炸问题的方法通常会使用一种叫”clip gradients “的方法.  它能]]></description><category></category></item><item><title><![CDATA[python assert 断言详细用法格式]]></title><link>https://blog.youkuaiyun.com/u010814042/article/details/75770901</link><guid>https://blog.youkuaiyun.com/u010814042/article/details/75770901</guid><author>u010814042</author><pubDate>Sat, 22 Jul 2017 17:05:24 +0800</pubDate><description><![CDATA[1.assert的作用当我们在写完一个程序，但是还不够完善的时候。我们想要程序在某个位置条件不满足时就终止程序，这时可以使用assert来提前中断程序，并显示自定义错误信息2.assert的用法assert 1==1
assert 2+2==2*2
assert len(['my boy',12])<10
assert range(4)==[0,1,2,3] assert stack == ex_s]]></description><category></category></item></channel></rss>