-
Notifications
You must be signed in to change notification settings - Fork 0
/
index.html
661 lines (443 loc) · 24.7 KB
/
index.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
<!DOCTYPE html>
<html>
<head>
<!-- hexo-inject:begin --><!-- hexo-inject:end --><meta charset="utf-8">
<title>研读小屋</title>
<meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1">
<meta property="og:type" content="website">
<meta property="og:title" content="研读小屋">
<meta property="og:url" content="http://yoursite.com/index.html">
<meta property="og:site_name" content="研读小屋">
<meta name="twitter:card" content="summary">
<meta name="twitter:title" content="研读小屋">
<link rel="icon" href="/favicon.png">
<link rel="stylesheet" href="/css/style.css">
<link rel="stylesheet" href="/fancybox/jquery.fancybox.css">
<link rel="stylesheet" href="/scrollLoading/style.css">
<link href='//fonts.useso.com/css?family=Titillium+Web:300,400,600' rel='stylesheet' type='text/css'>
<link href="//fonts.useso.com/css?family=Source+Code+Pro" rel="stylesheet" type="text/css">
<link rel="stylesheet" href="/font-awesome/css/font-awesome.min.css"><!-- hexo-inject:begin --><!-- hexo-inject:end -->
</head>
<body>
<!-- hexo-inject:begin --><!-- hexo-inject:end --><div id="wrap">
<header id="header">
<div id="header-outer" class="outer">
<div class="container">
<div class="container-inner">
<div id="header-title">
<h1 class="logo-wrap">
<a href="/" class="logo"></a>
</h1>
<h2 class="subtitle-wrap">
<p class="subtitle">君子食无求饱，居无求安，敏于事而慎于言</p>
</h2>
</div>
<div id="header-inner" class="nav-container">
<a id="main-nav-toggle" class="nav-icon fa fa-bars"></a>
<div class="nav-container-inner">
<ul id="main-nav">
<li class="main-nav-list-item" ><a class="main-nav-list-link" href="/">主页</a></li>
<ul class="main-nav-list"><li class="main-nav-list-item"><a class="main-nav-list-link" href="/categories/技术/">技术</a></li></ul>
<li class="main-nav-list-item" ><a class="main-nav-list-link" href="/2015/10/25/about">关于</a></li>
</ul>
<nav id="sub-nav">
<div id="search-form-wrap">
<form action="//google.com/search" method="get" accept-charset="UTF-8" class="search-form"><input type="search" name="q" class="search-form-input" placeholder="搜索"><input type="hidden" name="sitesearch" value="http://yoursite.com"></form>
</div>
</nav>
</div>
</div>
</div>
</div>
</div>
</header>
<div class="container">
<div class="main-body container-inner">
<div class="main-body-inner">
<section id="main">
<div class="main-body-header">
<h1 class="header"><em class="page-title-link" data-url="home">主页</em></h1>
</div>
<div class="main-body-content">
<div class="article-row">
<article class="article article-summary">
<div class="article-summary-inner">
<a href="/2017/05/06/adaboost/" class="thumbnail">
<span style="background-image:url(/images/adaboost_decision_boundary.png
)" alt="Adaboost" class="thumbnail-image"></span>
</a>
<div class="article-meta">
<p class="category"><a class="article-category-link" href="/categories/技术/">技术</a></p>
<p class="date"><time datetime="2017-05-06T03:25:23.000Z" itemprop="datePublished">2017-05-06</time></p>
</div>
<h2 class="article-title">
<a href="/2017/05/06/adaboost/" class="title">Adaboost</a>
</h2>
<p class="article-excerpt">
Adaboost算法boost的思想是组合一堆弱分类器来组成一个强分类器，Adaboost便是其中一个最著名的例子。Adaboost的推导可以由以下式子来完成：首先Adaboost是一堆弱分类器的组合，可以写为$f_m(x) = \frac{1}{2}\sum_{j}^m\alpha_jy_j(x)$，它的损失函数采用loss损失函数，所以最终可以写为：$$L(x) = \sum_i^n\exp(
</p>
</div>
</article>
<article class="article article-summary">
<div class="article-summary-inner">
<a href="/2017/03/07/svm/" class="thumbnail">
<span style="background-image:url(/images/svm_1.png
)" alt="svm" class="thumbnail-image"></span>
</a>
<div class="article-meta">
<p class="category"><a class="article-category-link" href="/categories/技术/">技术</a></p>
<p class="date"><time datetime="2017-03-07T14:51:33.000Z" itemprop="datePublished">2017-03-07</time></p>
</div>
<h2 class="article-title">
<a href="/2017/03/07/svm/" class="title">svm</a>
</h2>
<p class="article-excerpt">
SVMLagrange对偶问题首先一个约束问题$$\begin{cases}\min \ f(x) \\ s.t.\ \ g_i(x) \ge 0\\ \quad \quad h_j(x) = 0\end{cases}$$
该问题可以转化成为等价的问题，引入拉格朗日函数$$L(x, w, v) = f(x) - \sum_iw_ig_i(x) - \sum_jv_jh_j(x),w_i \ge 0
</p>
</div>
</article>
</div>
<div class="article-row">
<article class="article article-summary">
<div class="article-summary-inner">
<a href="/2016/12/20/sampling-method/" class="thumbnail">
<span style="background-image:url(/images/rejection_sampling.png
)" alt="采样方法" class="thumbnail-image"></span>
</a>
<div class="article-meta">
<p class="category"><a class="article-category-link" href="/categories/技术/">技术</a></p>
<p class="date"><time datetime="2016-12-20T13:31:55.000Z" itemprop="datePublished">2016-12-20</time></p>
</div>
<h2 class="article-title">
<a href="/2016/12/20/sampling-method/" class="title">采样方法</a>
</h2>
<p class="article-excerpt">
对一些常用的采用进行总结，主要参考了PRML。
整个采样的关键思想：求$$E[f] = \int f(z)p(z)dz$$
但是通常数值分析的方法很难求，寻求一种近似的方法$$E[f] = \frac{1}{L}\sum f(z^{(l)})$$
其中$z^{(l)} \sim P(z)$，通常来说10~20个样本就可以很好的拟合，并且这个方法是不取决于z的维度的。
但是通常情况下，可能一些非常小
</p>
</div>
</article>
<article class="article article-summary">
<div class="article-summary-inner">
<a href="/2016/12/20/decision-tree/" class="thumbnail">
<span style="background-image:url(/images/decision_tree.png
)" alt="决策树" class="thumbnail-image"></span>
</a>
<div class="article-meta">
<p class="category"><a class="article-category-link" href="/categories/技术/">技术</a></p>
<p class="date"><time datetime="2016-12-20T13:02:39.000Z" itemprop="datePublished">2016-12-20</time></p>
</div>
<h2 class="article-title">
<a href="/2016/12/20/decision-tree/" class="title">决策树</a>
</h2>
<p class="article-excerpt">
常用分类方法的一种，对于训练数据来说，每次找到最优的一个特征，对该特征进行划分。划分之后得到不同的子集，再分别在不同的子集进行划分，直到满足某个划分标准则停止。
如何选择最优的特征有不同的算法。常见的决策树算法有ID3，C4.5算法，对于ID3算法，采用信息增益最大的特征来选择最优的特征。算法流程如下：对于在未划分之前，首先算出该数据集$D$的熵。假设该数据集有$K$类，训练集大小为$N$.其中属
</p>
</div>
</article>
</div>
<div class="article-row">
<article class="article article-summary">
<div class="article-summary-inner">
<a href="/2016/10/24/kd-tree/" class="thumbnail">
<span style="background-image:url(/images/k_nearest.png
)" alt="K近邻" class="thumbnail-image"></span>
</a>
<div class="article-meta">
<p class="category"><a class="article-category-link" href="/categories/技术/">技术</a></p>
<p class="date"><time datetime="2016-10-24T13:40:44.000Z" itemprop="datePublished">2016-10-24</time></p>
</div>
<h2 class="article-title">
<a href="/2016/10/24/kd-tree/" class="title">K近邻</a>
</h2>
<p class="article-excerpt">
$k$近邻是最简单的分类算法了，对于一个实例$x$，找到它最近的邻居，根据邻居的类标来进行一个投票。
对于找到k近邻而言，对每个点采用暴力遍历，取前k个最小的即可。然后进行投票。
代码如下：k_nearest_neighbours
产生的决策面如下所示：
对于最近邻，可能暴力搜索会造成过高的耗时，为了减少这种耗时，采用了一种叫做KD树的数据结构。思想是将空间进行划分，拿二维平面举例：首先按照某个轴
</p>
</div>
</article>
<article class="article article-summary">
<div class="article-summary-inner">
<a href="/2016/10/06/elastic-net/" class="thumbnail">
<span style="background-image:url(/images/elastic_net_1.png
)" alt="ElasticNet" class="thumbnail-image"></span>
</a>
<div class="article-meta">
<p class="category"><a class="article-category-link" href="/categories/技术/">技术</a></p>
<p class="date"><time datetime="2016-10-06T13:27:00.000Z" itemprop="datePublished">2016-10-06</time></p>
</div>
<h2 class="article-title">
<a href="/2016/10/06/elastic-net/" class="title">ElasticNet</a>
</h2>
<p class="article-excerpt">
Linear Regression with Elastic Net线性回归线性回归最简单的形式为$$y = wx$$
损失函数为$$L(y,x;w) = \frac{1}{2n}\sum_{i}||y_i - w^Tx_i||_2^2$$
这个问题可以直接求解，令梯度为0，得到$$w = (X^TX)^{-1}X^Ty$$
但是通常来说$(X^TX)$不是满秩的，数值计算上会有问题，将会有无穷多
</p>
</div>
</article>
</div>
<div class="article-row">
<article class="article article-summary">
<div class="article-summary-inner">
<a href="/2016/09/04/gaussian-process/" class="thumbnail">
<span style="background-image:url(/images/gaussian_process_1.png
)" alt="Gaussian Process" class="thumbnail-image"></span>
</a>
<div class="article-meta">
<p class="category"><a class="article-category-link" href="/categories/技术/">技术</a></p>
<p class="date"><time datetime="2016-09-04T11:33:01.000Z" itemprop="datePublished">2016-09-04</time></p>
</div>
<h2 class="article-title">
<a href="/2016/09/04/gaussian-process/" class="title">Gaussian Process</a>
</h2>
<p class="article-excerpt">
高斯过程对于基本的线性回归，我们会假设$f(x)$的一个形式，比如说$f(x) = mx + c$或者$f(x) = ax^2 + bx + c$等等，然而高斯过程不直接假设函数的形式，而是让数据自己说话，对函数的分布进行推断。即后验$p(f|D)$，但是对函数后验$p(f|D)$分布进行推断比较困难，我们对这些函数在$x_1, x_2, \cdots, x_n$上的值进行推断。高斯过程即假设$p
</p>
</div>
</article>
<article class="article article-summary">
<div class="article-summary-inner">
<a href="/2016/08/08/dimension-reduction/" class="thumbnail">
<span style="background-image:url(/images/pca_1.png
)" alt="数据降维" class="thumbnail-image"></span>
</a>
<div class="article-meta">
<p class="category"><a class="article-category-link" href="/categories/技术/">技术</a></p>
<p class="date"><time datetime="2016-08-08T13:25:34.000Z" itemprop="datePublished">2016-08-08</time></p>
</div>
<h2 class="article-title">
<a href="/2016/08/08/dimension-reduction/" class="title">数据降维</a>
</h2>
<p class="article-excerpt">
给定数据$x_1,x_2,\cdots,x_N$，其中每个$x_i$是一个$D$维向量，现在要将其降低到M维，降维有以下几种方法：
PCAPCA可以以两种方式来看待，一是最大化方差，二是最小化误差
最大化方差考虑$D=2$的情况,假设$M=1$,找到的投影方向为$\mu_1$,那么最大化方差即为$$\max v = \frac{1}{N}\sum_{i=1}^{N}(\mu_1^Tx_i - \m
</p>
</div>
</article>
</div>
<div class="article-row">
<article class="article article-summary">
<div class="article-summary-inner">
<a href="/2016/07/12/bp-detail/" class="thumbnail">
<span style="background-image:url(/images/MLP.png
)" alt="神经网络求导细节" class="thumbnail-image"></span>
</a>
<div class="article-meta">
<p class="category"><a class="article-category-link" href="/categories/技术/">技术</a></p>
<p class="date"><time datetime="2016-07-12T09:24:23.000Z" itemprop="datePublished">2016-07-12</time></p>
</div>
<h2 class="article-title">
<a href="/2016/07/12/bp-detail/" class="title">神经网络求导细节</a>
</h2>
<p class="article-excerpt">
最近终于把RNN的求导细节给弄明白了，写一篇文章来总结一下DNN，CNN以及RNN的具体求导细节。在网上搜索资料的同时，发现很多时候RNN的求导大多数给的是矢量化的形式，并不容易去理解，比如说$y$是一个标量，$y = \sum_n||y_n - t_n||^2$，通常用的error形式，对一个权重矩阵$w$求导，也应该是一个矩阵的形式。但是一旦涉及到微积分的链式法则，通常很难以去理解。比如说$$
</p>
</div>
</article>
<article class="article article-summary">
<div class="article-summary-inner">
<a href="/2016/03/25/EMAlgorithm/" class="thumbnail">
<span style="background-image:url(/images/em_1.png
)" alt="EMAlgorithm" class="thumbnail-image"></span>
</a>
<div class="article-meta">
<p class="category"><a class="article-category-link" href="/categories/技术/">技术</a></p>
<p class="date"><time datetime="2016-03-25T08:38:37.000Z" itemprop="datePublished">2016-03-25</time></p>
</div>
<h2 class="article-title">
<a href="/2016/03/25/EMAlgorithm/" class="title">EMAlgorithm</a>
</h2>
<p class="article-excerpt">
EM算法具体例子为Gaussian Mixture Model,由k个component组成。$$P(z_k = 1) = \pi_k, \sum_k\pi_k = 1, 0 \le \pi_k \le 1$$$$P(x|z) = \prod_k\mathcal{N}(x|\mu_k,\Sigma_k)^{z_k}$$$$P(x) = \sum_zP(z)P(x|z) = \sum_k\pi_k\
</p>
</div>
</article>
</div>
<nav id="page-nav">
<span class="pages">第 1 页，共 2 页</span>
<span class="page-number current">1</span><a class="page-number" href="/page/2/">2</a><a class="extend next" rel="next" href="/page/2/">»</a>
</nav>
</div>
</section>
<aside id="sidebar">
<a class="sidebar-toggle" title="Expand Sidebar"><i class="toggle icon"></i></a>
<div class="sidebar-top">
<p>关注我 :</p>
<ul class="social-links">
<li><a class="social-tooltip" title="twitter" href="/" target="_blank"><i class="icon fa fa-twitter"></i></a></li>
<li><a class="social-tooltip" title="facebook" href="/" target="_blank"><i class="icon fa fa-facebook"></i></a></li>
<li><a class="social-tooltip" title="github" href="https://github.com/xcszbdnl" target="_blank"><i class="icon fa fa-github"></i></a></li>
<li><a class="social-tooltip" title="weibo" href="/" target="_blank"><i class="icon fa fa-weibo"></i></a></li>
</ul>
</div>
<div class="widgets-container">
<div class="widget-wrap">
<h3 class="widget-title">最新文章</h3>
<div class="widget">
<ul id="recent-post" class="">
<li>
<div class="item-thumbnail">
<a href="/2017/05/06/adaboost/" class="thumbnail">
<span style="background-image:url(/images/adaboost_decision_boundary.png
)" alt="Adaboost" class="thumbnail-image"></span>
</a>
</div>
<div class="item-inner">
<p class="item-category"><a class="article-category-link" href="/categories/技术/">技术</a></p>
<p class="item-title"><a href="/2017/05/06/adaboost/" class="title">Adaboost</a></p>
<p class="item-date"><time datetime="2017-05-06T03:25:23.000Z" itemprop="datePublished">2017-05-06</time></p>
</div>
</li>
<li>
<div class="item-thumbnail">
<a href="/2017/03/07/svm/" class="thumbnail">
<span style="background-image:url(/images/svm_1.png
)" alt="svm" class="thumbnail-image"></span>
</a>
</div>
<div class="item-inner">
<p class="item-category"><a class="article-category-link" href="/categories/技术/">技术</a></p>
<p class="item-title"><a href="/2017/03/07/svm/" class="title">svm</a></p>
<p class="item-date"><time datetime="2017-03-07T14:51:33.000Z" itemprop="datePublished">2017-03-07</time></p>
</div>
</li>
<li>
<div class="item-thumbnail">
<a href="/2016/12/20/sampling-method/" class="thumbnail">
<span style="background-image:url(/images/rejection_sampling.png
)" alt="采样方法" class="thumbnail-image"></span>
</a>
</div>
<div class="item-inner">
<p class="item-category"><a class="article-category-link" href="/categories/技术/">技术</a></p>
<p class="item-title"><a href="/2016/12/20/sampling-method/" class="title">采样方法</a></p>
<p class="item-date"><time datetime="2016-12-20T13:31:55.000Z" itemprop="datePublished">2016-12-20</time></p>
</div>
</li>
<li>
<div class="item-thumbnail">
<a href="/2016/12/20/decision-tree/" class="thumbnail">
<span style="background-image:url(/images/decision_tree.png
)" alt="决策树" class="thumbnail-image"></span>
</a>
</div>
<div class="item-inner">
<p class="item-category"><a class="article-category-link" href="/categories/技术/">技术</a></p>
<p class="item-title"><a href="/2016/12/20/decision-tree/" class="title">决策树</a></p>
<p class="item-date"><time datetime="2016-12-20T13:02:39.000Z" itemprop="datePublished">2016-12-20</time></p>
</div>
</li>
<li>
<div class="item-thumbnail">
<a href="/2016/10/24/kd-tree/" class="thumbnail">
<span style="background-image:url(/images/k_nearest.png
)" alt="K近邻" class="thumbnail-image"></span>
</a>
</div>
<div class="item-inner">
<p class="item-category"><a class="article-category-link" href="/categories/技术/">技术</a></p>
<p class="item-title"><a href="/2016/10/24/kd-tree/" class="title">K近邻</a></p>
<p class="item-date"><time datetime="2016-10-24T13:40:44.000Z" itemprop="datePublished">2016-10-24</time></p>
</div>
</li>
</ul>
</div>
</div>
<div class="widget-wrap widget-list">
<h3 class="widget-title">分类</h3>
<div class="widget">
<ul class="category-list"><li class="category-list-item"><a class="category-list-link" href="/categories/技术/">技术</a><span class="category-list-count">10</span></li></ul>
</div>
</div>
<div class="widget-wrap widget-list">
<h3 class="widget-title">归档</h3>
<div class="widget">
<ul class="archive-list"><li class="archive-list-item"><a class="archive-list-link" href="/archives/2017/05/">五月 2017</a><span class="archive-list-count">1</span></li><li class="archive-list-item"><a class="archive-list-link" href="/archives/2017/03/">三月 2017</a><span class="archive-list-count">1</span></li><li class="archive-list-item"><a class="archive-list-link" href="/archives/2016/12/">十二月 2016</a><span class="archive-list-count">2</span></li><li class="archive-list-item"><a class="archive-list-link" href="/archives/2016/10/">十月 2016</a><span class="archive-list-count">2</span></li><li class="archive-list-item"><a class="archive-list-link" href="/archives/2016/09/">九月 2016</a><span class="archive-list-count">1</span></li><li class="archive-list-item"><a class="archive-list-link" href="/archives/2016/08/">八月 2016</a><span class="archive-list-count">1</span></li><li class="archive-list-item"><a class="archive-list-link" href="/archives/2016/07/">七月 2016</a><span class="archive-list-count">1</span></li><li class="archive-list-item"><a class="archive-list-link" href="/archives/2016/03/">三月 2016</a><span class="archive-list-count">1</span></li><li class="archive-list-item"><a class="archive-list-link" href="/archives/2015/10/">十月 2015</a><span class="archive-list-count">1</span></li></ul>
</div>
</div>
<div class="widget-wrap widget-list">
<h3 class="widget-title">标签</h3>
<div class="widget">
<ul class="tag-list"><li class="tag-list-item"><a class="tag-list-link" href="/tags/Cluster/">Cluster</a><span class="tag-list-count">1</span></li><li class="tag-list-item"><a class="tag-list-link" href="/tags/DeepLearning/">DeepLearning</a><span class="tag-list-count">2</span></li><li class="tag-list-item"><a class="tag-list-link" href="/tags/MachineLearning/">MachineLearning</a><span class="tag-list-count">9</span></li><li class="tag-list-item"><a class="tag-list-link" href="/tags/Sampling/">Sampling</a><span class="tag-list-count">1</span></li><li class="tag-list-item"><a class="tag-list-link" href="/tags/Sparsity/">Sparsity</a><span class="tag-list-count">1</span></li></ul>
</div>
</div>
<div class="widget-wrap widget-float">
<h3 class="widget-title">标签云</h3>
<div class="widget tagcloud">
<a href="/tags/Cluster/" style="font-size: 10px;">Cluster</a> <a href="/tags/DeepLearning/" style="font-size: 15px;">DeepLearning</a> <a href="/tags/MachineLearning/" style="font-size: 20px;">MachineLearning</a> <a href="/tags/Sampling/" style="font-size: 10px;">Sampling</a> <a href="/tags/Sparsity/" style="font-size: 10px;">Sparsity</a>
</div>
</div>
<div class="widget-wrap widget-list">
<h3 class="widget-title">链接</h3>
<div class="widget">
<ul>
<li>
<a href="http://hexo.io">Hexo</a>
</li>
</ul>
</div>
</div>
</div>
</aside>
</div>
</div>
</div>
<footer id="footer">
<div class="container">
<div class="container-inner">
<a id="back-to-top" href="javascript:;"><i class="icon fa fa-angle-up"></i></a>
<div class="credit">
<h1 class="logo-wrap">
<a href="/" class="logo"></a>
</h1>
<p>© 2017 ChangshengXiang</p>
<p>Powered by <a href="//hexo.io/" target="_blank">Hexo</a>. Theme by <a href="//github.com/ppoffice" target="_blank">PPOffice</a></p>
</div>
</div>
</div>
</footer>
<script>
var disqus_shortname = 'xcszbdnl';
(function(){
var dsq = document.createElement('script');
dsq.type = 'text/javascript';
dsq.async = true;
dsq.src = '//' + disqus_shortname + '.disqus.com/count.js';
(document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq);
})();
</script>
<script src="http://code.jquery.com/jquery-2.1.3.min.js"></script>
<script src="/fancybox/jquery.fancybox.pack.js"></script>
<script src="/scrollLoading/jquery.scrollLoading.js"></script>
<script src="/scrollLoading/main.js"></script>
<script src="/js/html-patch.js"></script>
<script src="/js/script.js"></script>
</div><!-- hexo-inject:begin --><!-- Begin: Injected MathJax -->
<script type="text/x-mathjax-config">
MathJax.Hub.Config({"tex2jax":{"inlineMath":[["$","$"],["\\(","\\)"]],"skipTags":["script","noscript","style","textarea","pre","code"],"processEscapes":true},"TeX":{"equationNumbers":{"autoNumber":"AMS"}}});
</script>
<script type="text/x-mathjax-config">
MathJax.Hub.Queue(function() {
var all = MathJax.Hub.getAllJax(), i;
for(i=0; i < all.length; i += 1) {
all[i].SourceElement().parentNode.className += ' has-jax';
}
});
</script>
<script type="text/javascript" src="//cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML">
</script>
<!-- End: Injected MathJax -->
<!-- hexo-inject:end -->
</body>
</html>