更新文档数据

docs/1.机器学习基础.md

@@ -14,7 +14,7 @@
 机器学习（machine learning）: 机器学习是最基础的（当下初创公司和研究实验室的热点领域之一）。
     在90年代初，人们开始意识到一种可以更有效地构建模式识别算法的方法，那就是用数据（可以通过廉价劳动力采集获得）去替换专家（具有很多图像方面知识的人）。
     “机器学习”强调的是，在给计算机程序（或者机器）输入一些数据后，它必须做一些事情，那就是学习这些数据，而这个学习的步骤是明确的。
-    机器学习（Machine Learning）是一门专门研究计算机怎样模拟或实现人类的学习行为，以获取新的知识或技能，重新组织已有的知识结构使之不断改善自身的性能的学科。
+    机器学习（Machine Learning）是一门专门研究计算机怎样模拟或实现人类的学习行为，以获取新的知识或技能，重新组织已有的知识结构使之不断改善自身性能的学科。
 深度学习（deep learning）: 深度学习是非常崭新和有影响力的前沿领域，我们甚至不会去思考-后深度学习时代。
     深度学习是机器学习研究中的一个新的领域，其动机在于建立、模拟人脑进行分析学习的神经网络，它模仿人脑的机制来解释数据，例如图像，声音和文本。
 
@@ -36,18 +36,18 @@ http://baike.baidu.com/link?url=76P-uA4EBrC3G-I__P1tqeO7eoDS709Kp4wYuHxc7GNkz_xn
 ## 机器学习的简单概述
 
 `机器学习`就是把无序的数据转换成有用的信息；机器学习将有助于我们穿越数据雾霾，从中抽取出有用的信息。
-* 1.需要获取海量的数据
-* 2.才能从海量数据中获取有用的信息
+* 1.获取海量的数据
+* 2.从海量数据中获取有用的信息
 
 ## 机器学习的主要任务
 
 > 机器学习的主要任务就是分类和回归
 
 * 分类：将实例数据划分到合适的类别中。
-* 回归：主要用于预测数值型数据。（例子———数据拟合曲线：通过给定数据点的最优拟合曲线）
+* 回归：主要用于预测数值型数据。（示例：数据通过给定数据点来拟合最优曲线）
 * 目标变量
     * 目标变量是机器学习预测算法的测试结果。
-    * 在分类算法中目标变量的类型通常是标称型的，而在回归算法中通常是连续型的。
+    * 在分类算法中目标变量的类型通常是标称型(如：真与假)，而在回归算法中通常是连续型(如：1~100)。
 
 * 机器学习的训练过程
 * ![机器学习训练过程图](/images/1.MLFoundation/机器学习基础训练过程.png)
@@ -57,7 +57,7 @@ http://baike.baidu.com/link?url=76P-uA4EBrC3G-I__P1tqeO7eoDS709Kp4wYuHxc7GNkz_xn
 * 必须知道预测什么，即必须知道目标变量的分类信息。分类和回归属于监督学习。
 * 样本集：训练数据 + 测试数据
     * 训练样本 = 特征(feature) + 目标变量(label)
-    * 训练样本的集合称为训练样本集，训练样本集必须确定知道目标变量的值，以便机器学习算法可以发现特征和目标变量之间的关系。
+    * 训练样本的集合称为训练样本集，训练样本集必须确定目标变量的值，以便机器学习算法可以发现特征和目标变量之间的关系。
 * 特征(feature-是否有缺失情况) + 目标变量(分类-离散值<A/B/C、 是/否>/回归-连续值<0~100、 -999～999>)
     * 特征或者属性通常是训练样本集的列，它们是独立测量得到的结果，多个特征联系在一起共同组成一个训练样本。
 * `知识表示`：(例如-机器已经学会如何识别鸟类的过程)
@@ -76,7 +76,7 @@ http://baike.baidu.com/link?url=76P-uA4EBrC3G-I__P1tqeO7eoDS709Kp4wYuHxc7GNkz_xn
 
 ![算法汇总](/images/1.MLFoundation/ml_algorithm.jpg)
 
-## 学习机器学习的原因
+## 学习机器学习
 
 * 选择算法需要考虑的两个问题
     * 使用机器学习算法的目的

docs/5.Logistic回归.md

@@ -24,8 +24,8 @@
 > Sigmoid函数简介
 
 ```
-    我们想要的函数应该是，能接受所有的输入然后预测出类别。例如，在两个类的情况下，上述函数输出 0 和 1 。这类函数称为海维塞得阶跃函数，或者直接称之为 单位阶跃函数。
-    但是，海维塞得阶跃函数的问题在于：该函数在跳跃点上从 0 瞬间跳跃到 1，这个瞬间跳跃过程有时候很难处理。幸好，另外的一个函数也有这样的性质(这里的性质指的是可以输出0和1的性质),
+    我们想要的函数应该是，能接受所有的输入然后预测出类别。例如，在两个类的情况下，上述函数输出 0 和 1 。这类函数称为海维塞德阶跃函数，或者直接称之为 单位阶跃函数。
+    但是，海维塞德阶跃函数的问题在于：该函数在跳跃点上从 0 瞬间跳跃到 1，这个瞬间跳跃过程有时候很难处理。幸好，另外的一个函数也有这样的性质(这里的性质指的是可以输出0和1的性质),
     且数学上更易处理，这就是我们下边要介绍的 Sigmoid 函数。
 
     Sigmoid函数具体的计算公式如下：

input/16.RecommenderSystems/ml-100k/README

@@ -0,0 +1,157 @@
+SUMMARY & USAGE LICENSE
+=============================================
+
+MovieLens data sets were collected by the GroupLens Research Project
+at the University of Minnesota.
+ 
+This data set consists of:
+	* 100,000 ratings (1-5) from 943 users on 1682 movies. 
+	* Each user has rated at least 20 movies. 
+        * Simple demographic info for the users (age, gender, occupation, zip)
+
+The data was collected through the MovieLens web site
+(movielens.umn.edu) during the seven-month period from September 19th, 
+1997 through April 22nd, 1998. This data has been cleaned up - users
+who had less than 20 ratings or did not have complete demographic
+information were removed from this data set. Detailed descriptions of
+the data file can be found at the end of this file.
+
+Neither the University of Minnesota nor any of the researchers
+involved can guarantee the correctness of the data, its suitability
+for any particular purpose, or the validity of results based on the
+use of the data set.  The data set may be used for any research
+purposes under the following conditions:
+
+     * The user may not state or imply any endorsement from the
+       University of Minnesota or the GroupLens Research Group.
+
+     * The user must acknowledge the use of the data set in
+       publications resulting from the use of the data set
+       (see below for citation information).
+
+     * The user may not redistribute the data without separate
+       permission.
+
+     * The user may not use this information for any commercial or
+       revenue-bearing purposes without first obtaining permission
+       from a faculty member of the GroupLens Research Project at the
+       University of Minnesota.
+
+If you have any further questions or comments, please contact GroupLens
+<grouplens-info@cs.umn.edu>. 
+
+CITATION
+==============================================
+
+To acknowledge use of the dataset in publications, please cite the 
+following paper:
+
+F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets:
+History and Context. ACM Transactions on Interactive Intelligent
+Systems (TiiS) 5, 4, Article 19 (December 2015), 19 pages.
+DOI=http://dx.doi.org/10.1145/2827872
+
+
+ACKNOWLEDGEMENTS
+==============================================
+
+Thanks to Al Borchers for cleaning up this data and writing the
+accompanying scripts.
+
+PUBLISHED WORK THAT HAS USED THIS DATASET
+==============================================
+
+Herlocker, J., Konstan, J., Borchers, A., Riedl, J.. An Algorithmic
+Framework for Performing Collaborative Filtering. Proceedings of the
+1999 Conference on Research and Development in Information
+Retrieval. Aug. 1999.
+
+FURTHER INFORMATION ABOUT THE GROUPLENS RESEARCH PROJECT
+==============================================
+
+The GroupLens Research Project is a research group in the Department
+of Computer Science and Engineering at the University of Minnesota.
+Members of the GroupLens Research Project are involved in many
+research projects related to the fields of information filtering,
+collaborative filtering, and recommender systems. The project is lead
+by professors John Riedl and Joseph Konstan. The project began to
+explore automated collaborative filtering in 1992, but is most well
+known for its world wide trial of an automated collaborative filtering
+system for Usenet news in 1996.  The technology developed in the
+Usenet trial formed the base for the formation of Net Perceptions,
+Inc., which was founded by members of GroupLens Research. Since then
+the project has expanded its scope to research overall information
+filtering solutions, integrating in content-based methods as well as
+improving current collaborative filtering technology.
+
+Further information on the GroupLens Research project, including
+research publications, can be found at the following web site:
+        
+        http://www.grouplens.org/
+
+GroupLens Research currently operates a movie recommender based on
+collaborative filtering:
+
+        http://www.movielens.org/
+
+DETAILED DESCRIPTIONS OF DATA FILES
+==============================================
+
+Here are brief descriptions of the data.
+
+ml-data.tar.gz   -- Compressed tar file.  To rebuild the u data files do this:
+                gunzip ml-data.tar.gz
+                tar xvf ml-data.tar
+                mku.sh
+
+u.data     -- The full u data set, 100000 ratings by 943 users on 1682 items.
+              Each user has rated at least 20 movies.  Users and items are
+              numbered consecutively from 1.  The data is randomly
+              ordered. This is a tab separated list of 
+	         user id | item id | rating | timestamp. 
+              The time stamps are unix seconds since 1/1/1970 UTC   
+
+u.info     -- The number of users, items, and ratings in the u data set.
+
+u.item     -- Information about the items (movies); this is a tab separated
+              list of
+              movie id | movie title | release date | video release date |
+              IMDb URL | unknown | Action | Adventure | Animation |
+              Children's | Comedy | Crime | Documentary | Drama | Fantasy |
+              Film-Noir | Horror | Musical | Mystery | Romance | Sci-Fi |
+              Thriller | War | Western |
+              The last 19 fields are the genres, a 1 indicates the movie
+              is of that genre, a 0 indicates it is not; movies can be in
+              several genres at once.
+              The movie ids are the ones used in the u.data data set.
+
+u.genre    -- A list of the genres.
+
+u.user     -- Demographic information about the users; this is a tab
+              separated list of
+              user id | age | gender | occupation | zip code
+              The user ids are the ones used in the u.data data set.
+
+u.occupation -- A list of the occupations.
+
+u1.base    -- The data sets u1.base and u1.test through u5.base and u5.test
+u1.test       are 80%/20% splits of the u data into training and test data.
+u2.base       Each of u1, ..., u5 have disjoint test sets; this if for
+u2.test       5 fold cross validation (where you repeat your experiment
+u3.base       with each training and test set and average the results).
+u3.test       These data sets can be generated from u.data by mku.sh.
+u4.base
+u4.test
+u5.base
+u5.test
+
+ua.base    -- The data sets ua.base, ua.test, ub.base, and ub.test
+ua.test       split the u data into a training set and a test set with
+ub.base       exactly 10 ratings per user in the test set.  The sets
+ub.test       ua.test and ub.test are disjoint.  These data sets can
+              be generated from u.data by mku.sh.
+
+allbut.pl  -- The script that generates training and test sets where
+              all but n of a users ratings are in the training data.
+
+mku.sh     -- A shell script to generate all the u data sets from u.data.

input/16.RecommenderSystems/ml-100k/allbut.pl

@@ -0,0 +1,34 @@
+#!/usr/local/bin/perl
+
+# get args
+if (@ARGV < 3) {
+	print STDERR "Usage: $0 base_name start stop max_test [ratings ...]\n";
+	exit 1;
+}
+$basename = shift;
+$start = shift;
+$stop = shift;
+$maxtest = shift;
+
+# open files
+open( TESTFILE, ">$basename.test" ) or die "Cannot open $basename.test for writing\n";
+open( BASEFILE, ">$basename.base" ) or die "Cannot open $basename.base for writing\n";
+
+# init variables
+$testcnt = 0;
+
+while (<>) {
+	($user) = split;
+	if (! defined $ratingcnt{$user}) {
+		$ratingcnt{$user} = 0;
+	}
+	++$ratingcnt{$user};
+	if (($testcnt < $maxtest || $maxtest <= 0)
+	&& $ratingcnt{$user} >= $start && $ratingcnt{$user} <= $stop) {
+		++$testcnt;
+		print TESTFILE;
+	}
+	else {
+		print BASEFILE;
+	}
+}

input/16.RecommenderSystems/ml-100k/mku.sh

@@ -0,0 +1,25 @@
+#!/bin/sh
+
+trap `rm -f tmp.$$; exit 1` 1 2 15
+
+for i in 1 2 3 4 5
+do
+	head -`expr $i \* 20000` u.data | tail -20000 > tmp.$$
+	sort -t"	" -k 1,1n -k 2,2n tmp.$$ > u$i.test
+	head -`expr \( $i - 1 \) \* 20000` u.data > tmp.$$
+	tail -`expr \( 5 - $i \) \* 20000` u.data >> tmp.$$
+	sort -t"	" -k 1,1n -k 2,2n tmp.$$ > u$i.base
+done
+
+allbut.pl ua 1 10 100000 u.data
+sort -t"	" -k 1,1n -k 2,2n ua.base > tmp.$$
+mv tmp.$$ ua.base
+sort -t"	" -k 1,1n -k 2,2n ua.test > tmp.$$
+mv tmp.$$ ua.test
+
+allbut.pl ub 11 20 100000 u.data
+sort -t"	" -k 1,1n -k 2,2n ub.base > tmp.$$
+mv tmp.$$ ub.base
+sort -t"	" -k 1,1n -k 2,2n ub.test > tmp.$$
+mv tmp.$$ ub.test
+

input/16.RecommenderSystems/ml-100k/u.genre

@@ -0,0 +1,20 @@
+unknown|0
+Action|1
+Adventure|2
+Animation|3
+Children's|4
+Comedy|5
+Crime|6
+Documentary|7
+Drama|8
+Fantasy|9
+Film-Noir|10
+Horror|11
+Musical|12
+Mystery|13
+Romance|14
+Sci-Fi|15
+Thriller|16
+War|17
+Western|18
+

input/16.RecommenderSystems/ml-100k/u.info

@@ -0,0 +1,3 @@
+943 users
+1682 items
+100000 ratings

input/16.RecommenderSystems/ml-100k/u.occupation

@@ -0,0 +1,21 @@
+administrator
+artist
+doctor
+educator
+engineer
+entertainment
+executive
+healthcare
+homemaker
+lawyer
+librarian
+marketing
+none
+other
+programmer
+retired
+salesman
+scientist
+student
+technician
+writer

input/16.RecommenderSystems/ml-100k/u.user

@@ -0,0 +1,943 @@
+1|24|M|technician|85711
+2|53|F|other|94043
+3|23|M|writer|32067
+4|24|M|technician|43537
+5|33|F|other|15213
+6|42|M|executive|98101
+7|57|M|administrator|91344
+8|36|M|administrator|05201
+9|29|M|student|01002
+10|53|M|lawyer|90703
+11|39|F|other|30329
+12|28|F|other|06405
+13|47|M|educator|29206
+14|45|M|scientist|55106
+15|49|F|educator|97301
+16|21|M|entertainment|10309
+17|30|M|programmer|06355
+18|35|F|other|37212
+19|40|M|librarian|02138
+20|42|F|homemaker|95660
+21|26|M|writer|30068
+22|25|M|writer|40206
+23|30|F|artist|48197
+24|21|F|artist|94533
+25|39|M|engineer|55107
+26|49|M|engineer|21044
+27|40|F|librarian|30030
+28|32|M|writer|55369
+29|41|M|programmer|94043
+30|7|M|student|55436
+31|24|M|artist|10003
+32|28|F|student|78741
+33|23|M|student|27510
+34|38|F|administrator|42141
+35|20|F|homemaker|42459
+36|19|F|student|93117
+37|23|M|student|55105
+38|28|F|other|54467
+39|41|M|entertainment|01040
+40|38|M|scientist|27514
+41|33|M|engineer|80525
+42|30|M|administrator|17870
+43|29|F|librarian|20854
+44|26|M|technician|46260
+45|29|M|programmer|50233
+46|27|F|marketing|46538
+47|53|M|marketing|07102
+48|45|M|administrator|12550
+49|23|F|student|76111
+50|21|M|writer|52245
+51|28|M|educator|16509
+52|18|F|student|55105
+53|26|M|programmer|55414
+54|22|M|executive|66315
+55|37|M|programmer|01331
+56|25|M|librarian|46260
+57|16|M|none|84010
+58|27|M|programmer|52246
+59|49|M|educator|08403
+60|50|M|healthcare|06472
+61|36|M|engineer|30040
+62|27|F|administrator|97214
+63|31|M|marketing|75240
+64|32|M|educator|43202
+65|51|F|educator|48118
+66|23|M|student|80521
+67|17|M|student|60402
+68|19|M|student|22904
+69|24|M|engineer|55337
+70|27|M|engineer|60067
+71|39|M|scientist|98034
+72|48|F|administrator|73034
+73|24|M|student|41850
+74|39|M|scientist|T8H1N
+75|24|M|entertainment|08816
+76|20|M|student|02215
+77|30|M|technician|29379
+78|26|M|administrator|61801
+79|39|F|administrator|03755
+80|34|F|administrator|52241
+81|21|M|student|21218
+82|50|M|programmer|22902
+83|40|M|other|44133
+84|32|M|executive|55369
+85|51|M|educator|20003
+86|26|M|administrator|46005
+87|47|M|administrator|89503
+88|49|F|librarian|11701
+89|43|F|administrator|68106
+90|60|M|educator|78155
+91|55|M|marketing|01913
+92|32|M|entertainment|80525
+93|48|M|executive|23112
+94|26|M|student|71457
+95|31|M|administrator|10707
+96|25|F|artist|75206
+97|43|M|artist|98006
+98|49|F|executive|90291
+99|20|M|student|63129
+100|36|M|executive|90254
+101|15|M|student|05146
+102|38|M|programmer|30220
+103|26|M|student|55108
+104|27|M|student|55108
+105|24|M|engineer|94043
+106|61|M|retired|55125
+107|39|M|scientist|60466
+108|44|M|educator|63130
+109|29|M|other|55423
+110|19|M|student|77840
+111|57|M|engineer|90630
+112|30|M|salesman|60613
+113|47|M|executive|95032
+114|27|M|programmer|75013
+115|31|M|engineer|17110
+116|40|M|healthcare|97232
+117|20|M|student|16125
+118|21|M|administrator|90210
+119|32|M|programmer|67401
+120|47|F|other|06260
+121|54|M|librarian|99603
+122|32|F|writer|22206
+123|48|F|artist|20008
+124|34|M|student|60615
+125|30|M|lawyer|22202
+126|28|F|lawyer|20015
+127|33|M|none|73439
+128|24|F|marketing|20009
+129|36|F|marketing|07039
+130|20|M|none|60115
+131|59|F|administrator|15237
+132|24|M|other|94612
+133|53|M|engineer|78602
+134|31|M|programmer|80236
+135|23|M|student|38401
+136|51|M|other|97365
+137|50|M|educator|84408
+138|46|M|doctor|53211
+139|20|M|student|08904
+140|30|F|student|32250
+141|49|M|programmer|36117
+142|13|M|other|48118
+143|42|M|technician|08832
+144|53|M|programmer|20910
+145|31|M|entertainment|V3N4P
+146|45|M|artist|83814
+147|40|F|librarian|02143
+148|33|M|engineer|97006
+149|35|F|marketing|17325
+150|20|F|artist|02139
+151|38|F|administrator|48103
+152|33|F|educator|68767
+153|25|M|student|60641
+154|25|M|student|53703
+155|32|F|other|11217
+156|25|M|educator|08360
+157|57|M|engineer|70808
+158|50|M|educator|27606
+159|23|F|student|55346
+160|27|M|programmer|66215
+161|50|M|lawyer|55104
+162|25|M|artist|15610
+163|49|M|administrator|97212
+164|47|M|healthcare|80123
+165|20|F|other|53715
+166|47|M|educator|55113
+167|37|M|other|L9G2B
+168|48|M|other|80127
+169|52|F|other|53705
+170|53|F|healthcare|30067
+171|48|F|educator|78750
+172|55|M|marketing|22207
+173|56|M|other|22306
+174|30|F|administrator|52302
+175|26|F|scientist|21911
+176|28|M|scientist|07030
+177|20|M|programmer|19104
+178|26|M|other|49512
+179|15|M|entertainment|20755
+180|22|F|administrator|60202
+181|26|M|executive|21218
+182|36|M|programmer|33884
+183|33|M|scientist|27708
+184|37|M|librarian|76013
+185|53|F|librarian|97403
+186|39|F|executive|00000
+187|26|M|educator|16801
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src/python/1.MLFoundation/NumPy.py

@@ -3,7 +3,7 @@
 '''
 Created on 2017-05-18
 Update  on 2017-05-18
-@author: Peter Harrington/山上有课树
+@author: Peter Harrington/1988/片刻
 《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
 '''
 from numpy import random, mat, eye

src/python/16.RecommenderSystems/sklearn-RS-demo-cf.py

@@ -0,0 +1,68 @@
+#!/usr/bin/python
+# coding:utf8
+
+from math import sqrt
+
+import numpy as np
+import pandas as pd
+from scipy.sparse.linalg import svds
+from sklearn import cross_validation as cv
+from sklearn.metrics import mean_squared_error
+from sklearn.metrics.pairwise import pairwise_distances
+
+# 加载数据集
+header = ['user_id', 'item_id', 'rating', 'timestamp']
+# http://files.grouplens.org/datasets/movielens/ml-100k.zip
+dataFile = 'input/16.RecommenderSystems/ml-100k/u.data'
+df = pd.read_csv(dataFile, sep='\t', names=header)
+
+n_users = df.user_id.unique().shape[0]
+n_items = df.item_id.unique().shape[0]
+print 'Number of users = ' + str(n_users) + ' | Number of movies = ' + str(n_items)
+
+# 拆分数据集
+train_data, test_data = cv.train_test_split(df, test_size=0.25)
+
+# 创建用户产品矩阵，针对测试数据和训练数据，创建两个矩阵：
+train_data_matrix = np.zeros((n_users, n_items))
+for line in train_data.itertuples():
+    train_data_matrix[line[1]-1, line[2]-1] = line[3]
+test_data_matrix = np.zeros((n_users, n_items))
+for line in test_data.itertuples():
+    test_data_matrix[line[1]-1, line[2]-1] = line[3]
+# 使用sklearn的pairwise_distances函数来计算余弦相似性。
+user_similarity = pairwise_distances(train_data_matrix, metric="cosine")
+item_similarity = pairwise_distances(train_data_matrix.T, metric="cosine")
+
+
+def predict(rating, similarity, type='user'):
+    if type == 'user':
+        mean_user_rating = rating.mean(axis=1)
+        rating_diff = (rating - mean_user_rating[:, np.newaxis])
+        pred = mean_user_rating[:, np.newaxis] + similarity.dot(rating_diff)/np.array([np.abs(similarity).sum(axis=1)]).T
+    elif type == 'item':
+        pred = rating.dot(similarity) / np.array([np.abs(similarity).sum(axis=1)])
+    return pred
+
+
+user_prediction = predict(train_data_matrix, user_similarity, type='user')
+item_prediction = predict(train_data_matrix, item_similarity, type='item')
+
+
+def rmse(prediction, ground_truth):
+    prediction = prediction[ground_truth.nonzero()].flatten()
+    ground_truth = ground_truth[ground_truth.nonzero()].flatten()
+    return sqrt(mean_squared_error(prediction, ground_truth))
+
+
+print 'User based CF RMSE: ' + str(rmse(user_prediction, test_data_matrix))
+print 'Item based CF RMSe: ' + str(rmse(item_prediction, test_data_matrix))
+
+sparsity = round(1.0 - len(df)/float(n_users*n_items), 3)
+print 'The sparsity level of MovieLen100K is ' + str(sparsity * 100) + '%'
+
+
+u, s, vt = svds(train_data_matrix, k=20)
+s_diag_matrix = np.diag(s)
+x_pred = np.dot(np.dot(u, s_diag_matrix), vt)
+print 'User-based CF MSE: ' + str(rmse(x_pred, test_data_matrix))

src/python/16.RecommenderSystems/sklearn-RS-demo-item.py

@@ -0,0 +1,30 @@
+#!/usr/bin/python
+# coding:utf8
+
+import numpy as np
+from sklearn.decomposition import NMF
+import matplotlib.pyplot as plt
+
+RATE_MATRIX = np.array(
+    [[5, 5, 3, 0, 5, 5],
+     [5, 0, 4, 0, 4, 4],
+     [0, 3, 0, 5, 4, 5],
+     [5, 4, 3, 3, 5, 5]]
+)
+
+nmf = NMF(n_components=2)
+user_distribution = nmf.fit_transform(RATE_MATRIX)
+item_distribution = nmf.components_
+
+item_distribution = item_distribution.T
+plt.plot(item_distribution[:, 0], item_distribution[:, 1], "b*")
+plt.xlim((-1, 3))
+plt.ylim((-1, 3))
+
+plt.title(u'the distribution of items (NMF)')
+count = 1
+for item in item_distribution:
+    plt.text(item[0], item[1], 'item '+str(count), bbox=dict(facecolor='red', alpha=0.2),)
+    count += 1
+
+plt.show()

src/python/16.RecommenderSystems/sklearn-RS-demo-user.py

@@ -0,0 +1,31 @@
+#!/usr/bin/python
+# coding:utf8
+
+import numpy as np
+from sklearn.decomposition import NMF
+import matplotlib.pyplot as plt
+
+RATE_MATRIX = np.array(
+    [[5, 5, 3, 0, 5, 5],
+     [5, 0, 4, 0, 4, 4],
+     [0, 3, 0, 5, 4, 5],
+     [5, 4, 3, 3, 5, 5]]
+)
+
+nmf = NMF(n_components=2)
+user_distribution = nmf.fit_transform(RATE_MATRIX)
+item_distribution = nmf.components_
+
+users = ['Ben', 'Tom', 'John', 'Fred']
+zip_data = zip(users, user_distribution)
+
+plt.title(u'the distribution of users (NMF)')
+plt.xlim((-1, 3))
+plt.ylim((-1, 4))
+for item in zip_data:
+    user_name = item[0]
+    data = item[1]
+    plt.plot(data[0], data[1], "b*")
+    plt.text(data[0], data[1], user_name, bbox=dict(facecolor='red', alpha=0.2),)
+
+plt.show()

src/python/16.RecommenderSystems/sklearn-RS-demo.py

@@ -0,0 +1,22 @@
+#!/usr/bin/python
+# coding:utf8
+
+import numpy as np
+from sklearn.decomposition import NMF
+import matplotlib.pyplot as plt
+
+RATE_MATRIX = np.array(
+    [[5, 5, 3, 0, 5, 5],
+     [5, 0, 4, 0, 4, 4],
+     [0, 3, 0, 5, 4, 5],
+     [5, 4, 3, 3, 5, 5]]
+)
+
+nmf = NMF(n_components=2)  # 设有2个隐主题
+user_distribution = nmf.fit_transform(RATE_MATRIX)
+item_distribution = nmf.components_
+
+print '用户的主题分布：'
+print user_distribution
+print '物品的主题分布：'
+print item_distribution