├── README.md
├── csv2js.py
├── data_util.js
├── decision-tree-demo.html
├── golf.csv
├── helpers.js
├── leaf.csv
├── learningjs.js
├── package.json
├── test_lr.js
├── test_tree.js
├── wpbc_real_test.csv
└── wpbc_real_train.csv


/README.md:
--------------------------------------------------------------------------------
 1 | LearningJS: A Javascript Implementation of Logistic Regression and C4.5 Decision Tree Algorithms
 2 | ==========
 3 | Author: Yandong Liu. Email: yandongl @ cs.cmu.edu
 4 | 
 5 | # Update
 6 | I've made some update on the data loading logic so now it reads in csv-format file. Previous version is still accessible but it's no longer supported.
 7 | 
 8 | # Introduction
 9 | Javascript implementation of several machine learning algorithms including Decision Tree and Logistic Regression this far. More to come.
10 | 
11 | # Online Demo
12 | Here's a online [demo](http://www.cs.cmu.edu/~yandongl/learningjs/decision-tree-demo.html) with visualization and a few datasets.
13 | 
14 | # Data format
15 | Input files need to be in CSV-format with 1st line being feature names. One of the features has to be called 'label'. E.g.  
16 | <pre>
17 | outlook, temp, humidity, wind, label
18 | text, real, text, text, feature_type
19 | 'Sunny',80,'High', 'Weak', 'No'
20 | 'Sunny',82,'High', 'Strong', 'No'
21 | 'Overcast',73,'High', 'Weak', 'Yes' 
22 | </pre>
23 | There's also an optional 2nd line for feature types and the 'label' column for 2nd line has to be called 'feature_type'. This is useful if feature types are mixed. For Logistic Regression, all features should be real numbers. E.g.
24 | <pre>
25 | label,a,b,c,d,e,f,g,h,i,j,k,l,m
26 | 1,1,0.72694,1.4742,0.32396,0.98535,1,0.83592,0.0046566,0.0039465,0.04779,0.12795,0.016108,0.0052323
27 | 2,2,0.74173,1.5257,0.36116,0.98152,0.99825,0.79867,0.0052423,0.0050016,0.02416,0.090476,0.0081195,0.002708
28 | 3,3,0.76722,1.5725,0.38998,0.97755,1,0.80812,0.0074573,0.010121,0.011897,0.057445,0.0032891,0.00092068
29 | 1,4,0.73797,1.4597,0.35376,0.97566,1,0.81697,0.0068768,0.0086068,0.01595,0.065491,0.0042707,0.0011544
30 | </pre>
31 | 
32 | 
33 | # Usage
34 | Data loading: data_util.js provides three methods:
35 | 
36 |  * `loadTextFile`: the csv-format file will be loaded from disk and columns are parsed as strings unless 2nd line specifies feature types.
37 |  * `loadRealFile`: the csv-format file will be loaded from disk and columns are parsed as real numbers.
38 |  * `loadString`: a big string will be chopped into lines and columns are parsed as strings unless 2nd line specifies feature types.
39 | 
40 | In the loading callback function you will obtain a data object D on which you can apply the learning methods. Note that only Decision Tree supports both real and categorical features. Logistic Regression works on real features only.  
41 | 
42 | 
43 | ```javascript
44 | <script type="text/javascript" src="http://code.jquery.com/jquery-1.8.1.min.js"></script>
45 | <script type="text/javascript" src="data_util.js"></script>
46 | <script type="text/javascript" src="learningjs.js"></script>
47 | loadString(content, function(D) {
48 |   var tree = new learningjs.tree();
49 |   tree.train(D, function(model, err){
50 |     if(err) {
51 |       console.log(err);
52 |     } else {
53 |       model.calcAccuracy(D.data, D.targets, function(acc, correct, total){
54 |         console.log( 'training: got '+correct +' correct out of '+total+' examples. accuracy:'+(acc*100.0).toFixed(2)+'%');
55 |       });
56 |     }
57 |   });
58 | }); 
59 | ```
60 | 
61 | # Use in Nodejs
62 | Similarly you need to import the lib and do the same:
63 | 
64 | ```javascript 
65 | var learningjs = require('learningjs.js');
66 | var data_util = require('data_util.js');
67 | var tree = new learningjs.tree();
68 | data_util.loadRealFile(fn_csv, function(D) {
69 | 
70 |   //normalize data
71 |   data_util.normalize(D.data, D.nfeatures); 
72 | 
73 |   //logistic regression. following params are optional
74 |   D.optimizer = 'sgd'; //default choice. other choice is 'gd'
75 |   D.learning_rate = 0.005;
76 |   D.l2_weight = 0.0;
77 |   D.iterations = 1000; //increase number of iterations for better performance
78 | 
79 |   new learningjs.logistic().train(D, function(model, err){
80 |     if(err) {
81 |       console.log(err);
82 |     } else {
83 |       model.calcAccuracy(D.data, D.targets, function(acc, correct, total){
84 |         console.log('training: got '+correct +' correct out of '+total+' examples. accuracy:'+(acc*100.0).toFixed(2)+'%');
85 |       });
86 |       data_util.loadRealFile(fn_test, function(T) {
87 |         model.calcAccuracy(T.data, T.targets, function(acc, correct, total){
88 |           console.log('    test: got '+correct +' correct out of '+total+' examples. accuracy:'+(acc*100.0).toFixed(2)+'%');
89 |         });
90 |       });
91 |     }
92 |   });
93 | }); 
94 | ```
95 | 
96 | # License
97 | MIT
98 | 


--------------------------------------------------------------------------------
/csv2js.py:
--------------------------------------------------------------------------------
 1 | '''
 2 |   convert csv file to json objects
 3 | 
 4 |   usage: cat csv_file | python csv2js.py > json_file
 5 | 
 6 |   note:
 7 |    1) 1st line must be header
 8 |    2) also remember to remove label from 'features' so it's not used for training
 9 |    3) then drag and drop to the drop zone of learningjs page
10 | '''
11 | import sys
12 | 
13 | header=True
14 | h=[]
15 | data=[]
16 | print 'var trainData=['
17 | lastfs=None
18 | for l in sys.stdin:
19 |   if(header):
20 |     fs = l.strip().split(',')
21 |     h = fs
22 |     header=False
23 |   else:
24 |     fs = l.strip().split(',')
25 |     if lastfs!=None:
26 |       print '{',
27 |       print ','.join([h[idx]+':\''+a+'\'' for idx,a in enumerate(lastfs)]),
28 |       print '},'
29 |     lastfs = fs
30 | print '{',
31 | print ','.join([h[idx]+':\''+a+'\'' for idx,a in enumerate(lastfs)]),
32 | print '}'
33 | print ']'
34 | 
35 | print 'var features=['
36 | print ','.join(['\''+a+'\'' for a in h])
37 | print ']'
38 | 
39 | 


--------------------------------------------------------------------------------
/data_util.js:
--------------------------------------------------------------------------------
  1 | ////////////////////////////////////////////////////////////////////////////////////////////////
  2 | // utilities for
  3 | //   loading a real number file
  4 | //   loading a text file
  5 | //   normalize matrix of real numbers
  6 | //
  7 | // author: yandong liu
  8 | //  email: yandongl _at_ cs.cmu.edu
  9 | ////////////////////////////////////////////////////////////////////////////////////////////////
 10 | 'use strict';
 11 | if(typeof require === 'function') {
 12 |   var fs = require('fs');
 13 |   var lazy = require("lazy");
 14 | }
 15 | 
 16 | var util = {
 17 | 
 18 | normalize: function(data, nfeatures) {
 19 |   for(var i=0;i<data.length;i++) {
 20 |     var mean=0.0, stderr = 0.0;
 21 |     for(var j=0;j<nfeatures;j++) {
 22 |       mean += data[i][j];
 23 |     }
 24 |     mean/=nfeatures;
 25 |     for(var j=0;j<nfeatures;j++) {
 26 |       var a = data[i][j]-mean;
 27 |       stderr += a*a;
 28 |     }
 29 |     stderr = Math.sqrt(stderr/nfeatures);
 30 |     for(var j=0;j<nfeatures;j++) {
 31 |       data[i][j] -= mean;
 32 |       data[i][j] /=stderr;
 33 |     }
 34 |   }
 35 | },
 36 | 
 37 | loadTextFile: function(fn, cb) {
 38 |   var data=[];
 39 |   var header = true;
 40 |   var targets=[];
 41 |   var label_col = -1;
 42 |   var l_features_id=[];
 43 |   var featuresType={};
 44 |   var l_features_name=[];
 45 |   var feature_name2id=[];
 46 |   var s_targets={};
 47 |   var l_targets=[];
 48 |   var nfeatures = 0; 
 49 |   if (!String.prototype.trim) {
 50 |     String.prototype.trim = function () {
 51 |       return this.replace(/^\s+|\s+$/g, '');
 52 |     };
 53 |   }
 54 |   new lazy(fs.createReadStream(fn))
 55 |   .on('end', function() {
 56 | 
 57 |     for(var key in s_targets) {
 58 |       if (s_targets.hasOwnProperty(key)) {
 59 |         l_targets.push(key);
 60 |       }
 61 |     }
 62 | 
 63 |     cb({data:data, l_featuresIndex:l_features_id, nfeatures:nfeatures, targets:targets, l_targets:l_targets, ntargets:l_targets.length, featureNames:l_features_name, feature_name2id:feature_name2id, featuresType:featuresType});
 64 |   })
 65 |   .lines
 66 |   .forEach(function(line){
 67 |     if(header) {
 68 |       header=false;
 69 |       var aa = line.toString().split(',');
 70 |       for(var i=0;i<aa.length;i++) {
 71 |         aa[i] = aa[i].trim();
 72 |         if (aa[i] === 'label') label_col = i;
 73 |         else {
 74 |           l_features_id.push(i);//id is the index in input file
 75 |           l_features_name.push(aa[i]);
 76 |           feature_name2id[aa[i]]=i;
 77 |         }
 78 |       }
 79 |       //console.log('l_featuresIndex:',l_features);
 80 |       //console.log('featureNames:',l_features_name);
 81 |       if(label_col === -1) {
 82 |         console.log('ERROR. No label column found');
 83 |         process.exit(1);
 84 |       }
 85 |       nfeatures = l_features_id.length;
 86 |     } else {
 87 |       var aa = line.toString().split(',');
 88 |       if (aa.length == (nfeatures+1)) {
 89 |         for(var i=0;i<aa.length;i++) {
 90 |           aa[i] = aa[i].trim();
 91 |         }
 92 |         if(aa[label_col] === 'feature_type') {
 93 |           for(var i=0;i<nfeatures;i++) {
 94 |             featuresType[l_features_name[i]]=aa[l_features_id[i]];//id points to index in input file
 95 |           }
 96 |         } else {
 97 |           var dd=[];
 98 |           for(var i=0;i<nfeatures;i++) {
 99 |             var a = aa[l_features_id[i]];
100 |             dd.push(a);
101 |           }
102 |           s_targets[aa[label_col]]=1;
103 |           data.push(dd);
104 |           targets.push(aa[label_col]);
105 |         }
106 |       } else {
107 |         console.log('skip line:', line.toString());
108 |       }
109 |     }
110 |   }); 
111 | },
112 | 
113 | loadRealFile: function(fn, cb) {
114 |   var data=[];
115 |   var header = true;
116 |   var targets=[];
117 |   var label_col = -1;
118 |   var l_features=[];
119 |   var l_features_name=[];
120 |   var s_targets={};
121 |   var l_targets=[];
122 |   var nfeatures = 0; 
123 |   if (!String.prototype.trim) {
124 |     String.prototype.trim = function () {
125 |       return this.replace(/^\s+|\s+$/g, '');
126 |     };
127 |   }
128 |   new lazy(fs.createReadStream(fn))
129 |   .on('end', function() {
130 | 
131 |     for(var key in s_targets) {
132 |       if (s_targets.hasOwnProperty(key)) {
133 |         l_targets.push(key);
134 |       }
135 |     } 
136 | 
137 |     var ntargets = l_targets.length;
138 |     cb({data:data, l_featuresIndex:l_features, nfeatures:nfeatures, targets:targets, l_targets:l_targets, ntargets:ntargets, featureNames:l_features_name});
139 |   })
140 |   .lines
141 |   .forEach(function(line){
142 |     if(header) {
143 |       header=false;
144 |       var aa = line.toString().split(',');
145 |       for(var i=0;i<aa.length;i++) {
146 |         aa[i] = aa[i].trim();
147 |         if (aa[i] === 'label') label_col = i;
148 |         else {
149 |           l_features.push(i);
150 |           l_features_name.push(aa[i]);
151 |         }
152 |       }
153 |       if(label_col === -1) {
154 |         console.log('ERROR. No label column found');
155 |         process.exit(1);
156 |       }
157 |       nfeatures = l_features.length;
158 |     } else {
159 |       var aa = line.toString().split(',');
160 |       var dd=[];
161 |       for(var i=0;i<aa.length;i++) {
162 |         aa[i] = aa[i].trim();
163 |       }
164 |       if(aa[label_col]==='feature_type') return true;
165 |       for(var i=0;i<nfeatures;i++) {
166 |         var a = parseFloat(aa[l_features[i]]);
167 |         dd.push(a||0);
168 |       }
169 |       s_targets[aa[label_col]]=1;
170 |       data.push(dd);
171 |       targets.push(aa[label_col]);
172 |     }
173 |   }); 
174 | } 
175 | 
176 | }
177 | 
178 | function loadString (content, cb) {
179 |   var data=[];
180 |   var header = true;
181 |   var targets=[];
182 |   var label_col = -1;
183 |   var l_features_id=[];
184 |   var l_features_name=[];
185 |   var feature_name2id=[];
186 |   var s_targets={};
187 |   var l_targets=[];
188 |   var nfeatures = 0; 
189 |   var featuresType={};
190 |   var data_start_line=1;
191 | 
192 |   if (!String.prototype.trim) {
193 |     String.prototype.trim = function () {
194 |       return this.replace(/^\s+|\s+$/g, '');
195 |     };
196 |   }
197 | 
198 |   var lines = content.split('\n');
199 |   //header
200 |   var aa = lines[0].toString().split(',');
201 |   for(var i=0;i<aa.length;i++) {
202 |     aa[i] = aa[i].trim();
203 |     if (aa[i] === 'label') label_col = i;
204 |     else {
205 |       l_features_id.push(i);
206 |       l_features_name.push(aa[i]);
207 |       feature_name2id[aa[i]]=i;
208 |     }
209 |   }
210 |   if(label_col === -1) {
211 |     console.log('ERROR. No label column found');
212 |     return;
213 |   }
214 | 
215 |   nfeatures = l_features_id.length;
216 | 
217 |   aa = lines[1].toString().split(',');
218 |   if(aa[label_col].trim() === 'feature_type') {
219 |     data_start_line++;
220 |     var cnt = 0;
221 |     for(var i=0;i<aa.length;i++) {
222 |       aa[i] = aa[i].trim();
223 |       if(i!==label_col) {
224 |         featuresType[l_features_name[cnt]]=aa[i];
225 |         cnt++;
226 |       }
227 |     }
228 |   }
229 | 
230 |   for(var j=data_start_line;j<lines.length;j++) {
231 |     var line = lines[j].toString();
232 |     aa = line.split(',');
233 |     if (aa.length == (nfeatures+1)) {
234 |       for(var i=0;i<aa.length;i++) {
235 |         aa[i] = aa[i].trim();
236 |       }
237 |       var dd=[];
238 |       for(var i=0;i<nfeatures;i++) {
239 |         var a = aa[l_features_id[i]];
240 |         dd.push(a);
241 |       }
242 |       s_targets[aa[label_col]]=1;
243 |       data.push(dd);
244 |       targets.push(aa[label_col]);
245 |     } else {
246 |       console.log('skip line:', line);
247 |     }
248 |   }
249 | 
250 |   for(var key in s_targets) {
251 |     if (s_targets.hasOwnProperty(key)) {
252 |       l_targets.push(key);
253 |     }
254 |   }
255 | 
256 |   var ntargets = l_targets.length;
257 | 
258 |   cb({data:data, l_featuresIndex:l_features_id, nfeatures:nfeatures, targets:targets, l_targets:l_targets, ntargets:ntargets, featureNames:l_features_name, feature_name2id:feature_name2id, featuresType:featuresType});
259 | 
260 | }
261 | 
262 | if(typeof module !== 'undefined' && module.exports) 
263 |   module.exports = util;
264 | 


--------------------------------------------------------------------------------
/decision-tree-demo.html:
--------------------------------------------------------------------------------
  1 | <html>
  2 | <body>
  3 | <head>
  4 |   <script type="text/javascript" src="js/jquery-1.11.0.min.js"></script>
  5 |   <script type="text/javascript" src="js/underscore-min.js"></script>
  6 |   <script type='text/javascript' src='https://www.google.com/jsapi?autoload={"modules":[{"name":"visualization","version":"1","packages":["orgchart"]}]}'></script>
  7 |   <script type="text/javascript" src="js/learningjs.js"></script> 
  8 |   <link rel="stylesheet" href="stylesheets/styles.css"/>
  9 | </head>
 10 | <h2>
 11 | A Javascript Implementation of Logistic Regression and C4.5 Decision Tree Algorithms
 12 | </h2>
 13 | <p> Author: Yandong Liu. Email: &#121;&#097;&#110;&#100;&#111;&#110;&#103;&#108;&#032;@&#032;&#099;&#115;&#046;&#099;&#109;&#117;&#046;&#101;&#100;&#117;. Date: 2013.5</p>
 14 | <p>
 15 | <span class="topic">Update:</span> I've made some update on the data loading logic so now it reads in csv-format file. <a href="https://www.cs.cmu.edu/~yandongl/learningjs_old/decision-tree-demo.html">Previous version</a> is still accessible but it's no longer supported.
 16 | </p>
 17 | <hr/>
 18 | 
 19 | <span class="topic">Drop a csv file (such as this <a href="wpbc_drop_real_train.csv">training</a> and <a href="wpbc_drop_real_test.csv">test</a> files) to below areas and see the learning in action!</span>
 20 | 
 21 | <table class="holder1">
 22 | <tr>
 23 | <td id="drop_train_zone" class="holder">
 24 | Drop training data file here
 25 | </td>
 26 | <td id="drop_test_zone" class="holder">
 27 | Drop test data file here
 28 | <td>
 29 | </tr>
 30 | </table>
 31 | 
 32 | <div id="status" class="status"></div>
 33 | <table id="samples" class="standard"> </table>
 34 | <div id="canvas"> </div>
 35 | <hr/>
 36 | 
 37 | <p>
 38 | <span class="topic">Introduction:</span> 
 39 | Javascript implementation of several machine learning algorithms including Decision Tree and Logistic Regression this far. More to come.
 40 | </p>
 41 | 
 42 | <p> 
 43 | <span class="topic">Data format: </span> Input files need to be in CSV-format with 1st line being feature names. One of the features has to be called 'label'. E.g. 
 44 | <pre>
 45 | outlook, temp, humidity, wind, label
 46 | text, real, text, text, feature_type
 47 | 'Sunny',80,'High', 'Weak', 'No'
 48 | 'Sunny',82,'High', 'Strong', 'No'
 49 | 'Overcast',73,'High', 'Weak', 'Yes' 
 50 | </pre>
 51 | There's also an optional 2nd line for feature types and the 'label' column for 2nd line has to be called 'feature_type'. This is useful if feature types are mixed.
 52 | </p>
 53 | <span class="topic">Usage: </span>
 54 | <p>
 55 | <li><span class="topic">Data loading:</span> data_util.js provides three methods: 
 56 | <ul>
 57 | <li><span class="var">loadTextFile</span>: the csv-format file will be loaded from disk and columns are parsed as strings unless 2nd line specifies feature types.
 58 | <li><span class="var">loadRealFile</span>: the csv-format file will be loaded from disk and columns are parsed as real numbers.
 59 | <li><span class="var">loadString</span>: a big string will be chopped into lines and columns are parsed as strings unless 2nd line specifies feature types.
 60 | </ul>
 61 | In the loading callback function you will obtain a data object <span class="italic">D</span> on which you can apply the learning methods. Note that only Decision Tree supports both real and categorical features. Logistic Regression works on real features only.
 62 | 
 63 | <p>
 64 | <li><span class="topic">Use in browser:</span>
 65 | <pre>
 66 | loadString(content, function(D) {
 67 |   var tree = new learningjs.tree();
 68 |   tree.train(D, function(model, err){
 69 |     if(err) {
 70 |       console.log(err);
 71 |     } else {
 72 |       model.calcAccuracy(D.data, D.targets, function(acc, correct, total){
 73 |         console.log( 'training: got '+correct +' correct out of '+total+' examples. accuracy:'+(acc*100.0).toFixed(2)+'%');
 74 |       });
 75 |     }
 76 |   });
 77 | }); 
 78 | </pre>
 79 | Check the source code of this page and see how it works on the dropped files.
 80 | </p>
 81 | 
 82 | <li><span class="topic">Use in nodejs:</span>
 83 | <pre>
 84 | data_util.loadRealFile(fn_csv, function(D) {
 85 | 
 86 |   //normalize data
 87 |   data_util.normalize(D.data, D.nfeatures); 
 88 | 
 89 |   //logistic regression. following params are optional
 90 |   D.optimizer = 'sgd'; //default choice. other choice is 'gd'
 91 |   D.learning_rate = 0.005;
 92 |   D.l2_weight = 0.000001;
 93 |   D.iterations = 1000; //increase number of iterations for better performance
 94 | 
 95 |   new learningjs.logistic().train(D, function(model, err){
 96 |     if(err) {
 97 |       console.log(err);
 98 |     } else {
 99 |       model.calcAccuracy(D.data, D.targets, function(acc, correct, total){
100 |         console.log('training: got '+correct +' correct out of '+total+' examples. accuracy:'+(acc*100.0).toFixed(2)+'%');
101 |       });
102 |       data_util.loadRealFile(fn_test, function(T) {
103 |         model.calcAccuracy(T.data, T.targets, function(acc, correct, total){
104 |           console.log('    test: got '+correct +' correct out of '+total+' examples. accuracy:'+(acc*100.0).toFixed(2)+'%');
105 |         });
106 |       });
107 |     }
108 |   });
109 | }); 
110 | </pre>
111 | Here's a sample code file for <a href="test_tree.js">tree</a> and <a href="test_lr.js">logistic regression</a> for its application in nodejs.
112 | 
113 | <hr/>
114 | <p>
115 | <span class="topic">License:</span> MIT
116 | </p> 
117 | 
118 | Also see the <a href="https://github.com/yandongliu/learningjs">source code</a>
119 | 
120 | <script type="text/javascript" src="./js/data_util.js"></script>
121 | <script type="text/javascript" src="./js/helpers.js"></script>
122 | </body>
123 | </html>
124 | 


--------------------------------------------------------------------------------
/golf.csv:
--------------------------------------------------------------------------------
 1 | outlook, temp, humidity, wind,label
 2 | 'Sunny','Hot','High', 'Weak','No'
 3 | 'Sunny','Hot','High', 'Strong','No'
 4 | 'Overcast','Hot','High', 'Weak','Yes'
 5 | 'Rain','Mild','High', 'Weak','Yes'
 6 | 'Rain','Cool','Normal', 'Weak','Yes'
 7 | 'Rain','Cool','Normal', 'Strong','No'
 8 | 'Overcast','Cool','Normal', 'Strong','Yes'
 9 | 'Sunny','Mild','High', 'Weak','No'
10 | 'Sunny','Cool','Normal', 'Weak','Yes'
11 | 'Rain','Mild','Normal', 'Weak','Yes'
12 | 'Sunny','Mild','Normal', 'Strong','Yes'
13 | 'Overcast','Mild','High', 'Strong','Yes'
14 | 'Overcast','Hot','Normal', 'Weak','Yes'
15 | 'Rain','Mild','High', 'Strong','No' 
16 | 


--------------------------------------------------------------------------------
/helpers.js:
--------------------------------------------------------------------------------
  1 | function renderTest (model, samples, features, targets, $el) {
  2 |   $el.append("<tr style='background:#eeeeee;'><td>"+features.join('</td><td>')+'</td><td>Prediction</td><td>Actual</td></tr>');
  3 |   //_.each(samples,function(s) {
  4 |   for(var i=0;i<samples.length;i++) {
  5 |     //var vals_for_sample = _.map(features,function(x){return s[x]});
  6 |     var s = samples[i];
  7 |     var target = targets[i];
  8 |     var vals_for_sample = s;
  9 |     $el.append("<tr><td>"+vals_for_sample.join('</td><td>')+"</td><td><b>"+model.predict(s)+"</b></td><td> "+target+"</td></tr>");
 10 |   }
 11 | } 
 12 | 
 13 | function processTestFile(f) {
 14 |   var reader = new FileReader(); 
 15 |   reader.onload = (function(theFile) {
 16 |     return function(e) { 
 17 |       loadString(e.target.result, function(D) {
 18 |         var start = (new Date).getTime();
 19 |         var tree = new learningjs.tree();
 20 |         if(typeof trained_model === 'undefined') {
 21 |           alert('You need to train a model first.');
 22 |         } else {
 23 |           $("#status").append('Testing in process</br>');
 24 |           console.log('model:',trained_model);
 25 |           var elapsed = 0.00;
 26 |           var diff = (new Date).getTime() - start;
 27 |           console.log('Testing took ' + diff.toFixed(0) + " ms.");
 28 |           trained_model.calcAccuracy(D.data, D.targets, function(acc, correct, total){
 29 |             console.log( 'Testing: got '+correct +' correct out of '+total+' examples. accuracy:'+(acc*100.0).toFixed(2)+'%');
 30 |             $("#status").append( 'Testing: got '+correct +' correct out of '+total+' examples. accuracy:'+(acc*100.0).toFixed(2)+'%<br/>');
 31 |           });
 32 |           renderTest(trained_model, D.data,D.featureNames, D.targets, $("#samples"));
 33 |         }
 34 |       });
 35 |     };
 36 |   })(f); 
 37 |   reader.readAsText(f);
 38 | }
 39 | 
 40 | function processTrainFile(f) {
 41 |   var reader = new FileReader(); 
 42 |   reader.onload = (function(theFile) {
 43 |     return function(e) { 
 44 |       loadString(e.target.result, function(D) {
 45 |         //console.log(D);
 46 |         var start = (new Date).getTime();
 47 |         var tree = new learningjs.tree();
 48 |         $("#status").append('Training in process</br>');
 49 |         tree.train(D, function(model, err){
 50 |           if(err) {
 51 |             console.log(err);
 52 |           } else {
 53 |             console.log('drawing tree');
 54 |             tree.drawGraph(model,'canvas', function(err){
 55 |               if(err)
 56 |                   $("#status").append('Error.'+err+'<br/>'); 
 57 |             });
 58 |             console.log('model:',model);
 59 |             trained_model = model;
 60 |             var elapsed = 0.00;
 61 |             var diff = (new Date).getTime() - start;
 62 |             console.log('training took ' + diff.toFixed(0) + " ms.");
 63 |             model.calcAccuracy(D.data, D.targets, function(acc, correct, total){
 64 |               console.log( 'training: got '+correct +' correct out of '+total+' examples. accuracy:'+(acc*100.0).toFixed(2)+'%');
 65 |             });
 66 |             $("#status").append('Model training has finished. Now drop your test file.</br>');
 67 |           }
 68 |         });
 69 |       });
 70 |     };
 71 |   })(f); 
 72 |   reader.readAsText(f);
 73 | }
 74 | 
 75 | function handleTestFileSelect(evt) {
 76 |   evt.stopPropagation();
 77 |   evt.preventDefault();
 78 |   this.className='holder';
 79 |   var f = evt.dataTransfer.files[0]; // FileList object
 80 |   console.log(f.type);
 81 |   processTestFile(f);
 82 | }
 83 | 
 84 | function handleTrainFileSelect(evt) {
 85 |   $('#canvas').html('');
 86 |   $('#status').html('');
 87 |   $('#samples').html('');
 88 |   evt.stopPropagation();
 89 |   evt.preventDefault();
 90 |   this.className='holder';
 91 |   var f = evt.dataTransfer.files[0]; // FileList object
 92 |   console.log(f.type);
 93 |   processTrainFile(f);
 94 | }
 95 | 
 96 | function handleDragOver(evt) {
 97 |   evt.stopPropagation();
 98 |   evt.preventDefault();
 99 |   evt.dataTransfer.dropEffect = 'copy';
100 |   this.className='holder hover';
101 | }
102 | 
103 | function handleLeave(evt) {
104 |   this.className='holder';
105 | }
106 | 
107 | $(function(){
108 |   var dropZone = document.getElementById('drop_train_zone');
109 |   dropZone.addEventListener('dragover', handleDragOver, false);
110 |   dropZone.addEventListener('dragleave', handleLeave, false);
111 |   dropZone.addEventListener('drop', handleTrainFileSelect, false);
112 |   var dropZone2 = document.getElementById('drop_test_zone');
113 |   dropZone2.addEventListener('dragover', handleDragOver, false);
114 |   dropZone2.addEventListener('dragleave', handleLeave, false);
115 |   dropZone2.addEventListener('drop', handleTestFileSelect, false);
116 | 
117 |   function deleteVars() {
118 |     $('#demoScript').remove();
119 |     delete _training_data;
120 |     delete training_data;
121 |     delete features;
122 |     delete classlabel;
123 |     delete test_data;
124 |   }
125 | 
126 |   $('#reset').click(function() {
127 |     deleteVars();
128 |     $('#canvas').html('');
129 |     $('#status').html('');
130 |     $('#samples').html('');
131 |   });
132 | });
133 | 
134 | var trained_model = undefined;
135 | 


--------------------------------------------------------------------------------
/leaf.csv:
--------------------------------------------------------------------------------
  1 | label,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o
  2 | 1,1,0.72694,1.4742,0.32396,0.98535,1,0.83592,0.0046566,0.0039465,0.04779,0.12795,0.016108,0.0052323,0.00027477,1.1756
  3 | 1,2,0.74173,1.5257,0.36116,0.98152,0.99825,0.79867,0.0052423,0.0050016,0.02416,0.090476,0.0081195,0.002708,7.4846e-05,0.69659
  4 | 1,3,0.76722,1.5725,0.38998,0.97755,1,0.80812,0.0074573,0.010121,0.011897,0.057445,0.0032891,0.00092068,3.7886e-05,0.44348
  5 | 1,4,0.73797,1.4597,0.35376,0.97566,1,0.81697,0.0068768,0.0086068,0.01595,0.065491,0.0042707,0.0011544,6.6272e-05,0.58785
  6 | 1,5,0.82301,1.7707,0.44462,0.97698,1,0.75493,0.007428,0.010042,0.0079379,0.045339,0.0020514,0.00055986,2.3504e-05,0.34214
  7 | 1,6,0.72997,1.4892,0.34284,0.98755,1,0.84482,0.0049451,0.0044506,0.010487,0.058528,0.0034138,0.0011248,2.4798e-05,0.34068
  8 | 1,7,0.82063,1.7529,0.44458,0.97964,0.99649,0.7677,0.0059279,0.0063954,0.018375,0.080587,0.0064523,0.0022713,4.1495e-05,0.53904
  9 | 1,8,0.77982,1.6215,0.39222,0.98512,0.99825,0.80816,0.0050987,0.0047314,0.024875,0.089686,0.0079794,0.0024664,0.00014676,0.66975
 10 | 1,9,0.83089,1.8199,0.45693,0.9824,1,0.77106,0.0060055,0.006564,0.0072447,0.040616,0.0016469,0.00038812,3.2863e-05,0.33696
 11 | 1,10,0.90631,2.3906,0.58336,0.97683,0.99825,0.66419,0.0084019,0.012848,0.0070096,0.042347,0.0017901,0.00045889,2.8251e-05,0.28082
 12 | 1,11,0.7459,1.4927,0.34116,0.98296,1,0.83088,0.0055665,0.0056395,0.0057679,0.036511,0.0013313,0.00030872,3.1839e-05,0.25026
 13 | 1,12,0.79606,1.6934,0.43387,0.98181,1,0.76985,0.0077992,0.011071,0.013677,0.057832,0.0033334,0.00081648,0.00013855,0.49751
 14 | 2,1,0.93361,2.7582,0.64257,0.98346,1,0.59851,0.0055336,0.0055731,0.029712,0.089889,0.0080153,0.0020648,0.00023883,0.91499
 15 | 2,2,0.91186,2.4994,0.60323,0.983,1,0.64916,0.0061494,0.0068823,0.018887,0.072486,0.0052267,0.0014887,8.3271e-05,0.67811
 16 | 2,3,0.89063,2.2927,0.56667,0.98732,1,0.66427,0.0028365,0.0014643,0.029272,0.091328,0.0082717,0.0022383,0.00020166,0.87177
 17 | 2,4,0.86755,2.009,0.51464,0.98691,1,0.70277,0.0054439,0.0053937,0.030348,0.092063,0.0084044,0.0022541,0.00019854,0.94545
 18 | 2,5,0.91852,2.5247,0.61648,0.9787,1,0.63037,0.0050494,0.0046404,0.02309,0.082029,0.0066839,0.0018929,0.00012452,0.71713
 19 | 2,6,0.88795,2.2038,0.56218,0.97835,0.99825,0.64158,0.0059242,0.0063874,0.032722,0.092969,0.0085691,0.0021199,0.00027729,1.008
 20 | 2,7,0.85121,1.9548,0.4892,0.98622,1,0.70267,0.0039733,0.0028733,0.020258,0.070841,0.0049933,0.0012274,0.00014929,0.74174
 21 | 2,8,0.89084,2.2979,0.57815,0.97389,1,0.64598,0.015271,0.042443,0.028461,0.086477,0.0074228,0.0018832,0.00024345,0.91307
 22 | 2,9,0.93062,2.8973,0.65828,0.98182,1,0.5795,0.0064894,0.0076645,0.023606,0.072237,0.005191,0.0011217,0.00025558,0.90513
 23 | 2,10,0.84113,1.86,0.46549,0.99039,1,0.75976,0.0046759,0.0039793,0.062798,0.13234,0.017213,0.0044528,0.00065523,1.653
 24 | 3,1,0.70273,1.2099,0.36317,0.9211,0.98772,0.60555,0.023597,0.10134,0.089301,0.20088,0.038786,0.015895,0.0004049,1.5371
 25 | 3,2,0.66307,1.2065,0.32559,0.94952,0.99649,0.75954,0.013388,0.032621,0.021815,0.097143,0.0093485,0.0040284,3.6303e-05,0.5341
 26 | 3,3,0.61289,1.0991,0.33117,0.92405,0.98421,0.61661,0.025545,0.11877,0.054687,0.1606,0.025145,0.011672,0.00012121,1.1076
 27 | 3,4,0.70668,1.251,0.38111,0.94226,0.99825,0.6925,0.019432,0.068724,0.031587,0.11502,0.013056,0.0053112,8.6352e-05,0.72247
 28 | 3,5,0.66889,1.1435,0.3846,0.90355,0.99649,0.60571,0.028329,0.14606,0.057506,0.15931,0.024752,0.010304,0.00018533,1.1365
 29 | 3,6,0.50139,1.0066,0.29593,0.91585,0.99825,0.64029,0.021782,0.086347,0.054635,0.1598,0.024899,0.011106,0.00016162,1.0511
 30 | 3,7,0.60803,1.0646,0.3446,0.90487,0.99649,0.67517,0.031915,0.18538,0.06245,0.16411,0.026225,0.010602,0.00022964,1.2307
 31 | 3,8,0.56599,1.0427,0.35318,0.89086,0.99825,0.62068,0.032971,0.19785,0.026348,0.10589,0.011088,0.0046505,5.2046e-05,0.62671
 32 | 3,9,0.68605,1.1632,0.37528,0.91639,0.99825,0.61136,0.030339,0.16753,0.058401,0.16858,0.027633,0.012886,0.00015525,1.0815
 33 | 3,10,0.59571,1.084,0.32592,0.92056,0.99825,0.65698,0.024753,0.11151,0.042051,0.14722,0.021213,0.010881,6.468e-05,0.83221
 34 | 4,1,0.39432,1.1191,0.17272,0.96806,0.99298,0.73417,0.012499,0.028432,0.073063,0.13718,0.01847,0.0040597,0.00090977,1.8893
 35 | 4,2,0.62011,1.3298,0.2836,0.96952,0.99825,0.77871,0.0086562,0.013637,0.04722,0.1186,0.013872,0.0037549,0.00044188,1.2024
 36 | 4,3,0.73429,1.5069,0.40827,0.94518,1,0.62558,0.014987,0.040879,0.038113,0.092188,0.0084269,0.0017773,0.00047378,1.335
 37 | 4,4,0.64161,1.3004,0.31556,0.93324,0.97895,0.65487,0.017963,0.058729,0.030897,0.08221,0.0067132,0.0014678,0.00029878,1.2359
 38 | 4,5,0.64056,1.3166,0.28616,0.97053,0.99649,0.77183,0.0091632,0.015281,0.020413,0.065575,0.0042817,0.00089749,0.00020433,0.86218
 39 | 4,6,0.74242,1.5529,0.38804,0.95593,0.99474,0.68188,0.012904,0.030306,0.020679,0.064242,0.0041101,0.00079505,0.00026812,0.87687
 40 | 4,7,0.39289,1.1286,0.17039,0.96405,1,0.79407,0.011761,0.025176,0.025409,0.078211,0.0060798,0.0015299,0.00015762,1.0326
 41 | 4,8,0.53017,1.2503,0.23254,0.97337,1,0.82703,0.0066263,0.0079913,0.048541,0.10941,0.011828,0.0027809,0.00048839,1.5452
 42 | 5,1,0.87844,1.8096,0.63151,0.83923,0.83684,0.37688,0.043563,0.34539,0.045468,0.12554,0.015515,0.0051589,0.00022842,1.1374
 43 | 5,2,0.88075,1.736,0.58345,0.83383,0.91754,0.41551,0.040582,0.29973,0.035786,0.10047,0.0099932,0.002759,0.00025802,1.0952
 44 | 5,3,0.86545,1.8803,0.62039,0.82443,0.85439,0.33077,0.047,0.40204,0.039518,0.1157,0.01321,0.0042406,0.00020084,1.0136
 45 | 5,4,0.93671,2.4151,0.7298,0.81793,0.86491,0.33439,0.080539,1.1805,0.048722,0.12051,0.014314,0.0039983,0.00037216,1.3083
 46 | 5,5,0.92676,2.222,0.6558,0.82432,0.89474,0.35618,0.038012,0.26298,0.059981,0.14901,0.021723,0.0075251,0.00028398,1.353
 47 | 5,6,0.90944,2.1139,0.69555,0.80873,0.85263,0.31039,0.047267,0.40663,0.039446,0.10517,0.010939,0.0028913,0.00029366,1.1602
 48 | 5,7,0.89493,2.0494,0.64925,0.81682,0.83509,0.35318,0.049955,0.45418,0.041808,0.12036,0.01428,0.0048007,0.00017096,1.0962
 49 | 5,8,0.93613,2.3898,0.67175,0.84434,0.90526,0.37007,0.039277,0.28077,0.048447,0.11955,0.014091,0.0037887,0.00042459,1.2593
 50 | 5,9,0.88172,1.774,0.63974,0.8499,0.87368,0.34354,0.051776,0.4879,0.06103,0.1519,0.022554,0.0080813,0.00025039,1.3805
 51 | 5,10,0.86224,1.7434,0.62464,0.85754,0.89298,0.32238,0.050792,0.46952,0.064428,0.16335,0.025989,0.010189,0.00023873,1.3343
 52 | 5,11,0.82268,1.6657,0.59909,0.84493,0.85614,0.32056,0.048803,0.43348,0.079282,0.18571,0.033338,0.013523,0.00027502,1.5377
 53 | 5,12,0.82135,1.6035,0.6025,0.84981,0.87544,0.31304,0.056079,0.57236,0.069239,0.16319,0.02594,0.0092918,0.00032134,1.4686
 54 | 6,1,0.46348,1.0544,0.72231,0.63754,0.47544,0.20039,0.14864,4.0212,0.010917,0.053494,0.0028534,0.00087543,2.9594e-05,0.53199
 55 | 6,2,0.50784,1.0544,0.623,0.73625,0.59649,0.27687,0.13296,3.2176,0.014816,0.067044,0.0044748,0.0013737,3.792e-05,0.52114
 56 | 6,3,0.47728,1.2115,0.68343,0.76364,0.56667,0.23896,0.10747,2.1022,0.024036,0.080623,0.0064581,0.0018312,0.00010648,0.90702
 57 | 6,4,0.52744,1.0419,0.51662,0.77201,0.84211,0.33784,0.11438,2.3811,0.0254,0.082047,0.0066868,0.0017982,0.00012196,0.944
 58 | 6,5,0.50964,1.2067,0.70618,0.72175,0.56491,0.21009,0.11264,2.3093,0.021181,0.080306,0.0064077,0.0020323,6.5828e-05,0.73274
 59 | 6,6,0.63965,1.2323,0.60663,0.77037,0.62105,0.24135,0.12438,2.8155,0.025438,0.096215,0.0091723,0.0034208,5.1566e-05,0.75194
 60 | 6,7,0.71847,1.2291,0.693,0.75574,0.66667,0.26004,0.098823,1.7774,0.021217,0.073201,0.0053299,0.0013612,0.00012452,0.79862
 61 | 6,8,0.47815,1.1032,0.52998,0.81654,0.8193,0.33753,0.11547,2.4268,0.006859,0.042071,0.0017668,0.00047564,1.4404e-05,0.30832
 62 | 7,1,0.84886,1.7011,0.5348,0.81465,0.97018,0.37276,0.035424,0.22838,0.038846,0.1192,0.014009,0.0057424,0.00010856,1.2269
 63 | 7,2,0.83755,1.5634,0.53329,0.76697,0.95088,0.27825,0.039592,0.28528,0.023152,0.087838,0.0076564,0.0028187,5.6309e-05,0.8079
 64 | 7,3,0.84315,1.8134,0.54611,0.84406,0.96667,0.49437,0.038509,0.2699,0.054653,0.14411,0.020345,0.0082654,0.00016275,1.495
 65 | 7,4,0.82602,1.6637,0.49244,0.8862,0.99298,0.57136,0.022152,0.089306,0.034444,0.1012,0.010138,0.0030708,0.00016513,1.1047
 66 | 7,5,0.86486,1.7986,0.55606,0.87745,0.97018,0.52669,0.029555,0.15898,0.027883,0.092866,0.0085504,0.0031577,0.00012091,0.9961
 67 | 7,6,0.84563,1.7418,0.49818,0.92748,1,0.62301,0.014708,0.039374,0.025028,0.077667,0.0059959,0.002105,0.00029619,1.1899
 68 | 7,7,0.87163,2.0659,0.55339,0.94868,1,0.60231,0.031671,0.18255,0.049706,0.1344,0.017742,0.0066921,0.00016133,1.4016
 69 | 7,8,0.86587,1.9999,0.52725,0.97215,0.99474,0.67283,0.0092956,0.015726,0.021192,0.073946,0.0054383,0.0014673,0.00015488,0.78201
 70 | 7,9,0.82373,1.6421,0.47458,0.85777,0.98421,0.50374,0.020049,0.073157,0.018698,0.075567,0.005678,0.0023517,8.791e-05,0.7658
 71 | 7,10,0.84285,1.8572,0.51931,0.91339,0.97544,0.60526,0.028734,0.15027,0.038283,0.1156,0.013186,0.0048867,0.00012558,1.1185
 72 | 8,1,0.98502,6.0596,0.83612,0.97628,0.98772,0.31546,0.02322,0.098133,0.03683,0.10661,0.011238,0.0032908,0.00022206,1.0185
 73 | 8,2,0.98487,6.0229,0.83628,0.96522,1,0.29509,0.024667,0.11074,0.0303,0.097095,0.0093394,0.0027833,0.00018341,0.85025
 74 | 8,3,0.98853,6.628,0.84928,0.96861,0.97719,0.27717,0.029032,0.1534,0.032722,0.10283,0.010462,0.0033387,0.00013116,0.94985
 75 | 8,4,0.98326,5.8308,0.82878,0.96489,1,0.30777,0.014672,0.039178,0.019278,0.073118,0.0053178,0.0014657,9.7882e-05,0.66403
 76 | 8,5,0.98592,6.0269,0.83884,0.96027,0.96842,0.28345,0.025004,0.11379,0.021226,0.080869,0.0064972,0.001957,9.1646e-05,0.64839
 77 | 8,6,0.98695,6.2904,0.84124,0.97085,1,0.29288,0.023221,0.09814,0.025859,0.089821,0.0080032,0.0024919,9.7883e-05,0.8112
 78 | 8,7,0.98717,6.5173,0.84726,0.96846,1,0.28899,0.022056,0.08854,0.042124,0.11842,0.013829,0.0043818,0.00020215,1.098
 79 | 8,8,0.98361,5.8719,0.82847,0.95488,0.98421,0.32134,0.040164,0.29359,0.039243,0.11622,0.013327,0.0043872,0.00019465,1.0006
 80 | 8,9,0.98643,6.1273,0.84737,0.95274,0.89649,0.29982,0.019421,0.068643,0.028986,0.095642,0.0090645,0.0027526,0.00014425,0.82529
 81 | 8,10,0.986,6.1763,0.83942,0.97837,0.99298,0.31607,0.011769,0.025208,0.051048,0.13524,0.017961,0.0060176,0.0003128,1.166
 82 | 8,11,0.98824,7.142,0.86069,0.96683,0.99474,0.26951,0.021697,0.08568,0.024361,0.087108,0.0075307,0.0023055,9.7934e-05,0.7396
 83 | 9,1,0.47821,1.2059,0.34976,0.90378,0.99649,0.51298,0.030166,0.16561,0.082509,0.17347,0.029213,0.0093049,0.00067871,1.5956
 84 | 9,2,0.53941,1.278,0.32286,0.91793,0.97368,0.63545,0.034884,0.22147,0.060967,0.15124,0.022362,0.0076389,0.00043953,1.2541
 85 | 9,3,0.54833,1.2733,0.31093,0.93409,0.99825,0.59707,0.017966,0.058744,0.043685,0.12505,0.015397,0.0051037,0.00042441,0.94617
 86 | 9,4,0.58938,1.3232,0.40145,0.89009,0.98421,0.43977,0.047907,0.4177,0.11488,0.19211,0.035592,0.0091838,0.0015424,2.1004
 87 | 9,5,0.64803,1.392,0.41362,0.89752,0.96316,0.50892,0.026737,0.1301,0.058284,0.15033,0.022099,0.0078269,0.00036426,1.184
 88 | 9,6,0.54029,1.2759,0.36992,0.88831,0.97719,0.54283,0.028138,0.1441,0.051554,0.14256,0.019917,0.0074501,0.00020354,1.1427
 89 | 9,7,0.4309,1.2825,0.38769,0.85341,0.96667,0.46204,0.052403,0.49979,0.067688,0.16198,0.025566,0.0091042,0.00043085,1.3596
 90 | 9,8,0.4659,1.2195,0.27095,0.94433,1,0.66599,0.013322,0.032299,0.024332,0.10405,0.01071,0.0047135,4.6307e-05,0.56447
 91 | 9,9,0.4867,1.2165,0.37691,0.90161,0.99474,0.54006,0.032181,0.18848,0.098716,0.18377,0.03267,0.009529,0.00095661,1.9018
 92 | 9,10,0.46098,1.2199,0.32967,0.90459,1,0.56329,0.027061,0.13328,0.092156,0.18249,0.03223,0.010076,0.00090962,1.7156
 93 | 9,11,0.73086,1.5285,0.46455,0.87291,0.97193,0.4865,0.034289,0.21399,0.02898,0.10191,0.01028,0.0035859,0.00010051,0.76676
 94 | 9,12,0.51577,1.2902,0.32454,0.9208,0.99474,0.62017,0.023879,0.10378,0.047501,0.12228,0.014732,0.0042129,0.00056945,1.1232
 95 | 9,13,0.59837,1.3539,0.35704,0.93647,0.99825,0.64448,0.019818,0.071484,0.045326,0.13137,0.016966,0.0059817,0.0003532,0.93682
 96 | 9,14,0.55112,1.2821,0.39683,0.88375,0.99649,0.49492,0.030576,0.17015,0.062015,0.14621,0.02093,0.0063685,0.00067747,1.2659
 97 | 10,1,0.44941,1.1374,0.38458,0.90736,0.97368,0.54595,0.047809,0.41601,0.11385,0.18608,0.033467,0.0082395,0.0012682,2.2422
 98 | 10,2,0.46727,1.1876,0.36748,0.91542,0.97719,0.57995,0.051335,0.47962,0.13764,0.18732,0.033899,0.0061636,0.0029358,2.5551
 99 | 10,3,0.55362,1.2438,0.39517,0.91092,0.97193,0.51113,0.046323,0.39055,0.083154,0.17465,0.0296,0.0094574,0.00074607,1.5807
100 | 10,4,0.32175,1.1782,0.32151,0.91803,0.97544,0.58719,0.045933,0.384,0.076818,0.14622,0.020931,0.0048174,0.0014091,1.6749
101 | 10,5,0.64414,1.37,0.42697,0.91457,0.96491,0.44764,0.055411,0.55882,0.1203,0.19149,0.035373,0.0084315,0.0018137,2.2137
102 | 10,6,0.55977,1.3442,0.34301,0.9298,0.97544,0.57879,0.053564,0.52218,0.14905,0.25543,0.061249,0.02381,0.00059701,2.413
103 | 10,7,0.50114,1.2499,0.30101,0.94477,0.9807,0.6743,0.0494,0.44415,0.096252,0.23034,0.050382,0.025877,0.00026934,1.4463
104 | 10,8,0.23041,1.1294,0.25496,0.94201,0.97368,0.6084,0.040722,0.3018,0.10957,0.23571,0.052636,0.02418,0.00042353,1.6648
105 | 10,9,0.46094,1.1366,0.22905,0.95565,0.97544,0.71234,0.038286,0.26678,0.11753,0.22041,0.046329,0.016554,0.00064766,1.9866
106 | 10,10,0.54913,1.2769,0.31283,0.93889,0.99123,0.5933,0.041539,0.31404,0.15188,0.2605,0.06355,0.02379,0.000843,2.203
107 | 10,11,0.32168,1.1127,0.27055,0.95426,0.99298,0.70006,0.05426,0.53584,0.10967,0.21507,0.044212,0.016252,0.00061195,1.8452
108 | 10,12,0.38564,1.0471,0.23328,0.95176,0.97368,0.67107,0.050832,0.47028,0.13563,0.24744,0.057693,0.022,0.00086835,1.9757
109 | 10,13,0.36127,1.0573,0.26213,0.93923,0.96667,0.59302,0.04962,0.44811,0.19067,0.28081,0.073089,0.023825,0.0012501,2.6604
110 | 11,1,0.51247,1.1116,0.65626,0.57724,0.59298,0.16867,0.11187,2.2776,0.016001,0.061238,0.0037361,0.00087861,0.00010284,0.6508
111 | 11,2,0.54893,1.1111,0.63983,0.56623,0.6,0.15743,0.13081,3.1143,0.021231,0.079722,0.0063155,0.0019123,7.3919e-05,0.71949
112 | 11,3,0.43425,1.095,0.68828,0.52398,0.54211,0.15396,0.13761,3.4462,0.021147,0.073202,0.0053299,0.00134,0.00016376,0.75496
113 | 11,4,0.38501,1.0656,0.63042,0.51223,0.59123,0.13705,0.12292,2.7498,0.033373,0.098907,0.0096879,0.0027872,0.00021426,1.0015
114 | 11,5,0.26758,1.1316,0.60128,0.54301,0.77368,0.20311,0.15354,4.2904,0.017945,0.07145,0.0050792,0.0014652,7.2478e-05,0.63273
115 | 11,6,0.24465,1.047,0.60511,0.56524,0.79474,0.21788,0.12522,2.854,0.037595,0.127,0.015874,0.006587,0.00010798,0.8331
116 | 11,7,0.39092,1.087,0.68174,0.50961,0.6614,0.15361,0.14082,3.6093,0.028638,0.089135,0.0078824,0.0021177,0.00021044,0.90082
117 | 11,8,0.4042,1.0965,0.65899,0.52833,0.68421,0.1771,0.13017,3.0837,0.029057,0.096836,0.0092902,0.0028927,0.0001265,0.82383
118 | 11,9,0.50692,1.127,0.67203,0.53024,0.75263,0.16792,0.13006,3.0788,0.015279,0.057592,0.0033059,0.00072847,0.00010983,0.67289
119 | 11,10,0.47565,1.0656,0.69172,0.5233,0.49649,0.14133,0.12987,3.0697,0.023977,0.0842,0.0070397,0.0020845,0.00011284,0.77399
120 | 11,11,0.52382,1.1117,0.67175,0.54701,0.62982,0.15157,0.13674,3.4028,0.026434,0.085792,0.0073064,0.0021373,0.00016583,0.90513
121 | 11,12,0.36462,1.0811,0.67755,0.49042,0.68772,0.14118,0.1243,2.8118,0.037866,0.11692,0.013485,0.0046475,0.0001769,0.9229
122 | 11,13,0.52212,1.1191,0.70988,0.50678,0.64912,0.1412,0.13192,3.1674,0.025478,0.085964,0.0073356,0.0021793,0.0001485,0.82809
123 | 11,14,0.38203,1.0405,0.6901,0.48549,0.63684,0.13165,0.11852,2.5565,0.027997,0.093312,0.008632,0.0026591,0.00012469,0.84994
124 | 11,15,0.27123,1.096,0.68075,0.49446,0.53684,0.13088,0.12815,2.9887,0.021943,0.072882,0.0052838,0.0013391,0.00022058,0.80402
125 | 11,16,0.44882,1.0118,0.6301,0.57134,0.81053,0.16187,0.11115,2.2486,0.027309,0.088889,0.0078393,0.0022734,0.00017545,0.86
126 | 12,1,0.88374,2.4765,0.61389,0.8581,0.98246,0.40448,0.054511,0.54081,0.06489,0.14657,0.021032,0.0067378,0.00037204,1.6461
127 | 12,2,0.91579,2.5959,0.63023,0.94977,1,0.4976,0.024404,0.10839,0.067044,0.1528,0.022814,0.0075708,0.00031405,1.6221
128 | 12,3,0.82866,1.9848,0.50917,0.9418,0.99825,0.55942,0.025524,0.11857,0.080103,0.16692,0.027107,0.0086548,0.0004265,1.8038
129 | 12,4,0.87863,2.4734,0.60898,0.87869,0.99123,0.45758,0.056994,0.59119,0.076471,0.16369,0.026095,0.0087482,0.00034348,1.8363
130 | 12,5,0.92876,2.8218,0.66081,0.93335,0.97544,0.43699,0.046152,0.38767,0.050806,0.13408,0.017661,0.0064421,0.00017937,1.4027
131 | 12,6,0.91093,2.6184,0.63236,0.92868,0.99123,0.51584,0.021091,0.080956,0.05094,0.12676,0.015815,0.0047138,0.00031804,1.3323
132 | 12,7,0.92479,2.5638,0.62255,0.96226,0.97895,0.57504,0.0097927,0.017453,0.026156,0.10029,0.009958,0.0040505,4.4661e-05,0.77718
133 | 12,8,0.90037,2.3499,0.60125,0.93713,0.98421,0.46172,0.03166,0.18242,0.096235,0.18081,0.031658,0.0105,0.00050824,2.3052
134 | 12,9,0.8728,2.3151,0.58751,0.90716,0.98772,0.45257,0.058029,0.61287,0.07597,0.17378,0.029313,0.011448,0.00025642,1.6895
135 | 12,10,0.93377,2.8883,0.66665,0.93026,0.96316,0.43032,0.03571,0.23209,0.047675,0.12917,0.016412,0.0057866,0.00016113,1.321
136 | 12,11,0.88185,2.1732,0.57028,0.93013,0.99825,0.45014,0.049062,0.43809,0.072246,0.16601,0.02682,0.010288,0.0002503,1.7405
137 | 12,12,0.87259,2.1845,0.56922,0.93969,0.99474,0.52611,0.044598,0.36199,0.060388,0.14227,0.019839,0.0062705,0.00031494,1.4969
138 | 13,1,0.71763,1.5041,0.33723,0.98803,1,0.82342,0.0040421,0.0029736,0.050064,0.12542,0.015486,0.0046629,0.00027091,1.3598
139 | 13,2,0.73281,1.5437,0.36231,0.98616,1,0.79359,0.0052593,0.0050342,0.046731,0.1203,0.014265,0.0041563,0.00026442,1.2681
140 | 13,3,0.75285,1.5603,0.38474,0.98701,0.99649,0.78037,0.004611,0.0038695,0.046705,0.11723,0.013556,0.0038164,0.00028371,1.3539
141 | 13,4,0.73935,1.5319,0.34987,0.98479,1,0.81067,0.0078079,0.011095,0.027888,0.11472,0.01299,0.0060166,5.0098e-05,0.59895
142 | 13,5,0.63969,1.3612,0.28845,0.98738,1,0.82125,0.0042318,0.0032593,0.080446,0.15813,0.024394,0.0066506,0.00067629,1.8274
143 | 13,6,0.62033,1.3105,0.26312,0.98535,1,0.82478,0.0049896,0.0045311,0.070042,0.14655,0.021025,0.0058018,0.00053019,1.705
144 | 13,7,0.64536,1.3743,0.28162,0.98646,1,0.84494,0.005427,0.0053602,0.026853,0.085949,0.007333,0.0020334,0.00012813,0.94296
145 | 13,8,0.70115,1.4925,0.34048,0.98262,1,0.78473,0.0061511,0.0068862,0.08405,0.17102,0.028418,0.0087725,0.00052855,1.7782
146 | 13,9,0.70275,1.5016,0.34772,0.98776,0.99825,0.80848,0.0037534,0.002564,0.055512,0.12613,0.015659,0.004022,0.00050106,1.4395
147 | 13,10,0.69686,1.4507,0.33575,0.9851,1,0.80461,0.0036582,0.0024356,0.038211,0.10889,0.011717,0.003581,0.00016657,1.1363
148 | 13,11,0.66351,1.4024,0.31179,0.98529,1,0.81091,0.0068704,0.0085907,0.068206,0.13895,0.01894,0.0049098,0.00062422,1.7727
149 | 13,12,0.67362,1.3988,0.30608,0.98301,1,0.79998,0.0087621,0.013973,0.1196,0.17804,0.030726,0.0063197,0.0016787,2.5094
150 | 13,13,0.64314,1.3574,0.28063,0.98631,0.99825,0.81381,0.0052732,0.0050608,0.058531,0.13108,0.016892,0.0044631,0.00048306,1.5136
151 | 14,1,0.91298,2.455,0.61097,0.96979,0.99825,0.5621,0.0095958,0.016758,0.090447,0.1871,0.033823,0.0115,0.000564,1.6879
152 | 14,2,0.9136,2.5213,0.61341,0.97227,1,0.56248,0.0078474,0.011208,0.068427,0.14325,0.020108,0.0055763,0.00057194,1.7187
153 | 14,3,0.91355,2.457,0.61108,0.97034,0.97544,0.5645,0.0077029,0.010799,0.092547,0.18786,0.034088,0.011708,0.00046097,1.8501
154 | 14,4,0.91378,2.4132,0.60362,0.96618,0.99649,0.56153,0.0091377,0.015197,0.10604,0.20316,0.039637,0.013227,0.00072984,1.8526
155 | 14,5,0.85897,1.9723,0.51434,0.97407,0.99825,0.6792,0.0068392,0.0085129,0.046518,0.13848,0.018816,0.0073898,0.00015454,1.014
156 | 14,6,0.91296,2.4862,0.62315,0.96188,1,0.51041,0.010684,0.020775,0.064539,0.13678,0.018365,0.0048636,0.00052757,1.6875
157 | 14,7,0.93802,2.8087,0.65774,0.97091,1,0.58194,0.0067321,0.0082485,0.052218,0.13491,0.017874,0.0057374,0.00030246,1.2161
158 | 14,8,0.94058,3.0366,0.69248,0.93952,0.99474,0.42979,0.031219,0.17738,0.038691,0.1076,0.011445,0.0032156,0.00024816,1.0843
159 | 14,9,0.89672,2.2534,0.57689,0.96488,1,0.57906,0.0081422,0.012066,0.062778,0.1512,0.022351,0.0078553,0.00031192,1.4575
160 | 14,10,0.89507,2.2045,0.55605,0.97701,0.99649,0.64214,0.0053433,0.0051962,0.102,0.19598,0.036988,0.012562,0.00048437,2.0484
161 | 14,11,0.88246,2.1,0.5486,0.96929,0.99825,0.61897,0.0073021,0.0097043,0.10152,0.19717,0.03742,0.012995,0.00049125,2.0094
162 | 14,12,0.90831,2.4038,0.59263,0.97688,0.99825,0.59259,0.0064679,0.0076137,0.063688,0.1472,0.021208,0.0067017,0.00032702,1.5332
163 | 15,1,0.4132,1.0384,0.48465,0.78118,0.87018,0.30478,0.080722,1.1859,0.047303,0.12619,0.015674,0.0058992,0.00019037,1.489
164 | 15,2,0.56324,1.1928,0.474,0.77564,0.93333,0.34791,0.069404,0.87668,0.10401,0.17923,0.031125,0.0094494,0.00074739,2.4883
165 | 15,3,0.48967,1.1232,0.46175,0.80214,0.9,0.31034,0.073462,0.9822,0.10125,0.17926,0.031133,0.010102,0.00068131,2.4808
166 | 15,4,0.60319,1.088,0.51739,0.7638,0.90175,0.23802,0.077022,1.0797,0.10994,0.17976,0.031303,0.0088846,0.00087888,2.6613
167 | 15,5,0.53622,1.2341,0.44979,0.7882,0.94386,0.38426,0.072253,0.95013,0.042992,0.12337,0.014993,0.0056501,0.00013103,1.2646
168 | 15,6,0.37902,1.0738,0.5242,0.71586,0.85439,0.26309,0.065312,0.77635,0.067161,0.14275,0.019969,0.0059582,0.00040634,1.8527
169 | 15,7,0.53693,1.0751,0.55667,0.74395,0.77544,0.27241,0.080278,1.1729,0.091322,0.16265,0.025773,0.007318,0.00080697,2.2704
170 | 15,8,0.51153,1.0767,0.47053,0.77952,0.90702,0.28838,0.076992,1.0789,0.084711,0.17213,0.028776,0.0099656,0.00039644,2.0412
171 | 15,9,0.44271,1.0836,0.49548,0.74434,0.87018,0.26478,0.079481,1.1497,0.10151,0.1726,0.028929,0.0076406,0.00084487,2.389
172 | 15,10,0.30574,1.1176,0.58847,0.66502,0.84737,0.24074,0.10064,1.8435,0.074621,0.14273,0.019966,0.0054026,0.00062391,2.1403
173 | 22,1,0.92522,2.695,0.63571,0.92994,0.99298,0.505,0.040707,0.30158,0.055742,0.15592,0.023734,0.0096617,0.00030237,1.0351
174 | 22,2,0.90557,2.3423,0.58487,0.95943,0.97368,0.55537,0.023542,0.10087,0.045897,0.13433,0.017724,0.0066757,0.00024528,0.9943
175 | 22,3,0.91885,2.638,0.62859,0.96692,0.99123,0.52269,0.020081,0.07339,0.054145,0.1674,0.027257,0.013322,0.00016644,0.8989
176 | 22,4,0.88965,2.3863,0.59051,0.90564,0.98421,0.5136,0.048934,0.43581,0.062886,0.17454,0.029562,0.013364,0.00026357,1.0734
177 | 22,5,0.9456,3.1174,0.70912,0.79489,0.89298,0.33915,0.13457,3.2956,0.052685,0.14875,0.021648,0.008569,0.00025614,1.0503
178 | 22,6,0.91747,2.7054,0.64091,0.87687,0.98947,0.42531,0.089364,1.4534,0.042216,0.12098,0.014424,0.0047692,0.0003061,1.0097
179 | 22,7,0.86049,2.0997,0.532,0.9358,0.98772,0.5343,0.033994,0.21032,0.040254,0.13592,0.01814,0.0080226,0.00010143,0.82059
180 | 22,8,0.89808,2.4056,0.59137,0.93186,0.98596,0.51229,0.029398,0.15729,0.044185,0.15479,0.0234,0.012336,7.6339e-05,0.78887
181 | 22,9,0.86988,2.1277,0.53926,0.96257,1,0.64927,0.027899,0.14166,0.026849,0.096968,0.0093152,0.0032376,0.00010402,0.7271
182 | 22,10,0.86675,2.0417,0.52999,0.94477,0.98947,0.55141,0.028749,0.15043,0.031023,0.12007,0.014211,0.0064538,6.947e-05,0.64966
183 | 22,11,0.96652,3.8162,0.74273,0.82566,0.94737,0.32925,0.1446,3.8052,0.022715,0.10148,0.010193,0.0046292,4.4517e-05,0.52175
184 | 22,12,0.93396,2.9367,0.66689,0.92543,0.99474,0.45676,0.030052,0.16437,0.015159,0.071198,0.0050436,0.0017096,4.8527e-05,0.46762
185 | 23,1,0.61823,1.2435,0.33084,0.94147,0.9614,0.64089,0.024255,0.10708,0.017543,0.09745,0.0094072,0.0051286,1.2841e-05,0.37324
186 | 23,2,0.61717,1.2296,0.34897,0.93189,0.97368,0.60176,0.025375,0.11719,0.012906,0.070197,0.0049035,0.0019323,2.4448e-05,0.37728
187 | 23,3,0.62404,1.1599,0.47899,0.91562,0.85088,0.45154,0.097205,1.7197,0.013983,0.076408,0.0058043,0.0024109,2.5646e-05,0.36029
188 | 23,4,0.48229,1.1574,0.42617,0.91741,0.92456,0.46699,0.063396,0.73147,0.026522,0.105,0.010904,0.0044681,6.7833e-05,0.63468
189 | 23,5,0.5684,1.1876,0.48001,0.91247,0.79474,0.3885,0.089162,1.4469,0.027113,0.10182,0.010261,0.0037888,0.00010673,0.65567
190 | 23,6,0.62733,1.2441,0.50214,0.90049,0.86667,0.47758,0.092591,1.5603,0.02034,0.091645,0.0083289,0.0033696,4.9071e-05,0.49195
191 | 23,7,0.58637,1.1419,0.30339,0.93305,0.92105,0.57323,0.041282,0.31016,0.022886,0.093704,0.0087041,0.0032291,0.00010441,0.54481
192 | 23,8,0.50771,1.1084,0.25071,0.93529,0.98596,0.64927,0.02595,0.12256,0.013493,0.073509,0.0053745,0.0021246,3.1877e-05,0.3475
193 | 23,9,0.6018,1.2254,0.41835,0.94269,0.95263,0.54871,0.058835,0.62999,0.016202,0.074883,0.0055761,0.0018797,7.1264e-05,0.43769
194 | 23,10,0.63548,1.202,0.47365,0.89373,0.86842,0.44347,0.077616,1.0964,0.022403,0.10386,0.010672,0.005084,3.1292e-05,0.48911
195 | 23,11,0.65048,1.2354,0.44987,0.90929,0.89123,0.57594,0.070699,0.9097,0.0093349,0.0592,0.0034924,0.0013047,2.2232e-05,0.26169
196 | 24,1,0.64604,1.4683,0.35457,0.9588,0.99123,0.70641,0.02021,0.074334,0.059026,0.12721,0.015925,0.0037362,0.0012496,1.3596
197 | 24,2,0.55457,1.2955,0.25519,0.97441,0.99825,0.7824,0.012063,0.026483,0.020129,0.076722,0.0058518,0.0016389,0.00012751,0.60629
198 | 24,3,0.4443,1.2035,0.19432,0.98319,1,0.833,0.010317,0.019373,0.029059,0.10265,0.010427,0.003627,0.00011748,0.73255
199 | 24,4,0.6368,1.4266,0.29976,0.9747,0.99825,0.78457,0.013385,0.032608,0.028272,0.084495,0.0070888,0.0017155,0.00031351,0.913
200 | 24,5,0.64168,1.4853,0.3362,0.95562,0.99825,0.72952,0.017439,0.05535,0.02413,0.074641,0.0055403,0.001194,0.00031921,0.82279
201 | 24,6,0.58232,1.343,0.29271,0.95055,0.99825,0.71499,0.026876,0.13146,0.068134,0.13204,0.017136,0.0036798,0.0014697,1.6315
202 | 24,7,0.44567,1.2107,0.20257,0.97104,0.99123,0.80094,0.016177,0.047628,0.020632,0.074115,0.0054631,0.0013865,0.00016174,0.66423
203 | 24,8,0.67925,1.487,0.33465,0.97307,0.98246,0.7713,0.012399,0.02798,0.0075996,0.044132,0.0019439,0.00049059,3.6857e-05,0.28857
204 | 24,9,0.11708,1.0944,0.1745,0.97545,1,0.81957,0.016114,0.047259,0.033007,0.087092,0.0075278,0.001568,0.0006066,1.0292
205 | 24,10,0.53283,1.3078,0.25925,0.96727,1,0.77065,0.017745,0.057312,0.05341,0.12259,0.014806,0.0035923,0.0012358,1.1886
206 | 24,11,0.52828,1.2496,0.2238,0.98317,1,0.82495,0.010372,0.01958,0.047482,0.10274,0.010444,0.0019794,0.0011976,1.341
207 | 24,12,0.35236,1.146,0.20076,0.96876,0.99649,0.71333,0.017729,0.057204,0.033249,0.099068,0.0097191,0.002653,0.00035728,0.87046
208 | 24,13,0.64897,1.4597,0.3304,0.97406,1,0.75323,0.016021,0.046715,0.012025,0.050266,0.0025203,0.00051116,0.00013299,0.49261
209 | 25,1,0.72162,1.4686,0.43475,0.91337,0.98596,0.60738,0.046324,0.39055,0.097628,0.18471,0.032992,0.0097255,0.001043,1.8296
210 | 25,2,0.71573,1.3808,0.37798,0.95035,0.99123,0.68138,0.025077,0.11445,0.10429,0.23183,0.051004,0.023571,0.00044425,1.5025
211 | 25,3,0.81957,1.56,0.49513,0.91036,0.98421,0.60691,0.045977,0.38473,0.097894,0.20665,0.040954,0.015521,0.0007191,1.5452
212 | 25,4,0.83029,1.633,0.49437,0.92597,0.96842,0.62529,0.045331,0.37399,0.13423,0.2648,0.065523,0.029786,0.00074416,1.7493
213 | 25,5,0.75259,1.5852,0.41155,0.91036,0.99298,0.65821,0.044162,0.35495,0.12959,0.25343,0.060352,0.026003,0.00063907,1.813
214 | 25,6,0.7831,1.5389,0.42639,0.94648,0.97544,0.65478,0.038706,0.27266,0.12607,0.24469,0.056492,0.023259,0.00066766,1.8504
215 | 25,7,0.78823,1.6027,0.44995,0.91357,0.95614,0.62781,0.060707,0.67072,0.12958,0.2522,0.059803,0.025228,0.0009264,1.7489
216 | 25,8,0.83094,1.6643,0.49439,0.96016,0.99649,0.65605,0.023841,0.10345,0.12266,0.24766,0.057789,0.025308,0.00078336,1.7001
217 | 25,9,0.7885,1.638,0.43032,0.95417,1,0.71101,0.026783,0.13055,0.12808,0.21441,0.04395,0.013032,0.0010116,2.2764
218 | 26,1,0.73365,1.5028,0.34887,0.9828,1,0.80144,0.0053647,0.005238,0.029255,0.10327,0.010553,0.0043013,9.1642e-05,0.88407
219 | 26,2,0.82775,1.842,0.48769,0.97383,0.99825,0.68405,0.01619,0.047706,0.022041,0.087963,0.0076781,0.0029651,6.8766e-05,0.68174
220 | 26,3,0.55626,1.2516,0.23975,0.9802,1,0.81411,0.0073087,0.009722,0.056955,0.17933,0.031157,0.017403,8.4502e-05,0.98858
221 | 26,4,0.29901,1.0375,0.10761,0.98386,1,0.84936,0.011462,0.023912,0.032883,0.096034,0.0091383,0.0025695,0.00018734,1.0837
222 | 26,5,0.48223,1.1577,0.16908,0.97886,1,0.71192,0.008864,0.0143,0.037174,0.11384,0.012794,0.0044407,0.00014852,1.0089
223 | 26,6,0.5895,1.2708,0.24364,0.98173,0.99649,0.82082,0.0074388,0.010071,0.041688,0.11576,0.013223,0.0041,0.00025724,1.1105
224 | 26,7,0.322,1.0698,0.13626,0.98017,0.99825,0.81953,0.012913,0.030347,0.076267,0.18383,0.03269,0.014159,0.00026309,1.4955
225 | 26,8,0.482,1.1415,0.15235,0.98536,1,0.85572,0.0072199,0.009487,0.021863,0.079283,0.0062465,0.0017479,0.00013143,0.69178
226 | 26,9,0.87402,2.0754,0.55373,0.96281,0.98421,0.56448,0.019656,0.070316,0.037553,0.10946,0.011839,0.0038499,0.00021407,1.0693
227 | 26,10,0.58138,1.2486,0.21485,0.98704,1,0.83511,0.0046582,0.0039492,0.04072,0.11304,0.012618,0.0036743,0.00033322,1.0368
228 | 26,11,0.40974,1.0732,0.14538,0.98155,1,0.8407,0.0042083,0.0032232,0.04953,0.1489,0.021691,0.010419,0.00010258,1.1781
229 | 26,12,0.82556,1.6903,0.47477,0.97153,1,0.70462,0.019154,0.066774,0.025981,0.10427,0.010756,0.0045965,4.4538e-05,0.67306
230 | 27,1,0.60267,1.254,0.20587,0.98883,1,0.80193,0.0084333,0.012944,0.017777,0.061786,0.003803,0.00091963,0.00015231,0.85839
231 | 27,2,0.66906,1.2751,0.30877,0.9877,1,0.85816,0.0048599,0.0042987,0.031488,0.094241,0.0088032,0.0025337,0.00019057,1.0378
232 | 27,3,0.8138,1.737,0.42956,0.98956,1,0.79117,0.0086131,0.013502,0.033871,0.10399,0.010699,0.0035528,0.00015394,1.0284
233 | 27,4,0.72719,1.4779,0.3298,0.99388,1,0.8423,0.0029668,0.0016019,0.02634,0.081903,0.0066634,0.0017846,0.0001939,0.9805
234 | 27,5,0.79923,1.6549,0.40919,0.98964,1,0.815,0.0048968,0.0043641,0.028693,0.091008,0.0082145,0.0025009,0.00013606,0.98834
235 | 27,6,0.81354,1.6924,0.41016,0.99256,1,0.78909,0.0046348,0.0039096,0.031342,0.096493,0.0092251,0.0028704,0.00017007,1.0171
236 | 27,7,0.8561,1.873,0.49654,0.98047,1,0.6913,0.0092289,0.015501,0.047961,0.1039,0.010681,0.0024623,0.00080703,1.6203
237 | 27,8,0.75968,1.5763,0.38954,0.98731,1,0.82323,0.005582,0.0056708,0.034734,0.098208,0.0095526,0.0027074,0.00023404,1.1433
238 | 27,9,0.8031,1.652,0.40268,0.97695,1,0.79833,0.010883,0.021558,0.046764,0.10227,0.010352,0.0021515,0.00074063,1.5053
239 | 27,10,0.79389,1.5303,0.42937,0.96802,0.99649,0.79209,0.016566,0.049949,0.04703,0.11769,0.013661,0.0041812,0.00034846,1.42
240 | 27,11,0.66551,1.3617,0.26804,0.98949,1,0.76603,0.005179,0.0048816,0.017792,0.074392,0.0055037,0.0018166,5.245e-05,0.62317
241 | 28,1,0.946,3.3475,0.7081,0.96212,1,0.45671,0.035434,0.22851,0.054986,0.13106,0.016886,0.0047646,0.00051238,1.3088
242 | 28,2,0.93038,2.8852,0.67525,0.95738,1,0.50964,0.027111,0.13377,0.050703,0.12329,0.014972,0.0041932,0.00032166,1.3848
243 | 28,3,0.91549,2.6796,0.62862,0.96381,1,0.55874,0.019083,0.066276,0.10351,0.19882,0.038028,0.012751,0.00057453,1.9702
244 | 28,4,0.87202,2.1536,0.53817,0.9788,1,0.65619,0.020327,0.075203,0.081306,0.18726,0.033878,0.013211,0.00036142,1.4606
245 | 28,5,0.91328,2.5654,0.61185,0.98179,1,0.60313,0.006726,0.0082335,0.093484,0.19225,0.035644,0.012245,0.00061603,1.6701
246 | 28,6,0.91996,2.6668,0.63083,0.97894,1,0.58002,0.010311,0.019349,0.1125,0.19785,0.037671,0.011205,0.00078875,2.2241
247 | 28,7,0.91677,2.6955,0.62998,0.98275,1,0.59459,0.0049218,0.0044087,0.10467,0.1876,0.033997,0.0096762,0.00085264,2.0585
248 | 28,8,0.91397,2.7289,0.64656,0.96685,0.99298,0.55519,0.022351,0.090925,0.068514,0.1525,0.022728,0.0070313,0.00041076,1.5681
249 | 28,9,0.8762,2.2436,0.56314,0.98425,1,0.65133,0.010195,0.018915,0.14482,0.22485,0.048122,0.013423,0.0012638,2.4685
250 | 28,10,0.92081,2.9198,0.65845,0.96861,0.99825,0.52299,0.011587,0.024437,0.064788,0.16842,0.027581,0.01122,0.00024357,1.2406
251 | 28,11,0.86832,2.141,0.54004,0.98483,1,0.69313,0.009048,0.0149,0.11927,0.2052,0.040405,0.011659,0.0010933,2.1073
252 | 28,12,0.93323,3.222,0.69174,0.95791,1,0.48142,0.022654,0.093403,0.075307,0.17561,0.029915,0.011062,0.00039957,1.4215
253 | 29,1,0.8375,1.9512,0.4905,0.968,0.98246,0.65138,0.016224,0.047908,0.0051195,0.035621,0.0012673,0.0003224,1.1238e-05,0.23514
254 | 29,2,0.83275,2.0014,0.50157,0.9557,0.99649,0.67019,0.028422,0.14702,0.0092534,0.051082,0.0026026,0.00073282,4.0071e-05,0.32567
255 | 29,3,0.84579,2.0052,0.49931,0.96585,0.99825,0.65149,0.019981,0.07266,0.0050343,0.036189,0.0013079,0.00035119,8.7642e-06,0.22547
256 | 29,4,0.74053,1.6378,0.3892,0.9666,1,0.74844,0.025032,0.11404,0.0083497,0.045391,0.0020561,0.00054932,2.9454e-05,0.38449
257 | 29,5,0.82924,1.8903,0.47248,0.96435,0.99825,0.661,0.023836,0.10341,0.0057361,0.037634,0.0014143,0.00036897,1.3273e-05,0.27099
258 | 29,6,0.82256,1.9016,0.47622,0.96505,1,0.69746,0.025026,0.11398,0.0056632,0.033415,0.0011153,0.00022942,3.7442e-05,0.29502
259 | 29,7,0.84501,2.0918,0.51732,0.9451,1,0.6071,0.039781,0.28802,0.0081002,0.044849,0.0020074,0.00050789,2.9215e-05,0.34316
260 | 29,8,0.8187,1.9126,0.47835,0.94904,0.99298,0.58585,0.03806,0.26364,0.0050219,0.034026,0.0011564,0.00029213,1.5463e-05,0.26037
261 | 29,9,0.78965,1.8186,0.45353,0.94929,1,0.63343,0.030371,0.16787,0.008049,0.04345,0.0018843,0.00045429,2.9147e-05,0.36533
262 | 29,10,0.80728,1.857,0.46197,0.95012,0.99298,0.60913,0.028443,0.14724,0.0096387,0.060095,0.0035984,0.0013452,1.6802e-05,0.27688
263 | 29,11,0.80393,1.8829,0.4698,0.93066,0.99298,0.58011,0.043787,0.34894,0.010183,0.061975,0.0038262,0.0014329,1.8914e-05,0.28509
264 | 29,12,0.85451,2.06,0.51569,0.968,0.98421,0.60909,0.025453,0.11791,0.0079224,0.044948,0.0020163,0.00051981,2.6189e-05,0.32598
265 | 30,1,0.50924,1.2166,0.27943,0.93462,0.99123,0.4092,0.016089,0.047113,0.096518,0.17131,0.028511,0.006963,0.0015453,1.851
266 | 30,2,0.52914,1.2066,0.25595,0.93945,0.97895,0.51693,0.019009,0.065765,0.14675,0.19841,0.037876,0.0073471,0.0023476,2.7085
267 | 30,3,0.2458,1.1011,0.20617,0.94995,0.99298,0.53189,0.012818,0.029901,0.14279,0.20537,0.04047,0.0090273,0.0018094,2.579
268 | 30,4,0.55518,1.2318,0.25226,0.94783,0.99123,0.54965,0.015115,0.041582,0.059148,0.13996,0.019211,0.0057336,0.00042099,1.3579
269 | 30,5,0.35141,1.1428,0.24265,0.93638,0.99123,0.47846,0.019721,0.070781,0.1074,0.18794,0.034115,0.0089893,0.0015026,1.9019
270 | 30,6,0.4803,1.2022,0.26797,0.94544,0.99825,0.52139,0.022778,0.09443,0.066259,0.14818,0.021485,0.0061942,0.00073522,1.3669
271 | 30,7,0.19287,1.0551,0.25044,0.93641,0.99474,0.47284,0.019693,0.070581,0.055935,0.1303,0.016694,0.0045832,0.00044001,1.4111
272 | 30,8,0.49634,1.1832,0.22855,0.94687,0.98947,0.58613,0.017327,0.054638,0.047106,0.12748,0.015992,0.0051731,0.00023865,1.1489
273 | 30,9,0.33254,1.1208,0.27473,0.93625,0.99474,0.49836,0.024394,0.1083,0.10809,0.16882,0.027709,0.0059808,0.0012335,2.4866
274 | 30,10,0.39606,1.1647,0.29415,0.94064,0.99298,0.5486,0.025244,0.11598,0.051625,0.12014,0.014228,0.003721,0.00038216,1.4943
275 | 30,11,0.41933,1.1928,0.30567,0.93428,0.98947,0.50688,0.020948,0.079866,0.057408,0.12508,0.015404,0.003903,0.00045984,1.6497
276 | 30,12,0.45767,1.1629,0.24078,0.94275,0.99298,0.63919,0.020279,0.074844,0.028432,0.095746,0.0090841,0.002812,0.0001478,0.78535
277 | 31,1,0.99541,9.4912,0.91397,0.84991,0.97368,0.17429,0.082108,1.227,0.012512,0.060688,0.0036695,0.0010735,5.216e-05,0.41928
278 | 31,2,0.99611,11.006,0.92239,0.86962,0.99123,0.14581,0.064792,0.76404,0.011176,0.057808,0.0033307,0.00098398,3.9298e-05,0.3731
279 | 31,3,0.99767,12.009,0.94529,0.67005,0.94386,0.11114,0.19898,7.2062,0.010215,0.051104,0.0026048,0.00066765,4.5324e-05,0.39905
280 | 31,4,0.99456,9.7351,0.90444,0.93346,0.98421,0.19638,0.020897,0.079479,0.016599,0.075247,0.0056302,0.0019012,4.441e-05,0.48992
281 | 31,5,0.997,12.548,0.92397,0.93876,0.96842,0.16105,0.026941,0.1321,0.010116,0.057812,0.0033311,0.0010891,2.9179e-05,0.3101
282 | 31,6,0.99698,10.913,0.93836,0.70085,0.93684,0.11552,0.15334,4.2796,0.010649,0.055629,0.0030851,0.00089618,3.6128e-05,0.37415
283 | 31,7,0.99551,9.6915,0.91233,0.88256,0.93509,0.17567,0.049695,0.44946,0.013721,0.067649,0.0045556,0.0015063,4.4371e-05,0.41054
284 | 31,8,0.99502,8.991,0.92789,0.68687,0.86842,0.14478,0.18853,6.4689,0.013268,0.068973,0.0047347,0.001673,3.4802e-05,0.37492
285 | 31,9,0.99583,9.1366,0.92503,0.69234,0.90702,0.15101,0.17918,5.8432,0.0054072,0.044172,0.0019474,0.00070285,6.9241e-06,0.1694
286 | 31,10,0.99593,10.12,0.92461,0.80662,0.90702,0.15053,0.12257,2.7342,0.0082902,0.052333,0.0027313,0.00090459,1.5362e-05,0.27303
287 | 31,11,0.99422,9.1985,0.91119,0.8375,0.98421,0.17712,0.055414,0.55887,0.016168,0.079996,0.0063587,0.0024534,5.5348e-05,0.38686
288 | 32,1,0.86224,2.0735,0.52269,0.98686,0.99474,0.70529,0.010097,0.018554,0.041404,0.12163,0.014579,0.0048689,0.00027608,0.9458
289 | 32,2,0.87024,2.1094,0.52863,0.9836,0.99298,0.60784,0.0031737,0.0018332,0.026902,0.091391,0.0082832,0.0024385,0.00016093,0.73904
290 | 32,3,0.86248,2.0398,0.51338,0.98831,1,0.71419,0.0057488,0.0060148,0.052681,0.13941,0.019064,0.006376,0.00045928,1.0667
291 | 32,4,0.90305,2.3455,0.57721,0.98504,1,0.58743,0.0044044,0.0035306,0.022443,0.077826,0.0060204,0.0015394,0.00021043,0.68561
292 | 32,5,0.88566,2.2029,0.54993,0.98635,1,0.59039,0.0037601,0.0025732,0.039469,0.12459,0.015285,0.0056604,0.00020409,0.84271
293 | 32,6,0.88078,2.147,0.54028,0.98989,1,0.71926,0.0031646,0.0018226,0.044894,0.12906,0.016385,0.0056383,0.00032291,0.96576
294 | 32,7,0.88485,2.2398,0.55754,0.97997,0.99825,0.67974,0.0091286,0.015166,0.025658,0.087206,0.0075476,0.0021518,0.00017873,0.75154
295 | 32,8,0.88661,2.2553,0.56472,0.98284,1,0.67537,0.012123,0.026749,0.022251,0.083617,0.0069433,0.0020893,0.00016304,0.60779
296 | 32,9,0.83188,1.8445,0.46807,0.98691,1,0.75011,0.0049353,0.0044331,0.034814,0.10867,0.011672,0.0036787,0.0003005,0.80348
297 | 32,10,0.85278,1.9935,0.50107,0.98449,0.99825,0.72127,0.0068277,0.0084843,0.015559,0.070923,0.005005,0.0015733,6.9367e-05,0.44798
298 | 32,11,0.78676,1.6815,0.41383,0.97686,0.99825,0.7686,0.013524,0.033289,0.016538,0.076334,0.0057932,0.0019786,7.0661e-05,0.43227
299 | 33,1,0.69887,1.4947,0.34694,0.97906,1,0.74423,0.0070445,0.0090318,0.067277,0.15809,0.024383,0.0079843,0.00066857,1.2554
300 | 33,2,0.60555,1.3891,0.29569,0.97439,1,0.73603,0.010821,0.02131,0.1363,0.20829,0.04158,0.010017,0.0020084,2.2733
301 | 33,3,0.79495,1.7056,0.42041,0.98477,1,0.76317,0.0074486,0.010098,0.064127,0.14621,0.020929,0.0061549,0.00065287,1.3662
302 | 33,4,0.75535,1.6662,0.4065,0.97627,1,0.75126,0.0098445,0.017638,0.020477,0.09052,0.0081274,0.0031335,0.00012137,0.4391
303 | 33,5,0.69398,1.4542,0.32457,0.98263,0.99474,0.79449,0.0063863,0.0074227,0.079016,0.16552,0.026666,0.0079652,0.00077178,1.5438
304 | 33,6,0.81992,1.8698,0.47287,0.97829,0.99825,0.65353,0.0074141,0.010004,0.06789,0.142,0.019765,0.004991,0.00096699,1.4857
305 | 33,7,0.68878,1.4399,0.32986,0.98318,1,0.7847,0.0070802,0.0091235,0.083675,0.17243,0.028872,0.008786,0.0010143,1.5313
306 | 33,8,0.72174,1.5301,0.35707,0.97994,0.99649,0.78622,0.013545,0.03339,0.040677,0.11435,0.012908,0.0038198,0.00036093,0.98926
307 | 33,9,0.73067,1.5145,0.34921,0.98686,1,0.81119,0.017693,0.056975,0.081485,0.16841,0.02758,0.0081927,0.00084762,1.552
308 | 33,10,0.65119,1.4026,0.30387,0.9799,1,0.80081,0.0082573,0.012409,0.10567,0.185,0.033093,0.0087245,0.0013911,1.9632
309 | 33,11,0.71999,1.569,0.37089,0.97599,0.99825,0.77145,0.013975,0.035546,0.11997,0.19286,0.035863,0.0086099,0.001837,2.1311
310 | 34,1,0.99571,10.352,0.90609,0.90652,0.97895,0.16732,0.025787,0.12103,0.023523,0.089455,0.0079387,0.0028882,6.9318e-05,0.73774
311 | 34,2,0.99569,9.9653,0.90578,0.91515,0.9807,0.17113,0.023896,0.10392,0.020381,0.082596,0.0067759,0.0024074,5.0484e-05,0.67264
312 | 34,3,0.9967,11.361,0.914,0.91815,0.93684,0.14435,0.019976,0.072629,0.020667,0.085514,0.0072595,0.0028469,4.6487e-05,0.67925
313 | 34,4,0.99841,16.832,0.94116,0.85531,0.9,0.094537,0.035845,0.23384,0.012341,0.066682,0.0044268,0.0017052,1.7724e-05,0.41828
314 | 34,5,0.99799,15.068,0.93667,0.8807,0.95789,0.12132,0.032164,0.18828,0.011686,0.054827,0.002997,0.00086356,4.3787e-05,0.50576
315 | 34,6,0.99512,10.377,0.90564,0.92135,0.99825,0.17941,0.016647,0.050433,0.0204,0.071662,0.0051092,0.0012661,0.00017021,0.71514
316 | 34,7,0.99505,10.736,0.90851,0.92586,0.99649,0.18236,0.028571,0.14856,0.017761,0.06961,0.0048222,0.0013129,0.00013111,0.59218
317 | 34,8,0.99518,10.421,0.90385,0.96551,0.98947,0.20051,0.0077228,0.010855,0.021258,0.080385,0.0064203,0.0019168,9.5565e-05,0.66405
318 | 34,9,0.9953,10.63,0.90598,0.88866,0.95789,0.15967,0.025636,0.11961,0.014782,0.065416,0.0042611,0.0012831,6.4365e-05,0.52178
319 | 34,10,0.99515,10.007,0.90328,0.91594,0.97719,0.16244,0.033129,0.19975,0.017258,0.070646,0.004966,0.0014641,7.1555e-05,0.59946
320 | 34,11,0.99871,19.038,0.94834,0.851,0.90702,0.086183,0.073048,0.97117,0.0078165,0.048089,0.0023072,0.00075332,1.2922e-05,0.34029
321 | 35,1,0.9109,2.5488,0.6106,0.97388,0.99825,0.55818,0.01972,0.070775,0.10132,0.17022,0.028158,0.0074614,0.0010974,2.3847
322 | 35,2,0.90391,2.458,0.5977,0.9861,1,0.61429,0.0067013,0.0081731,0.08902,0.16706,0.027151,0.007219,0.00096593,1.8937
323 | 35,3,0.90755,2.582,0.62394,0.96837,0.99825,0.55674,0.031714,0.18305,0.079387,0.16213,0.025613,0.0074124,0.00069878,1.6951
324 | 35,4,0.91708,2.6498,0.62919,0.98493,1,0.57926,0.0068053,0.0084289,0.082187,0.16877,0.027693,0.0085027,0.00064002,1.7157
325 | 35,5,0.9166,2.6711,0.6333,0.98228,1,0.51619,0.017465,0.055514,0.10368,0.181,0.031721,0.0081985,0.0014211,1.973
326 | 35,6,0.8942,2.4203,0.59323,0.98407,1,0.60794,0.01743,0.055294,0.10102,0.18748,0.033957,0.0098155,0.0011793,1.8275
327 | 35,7,0.93847,3.0198,0.67312,0.98733,1,0.53234,0.0076393,0.010621,0.07629,0.18017,0.03144,0.011932,0.00072251,1.2434
328 | 35,8,0.89518,2.3875,0.581,0.98796,1,0.63833,0.0077926,0.011052,0.07304,0.1591,0.024688,0.0073807,0.00084138,1.408
329 | 35,9,0.91707,2.6504,0.63359,0.96002,0.99298,0.53972,0.012062,0.026481,0.10121,0.18433,0.032861,0.0089657,0.0014149,1.7979
330 | 35,10,0.92535,2.803,0.65133,0.976,1,0.48413,0.015435,0.04336,0.098946,0.18338,0.032533,0.0091801,0.0013297,1.8033
331 | 35,11,0.91861,2.8114,0.64707,0.94843,0.95614,0.51186,0.045037,0.36915,0.041345,0.13297,0.017374,0.0070233,0.00018923,0.83183
332 | 36,1,0.39093,1.1025,0.73351,0.72022,0.69474,0.17954,0.076072,1.0532,0.059213,0.15747,0.024197,0.0095408,0.00024731,1.2042
333 | 36,2,0.47124,1.1349,0.81159,0.65915,0.47368,0.093982,0.096492,1.6945,0.098618,0.21062,0.042478,0.016848,0.00058101,1.5913
334 | 36,3,0.3687,1.0456,0.77124,0.74413,0.77368,0.22278,0.075187,1.0289,0.074488,0.17993,0.031359,0.012414,0.0003469,1.3541
335 | 36,4,0.14986,1.0558,0.77733,0.73454,0.66316,0.1688,0.08041,1.1768,0.10395,0.1806,0.031585,0.0080165,0.0014315,1.9975
336 | 36,5,0.68069,1.1866,0.78745,0.73496,0.6,0.14029,0.072447,0.95524,0.09277,0.18451,0.032923,0.010852,0.00059383,1.8568
337 | 36,6,0.37522,1.1417,0.81725,0.68511,0.58772,0.12523,0.09186,1.5358,0.11488,0.20861,0.041703,0.013344,0.00082033,2.0281
338 | 36,7,0.28064,1.0849,0.75319,0.72152,0.71404,0.13686,0.078996,1.1358,0.14122,0.2183,0.045488,0.012002,0.0015154,2.4059
339 | 36,8,0.35344,1.0329,0.78147,0.70737,0.61579,0.13503,0.089763,1.4664,0.097663,0.20703,0.041101,0.016123,0.00045288,1.6935
340 | 36,9,0.59988,1.1427,0.71532,0.66101,0.47544,0.15747,0.11337,2.3394,0.050389,0.13585,0.018121,0.00619,0.00026454,1.1526
341 | 36,10,0.47195,1.0901,0.85409,0.53598,0.39649,0.078376,0.13227,3.184,0.082007,0.18782,0.034074,0.013487,0.00032855,1.5623
342 | 


--------------------------------------------------------------------------------
/learningjs.js:
--------------------------------------------------------------------------------
  1 | ////////////////////////////////////////////////////////////////////////////////////////////////
  2 | // javascript implementation of 
  3 | //   decision tree
  4 | //   logistic regression
  5 | //
  6 | // author: yandong liu
  7 | //  email: yandongl _at_ cs.cmu.edu
  8 | ////////////////////////////////////////////////////////////////////////////////////////////////
  9 | 
 10 | 
 11 | var learningjs=(function (exports) {
 12 |   'use strict'; 
 13 | 
 14 |   var _und;
 15 | 
 16 |   if(typeof _ === 'undefined')
 17 |     if(typeof require === 'function')
 18 |       var __ = require('underscore');
 19 | 
 20 |   if(typeof _ !== 'undefined')
 21 |     _und = _;
 22 |   else if(typeof __ !== 'undefined')
 23 |     _und = __;
 24 |   else
 25 |     throw 'underscore.js isn\'t found!'
 26 | 
 27 |   var debug=true;
 28 | 
 29 |   function debugp(msg, depth) {
 30 |     if(!debug) return;
 31 |     var s='';
 32 |     for(var i=0;i<depth*2;i++) {
 33 |       s+=' ';
 34 |     }
 35 |     console.log(s,msg);
 36 |   }
 37 | 
 38 |   var tree = function () {}, debug = false; 
 39 | 
 40 |   tree.prototype = {
 41 | 
 42 |     train: function(D, cb) {
 43 |       var major_label = this.mostCommon(D.targets);
 44 |       cb({
 45 |         model:this._c45(D.data, D.targets, D.l_featuresIndex, D.featureNames, D.featuresType, major_label),
 46 |         classify: function(sample) {
 47 |           var root = this.model;
 48 |           if(typeof root === 'undefined') {
 49 |             return 'null';
 50 |           }
 51 |           while(root.type != "result") {
 52 |             var childNode;
 53 |             if(root.type === 'feature_real') {
 54 |               var feature_name = root.name;
 55 |               var sampleVal = parseFloat(sample[D.feature_name2id[feature_name]]);
 56 |               if(sampleVal<=root.cut)
 57 |                 childNode=root.vals[1];
 58 |               else
 59 |                 childNode=root.vals[0]; 
 60 |             } else {
 61 |               var attr = root.name;
 62 |               var sampleVal = sample[D.feature_name2id[attr]];
 63 |               childNode = _und.detect(root.vals,function(x){return x.name == sampleVal});
 64 |             }
 65 |             //unseen feature value (didn't appear in training data)
 66 |             if(typeof childNode === 'undefined') {
 67 |               //console.log('unseen feature value:',root.name,'sample:',sample);
 68 |               return major_label;
 69 |             }
 70 |             root = childNode.child;
 71 |           }
 72 |           return root.val;
 73 |         },
 74 |         calcAccuracy: function(samples, targets, cb) {
 75 |           var total = samples.length;
 76 |           var correct = 0;
 77 |           for(var i=0;i<samples.length;i++) {
 78 |             var pred = this.classify(samples[i]);
 79 |             var actual = targets[i];
 80 |             //console.log('predict:'+pred,' actual:'+actual);
 81 |             if(pred === actual){
 82 |               correct++;
 83 |             }
 84 |           }
 85 |           if(total>0)
 86 |             cb(correct/total, correct, total);
 87 |           else
 88 |             cb(0.0);
 89 |         },
 90 |       }, undefined);
 91 |     },
 92 | 
 93 |     _c45: function(data, targets, l_features_id, l_features_name, featuresType, major_label) {
 94 |       var node;
 95 |       if (targets.length == 0) {
 96 |         debugp("==no data",0);
 97 |         return {type:"result", val: major_label, name: major_label,alias:major_label+this.randomTag() }; 
 98 |       }
 99 |       if (targets.length == 1) {
100 |         debugp("==end node "+targets[0],0);
101 |         return {type:"result", val: targets[0], name: targets[0],alias:targets[0]+this.randomTag() }; 
102 |       }
103 |       if(l_features_name.length == 0) {
104 |         debugp("==returning the most dominate feature", 0);
105 |         var topTarget = this.mostCommon(targets);
106 |         return {type:"result", val: topTarget, name: topTarget, alias: topTarget+this.randomTag()};
107 |       }
108 |       var bestFeatureData = this.maxGain(data,targets,l_features_id, l_features_name, featuresType);
109 |       var best_id = bestFeatureData.feature_id;//feature_id is index in data file
110 |       var best_name = bestFeatureData.feature_name;
111 |       //console.log('bestFeatureData:',bestFeatureData);
112 |       //console.log(featuresType[bestFeatureData.feature_name]);
113 |       var remainingFeaturesId = _und.without(l_features_id, best_id);
114 |       var remainingFeaturesName = _und.without(l_features_name, best_name);
115 |       if(featuresType[best_name]==='real') {
116 |         node = {name: best_name, id:best_id,alias: best_name+this.randomTag()};
117 |         node.type = "feature_real";
118 |         node.cut = bestFeatureData.cut;
119 |         node.vals=[];
120 | 
121 |         var _newS_r = this.filterByCutGreater(data, targets, bestFeatureData.cut, best_id);
122 |         //printDataset(_newS_r,bestFeature, 'label','>'+bestFeatureData.cut);
123 |         var child_node_r = {name:bestFeatureData.cut.toString(),alias:'>'+bestFeatureData.cut.toString()+this.randomTag(),type: "feature_value"};
124 |         child_node_r.child = this._c45(_newS_r[0], _newS_r[1], remainingFeaturesId, remainingFeaturesName, featuresType, major_label);
125 |         node.vals.push(child_node_r);
126 | 
127 |         var _newS_l = this.filterByCutLessEqual(data, targets, bestFeatureData.cut, best_id);
128 |         //printDataset(_newS_l,bestFeature, 'label','<='+bestFeatureData.cut);
129 |         var child_node_l= {name:bestFeatureData.cut.toString(),alias:'<='+bestFeatureData.cut.toString()+this.randomTag(),type: "feature_value"};
130 |         child_node_l.child = this._c45(_newS_l[0],_newS_l[1], remainingFeaturesId, remainingFeaturesName, featuresType, major_label);
131 |         node.vals.push(child_node_l);
132 | 
133 |       } else{ //default is text
134 |         var possibleValues = _und.unique(this.getCol(data, best_id));
135 |         node = {name: best_name, alias: best_name+this.randomTag()};
136 |         node.type = "feature_category";
137 |         node.vals=[];
138 | 
139 |         for(var i=0;i<possibleValues.length;i++) {
140 |           var _newS = this.filterByValue(data,targets,best_id, possibleValues[i]);
141 |           var child_node = {name:possibleValues[i], alias:possibleValues[i]+this.randomTag(),type: "feature_value"};
142 |           child_node.child = this._c45(_newS[0],_newS[1],remainingFeaturesId,remainingFeaturesName, featuresType, major_label);
143 |           node.vals.push(child_node);
144 |         }
145 |       }
146 |       return node;
147 |     }, 
148 | 
149 |     //node(alias, vals(node))
150 |     addEdges:function(node, colors, h_color, g){
151 |       var that = this;
152 |       if(node.type == 'feature_real'||node.type=='feature_category'){
153 |         _und.each(node.vals,function(m){
154 |           g.push(['val:'+m.alias+'</span>',node.alias+'','node']);
155 |           g = that.addEdges(m, colors, h_color, g);
156 |         });
157 |         return g;
158 |       } else if(node.type == 'feature_value'){ 
159 |         if(node.child.type != 'result'){
160 |           g.push([node.child.alias+'','val:'+node.alias+'</span>','value']);
161 |           g = this.addEdges(node.child, colors, h_color, g);
162 |         } else {
163 |           var color='black';
164 |           if(node.child.name in h_color) {
165 |             color = h_color[node.child.name];
166 |           } else {
167 |             var _sz = Object.keys(h_color).length;
168 |             if (_sz >=colors.length) color='black';
169 |             else color = colors[_sz];
170 |             h_color[node.child.name]=color;
171 |           }
172 |           g.push(['<span style="color:'+color+';font-weight:bold;">'+node.child.alias+'</span>','val:'+node.alias+'</span>','value']);
173 |         }
174 |         return g;
175 |       }
176 |       return g;
177 |     } ,
178 | 
179 |     drawGraph: function(model,divId, cb){
180 |       if(typeof google==='undefined') {
181 |         cb('google visualization APIs are not defined');
182 |         return;
183 |       }
184 |       var g = new Array();
185 |       var colors=['red','blue','green','yellow','black','fuchsia','gold','indigo','lime','mintcream','navy','olive','salmon','skyblue'];
186 |       var h_color={};
187 |       g = this.addEdges(model.model, colors,h_color,g).reverse();
188 |       window.g = g;
189 |       var data = google.visualization.arrayToDataTable(g.concat(g));
190 |       var chart = new google.visualization.OrgChart(document.getElementById(divId));
191 |       google.visualization.events.addListener(chart, 'ready',function(){
192 |          _und.each($('.google-visualization-orgchart-node'),function(x){
193 |             var oldVal = $(x).html();
194 |             if(oldVal){
195 |                 var cleanVal = oldVal.replace(/_r[0-9]+/,'');
196 |                 cleanVal = cleanVal.replace(/val:/,'<span style="color:olivedrab;">');
197 |                 $(x).html(cleanVal);
198 |             }
199 |           }); 
200 |       });
201 |       chart.draw(data, {allowHtml: true}); 
202 |       cb();
203 |     }, 
204 | 
205 |     getCol:function(d, colIdx) {
206 |       var col = [];
207 |       for(var i=0;i<d.length;i++) col.push(d[i][colIdx]);
208 |       return col;
209 |     },
210 | 
211 |     filterByCutLessEqual:function(d, targets, cut, col) {
212 |       var nd = [];
213 |       var nt = [];
214 |       if(d.length != targets.length) {
215 |         console.log('ERRROR: difft dimensions');
216 |       }
217 |       for(var i=0;i<d.length;i++) 
218 |         if(parseFloat(d[i][col])<=cut) {
219 |           nd.push(d[i]);
220 |           nt.push(targets[i]);
221 |         }
222 |       return [nd, nt];
223 |     },
224 | 
225 |     filterByCutGreater:function(d, targets, cut, col) {
226 |       var nd = [];
227 |       var nt = [];
228 |       if(d.length != targets.length) {
229 |         console.log('ERRROR: difft dimensions');
230 |       }
231 |       for(var i=0;i<d.length;i++) 
232 |         if(parseFloat(d[i][col])>cut) {
233 |           nd.push(d[i]);
234 |           nt.push(targets[i]);
235 |         }
236 |       return [nd, nt];
237 |     },
238 | 
239 |     //filter data, target at the same time
240 |     filterByValue:function(d,t, featureIdx, val) {
241 |       var nd = [];
242 |       var nt = [];
243 |       for(var i=0;i<d.length;i++) 
244 |         if(d[i][featureIdx]===val) {
245 |           nd.push(d[i]);
246 |           nt.push(t[i]);
247 |         }
248 |       return [nd,nt];
249 |     },
250 | 
251 |     //compute info gain for this feature. feature can be category or real type
252 |     gain: function(data,targets, feature_id, featureName, featuresType) {
253 |       if(data.length != targets.length) {
254 |         console.log('ERRROR: difft dimensions');
255 |       }
256 |       var setEntropy = this.entropy(targets);
257 |       //console.log('setEntropy:',setEntropy);
258 |       var vals = _und.unique(this.getCol(data,feature_id));
259 |       if(featuresType[featureName] === 'real') {
260 |         var gainVals = [];
261 |         for(var i=0;i<vals.length;i++) {
262 |           var cutf=parseFloat(vals[i]);
263 |           var _gain = setEntropy-this.conditionalEntropy(data, targets, feature_id, cutf);
264 |           gainVals.push({feature_id:feature_id, feature_name:featureName, gain:_gain, cut:cutf});
265 |         }
266 |         var _maxgain= _und.max(gainVals, function(e){return e.gain});
267 |         //debugp('real maxgain: '+_maxgain.cut+' '+_maxgain.gain,0);
268 |         return _maxgain;
269 |       } else{//default is text
270 |         var setSize = data.length;
271 |         var entropies = [];
272 |         for(var i=0;i<vals.length;i++) {
273 |           var subset = this.filterByValue(data, targets, feature_id, vals[i]);
274 |           entropies.push((subset[0].length/setSize)*this.entropy(subset[1]));
275 |         }
276 |         //console.log(featureName,' entropies:',entropies);
277 |         var sumOfEntropies =  _und(entropies).reduce(function(a,b){return a+b},0);
278 |         //console.log(featureName,' sumOfEntropies:',sumOfEntropies);
279 |         return {feature_id:feature_id, feature_name:featureName, gain:setEntropy - sumOfEntropies, cut:0};
280 |       } 
281 |     },
282 | 
283 |     entropy: function (vals){
284 |       var that = this;
285 |       var uniqueVals = _und.unique(vals);
286 |       var probs = uniqueVals.map(function(x){return that.prob(x,vals)});
287 |       var logVals = probs.map(function(p){return -p*that.log2(p) });
288 |       return logVals.reduce(function(a,b){return a+b},0);
289 |     }, 
290 | 
291 |     //conditional entropy if data is split to two
292 |     conditionalEntropy: function(_s, targets, feature_id, cut) {
293 |       var subset1 = this.filterByCutLessEqual(_s, targets, cut, feature_id);
294 |       var subset2 = this.filterByCutGreater(_s, targets, cut, feature_id);
295 |       var setSize = _s.length;
296 |       return subset1[0].length/setSize*this.entropy(subset1[1]) + subset2[0].length/setSize*this.entropy(subset1[1]);
297 |     }, 
298 | 
299 |     maxGain: function (data, targets, l_features_id, l_features_name, featuresType){
300 |       var g45 = [];
301 |       for(var i=0;i<l_features_id.length;i++) {
302 |         //console.log('maxgain feature:'+l_features_id[i]+' '+l_features_name[i]);
303 |         g45.push(this.gain(data,targets,l_features_id[i], l_features_name[i], featuresType));
304 |       }
305 |       return _und.max(g45,function(e){
306 |         return e.gain;
307 |       });
308 |     },
309 | 
310 |     prob: function(val,vals){
311 |      var instances = _und.filter(vals,function(x) {return x === val}).length;
312 |      var total = vals.length;
313 |      return instances/total;
314 |     },
315 | 
316 |     log2: function (n){
317 |      return Math.log(n)/Math.log(2);
318 |     }, 
319 | 
320 |     mostCommon: function(l){
321 |       var that=this;
322 |       return  _und.sortBy(l,function(a){
323 |         return that.count(a,l);
324 |       }).reverse()[1];
325 |     },
326 | 
327 |     count: function (a,l){
328 |       return _und.filter(l,function(b) { return b === a}).length
329 |     },
330 | 
331 |     randomTag: function (){
332 |       return "_r"+Math.round(Math.random()*1000000).toString();
333 |     }
334 | 
335 |  }
336 | 
337 |   //logistic regression
338 |   var lr = function () {};
339 | 
340 |   lr.prototype = {
341 | 
342 |     train: function(D, cb) {
343 |       cb({
344 |         that:this,
345 |         thetas:this.optimize(D),
346 |         classify: function(sample) {
347 |           var max_p = this.that.compThetaXProduct(this.thetas[D.l_targets[0]], sample, D.nfeatures);
348 |           var max_t = D.l_targets[0];
349 |           for(var i=1;i<D.ntargets;i++) {
350 |             var target = D.l_targets[i];
351 |             var p = this.that.compThetaXProduct(this.thetas[target], sample, D.nfeatures);
352 |             if(max_p<p) {
353 |               max_p = p;
354 |               max_t = target;
355 |             }
356 |           }
357 |           return max_t;
358 |         },
359 |         calcAccuracy: function(samples, targets, cb) {
360 |           var total = samples.length;
361 |           var correct = 0;
362 |           for(var i=0;i<samples.length;i++) {
363 |             var pred = this.classify(samples[i]);
364 |             var actual = targets[i];
365 |             //console.log('predict:'+pred,' actual:'+actual);
366 |             if(pred === actual){
367 |               correct++;
368 |             }
369 |           }
370 |           if(total>0)
371 |             cb(correct/total, correct, total);
372 |           else
373 |             cb(0.0);
374 |         },
375 |       }, undefined);
376 |     },
377 | 
378 |     printThetas: function(thetas, ntargets, l_targets, nfeatures) {
379 |       for(var i=0;i<ntargets;i++) {
380 |         console.log(l_targets[i]);
381 |         for(var j=0;j<nfeatures;j++) {
382 |           process.stdout.write(thetas[l_targets[i]][j]+' ');
383 |         }
384 |         console.log(' ');
385 |       }
386 |     },
387 | 
388 |     optimize: function(D) {
389 | 
390 |       if(!('optimizer' in D)) D.optimizer = 'sgd';
391 |       if(!('learning_rate' in D)) D.learning_rate = 0.005;
392 |       if(!('l2_weight' in D)) D.l2_weight = 0.000001;
393 |       if(!('iterations' in D)) D.iterations = 50;
394 | 
395 |       var thetas={};
396 |       for(var i=0;i<D.ntargets;i++) {
397 |         var theta=[];
398 |         for(var j=0;j<D.nfeatures;j++) {
399 |           theta.push(0.0);
400 |         }
401 |         thetas[D.l_targets[i]]=theta; 
402 |       }
403 |       for(var i=0;i<D.iterations;i++) {
404 |         if(D.optimizer === 'sgd')
405 |           this.sgd_once(thetas, D.data, D.nfeatures, D.targets,D.l_targets, D.ntargets, D.learning_rate, D.l2_weight);
406 |         else if (D.optimizer === 'gd') 
407 |           this.gd_batch(thetas, D.data, D.nfeatures,D.targets,D.l_targets, D.ntargets, D.learning_rate, D.l2_weight);
408 |         else {
409 |           console.log('unrecognized optimizer:'+D.optimizer);
410 |           break;
411 |         }
412 |       }
413 |       //this.printThetas(thetas, D.ntargets, D.l_targets, D.nfeatures);
414 |       return thetas;
415 |     },
416 | 
417 |     gd_batch: function(thetas, training, nfeatures,targets,l_targets, ntargets, learning_rate, l2_weight){
418 |       for(var t=0;t<ntargets;t++) {
419 |         var gradient=[];
420 |         for(var k=0;k<nfeatures;k++) {
421 |           gradient.push(0.0);
422 |         }
423 |         var target = l_targets[t];
424 | 
425 |         for(var i=0;i<training.length;i++) {
426 |           var prdt=[], this_prdt;
427 |           prdt.push(this.compThetaXProduct(thetas[l_targets[0]], training[i], nfeatures));
428 |           if(t==0) this_prdt = prdt[0];
429 |           var max_prdt = prdt[0];
430 | 
431 |           for(var j=1;j<ntargets;j++) {
432 |             var prdt1 = this.compThetaXProduct(thetas[l_targets[j]], training[i], nfeatures);
433 |             prdt[j] = prdt1;
434 |             if(t==j) this_prdt = prdt1;
435 |             if(max_prdt < prdt1) max_prdt= prdt1;
436 |           }
437 |           var z=0.0;
438 |           for(var j=0;j<ntargets;j++) {
439 |             z+=Math.exp(prdt[j]-max_prdt);
440 |           }
441 |           var p = Math.exp(this_prdt-max_prdt)/z;
442 |           for(var k=0;k<nfeatures;k++) {
443 |             if(target === targets[i]) {
444 |               gradient[k] += ((1.0-p)*training[i][k]);
445 |             } else {
446 |               gradient[k] += ((0.0-p)*training[i][k]);
447 |             }
448 |           }
449 |         }
450 |         var theta = thetas[target];
451 |         for(var k=0;k<nfeatures;k++) {
452 |           theta[k] += (learning_rate* gradient[k] - 2*training.length*l2_weight*theta[k]);
453 |         }
454 |       }
455 |     },
456 | 
457 |     sgd_once: function(thetas, training, nfeatures,targets,l_targets, ntargets, learning_rate, l2_weight){
458 |       for(var i=0;i<training.length;i++) {
459 |         var prdt=[];
460 |         prdt.push(this.compThetaXProduct(thetas[l_targets[0]], training[i], nfeatures));
461 |         var max_prdt = prdt[0];
462 |         for(var j=1;j<ntargets;j++) {
463 |           var prdt1 = this.compThetaXProduct(thetas[l_targets[j]], training[i], nfeatures);
464 |           prdt[j] = prdt1;
465 |           if(max_prdt < prdt1) max_prdt= prdt1;
466 |         }
467 |         var z=0.0;
468 |         for(var j=0;j<ntargets;j++) {
469 |           z+=Math.exp(prdt[j]-max_prdt);
470 |         }
471 |         for(var j=0;j<ntargets;j++) {
472 |           var p = Math.exp(prdt[j]-max_prdt)/z;
473 |           var target = l_targets[j];
474 |           var theta = thetas[target];
475 |           for(var k=0;k<nfeatures;k++) {
476 |             if(target === targets[i]) {
477 |               theta[k] += (learning_rate*(1.0-p)*training[i][k] - 2*l2_weight*theta[k]);
478 |             } else {
479 |               theta[k] += (learning_rate*(0.0-p)*training[i][k] - 2*l2_weight*theta[k]);
480 |             }
481 |           }
482 |         }
483 |       }
484 |     }, 
485 | 
486 |     compThetaXProduct:function(theta, sample, nfeatures) {
487 |       var a=0;
488 |       for(var i=0;i<nfeatures;i++) {
489 |         a += theta[i]*sample[i];
490 |       }
491 |       return a;
492 |     }
493 |  }
494 | 
495 |   var exports = exports||{};
496 |   exports.logistic = lr;
497 |   exports.tree = tree;
498 |   return exports;
499 | 
500 | })(typeof module != 'undefined' && module.exports); 
501 | 


--------------------------------------------------------------------------------
/package.json:
--------------------------------------------------------------------------------
 1 | {
 2 |   "name": "learningjs",
 3 |   "version": "0.1.1",
 4 |   "description": "logistic regression/c4.5 decision tree algorithm",
 5 |   "main": "learningjs.js",
 6 |   "scripts": {
 7 |     "test_tree": "node test_lr.js",
 8 |     "test_logistic": "node test_tree.js"
 9 |   },
10 |   "repository": {
11 |     "type": "git",
12 |     "url": "https://github.com/yandongliu/learningjs"
13 |   },
14 |   "dependencies": {
15 |     "underscore": "*",
16 |     "lazy": "*"
17 |   },
18 |   "keywords": [
19 |     "logistic regression",
20 |     "decision tree",
21 |     "machine learning",
22 |     "classifier",
23 |     "c4.5"
24 |   ],
25 |   "author": "Yandong Liu",
26 |   "license": "MIT",
27 |   "bugs": {
28 |     "url": "https://github.com/yandongliu/learningjs/issues"
29 |   }
30 | }
31 | 


--------------------------------------------------------------------------------
/test_lr.js:
--------------------------------------------------------------------------------
 1 | //////////////////////////////////////////////////////////////////
 2 | /// sample code using logistic regression for classification
 3 | //  some datasets can be found at 
 4 | //    https://archive.ics.uci.edu/ml/datasets.html
 5 | //
 6 | //  you have to manually add the header(features) to each data file
 7 | //////////////////////////////////////////////////////////////////
 8 | 'use strict';
 9 | 
10 | var learningjs = require('learningjs.js');
11 | var data_util = require("data_util.js");
12 | 
13 | if(process.argv.length<4) {
14 |   console.log('usage: %s %s training_file test_file', process.argv[0], process.argv[1]);
15 |   process.exit(0);
16 | }
17 | var fn = process.argv[2];
18 | var fn_test = process.argv[3];
19 | 
20 | console.log('=== TRAIN:%s ===', fn);
21 | console.log('=== TEST:%s ===', fn_test);
22 | 
23 | 
24 | data_util.loadRealFile(fn, function(D) {
25 | 
26 |   //normalize data
27 |   data_util.normalize(D.data, D.nfeatures); 
28 | 
29 | 
30 |   //logistic regression. following params are optional
31 |   D.optimizer = 'sgd'; //default choice. other choice is 'gd'
32 |   D.learning_rate = 0.005;
33 |   D.l2_weight = 0.0;
34 |   D.iterations = 1000; //increase number of iterations for better performance
35 | 
36 |   var start = process.hrtime();
37 |   new learningjs.logistic().train(D, function(model, err){
38 |     if(err) {
39 |       console.log(err);
40 |     } else {
41 |       var elapsed = process.hrtime(start)[1] / 1000000;
42 |       console.log('training took ' + process.hrtime(start)[0] + " s, " + elapsed.toFixed(2) + " ms.");
43 |       model.calcAccuracy(D.data, D.targets, function(acc, correct, total){
44 |         console.log('training: got '+correct +' correct out of '+total+' examples. accuracy:'+(acc*100.0).toFixed(2)+'%');
45 |       });
46 |       data_util.loadRealFile(fn_test, function(T) {
47 |         model.calcAccuracy(T.data, T.targets, function(acc, correct, total){
48 |           console.log('    test: got '+correct +' correct out of '+total+' examples. accuracy:'+(acc*100.0).toFixed(2)+'%');
49 |         });
50 |       });
51 |     }
52 |   });
53 | }); 
54 | 


--------------------------------------------------------------------------------
/test_tree.js:
--------------------------------------------------------------------------------
 1 | //////////////////////////////////////////////////////////////////
 2 | /// sample code using decision tree for classification
 3 | //  some datasets can be found at 
 4 | //    https://archive.ics.uci.edu/ml/datasets.html
 5 | //
 6 | //  you have to manually add the header(features) to each data file
 7 | //////////////////////////////////////////////////////////////////
 8 | 'use strict';
 9 | 
10 | var learningjs = require('./js/learningjs.js');
11 | var data_util = require("./js/data_util.js");
12 | 
13 | if(process.argv.length<4) {
14 |   console.log('usage: %s %s training_file test_file', process.argv[0], process.argv[1]);
15 |   process.exit(0);
16 | }
17 | var fn = process.argv[2];
18 | var fn_test = process.argv[3];
19 | 
20 | console.log('=== TRAIN:%s ===', fn);
21 | console.log('=== TEST:%s ===', fn_test);
22 | 
23 | data_util.loadTextFile(fn, function(D) {
24 | 
25 |   //decision tree deals with both numeric/categorical features
26 |   //but you have to specify its type individually in 2nd line of the file
27 | 
28 |   var start = process.hrtime();
29 |   new learningjs.tree().train(D, function(model, err){
30 |     if(err) {
31 |       console.log(err);
32 |     } else {
33 |       //console.log('model:',model);
34 |       var elapsed = process.hrtime(start)[1] / 1000000;
35 |       console.log('training took ' + process.hrtime(start)[0] + " s, " + elapsed.toFixed(2) + " ms.");
36 |       model.calcAccuracy(D.data, D.targets, function(acc, correct, total){
37 |         console.log('training: got '+correct +' correct out of '+total+' examples. accuracy:'+(acc*100.0).toFixed(2)+'%');
38 |       });
39 |       data_util.loadTextFile(fn_test, function(T) {
40 |         model.calcAccuracy(T.data, T.targets, function(acc, correct, total){
41 |           console.log('    test: got '+correct +' correct out of '+total+' examples. accuracy:'+(acc*100.0).toFixed(2)+'%');
42 |         });
43 |       });
44 |     }
45 |   });
46 | }); 
47 | 


--------------------------------------------------------------------------------
/wpbc_real_test.csv:
--------------------------------------------------------------------------------
 1 | label,f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f13,f14,f15,f16,f17,f18,f19,f20,f21,f22,f23,f24,f25,f26,f27,f28,f29,f30,f31,f32,f33
 2 | feature_type,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real
 3 | N,13,17.27,25.42,112.4,928.8,0.08331,0.1109,0.1204,0.05736,0.1467,0.05407,0.51,1.679,3.283,58.38,0.008109,0.04308,0.04942,0.01742,0.01594,0.003739,20.38,35.46,132.8,1284,0.1436,0.4122,0.5036,0.1739,0.25,0.07944,9,0
 4 | N,23,18.03,16.85,117.5,990,0.08947,0.1232,0.109,0.06254,0.172,0.0578,0.2986,0.5906,1.921,35.77,0.004117,0.0156,0.02975,0.009753,0.01295,0.002436,20.38,22.02,133.3,1292,0.1263,0.2666,0.429,0.1535,0.2842,0.08225,2.7,0
 5 | R,16,17.75,28.03,117.3,981.6,0.09997,0.1314,0.1698,0.08293,0.1713,0.05916,0.3897,1.077,2.873,43.95,0.004714,0.02015,0.03697,0.0111,0.01237,0.002556,21.53,38.54,145.4,1437,0.1401,0.3762,0.6399,0.197,0.2972,0.09075,1.5,7
 6 | N,34,21.1,20.52,138.1,1384,0.09684,0.1175,0.1572,0.1155,0.1554,0.0566,0.6643,1.361,4.542,81.89,0.005467,0.02075,0.03185,0.01466,0.01029,0.002205,25.68,32.07,168.2,2022,0.1368,0.3101,0.4399,0.228,0.2268,0.07425,2.8,0
 7 | R,9,19.59,25,127.7,1191,0.1032,0.09871,0.1655,0.09063,0.1663,0.05391,0.4674,1.375,2.916,56.18,0.0119,0.01929,0.04907,0.01499,0.01641,0.001807,21.44,30.96,139.8,1421,0.1528,0.1845,0.3977,0.1466,0.2293,0.06091,2,7
 8 | N,32,17.6,23.33,119,980.5,0.09289,0.2004,0.2136,0.1002,0.1696,0.07369,0.9289,1.465,5.801,104.9,0.006766,0.07025,0.06591,0.02311,0.01673,0.0113,21.57,28.87,143.6,1437,0.1207,0.4785,0.5165,0.1996,0.2301,0.1224,1,0
 9 | N,8,16.25,19.51,109.8,815.8,0.1026,0.1893,0.2236,0.09194,0.2151,0.06578,0.3147,0.9857,3.07,33.12,0.009197,0.0547,0.08079,0.02215,0.02773,0.006355,17.39,23.05,122.1,939.7,0.1377,0.4462,0.5897,0.1775,0.3318,0.09136,1,0
10 | R,14,19.44,18.82,128.1,1167,0.1089,0.1448,0.2256,0.1194,0.1823,0.06115,0.5659,1.408,3.631,67.74,0.005288,0.02833,0.04256,0.01176,0.01717,0.003211,23.96,30.39,153.9,1740,0.1514,0.3725,0.5936,0.206,0.3266,0.09009,7,9
11 | N,36,16.69,20.2,107.1,857.6,0.07497,0.07112,0.03649,0.02307,0.1846,0.05325,0.2473,0.5679,1.775,22.95,0.002667,0.01446,0.01423,0.005297,0.01961,0.0017,19.18,26.56,127.3,1084,0.1009,0.292,0.2477,0.08737,0.4677,0.07623,2.5,0
12 | R,12,18.01,20.56,118.4,1007,0.1001,0.1289,0.117,0.07762,0.2116,0.06077,0.7548,1.288,5.353,89.74,0.007997,0.027,0.03737,0.01648,0.02897,0.003996,21.53,26.06,143.4,1426,0.1309,0.2327,0.2544,0.1489,0.3251,0.07625,3.5,2
13 | N,38,18.49,17.52,121.3,1068,0.1012,0.1317,0.1491,0.09183,0.1832,0.06697,0.7923,1.045,4.851,95.77,0.007974,0.03214,0.04435,0.01573,0.01617,0.005255,22.75,22.88,146.4,1600,0.1412,0.3089,0.3533,0.1663,0.251,0.09445,3,0
14 | R,11,20.59,21.24,137.8,1320,0.1085,0.1644,0.2188,0.1121,0.1848,0.06222,0.5904,1.216,4.206,75.09,0.006666,0.02791,0.04062,0.01479,0.01117,0.003727,23.86,30.76,163.2,1760,0.1464,0.3597,0.5179,0.2113,0.248,0.08999,5.5,20
15 | N,34,13.82,24.49,92.33,595.9,0.1162,0.1681,0.1357,0.06759,0.2275,0.07237,0.4751,1.528,2.974,39.05,0.00968,0.03856,0.03476,0.01616,0.02434,0.006995,16.01,32.94,106,788,0.1794,0.3966,0.3381,0.1521,0.3651,0.1183,1,0
16 | R,7,24.24,18.74,159.6,1945,0.08938,0.1136,0.1727,0.1071,0.1591,0.05175,1.73,0.8535,11.56,316,0.005137,0.01279,0.02353,0.01508,0.01615,0.002252,35.13,23.36,232.2,3903,0.1154,0.1772,0.2917,0.1795,0.2336,0.06259,3,4
17 | N,41,15.46,23.95,103.8,731.3,0.1183,0.187,0.203,0.0852,0.1807,0.07083,0.3331,1.961,2.937,32.52,0.009538,0.0494,0.06019,0.02041,0.02105,0.006,17.11,36.33,117.7,909.4,0.1732,0.4967,0.5911,0.2163,0.3013,0.1067,2.5,1
18 | N,38,15.05,19.07,97.26,701.9,0.09215,0.08597,0.07486,0.04335,0.1561,0.05915,0.386,1.198,2.63,38.49,0.004952,0.0163,0.02967,0.009423,0.01152,0.001718,17.58,28.06,113.8,967,0.1246,0.2101,0.2866,0.112,0.2282,0.06954,2.5,8
19 | N,40,18.31,20.58,120.8,1052,0.1068,0.1248,0.1569,0.09451,0.186,0.05941,0.5449,0.9225,3.218,67.36,0.006176,0.01877,0.02913,0.01046,0.01559,0.002725,21.86,26.2,142.2,1493,0.1492,0.2536,0.3759,0.151,0.3074,0.07863,1.2,1
20 | N,41,19.89,20.26,130.5,1214,0.1037,0.131,0.1411,0.09431,0.1802,0.06188,0.5079,0.8737,3.654,59.7,0.005089,0.02303,0.03052,0.01178,0.01057,0.003391,23.73,25.23,160.5,1646,0.1417,0.3309,0.4185,0.1613,0.2549,0.09136,8,4
21 | N,39,24.63,21.6,165.5,1841,0.103,0.2106,0.231,0.1471,0.1991,0.06739,0.9915,0.9004,7.05,139.9,0.004989,0.03212,0.03571,0.01597,0.01879,0.00476,29.92,26.93,205.7,2642,0.1342,0.4188,0.4658,0.2475,0.3157,0.09671,1,1
22 | N,1,18.45,31.39,120.8,1051,0.09889,0.1108,0.1275,0.07493,0.1855,0.05593,0.3729,2.183,2.922,43.45,0.005957,0.02474,0.04066,0.01499,0.01703,0.003462,20.36,44.46,136.5,1286,0.1289,0.249,0.3641,0.1555,0.2847,0.07684,2,1
23 | N,30,15.22,30.62,103.4,716.9,0.1048,0.2087,0.255,0.09429,0.2128,0.07152,0.2602,1.205,2.362,22.65,0.004625,0.04844,0.07359,0.01608,0.02137,0.006142,17.52,42.79,128.7,915,0.1417,0.7917,1.17,0.2356,0.4089,0.1409,2.5,0
24 | N,17,20.92,25.09,143,1347,0.1099,0.2236,0.3174,0.1474,0.2149,0.06879,0.9622,1.026,8.758,118.8,0.006399,0.0431,0.07845,0.02624,0.02057,0.006213,24.29,29.41,179.1,1819,0.1407,0.4186,0.6599,0.2542,0.2929,0.09873,1.5,0
25 | N,28,21.56,22.39,142,1479,0.111,0.1159,0.2439,0.1389,0.1726,0.05623,1.176,1.256,7.673,158.7,0.0103,0.02891,0.05198,0.02454,0.01114,0.004239,25.45,26.4,166.1,2027,0.141,0.2113,0.4107,0.2216,0.206,0.07115,1.2,0
26 | N,20,20.13,28.25,131.2,1261,0.0978,0.1034,0.144,0.09791,0.1752,0.05533,0.7655,2.463,5.203,99.04,0.005769,0.02423,0.0395,0.01678,0.01898,0.002498,23.69,38.25,155,1731,0.1166,0.1922,0.3215,0.1628,0.2572,0.06637,1,0
27 | N,16,16.6,28.08,108.3,858.1,0.08455,0.1023,0.09251,0.05302,0.159,0.05648,0.4564,1.075,3.425,48.55,0.005903,0.03731,0.0473,0.01557,0.01318,0.003892,18.98,34.12,126.7,1124,0.1139,0.3094,0.3403,0.1418,0.2218,0.0782,3.5,1
28 | N,26,13.86,23.83,89.6,595.8,0.1067,0.08318,0.07671,0.05627,0.1433,0.05484,0.6142,2.25,4.334,51.14,0.0142,0.01844,0.03254,0.01999,0.02719,0.001925,15.51,30,101.2,730.6,0.1467,0.1279,0.1677,0.1114,0.218,0.06057,1.5,1
29 | N,27,18.11,26.17,118.7,970.2,0.09867,0.1716,0.2177,0.09718,0.2063,0.06194,0.5854,1.386,4.066,48.23,0.0078,0.04768,0.1015,0.02349,0.02759,0.004116,21.88,35.4,145.1,1251,0.1464,0.5127,0.8774,0.2603,0.3941,0.09318,2.4,2
30 | N,29,21.93,30.64,146.7,1487,0.08679,0.1723,0.2053,0.101,0.1796,0.05715,0.8842,1.152,6.92,115.8,0.007661,0.05912,0.05962,0.02539,0.02899,0.004636,25.24,34.07,171.5,1951,0.1168,0.4072,0.4494,0.1886,0.2784,0.07353,3.5,0
31 | R,14,17.53,25.28,114,966.6,0.09278,0.09175,0.1105,0.06741,0.1424,0.05563,0.4587,1.318,3.032,52.27,0.00454,0.01312,0.0172,0.007299,0.009783,0.001526,21.75,40.14,142.6,1483,0.1287,0.2472,0.2753,0.1372,0.2404,0.07156,10,9
32 | R,1,18.11,30.99,115.8,984.4,0.08625,0.0924,0.06214,0.05598,0.1603,0.05468,0.4236,1.176,2.189,36.01,0.004733,0.01621,0.02044,0.01142,0.01411,0.002098,21.05,38.93,128,1214,0.1194,0.2088,0.2385,0.1333,0.2652,0.07006,2.7,4
33 | N,24,24.29,25.48,161.8,1715,0.09374,0.2284,0.2702,0.1369,0.2307,0.06308,0.55,0.7052,3.853,76.08,0.00403,0.02513,0.04598,0.01184,0.02346,0.002403,27.53,30.44,184.8,2213,0.1247,0.3935,0.6118,0.2063,0.3983,0.07978,1.2,0
34 | N,13,15.6,26.79,99.85,760,0.07885,0.0524,0.03778,0.02876,0.158,0.05395,0.3292,0.9247,2.252,33.37,0.004508,0.007347,0.01094,0.007723,0.01673,0.00131,18.16,33.64,117.2,1027,0.1097,0.09745,0.1263,0.08222,0.2805,0.06378,2,2
35 | N,13,15.78,17.1,102.2,769,0.09668,0.0903,0.07268,0.04475,0.189,0.0569,0.2059,0.6052,1.153,20.04,0.00475,0.01527,0.01893,0.008301,0.01477,0.002027,17.37,22.52,111.7,921.4,0.1386,0.264,0.2901,0.1234,0.3261,0.07752,0.8,0
36 | N,12,19.28,20.88,124.9,1127,0.09033,0.1197,0.06435,0.0887,0.1971,0.05417,1.347,1.874,9.142,167.9,0.002889,0.009586,0.02836,0.0366,0.02385,0.003856,20.03,22.43,130,1214,0.09273,0.1259,0.08201,0.1116,0.2147,0.05644,2.5,0
37 | N,15,15.66,24.51,102,771.1,0.08886,0.08731,0.09483,0.04286,0.1995,0.05626,0.5016,1.824,4.013,52.6,0.005164,0.0231,0.03871,0.0129,0.01923,0.001897,18.47,35.09,124,1066,0.1182,0.2486,0.3508,0.1273,0.2964,0.06591,2,4
38 | N,11,22.44,27.42,150.6,1504,0.1211,0.2082,0.3579,0.1842,0.2524,0.05848,1.083,3.503,7.164,123.9,0.008771,0.03241,0.07453,0.02372,0.03172,0.003287,23.99,32.51,161.2,1689,0.133,0.258,0.4741,0.2135,0.2809,0.06318,1,0
39 | N,17,17.98,23.96,120,995,0.1157,0.1739,0.1954,0.1219,0.1981,0.06306,0.7937,2.595,5.639,90.08,0.007286,0.03164,0.04003,0.0205,0.02609,0.005366,20.05,32.23,135.2,1225,0.134,0.2606,0.3124,0.1753,0.2746,0.0783,2.5,0
40 | N,17,13.63,24.7,89.65,569.2,0.1055,0.1312,0.1161,0.06403,0.1791,0.07058,0.3223,1.148,2.797,25.79,0.007254,0.03085,0.03225,0.01585,0.01783,0.004359,15.33,31.96,107.7,712.2,0.1554,0.3515,0.3409,0.1689,0.2739,0.09945,2,0
41 | N,9,23.01,33.87,156.8,1705,0.1157,0.1948,0.2979,0.1522,0.1799,0.0634,1.819,2.708,13.28,253.8,0.008461,0.03397,0.05986,0.02232,0.03215,0.003898,28.39,41.66,197.8,2481,0.1432,0.2895,0.4937,0.211,0.2774,0.07542,4,0
42 | N,10,22.41,29.95,145.5,1528,0.1119,0.1699,0.3076,0.1594,0.2099,0.06803,1.463,1.717,9.939,233.7,0.003676,0.04185,0.06688,0.01772,0.01538,0.005663,29.27,38.4,194.5,2690,0.1251,0.3832,0.5842,0.2422,0.2729,0.09391,1,1
43 | N,15,12.53,30.98,80.41,490.9,0.09252,0.06271,0.06151,0.03938,0.1993,0.06554,0.3708,1.47,2.546,30.45,0.009003,0.01059,0.02372,0.01286,0.01837,0.003858,14.95,41.05,96.75,694.6,0.156,0.1358,0.2436,0.1259,0.3292,0.09406,3,7
44 | N,6,19.8,20.46,130.2,1235,0.09652,0.1077,0.1599,0.08705,0.162,0.05731,0.8215,1.26,6.436,116.7,0.009331,0.02827,0.06039,0.02226,0.01728,0.002806,22.61,25.78,155.1,1623,0.1373,0.2368,0.3931,0.184,0.2382,0.06831,2.1,2
45 | N,8,19.96,27.41,130.8,1238,0.09075,0.1167,0.1355,0.08397,0.16,0.05461,0.5299,1.562,3.39,66.13,0.005734,0.02089,0.04279,0.01392,0.01051,0.0037,22.75,36.88,150.3,1603,0.1293,0.2513,0.4106,0.1638,0.2245,0.07842,4,0
46 | N,3,19.22,27.18,128.8,1134,0.109,0.1777,0.2138,0.1116,0.1924,0.06389,0.497,2.403,4.493,61.18,0.006133,0.04109,0.05917,0.01756,0.0218,0.004305,22.57,44.6,161,1565,0.1501,0.513,0.6841,0.2099,0.3484,0.09774,0.8,1
47 | N,3,14.72,25.26,99.28,657.5,0.1174,0.2112,0.1729,0.09465,0.2079,0.07496,0.3405,1.158,2.188,26.12,0.004957,0.04553,0.05166,0.01597,0.02539,0.005548,16.67,31.97,111.6,814.8,0.1464,0.5352,0.5655,0.1974,0.3778,0.1132,1.7,21
48 | N,10,22.52,21.92,146.9,1597,0.07592,0.09162,0.06862,0.06367,0.1728,0.05262,1.374,2.312,9.938,192.4,0.004422,0.02679,0.02433,0.02036,0.02486,0.003922,24.67,24.81,162.1,1902,0.08191,0.1319,0.1056,0.09378,0.2061,0.05788,6,2
49 | N,8,15.44,31.18,101,740.4,0.09399,0.1062,0.1375,0.065,0.1735,0.06105,0.3235,1.839,1.971,32.61,0.006178,0.02088,0.02834,0.01079,0.02122,0.003168,17.34,41.48,112.6,929,0.1272,0.2362,0.2975,0.1286,0.2914,0.08024,1.5,0
50 | N,12,17.17,29.19,110,915.3,0.08952,0.06655,0.06583,0.05068,0.1793,0.05392,0.6101,1.425,3.827,63.02,0.00689,0.01556,0.03155,0.009714,0.02734,0.001377,20.76,36.66,132.5,1295,0.1261,0.1572,0.2141,0.0952,0.3362,0.06033,3.7,0
51 | R,3,21.42,22.84,145,1440,0.107,0.1939,0.238,0.1318,0.1884,0.06472,1.085,0.8469,8.308,142.5,0.006831,0.03541,0.04948,0.0177,0.01481,0.003979,28.34,27.98,198.3,2375,0.1498,0.4379,0.5411,0.2215,0.2832,0.08981,3,?
52 | N,6,16.7,28.13,110.3,885.4,0.08896,0.1131,0.1012,0.04989,0.189,0.06035,0.6052,1.235,4.243,63.91,0.007767,0.02631,0.03957,0.01411,0.01957,0.003606,19.74,34.92,128.8,1213,0.133,0.2808,0.3455,0.1317,0.3035,0.08036,3.5,0
53 | 


--------------------------------------------------------------------------------
/wpbc_real_train.csv:
--------------------------------------------------------------------------------
  1 | label,f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f13,f14,f15,f16,f17,f18,f19,f20,f21,f22,f23,f24,f25,f26,f27,f28,f29,f30,f31,f32,f33
  2 | feature_type,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real,real
  3 | N,31,18.02,27.6,117.5,1013,0.09489,0.1036,0.1086,0.07055,0.1865,0.06333,0.6249,1.89,3.972,71.55,0.004433,0.01421,0.03233,0.009854,0.01694,0.003495,21.63,37.08,139.7,1436,0.1195,0.1926,0.314,0.117,0.2677,0.08113,5,5
  4 | N,61,17.99,10.38,122.8,1001,0.1184,0.2776,0.3001,0.1471,0.2419,0.07871,1.095,0.9053,8.589,153.4,0.006399,0.04904,0.05373,0.01587,0.03003,0.006193,25.38,17.33,184.6,2019,0.1622,0.6656,0.7119,0.2654,0.4601,0.1189,3,2
  5 | N,116,21.37,17.44,137.5,1373,0.08836,0.1189,0.1255,0.0818,0.2333,0.0601,0.5854,0.6105,3.928,82.15,0.006167,0.03449,0.033,0.01805,0.03094,0.005039,24.9,20.98,159.1,1949,0.1188,0.3449,0.3414,0.2032,0.4334,0.09067,2.5,0
  6 | N,123,11.42,20.38,77.58,386.1,0.1425,0.2839,0.2414,0.1052,0.2597,0.09744,0.4956,1.156,3.445,27.23,0.00911,0.07458,0.05661,0.01867,0.05963,0.009208,14.91,26.5,98.87,567.7,0.2098,0.8663,0.6869,0.2575,0.6638,0.173,2,0
  7 | R,27,20.29,14.34,135.1,1297,0.1003,0.1328,0.198,0.1043,0.1809,0.05883,0.7572,0.7813,5.438,94.44,0.01149,0.02461,0.05688,0.01885,0.01756,0.005115,22.54,16.67,152.2,1575,0.1374,0.205,0.4,0.1625,0.2364,0.07678,3.5,0
  8 | R,77,12.75,15.29,84.6,502.7,0.1189,0.1569,0.1664,0.07666,0.1995,0.07164,0.3877,0.7402,2.999,30.85,0.007775,0.02987,0.04561,0.01357,0.01774,0.005114,15.51,20.37,107.3,733.2,0.1706,0.4196,0.5999,0.1709,0.3485,0.1179,2.5,0
  9 | N,60,18.98,19.61,124.4,1112,0.09087,0.1237,0.1213,0.0891,0.1727,0.05767,0.5285,0.8434,3.592,61.21,0.003703,0.02354,0.02222,0.01332,0.01378,0.003926,23.39,25.45,152.6,1593,0.1144,0.3371,0.299,0.1922,0.2726,0.09581,1.5,?
 10 | R,77,13.71,20.83,90.2,577.9,0.1189,0.1645,0.09366,0.05985,0.2196,0.07451,0.5835,1.377,3.856,50.96,0.008805,0.03029,0.02488,0.01448,0.01486,0.005412,17.06,28.14,110.6,897,0.1654,0.3682,0.2678,0.1556,0.3196,0.1151,4,10
 11 | N,119,13,21.82,87.5,519.8,0.1273,0.1932,0.1859,0.09353,0.235,0.07389,0.3063,1.002,2.406,24.32,0.005731,0.03502,0.03553,0.01226,0.02143,0.003749,15.49,30.73,106.2,739.3,0.1703,0.5401,0.539,0.206,0.4378,0.1072,2,1
 12 | N,76,12.46,24.04,83.97,475.9,0.1186,0.2396,0.2273,0.08543,0.203,0.08243,0.2976,1.599,2.039,23.94,0.007149,0.07217,0.07743,0.01432,0.01789,0.01008,15.09,40.68,97.65,711.4,0.1853,1.058,1.105,0.221,0.4366,0.2075,6,20
 13 | N,123,16.02,23.24,102.7,797.8,0.08206,0.06669,0.03299,0.03323,0.1528,0.05697,0.3795,1.187,2.466,40.51,0.004029,0.009269,0.01101,0.007591,0.0146,0.003042,19.19,33.88,123.8,1150,0.1181,0.1551,0.1459,0.09975,0.2948,0.08452,2,0
 14 | N,125,15.78,17.89,103.6,781,0.0971,0.1292,0.09954,0.06606,0.1842,0.06082,0.5058,0.9849,3.564,54.16,0.005771,0.04061,0.02791,0.01282,0.02008,0.004144,20.42,27.28,136.5,1299,0.1396,0.5609,0.3965,0.181,0.3792,0.1048,1.4,0
 15 | N,117,15.85,23.95,103.7,782.7,0.08401,0.1002,0.09938,0.05364,0.1847,0.05338,0.4033,1.078,2.903,36.58,0.009769,0.03126,0.05051,0.01992,0.02981,0.003002,16.84,27.66,112,876.5,0.1131,0.1924,0.2322,0.1119,0.2809,0.06287,1,0
 16 | R,36,14.54,27.54,96.73,658.8,0.1139,0.1595,0.1639,0.07364,0.2303,0.07077,0.37,1.033,2.879,32.55,0.005607,0.0424,0.04741,0.0109,0.01857,0.005466,17.46,37.13,124.1,943.2,0.1678,0.6577,0.7026,0.1712,0.4218,0.1341,6,6
 17 | N,123,14.68,20.13,94.74,684.5,0.09867,0.072,0.07395,0.05259,0.1586,0.05922,0.4727,1.24,3.195,45.4,0.005718,0.01162,0.01998,0.01109,0.0141,0.002085,19.07,30.88,123.4,1138,0.1464,0.1871,0.2914,0.1609,0.3029,0.08216,1.1,0
 18 | R,10,16.13,20.68,108.1,798.8,0.117,0.2022,0.1722,0.1028,0.2164,0.07356,0.5692,1.073,3.854,54.18,0.007026,0.02501,0.03188,0.01297,0.01689,0.004142,20.96,31.48,136.8,1315,0.1789,0.4233,0.4784,0.2073,0.3706,0.1142,3,1
 19 | N,20,15.34,14.26,102.5,704.4,0.1073,0.2135,0.2077,0.09756,0.2521,0.07032,0.4388,0.7096,3.384,44.91,0.006789,0.05328,0.06446,0.02252,0.03672,0.004394,18.07,19.08,125.1,980.9,0.139,0.5954,0.6305,0.2393,0.4667,0.09946,1.3,0
 20 | R,10,21.16,23.04,137.2,1404,0.09428,0.1022,0.1097,0.08632,0.1769,0.05278,0.6917,1.127,4.303,93.99,0.004728,0.01259,0.01715,0.01038,0.01083,0.001987,29.17,35.59,188,2615,0.1401,0.26,0.3155,0.2009,0.2822,0.07526,4,1
 21 | N,96,16.65,21.38,110,904.6,0.1121,0.1457,0.1525,0.0917,0.1995,0.0633,0.8068,0.9017,5.455,102.6,0.006048,0.01882,0.02741,0.0113,0.01468,0.002801,26.46,31.56,177,2215,0.1805,0.3578,0.4695,0.2095,0.3613,0.09564,3,0
 22 | N,116,17.14,16.4,116,912.7,0.1186,0.2276,0.2229,0.1401,0.304,0.07413,1.046,0.976,7.276,111.4,0.008029,0.03799,0.03732,0.02397,0.02308,0.007444,22.25,21.4,152.4,1461,0.1545,0.3949,0.3853,0.255,0.4066,0.1059,4.4,1
 23 | N,103,14.58,21.53,97.41,644.8,0.1054,0.1868,0.1425,0.08783,0.2252,0.06924,0.2545,0.9832,2.11,21.05,0.004452,0.03055,0.02681,0.01352,0.01454,0.003711,17.62,33.21,122.4,896.9,0.1525,0.6643,0.5539,0.2701,0.4264,0.1275,2.5,0
 24 | N,16,18.61,20.25,122.1,1094,0.0944,0.1066,0.149,0.07731,0.1697,0.05699,0.8529,1.849,5.632,93.54,0.01075,0.02722,0.05081,0.01911,0.02293,0.004217,21.31,27.26,139.9,1403,0.1338,0.2117,0.3446,0.149,0.2341,0.07421,3.4,13
 25 | N,52,15.3,25.27,102.4,732.4,0.1082,0.1697,0.1683,0.08751,0.1926,0.0654,0.439,1.012,3.498,43.5,0.005233,0.03057,0.03576,0.01083,0.01768,0.002967,20.27,36.71,149.3,1269,0.1641,0.611,0.6335,0.2024,0.4027,0.09876,2,0
 26 | N,94,17.57,15.05,115,955.1,0.09847,0.1157,0.09875,0.07953,0.1739,0.06149,0.6003,0.8225,4.655,61.1,0.005627,0.03033,0.03407,0.01354,0.01925,0.003742,20.01,19.52,134.9,1227,0.1255,0.2812,0.2489,0.1456,0.2756,0.07919,4,0
 27 | N,116,11.84,18.7,77.93,440.6,0.1109,0.1516,0.1218,0.05182,0.2301,0.07799,0.4825,1.03,3.475,41,0.005551,0.03414,0.04205,0.01044,0.02273,0.005667,16.82,28.12,119.4,888.7,0.1637,0.5775,0.6956,0.1546,0.4761,0.1402,3,2
 28 | N,87,17.02,23.98,112.8,899.3,0.1197,0.1496,0.2417,0.1203,0.2248,0.06382,0.6009,1.398,3.999,67.78,0.008268,0.03082,0.05042,0.01112,0.02102,0.003854,20.88,32.09,136.1,1344,0.1634,0.3559,0.5588,0.1847,0.353,0.08482,1.3,1
 29 | N,53,19.27,26.47,127.9,1162,0.09401,0.1719,0.1657,0.07593,0.1853,0.06261,0.5558,0.6062,3.528,68.17,0.005015,0.03318,0.03497,0.009643,0.01543,0.003896,24.15,30.9,161.4,1813,0.1509,0.659,0.6091,0.1785,0.3672,0.1123,3.5,0
 30 | N,109,16.13,17.88,107,807.2,0.104,0.1559,0.1354,0.07752,0.1998,0.06515,0.334,0.6857,2.183,35.03,0.004185,0.02868,0.02664,0.009067,0.01703,0.003817,20.21,27.26,132.7,1261,0.1446,0.5804,0.5274,0.1864,0.427,0.1233,2.5,0
 31 | N,12,16.74,21.59,110.1,869.5,0.0961,0.1336,0.1348,0.06018,0.1896,0.05656,0.4615,0.9197,3.008,45.19,0.005776,0.02499,0.03695,0.01195,0.02789,0.002665,20.01,29.02,133.5,1229,0.1563,0.3835,0.5409,0.1813,0.4863,0.08633,1.5,?
 32 | N,31,14.25,21.72,93.63,633,0.09823,0.1098,0.1319,0.05598,0.1885,0.06125,0.286,1.019,2.657,24.91,0.005878,0.02995,0.04815,0.01161,0.02028,0.004022,15.89,30.36,116.2,799.6,0.1446,0.4238,0.5186,0.1447,0.3591,0.1014,3,13
 33 | N,72,14.99,25.2,95.54,698.8,0.09387,0.05131,0.02398,0.02899,0.1565,0.05504,1.214,2.188,8.077,106,0.006883,0.01094,0.01818,0.01917,0.007882,0.001754,14.99,25.2,95.54,698.8,0.09387,0.05131,0.02398,0.02899,0.1565,0.05504,3.5,10
 34 | N,76,13.48,20.82,88.4,559.2,0.1016,0.1255,0.1063,0.05439,0.172,0.06419,0.213,0.5914,1.545,18.52,0.005367,0.02239,0.03049,0.01262,0.01377,0.003187,15.53,26.02,107.3,740.4,0.161,0.4225,0.503,0.2258,0.2807,0.1071,2.5,0
 35 | N,34,13.44,21.58,86.18,563,0.08162,0.06031,0.0311,0.02031,0.1784,0.05587,0.2385,0.8265,1.572,20.53,0.00328,0.01102,0.0139,0.006881,0.0138,0.001286,15.93,30.25,102.5,787.9,0.1094,0.2043,0.2085,0.1112,0.2994,0.07146,1.5,0
 36 | N,99,10.95,21.35,71.9,371.1,0.1227,0.1218,0.1044,0.05669,0.1895,0.0687,0.2366,1.428,1.822,16.97,0.008064,0.01764,0.02595,0.01037,0.01357,0.00304,12.84,35.34,87.22,514,0.1909,0.2698,0.4023,0.1424,0.2964,0.09606,2.7,0
 37 | R,9,19.07,24.81,128.3,1104,0.09081,0.219,0.2107,0.09961,0.231,0.06343,0.9811,1.666,8.83,104.9,0.006548,0.1006,0.09723,0.02638,0.05333,0.007646,24.09,33.17,177.4,1651,0.1247,0.7444,0.7242,0.2493,0.467,0.1038,2.3,0
 38 | N,114,13.28,20.28,87.32,545.2,0.1041,0.1436,0.09847,0.06158,0.1974,0.06782,0.3704,0.8249,2.427,31.33,0.005072,0.02147,0.02185,0.00956,0.01719,0.003317,17.38,28,113.1,907.2,0.153,0.3724,0.3664,0.1492,0.3739,0.1027,2.6,1
 39 | N,76,13.17,21.81,85.42,531.5,0.09714,0.1047,0.08259,0.05252,0.1746,0.06177,0.1938,0.6123,1.334,14.49,0.00335,0.01384,0.01452,0.006853,0.01113,0.00172,16.23,29.89,105.5,740.7,0.1503,0.3904,0.3728,0.1607,0.3693,0.09618,1.7,1
 40 | R,8,18.65,17.6,123.7,1076,0.1099,0.1686,0.1974,0.1009,0.1907,0.06049,0.6289,0.6633,4.293,71.56,0.006294,0.03994,0.05554,0.01695,0.02428,0.003535,22.82,21.32,150.6,1567,0.1679,0.509,0.7345,0.2378,0.3799,0.09185,1.8,0
 41 | N,111,13.17,18.66,85.98,534.6,0.1158,0.1231,0.1226,0.0734,0.2128,0.06777,0.2871,0.8937,1.897,24.25,0.006532,0.02336,0.02905,0.01215,0.01743,0.003643,15.67,27.95,102.8,759.4,0.1786,0.4166,0.5006,0.2088,0.39,0.1179,1,1
 42 | R,48,15.1,22.02,97.26,712.8,0.09056,0.07081,0.05253,0.03334,0.1616,0.05684,0.3105,0.8339,2.097,29.91,0.004675,0.0103,0.01603,0.009222,0.01095,0.001629,18.1,31.69,117.7,1030,0.1389,0.2057,0.2712,0.153,0.2675,0.07873,2.5,0
 43 | R,11,19.21,18.57,125.5,1152,0.1053,0.1267,0.1323,0.08994,0.1917,0.05961,0.7275,1.193,4.837,102.5,0.006458,0.02306,0.02945,0.01538,0.01852,0.002608,26.14,28.14,170.1,2145,0.1624,0.3511,0.3879,0.2091,0.3537,0.08294,3.2,13
 44 | N,62,14.71,21.59,95.55,656.9,0.1137,0.1365,0.1293,0.08123,0.2027,0.06758,0.4226,1.15,2.735,40.09,0.003659,0.02855,0.02572,0.01272,0.01817,0.004108,17.87,30.7,115.7,985.5,0.1368,0.429,0.3587,0.1834,0.3698,0.1094,6.5,6
 45 | R,34,14.25,22.15,96.42,645.7,0.1049,0.2008,0.2135,0.08653,0.1949,0.07292,0.7036,1.268,5.373,60.78,0.009407,0.07056,0.06899,0.01848,0.017,0.006113,17.67,29.51,119.1,959.5,0.164,0.6247,0.6922,0.1785,0.2844,0.1132,1.5,0
 46 | R,19,12.68,23.84,82.69,499,0.1122,0.1262,0.1128,0.06873,0.1905,0.0659,0.4255,1.178,2.927,36.46,0.007781,0.02648,0.02973,0.0129,0.01635,0.003601,17.09,33.47,111.8,888.3,0.1851,0.4061,0.4024,0.1716,0.3383,0.1031,1.2,1
 47 | N,106,14.78,23.94,97.4,668.3,0.1172,0.1479,0.1267,0.09029,0.1953,0.06654,0.3577,1.281,2.45,35.24,0.006703,0.0231,0.02315,0.01184,0.019,0.003224,17.31,33.39,114.6,925.1,0.1648,0.3416,0.3024,0.1614,0.3321,0.08911,1.2,0
 48 | N,70,17.2,24.52,114.2,929.4,0.1071,0.183,0.1692,0.07944,0.1927,0.06487,0.5907,1.041,3.705,69.47,0.00582,0.05616,0.04252,0.01127,0.01527,0.006299,23.32,33.82,151.6,1681,0.1585,0.7394,0.6566,0.1899,0.3313,0.1339,5,4
 49 | R,26,13.8,15.79,90.43,584.1,0.1007,0.128,0.07789,0.05069,0.1662,0.06566,0.2787,0.6205,1.957,23.35,0.004717,0.02065,0.01759,0.009206,0.0122,0.00313,16.57,20.86,110.3,812.4,0.1411,0.3542,0.2779,0.1383,0.2589,0.103,3,2
 50 | N,97,19.55,15.49,128,1156,0.1079,0.1747,0.2352,0.1196,0.2616,0.06752,1.223,0.4489,7.87,168.8,0.01101,0.04272,0.08624,0.02737,0.06041,0.007503,23.73,17.21,153.4,1633,0.1534,0.3391,0.5819,0.22,0.4714,0.09721,3,0
 51 | R,40,19.1,26.29,129.1,1132,0.1215,0.1791,0.1937,0.1469,0.1634,0.07224,0.519,2.91,5.801,67.1,0.007545,0.0605,0.02134,0.01843,0.03056,0.01039,20.33,32.72,141.3,1298,0.1392,0.2817,0.2432,0.1841,0.2311,0.09203,3,1
 52 | N,91,15.06,19.83,100.3,705.6,0.1039,0.1553,0.17,0.08815,0.1855,0.06284,0.4768,0.9644,3.706,47.14,0.00925,0.03715,0.04867,0.01851,0.01498,0.00352,18.23,24.23,123.5,1025,0.1551,0.4203,0.5203,0.2115,0.2834,0.08234,1.5,0
 53 | R,10,20.77,22.83,137.4,1336,0.1033,0.1515,0.1637,0.1015,0.18,0.05641,0.7569,1.458,5.095,92.99,0.006852,0.04575,0.03694,0.01598,0.02958,0.004069,24.22,29.43,159.9,1816,0.1385,0.4107,0.3757,0.1881,0.3371,0.07651,4,2
 54 | N,105,14.42,19.77,94.48,642.5,0.09752,0.1141,0.09388,0.05839,0.1879,0.0639,0.2895,1.851,2.376,26.85,0.008005,0.02895,0.03321,0.01424,0.01462,0.004452,16.33,30.86,109.5,826.4,0.1431,0.3026,0.3194,0.1565,0.2718,0.09353,0.4,1
 55 | N,68,14.19,26.02,96.29,632.4,0.105,0.1836,0.2013,0.07798,0.1894,0.06625,0.4634,1.416,5.357,43.65,0.007494,0.06696,0.06789,0.01714,0.01327,0.005693,17.05,35.37,126.8,905.4,0.1521,0.6461,0.6439,0.179,0.2655,0.1061,1.4,0
 56 | N,101,13.11,15.56,87.21,530.2,0.1398,0.1765,0.2071,0.09601,0.1925,0.07692,0.3908,0.9238,2.41,34.66,0.007162,0.02912,0.05473,0.01388,0.01547,0.007098,16.31,22.4,106.4,827.2,0.1862,0.4099,0.6376,0.1986,0.3147,0.1405,1.5,0
 57 | N,44,14.87,16.67,98.64,682.5,0.1162,0.1649,0.169,0.08923,0.2157,0.06768,0.4266,0.9489,2.989,41.18,0.006985,0.02563,0.03011,0.01271,0.01602,0.003884,18.81,27.37,127.1,1095,0.1878,0.448,0.4704,0.2027,0.3585,0.1065,1.5,4
 58 | N,104,15.78,22.91,105.7,782.6,0.1155,0.1752,0.2133,0.09479,0.2096,0.07331,0.552,1.072,3.598,58.63,0.008699,0.03976,0.0595,0.0139,0.01495,0.005984,20.19,30.5,130.3,1272,0.1855,0.4925,0.7356,0.2034,0.3274,0.1252,3.5,2
 59 | R,35,17.95,20.01,114.2,982,0.08402,0.06722,0.07293,0.05596,0.2129,0.05025,0.5506,1.214,3.357,54.04,0.004024,0.008422,0.02291,0.009863,0.05014,0.001902,20.58,27.83,129.2,1261,0.1072,0.1202,0.2249,0.1185,0.4882,0.06111,3,1
 60 | N,108,18.66,17.12,121.4,1077,0.1054,0.11,0.1457,0.08665,0.1966,0.06213,0.7128,1.581,4.895,90.47,0.008102,0.02101,0.03342,0.01601,0.02045,0.00457,22.25,24.9,145.4,1549,0.1503,0.2291,0.3272,0.1674,0.2894,0.08456,7,17
 61 | N,14,24.25,20.2,166.2,1761,0.1447,0.2867,0.4268,0.2012,0.2655,0.06877,1.509,3.12,9.807,233,0.02333,0.09806,0.1278,0.01822,0.04547,0.009875,26.02,23.99,180.9,2073,0.1696,0.4244,0.5803,0.2248,0.3222,0.08009,3.5,0
 62 | R,73,19,18.91,123.4,1138,0.08217,0.08028,0.09271,0.05627,0.1946,0.05044,0.6896,1.342,5.216,81.23,0.004428,0.02731,0.0404,0.01361,0.0203,0.002686,22.32,25.73,148.2,1538,0.1021,0.2264,0.3207,0.1218,0.2841,0.06541,3.5,15
 63 | N,88,19.79,25.12,130.4,1192,0.1015,0.1589,0.2545,0.1149,0.2202,0.06113,0.4953,1.199,2.765,63.33,0.005033,0.03179,0.04755,0.01043,0.01578,0.003224,22.63,33.58,148.7,1589,0.1275,0.3861,0.5673,0.1732,0.3305,0.08465,3,11
 64 | N,104,16.16,21.54,106.2,809.8,0.1008,0.1284,0.1043,0.05613,0.216,0.05891,0.4332,1.265,2.844,43.68,0.004877,0.01952,0.02219,0.009231,0.01535,0.002373,19.47,31.68,129.7,1175,0.1395,0.3055,0.2992,0.1312,0.348,0.07619,1.2,0
 65 | R,7,20.29,21.49,132.8,1291,0.09258,0.1205,0.1523,0.08636,0.2356,0.05635,0.8309,1.14,5.459,115.5,0.004934,0.02833,0.04284,0.01587,0.03187,0.003273,26.51,28.07,171.6,2196,0.1225,0.3449,0.4509,0.1943,0.4416,0.07863,2.5,9
 66 | N,10,12.77,22.47,81.72,506.3,0.09055,0.05761,0.04711,0.02704,0.1585,0.06065,0.2367,1.38,1.457,19.87,0.007499,0.01202,0.02332,0.00892,0.01647,0.002629,14.49,33.37,92.04,653.6,0.1419,0.1523,0.2177,0.09331,0.2829,0.08067,1,0
 67 | N,36,14.95,17.57,96.85,678.1,0.1167,0.1305,0.1539,0.08624,0.1957,0.06216,1.296,1.452,8.419,101.9,0.01,0.0348,0.06577,0.02801,0.05168,0.002887,18.55,21.43,121.4,971.4,0.1411,0.2164,0.3355,0.1667,0.3414,0.07147,1.2,8
 68 | N,100,16.11,18.05,105.1,813,0.09721,0.1137,0.09447,0.05943,0.1861,0.06248,0.7049,1.332,4.533,74.08,0.00677,0.01938,0.03067,0.01167,0.01875,0.003434,19.92,25.27,129,1233,0.1314,0.2236,0.2802,0.1216,0.2792,0.08158,1.5,1
 69 | N,43,11.8,16.58,78.99,432,0.1091,0.17,0.1659,0.07415,0.2678,0.07371,0.3197,1.426,2.281,24.72,0.005427,0.03633,0.04649,0.01843,0.05628,0.004635,13.74,26.38,91.93,591.7,0.1385,0.4092,0.4504,0.1865,0.5774,0.103,1.9,0
 70 | R,44,17.68,20.74,117.4,963.7,0.1115,0.1665,0.1855,0.1054,0.1971,0.06166,0.8113,1.4,5.54,93.91,0.009037,0.04954,0.05206,0.01841,0.01778,0.004968,20.47,25.11,132.9,1302,0.1418,0.3498,0.3583,0.1515,0.2463,0.07738,9,7
 71 | N,84,19.89,16.89,130.2,1229,0.1005,0.1134,0.1498,0.1121,0.173,0.06147,1.051,0.9349,7.382,121.9,0.008231,0.03082,0.0416,0.02785,0.03763,0.003785,22.25,19.08,147.5,1508,0.1197,0.1873,0.2487,0.1679,0.2643,0.07048,6,2
 72 | R,8,19.59,18.15,130.7,1214,0.112,0.1666,0.2508,0.1286,0.2027,0.06082,0.7364,1.048,4.792,97.07,0.004057,0.02277,0.04029,0.01303,0.01686,0.003318,26.73,26.39,174.9,2232,0.1438,0.3846,0.681,0.2247,0.3643,0.09223,3.5,0
 73 | N,62,23.27,22.04,152.1,1686,0.08439,0.1145,0.1324,0.09702,0.1801,0.05553,0.6642,0.8561,4.603,97.85,0.00491,0.02544,0.02822,0.01623,0.01956,0.00374,28.01,28.22,184.2,2403,0.1228,0.3583,0.3948,0.2346,0.3589,0.09187,1,3
 74 | N,5,16.78,18.8,109.3,886.3,0.08865,0.09182,0.08422,0.06576,0.1893,0.05534,0.599,1.391,4.129,67.34,0.006123,0.0247,0.02626,0.01604,0.02091,0.003493,20.05,26.3,130.7,1260,0.1168,0.2119,0.2318,0.1474,0.281,0.07228,3,1
 75 | N,65,18.98,24.12,122,1030,0.1198,0.1612,0.2119,0.1155,0.2375,0.06542,0.8541,1.283,4.972,76.81,0.008978,0.02448,0.04385,0.01547,0.03414,0.004493,21.77,27.44,140.9,1328,0.1481,0.2397,0.355,0.1638,0.34,0.08003,2,2
 76 | N,84,13.43,19.63,85.84,565.4,0.09048,0.06288,0.05858,0.03438,0.1598,0.05671,0.4697,1.147,3.142,43.4,0.006003,0.01063,0.02151,0.009443,0.0152,0.001868,17.98,29.87,116.6,993.6,0.1401,0.1546,0.2644,0.116,0.2884,0.07371,2,1
 77 | N,98,15.46,11.89,102.5,736.9,0.1257,0.1555,0.2032,0.1097,0.1966,0.07069,0.4209,0.6583,2.805,44.64,0.005393,0.02321,0.04303,0.0132,0.01792,0.004168,18.79,17.04,125,1102,0.1531,0.3583,0.583,0.1827,0.3216,0.101,1.8,1
 78 | R,17,27.22,21.87,182.1,2250,0.1094,0.1914,0.2871,0.1878,0.18,0.0577,0.8361,1.481,5.82,128.7,0.004631,0.02537,0.03109,0.01241,0.01575,0.002747,33.12,32.85,220.8,3216,0.1472,0.4034,0.534,0.2688,0.2856,0.08082,4,4
 79 | N,53,21.09,26.57,142.7,1311,0.1141,0.2832,0.2487,0.1496,0.2395,0.07398,0.6298,0.7629,4.414,81.46,0.004253,0.04759,0.03872,0.01567,0.01798,0.005295,26.68,33.48,176.5,2089,0.1491,0.7584,0.678,0.2903,0.4098,0.1284,4,7
 80 | N,65,15.7,20.31,101.2,766.6,0.09597,0.08799,0.06593,0.05189,0.1618,0.05549,0.3699,1.15,2.406,40.98,0.004626,0.02263,0.01954,0.009767,0.01547,0.00243,20.11,32.82,129.3,1269,0.1414,0.3547,0.2902,0.1541,0.3437,0.08631,2,1
 81 | N,81,15.28,22.41,98.92,710.6,0.09057,0.1052,0.05375,0.03263,0.1727,0.06317,0.2054,0.4956,1.344,19.53,0.00329,0.01395,0.01774,0.006009,0.01172,0.002575,17.8,28.03,113.8,973.1,0.1301,0.3299,0.363,0.1226,0.3175,0.09772,1.6,0
 82 | N,91,18.31,18.58,118.6,1041,0.08588,0.08468,0.08169,0.05814,0.1621,0.05425,0.2577,0.4757,1.817,28.92,0.002866,0.009181,0.01412,0.006719,0.01069,0.001087,21.31,26.36,139.2,1410,0.1234,0.2445,0.3538,0.1571,0.3206,0.06938,6,3
 83 | N,86,14.22,23.12,94.37,609.9,0.1075,0.2413,0.1981,0.06618,0.2384,0.07542,0.286,2.11,2.112,31.72,0.00797,0.1354,0.1166,0.01666,0.05113,0.01172,15.74,37.18,106.4,762.4,0.1533,0.9327,0.8488,0.1772,0.5166,0.1446,1.2,0
 84 | R,74,12.34,26.86,81.15,477.4,0.1034,0.1353,0.1085,0.04562,0.1943,0.06937,0.4053,1.809,2.642,34.44,0.009098,0.03845,0.03763,0.01321,0.01878,0.005672,15.65,39.34,101.7,768.9,0.1785,0.4706,0.4425,0.1459,0.3215,0.1205,1.2,4
 85 | N,75,14.86,23.21,100.4,671.4,0.1044,0.198,0.1697,0.08878,0.1737,0.06672,0.2796,0.9622,3.591,25.2,0.008081,0.05122,0.05551,0.01883,0.02545,0.004312,16.08,27.78,118.6,784.7,0.1316,0.4648,0.4589,0.1727,0.3,0.08701,8,9
 86 | N,91,13.77,22.29,90.63,588.9,0.12,0.1267,0.1385,0.06526,0.1834,0.06877,0.6191,2.112,4.906,49.7,0.0138,0.03348,0.04665,0.0206,0.02689,0.004306,16.39,34.01,111.6,806.9,0.1737,0.3122,0.3809,0.1673,0.308,0.09333,1.2,0
 87 | N,5,19.18,22.49,127.5,1148,0.08523,0.1428,0.1114,0.06772,0.1767,0.05529,0.4357,1.073,3.833,54.22,0.005524,0.03698,0.02706,0.01221,0.01415,0.003397,23.36,32.06,166.4,1688,0.1322,0.5601,0.3865,0.1708,0.3193,0.09221,3,1
 88 | N,72,14.45,20.22,94.49,642.7,0.09872,0.1206,0.118,0.0598,0.195,0.06466,0.2092,0.6509,1.446,19.42,0.004044,0.01597,0.02,0.007303,0.01522,0.001976,18.33,30.12,117.9,1044,0.1552,0.4056,0.4967,0.1838,0.4753,0.1013,1.5,?
 89 | N,5,23.29,26.67,158.9,1685,0.1141,0.2084,0.3523,0.162,0.22,0.06229,0.5539,1.56,4.667,83.16,0.009327,0.05121,0.08958,0.02465,0.02175,0.005195,25.12,32.68,177,1986,0.1536,0.4167,0.7892,0.2733,0.3198,0.08762,9,14
 90 | N,38,13.81,23.75,91.56,597.8,0.1323,0.1768,0.1558,0.09176,0.2251,0.07421,0.5648,1.93,3.909,52.72,0.008824,0.03108,0.03112,0.01291,0.01998,0.004506,19.2,41.85,128.5,1153,0.2226,0.5209,0.4646,0.2013,0.4432,0.1086,1.5,0
 91 | R,17,15.12,16.68,98.78,716.6,0.08876,0.09588,0.0755,0.04079,0.1594,0.05986,0.2711,0.3621,1.974,26.44,0.005472,0.01919,0.02039,0.00826,0.01523,0.002881,17.77,20.24,117.7,989.5,0.1491,0.3331,0.3327,0.1252,0.3415,0.0974,1.5,0
 92 | N,35,19.37,24.03,129.9,1174,0.09866,0.1629,0.1961,0.1054,0.212,0.06246,0.631,0.9409,4.794,75.93,0.005199,0.02804,0.03108,0.01319,0.019,0.003708,23.87,28.43,165.5,1725,0.1269,0.3535,0.3607,0.1832,0.3479,0.08793,3.2,0
 93 | N,74,17.42,25.56,114.5,948,0.1006,0.1146,0.1682,0.06597,0.1308,0.05866,0.5296,1.667,3.767,58.53,0.03113,0.08555,0.1438,0.03927,0.02175,0.01256,18.07,28.07,120.4,1021,0.1243,0.1793,0.2803,0.1099,0.1603,0.06818,1.5,1
 94 | N,73,14.19,23.81,92.87,610.7,0.09463,0.1306,0.1115,0.06462,0.2235,0.06433,0.4207,1.845,3.534,31,0.01088,0.0371,0.03688,0.01627,0.04499,0.004768,16.86,34.85,115,811.3,0.1559,0.4059,0.3744,0.1772,0.4724,0.1026,3,0
 95 | N,73,13.86,16.93,90.96,578.9,0.1026,0.1517,0.09901,0.05602,0.2106,0.06916,0.2563,1.194,1.933,22.69,0.00596,0.03438,0.03909,0.01435,0.01939,0.00456,15.75,26.93,104.4,750.1,0.146,0.437,0.4636,0.1654,0.363,0.1059,4,2
 96 | N,48,19.8,21.56,129.7,1230,0.09383,0.1306,0.1272,0.08691,0.2094,0.05581,0.9553,1.186,6.487,124.4,0.006804,0.03169,0.03446,0.01712,0.01897,0.004045,25.73,28.64,170.3,2009,0.1353,0.3235,0.3617,0.182,0.307,0.08255,2.6,0
 97 | N,74,19.53,32.47,128,1223,0.0842,0.113,0.1145,0.06637,0.1428,0.05313,0.7392,1.321,4.722,109.9,0.005539,0.02644,0.02664,0.01078,0.01332,0.002256,27.9,45.41,180.2,2477,0.1408,0.4097,0.3995,0.1625,0.2713,0.07568,2.8,0
 98 | N,68,16.34,20.81,109.4,829.8,0.1031,0.1589,0.1769,0.08451,0.1981,0.06613,0.2958,1.259,2.278,29.03,0.006518,0.025,0.04087,0.01344,0.01797,0.003626,18.88,31.95,128.9,1085,0.1543,0.3456,0.5978,0.1843,0.3584,0.09975,1,1
 99 | N,77,12.83,22.33,85.26,503.2,0.1088,0.1799,0.1695,0.06861,0.2123,0.07254,0.3061,1.069,2.257,25.13,0.006983,0.03858,0.04683,0.01499,0.0168,0.005617,15.2,30.15,105.3,706,0.1777,0.5343,0.6282,0.1977,0.3407,0.1243,3,27
100 | N,63,17.05,19.08,113.4,895,0.1141,0.1572,0.191,0.109,0.2131,0.06325,0.2959,0.679,2.153,31.98,0.005532,0.02008,0.03055,0.01384,0.01177,0.002336,19.59,24.89,133.5,1189,0.1703,0.3934,0.5018,0.2543,0.3109,0.09061,1.5,5
101 | N,67,20.51,27.81,134.4,1319,0.09159,0.1074,0.1554,0.0834,0.1448,0.05592,0.524,1.189,3.767,70.01,0.00502,0.02062,0.03457,0.01091,0.01298,0.002887,24.47,37.38,162.7,1872,0.1223,0.2761,0.4146,0.1563,0.2437,0.08328,9,24
102 | N,83,23.21,26.97,153.5,1670,0.09509,0.1682,0.195,0.1237,0.1909,0.06309,1.058,0.9635,7.247,155.8,0.006428,0.02863,0.04497,0.01716,0.0159,0.003053,31.01,34.51,206,2944,0.1481,0.4126,0.582,0.2593,0.3103,0.08677,1.5,1
103 | N,31,17.46,39.28,113.4,920.6,0.09812,0.1298,0.1417,0.08811,0.1809,0.05966,0.5366,0.8561,3.002,49,0.00486,0.02785,0.02602,0.01374,0.01226,0.002759,22.51,44.87,141.2,1408,0.1365,0.3735,0.3241,0.2066,0.2853,0.08496,2,0
104 | N,62,19.4,23.5,129.1,1155,0.1027,0.1558,0.2049,0.08886,0.1978,0.06,0.5243,1.802,4.037,60.41,0.01061,0.03252,0.03915,0.01559,0.02186,0.003949,21.65,30.53,144.9,1417,0.1463,0.2968,0.3458,0.1564,0.292,0.07614,1.8,1
105 | N,76,17.3,17.08,113,928.2,0.1008,0.1041,0.1266,0.08353,0.1813,0.05613,0.3093,0.8568,2.193,33.63,0.004757,0.01503,0.02332,0.01262,0.01394,0.002362,19.85,25.09,130.9,1222,0.1416,0.2405,0.3378,0.1857,0.3138,0.08113,1.2,0
106 | N,56,19.45,19.33,126.5,1169,0.1035,0.1188,0.1379,0.08591,0.1776,0.05647,0.5959,0.6342,3.797,71,0.004649,0.018,0.02749,0.01267,0.01365,0.00255,25.7,24.57,163.1,1972,0.1497,0.3161,0.4317,0.1999,0.3379,0.0895,9,7
107 | N,58,13.96,17.05,91.43,602.4,0.1096,0.1279,0.09789,0.05246,0.1908,0.0613,0.425,0.8098,2.563,35.74,0.006351,0.02679,0.03119,0.01342,0.02062,0.002695,16.39,22.07,108.1,826,0.1512,0.3262,0.3209,0.1374,0.3068,0.07957,1,0
108 | R,19,19.55,28.77,133.6,1207,0.0926,0.2063,0.1784,0.1144,0.1893,0.06232,0.8426,1.199,7.158,106.4,0.006356,0.04765,0.03863,0.01519,0.01936,0.005252,25.05,36.27,178.6,1926,0.1281,0.5329,0.4251,0.1941,0.2818,0.1005,6,15
109 | N,56,15.32,17.27,103.2,713.3,0.1335,0.2284,0.2448,0.1242,0.2398,0.07596,0.6592,1.059,4.061,59.46,0.01015,0.04588,0.04983,0.02127,0.01884,0.00866,17.73,22.66,119.8,928.8,0.1765,0.4503,0.4429,0.2229,0.3258,0.1191,0.8,0
110 | N,64,15.66,23.2,110.2,773.5,0.1109,0.3114,0.3176,0.1377,0.2495,0.08104,1.292,2.454,10.12,138.5,0.01236,0.05995,0.08232,0.03024,0.02337,0.006042,19.85,31.64,143.7,1226,0.1504,0.5172,0.6181,0.2462,0.3277,0.1019,0.6,0
111 | N,27,15.53,33.56,103.7,744.9,0.1063,0.1639,0.1751,0.08399,0.2091,0.0665,0.2419,1.278,1.903,23.02,0.005345,0.02556,0.02889,0.01022,0.009947,0.003359,18.49,49.54,126.3,1035,0.1883,0.5564,0.5703,0.2014,0.3512,0.1204,2.5,3
112 | R,39,20.31,27.06,132.9,1288,0.1,0.1088,0.1519,0.09333,0.1814,0.05572,0.3977,1.033,2.587,52.34,0.005043,0.01578,0.02117,0.008185,0.01282,0.001892,24.33,39.16,162.3,1844,0.1522,0.2945,0.3788,0.1697,0.3151,0.07999,1.8,1
113 | R,12,17.29,22.13,114.4,947.8,0.08999,0.1273,0.09697,0.07507,0.2108,0.05464,0.8348,1.633,6.146,90.94,0.006717,0.05981,0.04638,0.02149,0.02747,0.005838,20.39,27.24,137.9,1295,0.1134,0.2867,0.2298,0.1528,0.3067,0.07484,4,1
114 | N,62,15.61,19.38,100,758.6,0.0784,0.05616,0.04209,0.02847,0.1547,0.05443,0.2298,0.9988,1.534,22.18,0.002826,0.009105,0.01311,0.005174,0.01013,0.001345,17.91,31.67,115.9,988.6,0.1084,0.1807,0.226,0.08568,0.2683,0.06829,2,0
115 | R,37,17.19,22.07,111.6,928.3,0.09726,0.08995,0.09061,0.06527,0.1867,0.0558,0.4203,0.7383,2.819,45.42,0.004493,0.01206,0.02048,0.009875,0.01144,0.001575,21.58,29.33,140.5,1436,0.1558,0.2567,0.3889,0.1984,0.3216,0.0757,8.5,6
116 | N,63,20.73,31.12,135.7,1419,0.09469,0.1143,0.1367,0.08646,0.1769,0.05674,1.172,1.617,7.749,199.7,0.004551,0.01478,0.02143,0.00928,0.01367,0.002299,32.49,47.16,214,3432,0.1401,0.2644,0.3442,0.1659,0.2868,0.08218,3,2
117 | R,5,21.75,20.99,147.3,1491,0.09401,0.1961,0.2195,0.1088,0.1721,0.06194,1.167,1.352,8.867,156.8,0.005687,0.0496,0.06329,0.01561,0.01924,0.004614,28.19,28.18,195.9,2384,0.1272,0.4725,0.5807,0.1841,0.2833,0.08858,2.5,11
118 | N,59,17.93,24.48,115.2,998.9,0.08855,0.07027,0.05699,0.04744,0.1538,0.0551,0.4212,1.433,2.765,45.81,0.005444,0.01169,0.01622,0.008522,0.01419,0.002751,20.92,34.69,135.1,1320,0.1315,0.1806,0.208,0.1136,0.2504,0.07948,1.5,0
119 | N,55,19.16,26.6,126.2,1138,0.102,0.1453,0.1921,0.09664,0.1902,0.0622,0.6361,1.001,4.321,69.65,0.007392,0.02449,0.03988,0.01293,0.01435,0.003446,23.72,35.9,159.8,1724,0.1782,0.3841,0.5754,0.1872,0.3258,0.0972,1.5,0
120 | N,17,19.71,19.06,129.5,1192,0.1018,0.1352,0.1696,0.1043,0.1895,0.05863,0.4352,1.049,3.157,52.6,0.004996,0.02395,0.02867,0.01117,0.02266,0.002668,23.33,29.87,157.6,1659,0.1411,0.3993,0.4645,0.1925,0.4054,0.08621,4,15
121 | N,8,16.24,18.77,108.8,805.1,0.1066,0.1802,0.1948,0.09052,0.1876,0.06684,0.2873,0.9173,2.464,28.09,0.004563,0.03481,0.03872,0.01209,0.01388,0.004081,18.55,25.09,126.9,1031,0.1365,0.4706,0.5026,0.1732,0.277,0.1063,1,0
122 | N,1,11.76,18.14,75,431.1,0.09968,0.05914,0.02685,0.03515,0.1619,0.06287,0.645,2.105,4.138,49.11,0.005596,0.01005,0.01272,0.01432,0.01575,0.002758,13.36,23.39,85.1,553.6,0.1137,0.07974,0.0612,0.0716,0.1978,0.06915,10,18
123 | R,58,19.53,18.9,129.5,1217,0.115,0.1642,0.2197,0.1062,0.1792,0.06552,1.111,1.161,7.237,133,0.006056,0.03203,0.05638,0.01733,0.01884,0.004787,25.93,26.24,171.1,2053,0.1495,0.4116,0.6121,0.198,0.2968,0.09929,2,0
124 | N,34,20.09,23.86,134.7,1247,0.108,0.1838,0.2283,0.128,0.2249,0.07469,1.072,1.743,7.804,130.8,0.007964,0.04732,0.07649,0.01936,0.02736,0.005928,23.68,29.43,158.8,1696,0.1347,0.3391,0.4932,0.1923,0.3294,0.09469,5,11
125 | N,70,18.22,18.87,118.7,1027,0.09746,0.1117,0.113,0.0795,0.1807,0.05664,0.4041,0.5503,2.547,48.9,0.004821,0.01659,0.02408,0.01143,0.01275,0.002451,21.84,25,140.9,1485,0.1434,0.2763,0.3853,0.1776,0.2812,0.08198,2,0
126 | N,66,20.16,19.66,131.1,1274,0.0802,0.08564,0.1155,0.07726,0.1928,0.05096,0.5925,0.6863,3.868,74.85,0.004536,0.01376,0.02645,0.01247,0.02193,0.001589,23.06,23.03,150.2,1657,0.1054,0.1537,0.2606,0.1425,0.3055,0.05933,2.5,1
127 | N,66,20.34,21.51,135.9,1264,0.117,0.1875,0.2565,0.1504,0.2569,0.0667,0.5702,1.023,4.012,69.06,0.005485,0.02431,0.0319,0.01369,0.02768,0.003345,25.3,31.86,171.1,1938,0.1592,0.4492,0.5344,0.2685,0.5558,0.1024,0.4,2
128 | R,78,16.27,20.71,106.9,813.7,0.1169,0.1319,0.1478,0.08488,0.1948,0.06277,0.4375,1.232,3.27,44.41,0.006697,0.02083,0.03248,0.01392,0.01536,0.002789,19.28,30.38,129.8,1121,0.159,0.2947,0.3597,0.1583,0.3103,0.082,2.5,2
129 | N,34,17.06,21,111.8,918.6,0.1119,0.1056,0.1508,0.09934,0.1727,0.06071,0.8161,2.129,6.076,87.17,0.006455,0.01797,0.04502,0.01744,0.01829,0.003733,20.99,33.15,143.2,1362,0.1449,0.2053,0.392,0.1827,0.2623,0.07599,1.5,0
130 | N,10,18.77,21.43,122.9,1092,0.09116,0.1402,0.106,0.0609,0.1953,0.06083,0.6422,1.53,4.369,88.25,0.007548,0.03897,0.03914,0.01816,0.02168,0.004445,24.54,34.37,161.1,1873,0.1498,0.4827,0.4634,0.2048,0.3679,0.0987,2,0
131 | R,24,23.51,24.27,155.1,1747,0.1069,0.1283,0.2308,0.141,0.1797,0.05506,1.009,0.9245,6.462,164.1,0.006292,0.01971,0.03582,0.01301,0.01479,0.003118,30.67,30.73,202.4,2906,0.1515,0.2678,0.4819,0.2089,0.2593,0.07738,3.5,4
132 | R,33,19.68,21.68,129.9,1194,0.09797,0.1339,0.1863,0.1103,0.2082,0.05715,0.6226,2.284,5.173,67.66,0.004756,0.03368,0.04345,0.01806,0.03756,0.003288,22.75,34.66,157.6,1540,0.1218,0.3458,0.4734,0.2255,0.4045,0.07918,1.5,13
133 | N,58,15.75,19.22,107.1,758.6,0.1243,0.2364,0.2914,0.1242,0.2375,0.07603,0.5204,1.324,3.477,51.22,0.009329,0.06559,0.09953,0.02283,0.05543,0.00733,17.36,24.17,119.4,915.3,0.155,0.5046,0.6872,0.2135,0.4245,0.105,1.8,0
134 | R,4,25.73,17.46,174.2,2010,0.1149,0.2363,0.3368,0.1913,0.1956,0.06121,0.9948,0.8509,7.222,153.1,0.006369,0.04243,0.04266,0.01508,0.02335,0.003385,33.13,23.58,229.3,3234,0.153,0.5937,0.6451,0.2756,0.369,0.08815,4.5,0
135 | R,5,15.08,25.74,98,716.6,0.1024,0.09769,0.1235,0.06553,0.1647,0.06464,0.6534,1.506,4.174,63.37,0.01052,0.02431,0.04912,0.01746,0.0212,0.004867,18.51,33.22,121.2,1050,0.166,0.2356,0.4029,0.1526,0.2654,0.09438,2.6,0
136 | N,61,20.44,21.78,133.8,1293,0.0915,0.1131,0.09799,0.07785,0.1618,0.05557,0.5781,0.9168,4.218,72.44,0.006208,0.01906,0.02375,0.01461,0.01445,0.001906,24.31,26.37,161.2,1780,0.1327,0.2376,0.2702,0.1765,0.2609,0.06735,1,0
137 | N,51,20.2,26.83,133.7,1234,0.09905,0.1669,0.1641,0.1265,0.1875,0.0602,0.9761,1.892,7.128,103.6,0.008439,0.04674,0.05904,0.02536,0.0371,0.004286,24.19,33.81,160,1671,0.1278,0.3416,0.3703,0.2152,0.3271,0.07632,2.5,2
138 | R,2,22.01,21.9,147.2,1482,0.1063,0.1954,0.2448,0.1501,0.1824,0.0614,1.008,0.6999,7.561,130.2,0.003978,0.02821,0.03576,0.01471,0.01518,0.003796,27.66,25.8,195,2227,0.1294,0.3885,0.4756,0.2432,0.2741,0.08574,3,1
139 | N,54,20.64,17.35,134.8,1335,0.09446,0.1076,0.1527,0.08941,0.1571,0.05478,0.6137,0.6575,4.119,77.02,0.006211,0.01895,0.02681,0.01232,0.01276,0.001711,25.37,23.17,166.8,1946,0.1562,0.3055,0.4159,0.2112,0.2689,0.07055,4,0
140 | N,7,11.08,18.83,73.3,361.6,0.1216,0.2154,0.1689,0.06367,0.2196,0.0795,0.2114,1.027,1.719,13.99,0.007405,0.04549,0.04588,0.01339,0.01738,0.004435,13.24,32.82,91.76,508.1,0.2184,0.9379,0.8402,0.2524,0.4154,0.1403,2,0
141 | N,57,14.6,23.29,93.97,664.7,0.08682,0.06636,0.0839,0.05271,0.1627,0.05416,0.4157,1.627,2.914,33.01,0.008312,0.01742,0.03389,0.01576,0.0174,0.002871,15.79,31.71,102.2,758.2,0.1312,0.1581,0.2675,0.1359,0.2477,0.06836,3,0
142 | N,13,19.55,23.21,128.9,1174,0.101,0.1318,0.1856,0.1021,0.1989,0.05884,0.6107,2.836,5.383,70.1,0.01124,0.04097,0.07469,0.03441,0.02768,0.00624,20.82,30.44,142,1313,0.1251,0.2414,0.3829,0.1825,0.2576,0.07602,4,13
143 | N,58,21.61,22.28,144.4,1407,0.1167,0.2087,0.281,0.1562,0.2162,0.06606,0.6242,0.9209,4.158,80.99,0.005215,0.03726,0.04718,0.01288,0.02045,0.004028,26.23,28.74,172,2081,0.1502,0.5717,0.7053,0.2422,0.3828,0.1007,3.3,16
144 | N,57,17.99,20.66,117.8,991.7,0.1036,0.1304,0.1201,0.08824,0.1992,0.06069,0.4537,0.8733,3.061,49.81,0.007231,0.02772,0.02509,0.0148,0.01414,0.003336,21.08,25.41,138.1,1349,0.1482,0.3735,0.3301,0.1974,0.306,0.08503,3,3
145 | N,51,15.13,29.81,96.71,719.5,0.0832,0.04605,0.04686,0.02739,0.1852,0.05294,0.4681,1.627,3.043,45.38,0.006831,0.01427,0.02489,0.009087,0.03151,0.00175,17.26,36.91,110.1,931.4,0.1148,0.09866,0.1547,0.06575,0.3233,0.06165,4,13
146 | R,49,15.5,21.08,102.9,803.1,0.112,0.1571,0.1522,0.08481,0.2085,0.06864,1.37,1.213,9.424,176.5,0.008198,0.03889,0.04493,0.02139,0.02018,0.005815,23.17,27.65,157.1,1748,0.1517,0.4002,0.4211,0.2134,0.3003,0.1048,0.4,0
147 | N,55,17.29,21.43,112.7,938.6,0.1004,0.1124,0.1117,0.08377,0.1749,0.05822,0.9047,1.305,6.378,105.2,0.005609,0.02121,0.03387,0.01494,0.01537,0.003042,22.51,26.7,149.4,1558,0.1309,0.2352,0.327,0.1549,0.2609,0.07545,1.5,1
148 | R,1,20.18,19.54,133.8,1250,0.1133,0.1489,0.2133,0.1259,0.1724,0.06053,0.4331,1.001,3.008,52.49,0.009087,0.02715,0.05546,0.0191,0.02451,0.004005,22.03,25.07,146,1479,0.1665,0.2942,0.5308,0.2173,0.3032,0.08075,4.5,27
149 | N,47,18.82,21.97,123.7,1110,0.1018,0.1389,0.1594,0.08744,0.1943,0.06132,0.8191,1.931,4.493,103.9,0.008074,0.04088,0.05321,0.01834,0.02383,0.004515,22.66,30.93,145.3,1603,0.139,0.3463,0.3912,0.1708,0.3007,0.08314,1,0
150 | R,9,13.98,19.62,91.12,599.5,0.106,0.1133,0.1126,0.06463,0.1669,0.06544,0.2208,0.9533,1.602,18.85,0.005314,0.01791,0.02185,0.009567,0.01223,0.002846,17.04,30.8,113.9,869.3,0.1613,0.3568,0.4069,0.1827,0.3179,0.1055,3.5,4
151 | 


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