-
Notifications
You must be signed in to change notification settings - Fork 0
/
ML5.java
95 lines (77 loc) · 3.59 KB
/
ML5.java
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.classifiers.functions.Logistic;
import weka.classifiers.functions.SMO;
import weka.classifiers.meta.CVParameterSelection;
import weka.classifiers.meta.GridSearch;
import weka.classifiers.meta.LogitBoost;
import weka.classifiers.meta.Vote;
import weka.classifiers.trees.J48;
import weka.classifiers.trees.NBTree;
import weka.core.Instances;
import weka.core.Utils;
import java.io.BufferedReader;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.PrintWriter;
public class ML5 {
public static void main(String[] args) throws Exception{
// load data sets
Instances train = new Instances(
new BufferedReader(
new FileReader("/Weka-3-6/ProjectMilestone5/arrhythmia_train.arff")));
Instances test = new Instances(
new BufferedReader(
new FileReader("/Weka-3-6/ProjectMilestone5/arrhythmia_test.arff")));
test.setClassIndex(test.numAttributes()-1);
SMO ps=new SMO();
Classifier[] ClassifierArray=new Classifier[3];
ClassifierArray[1]=new J48();
ClassifierArray[0]=new NaiveBayes();
ClassifierArray[2]=new LogitBoost();
Vote vs=new Vote();
//String[] options=new String[3];
//options[2]="-R MAJ";
//options[1]="-B weka.classifiers.functions.SMO -B weka.classifiers.bayes.NaiveBayes";
//options[0]="-S <2>";
//vs.setOptions(options);
//GridSearch ps = new GridSearch();
//ps.setOptions(weka.core.Utils.splitOptions("-P \"I 1.0 10.0 1.0\" -P \"P 1.0 100.0 10.0\" -W \"weka.classifiers.meta.AdaBoostM1\" -- -P 100 -S 1 -I 10 -W \"weka.classifiers.functions.SMO\" -- -V -1 -C 1 -P 1.0E-12"));
vs.setOptions(weka.core.Utils.splitOptions("-B \"weka.classifiers.functions.SMO -C 1 -L 0.001 -P 1E-10\" -B \"weka.classifiers.trees.NBTree\" -B \"weka.classifiers.trees.RandomForest -I 50 -K 20 -depth 10\" -R AVG"));
//String[] options=ps.getBestClassifierOptions();
//vs.setOptions(options);
System.out.println(Utils.joinOptions(vs.getOptions()));
vs.setClassifiers(ClassifierArray);
ps.buildClassifier(train);
vs.buildClassifier(train);
//find optimal parameter
//Dagging cls = new Dagging();
//change the base classifier
//cls.setClassifier(new NBTree());
//change the parameter for dagging
//cls.setNumFolds(1);
//cls.setSeed(7);
//cls.buildClassifier(train);
//System.out.println(vs.getCombinationRule());
//System.out.println(vs.getOptions());
PrintWriter pw=new PrintWriter(new FileWriter("/Weka-3-6/ProjectMilestone5/arrhythmia1.txt"));
PrintWriter pw2=new PrintWriter(new FileWriter("/Weka-3-6/ProjectMilestone5/arrhythmia0.txt"));
//System.out.println(Utils.joinOptions(ps.getBestClassifierOptions()));
for (int i = 0; i < test.numInstances(); i++) {
double pred = vs.classifyInstance(test.instance(i));
pw.println(pred);
}
pw.close();
for (int i = 0; i < test.numInstances(); i++) {
double pred = ps.classifyInstance(test.instance(i));
pw2.println(pred);
}
pw2.close();
//weka.core.SerializationHelper.write("/Weka-3-6/ProjectMilestone3/ionosphere.model", vs);
//Evaluation eval=new Evaluation(train);
//eval.evaluateModel(vs,test);
//Double error_c=eval.errorRate();
//System.out.println(error_c);
}
}