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--- med:lab_5 [2018/05/12 23:19]
pszwed [Kod]
+++ med:lab_5 [2018/05/12 23:27]
pszwed [Weka]
@@ Line 18: / Line 18: @@
 Brak specjalnych wymagań
+Biblioteka weka.jar:  ''/opt/weka/weka.jar''
 ===== IDE =====
@@ Line 24: / Line 26: @@
   * Utwórz projekt
   * Wybierz: //Project -> Properties -> Java Build Path.// Następnie zakładkę //Libraries// i opcję //Add External JARs//. Dodaj bibliotekę ''weka.jar ''
+Dost
 ===== Kod =====
@@ Line 29: / Line 34: @@
+.1.1
 <code java>
 DataSource source = new DataSource("c-001.arff");
@@ Line 41: / Line 47: @@
 Utworznenie i uczenie klasyfikatora
+.1.3
 <code java>
 Classifier cls = new NaiveBayes();
@@ Line 48: / Line 55: @@
 Instancja do sklasyfikowania
+.1.4
 <code java>
 Instance inst = new DenseInstance (3);
@@ Line 55: / Line 63: @@
 </code>
+.1.5/6
 <code java>
+double y = cls.classifyInstance(inst);
 double[] distrib = cls.distributionForInstance(inst);
 System.out.printf(Locale.US,"%d->%f %d->%f\n",0,distrib[0],1,distrib[1]);
@@ Line 62: / Line 73: @@
+.2.2
 <code java>
 List<Attribute> atts = Arrays.asList(
@@ Line 73: / Line 85: @@
 </code>
+.2.3
 <code java>
 for(double x1=-10;x1<=10;x1+=0.1){
@@ Line 88: / Line 101: @@
 </code>
+.2.4
+<code java>
+ArffSaver saver = new ArffSaver();
+saver.setInstances(result);
+saver.setFile(new File("c-001-result.arff"));
+saver.writeBatch();
+</code>
+.4.2
+<code java>
+Classifier cls;
+cls = new NaiveBayes();
+Evaluation eval = new Evaluation(data);
+eval.crossValidateModel(cls, data, 10, new Random(1));
+eval.toSummaryString()
+eval.confusionMatrix()
+System.out.printf(Locale.US,
+"[prec recall fmeasure]:\t%f\t%f\t%f\n",
+eval.weightedPrecision(),
+eval.weightedRecall(),
+eval.weightedFMeasure());
+</code>