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1 import java.io.IOException;
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2 import java.util.Enumeration;
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3
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4 import weka.classifiers.Classifier;
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5 import weka.core.Instance;
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6 import weka.core.Instances;
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7 import weka.core.converters.ConverterUtils.DataSource;
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8
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9
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10 public class MLTest {
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11
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12 /**
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13 * @param args
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14 */
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15 public static void main(String[] args) {
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16 // Dataset data = null;
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17 // try {
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18 // data = FileHandler.loadDataset(new File("/Users/jdamerow/MPIWG/EndangeredSpecies/workspace/de.mpiwg.anteater/training/ApplicantTrainingDataData.data"), 0, ";");
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19 // } catch (IOException e1) {
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20 // // TODO Auto-generated catch block
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21 // e1.printStackTrace();
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22 // return;
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23 // }
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24 /* Contruct a KNN classifier that uses 5 neighbors to make a
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25 // *decision. */
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26 // Classifier knn = new KNearestNeighbors(5);
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27 // knn.buildClassifier(data);
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28
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29
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30 // int correct = 0, wrong = 0;
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31 // /* Classify all instances and check with the correct class values */
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32 // for (Instance inst : dataForClassification) {
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33 // Object predictedClassValue = knn.classify(inst);
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34 // Object realClassValue = inst.classValue();
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35 // if (predictedClassValue == realClassValue)
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36 // correct++;
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37 // else
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38 // wrong++;
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39 // }
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40
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41 //LMT lmt = new LMT();
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42
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43 Classifier cls = null;
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44 try {
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45 cls = (Classifier) weka.core.SerializationHelper.read("/Users/jdamerow/MPIWG/EndangeredSpecies/workspace/de.mpiwg.anteater/src/LMT.model");
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46 } catch (Exception e1) {
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47 // TODO Auto-generated catch block
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48 e1.printStackTrace();
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49 }
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50
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51 Instances testData = null;
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52 try {
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53 testData = DataSource.read("/Users/jdamerow/MPIWG/EndangeredSpecies/workspace/de.mpiwg.anteater/mlAnalysis/00-18565.arff");
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54 } catch (IOException e) {
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55 // TODO Auto-generated catch block
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56 e.printStackTrace();
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57 } catch (Exception e) {
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58 // TODO Auto-generated catch block
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59 e.printStackTrace();
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60 }
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61 testData.setClassIndex(0);
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62
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63 // Instances trainData = null;
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64 // try {
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65 // trainData = DataSource.read("/Users/jdamerow/MPIWG/EndangeredSpecies/workspace/de.mpiwg.anteater/training/ApplicantTrainingDataWithoutTID.arff");
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66 // } catch (IOException e) {
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67 // // TODO Auto-generated catch block
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68 // e.printStackTrace();
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69 // } catch (Exception e) {
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70 // // TODO Auto-generated catch block
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71 // e.printStackTrace();
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72 // }
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73 // trainData.setClassIndex(0);
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74
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75 Enumeration<Instance> en = testData.enumerateInstances();
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76 while (en.hasMoreElements()) {
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77 try {
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78 System.out.println(cls.classifyInstance(en.nextElement()));
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79 } catch (Exception e) {
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80 // TODO Auto-generated catch block
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81 e.printStackTrace();
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82 }
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83 }
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84
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85 // Evaluation eval;
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86 // try {
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87 // eval = new Evaluation(trainData);
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88 // double[] results = eval.evaluateModel(cls, testData);
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89 // Enumeration<?> en = eval.predictions().elements();
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90 // while (en.hasMoreElements()) {
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91 // System.out.println();
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92 // System.out.println(en.nextElement());
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93 // }
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94 // System.out.println(eval.toMatrixString());
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95 // System.out.println("correct " + eval.pctCorrect());
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96 // System.out.println("incorrect " + eval.pctIncorrect());
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97 // } catch (Exception e) {
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98 // // TODO Auto-generated catch block
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99 // e.printStackTrace();
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100 // }
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101
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102 /* Wrap Weka classifier in bridge */
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103 // Classifier javamlsmo = new WekaClassifier(smo);
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104 // /* Initialize cross-validation */
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105 // CrossValidation cv = new CrossValidation(javamlsmo);
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106 // /* Perform cross-validation */
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107 // Map<Object, PerformanceMeasure> pm = cv.crossValidation(data);
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108
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109 // /* Load a dataset */
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110 // Dataset dataForClassification = null;
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111 // try {
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112 // dataForClassification = FileHandler.loadDataset(new File("/Users/jdamerow/MPIWG/EndangeredSpecies/workspace/de.mpiwg.anteater/training/ApplicantTrainingDataData.data"), 0, ";");
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113 // } catch (IOException e) {
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114 // // TODO Auto-generated catch block
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115 // e.printStackTrace();
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116 // return;
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117 // }
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118 // int correct = 0, wrong = 0;
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119 // /* Classify all instances and check with the correct class values */
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120 // for (Instance inst : dataForClassification) {
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121 // Object predictedClassValue = javamlsmo.classify(inst);
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122 // Object realClassValue = inst.classValue();
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123 // if (predictedClassValue == realClassValue)
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124 // correct++;
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125 // else
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126 // wrong++;
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127 // }
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128 //
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129
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130 // System.out.println("right " + correct);
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131 // System.out.println("wrong " + wrong);
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132 }
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133
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134 }
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