Example usage for weka.core Instance value

Introduction

In this page you can find the example usage for weka.core Instance value.

Prototype

public double value(Attribute att);

Source Link

Document

Returns an instance's attribute value in internal format.

Usage

From source file:ai.GiniFunction.java

License:GNU General Public License

/**
 * Evaluate a single instance based on the current 
 * state of the split function//from w ww  .  j av a2s .  c o m
 * 
 * @param instance sample to evaluate
 * @return false if the instance is on the right of the splitting point, true if it's on the left 
 */
public boolean evaluate(Instance instance) {
    if (allSame)
        return true;
    else
        return instance.value(this.index) < this.threshold;
}

---

From source file:ANN.MultilayerPerceptron.java

@Override
public double classifyInstance(Instance i) {
    //buat list input
    ArrayList<Double> listInput = new ArrayList<>();
    listInput.add(1.0);//from  ww w . ja  v  a 2  s . c o  m
    for (int idx = 0; idx < i.numAttributes() - 1; idx++) {
        listInput.add(i.value(idx));
    }

    //hitung output layer
    ArrayList<Double> hiddenOutput = new ArrayList<>();
    hiddenOutput.add(1.0);
    for (int idxOutput = 1; idxOutput < listHidden.size(); idxOutput++) {
        output(listHidden, listInput, idxOutput);
        hiddenOutput.add(listHidden.get(idxOutput).getValue());
        //                        System.out.println(outputVal);
    }
    //hitung output layer
    for (int idxOutput = 0; idxOutput < listOutput.size(); idxOutput++) {
        output(listOutput, hiddenOutput, idxOutput);
    }
    double result = maxIdxValue(listOutput);
    return result;
}

---

From source file:ANN.MultiplePerceptron.java

@Override
public double classifyInstance(Instance i) {
    ArrayList<Double> listInput = new ArrayList<>();
    listInput.add(1.0);//from w w  w.j a  v a  2 s.c o  m
    for (int idxInstanceVal = 0; idxInstanceVal < i.numAttributes() - 1; idxInstanceVal++) {
        listInput.add(i.value(idxInstanceVal));
    }

    ArrayList<Double> listOutputHidden = new ArrayList<>();
    listOutputHidden.add(1.0);
    //set output hidden layer
    for (int idxNodeHidden = 1; idxNodeHidden < listNodeHidden.size(); idxNodeHidden++) {
        double outputVal = listNodeHidden.get(idxNodeHidden).output(listInput);
        listNodeHidden.get(idxNodeHidden).setValue(outputVal);
        listOutputHidden.add(outputVal);
    }

    //set output layer
    for (int idxNodeHidden = 0; idxNodeHidden < listNodeOutput.size(); idxNodeHidden++) {
        double outputVal = listNodeOutput.get(idxNodeHidden).output(listOutputHidden);
        listNodeOutput.get(idxNodeHidden).setValue(outputVal);
        //            System.out.printf("%f ", outputVal);
    }
    //        System.out.println();
    return getIdxMax(listNodeOutput);
}

---

From source file:ANN.MultiplePerceptron.java

public void calculateWeight(Instance i) {

    ArrayList<Double> listInput = new ArrayList<>();
    listInput.add(1.0);// w  ww . jav a  2 s.  c om
    for (int idxInstanceVal = 0; idxInstanceVal < i.numAttributes(); idxInstanceVal++) {
        listInput.add(i.value(idxInstanceVal));
    }

    //set weight hidden
    //        for (int index = 0; index < listNodeHidden.size();index++) {
    //            for (int indexDalem = 0 ; indexDalem < listNodeHidden.get(index).getWeightSize();indexDalem++) {
    //                double delta = learningRate*listNodeHidden.get(index).getError()*listInput.get(indexDalem);
    //                double newWeight = delta + listNodeHidden.get(index).getWeightFromList(indexDalem);
    //                listNodeHidden.get(index).setWeight(indexDalem, newWeight);
    ////                System.out.println(index+" "+indexDalem+" "+newWeight);
    //            }
    //        }
    for (int idxHidden = 0; idxHidden < listNodeHidden.size(); idxHidden++) {
        for (int idxweight = 0; idxweight < listNodeHidden.get(idxHidden).getWeightSize(); idxweight++) {
            double oldVal = listNodeHidden.get(idxHidden).getWeightFromList(idxweight);
            //                System.out.println(oldVal);
            oldVal += learningRate * listInput.get(idxweight) * listNodeHidden.get(idxHidden).getError();
            listNodeHidden.get(idxHidden).setWeight(idxweight, oldVal);
            //                System.out.println(listNodeHidden.get(idxHidden).getWeightFromList(idxweight));
        }
    }
    //        System.out.println(listNodeOutput.get(0).getWeightSize()+" "+listOutputHidden);
    //set weight output
    for (int index = 0; index < listNodeOutput.size(); index++) {
        for (int indexDalem = 0; indexDalem < listNodeHidden.size(); indexDalem++) {
            double delta = learningRate * listNodeOutput.get(index).getError()
                    * listNodeHidden.get(indexDalem).getValue();
            double newWeight = delta + listNodeOutput.get(index).getWeightFromList(indexDalem);
            listNodeOutput.get(index).setWeight(indexDalem, newWeight);
            //                System.out.println(newWeight);
        }
    }
    //        for (int idxOutput=0; idxOutput < listNodeOutput.size(); idxOutput++) {
    //            for (int idxweight=0;idxweight<listNodeOutput.get(idxOutput).getWeightSize();idxweight++) {
    //                double oldVal = listNodeOutput.get(idxOutput).getWeightFromList(idxweight);
    //                oldVal += learningRate*listOutputHidden.get(idxweight) * listNodeOutput.get(idxOutput).getError();
    //                listNodeOutput.get(idxOutput).setWeight(idxweight, oldVal);
    //            }
    //        }

}

---

From source file:ann.MyANN.java

/**
 * mengubah Instance menjadi Data//  www. ja v a2s . c  o  m
 * @param instance Instance yang akan diubah menjadi kelas Data
 * @return kelas Data dari input
 */
private Data instanceToData(Instance instance) {
    ArrayList<Double> input = new ArrayList<>();
    ArrayList<Double> target = new ArrayList<>();
    for (int j = 0; j < instance.numAttributes() - 1; j++) {
        input.add(0.0);
    }
    if (instance.classAttribute().isNominal()) {
        for (int j = 0; j < instance.classAttribute().numValues(); j++) {
            target.add(0.0);
        }
    } else {
        target.add(0.0);
    }
    for (int j = 0; j < instance.numAttributes(); j++) {
        if (j == instance.classIndex()) {
            if (instance.attribute(j).isNominal())
                target.set((int) instance.value(j), 1.0);
            else
                target.add(instance.value(j));
        } else {
            input.set(j, instance.value(j));
        }
    }
    return new Data(input, target);
}

---

From source file:ann.SingleLayerPerceptron.java

public double classifyInstance(Instance inst) throws Exception {
    double instanceClass = 0.0;

    ntb.input(inst);//from  w w w.j a  v  a  2s.  c o  m
    inst = ntb.output();
    normalize.input(inst);
    inst = normalize.output();
    System.out.println(inst.toString());
    int outputSize = output.size();
    double[] result = new double[outputSize];
    for (int j = 0; j < outputSize; j++) {
        result[j] = 0.0;
        for (int k = 0; k < inst.numAttributes(); k++) {
            double input = 1;
            if (k < inst.numAttributes() - 1) {
                input = inst.value(k);
            }
            System.out.println(output.get(j).weights.get(k) + "*" + input);
            result[j] += output.get(j).weights.get(k) * input;
        }
        result[j] = Util.activationFunction(result[j], annOptions);
    }

    if (outputSize >= 2) {
        for (int j = 0; j < outputSize; j++) {
            System.out.print(result[j] + "-");
            if (result[j] > result[(int) instanceClass]) {

                instanceClass = j;
                System.out.println(instanceClass);
            }
        }
    } else {
        instanceClass = (int) result[0];
    }

    return instanceClass;
}

---

From source file:anndl.Anndl.java

private void ClassifyInstance(String fileinput) throws Exception {
    System.out.println("Membaca File Model ..");
    Classifier cls = (Classifier) weka.core.SerializationHelper.read(fileinput);

    System.out.println("Masukan Data yang ingin diklasifikasikan, pisahkan dengan spasi :");
    String testset = scanner.nextLine();
    testset = "1 1 0";
    String[] exploded = testset.split(" ");

    Instance newinstance = new Instance(exploded.length);
    for (int i = 0; i < exploded.length; i++) {
        String exploded1 = exploded[i];
        System.out.println(exploded[i] + ";");

        newinstance.setValue(i, Integer.valueOf(exploded[i]));
    }// ww  w.  j av  a  2 s .com

    System.out.println("Melakukan klasifikasi ... ");
    double result = cls.classifyInstance(newinstance);
    System.out.println("Data ini:");
    System.out.println(testset);
    System.out.println("memiliki label :");
    System.out.println(newinstance.value(newinstance.classIndex()));
}

---

From source file:ANN_Single.SinglelayerPerceptron.java

@Override
public double classifyInstance(Instance i) {
    //buat list input
    System.out.println("classify");
    ArrayList<Double> listInput = new ArrayList<>();
    listInput.add(1.0);//from   w w  w  .j av  a  2s  .  c om
    for (int idx = 0; idx < i.numAttributes() - 1; idx++) {
        listInput.add(i.value(idx));
    }

    //hitung output layer
    for (int idxOutput = 0; idxOutput < listOutput.size(); idxOutput++) {
        outputC(listInput, idxOutput);
        //           listOutput.get(idxOutput).output(listInput);
        //           listOutput.get(idxOutput).setValue(outputVal);
        //           System.out.println(idxOutput+" "+outputVal);
        //            System.out.print(listOutput.get(idxOutput).getValue()+" ");
    }
    System.out.println();
    double result = maxIdxValue(listOutput);
    //       System.out.println(result+" "+i.stringValue(i.classIndex()));
    System.out.println("end c");
    return result;
}

---

From source file:ANN_single2.MultilayerPerceptron.java

@Override
public double classifyInstance(Instance i) {
    ArrayList<Double> listInput = new ArrayList<>();
    listInput.add(1.0); //ini untuk bias
    for (int ins = 0; ins < i.numAttributes() - 1; ins++) {
        listInput.add(i.value(ins));
    }/*from   w w w  .  jav  a  2 s . c om*/

    ArrayList<Double> listHide = new ArrayList<>();
    listHide.add(1.0);
    //Hitung output hidden layer
    for (int idxHidden = 1; idxHidden < listHidden.size(); idxHidden++) {
        output(listHidden, listInput, idxHidden);
        listHide.add(listHidden.get(idxHidden).getValue());
    }

    //Hitung ouput output lyer
    for (int idxOutput = 0; idxOutput < listOutput.size(); idxOutput++) {
        output(listOutput, listHide, idxOutput);
    }
    return maxValue();
}

---

From source file:ANN_single2.SinglelayerPerceptron.java

@Override
public double classifyInstance(Instance i) {
    ArrayList<Double> listInput = new ArrayList<>();
    listInput.add(1.0); //ini bias
    for (int idx = 0; idx < i.numAttributes() - 1; idx++) {
        listInput.add(i.value(idx));
    }/*from   w w w  .ja  va  2s  .c  o m*/

    //Hitung output rumus = sigmoid dari sigma
    for (int idxOut = 0; idxOut < listOutput.size(); idxOut++) {
        output(listInput, idxOut);
    }
    return maxValue();
}

---

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