Example usage for weka.core Instance numAttributes

Introduction

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

Prototype

public int numAttributes();

Source Link

Document

Returns the number of attributes.

Usage

From source file:ann.MyANN.java

/**
 * mengubah Instance menjadi Data//  w w w .  j a v a  2 s. co 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 ww. j a va2 s . com
    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: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  a  v a 2 s. co  m*/
    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 ww . j  ava 2s  . c o m
    }

    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 ww  . j  a  v  a 2  s . c o  m
    }

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

From source file:ant.Game.java

private char keyFromWeka(int classifier) {

    char key = W;

    Instance i = new Instance(m_ant.getNumAttributes() + 1);
    i.setDataset(m_data);//from ww  w.  j av a 2  s. c  om
    for (int j = 0; j < i.numAttributes() - 1; ++j) {
        i.setValue(new Attribute(Integer.toString(j), j), see(j));
    }

    i.setClassValue(1);
    double p = -1;
    try {
        p = m_wrapper10x1.classifyInstance(i);
    } catch (Exception ex) {
        Logger.getLogger(Game.class.getName()).log(Level.SEVERE, null, ex);
    }

    switch ((int) p) {
    case 0:
        key = W;
        break;
    case 1:
        key = A;
        break;
    case 2:
        key = S;
        break;
    case 3:
        key = D;
        break;
    default:
        System.err.println("Unexpected classifier output!");
        break;
    }

    return key;
}

From source file:assign00.KNNClassifier.java

double EuclideanDistance(Instance instanceLHS, Instance instanceRHS) {
    double distance = 0;
    for (int i = 0; i < instanceLHS.numAttributes() - 1 && i < instanceRHS.numAttributes() - 1; i++) {
        if (instanceLHS.attribute(i).isNumeric() && instanceRHS.attribute(i).isNumeric()) {
            distance += pow(instanceLHS.value(i) - instanceRHS.value(i), 2);
        } else {/* ww w  . j  av  a  2 s  . c o m*/
            if (instanceLHS.stringValue(i).equals(instanceRHS.stringValue(i))) {
                distance += 0;
            } else {
                distance += 1;
            }
        }
    }

    return distance;
}

From source file:assign00.KNNClassifier.java

double ManhattenDistance(Instance instanceLHS, Instance instanceRHS) {
    double distance = 0;
    for (int i = 0; i < instanceLHS.numAttributes() - 1 && i < instanceRHS.numAttributes() - 1; i++) {
        if (instanceLHS.attribute(i).isNumeric() && instanceRHS.attribute(i).isNumeric()) {
            distance += abs(instanceLHS.value(i) - instanceRHS.value(i));
        } else {//w w  w  . j a  v a2s  .  co  m
            if (instanceLHS.stringValue(i).equals(instanceRHS.stringValue(i))) {
                distance += 0;
            } else {
                distance += 1;
            }
        }
    }

    return distance;
}

From source file:bme.mace.logicdomain.Evaluation.java

License:Open Source License

/**
 * Builds a string listing the attribute values in a specified range of
 * indices, separated by commas and enclosed in brackets.
 * // w  w  w  .j  a v  a  2  s  .c  om
 * @param instance the instance to print the values from
 * @param attRange the range of the attributes to list
 * @return a string listing values of the attributes in the range
 */
protected static String attributeValuesString(Instance instance, Range attRange) {
    StringBuffer text = new StringBuffer();
    if (attRange != null) {
        boolean firstOutput = true;
        attRange.setUpper(instance.numAttributes() - 1);
        for (int i = 0; i < instance.numAttributes(); i++) {
            if (attRange.isInRange(i) && i != instance.classIndex()) {
                if (firstOutput) {
                    text.append("(");
                } else {
                    text.append(",");
                }
                text.append(instance.toString(i));
                firstOutput = false;
            }
        }
        if (!firstOutput) {
            text.append(")");
        }
    }
    return text.toString();
}

From source file:ca.uottawa.balie.WekaLearner.java

License:Open Source License

private Instance CreateDoubleInstance(Object[] pi_Instance, String pi_Class) {
    double[] instval = new double[pi_Instance.length];
    for (int i = 0; i != pi_Instance.length; ++i) {
        instval[i] = ((Double) pi_Instance[i]).doubleValue();
    }// ww  w .jav a 2  s  .  c om
    Instance inst = new Instance(1, instval);
    inst.insertAttributeAt(inst.numAttributes());
    inst.setValue((Attribute) m_WekaAttributes.lastElement(), pi_Class);
    return inst;
}