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:WLSVM.java

License:Open Source License

/**
 * Converts an ARFF Instance into a string in the sparse format accepted by
 * LIBSVM/* w  w  w  . j  av a  2s. co m*/
 * 
 * @param instance
 * @return
 */
protected String InstanceToSparse(Instance instance) {
    String line = new String();
    int c = (int) instance.classValue();
    if (c == 0)
        c = -1;
    line = c + " ";
    for (int j = 1; j < instance.numAttributes(); j++) {
        if (j - 1 == instance.classIndex()) {
            continue;
        }
        if (instance.isMissing(j - 1))
            continue;
        if (instance.value(j - 1) != 0)
            line += " " + j + ":" + instance.value(j - 1);
    }
    // System.out.println(line); 
    return (line + "\n");
}

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From source file:PrincipalComponents.java

License:Open Source License

/**
 * Convert a pc transformed instance back to the original space
 *
 * @param inst the instance to convert/*from  www. j  av a  2 s.c  o  m*/
 * @return the processed instance
 * @throws Exception if something goes wrong
 */
private Instance convertInstanceToOriginal(Instance inst) throws Exception {
    double[] newVals = null;

    if (m_hasClass) {
        newVals = new double[m_numAttribs + 1];
    } else {
        newVals = new double[m_numAttribs];
    }

    if (m_hasClass) {
        // class is always appended as the last attribute
        newVals[m_numAttribs] = inst.value(inst.numAttributes() - 1);
    }

    for (int i = 0; i < m_eTranspose[0].length; i++) {
        double tempval = 0.0;
        for (int j = 1; j < m_eTranspose.length; j++) {
            tempval += (m_eTranspose[j][i] * inst.value(j - 1));
        }
        newVals[i] = tempval;
        if (!m_center) {
            newVals[i] *= m_stdDevs[i];
        }
        newVals[i] += m_means[i];
    }

    if (inst instanceof SparseInstance) {
        return new SparseInstance(inst.weight(), newVals);
    } else {
        return new DenseInstance(inst.weight(), newVals);
    }
}

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From source file:adams.data.conversion.AbstractMatchWekaInstanceAgainstHeader.java

License:Open Source License

/**
 * Checks the instance against the header, whether they are compatible.
 *
 * @param input   the input instance/*from   w  ww  .  j a  va2s . c om*/
 * @return      null if compatible, otherwise error message
 */
protected String isCompatible(Instance input) {
    String result;
    int i;
    int typeInput;
    int typeHeader;

    result = null;
    if (input.numAttributes() != m_Dataset.numAttributes())
        result = "Number of attributes differ";

    if (result == null) {
        for (i = 0; i < m_Dataset.numAttributes(); i++) {
            typeInput = input.attribute(i).type();
            typeHeader = m_Dataset.attribute(i).type();
            if (typeInput == typeHeader)
                continue;
            if ((typeInput == Attribute.NOMINAL) && (typeHeader == Attribute.STRING))
                continue;
            if ((typeInput == Attribute.STRING) && (typeHeader == Attribute.NOMINAL))
                continue;
            result = "Attribute types at #" + (i + 1) + "  are not ";
            break;
        }
    }

    return result;
}

---

From source file:adams.flow.transformer.WekaGetInstanceValue.java

License:Open Source License

/**
 * Executes the flow item./*from  www.j  a va 2  s .  co  m*/
 *
 * @return      null if everything is fine, otherwise error message
 */
@Override
protected String doExecute() {
    String result;
    Instance inst;
    int index;

    result = null;

    inst = (Instance) m_InputToken.getPayload();

    try {
        if (m_AttributeName.length() > 0) {
            index = inst.dataset().attribute(m_AttributeName).index();
        } else {
            m_Index.setMax(inst.numAttributes());
            index = m_Index.getIntIndex();
        }
        if (inst.isMissing(index)) {
            m_OutputToken = new Token("?");
        } else {
            switch (inst.attribute(index).type()) {
            case Attribute.NUMERIC:
                m_OutputToken = new Token(inst.value(index));
                break;

            case Attribute.DATE:
            case Attribute.NOMINAL:
            case Attribute.STRING:
            case Attribute.RELATIONAL:
                m_OutputToken = new Token(inst.stringValue(index));
                break;

            default:
                result = "Unhandled attribute type: " + inst.attribute(index).type();
            }
        }
    } catch (Exception e) {
        result = handleException("Failed to obtain value from instance:\n" + inst, e);
    }

    return result;
}

---

From source file:adams.flow.transformer.WekaInstanceDumper.java

License:Open Source License

/**
 * Turns the row into the appropriate format.
 *
 * @param row      the row to convert//from w  w  w . j  ava 2  s . c  o  m
 * @return      the generated output
 */
protected String createRow(Instance row) {
    StringBuilder result;
    int i;

    result = new StringBuilder();

    switch (m_OutputFormat) {
    case ARFF:
        result.append(row.toString());
        break;

    case CSV:
        for (i = 0; i < row.numAttributes(); i++) {
            if (i > 0)
                result.append(",");
            result.append(row.toString(i));
        }
        break;

    case TAB:
        for (i = 0; i < row.numAttributes(); i++) {
            if (i > 0)
                result.append("\t");
            result.append(row.toString(i));
        }
        break;

    default:
        throw new IllegalStateException("Unhandled output format: " + m_OutputFormat);
    }

    return result.toString();
}

---

From source file:adams.flow.transformer.WekaInstanceStreamPlotGenerator.java

License:Open Source License

/**
 * Executes the flow item./* w w  w. ja va 2  s.c  o m*/
 *
 * @return      null if everything is fine, otherwise error message
 */
@Override
protected String doExecute() {
    String result;
    Instance inst;
    SequencePlotterContainer cont;
    int[] indices;
    int i;

    result = null;

    inst = (Instance) m_InputToken.getPayload();

    m_Counter++;
    m_Containers.clear();
    m_Attributes.setMax(inst.numAttributes());
    indices = m_Attributes.getIntIndices();

    for (i = 0; i < indices.length; i++) {
        if (inst.attribute(indices[i]).isNominal())
            cont = new SequencePlotterContainer(inst.dataset().attribute(indices[i]).name(),
                    new Double(m_Counter), inst.stringValue(indices[i]));
        else
            cont = new SequencePlotterContainer(inst.dataset().attribute(indices[i]).name(),
                    new Double(m_Counter), inst.value(indices[i]));
        m_Containers.add(cont);
    }

    return result;
}

---

From source file:adaptedClusteringAlgorithms.MyFarthestFirst.java

License:Open Source License

/**
 * Updates the minimum and maximum values for all the attributes
 * based on a new instance.//  ww w . jav  a2s  . co m
 *
 * @param instance the new instance
 */
private void updateMinMax(Instance instance) {

    for (int j = 0; j < instance.numAttributes(); j++) {
        if (Double.isNaN(m_Min[j])) {
            m_Min[j] = instance.value(j);
            m_Max[j] = instance.value(j);
        } else {
            if (instance.value(j) < m_Min[j]) {
                m_Min[j] = instance.value(j);
            } else {
                if (instance.value(j) > m_Max[j]) {
                    m_Max[j] = instance.value(j);
                }
            }
        }
    }
}

---

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 w w  w.ja  v  a2 s . co 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 ww  .j  ava 2 s.c om
    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);/*from   ww w. j  a v a2  s  .c o m*/
    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);
    //            }
    //        }

}

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