Example usage for weka.core Range setUpper

List of usage examples for weka.core Range setUpper

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Introduction

In this page you can find the example usage for weka.core Range setUpper.

Prototype

public void setUpper(int newUpper)

Source Link

Document

Sets the value of "last".

Usage

From source file:MultiClassClassifier.java

License:Open Source License

/**
 * Returns the distribution for an instance.
 *
 * @param inst the instance to get the distribution for
 * @return the distribution/*  ww  w .j  a va 2 s.c o  m*/
 * @throws Exception if the distribution can't be computed successfully
 */
public double[] distributionForInstance(Instance inst) throws Exception {

    if (m_Classifiers.length == 1) {
        return m_Classifiers[0].distributionForInstance(inst);
    }

    double[] probs = new double[inst.numClasses()];

    if (m_Method == METHOD_1_AGAINST_1) {
        double[][] r = new double[inst.numClasses()][inst.numClasses()];
        double[][] n = new double[inst.numClasses()][inst.numClasses()];

        for (int i = 0; i < m_ClassFilters.length; i++) {
            if (m_Classifiers[i] != null) {
                Instance tempInst = (Instance) inst.copy();
                tempInst.setDataset(m_TwoClassDataset);
                double[] current = m_Classifiers[i].distributionForInstance(tempInst);
                Range range = new Range(((RemoveWithValues) m_ClassFilters[i]).getNominalIndices());
                range.setUpper(m_ClassAttribute.numValues());
                int[] pair = range.getSelection();
                if (m_pairwiseCoupling && inst.numClasses() > 2) {
                    r[pair[0]][pair[1]] = current[0];
                    n[pair[0]][pair[1]] = m_SumOfWeights[i];
                } else {
                    if (current[0] > current[1]) {
                        probs[pair[0]] += 1.0;
                    } else {
                        probs[pair[1]] += 1.0;
                    }
                }
            }
        }
        if (m_pairwiseCoupling && inst.numClasses() > 2) {
            return pairwiseCoupling(n, r);
        }
    } else {
        // error correcting style methods
        for (int i = 0; i < m_ClassFilters.length; i++) {
            m_ClassFilters[i].input(inst);
            m_ClassFilters[i].batchFinished();
            double[] current = m_Classifiers[i].distributionForInstance(m_ClassFilters[i].output());
            //Calibrate the binary classifier scores

            for (int j = 0; j < m_ClassAttribute.numValues(); j++) {
                if (((MakeIndicator) m_ClassFilters[i]).getValueRange().isInRange(j)) {
                    probs[j] += current[1];
                } else {
                    probs[j] += current[0];
                }
            }
        }
    }

    if (Utils.gr(Utils.sum(probs), 0)) {
        Utils.normalize(probs);
        return probs;
    } else {
        return m_ZeroR.distributionForInstance(inst);
    }
}

From source file:MultiClassClassifier.java

License:Open Source License

/**
   * Prints the classifiers./*from  w  w  w  .  java  2  s .  com*/
   * 
   * @return a string representation of the classifier
   */
public String toString() {

    if (m_Classifiers == null) {
        return "MultiClassClassifier: No model built yet.";
    }
    StringBuffer text = new StringBuffer();
    text.append("MultiClassClassifier\n\n");
    for (int i = 0; i < m_Classifiers.length; i++) {
        text.append("Classifier ").append(i + 1);
        if (m_Classifiers[i] != null) {
            if ((m_ClassFilters != null) && (m_ClassFilters[i] != null)) {
                if (m_ClassFilters[i] instanceof RemoveWithValues) {
                    Range range = new Range(((RemoveWithValues) m_ClassFilters[i]).getNominalIndices());
                    range.setUpper(m_ClassAttribute.numValues());
                    int[] pair = range.getSelection();
                    text.append(", " + (pair[0] + 1) + " vs " + (pair[1] + 1));
                } else if (m_ClassFilters[i] instanceof MakeIndicator) {
                    text.append(", using indicator values: ");
                    text.append(((MakeIndicator) m_ClassFilters[i]).getValueRange());
                }
            }
            text.append('\n');
            text.append(m_Classifiers[i].toString() + "\n\n");
        } else {
            text.append(" Skipped (no training examples)\n");
        }
    }

    return text.toString();
}

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

License:Open Source License

/**
 * Prints the header for the predictions output into a supplied StringBuffer
 * /* ww w  .  j av  a 2s.c  o m*/
 * @param test structure of the test set to print predictions for
 * @param attributesToOutput indices of the attributes to output
 * @param printDistribution prints the complete distribution for nominal
 *          attributes, not just the predicted value
 * @param text the StringBuffer to print to
 */
protected static void printClassificationsHeader(Instances test, Range attributesToOutput,
        boolean printDistribution, StringBuffer text) {
    // print header
    if (test.classAttribute().isNominal()) {
        if (printDistribution) {
            text.append(" inst#     actual  predicted error distribution");
        } else {
            text.append(" inst#     actual  predicted error prediction");
        }
    } else {
        text.append(" inst#     actual  predicted      error");
    }
    if (attributesToOutput != null) {
        attributesToOutput.setUpper(test.numAttributes() - 1);
        text.append(" (");
        boolean first = true;
        for (int i = 0; i < test.numAttributes(); i++) {
            if (i == test.classIndex()) {
                continue;
            }

            if (attributesToOutput.isInRange(i)) {
                if (!first) {
                    text.append(",");
                }
                text.append(test.attribute(i).name());
                first = false;
            }
        }
        text.append(")");
    }
    text.append("\n");
}

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.
 * //from w w  w .j ava  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:core.ClusterEvaluationEX.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.
 *
 * @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
 *//*w w  w . j  a v  a  2 s .  com*/
private 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)) {
                if (firstOutput)
                    text.append("(");
                else
                    text.append(",");
                text.append(instance.toString(i));
                firstOutput = false;
            }
        if (!firstOutput)
            text.append(")");
    }
    return text.toString();
}

From source file:cotraining.copy.Evaluation_D.java

License:Open Source License

/**
 * Prints the header for the predictions output into a supplied StringBuffer
 *
 * @param test structure of the test set to print predictions for
 * @param attributesToOutput indices of the attributes to output
 * @param printDistribution prints the complete distribution for nominal
 * attributes, not just the predicted value
 * @param text the StringBuffer to print to
 *//*from   w  w  w .  j a  v  a 2s  . co  m*/
protected static void printClassificationsHeader(Instances test, Range attributesToOutput,
        boolean printDistribution, StringBuffer text) {
    // print header
    if (test.classAttribute().isNominal())
        if (printDistribution)
            text.append(" inst#     actual  predicted error distribution");
        else
            text.append(" inst#     actual  predicted error prediction");
    else
        text.append(" inst#     actual  predicted      error");
    if (attributesToOutput != null) {
        attributesToOutput.setUpper(test.numAttributes() - 1);
        text.append(" (");
        boolean first = true;
        for (int i = 0; i < test.numAttributes(); i++) {
            if (i == test.classIndex())
                continue;

            if (attributesToOutput.isInRange(i)) {
                if (!first)
                    text.append(",");
                text.append(test.attribute(i).name());
                first = false;
            }
        }
        text.append(")");
    }
    text.append("\n");
}

From source file:cotraining.copy.Evaluation_D.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.
 *
 * @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
 *//*from  www. j  a  v a 2  s . com*/
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:org.uclab.mm.kcl.ddkat.datapreprocessor.MissingValueHandler.java

License:Apache License

/**
 * Method to replace the identified missing values.
 *
 * @throws Exception the exception/*from   ww w . ja va2  s.  co  m*/
 */
public void replaceMissingValues() throws Exception {

    this.confirmationMessage = new ArrayList<String>();

    Instances outputData;

    String inputFile = BASE_DIR + "OriginalDataSet.csv";

    // load CSV file
    CSVLoader fileLoader = new CSVLoader();
    fileLoader.setSource(new File(inputFile));
    outputData = fileLoader.getDataSet();

    int numInstances = outputData.numInstances();
    int numAttributes = outputData.numAttributes();

    final int NON_NUMERIC = -1;
    int[] m_AttributeIndices = null;

    Range m_Attributes = new Range("first-last");

    // attributes must be numeric
    m_Attributes.setUpper(outputData.numAttributes() - 1);
    m_AttributeIndices = m_Attributes.getSelection();

    for (int i = 0; i < m_AttributeIndices.length; i++) {
        // ignore class
        if (m_AttributeIndices[i] == outputData.classIndex()) {
            m_AttributeIndices[i] = NON_NUMERIC;
            continue;
        }
        // not numeric -> ignore it
        if (!outputData.attribute(m_AttributeIndices[i]).isNumeric())
            m_AttributeIndices[i] = NON_NUMERIC;
    }

    double sum;
    int missingCounter;
    double attributeMean;

    // identify the missing values               
    for (int attributeIndex = 0; attributeIndex < numAttributes; attributeIndex++) {

        // non-numeric attribute?
        if (m_AttributeIndices[attributeIndex] == NON_NUMERIC) {
            continue;
        }

        double tempArr[] = outputData.attributeToDoubleArray(attributeIndex);
        sum = 0;
        missingCounter = 0;
        for (int i = 0; i < tempArr.length; i++) {
            sum = sum + tempArr[i];
            if (tempArr[i] == 0)
                missingCounter++;
        }

        attributeMean = sum / (numInstances - missingCounter);

        for (int instanceIndex = 0; instanceIndex < numInstances; instanceIndex++) {

            // replace the missing values with attribute mean values
            if (outputData.instance(instanceIndex).value(attributeIndex) == 0) {
                outputData.instance(instanceIndex).setValue(attributeIndex, attributeMean);
            }
        }
    }

    outputData.deleteAttributeAt(outputData.numAttributes() - 1);
    outputData.deleteAttributeAt(outputData.numAttributes() - 1);

    saveFilledData(inputFile, outputData);

}

From source file:org.uclab.mm.kcl.ddkat.datapreprocessor.OutlierHandler.java

License:Apache License

/**
 * Method to replace the detected outlier values.
 *
 * @throws Exception the exception/* ww  w  .j  a  v  a 2  s.c  o  m*/
 */
public void replaceOutliers() throws Exception {

    Instances inputData, outputData;

    String inputFile = BASE_DIR + "OriginalDataSet.csv";

    // load CSV file
    CSVLoader fileLoader = new CSVLoader();
    fileLoader.setSource(new File(inputFile));
    inputData = fileLoader.getDataSet();
    this.setInputFormat(inputData);
    outputData = Filter.useFilter(inputData, this);

    int numInstances = outputData.numInstances();
    int numAttributes = outputData.numAttributes();

    final int NON_NUMERIC = -1;
    double[] outlier_AttributeValues = null;
    double[] extreme_AttributeValues = null;
    int[] m_AttributeIndices = null;

    Range m_Attributes = new Range("first-last");

    // attributes must be numeric
    m_Attributes.setUpper(outputData.numAttributes() - 1);
    m_AttributeIndices = m_Attributes.getSelection();

    for (int i = 0; i < m_AttributeIndices.length; i++) {
        // ignore class
        if (m_AttributeIndices[i] == outputData.classIndex()) {
            m_AttributeIndices[i] = NON_NUMERIC;
            continue;
        }
        // not numeric -> ignore it
        if (!outputData.attribute(m_AttributeIndices[i]).isNumeric())
            m_AttributeIndices[i] = NON_NUMERIC;
    }

    for (int instanceIndex = 0; instanceIndex < numInstances; instanceIndex++) {
        // access instance 
        Instance tempInstance = outputData.instance(instanceIndex);

        for (int attributeIndex = 0; attributeIndex < numAttributes; attributeIndex++) {
            // non-numeric attribute?
            if (m_AttributeIndices[attributeIndex] == NON_NUMERIC) {
                continue;
            }

            // detect the outlier values using Interquartile approach
            if (this.isOutlier(tempInstance, m_AttributeIndices[attributeIndex])) {

                double outlierValue = tempInstance.value(attributeIndex);
                int outlierColumnIndex = attributeIndex;
                double sum = 0.0;

                outlier_AttributeValues = outputData.attributeToDoubleArray(outlierColumnIndex);

                for (int i = 0; i < outlier_AttributeValues.length; i++) {
                    sum = sum + outlier_AttributeValues[i];
                }

                sum = sum - outlierValue;
                double replacedValue = sum / (outlier_AttributeValues.length - 1);
                replacedValue = Math.round(replacedValue * 100D) / 100D;

                // replace the outliers with attribute mean values
                outputData.instance(instanceIndex).setValue(outlierColumnIndex, replacedValue);
            }

            // extreme value?
            if (this.isExtremeValue(tempInstance, m_AttributeIndices[attributeIndex])) {

                double extremeValue = tempInstance.value(attributeIndex);
                int extremeColumnIndex = attributeIndex;
                double sum = 0.0;

                extreme_AttributeValues = outputData.attributeToDoubleArray(extremeColumnIndex);

                for (int i = 0; i < extreme_AttributeValues.length; i++) {
                    sum = sum + extreme_AttributeValues[i];
                }

                sum = sum - extremeValue;
                double replacedValue = sum / (extreme_AttributeValues.length - 1);
                replacedValue = Math.round(replacedValue * 100D) / 100D;
                outputData.instance(instanceIndex).setValue(extremeColumnIndex, replacedValue);
            }

        }
    }
    outputData.deleteAttributeAt(outputData.numAttributes() - 1);
    outputData.deleteAttributeAt(outputData.numAttributes() - 1);

    saveConsistentData(inputFile, outputData);

}