Example usage for weka.core DenseInstance DenseInstance

List of usage examples for weka.core DenseInstance DenseInstance

  1. HOME
  2. Java
  3. weka
  4. weka.core.*
  5. DenseInstance
  6. DenseInstance

Introduction

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

Prototype


public DenseInstance(double weight, double[] attValues)

Source Link

Document

Constructor that inititalizes instance variable with given values.

Usage

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 ww  w. java  2s .  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);
    }
}

From source file:PrincipalComponents.java

License:Open Source License

/**
 * Transform an instance in original (unormalized) format. Convert back to
 * the original space if requested./*w ww  . j  ava 2  s  .  c  o  m*/
 *
 * @param instance an instance in the original (unormalized) format
 * @return a transformed instance
 * @throws Exception if instance cant be transformed
 */
@Override
public Instance convertInstance(Instance instance) throws Exception {

    if (m_eigenvalues == null) {
        throw new Exception("convertInstance: Principal components not " + "built yet");
    }

    double[] newVals = new double[m_outputNumAtts];
    Instance tempInst = (Instance) instance.copy();
    if (!instance.dataset().equalHeaders(m_trainHeader)) {
        throw new Exception("Can't convert instance: header's don't match: " + "PrincipalComponents\n"
                + instance.dataset().equalHeadersMsg(m_trainHeader));
    }

    m_replaceMissingFilter.input(tempInst);
    m_replaceMissingFilter.batchFinished();
    tempInst = m_replaceMissingFilter.output();

    /*
     * if (m_normalize) { m_normalizeFilter.input(tempInst);
     * m_normalizeFilter.batchFinished(); tempInst =
     * m_normalizeFilter.output(); }
     */

    m_nominalToBinFilter.input(tempInst);
    m_nominalToBinFilter.batchFinished();
    tempInst = m_nominalToBinFilter.output();

    if (m_attributeFilter != null) {
        m_attributeFilter.input(tempInst);
        m_attributeFilter.batchFinished();
        tempInst = m_attributeFilter.output();
    }

    if (!m_center) {
        m_standardizeFilter.input(tempInst);
        m_standardizeFilter.batchFinished();
        tempInst = m_standardizeFilter.output();
    } else {
        m_centerFilter.input(tempInst);
        m_centerFilter.batchFinished();
        tempInst = m_centerFilter.output();
    }

    if (m_hasClass) {
        newVals[m_outputNumAtts - 1] = instance.value(instance.classIndex());
    }

    double cumulative = 0;
    int numAttAdded = 0;
    for (int i = m_numAttribs - 1; i >= 0; i--) {
        double tempval = 0.0;
        for (int j = 0; j < m_numAttribs; j++) {
            tempval += (m_eigenvectors[j][m_sortedEigens[i]] * tempInst.value(j));
        }
        newVals[m_numAttribs - i - 1] = tempval;
        cumulative += m_eigenvalues[m_sortedEigens[i]];
        if ((cumulative / m_sumOfEigenValues) >= m_coverVariance) {
            break;
        }
        if (numAttAdded > m_maxNumAttr) {
            break;
        }
        numAttAdded++;
    }

    if (!m_transBackToOriginal) {
        if (instance instanceof SparseInstance) {
            return new SparseInstance(instance.weight(), newVals);
        } else {
            return new DenseInstance(instance.weight(), newVals);
        }
    } else {
        if (instance instanceof SparseInstance) {
            return convertInstanceToOriginal(new SparseInstance(instance.weight(), newVals));
        } else {
            return convertInstanceToOriginal(new DenseInstance(instance.weight(), newVals));
        }
    }
}

From source file:aaa.util.test.CreateArff.java

License:Open Source License

/**
 * Generates the Instances object and outputs it in ARFF format to stdout.
 *
 * @param args   ignored//from   ww w .  j a  va  2 s .co m
 * @throws Exception   if generation of instances fails
 */
public static void main(String[] args) throws Exception {
    ArrayList<Attribute> atts;
    ArrayList<Attribute> attsRel;
    ArrayList<String> attVals;
    ArrayList<String> attValsRel;
    Instances data;
    Instances dataRel;
    double[] vals;
    double[] valsRel;
    int i;

    // 1. set up attributes
    atts = new ArrayList<Attribute>();
    // - numeric
    atts.add(new Attribute("att1"));
    // - nominal
    attVals = new ArrayList<String>();
    for (i = 0; i < 5; i++)
        attVals.add("val" + (i + 1));
    atts.add(new Attribute("att2", attVals));
    // - string
    atts.add(new Attribute("att3", (ArrayList<String>) null));
    // - date
    atts.add(new Attribute("att4", "yyyy-MM-dd"));
    // - relational
    attsRel = new ArrayList<Attribute>();
    // -- numeric
    attsRel.add(new Attribute("att5.1"));
    // -- nominal
    attValsRel = new ArrayList<String>();
    for (i = 0; i < 5; i++)
        attValsRel.add("val5." + (i + 1));
    attsRel.add(new Attribute("att5.2", attValsRel));
    dataRel = new Instances("att5", attsRel, 0);
    atts.add(new Attribute("att5", dataRel, 0));

    // 2. create Instances object
    data = new Instances("MyRelation", atts, 0);

    // 3. fill with data
    // first instance
    vals = new double[data.numAttributes()];
    // - numeric
    vals[0] = Math.PI;
    // - nominal
    vals[1] = attVals.indexOf("val3");
    // - string
    vals[2] = data.attribute(2).addStringValue("This is a string!");
    // - date
    vals[3] = data.attribute(3).parseDate("2001-11-09");
    // - relational
    dataRel = new Instances(data.attribute(4).relation(), 0);
    // -- first instance
    valsRel = new double[2];
    valsRel[0] = Math.PI + 1;
    valsRel[1] = attValsRel.indexOf("val5.3");
    dataRel.add(new DenseInstance(1.0, valsRel));
    // -- second instance
    valsRel = new double[2];
    valsRel[0] = Math.PI + 2;
    valsRel[1] = attValsRel.indexOf("val5.2");
    dataRel.add(new DenseInstance(1.0, valsRel));
    vals[4] = data.attribute(4).addRelation(dataRel);
    // add
    data.add(new DenseInstance(1.0, vals));

    // second instance
    vals = new double[data.numAttributes()]; // important: needs NEW array!
    // - numeric
    vals[0] = Math.E;
    // - nominal
    vals[1] = attVals.indexOf("val1");
    // - string
    vals[2] = data.attribute(2).addStringValue("And another one!");
    // - date
    vals[3] = data.attribute(3).parseDate("2000-12-01");
    // - relational
    dataRel = new Instances(data.attribute(4).relation(), 0);
    // -- first instance
    valsRel = new double[2];
    valsRel[0] = Math.E + 1;
    valsRel[1] = attValsRel.indexOf("val5.4");
    dataRel.add(new DenseInstance(1.0, valsRel));
    // -- second instance
    valsRel = new double[2];
    valsRel[0] = Math.E + 2;
    valsRel[1] = attValsRel.indexOf("val5.1");
    dataRel.add(new DenseInstance(1.0, valsRel));
    vals[4] = data.attribute(4).addRelation(dataRel);
    // add
    data.add(new DenseInstance(1.0, vals));

    // 4. output data
    System.out.println(data);
}

From source file:activeSegmentation.filterImpl.FilterUtil.java

License:Open Source License

/**
 * Create instance (feature vector) of a specific coordinate
 * /*w  w  w .j av  a 2 s .  c  o m*/
 * @param x x- axis coordinate
 * @param y y- axis coordinate
 * @param classValue class value to be assigned
 * @return corresponding instance
 */
public DenseInstance createInstance(int x, int y, int classValue, ImageStack stack, boolean colorFeatures,
        boolean oldColorFormat) {

    final int size = stack.getSize();

    final double[] values = new double[size + 1];
    int n = 0;

    if (colorFeatures == false || oldColorFormat == true) {
        for (int z = 0; z < size; z++, n++)
            values[z] = stack.getVoxel(x, y, z);
    } else {
        for (int z = 0; z < size; z++, n++) {
            int c = (int) stack.getVoxel(x, y, z);
            int r = (c & 0xff0000) >> 16;
            int g = (c & 0xff00) >> 8;
            int b = c & 0xff;
            values[z] = (r + g + b) / 3.0;
        }
    }

    // Assign class
    values[values.length - 1] = (double) classValue;

    return new DenseInstance(1.0, values);
}

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

License:Open Source License

/**
 * Matches the input instance against the header.
 *
 * @param input   the Instance to align to the header
 * @return      the aligned Instance//from w w  w.j  a v a 2s.  c  o  m
 */
protected Instance match(Instance input) {
    Instance result;
    double[] values;
    int i;

    values = new double[m_Dataset.numAttributes()];
    for (i = 0; i < m_Dataset.numAttributes(); i++) {
        values[i] = Utils.missingValue();
        switch (m_Dataset.attribute(i).type()) {
        case Attribute.NUMERIC:
        case Attribute.DATE:
            values[i] = input.value(i);
            break;
        case Attribute.NOMINAL:
            if (m_Dataset.attribute(i).indexOfValue(input.stringValue(i)) != -1)
                values[i] = m_Dataset.attribute(i).indexOfValue(input.stringValue(i));
            break;
        case Attribute.STRING:
            values[i] = m_Dataset.attribute(i).addStringValue(input.stringValue(i));
            break;
        case Attribute.RELATIONAL:
            values[i] = m_Dataset.attribute(i).addRelation(input.relationalValue(i));
            break;
        default:
            throw new IllegalStateException(
                    "Unhandled attribute type: " + Attribute.typeToString(m_Dataset.attribute(i).type()));
        }
    }

    if (input instanceof SparseInstance)
        result = new SparseInstance(input.weight(), values);
    else
        result = new DenseInstance(input.weight(), values);
    result.setDataset(m_Dataset);

    // fix class index, if necessary
    if ((input.classIndex() != m_Dataset.classIndex()) && (m_Dataset.classIndex() < 0))
        m_Dataset.setClassIndex(input.classIndex());

    return result;
}

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

License:Open Source License

/**
 * Performs the actual conversion./*from www . j a v a 2s.  c o  m*/
 *
 * @return      the converted data
 * @throws Exception   if something goes wrong with the conversion
 */
protected Object doConvert() throws Exception {
    Report report;
    Instance result;
    ArrayList atts;
    ArrayList attValues;
    int i;
    double[] values;

    report = (Report) m_Input;

    // generate header
    if (m_Header == null) {
        atts = new ArrayList();
        for (i = 0; i < m_Fields.length; i++) {
            switch (m_Fields[i].getDataType()) {
            case NUMERIC:
                atts.add(new Attribute(m_Fields[i].getName()));
                break;
            case BOOLEAN:
                attValues = new ArrayList();
                attValues.add("false");
                attValues.add("true");
                atts.add(new Attribute(m_Fields[i].getName(), attValues));
                break;
            default:
                atts.add(new Attribute(m_Fields[i].getName(), (List) null));
                break;
            }
        }
        m_Header = new Instances(getClass().getName(), atts, 0);
    }

    // generate instance
    values = new double[m_Header.numAttributes()];
    for (i = 0; i < m_Fields.length; i++) {
        if (report.hasValue(m_Fields[i])) {
            switch (m_Fields[i].getDataType()) {
            case NUMERIC:
                values[i] = report.getDoubleValue(m_Fields[i]);
                break;
            case BOOLEAN:
                if (report.getBooleanValue(m_Fields[i]))
                    values[i] = 1;
                else
                    values[i] = 0;
                break;
            default:
                values[i] = m_Header.attribute(i).addStringValue("" + report.getValue(m_Fields[i]));
                break;
            }
        } else {
            values[i] = weka.core.Utils.missingValue();
        }
    }
    result = new DenseInstance(1.0, values);
    result.setDataset(m_Header);

    return result;
}

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

License:Open Source License

/**
 * Performs the actual conversion.//  w  w  w.  j a  v a  2  s. c om
 *
 * @return      the converted data
 * @throws Exception   if something goes wrong with the conversion
 */
@Override
protected Object doConvert() throws Exception {
    Instances result;
    SpreadSheet sheet;
    DenseInstance inst;
    ArrayList<Attribute> atts;
    HashSet<String> unique;
    ArrayList<String> labels;
    Row row;
    Cell cell;
    int i;
    int n;
    double[] values;
    Collection<ContentType> types;
    ContentType type;
    boolean added;
    int[] classIndices;

    sheet = (SpreadSheet) m_Input;

    // create header
    atts = new ArrayList<>();
    for (i = 0; i < sheet.getColumnCount(); i++) {
        added = false;
        types = sheet.getContentTypes(i);
        if (types.contains(ContentType.DOUBLE))
            types.remove(ContentType.LONG);
        if (types.contains(ContentType.LONG)) {
            types.add(ContentType.DOUBLE);
            types.remove(ContentType.LONG);
        }

        if (types.size() == 1) {
            type = (ContentType) types.toArray()[0];
            if (type == ContentType.DOUBLE) {
                atts.add(new Attribute(sheet.getHeaderRow().getCell(i).getContent()));
                added = true;
            } else if (type == ContentType.DATE) {
                atts.add(new Attribute(sheet.getHeaderRow().getCell(i).getContent(),
                        Constants.TIMESTAMP_FORMAT));
                added = true;
            } else if (type == ContentType.TIME) {
                atts.add(new Attribute(sheet.getHeaderRow().getCell(i).getContent(), Constants.TIME_FORMAT));
                added = true;
            }
        }

        if (!added) {
            unique = new HashSet<>();
            for (n = 0; n < sheet.getRowCount(); n++) {
                row = sheet.getRow(n);
                cell = row.getCell(i);
                if ((cell != null) && !cell.isMissing())
                    unique.add(cell.getContent());
            }
            if ((unique.size() > m_MaxLabels) || (m_MaxLabels < 1)) {
                atts.add(new Attribute(sheet.getHeaderRow().getCell(i).getContent(), (FastVector) null));
            } else {
                labels = new ArrayList<>(unique);
                Collections.sort(labels);
                atts.add(new Attribute(sheet.getHeaderRow().getCell(i).getContent(), labels));
            }
        }
    }
    result = new Instances(Environment.getInstance().getProject(), atts, sheet.getRowCount());
    if (sheet.hasName())
        result.setRelationName(sheet.getName());

    // add data
    for (n = 0; n < sheet.getRowCount(); n++) {
        row = sheet.getRow(n);
        values = new double[result.numAttributes()];
        for (i = 0; i < result.numAttributes(); i++) {
            cell = row.getCell(i);
            values[i] = weka.core.Utils.missingValue();
            if ((cell != null) && !cell.isMissing()) {
                if (result.attribute(i).type() == Attribute.DATE) {
                    if (cell.isTime())
                        values[i] = cell.toTime().getTime();
                    else
                        values[i] = cell.toDate().getTime();
                } else if (result.attribute(i).isNumeric()) {
                    values[i] = Utils.toDouble(cell.getContent());
                } else if (result.attribute(i).isString()) {
                    values[i] = result.attribute(i).addStringValue(cell.getContent());
                } else {
                    values[i] = result.attribute(i).indexOfValue(cell.getContent());
                }
            }
        }
        inst = new DenseInstance(1.0, values);
        result.add(inst);
    }

    if (sheet instanceof Dataset) {
        classIndices = ((Dataset) sheet).getClassAttributeIndices();
        if (classIndices.length > 0)
            result.setClassIndex(classIndices[0]);
    }

    return result;
}

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

License:Open Source License

/**
 * Performs the actual conversion./*from  w ww.  j a va 2 s  .com*/
 *
 * @return      the converted data
 * @throws Exception   if something goes wrong with the conversion
 */
@Override
protected Object doConvert() throws Exception {
    Instances result;
    ArrayList<Attribute> atts;
    Instance inst;
    Timeseries series;
    TimeseriesPoint point;
    double[] value;

    series = (Timeseries) m_Input;

    atts = new ArrayList<Attribute>();
    atts.add(new Attribute("Timestamp", m_Format.getValue()));
    atts.add(new Attribute("Value"));

    result = new Instances(series.getID(), atts, series.size());
    for (Object obj : series.toList()) {
        point = (TimeseriesPoint) obj;
        value = new double[2];
        value[0] = point.getTimestamp().getTime();
        value[1] = point.getValue();
        inst = new DenseInstance(1.0, value);
        result.add(inst);
    }

    return result;
}

From source file:adams.data.featureconverter.Weka.java

License:Open Source License

/**
 * Performs the actual generation of a row from the raw data.
 * /* w  w  w . ja  v  a 2 s . co m*/
 * @param data   the data of the row, elements can be null (= missing)
 * @return      the dataset structure
 */
@Override
protected Instance doGenerateRow(List<Object> data) {
    Instance result;
    int i;
    Object obj;
    double[] values;

    values = new double[m_Header.numAttributes()];

    for (i = 0; i < data.size(); i++) {
        obj = data.get(i);
        if (obj == null) {
            values[i] = Utils.missingValue();
            continue;
        }
        switch (m_HeaderDefinition.getType(i)) {
        case BOOLEAN:
            values[i] = ((Boolean) obj) ? 0.0 : 1.0;
            break;
        case NUMERIC:
            values[i] = ((Number) obj).doubleValue();
            break;
        case STRING:
        case UNKNOWN:
            values[i] = m_Header.attribute(i).addStringValue(obj.toString());
            break;
        }
    }

    result = new DenseInstance(1.0, values);
    result.setDataset(m_Header);

    return result;
}

From source file:adams.data.instances.AbstractInstanceGenerator.java

License:Open Source License

/**
 * For adding IDs, notes, additional fields to the data.
 *
 * @param data   the input data/*from  w w  w .j av  a 2s.  c o m*/
 * @param inst   the generated instance
 * @return      the processed instance
 */
protected Instance postProcessOutput(T data, Instance inst) {
    Instance result;
    double[] values;
    int index;
    Report report;

    values = inst.toDoubleArray();
    report = data.getReport();

    if (m_AddDatabaseID) {
        index = m_OutputHeader.attribute(ArffUtils.getDBIDName()).index();
        values[index] = report.getDatabaseID();
    }

    result = new DenseInstance(1.0, values);
    result.setDataset(m_OutputHeader);

    return result;
}