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:milk.visualize.PlotPanel.java

License:Open Source License

/**
 * Draws the distribution/*from w ww  .j a  va 2s .  c  om*/
 * @param gx the graphics context
 */
private void paintData(Graphics gx) {
    if ((plotExemplars == null) || (x == null))
        return;

    setFonts(gx);
    int w = this.getWidth();
    boolean[] xlabels = new boolean[w];

    Attribute classAtt = plotExemplars.classAttribute(), id = plotExemplars.idAttribute();
    int hf = m_labelMetrics.getAscent();

    for (int i = 0; i < plotExemplars.numExemplars(); i++) {
        Exemplar ex = plotExemplars.exemplar(i);
        if (classAtt.isNominal())
            gx.setColor((Color) colorList.elementAt((int) ex.classValue()));
        else {
            double r = (ex.classValue() - m_minC) / (m_maxC - m_minC);
            r = (r * 240) + 15;
            gx.setColor(new Color((int) r, 150, (int) (255 - r)));
        }

        double preY = 0;
        for (int j = 0; j < ex.getInstances().numInstances(); j++) {
            Instance ins = ex.getInstances().instance(j);
            double xValue = convertToAttribX(m_XaxisStart);
            double tmp = -(xValue - ins.value(x.index)) * (xValue - ins.value(x.index))
                    / (2.0 * stdDev * stdDev);
            preY += Math.exp(tmp) * ins.weight();
        }
        preY /= ex.getInstances().sumOfWeights();
        preY *= maxY;

        for (int k = m_XaxisStart + 1; k < m_XaxisEnd; k++) {
            double currY = 0;
            for (int l = 0; l < ex.getInstances().numInstances(); l++) {
                Instance ins = ex.getInstances().instance(l);
                double xValue = convertToAttribX(k);
                double tmp = -(xValue - ins.value(x.index)) * (xValue - ins.value(x.index))
                        / (2.0 * stdDev * stdDev);
                currY += Math.exp(tmp) * ins.weight();
            }
            currY /= ex.getInstances().sumOfWeights();
            currY *= maxY;

            // Draw the distribution         
            int plotPreY = (int) convertToPanelY(preY);
            int plotCurrY = (int) convertToPanelY(currY);
            gx.drawLine(k - 1, plotPreY, k, plotCurrY);

            // If peak or valley appears, specify the x value
            if (isUp && (preY > currY)) {
                Font old = gx.getFont();
                gx.setFont(new Font("Monospaced", Font.PLAIN, 10));
                String idvalue = Integer.toString((int) ex.idValue());
                gx.drawString(idvalue, k - 1 - m_labelMetrics.stringWidth(idvalue) / 2, plotCurrY);
                xlabels[k - 1] = true;
                gx.setFont(old);
            }
            isUp = (currY >= preY);
            preY = currY;
        }
    }

    // Draw the number labels on x axis where various peaks gather
    gx.setColor(m_axisColour);
    int start = 0, end = 0;
    while (start < w) {
        if (xlabels[start]) {
            end = start;
            int falseCount = 0;
            while (end < w) {
                while (xlabels[end++])
                    ; // Find the first false from start
                int m = end;
                // Count the number of falses
                for (falseCount = 0; (m < xlabels.length) && (!xlabels[m]); m++, falseCount++)
                    ;

                if ((falseCount < 28) && (m < xlabels.length))
                    end = m;
                else
                    break;
            }
            if (!xlabels[end])
                --end;

            int avg = (start + end) / 2;
            double xValue = convertToAttribX(avg);
            String stringX = Utils.doubleToString(xValue, 1);
            Font old = gx.getFont();
            gx.setFont(new Font("Monospaced", Font.PLAIN, 10));
            gx.drawString(stringX, avg - m_labelMetrics.stringWidth(stringX) / 2, m_YaxisEnd + hf + m_tickSize);
            gx.drawLine(avg, m_YaxisEnd, avg, m_YaxisEnd + m_tickSize);
            gx.setFont(old);
            start = end;
        } else
            ++start;
    }
}

---

From source file:minorpro.BitsUtility.java

public static String convertToBits(Instance ins) {
    String bits = "";
    for (int i = 0; i < ins.numAttributes() - 1; i++) {
        double d = 0, a = ins.value(i);
        switch (i) {
        case 0:/*from  www.  j ava  2s.  c  o m*/
            d = (a + 7) / 2;
            break;
        case 1:
            d = a - 4;
            break;
        case 2:
        case 3:
            d = a;
            break;
        }

        bits = bits + intToBits(d);
    }
    return bits;
}

---

From source file:ml.ann.BackPropagation.java

License:Open Source License

private void initInputAndTarget(Instance instance) {
    int classAttributeIdx = neuronTopology.instances.classIndex();
    if (neuronTopology.instances.classAttribute().isNumeric()) {
        neuronTopology.target = new double[1];
        neuronTopology.target[0] = instance.value(classAttributeIdx);
    } else if (neuronTopology.instances.classAttribute().isNominal()) {
        neuronTopology.target = new double[instance.numClasses()];
        for (int i = 0; i < instance.numClasses(); i++) {
            neuronTopology.target[i] = 0;
        }//from  www.  j  av  a  2  s. co  m
        int idxClassValue = (int) instance.classValue();
        neuronTopology.target[idxClassValue] = 1;
    }
    for (int i = 0; i < instance.numAttributes(); i++) {
        if (i == 0) {
            neuronTopology.input[i] = 1;
            neuronTopology.output[0][i] = 1;
        } else {
            neuronTopology.input[i] = instance.value(i - 1);
            neuronTopology.output[0][i] = instance.value(i - 1);
        }
    }
    //        System.out.println(neuronTopology.originInstances.instance(instancesIdx).toString());
}

---

From source file:ml.ann.BackPropagation.java

License:Open Source License

@Override
public double[] distributionForInstance(Instance instance) throws Exception {

    Instance currentInstance;
    // default model?
    if (useDefaultModel) {
        return zeroR.distributionForInstance(instance);
    }/*w  ww  . j  a  v  a 2 s  .c o m*/

    // Make a copy of the instance so that it isn't modified
    currentInstance = (Instance) instance.copy();

    for (int noa = 0; noa < currentInstance.numAttributes(); noa++) {
        if (noa != neuronTopology.instances.classIndex()) {
            if (attributeRanges[noa] != 0) {
                currentInstance.setValue(noa,
                        (currentInstance.value(noa) - attributeBases[noa]) / attributeRanges[noa]);
            } else {
                currentInstance.setValue(noa, currentInstance.value(noa) - attributeBases[noa]);
            }
        }
    }
    //        resetNetwork();

    //        initInputAndTarget(currentInstance);

    int lastLayerIdx = neuronTopology.numLayers - 1;
    for (int layers = 0; layers < neuronTopology.numLayers; layers++) {
        for (int neu = 0; neu < neuronTopology.numNeuronEachLayer[layers]; neu++) {
            neuronTopology.output[layers][neu] = sigmoidFunction(layers, neu);
        }
    }

    double[] jancuk = new double[neuronTopology.numNeuronEachLayer[lastLayerIdx]];

    for (int i = 0; i < neuronTopology.numNeuronEachLayer[lastLayerIdx]; i++) {
        jancuk[i] = neuronTopology.output[lastLayerIdx][i];
    }

    // now normalize the array
    double count = 0;
    for (int noa = 0; noa < neuronTopology.numClasses; noa++) {
        count += jancuk[noa];
    }
    if (count <= 0) {
        //            return zeroR.distributionForInstance(i);
    }
    for (int noa = 0; noa < neuronTopology.numClasses; noa++) {
        jancuk[noa] /= count;
    }
    return jancuk;
}

---

From source file:ml.ann.MultiClassPTR.java

@Override
public void buildClassifier(Instances instances) throws Exception {
    initAttributes(instances);/*w w  w.  j a va2  s  .  co m*/

    // REMEMBER: only works if class index is in the last position
    for (int instanceIdx = 0; instanceIdx < instances.numInstances(); instanceIdx++) {
        Instance instance = instances.get(instanceIdx);
        double[] inputInstance = inputInstances[instanceIdx];
        inputInstance[0] = 1.0; // initialize bias value
        for (int attrIdx = 0; attrIdx < instance.numAttributes() - 1; attrIdx++) {
            inputInstance[attrIdx + 1] = instance.value(attrIdx); // the first index of input instance is for bias
        }
    }

    // Initialize target values
    if (instances.classAttribute().isNominal()) {
        for (int instanceIdx = 0; instanceIdx < instances.numInstances(); instanceIdx++) {
            Instance instance = instances.instance(instanceIdx);
            for (int classIdx = 0; classIdx < instances.numClasses(); classIdx++) {
                targetInstances[instanceIdx][classIdx] = 0.0;
            }
            targetInstances[instanceIdx][(int) instance.classValue()] = 1.0;
        }
    } else {
        for (int instanceIdx = 0; instanceIdx < instances.numInstances(); instanceIdx++) {
            Instance instance = instances.instance(instanceIdx);
            targetInstances[instanceIdx][0] = instance.classValue();
        }
    }

    if (algo == 1) {
        setActFunction();
        buildClassifier();
    } else if (algo == 2) {
        buildClassifier();
    } else if (algo == 3) {
        buildClassifierBatch();
    }
}

---

From source file:ml.ann.MultiClassPTR.java

@Override
public double[] distributionForInstance(Instance instance) throws Exception {
    double[] input = new double[numInput]; // remember the first index use for bias input
    input[0] = 1.0;/*from w  w  w. j  a v a2s.c  o m*/
    for (int attrIdx = 0; attrIdx < instance.numAttributes() - 1; attrIdx++) { // we ignore the class value
        input[attrIdx + 1] = instance.value(attrIdx);
    }

    double[] output = new double[numOutput];
    double totalVal = 0.0;
    System.out.println("=========================");
    for (int outputIdx = 0; outputIdx < numOutput; outputIdx++) {
        output[outputIdx] = 0.0;
        for (int inputIdx = 0; inputIdx < numInput; inputIdx++) {
            output[outputIdx] += input[inputIdx] * weight[inputIdx][outputIdx];
        }
        output[outputIdx] = actFunction(output[outputIdx]);
        System.out.print(output[outputIdx] + " ");
        totalVal += Math.exp(output[outputIdx]);
    }
    for (int out = 0; out < numOutput; out++) {
        output[out] = Math.exp(output[out]) / totalVal;
    }
    return output;
}

---

From source file:mlda.attributes.MeanKurtosis.java

License:Open Source License

/**
 * Calculate metric value//  ww w  .j a v  a 2s  .c o m
 * 
 * @param mlData Multi-label dataset to which calculate the metric
 * @return Value of the metric
 */
public double calculate(MultiLabelInstances mlData) {
    Instances instances = mlData.getDataSet();
    int nInstances = mlData.getNumInstances();

    double avg;
    double var2;
    double var4;
    double val;
    int nNumeric = 0;
    double mean = 0;

    Set<Attribute> attributesSet = mlData.getFeatureAttributes();

    for (Attribute att : attributesSet) {
        if (att.isNumeric()) {
            nNumeric++;
            avg = instances.meanOrMode(att);
            var2 = 0;
            var4 = 0;

            for (Instance inst : instances) {
                val = inst.value(att);
                var2 += Math.pow(val - avg, 2);
                var4 += Math.pow(val - avg, 4);
            }

            double kurtosis = (nInstances * var4 / Math.pow(var2, 2)) - 3;
            double sampleKurtosis = (kurtosis * (nInstances + 1) + 6) * (nInstances - 1)
                    / ((nInstances - 2) * (nInstances - 3));
            mean += sampleKurtosis;
        }
    }
    if (nNumeric > 0) {
        mean = mean / nNumeric;
    } else {
        mean = Double.NaN;
    }

    this.value = mean;
    return value;
}

---

From source file:mlda.attributes.MeanSkewnessNumericAttributes.java

License:Open Source License

/**
 * Calculate metric value// www .j  a  va2s .  c  om
 * 
 * @param mlData Multi-label dataset to which calculate the metric
 * @return Value of the metric
 */
public double calculate(MultiLabelInstances mlData) {
    Instances instances = mlData.getDataSet();
    int nInstances = mlData.getNumInstances();

    Set<Attribute> attributesSet = mlData.getFeatureAttributes();

    int nNumeric = 0;
    double mean = 0;
    double avg;
    double var;
    double stdev;

    for (Attribute att : attributesSet) {
        if (att.isNumeric()) {
            nNumeric++;
            avg = instances.meanOrMode(att);
            var = 0;
            for (Instance inst : instances) {
                var += Math.pow(inst.value(att) - avg, 3);
            }
            stdev = Math.sqrt(instances.variance(att));
            mean += nInstances * var / ((nInstances - 1) * (nInstances - 2) * Math.pow(stdev, 3));
        }
    }

    if (nNumeric > 0) {
        this.value = mean / nNumeric;
    } else {
        this.value = Double.NaN;
    }

    //this.value = mean;
    return value;
}

---

From source file:mlda.labelsRelation.SCUMBLE.java

License:Open Source License

/**
 * Calculate metric value//from w w w.j a  va  2 s .c  om
 * 
 * @param mlData Multi-label dataset to which calculate the metric
 * @return Value of the metric
 */
public double calculate(MultiLabelInstances mlData) {

    double SCUMBLE = 0.0;

    //        ImbalancedFeature [] imbalanced_data =  Utils.getImbalancedWithIR(mlData, Utils.getSortedByFrequency(Utils.getAppearancesPerLabel(mlData)));
    //        
    //        ImbalancedFeature[] new_imbalanced_data = new ImbalancedFeature[imbalanced_data.length];
    //        
    //        for(int i=0; i<imbalanced_data.length; i++){
    //            for(int j=0; j<imbalanced_data.length; j++){
    //                if(mlData.getLabelNames()[i].equals(imbalanced_data[j].getName())){
    //                    new_imbalanced_data[i] = imbalanced_data[j];
    //                }
    //            }
    //        }

    double[] ir = getIRperLabel(mlData);

    int nLabels = mlData.getNumLabels();
    Instances instances = mlData.getDataSet();
    double IRLmean = 0;
    int nActive = 0;
    double prod = 1;
    double sum = 0;

    int[] labelIndices = mlData.getLabelIndices();

    for (Instance inst : instances) {
        IRLmean = 0;
        prod = 1;
        nActive = 0;

        for (int l = 0; l < nLabels; l++) {
            if (inst.value(labelIndices[l]) == 1) {
                prod *= ir[l];
                IRLmean += ir[l];
                nActive++;
            }
            //              else{
            //                 prod *= 0;
            //              }              
        }

        if (nActive == 0) {
            sum += 0;
        } else {
            IRLmean /= nActive;

            //               System.out.println(IRLmean);

            sum += 1 - (Math.pow(prod, 1.0 / nActive) / IRLmean);
        }

    }

    SCUMBLE = sum / mlData.getNumInstances();

    this.value = SCUMBLE;
    return value;
}

---

From source file:mlda.labelsRelation.SCUMBLE.java

License:Open Source License

public int[] getAppearances(MultiLabelInstances mlData) {
    int[] appearances = new int[mlData.getNumLabels()];
    int[] labelIndices = mlData.getLabelIndices();

    for (Instance instance : mlData.getDataSet()) {
        for (int label = 0; label < mlData.getNumLabels(); label++) {
            if (instance.value(labelIndices[label]) == 1) {
                appearances[label]++;/*from w  ww .  j  a v a2s  .c  om*/
            }
        }
    }

    return appearances;
}

---

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