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:machinelearning_cw.KNN.java

@Override
public double classifyInstance(Instance instance) throws Exception {
    // Check that classifier has been trained
    if (trainingData == null) {
        throw new Exception("Classifier has not been trained." + " No call to buildClassifier() was made");
    }/*from  w w  w  . j  a v  a 2s .  co m*/

    if (useStandardisedAttributes) {
        if (!isMeanAndStdDevInitialised) {
            // throw exception
        } else {
            /* Standardise test instance */
            for (int i = 0; i < instance.numAttributes() - 1; i++) {
                double value = instance.value(i);
                double standardisedValue = (value - mean[i]) / standardDeviation[i];
                instance.setValue(i, standardisedValue);
            }
        }
    }

    if (!useWeightedVoting) {
        return super.classifyInstance(instance);
    } else {

        if (!useAcceleratedNNSearch) {
            /* Calculate euclidean distances */
            double[] distances = Helpers.findEuclideanDistances(trainingData, instance);

            /* 
             * Create a list of dictionaries where each dictionary contains
             * the keys "distance", "weight" and "id".
             * The distance key stores the euclidean distance for an  
             * instance and the id key stores the hashcode for that 
             * instance object.
             */
            ArrayList<HashMap<String, Object>> table = Helpers.buildDistanceTable(trainingData, distances);

            /* Find the k smallest distances */
            Object[] kClosestRows = new Object[k];
            Object[] kClosestInstances = new Object[k];
            double[] classValues = new double[k];

            for (int i = 1; i <= k; i++) {
                ArrayList<Integer> tieIndices = new ArrayList<Integer>();

                /* Find the positions in the table of the ith closest 
                 * neighbour.
                 */
                int[] closestRowIndices = this.findNthClosestNeighbourByWeights(table, i);

                if (closestRowIndices.length > 0) {
                    /* Keep track of distance ties */
                    for (int j = 0; j < closestRowIndices.length; j++) {
                        tieIndices.add(closestRowIndices[j]);
                    }

                    /* Break ties (by choosing winner at random) */
                    Random rand = new Random();
                    int matchingNeighbourPosition = tieIndices.get(rand.nextInt(tieIndices.size()));
                    HashMap<String, Object> matchingRow = table.get(matchingNeighbourPosition);
                    kClosestRows[i - 1] = matchingRow;
                }
            }

            /* 
             * Find the closestInstances from their rows in the table and 
             * also get their class values.
             */
            for (int i = 0; i < kClosestRows.length; i++) {
                /* Build up closestInstances array */
                for (int j = 0; j < trainingData.numInstances(); j++) {
                    Instance inst = trainingData.get(j);
                    HashMap<String, Object> row = (HashMap<String, Object>) kClosestRows[i];
                    if (Integer.toHexString(inst.hashCode()).equals(row.get("id"))) {
                        kClosestInstances[i] = inst;
                    }
                }
            }

            /* Vote by weights */
            /* Get max class value */
            double[] possibleClassValues = trainingData.attributeToDoubleArray(trainingData.classIndex());
            int maxClassIndex = Utils.maxIndex(possibleClassValues);
            double maxClassValue = possibleClassValues[maxClassIndex];
            ArrayList<Double> weightedVotes = new ArrayList<Double>();

            /* Calculate the sum of votes for each class */
            for (double i = 0; i <= maxClassValue; i++) {
                double weightCount = 0;

                /* Calculate sum */
                for (int j = 0; j < kClosestInstances.length; j++) {
                    Instance candidateInstance = (Instance) kClosestInstances[j];
                    if (candidateInstance.classValue() == i) {
                        // Get weight
                        HashMap<String, Object> row = (HashMap<String, Object>) kClosestRows[(int) j];
                        weightCount += (double) row.get("weight");
                    }
                }

                weightedVotes.add(weightCount);
            }

            /* Select instance with highest vote */
            Double[] votesArray = new Double[weightedVotes.size()];
            weightedVotes.toArray(votesArray);
            double greatestSoFar = votesArray[0];
            int greatestIndex = 0;
            for (int i = 0; i < votesArray.length; i++) {
                if (votesArray[i] > greatestSoFar) {
                    greatestSoFar = votesArray[i];
                    greatestIndex = i;
                }
            }

            /* 
             * Class value will be the index because classes are indexed 
             * from 0 upwards.
             */
            return greatestIndex;

        }
        /* Use Orchards algorithm to accelerate NN search */
        else {
            // find k nearest neighbours
            ArrayList<Instance> nearestNeighbours = new ArrayList<Instance>();
            for (int i = 0; i < k; i++) {
                nearestNeighbours.add(findNthClosestWithOrchards(instance, trainingData, i));
            }

            // Find their class values
            double[] classValues = new double[nearestNeighbours.size()];

            for (int i = 0; i < nearestNeighbours.size(); i++) {
                classValues[i] = nearestNeighbours.get(i).classValue();
            }

            return Helpers.mode(Helpers.arrayToArrayList(classValues));
        }

    }

}

---

From source file:meka.core.MLUtils.java

License:Open Source License

/**
 * CopyValues - Set x_dest[j+offset] = x_src[i+from].
 */// w w w. j  a  v a 2s .  com
public static final Instance copyValues(Instance x_dest, Instance x_src, int from, int offset) {
    int d = x_src.numAttributes();
    for (int i = from, j = 0; i < d; i++, j++) {
        x_dest.setValue(j + offset, x_src.value(i));
    }
    return x_dest;
}

---

From source file:meka.experiment.statisticsexporters.WekaFilter.java

License:Open Source License

/**
 * Converts the Instances back into statistics.
 *
 * @param data          the data to convert
 * @return              the generated statistics
 *///from w  w  w . j a v a2 s  .co m
protected List<EvaluationStatistics> fromInstances(Instances data) {
    List<EvaluationStatistics> result;
    EvaluationStatistics stat;
    MultiLabelClassifier cls;
    String rel;
    int i;
    int n;
    Instance inst;

    result = new ArrayList<>();

    if (data.attribute(EvaluationStatistics.KEY_CLASSIFIER) == null) {
        log("Failed to locate attribute: " + EvaluationStatistics.KEY_CLASSIFIER);
        return result;
    }
    if (data.attribute(EvaluationStatistics.KEY_RELATION) == null) {
        log("Failed to locate attribute: " + EvaluationStatistics.KEY_RELATION);
        return result;
    }

    for (i = 0; i < data.numInstances(); i++) {
        inst = data.instance(i);
        try {
            cls = OptionUtils.fromCommandLine(MultiLabelClassifier.class,
                    inst.stringValue(data.attribute(EvaluationStatistics.KEY_CLASSIFIER)));
            rel = inst.stringValue(data.attribute(EvaluationStatistics.KEY_RELATION));
            stat = new EvaluationStatistics(cls, rel, null);
            for (n = 0; n < inst.numAttributes(); n++) {
                if (inst.attribute(n).isNumeric() && !inst.isMissing(n)) {
                    stat.put(inst.attribute(n).name(), inst.value(n));
                }
            }
            result.add(stat);
        } catch (Exception e) {
            handleException("Failed to process instance: " + inst, e);
        }
    }

    return result;
}

---

From source file:milk.classifiers.MINND.java

License:Open Source License

/**
 * Calculates the distance between two instances
 *
 * @param first the first instance/*from   w  w  w . j  a va  2  s. co m*/
 * @param second the second instance
 * @return the distance between the two given instances
 */
private double distance(Instance first, double[] mean, double[] var, int pos) {

    double diff, distance = 0;
    int j = 0;
    for (int i = 0; i < first.numAttributes(); i++) {
        // Skipp nominal attributes (incl. class & ID)
        if ((i == m_ClassIndex) || (i == m_IdIndex))
            continue;

        // If attribute is numeric
        if (first.attribute(i).isNumeric()) {
            if (!first.isMissing(i)) {
                diff = first.value(i) - mean[j];
                if (Utils.gr(var[j], m_ZERO))
                    distance += m_Change[pos][j] * var[j] * diff * diff;
                else
                    distance += m_Change[pos][j] * diff * diff;
            } else {
                if (Utils.gr(var[j], m_ZERO))
                    distance += m_Change[pos][j] * var[j];
                else
                    distance += m_Change[pos][j] * 1.0;
            }
        }
        j++;
    }

    return distance;
}

---

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 ww  w  . j a  v a 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;
        }/*  w  ww . j a v  a 2 s  .  c  o  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);
    }/*from   w w  w.ja  v a2 s . com*/

    // 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  v a  2s. c o  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  ww w .j  a  v a  2s  .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:moa.classifiers.bayes.NaiveBayes.java

License:Open Source License

@Override
public void trainOnInstanceImpl(Instance inst) {
    this.observedClassDistribution.addToValue((int) inst.classValue(), inst.weight());
    for (int i = 0; i < inst.numAttributes() - 1; i++) {
        int instAttIndex = modelAttIndexToInstanceAttIndex(i, inst);
        AttributeClassObserver obs = this.attributeObservers.get(i);
        if (obs == null) {
            obs = inst.attribute(instAttIndex).isNominal() ? newNominalClassObserver()
                    : newNumericClassObserver();
            this.attributeObservers.set(i, obs);
        }//from  www.j av  a2s .c o m
        obs.observeAttributeClass(inst.value(instAttIndex), (int) inst.classValue(), inst.weight());
    }
}

---

• «
• 1
• 2
• 3
• 4
• 5
• 6
• 7
• 8
• 9
• 10
• 11
• 12
• 13
• 14
• 15
• 16
• 17
• 18
• 19
• 20
• 21
• 22
• 23
• 24
• 25
• 26
• 27
• »