Example usage for weka.core Instance classValue

Introduction

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

Prototype

public double classValue();

Source Link

Document

Returns an instance's class value as a floating-point number.

Usage

From source file:edu.oregonstate.eecs.mcplan.abstraction.PairDataset.java

License:Open Source License

public static <S, X extends FactoredRepresentation<S>, A extends VirtualConstructor<A>> ArrayList<PairInstance> makePairDataset(
        final RandomGenerator rng, final int max_pairwise_instances, final Instances single) {
    final ReservoirSampleAccumulator<PairInstance> negative = new ReservoirSampleAccumulator<PairInstance>(rng,
            max_pairwise_instances);//from   w  w  w.j a v  a2  s .  com
    final ReservoirSampleAccumulator<PairInstance> positive = new ReservoirSampleAccumulator<PairInstance>(rng,
            max_pairwise_instances);

    for (int i = 0; i < single.size(); ++i) {
        for (int j = i + 1; j < single.size(); ++j) {
            final Instance ii = single.get(i);
            final Instance ij = single.get(j);
            final int label;
            if (ii.classValue() == ij.classValue()) {
                label = 1;
                if (positive.acceptNext()) {
                    final PairInstance pair_instance = new PairInstance(ii.toDoubleArray(), ij.toDoubleArray(),
                            label);
                    positive.addPending(pair_instance);
                }
            } else {
                label = 0;
                if (negative.acceptNext()) {
                    final PairInstance pair_instance = new PairInstance(ii.toDoubleArray(), ij.toDoubleArray(),
                            label);
                    negative.addPending(pair_instance);
                }
            }
        }
    }

    final ArrayList<PairInstance> result = new ArrayList<PairInstance>(negative.n() + positive.n());
    result.addAll(negative.samples());
    result.addAll(positive.samples());
    return result;
}

---

From source file:edu.oregonstate.eecs.mcplan.abstraction.PairDataset.java

License:Open Source License

public static <S, X extends FactoredRepresentation<S>, A extends VirtualConstructor<A>> Instances makePairDataset(
        final RandomGenerator rng, final int max_pairwise_instances, final Instances single,
        final InstanceCombiner combiner) {
    //      final int max_pairwise = config.getInt( "training.max_pairwise" );
    final ReservoirSampleAccumulator<Instance> negative = new ReservoirSampleAccumulator<Instance>(rng,
            max_pairwise_instances);//from   w  w  w.  j  a  va2 s  .  c o  m
    final ReservoirSampleAccumulator<Instance> positive = new ReservoirSampleAccumulator<Instance>(rng,
            max_pairwise_instances);

    for (int i = 0; i < single.size(); ++i) {
        //         if( i % 100 == 0 ) {
        //            System.out.println( "i = " + i );
        //         }
        for (int j = i + 1; j < single.size(); ++j) {
            final Instance ii = single.get(i);
            final Instance ij = single.get(j);
            final int label;
            if (ii.classValue() == ij.classValue()) {
                label = 1;
                if (positive.acceptNext()) {
                    final Instance pair_instance = combiner.apply(ii, ij, label);
                    positive.addPending(pair_instance);
                }
            } else {
                label = 0;
                if (negative.acceptNext()) {
                    final Instance pair_instance = combiner.apply(ii, ij, label);
                    negative.addPending(pair_instance);
                }
            }
        }
    }

    final int N = Math.min(negative.samples().size(), positive.samples().size());
    final String dataset_name = "train_" + combiner.keyword() + "_" + max_pairwise_instances;
    final Instances x = new Instances(dataset_name, combiner.attributes(), 2 * N);
    x.setClassIndex(x.numAttributes() - 1);
    for (final Instance ineg : negative.samples()) {
        x.add(ineg);
    }
    for (final Instance ipos : positive.samples()) {
        x.add(ipos);
    }

    return x;
    //      return new PairDataset( x, combiner );
}

---

From source file:edu.oregonstate.eecs.mcplan.abstraction.PairDataset.java

License:Open Source License

/**
 * Constructs one positive pair and one negative pair involving each
 * data point in 'single'./*from   www  . java  2s. com*/
 * @param rng
 * @param max_pairwise_instances
 * @param single
 * @param combiner
 * @return
 */
public static <S, X extends FactoredRepresentation<S>, A extends VirtualConstructor<A>> PairDataset makeBalancedPairDataset(
        final RandomGenerator rng, final int negative_per_instance, final int positive_per_instance,
        final Instances single, final InstanceCombiner combiner) {
    final int Nnegative = negative_per_instance * single.size();
    final int Npositive = positive_per_instance * single.size();
    //      final int max_pairwise = config.getInt( "training.max_pairwise" );
    final ReservoirSampleAccumulator<Pair<Instance, int[]>> negative = new ReservoirSampleAccumulator<Pair<Instance, int[]>>(
            rng, Nnegative);
    final ReservoirSampleAccumulator<Pair<Instance, int[]>> positive = new ReservoirSampleAccumulator<Pair<Instance, int[]>>(
            rng, Npositive);

    for (int i = 0; i < single.size(); ++i) {
        //         if( i % 100 == 0 ) {
        //            System.out.println( "i = " + i );
        //         }
        for (int j = i + 1; j < single.size(); ++j) {
            final Instance ii = single.get(i);
            final Instance ij = single.get(j);
            final int label;
            if (ii.classValue() == ij.classValue()) {
                label = 1;
                if (positive.acceptNext()) {
                    final Instance pair_instance = combiner.apply(ii, ij, label);
                    positive.addPending(Pair.makePair(pair_instance, new int[] { i, j }));
                }
            } else {
                label = 0;
                if (negative.acceptNext()) {
                    final Instance pair_instance = combiner.apply(ii, ij, label);
                    negative.addPending(Pair.makePair(pair_instance, new int[] { i, j }));
                }
            }
        }
    }

    final int N = Math.min(negative.samples().size(), positive.samples().size());
    final String dataset_name = "train_" + combiner.keyword() + "_" + Nnegative + "x" + Npositive;
    final Instances x = new Instances(dataset_name, combiner.attributes(), Nnegative + Npositive);
    x.setClassIndex(x.numAttributes() - 1);
    final ArrayList<int[]> matches = new ArrayList<int[]>();
    for (final Pair<Instance, int[]> ineg : negative.samples()) {
        WekaUtil.addInstance(x, ineg.first);
        matches.add(ineg.second);
    }
    for (final Pair<Instance, int[]> ipos : positive.samples()) {
        WekaUtil.addInstance(x, ipos.first);
        matches.add(ipos.second);
    }

    return new PairDataset(x, matches, combiner);
}

---

From source file:edu.oregonstate.eecs.mcplan.abstraction.PairDataset.java

License:Open Source License

/**
 * Constructs one positive pair and one negative pair involving each
 * data point in 'single'.// w  w  w.ja v a2s.  c  om
 * @param rng
 * @param max_pairwise_instances
 * @param single
 * @param combiner
 * @return
 */
public static <S, X extends FactoredRepresentation<S>, A extends VirtualConstructor<A>> PairDataset makePlausiblePairDataset(
        final RandomGenerator rng, final int negative_per_instance, final int positive_per_instance,
        final Instances single, final InstanceCombiner combiner,
        final Fn.Function2<Boolean, Instance, Instance> plausible_p) {
    final int Nnegative = negative_per_instance * single.size();
    final int Npositive = positive_per_instance * single.size();
    //      final int max_pairwise = config.getInt( "training.max_pairwise" );
    final ReservoirSampleAccumulator<Pair<Instance, int[]>> negative = new ReservoirSampleAccumulator<Pair<Instance, int[]>>(
            rng, Nnegative);
    final ReservoirSampleAccumulator<Pair<Instance, int[]>> positive = new ReservoirSampleAccumulator<Pair<Instance, int[]>>(
            rng, Npositive);

    for (int i = 0; i < single.size(); ++i) {
        //         if( i % 100 == 0 ) {
        //            System.out.println( "i = " + i );
        //         }
        for (int j = i + 1; j < single.size(); ++j) {
            final Instance ii = single.get(i);
            final Instance ij = single.get(j);

            if (!plausible_p.apply(ii, ij)) {
                //               System.out.println( "Not plausible: " + ii + " != " + ij );
                continue;
            }

            //            System.out.println( "! Plausible: " + ii + " == " + ij );

            final int label;
            if (ii.classValue() == ij.classValue()) {
                label = 1;
                if (positive.acceptNext()) {
                    final Instance pair_instance = combiner.apply(ii, ij, label);
                    positive.addPending(Pair.makePair(pair_instance, new int[] { i, j }));
                }
            } else {
                label = 0;
                if (negative.acceptNext()) {
                    final Instance pair_instance = combiner.apply(ii, ij, label);
                    negative.addPending(Pair.makePair(pair_instance, new int[] { i, j }));
                }
            }
        }
    }

    final int N = Math.min(negative.samples().size(), positive.samples().size());
    final String dataset_name = "train_" + combiner.keyword() + "_" + negative.samples().size() + "x"
            + positive.samples().size();
    final Instances x = new Instances(dataset_name, combiner.attributes(), Nnegative + Npositive);
    x.setClassIndex(x.numAttributes() - 1);
    final ArrayList<int[]> matches = new ArrayList<int[]>();
    for (final Pair<Instance, int[]> ineg : negative.samples()) {
        WekaUtil.addInstance(x, ineg.first);
        matches.add(ineg.second);
    }
    for (final Pair<Instance, int[]> ipos : positive.samples()) {
        WekaUtil.addInstance(x, ipos.first);
        matches.add(ipos.second);
    }

    return new PairDataset(x, matches, combiner);
}

---

From source file:edu.oregonstate.eecs.mcplan.abstraction.WekaUtil.java

License:Open Source License

public static Pair<ArrayList<double[]>, int[]> splitLabels(final Instances train) {
    assert (train.classAttribute() != null);

    final ArrayList<double[]> X = new ArrayList<double[]>();
    final int[] Y = new int[train.size()];

    for (int i = 0; i < train.size(); ++i) {
        final Instance inst = train.get(i);
        final double[] x = new double[train.numAttributes() - 1];
        int idx = 0;
        for (int j = 0; j < train.numAttributes(); ++j) {
            if (j == train.classIndex()) {
                Y[i] = (int) inst.classValue();
            } else {
                x[idx++] = inst.value(j);
            }//from w  w w. j  a va2s  .c  o  m
        }
        X.add(x);
    }

    return Pair.makePair(X, Y);
}

---

From source file:edu.oregonstate.eecs.mcplan.abstraction.WekaUtil.java

License:Open Source License

public static Instances powerSet(final Instances D, final int n) {
    final Attribute class_attr = D.classAttribute();

    final ImmutableSet.Builder<Integer> b = new ImmutableSet.Builder<Integer>();
    final int Nattr = class_attr != null ? D.numAttributes() - 1 : D.numAttributes();
    for (final int i : Fn.range(1, Nattr)) {
        b.add(i);/* www.j  ava2s . co  m*/
    }
    final Set<Set<Integer>> index = Sets.powerSet(b.build());

    final ArrayList<Attribute> attributes = new ArrayList<Attribute>();
    for (final Set<Integer> subset : index) {
        if (subset.isEmpty() || subset.size() > n) {
            continue;
        }

        final StringBuilder attr_name = new StringBuilder();
        int count = 0;
        for (final Integer i : subset) {
            if (count++ > 0) {
                attr_name.append("_x_");
            }
            attr_name.append(D.attribute(i).name());
        }

        attributes.add(new Attribute(attr_name.toString()));
    }
    if (class_attr != null) {
        assert (class_attr.isNominal());
        attributes.add(WekaUtil.createNominalAttribute(class_attr.name(), class_attr.numValues()));
    }

    final String Pname = "P" + n + "_" + D.relationName();
    final Instances P = new Instances(Pname, attributes, 0);
    if (class_attr != null) {
        P.setClassIndex(attributes.size() - 1);
    }

    for (final Instance inst : D) {
        final double[] xp = new double[attributes.size()];
        int idx = 0;
        for (final Set<Integer> subset : index) {
            if (subset.isEmpty() || subset.size() > n) {
                continue;
            }

            double p = 1.0;
            for (final Integer i : subset) {
                p *= inst.value(i);
            }
            xp[idx++] = p;
        }
        if (class_attr != null) {
            xp[idx++] = inst.classValue();
        }

        WekaUtil.addInstance(P, new DenseInstance(inst.weight(), xp));
    }

    return P;
}

---

From source file:edu.oregonstate.eecs.mcplan.domains.frogger.FroggerRepresentationConverter.java

License:Open Source License

public static Instances absoluteToRelative(final FroggerParameters params, final Instances src,
        final int vision) {
    final ArrayList<Attribute> attributes = new ArrayList<Attribute>();
    attributes.add(new Attribute("x"));
    attributes.add(new Attribute("y"));
    for (int i = vision; i >= -vision; --i) {
        for (int j = -vision; j <= vision; ++j) {
            if (i == 0 && j == 0) {
                continue;
            }/*from w w  w  .j a v  a2  s. co m*/

            final String name = "car_x" + (j >= 0 ? "+" : "") + j + "_y" + (i >= 0 ? "+" : "") + i;
            attributes.add(new Attribute(name));
        }
    }
    attributes.add(src.classAttribute());

    final Instances dest = new Instances(src.relationName() + "_relative", attributes, src.size());
    for (final Instance inst : src) {
        final double[] phi = new double[attributes.size()];
        int idx = 0;

        final int x = (int) inst.value(0);
        final int y = (int) inst.value(1);
        phi[idx++] = x;
        phi[idx++] = y;

        for (int i = vision; i >= -vision; --i) {
            for (int j = -vision; j <= vision; ++j) {
                if (i == 0 && j == 0) {
                    continue;
                }

                final int xoff = x + j;
                final int yoff = y + i;

                if (xoff >= 0 && xoff < params.road_length && yoff >= 1 && yoff <= params.lanes) {
                    final int car = (int) inst.value(2 + (yoff - 1) * params.road_length + xoff);
                    phi[idx] = car; // s.grid[dy][dx] == Tile.Car ? 1.0 : 0.0; // fv[2 + (dy-1)*road_length + dx]
                }
                idx += 1;
            }
        }

        phi[idx++] = inst.classValue();
        assert (idx == phi.length);

        WekaUtil.addInstance(dest, new DenseInstance(inst.weight(), phi));
    }

    return dest;
}

---

From source file:edu.oregonstate.eecs.mcplan.ml.WekaGlue.java

License:Open Source License

public static SequentialProjectionHashLearner createSequentialProjectionHashLearner(final RandomGenerator rng,
        final Instances labeled, final Instances unlabeled, final int K, final double eta, final double alpha) {
    assert (labeled.classIndex() >= 0);
    final int Nfeatures = labeled.numAttributes() - 1;

    final RealMatrix X = new Array2DRowRealMatrix(Nfeatures, labeled.size() + unlabeled.size());
    final RealMatrix XL = new Array2DRowRealMatrix(Nfeatures, labeled.size() * 2);
    final RealMatrix S = new Array2DRowRealMatrix(XL.getColumnDimension(), XL.getColumnDimension());

    for (int j = 0; j < labeled.size(); ++j) {
        final Instance inst = labeled.get(j);
        for (int i = 0; i < XL.getRowDimension(); ++i) {
            X.setEntry(i, j, inst.value(i));
            XL.setEntry(i, j, inst.value(i));
        }//from  w  w w  .  jav a  2  s .co  m

        int sj = -1;
        Instance s = null;
        do {
            sj = rng.nextInt(labeled.size());
            s = labeled.get(sj);
        } while (s == inst || s.classValue() != inst.classValue());
        S.setEntry(j, sj, 1);

        int dj = -1;
        Instance d = null;
        do {
            dj = rng.nextInt(labeled.size());
            d = labeled.get(dj);
        } while (d == inst || d.classValue() == inst.classValue());
        S.setEntry(j, dj, -1);
    }

    for (int j = 0; j < unlabeled.size(); ++j) {
        final Instance inst = unlabeled.get(j);
        for (int i = 0; i < X.getRowDimension(); ++i) {
            X.setEntry(i, labeled.size() + j, inst.value(i));
        }
    }

    return new SequentialProjectionHashLearner(X, XL, S, K, eta, alpha);
}

---

From source file:edu.umbc.cs.maple.utils.WekaUtils.java

License:Open Source License

/** Determines whether a data set has equal class priors.
 * @param data/*from   w  ww . j a  v  a2  s .  c  o  m*/
 * @return whether the data set has equal class priors
 */
public static boolean equalClassPriors(Instances data) {
    double[] counts = new double[data.numClasses()];
    int numInstances = data.numInstances();
    for (int i = 0; i < numInstances; i++) {
        Instance inst = data.instance(i);
        int classValueIdx = (int) Math.round(inst.classValue());
        counts[classValueIdx] = counts[classValueIdx] + 1;
    }

    // compute the mean
    double meanCount = MathUtils.sum(counts) / counts.length;
    double[] meanArray = new double[counts.length];
    for (int i = 0; i < meanArray.length; i++) {
        meanArray[i] = meanCount;
    }

    // compute the rmse
    double rmse = MathUtils.rmse(meanArray, counts);

    // compute 2.5% of the possible 
    double deviationAllowed = Math.ceil(0.025 * meanCount);

    if (rmse <= deviationAllowed)
        return true;
    else
        return false;
}

---

From source file:edu.umbc.cs.maple.utils.WekaUtils.java

License:Open Source License

/** Converts a set of instances to svm-light format
 * @param data the weka instances/*w  w  w .j  av  a2 s .  co m*/
 * @return the weka instances in svm-light format
 */
public static String arffToSVMLight(Instances data, SVMLightLabelFormat labelFormat) {

    if (labelFormat == SVMLightLabelFormat.CLASSIFICATION && data.numClasses() != 2) {
        throw new IllegalArgumentException(
                "SVM-light classification label format requires that the data contain only two classes.");
    }

    String str = "";
    String endline = System.getProperty("line.separator");

    int numInstances = data.numInstances();
    int numAttributes = data.numAttributes();
    int classAttIdx = data.classIndex();

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

        Instance inst = data.instance(instIdx);

        // convert the instance label
        if (labelFormat == SVMLightLabelFormat.CLASSIFICATION) {
            str += (inst.classValue() == 0) ? "-1" : "1";
        } else {
            str += inst.classValue();
        }

        str += " ";

        // convert each feature
        for (int attIdx = 0; attIdx < numAttributes; attIdx++) {
            // skip the class attribute
            if (attIdx == classAttIdx)
                continue;
            str += (attIdx + 1) + ":" + inst.value(attIdx) + " ";
        }

        // append the instance info string
        str += "# " + instIdx;

        str += endline;
    }

    return str;
}

---

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