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:dkpro.similarity.experiments.sts2013baseline.util.Evaluator.java

License:Open Source License

public static void runLinearRegressionCV(Mode mode, Dataset... datasets) throws Exception {
    for (Dataset dataset : datasets) {
        // Set parameters
        int folds = 10;
        Classifier baseClassifier = new LinearRegression();

        // Set up the random number generator
        long seed = new Date().getTime();
        Random random = new Random(seed);

        // Add IDs to the instances
        AddID.main(new String[] { "-i",
                MODELS_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".arff", "-o",
                MODELS_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString()
                        + "-plusIDs.arff" });

        String location = MODELS_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString()
                + "-plusIDs.arff";

        Instances data = DataSource.read(location);

        if (data == null) {
            throw new IOException("Could not load data from: " + location);
        }//from  w  w  w  .j  a va 2s  . c o  m

        data.setClassIndex(data.numAttributes() - 1);

        // Instantiate the Remove filter
        Remove removeIDFilter = new Remove();
        removeIDFilter.setAttributeIndices("first");

        // Randomize the data
        data.randomize(random);

        // Perform cross-validation
        Instances predictedData = null;
        Evaluation eval = new Evaluation(data);

        for (int n = 0; n < folds; n++) {
            Instances train = data.trainCV(folds, n, random);
            Instances test = data.testCV(folds, n);

            // Apply log filter
            Filter logFilter = new LogFilter();
            logFilter.setInputFormat(train);
            train = Filter.useFilter(train, logFilter);
            logFilter.setInputFormat(test);
            test = Filter.useFilter(test, logFilter);

            // Copy the classifier
            Classifier classifier = AbstractClassifier.makeCopy(baseClassifier);

            // Instantiate the FilteredClassifier
            FilteredClassifier filteredClassifier = new FilteredClassifier();
            filteredClassifier.setFilter(removeIDFilter);
            filteredClassifier.setClassifier(classifier);

            // Build the classifier
            filteredClassifier.buildClassifier(train);

            // Evaluate
            eval.evaluateModel(classifier, test);

            // Add predictions
            AddClassification filter = new AddClassification();
            filter.setClassifier(classifier);
            filter.setOutputClassification(true);
            filter.setOutputDistribution(false);
            filter.setOutputErrorFlag(true);
            filter.setInputFormat(train);
            Filter.useFilter(train, filter); // trains the classifier

            Instances pred = Filter.useFilter(test, filter); // performs predictions on test set
            if (predictedData == null) {
                predictedData = new Instances(pred, 0);
            }
            for (int j = 0; j < pred.numInstances(); j++) {
                predictedData.add(pred.instance(j));
            }
        }

        // Prepare output scores
        double[] scores = new double[predictedData.numInstances()];

        for (Instance predInst : predictedData) {
            int id = new Double(predInst.value(predInst.attribute(0))).intValue() - 1;

            int valueIdx = predictedData.numAttributes() - 2;

            double value = predInst.value(predInst.attribute(valueIdx));

            scores[id] = value;

            // Limit to interval [0;5]
            if (scores[id] > 5.0) {
                scores[id] = 5.0;
            }
            if (scores[id] < 0.0) {
                scores[id] = 0.0;
            }
        }

        // Output
        StringBuilder sb = new StringBuilder();
        for (Double score : scores) {
            sb.append(score.toString() + LF);
        }

        FileUtils.writeStringToFile(
                new File(OUTPUT_DIR + "/" + mode.toString().toLowerCase() + "/" + dataset.toString() + ".csv"),
                sb.toString());
    }
}

---

From source file:edu.drexel.psal.jstylo.verifiers.WLSVM.java

License:Open Source License

/**
 * Converts an ARFF Instance into a string in the sparse format accepted by
 * LIBSVM/*ww  w  .jav a2 s .c  om*/
 * 
 * @param instance
 * @return
 */
protected String InstanceToSparse(Instance instance) {
    String line = new String();
    int c = (int) instance.classValue();
    if (c == 0)
        c = -1;
    line = c + " ";
    for (int j = 1; j < instance.numAttributes(); j++) {
        if (j - 1 == instance.classIndex()) {
            continue;
        }
        if (instance.isMissing(j - 1))
            continue;
        if (instance.value(j - 1) != 0)
            line += " " + j + ":" + instance.value(j - 1);
    }
    // LOG.info(line); 
    return (line + "\n");
}

---

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

License:Open Source License

private static Fn.Function2<Boolean, Instance, Instance> createPlausiblePredicate(final Configuration config) {
    if ("fuelworld".equals(config.domain)) {
        return new Fn.Function2<Boolean, Instance, Instance>() {
            final FuelWorldState s = FuelWorldState.createDefaultWithChoices(null);

            @Override//ww  w  . j av  a2 s .  c o  m
            public Boolean apply(final Instance a, final Instance b) {
                // The same action can't put you in two different locations
                if (a.value(0) != b.value(0)) {
                    return false;
                } else if (Math.abs(a.value(1) - b.value(1)) > s.fuel_consumption
                        && !s.fuel_depots.contains((int) a.value(0))) {
                    return false;
                }

                return true;
            }
        };
    } else if ("tamarisk".equals(config.domain)) {
        return new Fn.Function2<Boolean, Instance, Instance>() {
            @Override
            public Boolean apply(final Instance a, final Instance b) {
                return true;
            }
        };
    } else if ("yahtzee".equals(config.domain)) {
        return new Fn.Function2<Boolean, Instance, Instance>() {
            @Override
            public Boolean apply(final Instance a, final Instance b) {
                final int reroll_diff = (int) (a.value(6) - b.value(6));
                if (reroll_diff != 0) {
                    return false;
                }

                for (final int i : Fn.range(7, 19)) {
                    if (a.value(i) != b.value(i)) {
                        return false;
                    }
                }

                return true;
            }
        };
    } else {
        throw new IllegalArgumentException("domain = " + config.domain);
    }
}

---

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

License:Open Source License

/**
 * Constructs an InformationTheoreticMetricLearner from a set of labeled
 * state vector differences.// w w w  . java  2  s . c om
 * 
 * @param config
 * @param A0
 * @param XL A labeled set of state vector differences. The label must be
 * the last attribute, and it must be 1 if the states are similar and 0
 * if they are not.
 * @return
 */
private static <A> InformationTheoreticMetricLearner learnMetric(final Configuration config,
        final RealMatrix A0, final Instances XL) {
    final int d = XL.numAttributes() - 1; //XL.get( 0 ).getDimension();
    System.out.println("d = " + d);
    final double u;
    final double ell;
    final double gamma = config.getDouble("itml.gamma");
    // We will set 'ell' and 'u' using sample quantiles as described in
    // the ITML paper.
    final QuantileAccumulator qacc = new QuantileAccumulator(0.05, 0.95);

    final ArrayList<double[]> S = new ArrayList<double[]>();
    final ArrayList<double[]> D = new ArrayList<double[]>();
    for (int i = 0; i < XL.size(); ++i) {
        final Instance ii = XL.get(i);
        final double diff[] = new double[d];
        for (int j = 0; j < d; ++j) {
            diff[j] = ii.value(j);
        }

        if (ii.classValue() == 0.0) {
            D.add(diff);
        } else {
            S.add(diff);
        }

        qacc.add(Math.sqrt(HilbertSpace.inner_prod(diff, A0, diff)));
    }
    // Set bounds to quantile esimates
    ell = qacc.estimates[0];
    u = qacc.estimates[1];
    System.out.println("ITML: ell = " + ell);
    System.out.println("ITML: u = " + u);

    final InformationTheoreticMetricLearner itml = new InformationTheoreticMetricLearner(S, D, u, ell, A0,
            gamma, config.rng);
    itml.run();
    return itml;
}

---

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

License:Open Source License

public static double[] unlabeledFeatures(final Instance i) {
    assert (i.dataset() != null);
    assert (i.dataset().classIndex() == i.numAttributes() - 1);
    final double[] phi = new double[i.numAttributes() - 1];
    for (int j = 0; j < i.numAttributes() - 1; ++j) {
        phi[j] = i.value(j);
    }//from  w w w  .j  av  a2  s. c  o m
    return phi;
}

---

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);
            }//  w w w  .  j a v  a  2s  .  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);/*from   w w  w  .j av a 2  s .  c  om*/
    }
    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.abstraction.WekaUtil.java

License:Open Source License

public static Instances allPairwiseProducts(final Instances single, final boolean reflexive,
        final boolean symmetric) {
    final int c = single.classIndex();
    System.out.println("Class attribute = " + c);

    final ArrayList<Attribute> pair_attributes = new ArrayList<Attribute>();
    for (int i = 0; i < single.numAttributes(); ++i) {
        if (i == c) {
            continue;
        }//from w  ww  .  j  a  v  a  2s . c  o m
        final Attribute ai = single.attribute(i);
        final int j0 = (symmetric ? 0 : i);
        for (int j = j0; j < single.numAttributes(); ++j) {
            if (j == c) {
                continue;
            }
            if (!reflexive && i == j) {
                continue;
            }

            final Attribute aj = single.attribute(j);

            final String name = ai.name() + "_x_" + aj.name();
            pair_attributes.add(new Attribute(name));
        }
    }

    String pair_name = single.relationName();
    pair_name += "_x";
    if (reflexive) {
        pair_name += "r";
    }
    if (symmetric) {
        pair_name += "s";
    }
    pair_name += "_";
    pair_name += single.relationName();
    final Instances result = new Instances(pair_name, pair_attributes, 0);

    for (final Instance inst : single) {
        final double[] xp = new double[pair_attributes.size()];
        int idx = 0;
        for (int i = 0; i < single.numAttributes(); ++i) {
            if (i == c) {
                continue;
            }
            final double xi = inst.value(i);
            final int j0 = (symmetric ? 0 : i);
            for (int j = j0; j < single.numAttributes(); ++j) {
                if (j == c) {
                    continue;
                }
                if (!reflexive && i == j) {
                    continue;
                }
                final double xj = inst.value(j);
                xp[idx++] = xi * xj;
            }
        }
        WekaUtil.addInstance(result, new DenseInstance(inst.weight(), xp));
    }

    return result;
}

---

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 a2s. 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 RealVector toRealVector(final Instance inst) {
    final RealVector v = new ArrayRealVector(inst.numAttributes());
    for (int i = 0; i < inst.numAttributes(); ++i) {
        v.setEntry(i, inst.value(i));
    }/*from  ww w . jav a2 s .  c om*/
    return v;
}

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