Example usage for org.apache.commons.math3.linear RealVector getDimension

Introduction

In this page you can find the example usage for org.apache.commons.math3.linear RealVector getDimension.

Prototype

public abstract int getDimension();

Source Link

Document

Returns the size of the vector.

Usage

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

public static void main(final String[] args) throws FileNotFoundException {
    final File root = new File("test/KernelPrincipalComponentsAnalysis");
    root.mkdirs();//ww w  . j  a v a2  s . c  o  m
    final int seed = 42;
    final int N = 30;
    final RandomGenerator rng = new MersenneTwister(seed);
    final ArrayList<RealVector> data = new ArrayList<RealVector>();
    final ArrayList<RealVector> shuffled = new ArrayList<RealVector>();

    //      final double[][] covariance = new double[][] { {1.0, 0.0},
    //                                          {0.0, 1.0} };
    //      final MultivariateNormalDistribution p = new MultivariateNormalDistribution(
    //         rng, new double[] { 0.0, 0.0 }, covariance );
    //      final MultivariateNormalDistribution q = new MultivariateNormalDistribution(
    //         rng, new double[] { 10.0, 0.0 }, covariance );
    //
    //      for( int i = 0; i < N; ++i ) {
    //         data.add( new ArrayRealVector( p.sample() ) );
    //         data.add( new ArrayRealVector( q.sample() ) );
    //      }
    //      Fn.shuffle( rng, data );

    final double sigma = 0.01;
    final double[][] covariance = new double[][] { { sigma, 0.0 }, { 0.0, sigma } };
    final MultivariateNormalDistribution p = new MultivariateNormalDistribution(rng, new double[] { 0.0, 0.0 },
            covariance);

    for (final double r : new double[] { 1.0, 3.0, 5.0 }) {
        for (int i = 0; i < N; ++i) {
            final double theta = i * 2 * Math.PI / N;
            final double[] noise = p.sample();
            final RealVector v = new ArrayRealVector(
                    new double[] { r * Math.cos(theta) + noise[0], r * Math.sin(theta) + noise[1] });
            data.add(v);
            shuffled.add(v);
        }
    }
    Fn.shuffle(rng, shuffled);

    final Csv.Writer data_writer = new Csv.Writer(new PrintStream(new File(root, "data.csv")));
    for (final RealVector v : data) {
        for (int i = 0; i < v.getDimension(); ++i) {
            data_writer.cell(v.getEntry(i));
        }
        data_writer.newline();
    }
    data_writer.close();

    System.out.println("[Training]");
    final int Ncomponents = 2;
    final KernelPrincipalComponentsAnalysis<RealVector> kpca = new KernelPrincipalComponentsAnalysis<RealVector>(
            shuffled, new RadialBasisFunctionKernel(0.5), 1e-6);
    System.out.println("[Finished]");
    for (int i = 0; i < Ncomponents; ++i) {
        System.out.println(kpca.eigenvectors.get(i));
    }

    System.out.println("Transformed data:");
    final KernelPrincipalComponentsAnalysis.Transformer<RealVector> transformer = kpca
            .makeTransformer(Ncomponents);
    final Csv.Writer transformed_writer = new Csv.Writer(new PrintStream(new File(root, "transformed.csv")));
    for (final RealVector u : data) {
        final RealVector v = transformer.transform(u);
        System.out.println(v);
        for (int i = 0; i < v.getDimension(); ++i) {
            transformed_writer.cell(v.getEntry(i));
        }
        transformed_writer.newline();
    }
    transformed_writer.close();
}

---

From source file:com.cloudera.oryx.common.math.Vectors.java

/**
 * @return a vector of the same sparse-ness and dimension as {@code vec} but without data
 *//*www.  j a v a  2 s . co  m*/
public static RealVector like(RealVector vector) {
    int dimension = vector.getDimension();
    return vector instanceof SparseRealVector ? sparse(dimension) : dense(dimension);
}

---

From source file:lambertmrev.conversions.java

public static int[][] vec2kMat(RealVector in) {
    int dim = in.getDimension();
    int[][] mat = new int[dim][dim];
    for (int ii = 0; ii < dim; ii++) {
        int tmp = (int) in.getEntry(ii);
        mat[ii][tmp] = 1;//  w w  w . ja va 2 s  .  com
    }
    return mat;
}

---

From source file:edu.byu.nlp.stats.GammaDistribution.java

/**
 * Generates gamma samples, one for each element in shapes.
 * /*w w w.j  a v  a 2 s.co  m*/
 * @param shapes
 */
public static double[] sample(RealVector shapes, RandomGenerator rnd) {
    double[] gamma = new double[shapes.getDimension()];
    for (int i = 0; i < gamma.length; i++) {
        gamma[i] = sample(shapes.getEntry(i), rnd);
    }
    return gamma;
}

---

From source file:com.cloudera.oryx.kmeans.computation.VectorConvert.java

public static RealVector fromString(CharSequence input) {
    RealVector rv = VECTOR_FORMAT.parse(input.toString());
    int density = 0;
    for (int i = 0; i < rv.getDimension(); i++) {
        if (rv.getEntry(i) != 0.0) {
            density++;/*  ww w.j  a  v a  2 s.co  m*/
        }
    }
    if (2 * density < rv.getDimension()) {
        return new OpenMapRealVector(rv);
    } else {
        return rv;
    }
}

---

From source file:com.cloudera.oryx.kmeans.computation.pmml.ClusteringModelBuilder.java

private static String toString(RealVector vec) {
    StringBuilder sb = new StringBuilder();
    for (int i = 0; i < vec.getDimension(); i++) {
        if (i > 0) {
            sb.append(' ');
        }//  ww  w . j  a  va  2s  .  c om
        sb.append(vec.getEntry(i));
    }
    return sb.toString();
}

---

From source file:IO.java

public static void display(RealVector vec, String name) {
    int len = vec.getDimension();
    System.out.println(name + ":" + len);
    for (int i = 0; i < len; i++) {
        System.out.print(vec.getEntry(i) + ",");
    }/*from  ww  w .j  ava  2s. c  o m*/
    System.out.println();
}

---

From source file:com.cloudera.oryx.kmeans.computation.VectorConvert.java

public static MLVector fromVector(RealVector input) {
    final List<Double> values = Lists.newArrayList();
    MLVector.Builder vb = MLVector.newBuilder().setSize(input.getDimension()).setValues(values);
    if (input instanceof ArrayRealVector) {
        vb.setIndices(ImmutableList.<Integer>of());
        for (int i = 0; i < input.getDimension(); i++) {
            values.add(input.getEntry(i));
        }//w  w  w .  j  av a  2  s  .c  o  m
    } else {
        final List<Integer> indices = Lists.newArrayList();
        vb.setIndices(indices);
        input.walkInDefaultOrder(new AbstractRealVectorPreservingVisitor() {
            @Override
            public void visit(int index, double value) {
                indices.add(index);
                values.add(value);
            }
        });
    }
    if (input instanceof NamedRealVector) {
        vb.setId(((NamedRealVector) input).getName());
    } else {
        vb.setId("");
    }
    return vb.build();
}

---

From source file:edu.byu.nlp.math.RealVectors.java

public static double sum(RealVector v) {
    if (v == null) {
        return 0;
    }/*from  www .j a v  a 2 s  .  c  o m*/
    if (v.getDimension() == 0) {
        return 0;
    }
    return v.walkInOptimizedOrder(new RealVectorPreservingVisitor() {
        private double total = 0;

        @Override
        public void visit(int index, double value) {
            total += value;
        }

        @Override
        public void start(int dimension, int start, int end) {
        }

        @Override
        public double end() {
            return total;
        }
    });
}

---

From source file:edu.byu.nlp.math.RealVectors.java

/**
 * Adds vector a to b and stores the results in a.
 * /*from  ww  w .j  a v  a  2 s .c om*/
 * @throws NullPointerException if a or b are null 
 */
public static void addToSelf(RealVector a, RealVector b) {
    Preconditions.checkNotNull(a);
    Preconditions.checkNotNull(b);
    if (a.getDimension() != b.getDimension()) {
        throw new DimensionMismatchException(b.getDimension(), a.getDimension());
    }

    a.combineToSelf(1.0, 1.0, b);
}

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