Example usage for org.apache.commons.math3.linear RealMatrix addToEntry

Introduction

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

Prototype

void addToEntry(int row, int column, double increment) throws OutOfRangeException;

Source Link

Document

Adds (in place) the specified value to the specified entry of this matrix.

Usage

From source file:com.rapidminer.tools.math.LinearRegression.java

/** Calculates the coefficients of linear ridge regression. */
public static double[] performRegression(Matrix a, Matrix b, double ridge) {
    RealMatrix x = MatrixUtils.createRealMatrix(a.getArray());
    RealMatrix y = MatrixUtils.createRealMatrix(b.getArray());
    int numberOfColumns = x.getColumnDimension();
    double[] coefficients = new double[numberOfColumns];
    RealMatrix xTransposed = x.transpose();
    Matrix result;/*from ww w  . j av a 2 s.  com*/
    boolean finished = false;
    while (!finished) {
        RealMatrix xTx = xTransposed.multiply(x);

        for (int i = 0; i < numberOfColumns; i++) {
            xTx.addToEntry(i, i, ridge);
        }

        RealMatrix xTy = xTransposed.multiply(y);
        coefficients = xTy.getColumn(0);

        try {
            // do not use Apache LUDecomposition for solve instead because it creates different
            // results
            result = new Matrix(xTx.getData()).solve(new Matrix(coefficients, coefficients.length));
            for (int i = 0; i < numberOfColumns; i++) {
                coefficients[i] = result.get(i, 0);
            }
            finished = true;
        } catch (Exception ex) {
            double ridgeOld = ridge;
            if (ridge > 0) {
                ridge *= 10;
            } else {
                ridge = 0.0000001;
            }
            finished = false;
            logger.warning("Error during calculation: " + ex.getMessage() + ": Increasing ridge factor from "
                    + ridgeOld + " to " + ridge);
        }
    }
    return coefficients;
}

---

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

/**
 * Computes a Newton-Raphson update in-place to alpha.
 *//*from   w w  w .  j a va 2 s .c  o m*/
private static RealVector newtonRaphsonUpdate(final double[][] data, double[] alpha) {
    // We'll compute the gold-standard value the "long" way (taking the inverse of the Hessian)
    RealMatrix hessian = new Array2DRowRealMatrix(alpha.length, alpha.length);
    for (int r = 0; r < hessian.getRowDimension(); r++) {
        for (int c = 0; c < hessian.getColumnDimension(); c++) {
            hessian.addToEntry(r, c, data.length * Gamma.trigamma(DoubleArrays.sum(alpha)));
            if (r == c) {
                hessian.addToEntry(r, c, -data.length * Gamma.trigamma(alpha[r]));
            }
        }
    }
    RealVector derivative = new ArrayRealVector(alpha.length);
    for (int k = 0; k < alpha.length; k++) {
        derivative.setEntry(k,
                data.length * (Gamma.digamma(DoubleArrays.sum(alpha)) - Gamma.digamma(alpha[k])));
        for (double[] theta : data) {
            derivative.addToEntry(k, theta[k]);
        }
    }

    RealMatrix hessianInverse = new LUDecomposition(hessian).getSolver().getInverse();
    RealVector negDiff = hessianInverse.preMultiply(derivative);
    negDiff.mapMultiplyToSelf(-1.0);

    RealVector expected = new ArrayRealVector(alpha, true);
    return expected.add(negDiff);
}

---

From source file:com.anhth12.lambda.common.math.VectorMath.java

public static RealMatrix transposeTimesSelf(Collection<float[]> M) {
    if (M == null || M.isEmpty()) {
        return null;
    }//from   w w  w  .j  a  va2s . c o m
    int features = 0;
    RealMatrix result = null;
    for (float[] vector : M) {
        if (result == null) {
            features = vector.length;
            result = new Array2DRowRealMatrix(features, features);
        }
        for (int row = 0; row < features; row++) {
            float rowValue = vector[row];
            for (int col = 0; col < features; col++) {
                result.addToEntry(row, col, rowValue * vector[col]);
            }
        }
    }
    return result;
}

---

From source file:com.cloudera.oryx.als.computation.iterate.row.RowReduceFn.java

private static RealMatrix partialTransposeTimesSelf(LongObjectMap<float[]> M, int dimension,
        Iterable<LongFloatMap.MapEntry> entries) {
    RealMatrix result = new Array2DRowRealMatrix(dimension, dimension);
    for (LongFloatMap.MapEntry entry : entries) {
        float[] vector = M.get(entry.getKey());
        for (int row = 0; row < dimension; row++) {
            float rowValue = vector[row];
            for (int col = 0; col < dimension; col++) {
                result.addToEntry(row, col, rowValue * vector[col]);
            }//  ww  w .j  av a  2  s.  c  om
        }
    }
    return result;
}

---

From source file:com.cloudera.oryx.kmeans.common.ClusterValidityStatistics.java

public static ClusterValidityStatistics create(Iterable<WeightedRealVector> points, Centers test, Centers train,
        int replicaId) {
    Preconditions.checkArgument(test.size() == train.size());
    int k = test.size();
    double n = 0.0;
    double testCost = 0.0;
    double trainCost = 0.0;
    RealMatrix contingencyMatrix = new Array2DRowRealMatrix(k, k);
    double[] rowSums = new double[k];
    double[] colSums = new double[k];
    for (WeightedRealVector wv : points) {
        Distance d1 = test.getDistance(wv.thing());
        Distance d2 = train.getDistance(wv.thing());
        contingencyMatrix.addToEntry(d1.getClosestCenterId(), d2.getClosestCenterId(), wv.weight());
        rowSums[d1.getClosestCenterId()] += wv.weight();
        colSums[d2.getClosestCenterId()] += wv.weight();
        testCost += d1.getSquaredDistance() * wv.weight();
        trainCost += d2.getSquaredDistance() * wv.weight();
        n += wv.weight();/*from  ww w.ja va 2  s. co m*/
    }

    return new ClusterValidityStatistics(k, replicaId, testCost, trainCost,
            normVarInformation(contingencyMatrix, rowSums, colSums, n),
            normVanDongen(contingencyMatrix, rowSums, colSums, n));
}

---

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

/**
 * @param M tall, skinny matrix//from  w w  w. j ava  2 s.  co m
 * @return MT * M as a dense matrix
 */
public static RealMatrix transposeTimesSelf(Collection<float[]> M) {
    if (M == null || M.isEmpty()) {
        return null;
    }
    int features = 0;
    RealMatrix result = null;
    for (float[] vector : M) {
        if (result == null) {
            features = vector.length;
            result = new Array2DRowRealMatrix(features, features);
        }
        for (int row = 0; row < features; row++) {
            float rowValue = vector[row];
            for (int col = 0; col < features; col++) {
                result.addToEntry(row, col, rowValue * vector[col]);
            }
        }
    }
    return result;
}

---

From source file:cooccurrence.Omer_Levy.java

/**
 * Generic Method to take squareRoot of individual Values.
 * @param coVariance/*ww  w  .j av  a2 s . c o  m*/
 * @return 
 */
private static RealMatrix squareRoot(RealMatrix coVariance) {
    RealMatrix squareRoot = MatrixUtils.createRealMatrix(coVariance.getRowDimension(),
            coVariance.getColumnDimension());
    for (int i = 0; i < coVariance.getRowDimension(); i++) {
        for (int j = 0; j < coVariance.getColumnDimension(); j++) {
            double val = coVariance.getEntry(i, j);
            val = Math.sqrt(val);
            squareRoot.addToEntry(i, j, val);
        }
    }
    return squareRoot;
}

---

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

/**
 * @param M tall, skinny matrix/*from ww  w . ja v  a  2  s  .c  o  m*/
 * @return MT * M as a dense matrix
 */
public static RealMatrix transposeTimesSelf(LongObjectMap<float[]> M) {
    if (M == null || M.isEmpty()) {
        return null;
    }
    RealMatrix result = null;
    for (LongObjectMap.MapEntry<float[]> entry : M.entrySet()) {
        float[] vector = entry.getValue();
        int dimension = vector.length;
        if (result == null) {
            result = new Array2DRowRealMatrix(dimension, dimension);
        }
        for (int row = 0; row < dimension; row++) {
            float rowValue = vector[row];
            for (int col = 0; col < dimension; col++) {
                result.addToEntry(row, col, rowValue * vector[col]);
            }
        }
    }
    Preconditions.checkNotNull(result);
    return result;
}

---

From source file:com.yahoo.egads.utilities.SpectralMethods.java

public static RealMatrix averageHankelMatrix(RealMatrix hankelMat, int windowSize) {

    int k = hankelMat.getRowDimension();
    int m = hankelMat.getColumnDimension() / windowSize;
    int n = k + windowSize - 1;

    RealMatrix result = MatrixUtils.createRealMatrix(n, m);

    for (int t = 0; t < n; ++t) {
        int i = (t < windowSize) ? 0 : (t - windowSize + 1);
        int j = (t < windowSize) ? t : (windowSize - 1);
        int counter = 0;

        for (; i < k && j >= 0; ++i, --j, ++counter) {
            for (int h = 0; h < m; ++h) {
                result.addToEntry(t, h, hankelMat.getEntry(i, j * m + h));
            }/*  ww w. j av a 2s .c  om*/
        }

        for (int h = 0; h < m; ++h) {
            result.setEntry(t, h, result.getEntry(t, h) / counter);
        }
    }

    return result;
}

---

From source file:net.myrrix.common.math.MatrixUtils.java

/**
 * @param M tall, skinny matrix//from ww w .ja v a2 s. c o m
 * @return MT * M as a dense matrix
 */
public static RealMatrix transposeTimesSelf(FastByIDMap<float[]> M) {
    if (M == null || M.isEmpty()) {
        return null;
    }
    RealMatrix result = null;
    for (FastByIDMap.MapEntry<float[]> entry : M.entrySet()) {
        float[] vector = entry.getValue();
        int dimension = vector.length;
        if (result == null) {
            result = new Array2DRowRealMatrix(dimension, dimension);
        }
        for (int row = 0; row < dimension; row++) {
            float rowValue = vector[row];
            for (int col = 0; col < dimension; col++) {
                result.addToEntry(row, col, rowValue * vector[col]);
            }
        }
    }
    Preconditions.checkNotNull(result);
    return result;
}

---

• «
• 1
• 2
• 3
• »