Examples with DoubleMatrix2D cern.colt.matrix.DoubleMatrix2D used on opensource projects

Example 76 with DoubleMatrix2D

use of cern.colt.matrix.DoubleMatrix2D in project tdq-studio-se by Talend.

the class Sorting method zdemo1.

/**
 * Demonstrates advanced sorting.
 * Sorts by sum of row.
 */
public static void zdemo1() {
    Sorting sort = quickSort;
    DoubleMatrix2D matrix = DoubleFactory2D.dense.descending(4, 3);
    DoubleMatrix1DComparator comp = new DoubleMatrix1DComparator() {

        public int compare(DoubleMatrix1D a, DoubleMatrix1D b) {
            double as = a.zSum();
            double bs = b.zSum();
            return as < bs ? -1 : as == bs ? 0 : 1;
        }
    };
    System.out.println("unsorted:" + matrix);
    System.out.println("sorted  :" + sort.sort(matrix, comp));
}

Example 77 with DoubleMatrix2D

use of cern.colt.matrix.DoubleMatrix2D in project tdq-studio-se by Talend.

the class Sorting method zdemo6.

/**
 * Demonstrates advanced sorting.
 * Sorts by sum of row.
 */
public static void zdemo6() {
    Sorting sort = quickSort;
    double[][] values = { { 3, 7, 0 }, { 2, 1, 0 }, { 2, 2, 0 }, { 1, 8, 0 }, { 2, 5, 0 }, { 7, 0, 0 }, { 2, 3, 0 }, { 1, 0, 0 }, { 4, 0, 0 }, { 2, 0, 0 } };
    DoubleMatrix2D A = DoubleFactory2D.dense.make(values);
    DoubleMatrix2D B, C;
    /*
	DoubleMatrix1DComparator comp = new DoubleMatrix1DComparator() {
		public int compare(DoubleMatrix1D a, DoubleMatrix1D b) {
			double as = a.zSum(); double bs = b.zSum();
			return as < bs ? -1 : as == bs ? 0 : 1;
		}
	};
	*/
    System.out.println("\n\nunsorted:" + A);
    B = quickSort.sort(A, 1);
    C = quickSort.sort(B, 0);
    System.out.println("quick sorted  :" + C);
    B = mergeSort.sort(A, 1);
    C = mergeSort.sort(B, 0);
    System.out.println("merge sorted  :" + C);
}

Example 78 with DoubleMatrix2D

use of cern.colt.matrix.DoubleMatrix2D in project tdq-studio-se by Talend.

the class Statistic method covariance.

/**
 * Constructs and returns the covariance matrix of the given matrix.
 * The covariance matrix is a square, symmetric matrix consisting of nothing but covariance coefficients.
 * The rows and the columns represent the variables, the cells represent covariance coefficients.
 * The diagonal cells (i.e. the covariance between a variable and itself) will equal the variances.
 * The covariance of two column vectors x and y is given by <tt>cov(x,y) = (1/n) * Sum((x[i]-mean(x)) * (y[i]-mean(y)))</tt>.
 * See the <A HREF="http://www.cquest.utoronto.ca/geog/ggr270y/notes/not05efg.html"> math definition</A>.
 * Compares two column vectors at a time. Use dice views to compare two row vectors at a time.
 *
 * @param matrix any matrix; a column holds the values of a given variable.
 * @return the covariance matrix (<tt>n x n, n=matrix.columns</tt>).
 */
public static DoubleMatrix2D covariance(DoubleMatrix2D matrix) {
    int rows = matrix.rows();
    int columns = matrix.columns();
    DoubleMatrix2D covariance = new cern.colt.matrix.impl.DenseDoubleMatrix2D(columns, columns);
    double[] sums = new double[columns];
    DoubleMatrix1D[] cols = new DoubleMatrix1D[columns];
    for (int i = columns; --i >= 0; ) {
        cols[i] = matrix.viewColumn(i);
        sums[i] = cols[i].zSum();
    }
    for (int i = columns; --i >= 0; ) {
        for (int j = i + 1; --j >= 0; ) {
            double sumOfProducts = cols[i].zDotProduct(cols[j]);
            double cov = (sumOfProducts - sums[i] * sums[j] / rows) / rows;
            covariance.setQuick(i, j, cov);
            // symmetric
            covariance.setQuick(j, i, cov);
        }
    }
    return covariance;
}

Example 79 with DoubleMatrix2D

use of cern.colt.matrix.DoubleMatrix2D in project tdq-studio-se by Talend.

the class Statistic method demo2.

/**
 * Demonstrates usage of this class.
 */
public static void demo2(int rows, int columns, boolean print) {
    System.out.println("\n\ninitializing...");
    DoubleFactory2D factory = DoubleFactory2D.dense;
    DoubleMatrix2D A = factory.ascending(rows, columns);
    // double value = 1;
    // DoubleMatrix2D A = factory.make(rows,columns);
    // A.assign(value);
    System.out.println("benchmarking correlation...");
    cern.colt.Timer timer = new cern.colt.Timer().start();
    DoubleMatrix2D corr = correlation(covariance(A));
    timer.stop().display();
    if (print) {
        System.out.println("printing result...");
        System.out.println(corr);
    }
    System.out.println("done.");
}

Example 80 with DoubleMatrix2D

use of cern.colt.matrix.DoubleMatrix2D in project tdq-studio-se by Talend.

the class Statistic method distance.

/**
 * Constructs and returns the distance matrix of the given matrix.
 * The distance matrix is a square, symmetric matrix consisting of nothing but distance coefficients.
 * The rows and the columns represent the variables, the cells represent distance coefficients.
 * The diagonal cells (i.e. the distance between a variable and itself) will be zero.
 * Compares two column vectors at a time. Use dice views to compare two row vectors at a time.
 *
 * @param matrix any matrix; a column holds the values of a given variable (vector).
 * @param distanceFunction (EUCLID, CANBERRA, ..., or any user defined distance function operating on two vectors).
 * @return the distance matrix (<tt>n x n, n=matrix.columns</tt>).
 */
public static DoubleMatrix2D distance(DoubleMatrix2D matrix, VectorVectorFunction distanceFunction) {
    int columns = matrix.columns();
    DoubleMatrix2D distance = new cern.colt.matrix.impl.DenseDoubleMatrix2D(columns, columns);
    // cache views
    DoubleMatrix1D[] cols = new DoubleMatrix1D[columns];
    for (int i = columns; --i >= 0; ) {
        cols[i] = matrix.viewColumn(i);
    }
    // work out all permutations
    for (int i = columns; --i >= 0; ) {
        for (int j = i; --j >= 0; ) {
            double d = distanceFunction.apply(cols[i], cols[j]);
            distance.setQuick(i, j, d);
            // symmetric
            distance.setQuick(j, i, d);
        }
    }
    return distance;
}