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

Example 81 with DoubleMatrix2D

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

the class Statistic method demo1.

/**
 * Demonstrates usage of this class.
 */
public static void demo1() {
    double[][] values = { { 1, 2, 3 }, { 2, 4, 6 }, { 3, 6, 9 }, { 4, -8, -10 } };
    DoubleFactory2D factory = DoubleFactory2D.dense;
    DoubleMatrix2D A = factory.make(values);
    System.out.println("\n\nmatrix=" + A);
    System.out.println("\ncovar1=" + covariance(A));
// System.out.println(correlation(covariance(A)));
// System.out.println(distance(A,EUCLID));
// System.out.println(cern.colt.matrixpattern.Converting.toHTML(A.toString()));
// System.out.println(cern.colt.matrixpattern.Converting.toHTML(covariance(A).toString()));
// System.out.println(cern.colt.matrixpattern.Converting.toHTML(correlation(covariance(A)).toString()));
// System.out.println(cern.colt.matrixpattern.Converting.toHTML(distance(A,EUCLID).toString()));
}

Example 82 with DoubleMatrix2D

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

the class BenchmarkMatrix method run.

/**
 * Executes procedure repeatadly until more than minSeconds have elapsed.
 */
protected static void run(double minSeconds, String title, Double2DProcedure function, String[] types, int[] sizes, double[] densities) {
    // int[] sizes = {33,500,1000};
    // double[] densities = {0.001,0.01,0.99};
    // int[] sizes = {3,5,7,9,30,45,60,61,100,200,300,500,800,1000};
    // double[] densities = {0.001,0.01,0.1,0.999};
    // int[] sizes = {3};
    // double[] densities = {0.1};
    DoubleMatrix3D timings = DoubleFactory3D.dense.make(types.length, sizes.length, densities.length);
    cern.colt.Timer runTime = new cern.colt.Timer().start();
    for (int k = 0; k < types.length; k++) {
        // DoubleFactory2D factory = (k==0 ? DoubleFactory2D.dense : k==1 ? DoubleFactory2D.sparse : DoubleFactory2D.rowCompressed);
        // DoubleFactory2D factory = (k==0 ? DoubleFactory2D.dense : k==1 ? DoubleFactory2D.sparse : k==2 ? DoubleFactory2D.rowCompressed : DoubleFactory2D.rowCompressedModified);
        DoubleFactory2D factory = getFactory(types[k]);
        System.out.print("\n@");
        for (int i = 0; i < sizes.length; i++) {
            int size = sizes[i];
            System.out.print("x");
            for (int j = 0; j < densities.length; j++) {
                final double density = densities[j];
                System.out.print(".");
                // System.out.println("   doing density="+density+"...");
                float opsPerSec;
                // if (!((k==1 && density >= 0.1 && size >=100) || (size>5000 && (k==0 || density>1.0E-4) ))) {
                if (!((k > 0 && density >= 0.1 && size >= 500))) {
                    double val = 0.5;
                    // --> help gc before allocating new mem
                    function.A = null;
                    // --> help gc before allocating new mem
                    function.B = null;
                    // --> help gc before allocating new mem
                    function.C = null;
                    // --> help gc before allocating new mem
                    function.D = null;
                    DoubleMatrix2D A = factory.sample(size, size, val, density);
                    DoubleMatrix2D B = factory.sample(size, size, val, density);
                    function.setParameters(A, B);
                    // help gc
                    A = null;
                    // help gc
                    B = null;
                    double ops = function.operations();
                    double secs = BenchmarkKernel.run(minSeconds, function);
                    opsPerSec = (float) (ops / secs);
                } else {
                    // skip this parameter combination (not used in practice & would take a lot of memory and time)
                    opsPerSec = Float.NaN;
                }
                timings.set(k, i, j, opsPerSec);
            // System.out.println(secs);
            // System.out.println(opsPerSec+" Mops/sec\n");
            }
        }
    }
    runTime.stop();
    String sliceAxisName = "type";
    String rowAxisName = "size";
    // "density";
    String colAxisName = "d";
    // String[] sliceNames = {"dense", "sparse"};
    // String[] sliceNames = {"dense", "sparse", "rowCompressed"};
    String[] sliceNames = types;
    hep.aida.bin.BinFunctions1D F = hep.aida.bin.BinFunctions1D.functions;
    // {F.mean, F.median, F.sum};
    hep.aida.bin.BinFunction1D[] aggr = null;
    String[] rowNames = new String[sizes.length];
    String[] colNames = new String[densities.length];
    for (int i = sizes.length; --i >= 0; ) rowNames[i] = Integer.toString(sizes[i]);
    for (int j = densities.length; --j >= 0; ) colNames[j] = Double.toString(densities[j]);
    System.out.println("*");
    // show transposed
    String tmp = rowAxisName;
    rowAxisName = colAxisName;
    colAxisName = tmp;
    String[] tmp2 = rowNames;
    rowNames = colNames;
    colNames = tmp2;
    timings = timings.viewDice(0, 2, 1);
    System.out.println(new cern.colt.matrix.doublealgo.Formatter("%1.3G").toTitleString(timings, sliceNames, rowNames, colNames, sliceAxisName, rowAxisName, colAxisName, "Performance of " + title, aggr));
    /*
	title = "Speedup of dense over sparse";
	DoubleMatrix2D speedup = cern.colt.matrix.doublealgo.Transform.div(timings.viewSlice(0).copy(),timings.viewSlice(1));
	System.out.println("\n"+new cern.colt.matrix.doublealgo.Formatter("%1.3G").toTitleString(speedup,rowNames,colNames,rowAxisName,colAxisName,title,aggr));
	*/
    System.out.println("Run took a total of " + runTime + ". End of run.");
}

Example 83 with DoubleMatrix2D

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

the class BenchmarkMatrix method runSpecial.

/**
 * Executes procedure repeatadly until more than minSeconds have elapsed.
 */
protected static void runSpecial(double minSeconds, String title, Double2DProcedure function) {
    int[] sizes = { 10000 };
    double[] densities = { 0.00001 };
    boolean[] sparses = { true };
    DoubleMatrix2D timings = DoubleFactory2D.dense.make(sizes.length, 4);
    cern.colt.Timer runTime = new cern.colt.Timer().start();
    for (int i = 0; i < sizes.length; i++) {
        int size = sizes[i];
        double density = densities[i];
        boolean sparse = sparses[i];
        final DoubleFactory2D factory = (sparse ? DoubleFactory2D.sparse : DoubleFactory2D.dense);
        System.out.print("\n@");
        System.out.print("x");
        double val = 0.5;
        // --> help gc before allocating new mem
        function.A = null;
        // --> help gc before allocating new mem
        function.B = null;
        // --> help gc before allocating new mem
        function.C = null;
        // --> help gc before allocating new mem
        function.D = null;
        DoubleMatrix2D A = factory.sample(size, size, val, density);
        DoubleMatrix2D B = factory.sample(size, size, val, density);
        function.setParameters(A, B);
        // help gc
        A = null;
        // help gc
        B = null;
        float secs = BenchmarkKernel.run(minSeconds, function);
        double ops = function.operations();
        float opsPerSec = (float) (ops / secs);
        timings.viewRow(i).set(0, sparse ? 0 : 1);
        timings.viewRow(i).set(1, size);
        timings.viewRow(i).set(2, density);
        timings.viewRow(i).set(3, opsPerSec);
    // System.out.println(secs);
    // System.out.println(opsPerSec+" Mops/sec\n");
    }
    runTime.stop();
    hep.aida.bin.BinFunctions1D F = hep.aida.bin.BinFunctions1D.functions;
    // {F.mean, F.median, F.sum};
    hep.aida.bin.BinFunction1D[] aggr = null;
    String[] rowNames = null;
    String[] colNames = { "dense (y=1,n=0)", "size", "density", "flops/sec" };
    String rowAxisName = null;
    String colAxisName = null;
    System.out.println("*");
    System.out.println(new cern.colt.matrix.doublealgo.Formatter("%1.3G").toTitleString(timings, rowNames, colNames, rowAxisName, colAxisName, title, aggr));
    System.out.println("Run took a total of " + runTime + ". End of run.");
}

Example 84 with DoubleMatrix2D

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

the class BenchmarkMatrix2D method doubleBenchmark.

/**
 * Runs a bench on matrices holding double elements.
 */
public static void doubleBenchmark(int runs, int rows, int columns, String kind, boolean print, int initialCapacity, double minLoadFactor, double maxLoadFactor) {
    System.out.println("benchmarking double matrix");
    // certain loops need to be constructed so that the jitter can't optimize them away and we get fantastic numbers.
    // this involves primarly read-loops
    cern.colt.Timer timer1 = new cern.colt.Timer();
    cern.colt.Timer timer2 = new cern.colt.Timer();
    cern.colt.Timer timer3 = new cern.colt.Timer();
    cern.colt.Timer timer4 = new cern.colt.Timer();
    cern.colt.Timer emptyLoop = new cern.colt.Timer();
    cern.colt.Timer emptyLoop2 = new cern.colt.Timer();
    emptyLoop.start();
    int dummy = 0;
    for (int i = 0; i < runs; i++) {
        for (int column = 0; column < columns; column++) {
            for (int row = 0; row < rows; row++) {
                dummy++;
            }
        }
    }
    emptyLoop.stop();
    // !!! so that the jitter can't optimize away the whole loop
    System.out.println(dummy);
    emptyLoop2.start();
    dummy = 3;
    double dummy2 = 0;
    for (int i = 0; i < runs; i++) {
        for (int value = 0, column = 0; column < columns; column++) {
            for (int row = 0; row < rows; row++) {
                dummy2 += dummy;
            }
        }
    }
    emptyLoop2.stop();
    // !!! so that the jitter can't optimize away the whole loop
    System.out.println(dummy2);
    long before = Runtime.getRuntime().freeMemory();
    long size = (((long) rows) * columns) * runs;
    DoubleMatrix2D matrix = null;
    if (kind.equals("sparse"))
        matrix = new SparseDoubleMatrix2D(rows, columns, initialCapacity, minLoadFactor, maxLoadFactor);
    else if (kind.equals("dense"))
        matrix = new DenseDoubleMatrix2D(rows, columns);
    else
        // else if (kind.equals("denseArray")) matrix = new DoubleArrayMatrix2D(rows,columns);
        throw new RuntimeException("unknown kind");
    System.out.println("\nNow filling...");
    // if (kind.equals("sparse")) ((SparseDoubleMatrix2D)matrix).elements.hashCollisions = 0;
    for (int i = 0; i < runs; i++) {
        matrix.assign(0);
        matrix.ensureCapacity(initialCapacity);
        if (kind.equals("sparse"))
            ((SparseDoubleMatrix2D) matrix).ensureCapacity(initialCapacity);
        timer1.start();
        int value = 0;
        for (int row = 0; row < rows; row++) {
            for (int column = 0; column < columns; column++) {
                matrix.setQuick(row, column, value++);
            }
        }
        timer1.stop();
    }
    timer1.display();
    timer1.minus(emptyLoop).display();
    System.out.println(size / timer1.minus(emptyLoop).seconds() + " elements / sec");
    // invite gc
    Runtime.getRuntime().gc();
    try {
        Thread.currentThread().sleep(1000);
    } catch (InterruptedException exc) {
    }
    ;
    long after = Runtime.getRuntime().freeMemory();
    System.out.println("KB needed=" + (before - after) / 1024);
    System.out.println("bytes needed per non-zero=" + (before - after) / (double) matrix.cardinality());
    if (print) {
        System.out.println(matrix);
        if (kind.equals("sparse"))
            System.out.println("map=" + ((SparseDoubleMatrix2D) matrix).elements);
    }
    /*
	if (kind.equals("sparse")) {
		int hashCollisions = ((SparseDoubleMatrix2D)matrix).elements.hashCollisions;
		System.out.println("hashCollisions="+hashCollisions);
		System.out.println("--> "+ ((double)hashCollisions / (rows*columns)) +" hashCollisions/element on average.");
	}
	*/
    System.out.println("\nNow reading...");
    // if (kind.equals("sparse")) ((SparseDoubleMatrix2D)matrix).elements.hashCollisions = 0;
    timer2.start();
    double element = 0;
    for (int i = 0; i < runs; i++) {
        for (int row = 0; row < rows; row++) {
            for (int column = 0; column < columns; column++) {
                element += matrix.getQuick(row, column);
            }
        }
    }
    timer2.stop().display();
    timer2.minus(emptyLoop2).display();
    System.out.println(size / timer2.minus(emptyLoop2).seconds() + " elements / sec");
    if (print)
        System.out.println(matrix);
    // if (kind.equals("sparse")) System.out.println("hashCollisions="+((SparseDoubleMatrix2D)matrix).elements.hashCollisions);
    // !!! so that the jitter can't optimize away the whole loop
    System.out.println(element);
    System.out.println("\nNow reading view...");
    DoubleMatrix2D view = matrix.viewPart(0, 0, rows, columns);
    timer4.start();
    element = 0;
    for (int i = 0; i < runs; i++) {
        for (int row = 0; row < rows; row++) {
            for (int column = 0; column < columns; column++) {
                element += view.getQuick(row, column);
            }
        }
    }
    timer4.stop().display();
    timer4.minus(emptyLoop2).display();
    System.out.println(size / timer4.minus(emptyLoop2).seconds() + " elements / sec");
    if (print)
        System.out.println(view);
    // if (kind.equals("sparse")) System.out.println("hashCollisions="+((SparseDoubleMatrix2D)view).elements.hashCollisions);
    // !!! so that the jitter can't optimize away the whole loop
    System.out.println(element);
    System.out.println("\nNow removing...");
    before = Runtime.getRuntime().freeMemory();
    // if (kind.equals("sparse")) ((SparseDoubleMatrix2D)matrix).elements.hashCollisions = 0;
    for (int i = 0; i < runs; i++) {
        // initializing
        for (int row = 0; row < rows; row++) {
            for (int column = 0; column < columns; column++) {
                matrix.setQuick(row, column, 1);
            }
        }
        timer3.start();
        for (int row = 0; row < rows; row++) {
            for (int column = 0; column < columns; column++) {
                matrix.setQuick(row, column, 0);
            }
        }
        timer3.stop();
    }
    timer3.display();
    timer3.minus(emptyLoop).display();
    System.out.println(size / timer3.minus(emptyLoop).seconds() + " elements / sec");
    // invite gc
    Runtime.getRuntime().gc();
    try {
        Thread.currentThread().sleep(1000);
    } catch (InterruptedException exc) {
    }
    ;
    after = Runtime.getRuntime().freeMemory();
    System.out.println("KB needed=" + (before - after) / 1024);
    System.out.println("KB free=" + (after / 1024));
    if (print)
        System.out.println(matrix);
    // if (kind.equals("sparse")) System.out.println("hashCollisions"+((SparseDoubleMatrix2D)matrix).elements.hashCollisions);
    System.out.println("bye bye.");
}

Example 85 with DoubleMatrix2D

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

the class BenchmarkMatrix2D method doubleBenchmarkMult.

/**
 * Runs a bench on matrices holding double elements.
 */
public static void doubleBenchmarkMult(int runs, int rows, int columns, String kind, boolean print, int initialCapacity, double minLoadFactor, double maxLoadFactor) {
    System.out.println("benchmarking double matrix");
    // certain loops need to be constructed so that the jitter can't optimize them away and we get fantastic numbers.
    // this involves primarly read-loops
    cern.colt.Timer timer1 = new cern.colt.Timer();
    cern.colt.Timer timer2 = new cern.colt.Timer();
    long size = (((long) rows) * columns) * runs;
    DoubleMatrix2D matrix = null;
    if (kind.equals("sparse"))
        matrix = new SparseDoubleMatrix2D(rows, columns, initialCapacity, minLoadFactor, maxLoadFactor);
    else if (kind.equals("dense"))
        matrix = new DenseDoubleMatrix2D(rows, columns);
    else
        // else if (kind.equals("denseArray")) matrix = new DoubleArrayMatrix2D(rows,columns);
        throw new RuntimeException("unknown kind");
    System.out.println("\nNow multiplying...");
    matrix.assign(1);
    // if (kind.equals("sparse")) ((SparseDoubleMatrix2D)matrix).elements.hashCollisions = 0;
    for (int i = 0; i < runs; i++) {
        timer1.start();
        cern.colt.matrix.doublealgo.Transform.mult(matrix, 3);
        timer1.stop();
    }
    timer1.display();
    System.out.println(size / timer1.seconds() + " elements / sec");
    if (print) {
        System.out.println(matrix);
    }
    /*
	if (kind.equals("sparse")) {
		int hashCollisions = ((SparseDoubleMatrix2D)matrix).elements.hashCollisions;
		System.out.println("hashCollisions="+hashCollisions);
		System.out.println("--> "+ ((double)hashCollisions / (rows*columns)) +" hashCollisions/element on average.");
	}
	*/
    System.out.println("\nNow multiplying2...");
    matrix.assign(1);
    // if (kind.equals("sparse")) ((SparseDoubleMatrix2D)matrix).elements.hashCollisions = 0;
    for (int i = 0; i < runs; i++) {
        timer2.start();
        cern.colt.matrix.doublealgo.Transform.mult(matrix, 3);
        timer2.stop();
    }
    timer2.display();
    System.out.println(size / timer2.seconds() + " elements / sec");
    if (print) {
        System.out.println(matrix);
    }
    /*
	if (kind.equals("sparse")) {
		int hashCollisions = ((SparseDoubleMatrix2D)matrix).elements.hashCollisions;
		System.out.println("hashCollisions="+hashCollisions);
		System.out.println("--> "+ ((double)hashCollisions / (rows*columns)) +" hashCollisions/element on average.");
	}
	*/
    System.out.println("bye bye.");
}