Example usage for org.apache.commons.math3.linear ArrayRealVector ArrayRealVector

Introduction

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

Prototype

public ArrayRealVector(ArrayRealVector v) throws NullArgumentException 

Source Link

Document

Construct a vector from another vector, using a deep copy.

Usage

From source file:com.mycompany.myfirstindoorsapp.PagedActivity.java

@Override
public boolean onOptionsItemSelected(MenuItem item) {
    switch (item.getItemId()) {
    case R.id.pointMode:
        item.setChecked(true);/*from www  . ja  v a 2  s  .c  o  m*/
        mesureMode = POINTMODE;
        createdOverlays = refreshOverlay(createdOverlays);
        twoPoints[0] = null;
        twoPoints[1] = null;
        singlePoint = null;
        return true;
    case R.id.lineMode:
        item.setChecked(true);
        mesureMode = LINEMODE;
        createdOverlays = refreshOverlay(createdOverlays);
        twoPoints[0] = null;
        twoPoints[1] = null;
        singlePoint = null;
        return true;
    case R.id.menu_startMesurement:
        // begin mesurement
        if ((currentPosition == null) || ((singlePoint == null) && (twoPoints[0] == null))) {
            Toast.makeText(this, "Set pts", Toast.LENGTH_SHORT).show();
        } else {

            double[] coord = new double[4];
            coord[0] = currentPosition.x;
            coord[1] = currentPosition.y;
            coord[2] = 0;
            coord[3] = 0;
            RealVector x = new ArrayRealVector(coord);
            kalman = new Kalman(accelNoise, measurementNoise, x);
            accel.start(mesureMode);
            if ((mesureMode == POINTMODE) && (singlePoint != null)) {
                data = new Data(singlePoint);
                Toast.makeText(this, "new Data (singlePoint)", Toast.LENGTH_SHORT).show();
            }
            if ((mesureMode == LINEMODE) && (twoPoints[0] != null) && (twoPoints[1] != null)) {
                data = new Data(twoPoints[0], twoPoints[1], SystemClock.uptimeMillis());
                Toast.makeText(this, "new Data (twoPoints)", Toast.LENGTH_SHORT).show();
            }
            if ((twoPoints[0] != null) && (twoPoints[1] != null) && (singlePoint != null)) {
                Toast.makeText(this, "singlePoint!=!=!null!=!=!twoPoints[0][1]", Toast.LENGTH_SHORT).show();
            }
            if ((twoPoints[0] == null) && (twoPoints[1] == null) && (singlePoint == null)) {
                Toast.makeText(this, "singlePoint==null==twoPoints[0][1]", Toast.LENGTH_SHORT).show();
            }
        }
        return true;
    case R.id.menu_stopMesurement:
        if (null != data) {
            double[] standartDeviations = data.calc(SystemClock.uptimeMillis());
            accel.stop();
            kalman = null;
            fragment.addItem(pagedActivity, indoorsSurfaceFragment, "Deviation for indoors="
                    + standartDeviations[0] + "; and for Kalman=" + standartDeviations[1],
                    new Coordinate(0, 0, 0));

            Toast.makeText(this, "Deviation for indoors=" + standartDeviations[0] + "; and for " + "Kalman="
                    + standartDeviations[1], Toast.LENGTH_LONG).show();
            data = null;
            if (overlayKalman != null) {
                indoorsSurfaceFragment.removeOverlay(overlayKalman);
                indoorsSurfaceFragment.updateSurface();
            }
        }
        return true;
    default:
        return super.onOptionsItemSelected(item);
    }
}

---

From source file:com.joptimizer.algebra.CholeskySparseFactorizationTest.java

public void testSimple2() throws Exception {
    log.debug("testSimple2");
    double[][] A = new double[][] { { 4, 0, 0, 1 }, { 0, 4, 0, -1 }, { 0, 0, 6, 1 }, { 1, -1, 1, 6 } };

    CholeskySparseFactorization cs = new CholeskySparseFactorization(new SparseDoubleMatrix2D(A));
    cs.factorize();/*from w w w .  j  ava2s.c  o m*/
    DoubleMatrix2D L = cs.getL();
    DoubleMatrix2D LT = cs.getLT();
    log.debug("L : " + ArrayUtils.toString(L.toArray()));
    log.debug("LT: " + ArrayUtils.toString(LT.toArray()));

    //check the norm ||A.x-b||
    double[] b = new double[] { 1, 2, 3, 4 };
    double[] x = cs.solve(F1.make(b)).toArray();
    double norm = new Array2DRowRealMatrix(A).operate(new ArrayRealVector(x)).subtract(new ArrayRealVector(b))
            .getNorm();
    log.debug("norm: " + norm);
    assertEquals(0., norm, 1.e-15);

    //check the scaled residual
    double residual = Utils.calculateScaledResidual(A, x, b);
    log.debug("residual: " + residual);
    assertEquals(0., residual, Utils.getDoubleMachineEpsilon());
}

---

From source file:de.bund.bfr.math.LeastSquaresOptimization.java

@Override
public Result optimize(int nParameterSpace, int nOptimizations, boolean stopWhenSuccessful,
        Map<String, Double> minStartValues, Map<String, Double> maxStartValues, int maxIterations,
        DoubleConsumer progressListener, ExecutionContext exec) throws CanceledExecutionException {
    if (exec != null) {
        exec.checkCanceled();/*from ww  w.  jav  a  2  s  .  co m*/
    }

    progressListener.accept(0.0);

    List<ParamRange> ranges = MathUtils.getParamRanges(parameters, minStartValues, maxStartValues,
            nParameterSpace);
    RealVector targetVector = new ArrayRealVector(Doubles.toArray(targetValues));
    List<StartValues> startValuesList = MathUtils.createStartValuesList(ranges, nOptimizations,
            values -> targetVector
                    .getDistance(new ArrayRealVector(optimizerFunction.value(Doubles.toArray(values)))),
            progress -> progressListener.accept(0.5 * progress), exec);
    LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
    Result result = new Result();
    AtomicInteger count = new AtomicInteger(0);

    for (StartValues startValues : startValuesList) {
        if (exec != null) {
            exec.checkCanceled();
        }

        progressListener.accept(0.5 * count.get() / startValuesList.size() + 0.5);

        try {
            LeastSquaresBuilder builder = createLeastSquaresBuilder(startValues.getValues(), maxIterations);

            builder.checker((iteration, previous, current) -> {
                double currentProgress = (double) iteration / (double) maxIterations;

                if (exec != null) {
                    try {
                        exec.checkCanceled();
                    } catch (CanceledExecutionException e) {
                        return true;
                    }
                }

                progressListener.accept(0.5 * (count.get() + currentProgress) / startValuesList.size() + 0.5);
                return iteration == maxIterations;
            });

            LeastSquaresOptimizer.Optimum optimizerResults = optimizer.optimize(builder.build());

            if (exec != null) {
                exec.checkCanceled();
            }

            double cost = optimizerResults.getCost();

            if (result.sse == null || cost * cost < result.sse) {
                result = getResults(optimizerResults);

                if (result.sse == 0.0) {
                    break;
                }

                if (result.r2 != null && result.r2 > 0.0 && stopWhenSuccessful) {
                    break;
                }
            }
        } catch (TooManyEvaluationsException | TooManyIterationsException | ConvergenceException e) {
        }

        count.incrementAndGet();
    }

    return result;
}

---

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

public static ClusterContingencyTable evaluateClassifier(final Classifier classifier, final Instances test) {
    try {/*w ww  .  j a  v  a 2s .co m*/
        final Map<Integer, Set<RealVector>> Umap = new TreeMap<Integer, Set<RealVector>>();
        final Map<Integer, Set<RealVector>> Vmap = new TreeMap<Integer, Set<RealVector>>();

        final Remove rm_filter = new Remove();
        rm_filter.setAttributeIndicesArray(new int[] { test.classIndex() });
        rm_filter.setInputFormat(test);

        for (final Instance i : test) {
            rm_filter.input(i);
            final double[] phi = rm_filter.output().toDoubleArray();
            //            final double[] phi = WekaUtil.unlabeledFeatures( i );

            final int cluster = (int) classifier.classifyInstance(i);
            Set<RealVector> u = Umap.get(cluster);
            if (u == null) {
                u = new HashSet<RealVector>();
                Umap.put(cluster, u);
            }
            u.add(new ArrayRealVector(phi));

            final int true_label = (int) i.classValue();
            Set<RealVector> v = Vmap.get(true_label);
            if (v == null) {
                v = new HashSet<RealVector>();
                Vmap.put(true_label, v);
            }
            v.add(new ArrayRealVector(phi));
        }

        final ArrayList<Set<RealVector>> U = new ArrayList<Set<RealVector>>();
        for (final Map.Entry<Integer, Set<RealVector>> e : Umap.entrySet()) {
            U.add(e.getValue());
        }

        final ArrayList<Set<RealVector>> V = new ArrayList<Set<RealVector>>();
        for (final Map.Entry<Integer, Set<RealVector>> e : Vmap.entrySet()) {
            V.add(e.getValue());
        }

        return new ClusterContingencyTable(U, V);
    } catch (final RuntimeException ex) {
        throw ex;
    } catch (final Exception ex) {
        throw new RuntimeException(ex);
    }
}

---

From source file:gamlss.algorithm.AdditiveFit.java

/**
 * Performs a smoothing process - creates an approximating function
 * that attempts to capture important patterns in the data, while 
 * leaving out noise or other fine-scale structures/rapid phenomena.
 * @param xMat - design matrix// ww  w. ja  v a  2 s.  c om
 * @param y - response variable
 * @param sMat - smoother matrix
 * @param distParameter - distribution parameter
 * @return matrix of smoothed values
 */
public BlockRealMatrix fitSmoother(final ArrayRealVector y, final ArrayRealVector weights,
        final BlockRealMatrix xMat, final BlockRealMatrix sMat, final int distParameter) {

    this.s = sMat.copy();
    this.w = weights;
    this.whichDistParameter = distParameter;

    //residuals <- as.vector(y - s %*% rep(1, ncol(s)))   
    tempV = new ArrayRealVector(s.getColumnDimension());
    tempV.set(1.0);
    tempV = (ArrayRealVector) s.operate(tempV);
    residuals = MatrixFunctions.vecSub(y, tempV);
    tempV = null;

    //fit <- list(fitted.values = 0)
    fittedValues = new ArrayRealVector(residuals.getDimension());

    //rss <- weighted.mean(residuals^2, w)
    //rss = calculateRss(residuals, w);
    //tempArr = null;

    //rssold <- rss * 10
    //rssOld = rss*10;

    //nit <- 0
    nit = 0;

    //df <- rep(NA, length(who))
    //lambda <- rep(NA, length(who))
    //double[] df = new double[s.getColumnDimension()]; 

    //var <- s
    var = s.copy();

    //if(trace) cat("\nADDITIVE   iter   rss/n     term\n")
    if (Controls.BACKFIT_TRACE) {
        System.out.println("ADDITIVE   iter   rss/n     term");
    }

    //ndig <- -log10(tol) + 1
    //double ndig = -FastMath.log10(tol)+1;

    //RATIO <- tol + 1
    ratio = Controls.BACKFIT_TOL + 1;

    //while(RATIO > tol & nit < maxit) 
    while (ratio > Controls.BACKFIT_TOL & nit < Controls.BACKFIT_CYCLES) {

        //rssold <- rss
        //rssOld = rss;

        //nit <- nit + 1
        nit++;

        //z <- residuals + fit$fitted.values         
        z = residuals.add(fittedValues);

        //org.apache.commons.lang3.time.StopWatch watch = new org.apache.commons.lang3.time.StopWatch();
        //watch.start();         

        //fit <- lm.wfit(x, z, w, method="qr", ...)
        wls.setNoIntercept(Controls.NO_INTERCEPT[whichDistParameter - 1]);
        wls.newSampleData(z, xMat.copy(), w.copy());

        wls.setNoIntercept(false);

        //residuals <- fit$residuals
        fittedValues = (ArrayRealVector) wls.calculateFittedValues(Controls.IS_SVD);
        //watch.stop();
        //System.out.println(watch.getNanoTime()/(1e+9));
        //watch.reset();         

        //residuals = z.subtract(fittedValues);
        //residuals = (ArrayRealVector) wls.calculateResiduals();
        residuals = z.subtract(fittedValues);

        //rss <- weighted.mean(residuals^2, w)
        //rss = calculateRss(residuals, w);

        //if(trace) cat(nit, "   ", 
        //format(round(rss, ndig)), "  Parametric -- lm.wfit\n", sep = "")
        if (Controls.BACKFIT_TRACE) {
            //System.out.println(" " + nit + "  " + 
            //rss + " Parametric -- lm.wfit");
        }

        //deltaf <- 0
        deltaf = 0;

        //for(j in seq(names.calls)) 
        for (colNum = 0; colNum < s.getColumnDimension(); colNum++) {

            //old <- s[, j]
            old = (ArrayRealVector) s.getColumnVector(colNum);

            //z <- residuals + s[, j]
            z = residuals.add(old);

            //fit.call <- eval(smooth.calls[[j]])
            //residuals <- as.double(fit.call$residuals)      
            residuals = smoother.solve(this);

            //if(length(residuals) != n)
            //stop(paste(names.calls[j], "returns a vector 
            //of the wrong length"))
            if (residuals.getDimension() != y.getDimension()) {
                System.err.println(colNum + "  column of matrix s has a" + " vector of the wrong length");
            }

            //s[, j] <- z - residual
            s.setColumnVector(colNum, z.subtract(residuals));

            //if (length(fit.call$lambda)>1)
            //{for cases where there are multiple lambdas 
            //ambda[j] <- fit.call$lambda[1] 

            //coefSmo[[j]] <- if(is.null(fit.call$coefSmo))
            //0 else fit.call$coefSmo

            //deltaf <- deltaf + weighted.mean((s[, j] - old)^2, w)
            tempV = MatrixFunctions.vecSub((ArrayRealVector) s.getColumnVector(colNum), old);
            deltaf = deltaf + mean.evaluate(tempV.ebeMultiply(tempV).getDataRef(), w.getDataRef());
            tempV = null;

            //rss <- weighted.mean(residuals^2, w)
            //rss = calculateRss(residuals, w);

            // if(trace)
            if (Controls.BACKFIT_TRACE) {
                //cat(" ", nit, " ", format(round(rss, ndig)), 
                //"  Nonparametric -- ", names.calls[j], "\n", sep = "")
                //System.out.println("   " + nit +"   " + rss + "
                //Nonparametric " + "pb(column "+ i+")");
            }

            //df[j] <- fit.call$nl.df
            //df[i] = pb.getEdf();

            //if(se)
            if (Controls.IS_SE) {
                //var[, j] <- fit.call$var
                var.setColumnVector(colNum, smoother.getVar());
            }
        }

        //RATIO <- sqrt(deltaf/sum(w * apply(s, 1, sum)^2))   
        tempD = 0.0;
        tempM = new BlockRealMatrix(s.getRowDimension(), 1);
        for (int j = 0; j < s.getRowDimension(); j++) {
            for (int k = 0; k < s.getColumnDimension(); k++) {
                tempD = tempD + s.getEntry(j, k);
            }
            tempM.setEntry(j, 0, tempD);
            tempD = 0.0;
        }
        size = tempM.getRowDimension();
        for (int j = 0; j < size; j++) {
            tempD = tempD + tempM.getEntry(j, 0) * tempM.getEntry(j, 0) * w.getEntry(j);
        }
        ratio = FastMath.sqrt(deltaf / tempD);
        tempD = null;
        tempM = null;

        //if(trace)
        //cat("Relative change in functions:", 
        //format(round(RATIO, ndig)), "\n")
        if (Controls.BACKFIT_TRACE) {
            System.out.println("Relative change in functions:  " + ratio);
        }
    }

    //if(nit == maxit && maxit > 1)
    //warning(paste("additive.fit convergence not 
    //obtained in ", maxit, " iterations"))
    if (ratio > Controls.BACKFIT_TOL) {
        System.out.println(
                "AdditiveFit convergence is not obtained in " + Controls.BACKFIT_CYCLES + " iterations");
    }

    //fit$fitted.values <- y - residuals
    fittedValues = y.subtract(residuals);

    //rl <- c(fit, list(smooth = s, nl.df = 
    //sum(df), lambda=lambda, coefSmo=coefSmo))
    return s;
}

---

From source file:edu.stanford.cfuller.imageanalysistools.fitting.BisquareLinearFitTest.java

@Test
public void testwls6() {

    for (int i = 0; i < this.x.getDimension(); i++) {
        this.x.setEntry(i, i);
        this.w.setEntry(i, 1);
    }/* ww  w.  j  av  a2s.c o  m*/

    double[] testY = { 5, 17, 8, 2, 1 };

    this.y = new ArrayRealVector(testY);

    this.expected.setEntry(0, -2.3);
    this.expected.setEntry(1, 11.2);

    this.result = this.bslf.wlsFit(x, y, w);

    this.success &= (this.result.subtract(this.expected).getNorm() < this.eps);

    assertTrue(success);

}

---

From source file:com.github.thorbenlindhauer.factor.GaussianFactorTest.java

@Test
public void testInitializationFromConditionalLinearGaussian() {
    GaussianFactor factor = factorBuilder.scope("A", "B").conditional().conditioningScope("B").parameters(
            new ArrayRealVector(new double[] { 4.0d }), // mean of A
            new Array2DRowRealMatrix(new double[] { 2.0d }), // variance for A
            new Array2DRowRealMatrix(new double[] { 5.0d })); // weight of B

    // P(A = 3 | B = 1.5)
    assertThat(factor.getValueForAssignment(new double[] { 10.0d, 1.5d })).isEqualTo(0.160733d,
            TestConstants.DOUBLE_VALUE_TOLERANCE);

}

---

From source file:gamlss.distributions.JSUo.java

/** Calculate and set initial value of sigma.
    * @param y - vector of values of response variable
    * @return vector of initial values of sigma
    *//*  ww w .  j  ava 2  s .c o  m*/
private ArrayRealVector setSigmaInitial(final ArrayRealVector y) {
    //sigma.initial = expression(sigma<- rep(.1, length(y))),
    tempV = new ArrayRealVector(y.getDimension());
    tempV.set(0.1);
    return tempV;
}

---

From source file:gamlss.distributions.TF2.java

/** Calculate and set initial value of nu.
 * @param y - vector of values of response variable
 * @return vector of initial values of nu
 *///from  ww  w .  j  a  v a 2 s. c  o  m
private ArrayRealVector setNuInitial(final ArrayRealVector y) {
    //nu.initial = expression( nu <- rep(4, length(y)))
    tempV = new ArrayRealVector(y.getDimension());
    tempV.set(4.0);
    return tempV;
}

---

From source file:gamlss.distributions.TF.java

/** Calculate and set initial value of nu.
 * @param y - vector of values of response variable
 * @return vector of initial values of nu
 *//*w w w .j  av a 2  s  .c o m*/
private ArrayRealVector setNuInitial(final ArrayRealVector y) {
    //nu.initial = expression( nu <- rep(10, length(y)))
    tempV = new ArrayRealVector(y.getDimension());
    tempV.set(10.0);
    return tempV;
}

---

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