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Java RealMatrix.transpose方法代码示例

本文整理汇总了Java中org.apache.commons.math3.linear.RealMatrix.transpose方法的典型用法代码示例。如果您正苦于以下问题：Java RealMatrix.transpose方法的具体用法？Java RealMatrix.transpose怎么用？Java RealMatrix.transpose使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类org.apache.commons.math3.linear.RealMatrix的用法示例。


在下文中一共展示了RealMatrix.transpose方法的6个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。

示例1: updateReward

import org.apache.commons.math3.linear.RealMatrix; //导入方法依赖的package包/类
public void updateReward(User user, Article a, boolean clicked) {
	String aId = a.getId();
	// Collect Variables
	RealMatrix xta = MatrixUtils.createColumnRealMatrix(a.getFeatures());
	RealMatrix zta = makeZta(
			MatrixUtils.createColumnRealMatrix(user.getFeatures()), xta);

	RealMatrix Aa = AMap.get(aId);
	RealMatrix ba = bMap.get(aId);
	RealMatrix Ba = BMap.get(aId);

	// Find common transpose/inverse to save computation
	RealMatrix AaInverse = MatrixUtils.inverse(Aa);
	RealMatrix BaTranspose = Ba.transpose();
	RealMatrix xtaTranspose = xta.transpose();
	RealMatrix ztaTranspose = zta.transpose();

	// Update
	A0 = A0.add(BaTranspose.multiply(AaInverse).multiply(Ba));
	b0 = b0.add(BaTranspose.multiply(AaInverse).multiply(ba));
	Aa = Aa.add(xta.multiply(xtaTranspose));
	AMap.put(aId, Aa);
	Ba = Ba.add(xta.multiply(ztaTranspose));
	BMap.put(aId, Ba);
	if (clicked) {
		ba = ba.add(xta);
		bMap.put(aId, ba);
	}

	// Update A0 and b0 with the new values
	A0 = A0.add(zta.multiply(ztaTranspose)).subtract(
			Ba.transpose().multiply(MatrixUtils.inverse(Aa).multiply(Ba)));
	b0 = b0.subtract(Ba.transpose().multiply(MatrixUtils.inverse(Aa))
			.multiply(ba));
	if (clicked) {
		b0 = b0.add(zta);
	}
}
开发者ID:tankle，项目名称:Bandit4J，代码行数:39，代码来源:HybridLinUCB.java

示例2: computeBeta

import org.apache.commons.math3.linear.RealMatrix; //导入方法依赖的package包/类
/**
 *
 * @param y     the response vector
 * @param x     the design matrix
 */
private RealMatrix computeBeta(RealVector y, RealMatrix x) {
    if (solver == Solver.QR) {
        return computeBetaQR(y, x);
    } else {
        final int n = x.getRowDimension();
        final int p = x.getColumnDimension();
        final int offset = hasIntercept() ? 1 : 0;
        final RealMatrix xT = x.transpose();
        final RealMatrix xTxInv = new LUDecomposition(xT.multiply(x)).getSolver().getInverse();
        final RealVector betaVector = xTxInv.multiply(xT).operate(y);
        final RealVector residuals = y.subtract(x.operate(betaVector));
        this.rss = residuals.dotProduct(residuals);
        this.errorVariance = rss / (n - p);
        this.stdError = Math.sqrt(errorVariance);
        this.residuals = createResidualsFrame(residuals);
        final RealMatrix covMatrix = xTxInv.scalarMultiply(errorVariance);
        final RealMatrix result = new Array2DRowRealMatrix(p, 2);
        if (hasIntercept()) {
            result.setEntry(0, 0, betaVector.getEntry(0));      //Intercept coefficient
            result.setEntry(0, 1, covMatrix.getEntry(0, 0));    //Intercept variance
        }
        for (int i = 0; i < getRegressors().size(); i++) {
            final int index = i + offset;
            final double variance = covMatrix.getEntry(index, index);
            result.setEntry(index, 1, variance);
            result.setEntry(index, 0, betaVector.getEntry(index));
        }
        return result;
    }
}
开发者ID:zavtech，项目名称:morpheus-core，代码行数:36，代码来源:XDataFrameLeastSquares.java

示例3: weightedLinearCorr

import org.apache.commons.math3.linear.RealMatrix; //导入方法依赖的package包/类
/**
 *
 * @param y
 * @param x
 * @param sigmaRhoY
 * @return
 */
public static WeightedLinearCorrResults weightedLinearCorr(double[] y, double[] x, double[][] sigmaRhoY) {
    WeightedLinearCorrResults weightedLinearCorrResults = new WeightedLinearCorrResults();

    RealMatrix omega = new BlockRealMatrix(convertCorrelationsToCovariances(sigmaRhoY));
    RealMatrix invOmega = MatrixUtils.inverse(omega);
    int n = y.length;

    double mX = 0;
    double pX = 0;
    double pY = 0;
    double pXY = 0;
    double w = 0;

    for (int i = 0; i < n; i++) {
        for (int j = 0; j < n; j++) {
            double invOm = invOmega.getEntry(i, j);
            w += invOm;
            pX += (invOm * (x[i] + x[j]));
            pY += (invOm * (y[i] + y[j]));
            pXY += (invOm * (((x[i] * y[j]) + (x[j] * y[i]))));
            mX += (invOm * x[i] * x[j]);
        }
    }       
    double slope = ((2 * pXY * w) - (pX * pY)) / ((4 * mX * w) - (pX * pX));
    double intercept = (pY - (slope * pX)) / (2 * w);

    RealMatrix fischer = new BlockRealMatrix(new double[][]{{mX, pX / 2.0}, {pX / 2.0, w}});
    RealMatrix fischerInv = MatrixUtils.inverse(fischer);

    double slopeSig = Math.sqrt(fischerInv.getEntry(0, 0));
    double interceptSig = Math.sqrt(fischerInv.getEntry(1, 1));
    double slopeInterceptCov = fischerInv.getEntry(0, 1);
    
    RealMatrix resid = new BlockRealMatrix(n, 1);
    for (int i = 0; i < n; i++) {
        resid.setEntry(i, 0, y[i] - (slope * x[i]) - intercept);
    }

    RealMatrix residT = resid.transpose();
    RealMatrix mM = residT.multiply(invOmega).multiply(resid);

    double sumSqWtdResids = mM.getEntry(0, 0);
    double mswd = sumSqWtdResids / (n - 2);

    // http://commons.apache.org/proper/commons-math/apidocs/org/apache/commons/math3/distribution/FDistribution.html
    FDistribution fdist = new org.apache.commons.math3.distribution.FDistribution((n - 2), 1E9);
    double prob = 1.0 - fdist.cumulativeProbability(mswd);
    
    weightedLinearCorrResults.setBad(false);
    weightedLinearCorrResults.setSlope(slope);
    weightedLinearCorrResults.setIntercept(intercept);
    weightedLinearCorrResults.setSlopeSig(slopeSig);
    weightedLinearCorrResults.setInterceptSig(interceptSig);
    weightedLinearCorrResults.setSlopeInterceptCov(slopeInterceptCov);
    weightedLinearCorrResults.setMswd(mswd);
    weightedLinearCorrResults.setProb(prob);

    return weightedLinearCorrResults;
}
开发者ID:CIRDLES，项目名称:Squid，代码行数:67，代码来源:WeightedMeanCalculators.java

示例4: wtdAvCorr

import org.apache.commons.math3.linear.RealMatrix; //导入方法依赖的package包/类
/**
 *
 * @param values
 * @param varCov
 * @return
 */
public static WtdAvCorrResults wtdAvCorr(double[] values, double[][] varCov) {
    // assume varCov is variance-covariance matrix (i.e. SigRho = false)

    WtdAvCorrResults results = new WtdAvCorrResults();

    int n = varCov.length;
    RealMatrix omegaInv = new BlockRealMatrix(varCov);
    RealMatrix omega = MatrixUtils.inverse(omegaInv);

    double numer = 0.0;
    double denom = 0.0;

    for (int i = 0; i < n; i++) {
        for (int j = 0; j < n; j++) {
            numer += (values[i] + values[j]) * omega.getEntry(i, j);
            denom += omega.getEntry(i, j);
        }
    }

    // test denom
    if (denom > 0.0) {
        double meanVal = numer / denom / 2.0;
        double meanValSigma = Math.sqrt(1.0 / denom);

        double[][] unwtdResidsArray = new double[n][1];
        for (int i = 0; i < n; i++) {
            unwtdResidsArray[i][0] = values[i] - meanVal;
        }

        RealMatrix unwtdResids = new BlockRealMatrix(unwtdResidsArray);
        RealMatrix transUnwtdResids = unwtdResids.transpose();
        RealMatrix product = transUnwtdResids.multiply(omega);
        RealMatrix sumWtdResids = product.multiply(unwtdResids);

        double mswd = 0.0;
        double prob = 0.0;
        if (n > 1) {
            mswd = sumWtdResids.getEntry(0, 0) / (n - 1);

            // http://commons.apache.org/proper/commons-math/apidocs/org/apache/commons/math3/distribution/FDistribution.html
            FDistribution fdist = new org.apache.commons.math3.distribution.FDistribution((n - 1), 1E9);
            prob = 1.0 - fdist.cumulativeProbability(mswd);
        }

        results.setBad(false);
        results.setMeanVal(meanVal);
        results.setSigmaMeanVal(meanValSigma);
        results.setMswd(mswd);
        results.setProb(prob);
    }

    return results;

}
开发者ID:CIRDLES，项目名称:Squid，代码行数:61，代码来源:WeightedMeanCalculators.java

示例5: solve

import org.apache.commons.math3.linear.RealMatrix; //导入方法依赖的package包/类
private double[] solve(float tc, float m, float w, Range range) {
    kernel.set_tcmw(tc, m, w);
    kernel.execute(range);
    final float[] result = kernel.getResult();
    double[][] _A = new double[4][4];
    double[] _b = new double[4];
    _A[0][0] = N;

    /*
      A = [[ N,           result[fi],    result[gi],    result[hi]   ],
           [ result[fi],  result[fi2],   result[figi],  result[fihi] ],
           [ result[gi],  result[figi],  result[gi2],   result[gihi] ],
           [ result[hi],  result[fihi],  result[gihi],  result[hi2]  ]]
      b =  [ sum_yi,      sum_yi_fi,     sum_yi_gi,     sum_yi_hi ]
   */
    for (int i = 0; i < N; i++) {
        int offset = i * v;
        _A[0][1] += result[offset + FI];
        _A[0][2] += result[offset + GI];
        _A[0][3] += result[offset + HI];

        _A[1][0] += result[offset + FI];
        _A[1][1] += result[offset + FI2];
        _A[1][2] += result[offset + FIGI];
        _A[1][3] += result[offset + FIHI];

        _A[2][0] += result[offset + GI];
        _A[2][1] += result[offset + FIGI];
        _A[2][2] += result[offset + GI2];
        _A[2][3] += result[offset + GIHI];

        _A[3][0] += result[offset + HI];
        _A[3][1] += result[offset + FIHI];
        _A[3][2] += result[offset + GIHI];
        _A[3][3] += result[offset + HI2];

        _b[0] += result[offset + YI];
        _b[1] += result[offset + YIFI];
        _b[2] += result[offset + YIGI];
        _b[3] += result[offset + YIHI];
    }

    RealMatrix A = new Array2DRowRealMatrix(_A, false);
    RealMatrix b = new Array2DRowRealMatrix(new double[][]{_b}, false).transpose();
    RealMatrix At = A.transpose();
    RealMatrix x = MatrixUtils.inverse(At.multiply(A)).multiply(At).multiply(b);

    return x.getColumn(0);
}
开发者ID:KIC，项目名称:LPPL，代码行数:50，代码来源:ABCCSolver.java

示例6: chooseArm

import org.apache.commons.math3.linear.RealMatrix; //导入方法依赖的package包/类
public Article chooseArm(User user, List<Article> articles) {
	Article bestA = null;
	double bestArmP = Double.MIN_VALUE;

	RealMatrix Aa;
	RealMatrix Ba;
	RealMatrix ba;

	for (Article a : articles) {
		String aId = a.getId();
		if (!AMap.containsKey(aId)) {
			Aa = MatrixUtils.createRealIdentityMatrix(6);
			AMap.put(aId, Aa); // set as identity for now and we will update
								// in reward

			double[] zeros = { 0, 0, 0, 0, 0, 0 };
			ba = MatrixUtils.createColumnRealMatrix(zeros);
			bMap.put(aId, ba);

			double[][] BMapZeros = new double[6][36];
			for (double[] row : BMapZeros) {
				Arrays.fill(row, 0.0);
			}
			Ba = MatrixUtils.createRealMatrix(BMapZeros);
			BMap.put(aId, Ba);
		} else {
			Aa = AMap.get(aId);
			ba = bMap.get(aId);
			Ba = BMap.get(aId);
		}

		// Make column vector out of features
		RealMatrix xta = MatrixUtils
				.createColumnRealMatrix(a.getFeatures());
		RealMatrix zta = makeZta(
				MatrixUtils.createColumnRealMatrix(user.getFeatures()), xta);

		// Set up common variables
		RealMatrix A0Inverse = MatrixUtils.inverse(A0);
		RealMatrix AaInverse = MatrixUtils.inverse(Aa);
		RealMatrix ztaTranspose = zta.transpose();
		RealMatrix BaTranspose = Ba.transpose();
		RealMatrix xtaTranspose = xta.transpose();

		// Find theta
		RealMatrix theta = AaInverse.multiply(ba.subtract(Ba
				.multiply(BetaHat)));
		// Find sta
		RealMatrix staMatrix = ztaTranspose.multiply(A0Inverse).multiply(
				zta);
		staMatrix = staMatrix.subtract(ztaTranspose.multiply(A0Inverse)
				.multiply(BaTranspose).multiply(AaInverse).multiply(xta)
				.scalarMultiply(2));
		staMatrix = staMatrix.add(xtaTranspose.multiply(AaInverse)
				.multiply(xta));
		staMatrix = staMatrix.add(xtaTranspose.multiply(AaInverse)
				.multiply(Ba).multiply(A0Inverse).multiply(BaTranspose)
				.multiply(AaInverse).multiply(xta));

		// Find pta for arm
		RealMatrix ptaMatrix = ztaTranspose.multiply(BetaHat);
		ptaMatrix = ptaMatrix.add(xtaTranspose.multiply(theta));
		double ptaVal = ptaMatrix.getData()[0][0];
		double staVal = staMatrix.getData()[0][0];
		ptaVal = ptaVal + alpha * Math.sqrt(staVal);

		// Update argmax
		if (ptaVal > bestArmP) {
			bestArmP = ptaVal;
			bestA = a;
		}
	}
	return bestA;
}
开发者ID:tankle，项目名称:Bandit4J，代码行数:75，代码来源:HybridLinUCB.java


注：本文中的org.apache.commons.math3.linear.RealMatrix.transpose方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。