当前位置: 首页>>代码示例>>Java>>正文

Java RealVector.getEntry方法代码示例

本文整理汇总了Java中org.apache.commons.math3.linear.RealVector.getEntry方法的典型用法代码示例。如果您正苦于以下问题：Java RealVector.getEntry方法的具体用法？Java RealVector.getEntry怎么用？Java RealVector.getEntry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.RealVector的用法示例。


在下文中一共展示了RealVector.getEntry方法的10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: sim

import org.apache.commons.math3.linear.RealVector; //导入方法依赖的package包/类
@Override
public double sim(RealVector r1, RealVector r2, boolean sparse) {
    if (r1.getDimension() != r2.getDimension()) {
        return 0;
    }

    double min = 0.0;
    double sum = 0.0;

    for (int i = 0; i < r1.getDimension(); ++i) {
        if (r1.getEntry(i) > r2.getEntry(i)) {
            min += r2.getEntry(i);
        } else {
            min += r1.getEntry(i);
        }
        sum += r1.getEntry(i) + r2.getEntry(i);
    }

    if (sum == 0) {
        return 0;
    }

    double result = 2 * min / sum;
    return Math.abs(result);
}
开发者ID:Lambda-3，项目名称:Indra，代码行数:26，代码来源:DiceRelatednessFunction.java

示例2: sim

import org.apache.commons.math3.linear.RealVector; //导入方法依赖的package包/类
@Override
public double sim(RealVector r1, RealVector r2, boolean sparse) {
    if (r1.getDimension() != r2.getDimension()) {
        return 0;
    }

    double alpha = 0.99;
    double divergence = 0.0;

    for (int i = 0; i < r1.getDimension(); ++i) {
        if (r1.getEntry(i) > 0.0 && r2.getEntry(i) > 0.0) {
            divergence += r1.getEntry(i) * Math.log(r1.getEntry(i) / ((1 - alpha) * r1.getEntry(i) + alpha * r2.getEntry(i)));
        }
    }

    double result = (1 - (divergence / Math.sqrt(2 * Math.log(2))));
    return Math.abs(result);
}
开发者ID:Lambda-3，项目名称:Indra，代码行数:19，代码来源:AlphaSkewRelatednessFunction.java

示例3: sim

import org.apache.commons.math3.linear.RealVector; //导入方法依赖的package包/类
@Override
public double sim(RealVector r1, RealVector r2, boolean sparse) {
    if (r1.getDimension() != r2.getDimension()) {
        return 0;
    }

    double min = 0.0;
    double max = 0.0;

    for (int i = 0; i <r1.getDimension(); ++i) {
        if (r1.getEntry(i) > r2.getEntry(i)) {
            min +=r2.getEntry(i);
            max += r1.getEntry(i);
        } else {
            min += r1.getEntry(i);
            max += r2.getEntry(i);
        }
    }

    if (max == 0) {
        return 0;
    }

    return Math.abs(min / max);
}
开发者ID:Lambda-3，项目名称:Indra，代码行数:26，代码来源:Jaccard2RelatednessFunction.java

示例4: computeParameterStdErrors

import org.apache.commons.math3.linear.RealVector; //导入方法依赖的package包/类
/**
 * Calculates the standard errors of the regression parameters.
 * @param betaVar   the variance of the beta parameters
 * @throws DataFrameException   if this operation fails
 */
private void computeParameterStdErrors(RealVector betaVar) {
    try {
        final int offset = hasIntercept() ? 1 : 0;
        if (hasIntercept()) {
            final double interceptVariance = betaVar.getEntry(0);
            final double interceptStdError = Math.sqrt(interceptVariance);
            this.intercept.data().setDouble(0, Field.STD_ERROR, interceptStdError);
        }
        for (int i = 0; i < regressors.size(); i++) {
            final double betaVar_i = betaVar.getEntry(i + offset);
            final double betaStdError = Math.sqrt(betaVar_i);
            this.betas.data().setDouble(i, Field.STD_ERROR, betaStdError);
        }
    } catch (Exception ex) {
        throw new DataFrameException("Failed to calculate regression coefficient standard errors", ex);
    }
}
开发者ID:zavtech，项目名称:morpheus-core，代码行数:23，代码来源:XDataFrameLeastSquares.java

示例5: sim

import org.apache.commons.math3.linear.RealVector; //导入方法依赖的package包/类
@Override
public double sim(RealVector r1, RealVector r2, boolean sparse) {
    if (r1.getDimension() != r2.getDimension()) {
        return 0;
    }

    double divergence = 0.0;
    double avr = 0.0;

    for (int i = 0; i < r1.getDimension(); ++i) {
        avr = (r1.getEntry(i) + r2.getEntry(i)) / 2;

        if (r1.getEntry(i) > 0.0 && avr > 0.0) {
            divergence += r1.getEntry(i) * Math.log(r1.getEntry(i) / avr);
        }
    }
    for (int i = 0; i < r2.getDimension(); ++i) {
        avr = (r1.getEntry(i) + r2.getEntry(i)) / 2;

        if (r2.getEntry(i) > 0.0 && avr > 0.0) {
            divergence += r1.getEntry(i) * Math.log(r2.getEntry(i) / avr);
        }
    }

    double result = 1 - (divergence / (2 * Math.sqrt(2 * Math.log(2))));
    return Math.abs(result);
}
开发者ID:Lambda-3，项目名称:Indra，代码行数:28，代码来源:JensenShannonRelatednessFunction.java

示例6: thetaPred

import org.apache.commons.math3.linear.RealVector; //导入方法依赖的package包/类
private static double thetaPred(RealVector sumLogDens, RealVector thetas) {
    int thetaIndex = sumLogDens.getMaxIndex();
    double pred;
    if (thetaIndex == -1 || // NaN for all theta dists
            sumLogDens.getMaxValue() == sumLogDens.getMinValue()) { // uniform dist
        pred = Double.NaN;
    } else {
        pred = thetas.getEntry(thetaIndex);
    }
    return pred;
}
开发者ID:jasminezhoulab，项目名称:CancerLocator，代码行数:12，代码来源:CancerPrediction.java

示例7: ProjectiveMapping

import org.apache.commons.math3.linear.RealVector; //导入方法依赖的package包/类
/**
 * Constructor for more than 4 point pairs, finds a least-squares solution
 * for the homography parameters.
 * NOTE: this is UNFINISHED code!
 * @param P sequence of points (source)
 * @param Q sequence of points (target)
 * @param dummy unused (only to avoid duplicate signature)
 */
public ProjectiveMapping(Point2D[] P, Point2D[] Q, boolean dummy) {
	final int n = P.length;
	double[] ba = new double[2 * n];
	double[][] Ma = new double[2 * n][];
	for (int i = 0; i < n; i++) {
		double x = P[i].getX();
		double y = P[i].getY();
		double u = Q[i].getX();
		double v = Q[i].getY();
		ba[2 * i + 0] = u;
		ba[2 * i + 1] = v;
		Ma[2 * i + 0] = new double[] { x, y, 1, 0, 0, 0, -u * x, -u * y };
		Ma[2 * i + 1] = new double[] { 0, 0, 0, x, y, 1, -v * x, -v * y };
	}
	
	RealMatrix M = MatrixUtils.createRealMatrix(Ma);
	RealVector b = MatrixUtils.createRealVector(ba);
	DecompositionSolver solver = new SingularValueDecomposition(M).getSolver();
	RealVector h = solver.solve(b);
	a00 = h.getEntry(0);
	a01 = h.getEntry(1);
	a02 = h.getEntry(2);
	a10 = h.getEntry(3);
	a11 = h.getEntry(4);
	a12 = h.getEntry(5);
	a20 = h.getEntry(6);
	a21 = h.getEntry(7);
	a22 = 1;
}
开发者ID:imagingbook，项目名称:imagingbook-common，代码行数:38，代码来源:ProjectiveMapping.java

示例8: solveFToF

import org.apache.commons.math3.linear.RealVector; //导入方法依赖的package包/类
public float[] solveFToF(float[] b) {
  RealVector bVec = new ArrayRealVector(b.length);
  for (int i = 0; i < b.length; i++) {
    bVec.setEntry(i, b[i]);
  }
  RealVector resultVec = solver.solve(bVec);
  float[] result = new float[resultVec.getDimension()];
  for (int i = 0; i < result.length; i++) {
    result[i] = (float) resultVec.getEntry(i);
  }
  return result;
}
开发者ID:oncewang，项目名称:oryx2，代码行数:13，代码来源:Solver.java

示例9: computeParameterSignificance

import org.apache.commons.math3.linear.RealVector; //导入方法依赖的package包/类
/**
 * Computes the T-stats and the P-Value for all regression parameters
 */
private void computeParameterSignificance(RealVector betaVector) {
    try {
        final double residualDF = frame.rows().count() - (regressors.size() + 1);
        final TDistribution distribution = new TDistribution(residualDF);
        final double interceptParam = betaVector.getEntry(0);
        final double interceptStdError = intercept.data().getDouble(0, Field.STD_ERROR);
        final double interceptTStat = interceptParam / interceptStdError;
        final double interceptPValue = distribution.cumulativeProbability(-Math.abs(interceptTStat)) * 2d;
        final double interceptCI = interceptStdError * distribution.inverseCumulativeProbability(1d - alpha / 2d);
        this.intercept.data().setDouble(0, Field.PARAMETER, interceptParam);
        this.intercept.data().setDouble(0, Field.T_STAT, interceptTStat);
        this.intercept.data().setDouble(0, Field.P_VALUE, interceptPValue);
        this.intercept.data().setDouble(0, Field.CI_LOWER, interceptParam - interceptCI);
        this.intercept.data().setDouble(0, Field.CI_UPPER, interceptParam + interceptCI);
        final int offset = hasIntercept() ? 1 : 0;
        for (int i=0; i<regressors.size(); ++i) {
            final C regressor = regressors.get(i);
            final double betaParam = betaVector.getEntry(i + offset);
            final double betaStdError = betas.data().getDouble(regressor, Field.STD_ERROR);
            final double tStat = betaParam / betaStdError;
            final double pValue = distribution.cumulativeProbability(-Math.abs(tStat)) * 2d;
            final double betaCI = betaStdError * distribution.inverseCumulativeProbability(1d - alpha / 2d);
            this.betas.data().setDouble(regressor, Field.PARAMETER, betaParam);
            this.betas.data().setDouble(regressor, Field.T_STAT, tStat);
            this.betas.data().setDouble(regressor, Field.P_VALUE, pValue);
            this.betas.data().setDouble(regressor, Field.CI_LOWER, betaParam - betaCI);
            this.betas.data().setDouble(regressor, Field.CI_UPPER, betaParam + betaCI);
        }
    } catch (Exception ex) {
        throw new DataFrameException("Failed to compute regression coefficient t-stats and p-values", ex);
    }
}
开发者ID:zavtech，项目名称:morpheus-core，代码行数:36，代码来源:XDataFrameLeastSquares.java

示例10: run

import org.apache.commons.math3.linear.RealVector; //导入方法依赖的package包/类
@Override
public void run() {
	mixDens[featureIdx] = new BlockRealMatrix(nBetas,nThetas);
	for(int j = 0; j < nThetas;j++) {
		double theta = thetas.getEntry(j);
		RealVector betaDens = new ArrayRealVector(nBetas);
		for (int k = 0; k < nBetas; k++) {
			double beta = betas.getEntry(k);
			double lowerBound = FastMath.max(0, (beta - 1 + theta) / theta);
			if (Double.isNaN(lowerBound)) lowerBound = 0;
			double upperBound = FastMath.min(1,beta/theta);
			if (Double.isNaN(upperBound)) upperBound = 1;
			double step = (upperBound-lowerBound)/(nPoints-1);
			RealVector dens = new ArrayRealVector(nPoints);
			RealVector points = new ArrayRealVector(nPoints);
			RealVector allTumorDens = new ArrayRealVector(nPoints);
			RealVector allNormalDens = new ArrayRealVector(nPoints);
			RealVector allNormalDensRev = new ArrayRealVector(nPoints);
			// tumor
			for (int l = 0; l < nPoints; l++) {
				double tumorValue = lowerBound + l * step;
				points.setEntry(l, tumorValue);
				if (tumorValue == 0) tumorValue = 0.0001;
				if (tumorValue == 1) tumorValue = 0.9999;
				allTumorDens.setEntry(l,tumorDist.density(tumorValue));
			}
			// adjust the densities
			double calProb = tumorDist.probability(lowerBound,upperBound);
			double estProb = CancerLocator.integSimpson(points,allTumorDens);
			if (estProb!=0)	{
				allTumorDens.mapMultiplyToSelf(calProb/estProb);
			}
			else {
				allTumorDens.mapAddToSelf(1.0/allTumorDens.getDimension());
			}
			// normal
			RealVector normalPoints = new ArrayRealVector(nPoints);
			for (int l = 0; l < nPoints; l++) {
				double normalValue = (beta-theta*points.getEntry(l))/(1-theta);
				normalPoints.setEntry(nPoints-l-1,normalValue);
				if (normalValue == 0) normalValue = 0.0001;
				if (normalValue == 1) normalValue = 0.9999;
				double normalDens = normalDist.density(normalValue);
				allNormalDens.setEntry(l,normalDens);
				allNormalDensRev.setEntry(nPoints-l-1,normalDens);
			}
			calProb = normalDist.probability((beta-theta*upperBound)/(1-theta),(beta-theta*lowerBound)/(1-theta));
			estProb = CancerLocator.integSimpson(normalPoints, allNormalDensRev);
			if (estProb!=0)	{
				allNormalDens.mapMultiplyToSelf(calProb/estProb);
			}
			else {
				allNormalDens.mapAddToSelf(1.0/allNormalDens.getDimension());
			}
			//mixture
			for (int l = 0; l < nPoints; l++) {
				dens.setEntry(l,allTumorDens.getEntry(l)*allNormalDens.getEntry(l));
			}
			betaDens.setEntry(k,CancerLocator.integSimpson(points,dens));
		}
		double normTerm = CancerLocator.integSimpson(betas,betaDens); //normalization term
		if (normTerm!=0) {
			betaDens.mapDivideToSelf(normTerm);
		}
		else {
			betaDens.mapAddToSelf(1.0/betaDens.getDimension());
		}
		mixDens[featureIdx].setColumnVector(j, betaDens);
	}
}
开发者ID:jasminezhoulab，项目名称:CancerLocator，代码行数:71，代码来源:MixModel.java


注：本文中的org.apache.commons.math3.linear.RealVector.getEntry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。