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Java Vector.stack方法代码示例

本文整理汇总了Java中gov.sandia.cognition.math.matrix.Vector.stack方法的典型用法代码示例。如果您正苦于以下问题:Java Vector.stack方法的具体用法?Java Vector.stack怎么用?Java Vector.stack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gov.sandia.cognition.math.matrix.Vector的用法示例。


在下文中一共展示了Vector.stack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: convertToVector

import gov.sandia.cognition.math.matrix.Vector; //导入方法依赖的package包/类
@Override
public Vector convertToVector()
{
    Vector dof =
        VectorFactory.getDefault().copyValues( this.getDegreesOfFreedom() );
    Vector matrix = this.getInverseScale().convertToVector();
    return dof.stack(matrix);
}
 
开发者ID:algorithmfoundry,项目名称:Foundry,代码行数:9,代码来源:InverseWishartDistribution.java

示例2: convertToVector

import gov.sandia.cognition.math.matrix.Vector; //导入方法依赖的package包/类
public Vector convertToVector()
{
    Vector parameters = VectorFactory.getDefault().copyValues(
        this.getDegreesOfFreedom() );
    parameters = parameters.stack( this.getMean() );
    parameters = parameters.stack( this.getPrecision().convertToVector() );
    return parameters;
}
 
开发者ID:algorithmfoundry,项目名称:Foundry,代码行数:9,代码来源:MultivariateStudentTDistribution.java

示例3: testKnownConvertToVector

import gov.sandia.cognition.math.matrix.Vector; //导入方法依赖的package包/类
@Override
public void testKnownConvertToVector()
{
    System.out.println( "known convertToVector" );

    MultivariateGaussianInverseGammaDistribution instance = this.createInstance();
    Vector v1 = instance.getGaussian().convertToVector();
    Vector v2 = instance.getInverseGamma().convertToVector();
    Vector expected = v1.stack(v2);
    assertEquals( expected, instance.convertToVector() );
}
 
开发者ID:algorithmfoundry,项目名称:Foundry,代码行数:12,代码来源:MultivariateGaussianInverseGammaDistributionTest.java

示例4: convertToVector

import gov.sandia.cognition.math.matrix.Vector; //导入方法依赖的package包/类
@Override
public Vector convertToVector()
{
    Vector p = super.convertToVector();
    return p.stack( this.getBias() );
}
 
开发者ID:algorithmfoundry,项目名称:Foundry,代码行数:7,代码来源:MultivariateDiscriminantWithBias.java

示例5: evaluate

import gov.sandia.cognition.math.matrix.Vector; //导入方法依赖的package包/类
public Vector evaluate(
    Vector input )
{
    int M = input.getDimensionality();
    this.getState().addLast( input );
    
    // This is the mean of the arithmetic series from 0..(num-1):
    // 1->0;
    // 2->0.5;
    // 3->1;
    // 4->0+1+2+3->1.5;
    // 5->0+1+2+3+4->2;
    // Thus, meanx == (num-1) / 2.0;        
    int num = this.getState().size();
    
    Vector ms;
    Vector bs;
    RingAccumulator<Vector> sumy = new RingAccumulator<Vector>( this.getState() );
    Vector meany = sumy.getMean();
    if( num > 1 )
    {
        double meanx = -(num - 1) / 2.0;
        double sxx = 0.0;
        RingAccumulator<Vector> sumxy = new RingAccumulator<Vector>();
        int x = -num + 1;
        for (Vector y : this.getState())
        {
            double dx = x - meanx;
            sxx += dx * dx;
            sumxy.accumulate( y.minus( meany ).scale( dx ) );
            x++;
        }
        ms = sumxy.scaleSum( 1.0/sxx );
        bs = meany.minus( ms.scale(meanx) );
    }
    else
    {
        ms = VectorFactory.getDefault().createVector(M);
        bs = meany;
    }
    
    return bs.stack(ms);

}
 
开发者ID:algorithmfoundry,项目名称:Foundry,代码行数:45,代码来源:LinearRegressionCoefficientExtractor.java

示例6: convertToVector

import gov.sandia.cognition.math.matrix.Vector; //导入方法依赖的package包/类
public Vector convertToVector()
{
    Vector c = VectorFactory.getDefault().copyValues( this.covarianceDivisor );
    c = c.stack( this.gaussian.getMean() );
    return c.stack( this.inverseWishart.convertToVector() );
}
 
开发者ID:algorithmfoundry,项目名称:Foundry,代码行数:7,代码来源:NormalInverseWishartDistribution.java


注:本文中的gov.sandia.cognition.math.matrix.Vector.stack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。