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类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);
}
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;
}
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() );
}
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);
}
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() );
}