gov.sandia.cognition.math.matrix.Vector.norm2()方法的使用及代码示例
本文整理了Java中gov.sandia.cognition.math.matrix.Vector.norm2()方法的一些代码示例，展示了Vector.norm2()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Vector.norm2()方法的具体详情如下：
包路径：gov.sandia.cognition.math.matrix.Vector
类名称：Vector
方法名：norm2
Vector.norm2介绍
暂无
代码示例
代码示例来源：origin: gov.sandia.foundry/gov-sandia-cognition-common-core
/**
* Divides all of the given elements of the vector by the 2-norm (the square
* root of the sum of the squared values of the elements). If the 2-norm is
* zero (which means all the elements are zero), then the vector is not
* modified.
*
* @param vector
* The vector to divide the elements by the 2-norm. It is modified by
* this method.
*/
public static void divideByNorm2Equals(
final Vector vector)
{
final double norm2 = vector.norm2();
if (norm2 != 0.0)
{
vector.scaleEquals(1.0 / norm2);
}
}代码示例来源：origin: algorithmfoundry/Foundry
/**
* Divides all of the given elements of the vector by the 2-norm (the square
* root of the sum of the squared values of the elements). If the 2-norm is
* zero (which means all the elements are zero), then the vector is not
* modified.
*
* @param vector
* The vector to divide the elements by the 2-norm. It is modified by
* this method.
*/
public static void divideByNorm2Equals(
final Vector vector)
{
final double norm2 = vector.norm2();
if (norm2 != 0.0)
{
vector.scaleEquals(1.0 / norm2);
}
}代码示例来源：origin: algorithmfoundry/Foundry
/**
* Divides all of the given elements of the vector by the 2-norm (the square
* root of the sum of the squared values of the elements). If the 2-norm is
* zero (which means all the elements are zero), then the vector is not
* modified.
*
* @param vector
* The vector to divide the elements by the 2-norm. It is modified by
* this method.
*/
public static void divideByNorm2Equals(
final Vector vector)
{
final double norm2 = vector.norm2();
if (norm2 != 0.0)
{
vector.scaleEquals(1.0 / norm2);
}
}代码示例来源：origin: algorithmfoundry/Foundry
@Override
protected void initialize(
final LinearBinaryCategorizer target,
final Vector input,
final boolean actualCategory)
{
final double norm = input.norm2();
if (norm != 0.0)
{
final Vector weights = this.getVectorFactory().copyVector(input);
final double actual = actualCategory ? +1.0 : -1.0;
weights.scaleEquals(actual / input.norm2());
target.setWeights(weights);
}
}代码示例来源：origin: algorithmfoundry/Foundry
@Override
protected void initialize(
final LinearBinaryCategorizer target,
final Vector input,
final boolean actualCategory)
{
final double norm = input.norm2();
if (norm != 0.0)
{
final Vector weights = this.getVectorFactory().copyVector(input);
final double actual = actualCategory ? +1.0 : -1.0;
weights.scaleEquals(actual / input.norm2());
target.setWeights(weights);
}
}代码示例来源：origin: gov.sandia.foundry/gov-sandia-cognition-learning-core
@Override
protected void initialize(
final LinearBinaryCategorizer target,
final Vector input,
final boolean actualCategory)
{
final double norm = input.norm2();
if (norm != 0.0)
{
final Vector weights = this.getVectorFactory().copyVector(input);
final double actual = actualCategory ? +1.0 : -1.0;
weights.scaleEquals(actual / input.norm2());
target.setWeights(weights);
}
}代码示例来源：origin: algorithmfoundry/Foundry
double lambda = vlambda.norm2();
double dp = vunithat.minus( vunit ).norm2();
double dn = vunithat.plus( vunit ).norm2();
if (dn < dp)代码示例来源：origin: gov.sandia.foundry/gov-sandia-cognition-learning-core
/**
* Evaluate the this function on the provided cluster.
*
* @param cluster The cluster to calculate the function on.
* @return The result of applying this function to the cluster.
*/
public double evaluate(NormalizedCentroidCluster<V> cluster)
{
double total = 1.0;
Vector centroid = cluster.getCentroid().convertToVector();
Vector normalizedCentroid
= cluster.getNormalizedCentroid().convertToVector();
//if centroid is 0.0, cosine measure returns 0.0
if (centroid.norm2() != 0.0)
{
total -= centroid.dotProduct(normalizedCentroid) / centroid.norm2();
}
total *= cluster.getMembers().size();
return total;
}代码示例来源：origin: algorithmfoundry/Foundry
/**
* Evaluate the this function on the provided cluster.
*
* @param cluster The cluster to calculate the function on.
* @return The result of applying this function to the cluster.
*/
public double evaluate(NormalizedCentroidCluster<V> cluster)
{
double total = 1.0;
Vector centroid = cluster.getCentroid().convertToVector();
Vector normalizedCentroid
= cluster.getNormalizedCentroid().convertToVector();
//if centroid is 0.0, cosine measure returns 0.0
if (centroid.norm2() != 0.0)
{
total -= centroid.dotProduct(normalizedCentroid) / centroid.norm2();
}
total *= cluster.getMembers().size();
return total;
}代码示例来源：origin: algorithmfoundry/Foundry
/**
* Evaluate the this function on the provided cluster.
*
* @param cluster The cluster to calculate the function on.
* @return The result of applying this function to the cluster.
*/
public double evaluate(NormalizedCentroidCluster<V> cluster)
{
double total = 1.0;
Vector centroid = cluster.getCentroid().convertToVector();
Vector normalizedCentroid
= cluster.getNormalizedCentroid().convertToVector();
//if centroid is 0.0, cosine measure returns 0.0
if (centroid.norm2() != 0.0)
{
total -= centroid.dotProduct(normalizedCentroid) / centroid.norm2();
}
total *= cluster.getMembers().size();
return total;
}代码示例来源：origin: openimaj/openimaj
public boolean test_backtrack(Matrix W, Matrix grad, Matrix prox, double eta){
Matrix tmp = prox.clone();
tmp.minusEquals(W);
Vector tmpvec = tmp.getColumn(0);
return (
eval(prox) <= eval(W) + grad.getColumn(0).dotProduct(tmpvec) + 0.5*eta*tmpvec.norm2());
}
}代码示例来源：origin: gov.sandia.foundry/gov-sandia-cognition-learning-core
vectorFactory.createUniformRandom(this.dimensionality,
-initializationRange, initializationRange, this.random);
if (initialWeights.norm2() < (1.0 / sqrtLambda))代码示例来源：origin: algorithmfoundry/Foundry
vectorFactory.createUniformRandom(this.dimensionality,
-initializationRange, initializationRange, this.random);
if (initialWeights.norm2() < (1.0 / sqrtLambda))代码示例来源：origin: algorithmfoundry/Foundry
vectorFactory.createUniformRandom(this.dimensionality,
-initializationRange, initializationRange, this.random);
if (initialWeights.norm2() < (1.0 / sqrtLambda))代码示例来源：origin: gov.sandia.foundry/gov-sandia-cognition-learning-core
@Override
protected boolean step()
{
SumSquaredErrorCostFunction.Cache cost =
SumSquaredErrorCostFunction.Cache.compute( this.getResult(), this.getData() );
Vector lastParameters = this.lineFunction.getVectorOffset();
Vector direction = cost.JtJ.solve(cost.Jte);
double directionNorm = direction.norm2();
if( directionNorm > STEP_MAX )
{
direction.scaleEquals( STEP_MAX / directionNorm );
}
this.lineFunction.setDirection( direction );
InputOutputPair<Vector,Double> result = this.getLineMinimizer().minimizeAlongDirection(
this.lineFunction, cost.parameterCost, cost.Jte );
this.lineFunction.setVectorOffset( result.getInput() );
this.setResultCost( result.getOutput() );
Vector delta = result.getInput().minus( lastParameters );
this.getResult().convertFromVector( result.getInput() );
return !MinimizationStoppingCriterion.convergence(
result.getInput(), result.getOutput(), cost.Jte, delta, this.getTolerance() );
}代码示例来源：origin: algorithmfoundry/Foundry
@Override
protected boolean step()
{
SumSquaredErrorCostFunction.Cache cost =
SumSquaredErrorCostFunction.Cache.compute( this.getResult(), this.getData() );
Vector lastParameters = this.lineFunction.getVectorOffset();
Vector direction = cost.JtJ.solve(cost.Jte);
double directionNorm = direction.norm2();
if( directionNorm > STEP_MAX )
{
direction.scaleEquals( STEP_MAX / directionNorm );
}
this.lineFunction.setDirection( direction );
InputOutputPair<Vector,Double> result = this.getLineMinimizer().minimizeAlongDirection(
this.lineFunction, cost.parameterCost, cost.Jte );
this.lineFunction.setVectorOffset( result.getInput() );
this.setResultCost( result.getOutput() );
Vector delta = result.getInput().minus( lastParameters );
this.getResult().convertFromVector( result.getInput() );
return !MinimizationStoppingCriterion.convergence(
result.getInput(), result.getOutput(), cost.Jte, delta, this.getTolerance() );
}代码示例来源：origin: algorithmfoundry/Foundry
@Override
protected boolean step()
{
SumSquaredErrorCostFunction.Cache cost =
SumSquaredErrorCostFunction.Cache.compute( this.getResult(), this.getData() );
Vector lastParameters = this.lineFunction.getVectorOffset();
Vector direction = cost.JtJ.solve(cost.Jte);
double directionNorm = direction.norm2();
if( directionNorm > STEP_MAX )
{
direction.scaleEquals( STEP_MAX / directionNorm );
}
this.lineFunction.setDirection( direction );
InputOutputPair<Vector,Double> result = this.getLineMinimizer().minimizeAlongDirection(
this.lineFunction, cost.parameterCost, cost.Jte );
this.lineFunction.setVectorOffset( result.getInput() );
this.setResultCost( result.getOutput() );
Vector delta = result.getInput().minus( lastParameters );
this.getResult().convertFromVector( result.getInput() );
return !MinimizationStoppingCriterion.convergence(
result.getInput(), result.getOutput(), cost.Jte, delta, this.getTolerance() );
}代码示例来源：origin: gov.sandia.foundry/gov-sandia-cognition-learning-core
f.convertFromVector( wnew );
double delta = wnew.minus( w ).norm2();
return delta > this.getTolerance();代码示例来源：origin: algorithmfoundry/Foundry
f.convertFromVector( wnew );
double delta = wnew.minus( w ).norm2();
return delta > this.getTolerance();代码示例来源：origin: algorithmfoundry/Foundry
f.convertFromVector( wnew );
double delta = wnew.minus( w ).norm2();
return delta > this.getTolerance();内容来源于网络，如有侵权，请联系作者删除！
