本文整理汇总了C++中arma::Col::zeros方法的典型用法代码示例。如果您正苦于以下问题：C++ Col::zeros方法的具体用法？C++ Col::zeros怎么用？C++ Col::zeros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arma::Col的用法示例。
在下文中一共展示了Col::zeros方法的2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C++代码示例。
示例1: rules
double PellegMooreKMeans<MetricType, MatType>::Iterate(
const arma::mat& centroids,
arma::mat& newCentroids,
arma::Col<size_t>& counts)
{
newCentroids.zeros(centroids.n_rows, centroids.n_cols);
counts.zeros(centroids.n_cols);
// Create rules object.
typedef PellegMooreKMeansRules<MetricType, TreeType> RulesType;
RulesType rules(dataset, centroids, newCentroids, counts, metric);
// Use single-tree traverser.
typename TreeType::template SingleTreeTraverser<RulesType> traverser(rules);
// Now, do a traversal with a fake query index (since the query index is
// irrelevant; we are checking each node with all clusters.
traverser.Traverse(0, *tree);
distanceCalculations += rules.DistanceCalculations();
// Now, calculate how far the clusters moved, after normalizing them.
double residual = 0.0;
for (size_t c = 0; c < centroids.n_cols; ++c)
{
if (counts[c] > 0)
{
newCentroids.col(c) /= counts(c);
residual += std::pow(metric.Evaluate(centroids.col(c),
newCentroids.col(c)), 2.0);
}
}
distanceCalculations += centroids.n_cols;
return std::sqrt(residual);
}示例2: rules
double DualTreeKMeans<MetricType, MatType, TreeType>::Iterate(
const arma::mat& centroids,
arma::mat& newCentroids,
arma::Col<size_t>& counts)
{
newCentroids.zeros(centroids.n_rows, centroids.n_cols);
counts.zeros(centroids.n_cols);
if (clusterDistances.n_elem != centroids.n_cols + 1)
{
clusterDistances.set_size(centroids.n_cols + 1);
clusterDistances.fill(DBL_MAX / 2.0); // To prevent overflow.
}
// Build a tree on the centroids.
std::vector<size_t> oldFromNewCentroids;
TreeType* centroidTree = BuildTree<TreeType>(
const_cast<typename TreeType::Mat&>(centroids), oldFromNewCentroids);
// Now run the dual-tree algorithm.
typedef DualTreeKMeansRules<MetricType, TreeType> RulesType;
RulesType rules(dataset, centroids, newCentroids, counts, oldFromNewCentroids,
iteration, clusterDistances, distances, assignments, distanceIteration,
metric);
// Use the dual-tree traverser.
//typename TreeType::template DualTreeTraverser<RulesType> traverser(rules);
typename TreeType::template BreadthFirstDualTreeTraverser<RulesType>
traverser(rules);
traverser.Traverse(*centroidTree, *tree);
distanceCalculations += rules.DistanceCalculations();
// Now, calculate how far the clusters moved, after normalizing them.
double residual = 0.0;
clusterDistances.zeros();
for (size_t c = 0; c < centroids.n_cols; ++c)
{
if (counts[c] == 0)
{
newCentroids.col(c).fill(DBL_MAX); // Should have happened anyway I think.
}
else
{
const size_t oldCluster = oldFromNewCentroids[c];
newCentroids.col(oldCluster) /= counts(oldCluster);
const double dist = metric.Evaluate(centroids.col(c),
newCentroids.col(oldCluster));
if (dist > clusterDistances[centroids.n_cols])
clusterDistances[centroids.n_cols] = dist;
clusterDistances[oldCluster] = dist;
residual += std::pow(dist, 2.0);
}
}
Log::Info << clusterDistances.t();
delete centroidTree;
++iteration;
return std::sqrt(residual);
}