cluster-utils.h File Reference

#include <vector>
#include "matrix/matrix-lib.h"
#include "itf/clusterable-itf.h"

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Classes
struct	RefineClustersOptions
struct	ClusterKMeansOptions
struct	TreeClusterOptions

Namespaces
	kaldi
	This code computes Goodness of Pronunciation (GOP) and extracts phone-level pronunciation feature for mispronunciations detection tasks, the reference:

Functions
BaseFloat	SumClusterableObjf (const std::vector< Clusterable * > &vec)
	Returns the total objective function after adding up all the statistics in the vector (pointers may be NULL). More...
BaseFloat	SumClusterableNormalizer (const std::vector< Clusterable * > &vec)
	Returns the total normalizer (usually count) of the cluster (pointers may be NULL). More...
Clusterable *	SumClusterable (const std::vector< Clusterable * > &vec)
	Sums stats (ptrs may be NULL). Returns NULL if no non-NULL stats present. More...
void	EnsureClusterableVectorNotNull (std::vector< Clusterable * > *stats)
	Fills in any (NULL) holes in "stats" vector, with empty stats, because certain algorithms require non-NULL stats. More...
void	AddToClusters (const std::vector< Clusterable * > &stats, const std::vector< int32 > &assignments, std::vector< Clusterable * > *clusters)
	Given stats and a vector "assignments" of the same size (that maps to cluster indices), sums the stats up into "clusters." It will add to any stats already present in "clusters" (although typically "clusters" will be empty when called), and it will extend with NULL pointers for any unseen indices. More...
void	AddToClustersOptimized (const std::vector< Clusterable * > &stats, const std::vector< int32 > &assignments, const Clusterable &total, std::vector< Clusterable * > *clusters)
	AddToClustersOptimized does the same as AddToClusters (it sums up the stats within each cluster, except it uses the sum of all the stats ("total") to optimize the computation for speed, if possible. More...
BaseFloat	ClusterBottomUp (const std::vector< Clusterable * > &points, BaseFloat thresh, int32 min_clust, std::vector< Clusterable * > *clusters_out, std::vector< int32 > *assignments_out)
	A bottom-up clustering algorithm. More...
BaseFloat	ClusterBottomUpCompartmentalized (const std::vector< std::vector< Clusterable * > > &points, BaseFloat thresh, int32 min_clust, std::vector< std::vector< Clusterable * > > *clusters_out, std::vector< std::vector< int32 > > *assignments_out)
	This is a bottom-up clustering where the points are pre-clustered in a set of compartments, such that only points in the same compartment are clustered together. More...
BaseFloat	RefineClusters (const std::vector< Clusterable * > &points, std::vector< Clusterable * > *clusters, std::vector< int32 > *assignments, RefineClustersOptions cfg=RefineClustersOptions())
	RefineClusters is mainly used internally by other clustering algorithms. More...
BaseFloat	ClusterKMeans (const std::vector< Clusterable * > &points, int32 num_clust, std::vector< Clusterable * > *clusters_out, std::vector< int32 > *assignments_out, ClusterKMeansOptions cfg=ClusterKMeansOptions())
	ClusterKMeans is a K-means-like clustering algorithm. More...
BaseFloat	TreeCluster (const std::vector< Clusterable * > &points, int32 max_clust, std::vector< Clusterable * > *clusters_out, std::vector< int32 > *assignments_out, std::vector< int32 > *clust_assignments_out, int32 *num_leaves_out, TreeClusterOptions cfg=TreeClusterOptions())
	TreeCluster is a top-down clustering algorithm, using a binary tree (not necessarily balanced). More...
BaseFloat	ClusterTopDown (const std::vector< Clusterable * > &points, int32 max_clust, std::vector< Clusterable * > *clusters_out, std::vector< int32 > *assignments_out, TreeClusterOptions cfg=TreeClusterOptions())
	A clustering algorithm that internally uses TreeCluster, but does not give you the information about the structure of the tree. More...