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authorDeterminant <[email protected]>2015-08-14 11:51:42 +0800
committerDeterminant <[email protected]>2015-08-14 11:51:42 +0800
commit96a32415ab43377cf1575bd3f4f2980f58028209 (patch)
tree30a2d92d73e8f40ac87b79f6f56e227bfc4eea6e /kaldi_io/src/kaldi/tree/cluster-utils.h
parentc177a7549bd90670af4b29fa813ddea32cfe0f78 (diff)
add implementation for kaldi io (by ymz)
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+// tree/cluster-utils.h
+
+// Copyright 2012 Arnab Ghoshal
+// Copyright 2009-2011 Microsoft Corporation; Saarland University
+
+// See ../../COPYING for clarification regarding multiple authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
+// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
+// MERCHANTABLITY OR NON-INFRINGEMENT.
+// See the Apache 2 License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef KALDI_TREE_CLUSTER_UTILS_H_
+#define KALDI_TREE_CLUSTER_UTILS_H_
+
+#include <vector>
+#include "matrix/matrix-lib.h"
+#include "itf/clusterable-itf.h"
+
+namespace kaldi {
+
+/// \addtogroup clustering_group_simple
+/// @{
+
+/// Returns the total objective function after adding up all the
+/// statistics in the vector (pointers may be NULL).
+BaseFloat SumClusterableObjf(const std::vector<Clusterable*> &vec);
+
+/// Returns the total normalizer (usually count) of the cluster (pointers may be NULL).
+BaseFloat SumClusterableNormalizer(const std::vector<Clusterable*> &vec);
+
+/// Sums stats (ptrs may be NULL). Returns NULL if no non-NULL stats present.
+Clusterable *SumClusterable(const std::vector<Clusterable*> &vec);
+
+/** Fills in any (NULL) holes in "stats" vector, with empty stats, because
+ * certain algorithms require non-NULL stats. If "stats" nonempty, requires it
+ * to contain at least one non-NULL pointer that we can call Copy() on.
+ */
+void EnsureClusterableVectorNotNull(std::vector<Clusterable*> *stats);
+
+
+/** 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. Call EnsureClusterableStatsNotNull afterwards if you want to ensure
+ * all non-NULL clusters. Pointer in "clusters" are owned by caller. Pointers in
+ * "stats" do not have to be non-NULL.
+ */
+void AddToClusters(const std::vector<Clusterable*> &stats,
+ const std::vector<int32> &assignments,
+ 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. This will generally only be
+/// a significant speedup in the case where there are just two clusters, which
+/// can happen in algorithms that are doing binary splits; the idea is that we
+/// sum up all the stats in one cluster (the one with the fewest points in it),
+/// and then subtract from the total.
+void AddToClustersOptimized(const std::vector<Clusterable*> &stats,
+ const std::vector<int32> &assignments,
+ const Clusterable &total,
+ std::vector<Clusterable*> *clusters);
+
+/// @} end "addtogroup clustering_group_simple"
+
+/// \addtogroup clustering_group_algo
+/// @{
+
+// Note, in the algorithms below, it is assumed that the input "points" (which
+// is std::vector<Clusterable*>) is all non-NULL.
+
+/** A bottom-up clustering algorithm. There are two parameters that control how
+ * many clusters we get: a "max_merge_thresh" which is a threshold for merging
+ * clusters, and a min_clust which puts a floor on the number of clusters we want. Set
+ * max_merge_thresh = large to use the min_clust only, or min_clust to 0 to use
+ * the max_merge_thresh only.
+ *
+ * The algorithm is:
+ * \code
+ * while (num-clusters > min_clust && smallest_merge_cost <= max_merge_thresh)
+ * merge the closest two clusters.
+ * \endcode
+ *
+ * @param points [in] Points to be clustered (may not contain NULL pointers)
+ * @param thresh [in] Threshold on cost change from merging clusters; clusters
+ * won't be merged if the cost is more than this
+ * @param min_clust [in] Minimum number of clusters desired; we'll stop merging
+ * after reaching this number.
+ * @param clusters_out [out] If non-NULL, will be set to a vector of size equal
+ * to the number of output clusters, containing the clustered
+ * statistics. Must be empty when called.
+ * @param assignments_out [out] If non-NULL, will be resized to the number of
+ * points, and each element is the index of the cluster that point
+ * was assigned to.
+ * @return Returns the total objf change relative to all clusters being separate, which is
+ * a negative. Note that this is not the same as what the other clustering algorithms return.
+ */
+BaseFloat ClusterBottomUp(const std::vector<Clusterable*> &points,
+ BaseFloat thresh,
+ int32 min_clust,
+ std::vector<Clusterable*> *clusters_out,
+ 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. The compartment and pair of points with the smallest merge cost
+ * is selected and the points are clustered. The result stays in the same
+ * compartment. The code does not merge compartments, and hence assumes that
+ * the number of compartments is smaller than the 'min_clust' option.
+ * The clusters in "clusters_out" are newly allocated and owned by the caller.
+ */
+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);
+
+
+struct RefineClustersOptions {
+ int32 num_iters; // must be >= 0. If zero, does nothing.
+ int32 top_n; // must be >= 2.
+ RefineClustersOptions() : num_iters(100), top_n(5) {}
+ RefineClustersOptions(int32 num_iters_in, int32 top_n_in)
+ : num_iters(num_iters_in), top_n(top_n_in) {}
+ // include Write and Read functions because this object gets written/read as
+ // part of the QuestionsForKeyOptions class.
+ void Write(std::ostream &os, bool binary) const;
+ void Read(std::istream &is, bool binary);
+};
+
+/** RefineClusters is mainly used internally by other clustering algorithms.
+ *
+ * It starts with a given assignment of points to clusters and
+ * keeps trying to improve it by moving points from cluster to cluster, up to
+ * a maximum number of iterations.
+ *
+ * "clusters" and "assignments" are both input and output variables, and so
+ * both MUST be non-NULL.
+ *
+ * "top_n" (>=2) is a pruning value: more is more exact, fewer is faster. The
+ * algorithm initially finds the "top_n" closest clusters to any given point,
+ * and from that point only consider move to those "top_n" clusters. Since
+ * RefineClusters is called multiple times from ClusterKMeans (for instance),
+ * this is not really a limitation.
+ */
+BaseFloat RefineClusters(const std::vector<Clusterable*> &points,
+ std::vector<Clusterable*> *clusters /*non-NULL*/,
+ std::vector<int32> *assignments /*non-NULL*/,
+ RefineClustersOptions cfg = RefineClustersOptions());
+
+struct ClusterKMeansOptions {
+ RefineClustersOptions refine_cfg;
+ int32 num_iters;
+ int32 num_tries; // if >1, try whole procedure >once and pick best.
+ bool verbose;
+ ClusterKMeansOptions()
+ : refine_cfg(), num_iters(20), num_tries(2), verbose(true) {}
+};
+
+/** ClusterKMeans is a K-means-like clustering algorithm. It starts with
+ * pseudo-random initialization of points to clusters and uses RefineClusters
+ * to iteratively improve the cluster assignments. It does this for
+ * multiple iterations and picks the result with the best objective function.
+ *
+ *
+ * ClusterKMeans implicitly uses Rand(). It will not necessarily return
+ * the same value on different calls. Use sRand() if you want consistent
+ * results.
+ * The algorithm used in ClusterKMeans is a "k-means-like" algorithm that tries
+ * to be as efficient as possible. Firstly, since the algorithm it uses
+ * includes random initialization, it tries the whole thing cfg.num_tries times
+ * and picks the one with the best objective function. Each try, it does as
+ * follows: it randomly initializes points to clusters, and then for
+ * cfg.num_iters iterations it calls RefineClusters(). The options to
+ * RefineClusters() are given by cfg.refine_cfg. Calling RefineClusters once
+ * will always be at least as good as doing one iteration of reassigning points to
+ * clusters, but will generally be quite a bit better (without taking too
+ * much extra time).
+ *
+ * @param points [in] points to be clustered (must be all non-NULL).
+ * @param num_clust [in] number of clusters requested (it will always return exactly
+ * this many, or will fail if num_clust > points.size()).
+ * @param clusters_out [out] may be NULL; if non-NULL, should be empty when called.
+ * Will be set to a vector of statistics corresponding to the output clusters.
+ * @param assignments_out [out] may be NULL; if non-NULL, will be set to a vector of
+ * same size as "points", which says for each point which cluster
+ * it is assigned to.
+ * @param cfg [in] configuration class specifying options to the algorithm.
+ * @return Returns the objective function improvement versus everything being
+ * in the same cluster.
+ *
+ */
+BaseFloat ClusterKMeans(const std::vector<Clusterable*> &points,
+ int32 num_clust, // exact number of clusters
+ std::vector<Clusterable*> *clusters_out, // may be NULL
+ std::vector<int32> *assignments_out, // may be NULL
+ ClusterKMeansOptions cfg = ClusterKMeansOptions());
+
+struct TreeClusterOptions {
+ ClusterKMeansOptions kmeans_cfg;
+ int32 branch_factor;
+ BaseFloat thresh; // Objf change: if >0, may be used to control number of leaves.
+ TreeClusterOptions()
+ : kmeans_cfg(), branch_factor(2), thresh(0) {
+ kmeans_cfg.verbose = false;
+ }
+};
+
+/** TreeCluster is a top-down clustering algorithm, using a binary tree (not
+ * necessarily balanced). Returns objf improvement versus having all points
+ * in one cluster. The algorithm is:
+ * - Initialize to 1 cluster (tree with 1 node).
+ * - Maintain, for each cluster, a "best-binary-split" (using ClusterKMeans
+ * to do so). Always split the highest scoring cluster, until we can do no
+ * more splits.
+ *
+ * @param points [in] Data points to be clustered
+ * @param max_clust [in] Maximum number of clusters (you will get exactly this number,
+ * if there are at least this many points, except if you set the
+ * cfg.thresh value nonzero, in which case that threshold may limit
+ * the number of clusters.
+ * @param clusters_out [out] If non-NULL, will be set to the a vector whose first
+ * (*num_leaves_out) elements are the leaf clusters, and whose
+ * subsequent elements are the nonleaf nodes in the tree, in
+ * topological order with the root node last. Must be empty vector
+ * when this function is called.
+ * @param assignments_out [out] If non-NULL, will be set to a vector to a vector the
+ * same size as "points", where assignments[i] is the leaf node index i
+ * to which the i'th point gets clustered.
+ * @param clust_assignments_out [out] If non-NULL, will be set to a vector the same size
+ * as clusters_out which says for each node (leaf or nonleaf), the
+ * index of its parent. For the root node (which is last),
+ * assignments_out[i] == i. For each i, assignments_out[i]>=i, i.e.
+ * any node's parent is higher numbered than itself. If you don't need
+ * this information, consider using instead the ClusterTopDown function.
+ * @param num_leaves_out [out] If non-NULL, will be set to the number of leaf nodes
+ * in the tree.
+ * @param cfg [in] Configuration object that controls clustering behavior. Most
+ * important value is "thresh", which provides an alternative mechanism
+ * [other than max_clust] to limit the number of leaves.
+ */
+BaseFloat TreeCluster(const std::vector<Clusterable*> &points,
+ int32 max_clust, // max number of leaf-level clusters.
+ std::vector<Clusterable*> *clusters_out,
+ std::vector<int32> *assignments_out,
+ std::vector<int32> *clust_assignments_out,
+ int32 *num_leaves_out,
+ TreeClusterOptions cfg = TreeClusterOptions());
+
+
+/**
+ * A clustering algorithm that internally uses TreeCluster,
+ * but does not give you the information about the structure of the tree.
+ * The "clusters_out" and "assignments_out" may be NULL if the outputs are not
+ * needed.
+ *
+ * @param points [in] points to be clustered (must be all non-NULL).
+ * @param max_clust [in] Maximum number of clusters (you will get exactly this number,
+ * if there are at least this many points, except if you set the
+ * cfg.thresh value nonzero, in which case that threshold may limit
+ * the number of clusters.
+ * @param clusters_out [out] may be NULL; if non-NULL, should be empty when called.
+ * Will be set to a vector of statistics corresponding to the output clusters.
+ * @param assignments_out [out] may be NULL; if non-NULL, will be set to a vector of
+ * same size as "points", which says for each point which cluster
+ * it is assigned to.
+ * @param cfg [in] Configuration object that controls clustering behavior. Most
+ * important value is "thresh", which provides an alternative mechanism
+ * [other than max_clust] to limit the number of leaves.
+*/
+BaseFloat ClusterTopDown(const std::vector<Clusterable*> &points,
+ int32 max_clust, // max number of clusters.
+ std::vector<Clusterable*> *clusters_out,
+ std::vector<int32> *assignments_out,
+ TreeClusterOptions cfg = TreeClusterOptions());
+
+/// @} end of "addtogroup clustering_group_algo"
+
+} // end namespace kaldi.
+
+#endif // KALDI_TREE_CLUSTER_UTILS_H_