[ann] 04/08: Add autopkgtest.
Anton Gladky
gladk at moszumanska.debian.org
Sat May 31 17:55:22 UTC 2014
This is an automated email from the git hooks/post-receive script.
gladk pushed a commit to branch master
in repository ann.
commit 3b71f141b7b639502a748389371f07a7538b0d2d
Author: Anton Gladky <gladk at debian.org>
Date: Sat May 31 16:06:35 2014 +0200
Add autopkgtest.
---
debian/control | 1 +
debian/tests/ann_test.cpp | 1644 ++++++++++++++
debian/tests/build1 | 156 ++
debian/tests/build2 | 5313 +++++++++++++++++++++++++++++++++++++++++++++
debian/tests/control | 2 +
debian/tests/rand.cpp | 594 +++++
debian/tests/rand.h | 131 ++
7 files changed, 7841 insertions(+)
diff --git a/debian/control b/debian/control
index 64e462c..32e33e4 100644
--- a/debian/control
+++ b/debian/control
@@ -9,6 +9,7 @@ Standards-Version: 3.9.4
Homepage: http://www.cs.umd.edu/~mount/ANN/
Vcs-Git: git://anonscm.debian.org/debian-science/packages/ann.git
Vcs-Browser: http://anonscm.debian.org/gitweb/?p=debian-science/packages/ann.git
+XS-Testsuite: autopkgtest
Package: libann-dev
diff --git a/debian/tests/ann_test.cpp b/debian/tests/ann_test.cpp
new file mode 100644
index 0000000..093f5c6
--- /dev/null
+++ b/debian/tests/ann_test.cpp
@@ -0,0 +1,1644 @@
+//----------------------------------------------------------------------
+// File: ann_test.cpp
+// Programmer: Sunil Arya and David Mount
+// Description: test program for ANN (approximate nearest neighbors)
+// Last modified: 01/27/10 (Version 1.1.2)
+//----------------------------------------------------------------------
+// Copyright (c) 1997-2010 University of Maryland and Sunil Arya and
+// David Mount. All Rights Reserved.
+//
+// This software and related documentation is part of the Approximate
+// Nearest Neighbor Library (ANN). This software is provided under
+// the provisions of the Lesser GNU Public License (LGPL). See the
+// file ../ReadMe.txt for further information.
+//
+// The University of Maryland (U.M.) and the authors make no
+// representations about the suitability or fitness of this software for
+// any purpose. It is provided "as is" without express or implied
+// warranty.
+//----------------------------------------------------------------------
+// History:
+// Revision 0.1 03/04/98
+// Initial release
+// Revision 0.2 06/26/98
+// Added CLOCKS_PER_SEC definition if needed
+// Revision 1.0 04/01/05
+// Added comments (from "#" to eol)
+// Added clus_orth_flats and clus_ellipsoids distributions
+// Fixed order of fair and midpt in split_table
+// Added dump/load operations
+// Cleaned up C++ for modern compilers
+// Revision 1.1 05/03/05
+// Added fixed radius kNN search
+// Revision 1.1.1 08/04/06
+// Added planted distribution
+// Revision 1.1.2 01/27/10
+// Fixed minor compilation bugs for new versions of gcc
+// Allow round-off error in validation test
+//----------------------------------------------------------------------
+
+#include <ctime> // clock
+#include <cmath> // math routines
+#include <cstring> // C string ops
+#include <fstream> // file I/O
+
+#include <ANN/ANN.h> // ANN declarations
+#include <ANN/ANNx.h> // more ANN declarations
+#include <ANN/ANNperf.h> // performance evaluation
+
+#include "rand.h" // random point generation
+
+#ifndef CLOCKS_PER_SEC // define clocks-per-second if needed
+ #define CLOCKS_PER_SEC 1000000
+#endif
+
+using namespace std; // make std:: available
+
+//----------------------------------------------------------------------
+// ann_test
+//
+// This program is a driver for testing and evaluating the ANN library
+// for computing approximate nearest neighbors. It allows the user to
+// generate data and query sets of various sizes, dimensions, and
+// distributions, to build kd- and bbd-trees of various types, and then
+// run queries and outputting various performance statistics.
+//
+// Overview:
+// ---------
+// The test program is run as follows:
+//
+// ann_test < test_input > test_output
+//
+// where the test_input file contains a list of directives as described
+// below. Directives consist of a directive name, followed by list of
+// arguments (depending on the directive). Arguments and directives are
+// separated by white space (blank, tab, and newline). String arguments
+// are not quoted, and consist of a string of nonwhite chacters. A
+// character "#" denotes a comment. The following characters up to
+// the end of line are ignored. Comments may only be inserted between
+// directives (not within the argument list of a directive).
+//
+// Basic operations:
+// -----------------
+// The test program can perform the following operations. How these
+// operations are performed depends on the options which are described
+// later.
+//
+// Data Generation:
+// ----------------
+// read_data_pts <file> Create a set of data points whose
+// coordinates are input from file <file>.
+// gen_data_pts Create a set of data points whose
+// coordinates are generated from the
+// current point distribution.
+//
+// Building the tree:
+// ------------------
+// build_ann Generate an approximate nearest neighbor
+// structure for the current data set, using
+// the selected splitting rules. Any existing
+// tree will be destroyed.
+//
+// Query Generation/Searching:
+// ---------------------------
+// read_query_pts <file> Create a set of query points whose
+// coordinates are input from file <file>.
+// gen_query_pts Create a set of query points whose
+// coordinates are generated from the
+// current point distribution.
+// run_queries <string> Apply nearest neighbor searching to the
+// query points using the approximate nearest
+// neighbor structure and the given search
+// strategy. Possible strategies are:
+// standard = standard kd-tree search
+// priority = priority search
+//
+// Miscellaneous:
+// --------------
+// output_label Output a label to the output file.
+// dump <file> Dump the current structure to given file.
+// (The dump format is explained further in
+// the source file kd_tree.cc.)
+// load <file> Load a tree from a data file which was
+// created by the dump operation. Any
+// existing tree will be destroyed.
+//
+// Options:
+// --------
+// How these operations are performed depends on a set of options.
+// If an option is not specified, a default value is used. An option
+// retains its value until it is set again. String inputs are not
+// enclosed in quotes, and must contain no embedded white space (sorry,
+// this is C++'s convention).
+//
+// Options affecting search tree structure:
+// ----------------------------------------
+// split_rule <type> Type of splitting rule to use in building
+// the search tree. Choices are:
+// kd = optimized kd-tree
+// midpt = midpoint split
+// fair = fair split
+// sl_midpt = sliding midpt split
+// sl_fair = sliding fair split
+// suggest = authors' choice for best
+// The default is "suggest". See the file
+// kd_split.cc for more detailed information.
+//
+// shrink_rule <type> Type of shrinking rule to use in building
+// a bd-tree data structure. If "none" is
+// given, then no shrinking is performed and
+// the result is a kd-tree. Choices are:
+// none = perform no shrinking
+// simple = simple shrinking
+// centroid = centroid shrinking
+// suggest = authors' choice for best
+// The default is "none". See the file
+// bd_tree.cc for more information.
+// bucket_size <int> Bucket size, that is, the maximum number of
+// points stored in each leaf node.
+//
+// Options affecting data and query point generation:
+// --------------------------------------------------
+// dim <int> Dimension of space.
+// seed <int> Seed for random number generation.
+// data_size <int> Number of data points. When reading data
+// points from a file, this indicates the
+// maximum number of points for storage
+// allocation. Default = 100.
+// query_size <int> Same as data_size for query points.
+// std_dev <float> Standard deviation (used in gauss,
+// planted, and clustered distributions).
+// This is the "small" distribution for
+// clus_ellipsoids. Default = 1.
+// std_dev_lo <float> Low and high standard deviations (used in
+// std_dev_hi <float> clus_ellipsoids). Default = 1.
+// corr_coef <float> Correlation coefficient (used in co-gauss
+// and co_lapace distributions). Default = 0.05.
+// colors <int> Number of color classes (clusters) (used
+// in the clustered distributions). Default = 5.
+// new_clust Once generated, cluster centers are not
+// normally regenerated. This is so that both
+// query points and data points can be generated
+// using the same set of clusters. This option
+// forces new cluster centers to be generated
+// with the next generation of either data or
+// query points.
+// max_clus_dim <int> Maximum dimension of clusters (used in
+// clus_orth_flats and clus_ellipsoids).
+// Default = 1.
+// distribution <string> Type of input distribution
+// uniform = uniform over cube [-1,1]^d.
+// gauss = Gaussian with mean 0
+// laplace = Laplacian, mean 0 and var 1
+// co_gauss = correlated Gaussian
+// co_laplace = correlated Laplacian
+// clus_gauss = clustered Gaussian
+// clus_orth_flats = clusters of orth flats
+// clus_ellipsoids = clusters of ellipsoids
+// planted = planted distribution
+// See the file rand.cpp for further information.
+//
+// Options affecting nearest neighbor search:
+// ------------------------------------------
+// epsilon <float> Error bound for approx. near neigh. search.
+// near_neigh <int> Number of nearest neighbors to compute.
+// max_pts_visit <int> Maximum number of points to visit before
+// terminating. (Used in applications where
+// real-time performance is important.)
+// (Default = 0, which means no limit.)
+// radius_bound <float> Sets an upper bound on the nearest
+// neighbor search radius. If the bound is
+// positive, then fixed-radius nearest
+// neighbor searching is performed, and the
+// count of the number of points in the
+// range is returned. If the bound is
+// zero, then standard search is used.
+// This can only be used with standard, not
+// priority, search. (Default = 0, which
+// means standard search.)
+//
+// Options affection general program behavior:
+// -------------------------------------------
+// stats <string> Level of statistics output
+// silent = no output,
+// exec_time += execution time only
+// prep_stats += preprocessing statistics
+// query_stats += query performance stats
+// query_res += results of queries
+// show_pts += show the data points
+// show_struct += print search structure
+// validate <string> Validate experiment and compute average
+// error. Since validation causes exact
+// nearest neighbors to be computed by the
+// brute force method, this can take a long
+// time. Valid arguments are:
+// on = turn validation on
+// off = turn validation off
+// true_near_neigh <int> Number of true nearest neighbors to compute.
+// When validating, we compute the difference
+// in rank between each reported nearest neighbor
+// and the true nearest neighbor of the same
+// rank. Thus it is necessary to compute a
+// few more true nearest neighbors. By default
+// we compute 10 more than near_neigh. With
+// this option the exact number can be set.
+// (Used only when validating.)
+//
+// Example:
+// --------
+// output_label test_run_0 # output label for this run
+// validate off # do not perform validation
+// dim 16 # points in dimension 16
+// stats query_stats # output performance statistics for queries
+// seed 121212 # random number seed
+// data_size 1000
+// distribution uniform
+// gen_data_pts # 1000 uniform data points in dim 16
+// query_size 100
+// std_dev 0.05
+// distribution clus_gauss
+// gen_query_pts # 100 points in 10 clusters with std_dev 0.05
+// bucket_size 2
+// split_rule kd
+// shrink_rule none
+// build_ann # kd-tree, bucket size 2
+// epsilon 0.1
+// near_neigh 5
+// max_pts_visit 100 # stop search if more than 100 points seen
+// run_queries standard # run queries; 5 nearest neighbors, 10% error
+// data_size 500
+// read_data_pts data.in # read up to 500 points from file data.in
+// split_rule sl_midpt
+// shrink_rule simple
+// build_ann # bd-tree; simple shrink, sliding midpoint split
+// epsilon 0
+// run_queries priority # run same queries; 0 allowable error
+//
+//------------------------------------------------------------------------
+
+//------------------------------------------------------------------------
+// Constants
+//------------------------------------------------------------------------
+
+const int STRING_LEN = 500; // max string length
+const double ERR = 0.00001; // epsilon (for float compares)
+const double RND_OFF = 5E-16; // double round-off error
+
+//------------------------------------------------------------------------
+// Enumerated values and conversions
+//------------------------------------------------------------------------
+
+typedef enum {DATA, QUERY} PtType; // point types
+
+//------------------------------------------------------------------------
+// Statistics output levels
+//------------------------------------------------------------------------
+
+typedef enum { // stat levels
+ SILENT, // no output
+ EXEC_TIME, // just execution time
+ PREP_STATS, // preprocessing info
+ QUERY_STATS, // query performance
+ QUERY_RES, // query results
+ SHOW_PTS, // show data points
+ SHOW_STRUCT, // show tree structure
+ N_STAT_LEVELS} // number of levels
+ StatLev;
+
+const char stat_table[N_STAT_LEVELS][STRING_LEN] = {
+ "silent", // SILENT
+ "exec_time", // EXEC_TIME
+ "prep_stats", // PREP_STATS
+ "query_stats", // QUERY_STATS
+ "query_res", // QUERY_RES
+ "show_pts", // SHOW_PTS
+ "show_struct"}; // SHOW_STRUCT
+
+//------------------------------------------------------------------------
+// Distributions
+//------------------------------------------------------------------------
+
+typedef enum { // distributions
+ UNIFORM, // uniform over cube [-1,1]^d.
+ GAUSS, // Gaussian with mean 0
+ LAPLACE, // Laplacian, mean 0 and var 1
+ CO_GAUSS, // correlated Gaussian
+ CO_LAPLACE, // correlated Laplacian
+ CLUS_GAUSS, // clustered Gaussian
+ CLUS_ORTH_FLATS, // clustered on orthog flats
+ CLUS_ELLIPSOIDS, // clustered on ellipsoids
+ PLANTED, // planted distribution
+ N_DISTRIBS}
+ Distrib;
+
+const char distr_table[N_DISTRIBS][STRING_LEN] = {
+ "uniform", // UNIFORM
+ "gauss", // GAUSS
+ "laplace", // LAPLACE
+ "co_gauss", // CO_GAUSS
+ "co_laplace", // CO_LAPLACE
+ "clus_gauss", // CLUS_GAUSS
+ "clus_orth_flats", // CLUS_ORTH_FLATS
+ "clus_ellipsoids", // CLUS_ELLIPSOIS
+ "planted"}; // PLANTED
+
+//------------------------------------------------------------------------
+// Splitting rules for kd-trees (see ANN.h for types)
+//------------------------------------------------------------------------
+
+const int N_SPLIT_RULES = 6;
+const char split_table[N_SPLIT_RULES][STRING_LEN] = {
+ "standard", // standard optimized kd-tree
+ "midpt", // midpoint split
+ "fair", // fair split
+ "sl_midpt", // sliding midpt split
+ "sl_fair", // sliding fair split
+ "suggest"}; // authors' choice for best
+
+//------------------------------------------------------------------------
+// Shrinking rules for bd-trees (see ANN.h for types)
+//------------------------------------------------------------------------
+
+const int N_SHRINK_RULES = 4;
+const char shrink_table[N_SHRINK_RULES][STRING_LEN] = {
+ "none", // perform no shrinking (kd-tree)
+ "simple", // simple shrinking
+ "centroid", // centroid shrinking
+ "suggest"}; // authors' choice for best
+
+//----------------------------------------------------------------------
+// Short utility functions
+// Error - general error routine
+// printPoint - print a point to standard output
+// lookUp - look up a name in table and return index
+//----------------------------------------------------------------------
+
+void Error( // error routine
+ const char* msg, // error message
+ ANNerr level) // abort afterwards
+{
+ if (level == ANNabort) {
+ cerr << "ann_test: ERROR------->" << msg << "<-------------ERROR\n";
+ exit(1);
+ }
+ else {
+ cerr << "ann_test: WARNING----->" << msg << "<-------------WARNING\n";
+ }
+}
+
+void printPoint( // print point
+ ANNpoint p, // the point
+ int dim) // the dimension
+{
+ cout << "[";
+ for (int i = 0; i < dim; i++) {
+ cout << p[i];
+ if (i < dim-1) cout << ",";
+ }
+ cout << "]";
+}
+
+int lookUp( // look up name in table
+ const char* arg, // name to look up
+ const char (*table)[STRING_LEN], // name table
+ int size) // table size
+{
+ int i;
+ for (i = 0; i < size; i++) {
+ if (!strcmp(arg, table[i])) return i;
+ }
+ return i;
+}
+
+//------------------------------------------------------------------------
+// Function declarations
+//------------------------------------------------------------------------
+
+void generatePts( // generate data/query points
+ ANNpointArray &pa, // point array (returned)
+ int n, // number of points
+ PtType type, // point type
+ ANNbool new_clust, // new cluster centers desired?
+ ANNpointArray src = NULL, // source array (for PLANTED)
+ int n_src = 0); // source size (for PLANTED)
+
+void readPts( // read data/query points from file
+ ANNpointArray &pa, // point array (returned)
+ int &n, // number of points
+ char *file_nm, // file name
+ PtType type); // point type (DATA, QUERY)
+
+void doValidation(); // perform validation
+void getTrueNN(); // compute true nearest neighbors
+
+void treeStats( // print statistics on kd- or bd-tree
+ ostream &out, // output stream
+ ANNbool verbose); // print stats
+
+//------------------------------------------------------------------------
+// Default execution parameters
+//------------------------------------------------------------------------
+const int extra_nn = 10; // how many extra true nn's?
+
+const int def_dim = 2; // def dimension
+const int def_data_size = 100; // def data size
+const int def_query_size = 100; // def number of queries
+const int def_n_color = 5; // def number of colors
+const ANNbool def_new_clust = ANNfalse; // def new clusters flag
+const int def_max_dim = 1; // def max flat dimension
+const Distrib def_distr = UNIFORM; // def distribution
+const double def_std_dev = 1.00; // def standard deviation
+const double def_corr_coef = 0.05; // def correlation coef
+const int def_bucket_size = 1; // def bucket size
+const double def_epsilon = 0.0; // def error bound
+const int def_near_neigh = 1; // def number of near neighbors
+const int def_max_visit = 0; // def number of points visited
+const int def_rad_bound = 0; // def radius bound
+ // def number of true nn's
+const int def_true_nn = def_near_neigh + extra_nn;
+const int def_seed = 0; // def seed for random numbers
+const ANNbool def_validate = ANNfalse; // def validation flag
+ // def statistics output level
+const StatLev def_stats = QUERY_STATS;
+const ANNsplitRule // def splitting rule
+ def_split = ANN_KD_SUGGEST;
+const ANNshrinkRule // def shrinking rule
+ def_shrink = ANN_BD_NONE;
+
+//------------------------------------------------------------------------
+// Global variables - Execution options
+//------------------------------------------------------------------------
+
+int dim; // dimension
+int data_size; // data size
+int query_size; // number of queries
+int n_color; // number of colors
+ANNbool new_clust; // generate new clusters?
+int max_dim; // maximum flat dimension
+Distrib distr; // distribution
+double corr_coef; // correlation coef
+double std_dev; // standard deviation
+double std_dev_lo; // low standard deviation
+double std_dev_hi; // high standard deviation
+int bucket_size; // bucket size
+double epsilon; // error bound
+int near_neigh; // number of near neighbors
+int max_pts_visit; // max number of points to visit
+double radius_bound; // maximum radius search bound
+int true_nn; // number of true nn's
+ANNbool validate; // validation flag
+StatLev stats; // statistics output level
+ANNsplitRule split; // splitting rule
+ANNshrinkRule shrink; // shrinking rule
+
+//------------------------------------------------------------------------
+// More globals - pointers to dynamically allocated arrays and structures
+//
+// It is assumed that all these values are set to NULL when nothing
+// is allocated.
+//
+// data_pts, query_pts The data and query points
+// the_tree Points to the kd- or bd-tree for
+// nearest neighbor searching.
+// apx_nn_idx, apx_dists Record approximate near neighbor
+// indices and distances
+// apx_pts_in_range Counts of the number of points in
+// the in approx range, for fixed-
+// radius NN searching.
+// true_nn_idx, true_dists Record true near neighbor
+// indices and distances
+// min_pts_in_range, max_... Min and max counts of the number
+// of points in the in approximate
+// range.
+// valid_dirty To avoid repeated validation,
+// we only validate query results
+// once. This validation becomes
+// invalid, if a new tree, new data
+// points or new query points have
+// been generated.
+// tree_data_size The number of points in the
+// current tree. (This will be the
+// same a data_size unless points have
+// been added since the tree was
+// built.)
+//
+// The approximate and true nearest neighbor results are stored
+// in: apx_nn_idx, apx_dists, and true_nn_idx, true_dists.
+// They are really flattened 2-dimensional arrays. Each of these
+// arrays consists of query_size blocks, each of which contains
+// near_neigh (or true_nn) entries, one for each of the nearest
+// neighbors for a given query point.
+//------------------------------------------------------------------------
+
+ANNpointArray data_pts; // data points
+ANNpointArray query_pts; // query points
+ANNbd_tree* the_tree; // kd- or bd-tree search structure
+ANNidxArray apx_nn_idx; // storage for near neighbor indices
+ANNdistArray apx_dists; // storage for near neighbor distances
+int* apx_pts_in_range; // storage for no. of points in range
+ANNidxArray true_nn_idx; // true near neighbor indices
+ANNdistArray true_dists; // true near neighbor distances
+int* min_pts_in_range; // min points in approx range
+int* max_pts_in_range; // max points in approx range
+
+ANNbool valid_dirty; // validation is no longer valid
+
+//------------------------------------------------------------------------
+// Initialize global parameters
+//------------------------------------------------------------------------
+
+void initGlobals()
+{
+ dim = def_dim; // init execution parameters
+ data_size = def_data_size;
+ query_size = def_query_size;
+ distr = def_distr;
+ corr_coef = def_corr_coef;
+ std_dev = def_std_dev;
+ std_dev_lo = def_std_dev;
+ std_dev_hi = def_std_dev;
+ new_clust = def_new_clust;
+ max_dim = def_max_dim;
+ n_color = def_n_color;
+ bucket_size = def_bucket_size;
+ epsilon = def_epsilon;
+ near_neigh = def_near_neigh;
+ max_pts_visit = def_max_visit;
+ radius_bound = def_rad_bound;
+ true_nn = def_true_nn;
+ validate = def_validate;
+ stats = def_stats;
+ split = def_split;
+ shrink = def_shrink;
+ annIdum = -def_seed; // init. global seed for ran0()
+
+ data_pts = NULL; // initialize storage pointers
+ query_pts = NULL;
+ the_tree = NULL;
+ apx_nn_idx = NULL;
+ apx_dists = NULL;
+ apx_pts_in_range = NULL;
+ true_nn_idx = NULL;
+ true_dists = NULL;
+ min_pts_in_range = NULL;
+ max_pts_in_range = NULL;
+
+ valid_dirty = ANNtrue; // (validation must be done)
+}
+
+//------------------------------------------------------------------------
+// getDirective - skip comments and read next directive
+// Returns ANNtrue if directive read, and ANNfalse if eof seen.
+//------------------------------------------------------------------------
+
+ANNbool skipComment( // skip any comments
+ istream &in) // input stream
+{
+ char ch = 0;
+ // skip whitespace
+ do { in.get(ch); } while (isspace(ch) && !in.eof());
+ while (ch == '#' && !in.eof()) { // comment?
+ // skip to end of line
+ do { in.get(ch); } while(ch != '\n' && !in.eof());
+ // skip whitespace
+ do { in.get(ch); } while(isspace(ch) && !in.eof());
+ }
+ if (in.eof()) return ANNfalse; // end of file
+ in.putback(ch); // put character back
+ return ANNtrue;
+}
+
+ANNbool getDirective(
+ istream &in, // input stream
+ char *directive) // directive storage
+{
+ if (!skipComment(in)) // skip comments
+ return ANNfalse; // found eof along the way?
+ in >> directive; // read directive
+ return ANNtrue;
+}
+
+
+//------------------------------------------------------------------------
+// main program - driver
+// The main program reads input options, invokes the necessary
+// routines to process them.
+//------------------------------------------------------------------------
+
+int main(int argc, char** argv)
+{
+ long clock0; // clock time
+ char directive[STRING_LEN]; // input directive
+ char arg[STRING_LEN]; // all-purpose argument
+
+ cout << "------------------------------------------------------------\n"
+ << "ann_test: Version " << ANNversion << " " << ANNversionCmt << "\n"
+ << " Copyright: " << ANNcopyright << ".\n"
+ << " Latest Revision: " << ANNlatestRev << ".\n"
+ << "------------------------------------------------------------\n\n";
+
+ initGlobals(); // initialize global values
+
+ //--------------------------------------------------------------------
+ // Main input loop
+ //--------------------------------------------------------------------
+ // read input directive
+ while (getDirective(cin, directive)) {
+ //----------------------------------------------------------------
+ // Read options
+ //----------------------------------------------------------------
+ if (!strcmp(directive,"dim")) {
+ cin >> dim;
+ }
+ else if (!strcmp(directive,"colors")) {
+ cin >> n_color;
+ }
+ else if (!strcmp(directive,"new_clust")) {
+ new_clust = ANNtrue;
+ }
+ else if (!strcmp(directive,"max_clus_dim")) {
+ cin >> max_dim;
+ }
+ else if (!strcmp(directive,"std_dev")) {
+ cin >> std_dev;
+ }
+ else if (!strcmp(directive,"std_dev_lo")) {
+ cin >> std_dev_lo;
+ }
+ else if (!strcmp(directive,"std_dev_hi")) {
+ cin >> std_dev_hi;
+ }
+ else if (!strcmp(directive,"corr_coef")) {
+ cin >> corr_coef;
+ }
+ else if (!strcmp(directive, "data_size")) {
+ cin >> data_size;
+ }
+ else if (!strcmp(directive,"query_size")) {
+ cin >> query_size;
+ }
+ else if (!strcmp(directive,"bucket_size")) {
+ cin >> bucket_size;
+ }
+ else if (!strcmp(directive,"epsilon")) {
+ cin >> epsilon;
+ }
+ else if (!strcmp(directive,"max_pts_visit")) {
+ cin >> max_pts_visit;
+ valid_dirty = ANNtrue; // validation must be redone
+ }
+ else if (!strcmp(directive,"radius_bound")) {
+ cin >> radius_bound;
+ valid_dirty = ANNtrue; // validation must be redone
+ }
+ else if (!strcmp(directive,"near_neigh")) {
+ cin >> near_neigh;
+ true_nn = near_neigh + extra_nn; // also reset true near neighs
+ valid_dirty = ANNtrue; // validation must be redone
+ }
+ else if (!strcmp(directive,"true_near_neigh")) {
+ cin >> true_nn;
+ valid_dirty = ANNtrue; // validation must be redone
+ }
+ //----------------------------------------------------------------
+ // seed option
+ // The seed is reset by setting the global annIdum to the
+ // negation of the seed value. See rand.cpp.
+ //----------------------------------------------------------------
+ else if (!strcmp(directive,"seed")) {
+ cin >> annIdum;
+ annIdum = -annIdum;
+ }
+ //----------------------------------------------------------------
+ // validate option
+ //----------------------------------------------------------------
+ else if (!strcmp(directive,"validate")) {
+ cin >> arg; // input argument
+ if (!strcmp(arg, "on")) {
+ validate = ANNtrue;
+ cout << "validate = on "
+ << "(Warning: this may slow execution time.)\n";
+ }
+ else if (!strcmp(arg, "off")) {
+ validate = ANNfalse;
+ }
+ else {
+ cerr << "Argument: " << arg << "\n";
+ Error("validate argument must be \"on\" or \"off\"", ANNabort);
+ }
+ }
+ //----------------------------------------------------------------
+ // distribution option
+ //----------------------------------------------------------------
+ else if (!strcmp(directive,"distribution")) {
+ cin >> arg; // input name and translate
+ distr = (Distrib) lookUp(arg, distr_table, N_DISTRIBS);
+ if (distr >= N_DISTRIBS) { // not something we recognize
+ cerr << "Distribution: " << arg << "\n";
+ Error("Unknown distribution", ANNabort);
+ }
+ }
+ //----------------------------------------------------------------
+ // stats option
+ //----------------------------------------------------------------
+ else if (!strcmp(directive,"stats")) {
+ cin >> arg; // input name and translate
+ stats = (StatLev) lookUp(arg, stat_table, N_STAT_LEVELS);
+ if (stats >= N_STAT_LEVELS) { // not something we recognize
+ cerr << "Stats level: " << arg << "\n";
+ Error("Unknown statistics level", ANNabort);
+ }
+ if (stats > SILENT)
+ cout << "stats = " << arg << "\n";
+ }
+ //----------------------------------------------------------------
+ // split_rule option
+ //----------------------------------------------------------------
+ else if (!strcmp(directive,"split_rule")) {
+ cin >> arg; // input split_rule name
+ split = (ANNsplitRule) lookUp(arg, split_table, N_SPLIT_RULES);
+ if (split >= N_SPLIT_RULES) { // not something we recognize
+ cerr << "Splitting rule: " << arg << "\n";
+ Error("Unknown splitting rule", ANNabort);
+ }
+ }
+ //----------------------------------------------------------------
+ // shrink_rule option
+ //----------------------------------------------------------------
+ else if (!strcmp(directive,"shrink_rule")) {
+ cin >> arg; // input split_rule name
+ shrink = (ANNshrinkRule) lookUp(arg, shrink_table, N_SHRINK_RULES);
+ if (shrink >= N_SHRINK_RULES) { // not something we recognize
+ cerr << "Shrinking rule: " << arg << "\n";
+ Error("Unknown shrinking rule", ANNabort);
+ }
+ }
+ //----------------------------------------------------------------
+ // label operation
+ //----------------------------------------------------------------
+ else if (!strcmp(directive,"output_label")) {
+ cin >> arg;
+ if (stats > SILENT)
+ cout << "<" << arg << ">\n";
+ }
+ //----------------------------------------------------------------
+ // gen_data_pts operation
+ //----------------------------------------------------------------
+ else if (!strcmp(directive,"gen_data_pts")) {
+ if (distr == PLANTED) { // planted distribution
+ Error("Cannot use planted distribution for data points", ANNabort);
+ }
+ generatePts( // generate data points
+ data_pts, // data points
+ data_size, // data size
+ DATA, // data points
+ new_clust); // new clusters flag
+ valid_dirty = ANNtrue; // validation must be redone
+ new_clust = ANNfalse; // reset flag
+ }
+ //----------------------------------------------------------------
+ // gen_query_pts operation
+ // If the distribution is PLANTED, then the query points
+ // are planted near the data points (which must already be
+ // generated).
+ //----------------------------------------------------------------
+ else if (!strcmp(directive,"gen_query_pts")) {
+ if (distr == PLANTED) { // planted distribution
+ if (data_pts == NULL) {
+ Error("Must generate data points before query points for planted distribution", ANNabort);
+ }
+ generatePts( // generate query points
+ query_pts, // point array
+ query_size, // number of query points
+ QUERY, // query points
+ new_clust, // new clusters flag
+ data_pts, // plant around data pts
+ data_size);
+ }
+ else { // all other distributions
+ generatePts( // generate query points
+ query_pts, // point array
+ query_size, // number of query points
+ QUERY, // query points
+ new_clust); // new clusters flag
+ }
+ valid_dirty = ANNtrue; // validation must be redone
+ new_clust = ANNfalse; // reset flag
+ }
+ //----------------------------------------------------------------
+ // read_data_pts operation
+ //----------------------------------------------------------------
+ else if (!strcmp(directive,"read_data_pts")) {
+ cin >> arg; // input file name
+ readPts(
+ data_pts, // point array
+ data_size, // number of points
+ arg, // file name
+ DATA); // data points
+ valid_dirty = ANNtrue; // validation must be redone
+ }
+ //----------------------------------------------------------------
+ // read_query_pts operation
+ //----------------------------------------------------------------
+ else if (!strcmp(directive,"read_query_pts")) {
+ cin >> arg; // input file name
+ readPts(
+ query_pts, // point array
+ query_size, // number of points
+ arg, // file name
+ QUERY); // query points
+ valid_dirty = ANNtrue; // validation must be redone
+ }
+ //----------------------------------------------------------------
+ // build_ann operation
+ // We always invoke the constructor for bd-trees. Note
+ // that when the shrinking rule is NONE (which is true by
+ // default), then this constructs a kd-tree.
+ //----------------------------------------------------------------
+ else if (!strcmp(directive,"build_ann")) {
+ //------------------------------------------------------------
+ // Build the tree
+ //------------------------------------------------------------
+ if (the_tree != NULL) { // tree exists already
+ delete the_tree; // get rid of it
+ }
+ clock0 = clock(); // start time
+
+ the_tree = new ANNbd_tree( // build it
+ data_pts, // the data points
+ data_size, // number of points
+ dim, // dimension of space
+ bucket_size, // maximum bucket size
+ split, // splitting rule
+ shrink); // shrinking rule
+
+ //------------------------------------------------------------
+ // Print summary
+ //------------------------------------------------------------
+ long prep_time = clock() - clock0; // end of prep time
+
+ if (stats > SILENT) {
+ cout << "[Build ann-structure:\n";
+ cout << " split_rule = " << split_table[split] << "\n";
+ cout << " shrink_rule = " << shrink_table[shrink] << "\n";
+ cout << " data_size = " << data_size << "\n";
+ cout << " dim = " << dim << "\n";
+ cout << " bucket_size = " << bucket_size << "\n";
+
+ if (stats >= EXEC_TIME) { // output processing time
+ cout << " process_time = "
+ << double(prep_time)/CLOCKS_PER_SEC << " sec\n";
+ }
+
+ if (stats >= PREP_STATS) // output or check tree stats
+ treeStats(cout, ANNtrue); // print tree stats
+ else
+ treeStats(cout, ANNfalse); // check stats
+
+ if (stats >= SHOW_STRUCT) { // print the whole tree
+ cout << " (Structure Contents:\n";
+ the_tree->Print(ANNfalse, cout);
+ cout << " )\n";
+ }
+ cout << "]\n";
+ }
+ }
+ //----------------------------------------------------------------
+ // dump operation
+ //----------------------------------------------------------------
+ else if (!strcmp(directive,"dump")) {
+ cin >> arg; // input file name
+ if (the_tree == NULL) { // no tree
+ Error("Cannot dump. No tree has been built yet", ANNwarn);
+ }
+ else { // there is a tree
+ // try to open file
+ ofstream out_dump_file(arg);
+ if (!out_dump_file) {
+ cerr << "File name: " << arg << "\n";
+ Error("Cannot open dump file", ANNabort);
+ }
+ // dump the tree and points
+ the_tree->Dump(ANNtrue, out_dump_file);
+ if (stats > SILENT) {
+ cout << "(Tree has been dumped to file " << arg << ")\n";
+ }
+ }
+ }
+ //----------------------------------------------------------------
+ // load operation
+ // Since this not only loads a tree, but loads a new set
+ // of data points.
+ //----------------------------------------------------------------
+ else if (!strcmp(directive,"load")) {
+ cin >> arg; // input file name
+ if (the_tree != NULL) { // tree exists already
+ delete the_tree; // get rid of it
+ }
+ if (data_pts != NULL) { // data points exist already
+ delete data_pts; // get rid of them
+ }
+
+ ifstream in_dump_file(arg); // try to open file
+ if (!in_dump_file) {
+ cerr << "File name: " << arg << "\n";
+ Error("Cannot open file for loading", ANNabort);
+ }
+ // build tree by loading
+ the_tree = new ANNbd_tree(in_dump_file);
+
+ dim = the_tree->theDim(); // new dimension
+ data_size = the_tree->nPoints(); // number of points
+ data_pts = the_tree->thePoints(); // new points
+
+ valid_dirty = ANNtrue; // validation must be redone
+
+ if (stats > SILENT) {
+ cout << "(Tree has been loaded from file " << arg << ")\n";
+ }
+ if (stats >= SHOW_STRUCT) { // print the tree
+ cout << " (Structure Contents:\n";
+ the_tree->Print(ANNfalse, cout);
+ cout << " )\n";
+ }
+ }
+ //----------------------------------------------------------------
+ // run_queries operation
+ // This section does all the query processing. It consists
+ // of the following subsections:
+ //
+ // ** input the argument (standard or priority) and output
+ // the header describing the essential information.
+ // ** allocate space for the results to be stored.
+ // ** run the queries by invoking the appropriate search
+ // procedure on the query points. Print nearest neighbor
+ // if requested.
+ // ** print final summaries
+ //
+ // The approach for processing multiple nearest neighbors is
+ // pretty crude. We allocate an array whose size is the
+ // product of the total number of queries times the number of
+ // nearest neighbors (k), and then use each k consecutive
+ // entries to store the results of each query.
+ //----------------------------------------------------------------
+ else if (!strcmp(directive,"run_queries")) {
+
+ //------------------------------------------------------------
+ // Input arguments and print summary
+ //------------------------------------------------------------
+ enum {STANDARD, PRIORITY} method;
+
+ cin >> arg; // input argument
+ if (!strcmp(arg, "standard")) {
+ method = STANDARD;
+ }
+ else if (!strcmp(arg, "priority")) {
+ method = PRIORITY;
+ }
+ else {
+ cerr << "Search type: " << arg << "\n";
+ Error("Search type must be \"standard\" or \"priority\"",
+ ANNabort);
+ }
+ if (data_pts == NULL || query_pts == NULL) {
+ Error("Either data set and query set not constructed", ANNabort);
+ }
+ if (the_tree == NULL) {
+ Error("No search tree built.", ANNabort);
+ }
+
+ //------------------------------------------------------------
+ // Set up everything
+ //------------------------------------------------------------
+
+ #ifdef ANN_PERF // performance only
+ annResetStats(data_size); // reset statistics
+ #endif
+
+ clock0 = clock(); // start time
+ // deallocate existing storage
+ if (apx_nn_idx != NULL) delete [] apx_nn_idx;
+ if (apx_dists != NULL) delete [] apx_dists;
+ if (apx_pts_in_range != NULL) delete [] apx_pts_in_range;
+ // allocate apx answer storage
+ apx_nn_idx = new ANNidx[near_neigh*query_size];
+ apx_dists = new ANNdist[near_neigh*query_size];
+ apx_pts_in_range = new int[query_size];
+
+ annMaxPtsVisit(max_pts_visit); // set max points to visit
+
+ //------------------------------------------------------------
+ // Run the queries
+ //------------------------------------------------------------
+ // pointers for current query
+ ANNidxArray curr_nn_idx = apx_nn_idx;
+ ANNdistArray curr_dists = apx_dists;
+
+ for (int i = 0; i < query_size; i++) {
+ #ifdef ANN_PERF
+ annResetCounts(); // reset counters
+ #endif
+ apx_pts_in_range[i] = 0;
+
+ if (radius_bound == 0) { // no radius bound
+ if (method == STANDARD) {
+ the_tree->annkSearch(
+ query_pts[i], // query point
+ near_neigh, // number of near neighbors
+ curr_nn_idx, // nearest neighbors (returned)
+ curr_dists, // distance (returned)
+ epsilon); // error bound
+ }
+ else if (method == PRIORITY) {
+ the_tree->annkPriSearch(
+ query_pts[i], // query point
+ near_neigh, // number of near neighbors
+ curr_nn_idx, // nearest neighbors (returned)
+ curr_dists, // distance (returned)
+ epsilon); // error bound
+ }
+ else {
+ Error("Internal error - invalid method", ANNabort);
+ }
+ }
+ else { // use radius bound
+ if (method != STANDARD) {
+ Error("A nonzero radius bound assumes standard search",
+ ANNwarn);
+ }
+ apx_pts_in_range[i] = the_tree->annkFRSearch(
+ query_pts[i], // query point
+ ANN_POW(radius_bound), // squared radius search bound
+ near_neigh, // number of near neighbors
+ curr_nn_idx, // nearest neighbors (returned)
+ curr_dists, // distance (returned)
+ epsilon); // error bound
+ }
+ curr_nn_idx += near_neigh; // increment current pointers
+ curr_dists += near_neigh;
+
+ #ifdef ANN_PERF
+ annUpdateStats(); // update stats
+ #endif
+ }
+
+ long query_time = clock() - clock0; // end of query time
+
+ if (validate) { // validation requested
+ if (valid_dirty) getTrueNN(); // get true near neighbors
+ doValidation(); // validate
+ }
+
+ //------------------------------------------------------------
+ // Print summaries
+ //------------------------------------------------------------
+
+ if (stats > SILENT) {
+ cout << "[Run Queries:\n";
+ cout << " query_size = " << query_size << "\n";
+ cout << " dim = " << dim << "\n";
+ cout << " search_method = " << arg << "\n";
+ cout << " epsilon = " << epsilon << "\n";
+ cout << " near_neigh = " << near_neigh << "\n";
+ if (max_pts_visit != 0)
+ cout << " max_pts_visit = " << max_pts_visit << "\n";
+ if (radius_bound != 0)
+ cout << " radius_bound = " << radius_bound << "\n";
+ if (validate)
+ cout << " true_nn = " << true_nn << "\n";
+
+ if (stats >= EXEC_TIME) { // print exec time summary
+ cout << " query_time = " <<
+ double(query_time)/(query_size*CLOCKS_PER_SEC)
+ << " sec/query";
+ #ifdef ANN_PERF
+ cout << " (biased by perf measurements)";
+ #endif
+ cout << "\n";
+ }
+
+ if (stats >= QUERY_STATS) { // output performance stats
+ #ifdef ANN_PERF
+ cout.flush();
+ annPrintStats(validate);
+ #else
+ cout << " (Performance statistics unavailable.)\n";
+ #endif
+ }
+
+ if (stats >= QUERY_RES) { // output results
+ cout << " (Query Results:\n";
+ cout << " Pt\tANN\tDist\n";
+ curr_nn_idx = apx_nn_idx; // subarray pointers
+ curr_dists = apx_dists;
+ // output nearest neighbors
+ for (int i = 0; i < query_size; i++) {
+ cout << " " << setw(4) << i;
+ for (int j = 0; j < near_neigh; j++) {
+ // exit if no more neighbors
+ if (curr_nn_idx[j] == ANN_NULL_IDX) {
+ cout << "\t[no other pts in radius bound]\n";
+ break;
+ }
+ else { // output point info
+ cout << "\t" << curr_nn_idx[j]
+ << "\t" << ANN_ROOT(curr_dists[j])
+ << "\n";
+ }
+ }
+ // output range count
+ if (radius_bound != 0) {
+ cout << " pts_in_radius_bound = "
+ << apx_pts_in_range[i] << "\n";
+ }
+ // increment subarray pointers
+ curr_nn_idx += near_neigh;
+ curr_dists += near_neigh;
+ }
+ cout << " )\n";
+ }
+ cout << "]\n";
+ }
+ }
+ //----------------------------------------------------------------
+ // Unknown directive
+ //----------------------------------------------------------------
+ else {
+ cerr << "Directive: " << directive << "\n";
+ Error("Unknown directive", ANNabort);
+ }
+ }
+ //--------------------------------------------------------------------
+ // End of input loop (deallocate stuff that was allocated)
+ //--------------------------------------------------------------------
+ if (the_tree != NULL) delete the_tree;
+ if (data_pts != NULL) annDeallocPts(data_pts);
+ if (query_pts != NULL) annDeallocPts(query_pts);
+ if (apx_nn_idx != NULL) delete [] apx_nn_idx;
+ if (apx_dists != NULL) delete [] apx_dists;
+ if (apx_pts_in_range != NULL) delete [] apx_pts_in_range;
+
+ annClose(); // close ANN
+
+ return EXIT_SUCCESS;
+}
+
+//------------------------------------------------------------------------
+// generatePts - call appropriate routine to generate points of a
+// given distribution.
+//------------------------------------------------------------------------
+
+void generatePts(
+ ANNpointArray &pa, // point array (returned)
+ int n, // number of points to generate
+ PtType type, // point type
+ ANNbool new_clust, // new cluster centers desired?
+ ANNpointArray src, // source array (if distr=PLANTED)
+ int n_src) // source size (if distr=PLANTED)
+{
+ if (pa != NULL) annDeallocPts(pa); // get rid of any old points
+ pa = annAllocPts(n, dim); // allocate point storage
+
+ switch (distr) {
+ case UNIFORM: // uniform over cube [-1,1]^d.
+ annUniformPts(pa, n, dim);
+ break;
+ case GAUSS: // Gaussian with mean 0
+ annGaussPts(pa, n, dim, std_dev);
+ break;
+ case LAPLACE: // Laplacian, mean 0 and var 1
+ annLaplacePts(pa, n, dim);
+ break;
+ case CO_GAUSS: // correlated Gaussian
+ annCoGaussPts(pa, n, dim, corr_coef);
+ break;
+ case CO_LAPLACE: // correlated Laplacian
+ annCoLaplacePts(pa, n, dim, corr_coef);
+ break;
+ case CLUS_GAUSS: // clustered Gaussian
+ annClusGaussPts(pa, n, dim, n_color, new_clust, std_dev);
+ break;
+ case CLUS_ORTH_FLATS: // clustered on orthog flats
+ annClusOrthFlats(pa, n, dim, n_color, new_clust, std_dev, max_dim);
+ break;
+ case CLUS_ELLIPSOIDS: // clustered ellipsoids
+ annClusEllipsoids(pa, n, dim, n_color, new_clust, std_dev,
+ std_dev_lo, std_dev_hi, max_dim);
+ break;
+ case PLANTED: // planted distribution
+ annPlanted(pa, n, dim, src, n_src, std_dev);
+ break;
+ default:
+ Error("INTERNAL ERROR: Unknown distribution", ANNabort);
+ break;
+ }
+
+ if (stats > SILENT) {
+ if(type == DATA) cout << "[Generating Data Points:\n";
+ else cout << "[Generating Query Points:\n";
+ cout << " number = " << n << "\n";
+ cout << " dim = " << dim << "\n";
+ cout << " distribution = " << distr_table[distr] << "\n";
+ if (annIdum < 0)
+ cout << " seed = " << annIdum << "\n";
+ if (distr == GAUSS || distr == CLUS_GAUSS
+ || distr == CLUS_ORTH_FLATS)
+ cout << " std_dev = " << std_dev << "\n";
+ if (distr == CLUS_ELLIPSOIDS) {
+ cout << " std_dev = " << std_dev << " (small) \n";
+ cout << " std_dev_lo = " << std_dev_lo << "\n";
+ cout << " std_dev_hi = " << std_dev_hi << "\n";
+ }
+ if (distr == CO_GAUSS || distr == CO_LAPLACE)
+ cout << " corr_coef = " << corr_coef << "\n";
+ if (distr == CLUS_GAUSS || distr == CLUS_ORTH_FLATS
+ || distr == CLUS_ELLIPSOIDS) {
+ cout << " colors = " << n_color << "\n";
+ if (new_clust)
+ cout << " (cluster centers regenerated)\n";
+ }
+ if (distr == CLUS_ORTH_FLATS || distr == CLUS_ELLIPSOIDS) {
+ cout << " max_dim = " << max_dim << "\n";
+ }
+ }
+ // want to see points?
+ if ((type == DATA && stats >= SHOW_PTS) ||
+ (type == QUERY && stats >= QUERY_RES)) {
+ if(type == DATA) cout << "(Data Points:\n";
+ else cout << "(Query Points:\n";
+ for (int i = 0; i < n; i++) {
+ cout << " " << setw(4) << i << "\t";
+ printPoint(pa[i], dim);
+ cout << "\n";
+ }
+ cout << " )\n";
+ }
+ cout << "]\n";
+}
+
+//------------------------------------------------------------------------
+// readPts - read a collection of data or query points.
+//------------------------------------------------------------------------
+
+void readPts(
+ ANNpointArray &pa, // point array (returned)
+ int &n, // number of points
+ char *file_nm, // file name
+ PtType type) // point type (DATA, QUERY)
+{
+ int i;
+ //--------------------------------------------------------------------
+ // Open input file and read points
+ //--------------------------------------------------------------------
+ ifstream in_file(file_nm); // try to open data file
+ if (!in_file) {
+ cerr << "File name: " << file_nm << "\n";
+ Error("Cannot open input data/query file", ANNabort);
+ }
+ // allocate storage for points
+ if (pa != NULL) annDeallocPts(pa); // get rid of old points
+ pa = annAllocPts(n, dim);
+
+ for (i = 0; i < n; i++) { // read the data
+ if (!(in_file >> pa[i][0])) break;
+ for (int d = 1; d < dim; d++) {
+ in_file >> pa[i][d];
+ }
+ }
+
+ char ignore_me; // character for EOF test
+ in_file >> ignore_me; // try to get one more character
+ if (!in_file.eof()) { // exhausted space before eof
+ if (type == DATA)
+ Error("`data_size' too small. Input file truncated.", ANNwarn);
+ else
+ Error("`query_size' too small. Input file truncated.", ANNwarn);
+ }
+ n = i; // number of points read
+
+ //--------------------------------------------------------------------
+ // Print summary
+ //--------------------------------------------------------------------
+ if (stats > SILENT) {
+ if (type == DATA) {
+ cout << "[Read Data Points:\n";
+ cout << " data_size = " << n << "\n";
+ }
+ else {
+ cout << "[Read Query Points:\n";
+ cout << " query_size = " << n << "\n";
+ }
+ cout << " file_name = " << file_nm << "\n";
+ cout << " dim = " << dim << "\n";
+ // print if results requested
+ if ((type == DATA && stats >= SHOW_PTS) ||
+ (type == QUERY && stats >= QUERY_RES)) {
+ cout << " (Points:\n";
+ for (i = 0; i < n; i++) {
+ cout << " " << i << "\t";
+ printPoint(pa[i], dim);
+ cout << "\n";
+ }
+ cout << " )\n";
+ }
+ cout << "]\n";
+ }
+}
+
+//------------------------------------------------------------------------
+// getTrueNN
+// Computes the true nearest neighbors. For purposes of validation,
+// this intentionally done in a rather dumb (but safe way), by
+// invoking the brute-force search.
+//
+// The number of true nearest neighbors is somewhat larger than
+// the number of nearest neighbors. This is so that the validation
+// can determine the expected difference in element ranks.
+//
+// This procedure is invoked just prior to running queries. Since
+// the operation takes a long time, it is performed only if needed.
+// In particular, once generated, it will be regenerated only if
+// new query or data points are generated, or if the requested number
+// of true near neighbors or approximate near neighbors has changed.
+//
+// To validate fixed-radius searching, we compute two counts, one
+// with the original query radius (trueSqRadius) and the other with
+// a radius shrunken by the error factor (minSqradius). We then
+// check that the count of points inside the approximate range is
+// between these two bounds. Because fixed-radius search is
+// allowed to ignore points within the shrunken radius, we only
+// compute exact neighbors within this smaller distance (for we
+// cannot guarantee that we will even visit the other points).
+//------------------------------------------------------------------------
+
+void getTrueNN() // compute true nearest neighbors
+{
+ if (stats > SILENT) {
+ cout << "(Computing true nearest neighbors for validation. This may take time.)\n";
+ }
+ // deallocate existing storage
+ if (true_nn_idx != NULL) delete [] true_nn_idx;
+ if (true_dists != NULL) delete [] true_dists;
+ if (min_pts_in_range != NULL) delete [] min_pts_in_range;
+ if (max_pts_in_range != NULL) delete [] max_pts_in_range;
+
+ if (true_nn > data_size) { // can't get more nn than points
+ true_nn = data_size;
+ }
+
+ // allocate true answer storage
+ true_nn_idx = new ANNidx[true_nn*query_size];
+ true_dists = new ANNdist[true_nn*query_size];
+ min_pts_in_range = new int[query_size];
+ max_pts_in_range = new int[query_size];
+
+ ANNidxArray curr_nn_idx = true_nn_idx; // current locations in arrays
+ ANNdistArray curr_dists = true_dists;
+
+ // allocate search structure
+ ANNbruteForce *the_brute = new ANNbruteForce(data_pts, data_size, dim);
+ // compute nearest neighbors
+ for (int i = 0; i < query_size; i++) {
+ if (radius_bound == 0) { // standard kNN search
+ the_brute->annkSearch( // compute true near neighbors
+ query_pts[i], // query point
+ true_nn, // number of nearest neighbors
+ curr_nn_idx, // where to put indices
+ curr_dists); // where to put distances
+ }
+ else { // fixed radius kNN search
+ // search radii limits
+ ANNdist trueSqRadius = ANN_POW(radius_bound);
+ ANNdist minSqRadius = ANN_POW(radius_bound / (1+epsilon));
+ min_pts_in_range[i] = the_brute->annkFRSearch(
+ query_pts[i], // query point
+ minSqRadius, // shrunken search radius
+ true_nn, // number of near neighbors
+ curr_nn_idx, // nearest neighbors (returned)
+ curr_dists); // distance (returned)
+ max_pts_in_range[i] = the_brute->annkFRSearch(
+ query_pts[i], // query point
+ trueSqRadius, // true search radius
+ 0, NULL, NULL); // (ignore kNN info)
+ }
+ curr_nn_idx += true_nn; // increment nn index pointer
+ curr_dists += true_nn; // increment nn dist pointer
+ }
+ delete the_brute; // delete brute-force struct
+ valid_dirty = ANNfalse; // validation good for now
+}
+
+//------------------------------------------------------------------------
+// doValidation
+// Compares the approximate answers to the k-nearest neighbors
+// against the true nearest neighbors (computed earlier). It is
+// assumed that the true nearest neighbors and indices have been
+// computed earlier.
+//
+// First, we check that all the results are within their allowed
+// limits, and generate an internal error, if not. For the sake of
+// performance evaluation, we also compute the following two
+// quantities for nearest neighbors:
+//
+// Average Error
+// -------------
+// The relative error between the distance to a reported nearest
+// neighbor and the true nearest neighbor (of the same rank),
+//
+// Rank Error
+// ----------
+// The difference in rank between the reported nearest neighbor and
+// its position (if any) among the true nearest neighbors. If we
+// cannot find this point among the true nearest neighbors, then
+// it assumed that the rank of the true nearest neighbor is true_nn+1.
+//
+// Because of the possibility of duplicate distances, this is computed
+// as follows. For the j-th reported nearest neighbor, we count the
+// number of true nearest neighbors that are at least this close. Let
+// this be rnk. Then the rank error is max(0, j-rnk). (In the code
+// below, j is an array index and so the first item is 0, not 1. Thus
+// we take max(0, j+1-rnk) instead.)
+//
+// For the results of fixed-radious range count, we verify that the
+// reported number of points in the range lies between the actual
+// number of points in the shrunken and the true search radius.
+//------------------------------------------------------------------------
+
+void doValidation() // perform validation
+{
+ int* curr_apx_idx = apx_nn_idx; // approx index pointer
+ ANNdistArray curr_apx_dst = apx_dists; // approx distance pointer
+ int* curr_tru_idx = true_nn_idx; // true index pointer
+ ANNdistArray curr_tru_dst = true_dists; // true distance pointer
+ int i, j;
+
+ if (true_nn < near_neigh) {
+ Error("Cannot validate with fewer true near neighbors than actual", ANNabort);
+ }
+
+ for (i = 0; i < query_size; i++) { // validate each query
+ //----------------------------------------------------------------
+ // Compute result errors
+ // In fixed radius search it is possible that not all k
+ // nearest neighbors were computed. Because the true
+ // results are computed over the shrunken radius, we should
+ // have at least as many true nearest neighbors as
+ // approximate nearest neighbors. (If not, an infinite
+ // error will be generated, and so an internal error will
+ // will be generated.
+ //
+ // Because nearest neighbors are sorted in increasing order
+ // of distance, as soon as we see a null index, we can
+ // terminate the distance checking. The error in the
+ // result should not exceed epsilon. However, if
+ // max_pts_visit is nonzero (meaning that the search is
+ // terminated early) this might happen.
+ //----------------------------------------------------------------
+ for (j = 0; j < near_neigh; j++) {
+ if (curr_tru_idx[j] == ANN_NULL_IDX)// no more true neighbors?
+ break;
+ // true i-th smallest distance
+ double true_dist = ANN_ROOT(curr_tru_dst[j]);
+ // reported i-th smallest
+ double rept_dist = ANN_ROOT(curr_apx_dst[j]);
+ // better than optimum?
+ if (rept_dist < true_dist*(1-ERR)) {
+ Error("INTERNAL ERROR: True nearest neighbor incorrect",
+ ANNabort);
+ }
+
+ double resultErr; // result error
+ if (true_dist == 0.0) { // let's not divide by zero
+ if (rept_dist != 0.0) resultErr = ANN_DBL_MAX;
+ else resultErr = 0.0;
+ }
+ else {
+ resultErr = (rept_dist - true_dist) / ((double) true_dist);
+ }
+
+ if (resultErr > epsilon + RND_OFF && max_pts_visit == 0) {
+ Error("INTERNAL ERROR: Actual error exceeds epsilon",
+ ANNabort);
+ }
+ #ifdef ANN_PERF
+ ann_average_err += resultErr; // update statistics error
+ #endif
+ }
+ //--------------------------------------------------------------------
+ // Compute rank errors (only needed for perf measurements)
+ //--------------------------------------------------------------------
+ #ifdef ANN_PERF
+ for (j = 0; j < near_neigh; j++) {
+ if (curr_tru_idx[i] == ANN_NULL_IDX) // no more true neighbors?
+ break;
+
+ double rnkErr = 0.0; // rank error
+ // reported j-th distance
+ ANNdist rept_dist = curr_apx_dst[j];
+ int rnk = 0; // compute rank of this item
+ while (rnk < true_nn && curr_tru_dst[rnk] <= rept_dist)
+ rnk++;
+ if (j+1-rnk > 0) rnkErr = (double) (j+1-rnk);
+ ann_rank_err += rnkErr; // update average rank error
+ }
+ #endif
+ //----------------------------------------------------------------
+ // Check range counts from fixed-radius query
+ //----------------------------------------------------------------
+ if (radius_bound != 0) { // fixed-radius search
+ if (apx_pts_in_range[i] < min_pts_in_range[i] ||
+ apx_pts_in_range[i] > max_pts_in_range[i])
+ Error("INTERNAL ERROR: Invalid fixed-radius range count",
+ ANNabort);
+ }
+
+ curr_apx_idx += near_neigh;
+ curr_apx_dst += near_neigh;
+ curr_tru_idx += true_nn; // increment current pointers
+ curr_tru_dst += true_nn;
+ }
+}
+
+//----------------------------------------------------------------------
+// treeStats
+// Computes a number of statistics related to kd_trees and
+// bd_trees. These statistics are printed if in verbose mode,
+// and otherwise they are only printed if they are deemed to
+// be outside of reasonable operating bounds.
+//----------------------------------------------------------------------
+
+#define log2(x) (log(x)/log(2.0)) // log base 2
+
+void treeStats(
+ ostream &out, // output stream
+ ANNbool verbose) // print stats
+{
+ const int MIN_PTS = 20; // min no. pts for checking
+ const float MAX_FRAC_TL = 0.50; // max frac of triv leaves
+ const float MAX_AVG_AR = 20; // max average aspect ratio
+
+ ANNkdStats st; // statistics structure
+
+ the_tree->getStats(st); // get statistics
+ // total number of nodes
+ int n_nodes = st.n_lf + st.n_spl + st.n_shr;
+ // should be O(n/bs)
+ int opt_n_nodes = (int) (2*(float(st.n_pts)/st.bkt_size));
+ int too_many_nodes = 10*opt_n_nodes;
+ if (st.n_pts >= MIN_PTS && n_nodes > too_many_nodes) {
+ out << "-----------------------------------------------------------\n";
+ out << "Warning: The tree has more than 10x as many nodes as points.\n";
+ out << "You may want to consider a different split or shrink method.\n";
+ out << "-----------------------------------------------------------\n";
+ verbose = ANNtrue;
+ }
+ // fraction of trivial leaves
+ float frac_tl = (st.n_lf == 0 ? 0 : ((float) st.n_tl)/ st.n_lf);
+ if (st.n_pts >= MIN_PTS && frac_tl > MAX_FRAC_TL) {
+ out << "-----------------------------------------------------------\n";
+ out << "Warning: A significant fraction of leaves contain no points.\n";
+ out << "You may want to consider a different split or shrink method.\n";
+ out << "-----------------------------------------------------------\n";
+ verbose = ANNtrue;
+ }
+ // depth should be O(dim*log n)
+ int too_many_levels = (int) (2.0 * st.dim * log2((double) st.n_pts));
+ int opt_levels = (int) log2(double(st.n_pts)/st.bkt_size);
+ if (st.n_pts >= MIN_PTS && st.depth > too_many_levels) {
+ out << "-----------------------------------------------------------\n";
+ out << "Warning: The tree is more than 2x as deep as (dim*log n).\n";
+ out << "You may want to consider a different split or shrink method.\n";
+ out << "-----------------------------------------------------------\n";
+ verbose = ANNtrue;
+ }
+ // average leaf aspect ratio
+ if (st.n_pts >= MIN_PTS && st.avg_ar > MAX_AVG_AR) {
+ out << "-----------------------------------------------------------\n";
+ out << "Warning: Average aspect ratio of cells is quite large.\n";
+ out << "This may slow queries depending on the point distribution.\n";
+ out << "-----------------------------------------------------------\n";
+ verbose = ANNtrue;
+ }
+
+ //------------------------------------------------------------------
+ // Print summaries if requested
+ //------------------------------------------------------------------
+ if (verbose) { // output statistics
+ out << " (Structure Statistics:\n";
+ out << " n_nodes = " << n_nodes
+ << " (opt = " << opt_n_nodes
+ << ", best if < " << too_many_nodes << ")\n"
+ << " n_leaves = " << st.n_lf
+ << " (" << st.n_tl << " contain no points)\n"
+ << " n_splits = " << st.n_spl << "\n"
+ << " n_shrinks = " << st.n_shr << "\n";
+ out << " empty_leaves = " << frac_tl*100
+ << " percent (best if < " << MAX_FRAC_TL*100 << " percent)\n";
+ out << " depth = " << st.depth
+ << " (opt = " << opt_levels
+ << ", best if < " << too_many_levels << ")\n";
+ out << " avg_aspect_ratio = " << st.avg_ar
+ << " (best if < " << MAX_AVG_AR << ")\n";
+ out << " )\n";
+ }
+}
diff --git a/debian/tests/build1 b/debian/tests/build1
new file mode 100755
index 0000000..1c13ef1
--- /dev/null
+++ b/debian/tests/build1
@@ -0,0 +1,156 @@
+#!/bin/sh
+# autopkgtest check: Build and run a program against libann
+# (C) 2014 Anton Gladky
+
+set -e
+
+WORKDIR=$(mktemp -d)
+trap "rm -rf $WORKDIR" 0 INT QUIT ABRT PIPE TERM
+cp ann_test.cpp $WORKDIR/
+cp rand.cpp $WORKDIR/
+cp rand.h $WORKDIR/
+
+cd $WORKDIR
+
+cat <<EOF > test1.in
+ validate on
+ stats query_stats
+ dim 2
+ data_size 20
+ query_size 10
+read_data_pts test1-data.pts
+read_query_pts test1-query.pts
+ bucket_size 1
+ near_neigh 3
+ split_rule suggest
+ shrink_rule none
+build_ann
+ epsilon 0.0
+run_queries standard
+run_queries priority
+
+EOF
+
+cat <<EOF > test1.save
+------------------------------------------------------------
+ann_test: Version 1.0
+ Copyright: David M. Mount and Sunil Arya.
+ Latest Revision: Mar 1, 2005.
+------------------------------------------------------------
+
+validate = on (Warning: this may slow execution time.)
+stats = query_stats
+[Read Data Points:
+ data_size = 20
+ file_name = test1-data.pts
+ dim = 2
+]
+[Read Query Points:
+ query_size = 10
+ file_name = test1-query.pts
+ dim = 2
+]
+[Build ann-structure:
+ split_rule = suggest
+ shrink_rule = none
+ data_size = 20
+ dim = 2
+ bucket_size = 1
+ process_time = 0 sec
+ (Structure Statistics:
+ n_nodes = 39 (opt = 40, best if < 400)
+ n_leaves = 20 (0 contain no points)
+ n_splits = 19
+ n_shrinks = 0
+ empty_leaves = 0 percent (best if < 50 percent)
+ depth = 6 (opt = 4, best if < 17)
+ avg_aspect_ratio = 1.48847 (best if < 20)
+ )
+]
+(Computing true nearest neighbors for validation. This may take time.)
+[Run Queries:
+ query_size = 10
+ dim = 2
+ search_method = standard
+ epsilon = 0
+ near_neigh = 3
+ true_nn = 13
+ query_time = 0 sec/query (biased by perf measurements)
+ (Performance stats: [ mean : stddev ]< min , max >
+ leaf_nodes = [ 6.3 : 2.751 ]< 4 , 11 >
+ splitting_nodes = [ 8.8 : 3.676 ]< 5 , 15 >
+ shrinking_nodes = [ 0 : 0 ]< 0 , 0 >
+ total_nodes = [ 15.1 : 6.35 ]< 9 , 26 >
+ points_visited = [ 6.3 : 2.751 ]< 4 , 11 >
+ coord_hits/pt = [ 0.57 : 0.2201 ]< 0.35 , 0.95 >
+ floating_ops_(K) = [ 0.156 : 0.0563 ]< 0.101 , 0.254 >
+ average_error = [ 0 : 0 ]< 0 , 0 >
+ rank_error = [ 0 : 0 ]< 0 , 0 >
+ )
+]
+[Run Queries:
+ query_size = 10
+ dim = 2
+ search_method = priority
+ epsilon = 0
+ near_neigh = 3
+ true_nn = 13
+ query_time = 0 sec/query (biased by perf measurements)
+ (Performance stats: [ mean : stddev ]< min , max >
+ leaf_nodes = [ 5.9 : 2.025 ]< 4 , 9 >
+ splitting_nodes = [ 8.7 : 3.498 ]< 5 , 15 >
+ shrinking_nodes = [ 0 : 0 ]< 0 , 0 >
+ total_nodes = [ 14.6 : 5.42 ]< 9 , 24 >
+ points_visited = [ 5.9 : 2.025 ]< 4 , 9 >
+ coord_hits/pt = [ 0.535 : 0.1667 ]< 0.35 , 0.8 >
+ floating_ops_(K) = [ 0.1719 : 0.05861 ]< 0.114 , 0.267 >
+ average_error = [ 0 : 0 ]< 0 , 0 >
+ rank_error = [ 0 : 0 ]< 0 , 0 >
+ )
+]
+
+EOF
+
+cat <<EOF > test1-data.pts
+-0.297462 0.176102
+0.565538 -0.361496
+0.909313 -0.182785
+0.920712 0.478408
+0.167682 0.0499836
+0.305223 -0.0805835
+0.114973 0.882453
+0.742916 0.16376
+0.0724605 -0.826775
+0.690960 -0.559284
+0.188485 -0.643934
+0.749427 -0.942415
+-0.970662 -0.223466
+0.916110 0.879597
+0.927417 -0.382593
+-0.711327 0.278713
+-0.519172 0.986146
+0.135338 0.924588
+-0.0837537 0.61687
+0.0520465 0.896306
+
+EOF
+
+cat <<EOF > test1-query.pts
+0.0902484 -0.207129
+-0.419567 0.485743
+0.826225 -0.30962
+0.694758 0.987088
+-0.410807 -0.465182
+-0.836501 0.490184
+0.588289 0.656408
+0.325807 0.38721
+-0.532226 -0.727036
+-0.52506 -0.853508
+
+EOF
+
+g++ -o demo ann_test.cpp rand.cpp -lann
+echo "build: OK"
+[ -x demo ]
+./demo < test1.in
+echo "run: OK"
diff --git a/debian/tests/build2 b/debian/tests/build2
new file mode 100755
index 0000000..a8cdbdc
--- /dev/null
+++ b/debian/tests/build2
@@ -0,0 +1,5313 @@
+#!/bin/sh
+# autopkgtest check: Build and run a program against libann
+# (C) 2014 Anton Gladky
+
+set -e
+
+WORKDIR=$(mktemp -d)
+trap "rm -rf $WORKDIR" 0 INT QUIT ABRT PIPE TERM
+cp ann_test.cpp $WORKDIR/
+cp rand.cpp $WORKDIR/
+cp rand.h $WORKDIR/
+
+cd $WORKDIR
+
+cat <<EOF > test2.in
+ validate on
+ stats query_stats
+ dim 8
+ data_size 5000
+read_data_pts test2-data.pts
+ query_size 100
+read_query_pts test2-query.pts
+ bucket_size 1
+ near_neigh 3
+ split_rule suggest
+ shrink_rule none
+build_ann
+ epsilon 0.0
+run_queries standard
+run_queries priority
+ epsilon 0.10
+run_queries standard
+run_queries priority
+ epsilon 0.50
+run_queries standard
+run_queries priority
+
+EOF
+
+cat <<EOF > test2.save
+------------------------------------------------------------
+ann_test: Version 1.0
+ Copyright: David M. Mount and Sunil Arya.
+ Latest Revision: Mar 1, 2005.
+------------------------------------------------------------
+
+validate = on (Warning: this may slow execution time.)
+stats = query_stats
+[Read Data Points:
+ data_size = 5000
+ file_name = test2-data.pts
+ dim = 8
+]
+[Read Query Points:
+ query_size = 100
+ file_name = test2-query.pts
+ dim = 8
+]
+[Build ann-structure:
+ split_rule = suggest
+ shrink_rule = none
+ data_size = 5000
+ dim = 8
+ bucket_size = 1
+ process_time = 0.18 sec
+ (Structure Statistics:
+ n_nodes = 9999 (opt = 10000, best if < 100000)
+ n_leaves = 5000 (0 contain no points)
+ n_splits = 4999
+ n_shrinks = 0
+ empty_leaves = 0 percent (best if < 50 percent)
+ depth = 17 (opt = 12, best if < 196)
+ avg_aspect_ratio = 2.03396 (best if < 20)
+ )
+]
+(Computing true nearest neighbors for validation. This may take time.)
+[Run Queries:
+ query_size = 100
+ dim = 8
+ search_method = standard
+ epsilon = 0
+ near_neigh = 3
+ true_nn = 13
+ query_time = 0.0008 sec/query (biased by perf measurements)
+ (Performance stats: [ mean : stddev ]< min , max >
+ leaf_nodes = [ 269.6 : 154.1 ]< 68 , 1046 >
+ splitting_nodes = [ 448.2 : 259.2 ]< 100 , 1858 >
+ shrinking_nodes = [ 0 : 0 ]< 0 , 0 >
+ total_nodes = [ 717.8 : 412.6 ]< 168 , 2904 >
+ points_visited = [ 269.6 : 154.1 ]< 68 , 1046 >
+ coord_hits/pt = [ 0.1975 : 0.1075 ]< 0.0446 , 0.6974 >
+ floating_ops_(K) = [ 8.492 : 4.716 ]< 1.939 , 32.61 >
+ average_error = [ 0 : 0 ]< 0 , 0 >
+ rank_error = [ 0 : 0 ]< 0 , 0 >
+ )
+]
+[Run Queries:
+ query_size = 100
+ dim = 8
+ search_method = priority
+ epsilon = 0
+ near_neigh = 3
+ true_nn = 13
+ query_time = 0.0011 sec/query (biased by perf measurements)
+ (Performance stats: [ mean : stddev ]< min , max >
+ leaf_nodes = [ 237.7 : 131.6 ]< 68 , 801 >
+ splitting_nodes = [ 408.1 : 227.7 ]< 100 , 1398 >
+ shrinking_nodes = [ 0 : 0 ]< 0 , 0 >
+ total_nodes = [ 645.8 : 358.5 ]< 168 , 2149 >
+ points_visited = [ 237.7 : 131.6 ]< 68 , 801 >
+ coord_hits/pt = [ 0.1679 : 0.08993 ]< 0.0472 , 0.5492 >
+ floating_ops_(K) = [ 10.83 : 6.344 ]< 2.638 , 38.3 >
+ average_error = [ 0 : 0 ]< 0 , 0 >
+ rank_error = [ 0 : 0 ]< 0 , 0 >
+ )
+]
+[Run Queries:
+ query_size = 100
+ dim = 8
+ search_method = standard
+ epsilon = 0.1
+ near_neigh = 3
+ true_nn = 13
+ query_time = 0.0006 sec/query (biased by perf measurements)
+ (Performance stats: [ mean : stddev ]< min , max >
+ leaf_nodes = [ 200.9 : 115.8 ]< 51 , 762 >
+ splitting_nodes = [ 344.9 : 202.4 ]< 77 , 1407 >
+ shrinking_nodes = [ 0 : 0 ]< 0 , 0 >
+ total_nodes = [ 545.9 : 317.4 ]< 128 , 2169 >
+ points_visited = [ 200.9 : 115.8 ]< 51 , 762 >
+ coord_hits/pt = [ 0.1548 : 0.08517 ]< 0.0348 , 0.5494 >
+ floating_ops_(K) = [ 6.606 : 3.703 ]< 1.513 , 25.14 >
+ average_error = [ 0 : 0 ]< 0 , 0 >
+ rank_error = [ 0 : 0 ]< 0 , 0 >
+ )
+]
+[Run Queries:
+ query_size = 100
+ dim = 8
+ search_method = priority
+ epsilon = 0.1
+ near_neigh = 3
+ true_nn = 13
+ query_time = 0.0007 sec/query (biased by perf measurements)
+ (Performance stats: [ mean : stddev ]< min , max >
+ leaf_nodes = [ 176.1 : 101.1 ]< 49 , 629 >
+ splitting_nodes = [ 314.3 : 186.9 ]< 77 , 1285 >
+ shrinking_nodes = [ 0 : 0 ]< 0 , 0 >
+ total_nodes = [ 490.4 : 286.6 ]< 128 , 1914 >
+ points_visited = [ 176.1 : 101.1 ]< 49 , 629 >
+ coord_hits/pt = [ 0.1309 : 0.07112 ]< 0.0374 , 0.4332 >
+ floating_ops_(K) = [ 8.205 : 4.999 ]< 2.032 , 33.27 >
+ average_error = [ 0 : 0 ]< 0 , 0 >
+ rank_error = [ 0 : 0 ]< 0 , 0 >
+ )
+]
+[Run Queries:
+ query_size = 100
+ dim = 8
+ search_method = standard
+ epsilon = 0.5
+ near_neigh = 3
+ true_nn = 13
+ query_time = 0.0002 sec/query (biased by perf measurements)
+ (Performance stats: [ mean : stddev ]< min , max >
+ leaf_nodes = [ 83.07 : 46.06 ]< 23 , 264 >
+ splitting_nodes = [ 163.4 : 94.86 ]< 42 , 512 >
+ shrinking_nodes = [ 0 : 0 ]< 0 , 0 >
+ total_nodes = [ 246.5 : 140.2 ]< 67 , 776 >
+ points_visited = [ 83.07 : 46.06 ]< 23 , 264 >
+ coord_hits/pt = [ 0.0765 : 0.03992 ]< 0.0182 , 0.2192 >
+ floating_ops_(K) = [ 3.224 : 1.734 ]< 0.891 , 9.572 >
+ average_error = [ 0.0009039 : 0.009619 ]< 0 , 0.1516 >
+ rank_error = [ 0 : 0 ]< 0 , 0 >
+ )
+]
+[Run Queries:
+ query_size = 100
+ dim = 8
+ search_method = priority
+ epsilon = 0.5
+ near_neigh = 3
+ true_nn = 13
+ query_time = 0.0004 sec/query (biased by perf measurements)
+ (Performance stats: [ mean : stddev ]< min , max >
+ leaf_nodes = [ 69.72 : 38.29 ]< 21 , 246 >
+ splitting_nodes = [ 146.8 : 81.69 ]< 40 , 475 >
+ shrinking_nodes = [ 0 : 0 ]< 0 , 0 >
+ total_nodes = [ 216.5 : 118.8 ]< 65 , 721 >
+ points_visited = [ 69.72 : 38.29 ]< 21 , 246 >
+ coord_hits/pt = [ 0.06206 : 0.03155 ]< 0.0182 , 0.194 >
+ floating_ops_(K) = [ 3.608 : 1.989 ]< 1.126 , 12.28 >
+ average_error = [ 0.001425 : 0.011 ]< 0 , 0.1516 >
+ rank_error = [ 0 : 0 ]< 0 , 0 >
+ )
+]
+
+EOF
+
+cat <<EOF > test2-data.pts
+-0.297462 0.176102 0.565538 -0.361496 0.909313 -0.182785 0.920712 0.478408
+0.167682 0.0499836 0.305223 -0.0805835 0.114973 0.882453 0.742916 0.16376
+0.0724605 -0.826775 0.69096 -0.559284 0.188485 -0.643934 0.749427 -0.942415
+-0.970662 -0.223466 0.91611 0.879597 0.927417 -0.382593 -0.711327 0.278713
+-0.519172 0.986146 0.135338 0.924588 -0.0837537 0.61687 0.0520465 0.896306
+0.901473 -0.325291 0.0652063 -0.120574 -0.488327 0.751363 -0.697499 -0.947413
+-0.987624 0.963139 0.369289 -0.211379 -0.883509 -0.708899 0.600525 -0.582872
+-0.643506 0.71497 0.575093 0.00459678 0.539812 -0.258652 0.869204 -0.00241996
+0.772744 -0.676832 -0.897886 -0.196002 -0.628767 0.30896 0.570148 0.26033
+-0.386577 -0.874337 -0.259992 0.312917 -0.0935399 0.573666 0.239825 0.85421
+-0.796208 0.678532 -0.642731 -0.143502 -0.381581 0.356034 0.65693 0.401868
+-0.218174 0.780909 0.499682 0.523531 0.214211 -0.29589 -0.291622 0.0764825
+0.091177 0.447957 -0.848885 -0.752714 -0.40904 0.733853 0.721019 0.0583175
+0.663434 -0.344447 0.696992 0.511178 0.723191 0.159443 -0.988504 -0.898227
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+-0.685114 0.0677462 -0.95481 -0.179017 0.857301 -0.87579 -0.329674 0.562246
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+-0.245092 -0.109308 -0.713837 -0.613438 -0.0236157 0.140357 -0.512282 -0.518118
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+-0.157901 0.836454 0.245372 0.589479 -0.930075 0.106183 0.420879 -0.510301
+-0.466877 -0.115361 -0.736242 0.493482 -0.785855 -0.55306 0.617798 -0.904856
+-0.830296 0.557992 -0.204564 -0.500123 0.153565 -0.773108 0.287265 0.823092
+0.986858 -0.323913 -0.914362 0.535652 -0.535098 -0.945814 0.974316 0.434805
+0.732686 0.221111 0.559403 0.584674 -0.447866 0.508206 0.907212 -0.782827
+-0.970814 -0.13434 -0.550199 0.523038 -0.373701 -0.359454 0.0321578 0.840907
+
+
+EOF
+
+g++ -o demo ann_test.cpp rand.cpp -lann
+echo "build: OK"
+[ -x demo ]
+./demo < test2.in
+echo "run: OK"
diff --git a/debian/tests/control b/debian/tests/control
new file mode 100644
index 0000000..d7d89de
--- /dev/null
+++ b/debian/tests/control
@@ -0,0 +1,2 @@
+Tests: build1 build2
+Depends: libann-dev, build-essential
diff --git a/debian/tests/rand.cpp b/debian/tests/rand.cpp
new file mode 100644
index 0000000..9bf6204
--- /dev/null
+++ b/debian/tests/rand.cpp
@@ -0,0 +1,594 @@
+//----------------------------------------------------------------------
+// File: rand.cpp
+// Programmer: Sunil Arya and David Mount
+// Description: Routines for random point generation
+// Last modified: 08/04/06 (Version 1.1.1)
+//----------------------------------------------------------------------
+// Copyright (c) 1997-2005 University of Maryland and Sunil Arya and
+// David Mount. All Rights Reserved.
+//
+// This software and related documentation is part of the Approximate
+// Nearest Neighbor Library (ANN). This software is provided under
+// the provisions of the Lesser GNU Public License (LGPL). See the
+// file ../ReadMe.txt for further information.
+//
+// The University of Maryland (U.M.) and the authors make no
+// representations about the suitability or fitness of this software for
+// any purpose. It is provided "as is" without express or implied
+// warranty.
+//----------------------------------------------------------------------
+// History:
+// Revision 0.1 03/04/98
+// Initial release
+// Revision 0.2 03/26/98
+// Changed random/srandom declarations for SGI's.
+// Revision 1.0 04/01/05
+// annClusGauss centers distributed over [-1,1] rather than [0,1]
+// Added annClusOrthFlats distribution
+// Changed procedure names to avoid namespace conflicts
+// Added annClusFlats distribution
+// Added rand/srand option and fixed annRan0() initialization.
+// Revision 1.1.1 08/04/06
+// Added planted distribution
+//----------------------------------------------------------------------
+
+#include "rand.h" // random generator declarations
+
+using namespace std; // make std:: accessible
+
+//----------------------------------------------------------------------
+// Globals
+//----------------------------------------------------------------------
+int annIdum = 0; // used for random number generation
+
+//------------------------------------------------------------------------
+// annRan0 - (safer) uniform random number generator
+//
+// The code given here is taken from "Numerical Recipes in C" by
+// William Press, Brian Flannery, Saul Teukolsky, and William
+// Vetterling. The task of the code is to do an additional randomizing
+// shuffle on the system-supplied random number generator to make it
+// safer to use.
+//
+// Returns a uniform deviate between 0.0 and 1.0 using the
+// system-supplied routine random() or rand(). Set the global
+// annIdum to any negative value to initialise or reinitialise
+// the sequence.
+//------------------------------------------------------------------------
+
+double annRan0()
+{
+ const int TAB_SIZE = 97; // table size: any large number
+ int j;
+
+ static double y, v[TAB_SIZE];
+ static int iff = 0;
+ const double RAN_DIVISOR = double(ANN_RAND_MAX + 1UL);
+ if (RAN_DIVISOR < 0) {
+ cout << "RAN_DIVISOR " << RAN_DIVISOR << endl;
+ exit(0);
+ }
+
+ //--------------------------------------------------------------------
+ // As a precaution against misuse, we will always initialize on the
+ // first call, even if "annIdum" is not set negative. Determine
+ // "maxran", the next integer after the largest representable value
+ // of type int. We assume this is a factor of 2 smaller than the
+ // corresponding value of type unsigned int.
+ //--------------------------------------------------------------------
+
+ if (annIdum < 0 || iff == 0) { // initialize
+ iff = 1;
+ ANN_SRAND(annIdum); // (re)seed the generator
+ annIdum = 1;
+
+ for (j = 0; j < TAB_SIZE; j++) // exercise the system routine
+ ANN_RAND(); // (values intentionally ignored)
+
+ for (j = 0; j < TAB_SIZE; j++) // then save TAB_SIZE-1 values
+ v[j] = ANN_RAND();
+ y = ANN_RAND(); // generate starting value
+ }
+
+ //--------------------------------------------------------------------
+ // This is where we start if not initializing. Use the previously
+ // saved random number y to get an index j between 1 and TAB_SIZE-1.
+ // Then use the corresponding v[j] for both the next j and as the
+ // output number.
+ //--------------------------------------------------------------------
+
+ j = int(TAB_SIZE * (y / RAN_DIVISOR));
+ y = v[j];
+ v[j] = ANN_RAND(); // refill the table entry
+ return y / RAN_DIVISOR;
+}
+
+//------------------------------------------------------------------------
+// annRanInt - generate a random integer from {0,1,...,n-1}
+//
+// If n == 0, then -1 is returned.
+//------------------------------------------------------------------------
+
+static int annRanInt(
+ int n)
+{
+ int r = (int) (annRan0()*n);
+ if (r == n) r--; // (in case annRan0() == 1 or n == 0)
+ return r;
+}
+
+//------------------------------------------------------------------------
+// annRanUnif - generate a random uniform in [lo,hi]
+//------------------------------------------------------------------------
+
+static double annRanUnif(
+ double lo,
+ double hi)
+{
+ return annRan0()*(hi-lo) + lo;
+}
+
+//------------------------------------------------------------------------
+// annRanGauss - Gaussian random number generator
+// Returns a normally distributed deviate with zero mean and unit
+// variance, using annRan0() as the source of uniform deviates.
+//------------------------------------------------------------------------
+
+static double annRanGauss()
+{
+ static int iset=0;
+ static double gset;
+
+ if (iset == 0) { // we don't have a deviate handy
+ double v1, v2;
+ double r = 2.0;
+ while (r >= 1.0) {
+ //------------------------------------------------------------
+ // Pick two uniform numbers in the square extending from -1 to
+ // +1 in each direction, see if they are in the circle of radius
+ // 1. If not, try again
+ //------------------------------------------------------------
+ v1 = annRanUnif(-1, 1);
+ v2 = annRanUnif(-1, 1);
+ r = v1 * v1 + v2 * v2;
+ }
+ double fac = sqrt(-2.0 * log(r) / r);
+ //-----------------------------------------------------------------
+ // Now make the Box-Muller transformation to get two normal
+ // deviates. Return one and save the other for next time.
+ //-----------------------------------------------------------------
+ gset = v1 * fac;
+ iset = 1; // set flag
+ return v2 * fac;
+ }
+ else { // we have an extra deviate handy
+ iset = 0; // so unset the flag
+ return gset; // and return it
+ }
+}
+
+//------------------------------------------------------------------------
+// annRanLaplace - Laplacian random number generator
+// Returns a Laplacian distributed deviate with zero mean and
+// unit variance, using annRan0() as the source of uniform deviates.
+//
+// prob(x) = b/2 * exp(-b * |x|).
+//
+// b is chosen to be sqrt(2.0) so that the variance of the Laplacian
+// distribution [2/(b^2)] becomes 1.
+//------------------------------------------------------------------------
+
+static double annRanLaplace()
+{
+ const double b = 1.4142136;
+
+ double laprand = -log(annRan0()) / b;
+ double sign = annRan0();
+ if (sign < 0.5) laprand = -laprand;
+ return(laprand);
+}
+
+//----------------------------------------------------------------------
+// annUniformPts - Generate uniformly distributed points
+// A uniform distribution over [-1,1].
+//----------------------------------------------------------------------
+
+void annUniformPts( // uniform distribution
+ ANNpointArray pa, // point array (modified)
+ int n, // number of points
+ int dim) // dimension
+{
+ for (int i = 0; i < n; i++) {
+ for (int d = 0; d < dim; d++) {
+ pa[i][d] = (ANNcoord) (annRanUnif(-1,1));
+ }
+ }
+}
+
+//----------------------------------------------------------------------
+// annGaussPts - Generate Gaussian distributed points
+// A Gaussian distribution with zero mean and the given standard
+// deviation.
+//----------------------------------------------------------------------
+
+void annGaussPts( // Gaussian distribution
+ ANNpointArray pa, // point array (modified)
+ int n, // number of points
+ int dim, // dimension
+ double std_dev) // standard deviation
+{
+ for (int i = 0; i < n; i++) {
+ for (int d = 0; d < dim; d++) {
+ pa[i][d] = (ANNcoord) (annRanGauss() * std_dev);
+ }
+ }
+}
+
+//----------------------------------------------------------------------
+// annLaplacePts - Generate Laplacian distributed points
+// Generates a Laplacian distribution (zero mean and unit variance).
+//----------------------------------------------------------------------
+
+void annLaplacePts( // Laplacian distribution
+ ANNpointArray pa, // point array (modified)
+ int n, // number of points
+ int dim) // dimension
+{
+ for (int i = 0; i < n; i++) {
+ for (int d = 0; d < dim; d++) {
+ pa[i][d] = (ANNcoord) annRanLaplace();
+ }
+ }
+}
+
+//----------------------------------------------------------------------
+// annCoGaussPts - Generate correlated Gaussian distributed points
+// Generates a Gauss-Markov distribution of zero mean and unit
+// variance.
+//----------------------------------------------------------------------
+
+void annCoGaussPts( // correlated-Gaussian distribution
+ ANNpointArray pa, // point array (modified)
+ int n, // number of points
+ int dim, // dimension
+ double correlation) // correlation
+{
+ double std_dev_w = sqrt(1.0 - correlation * correlation);
+ for (int i = 0; i < n; i++) {
+ double previous = annRanGauss();
+ pa[i][0] = (ANNcoord) previous;
+ for (int d = 1; d < dim; d++) {
+ previous = correlation*previous + std_dev_w*annRanGauss();
+ pa[i][d] = (ANNcoord) previous;
+ }
+ }
+}
+
+//----------------------------------------------------------------------
+// annCoLaplacePts - Generate correlated Laplacian distributed points
+// Generates a Laplacian-Markov distribution of zero mean and unit
+// variance.
+//----------------------------------------------------------------------
+
+void annCoLaplacePts( // correlated-Laplacian distribution
+ ANNpointArray pa, // point array (modified)
+ int n, // number of points
+ int dim, // dimension
+ double correlation) // correlation
+{
+ double wn;
+ double corr_sq = correlation * correlation;
+
+ for (int i = 0; i < n; i++) {
+ double previous = annRanLaplace();
+ pa[i][0] = (ANNcoord) previous;
+ for (int d = 1; d < dim; d++) {
+ double temp = annRan0();
+ if (temp < corr_sq)
+ wn = 0.0;
+ else
+ wn = annRanLaplace();
+ previous = correlation * previous + wn;
+ pa[i][d] = (ANNcoord) previous;
+ }
+ }
+}
+
+//----------------------------------------------------------------------
+// annClusGaussPts - Generate clusters of Gaussian distributed points
+// Cluster centers are uniformly distributed over [-1,1], and the
+// standard deviation within each cluster is fixed.
+//
+// Note: Once cluster centers have been set, they are not changed,
+// unless new_clust = true. This is so that subsequent calls generate
+// points from the same distribution. It follows, of course, that any
+// attempt to change the dimension or number of clusters without
+// generating new clusters is asking for trouble.
+//
+// Note: Cluster centers are not generated by a call to uniformPts().
+// Although this could be done, it has been omitted for
+// compatibility with annClusGaussPts() in the colored version,
+// rand_c.cc.
+//----------------------------------------------------------------------
+
+void annClusGaussPts( // clustered-Gaussian distribution
+ ANNpointArray pa, // point array (modified)
+ int n, // number of points
+ int dim, // dimension
+ int n_clus, // number of colors
+ ANNbool new_clust, // generate new clusters.
+ double std_dev) // standard deviation within clusters
+{
+ static ANNpointArray clusters = NULL;// cluster storage
+
+ if (clusters == NULL || new_clust) {// need new cluster centers
+ if (clusters != NULL) // clusters already exist
+ annDeallocPts(clusters); // get rid of them
+ clusters = annAllocPts(n_clus, dim);
+ // generate cluster center coords
+ for (int i = 0; i < n_clus; i++) {
+ for (int d = 0; d < dim; d++) {
+ clusters[i][d] = (ANNcoord) annRanUnif(-1,1);
+ }
+ }
+ }
+
+ for (int i = 0; i < n; i++) {
+ int c = annRanInt(n_clus); // generate cluster index
+ for (int d = 0; d < dim; d++) {
+ pa[i][d] = (ANNcoord) (std_dev*annRanGauss() + clusters[c][d]);
+ }
+ }
+}
+
+//----------------------------------------------------------------------
+// annClusOrthFlats - points clustered along orthogonal flats
+//
+// This distribution consists of a collection points clustered
+// among a collection of axis-aligned low dimensional flats in
+// the hypercube [-1,1]^d. A set of n_clus orthogonal flats are
+// generated, each whose dimension is a random number between 1
+// and max_dim. The points are evenly distributed among the clusters.
+// For each cluster, we generate points uniformly distributed along
+// the flat within the hypercube.
+//
+// This is done as follows. Each cluster is defined by a d-element
+// control vector whose components are either:
+//
+// CO_FLAG indicating that this component is to be generated
+// uniformly in [-1,1],
+// x a value other than CO_FLAG in the range [-1,1],
+// which indicates that this coordinate is to be
+// generated as x plus a Gaussian random deviation
+// with the given standard deviation.
+//
+// The number of zero components is the dimension of the flat, which
+// is a random integer in the range from 1 to max_dim. The points
+// are disributed between clusters in nearly equal sized groups.
+//
+// Note: Once cluster centers have been set, they are not changed,
+// unless new_clust = true. This is so that subsequent calls generate
+// points from the same distribution. It follows, of course, that any
+// attempt to change the dimension or number of clusters without
+// generating new clusters is asking for trouble.
+//
+// To make this a bad scenario at query time, query points should be
+// selected from a different distribution, e.g. uniform or Gaussian.
+//
+// We use a little programming trick to generate groups of roughly
+// equal size. If n is the total number of points, and n_clus is
+// the number of clusters, then the c-th cluster (0 <= c < n_clus)
+// is given floor((n+c)/n_clus) points. It can be shown that this
+// will exactly consume all n points.
+//
+// This procedure makes use of the utility procedure, genOrthFlat
+// which generates points in one orthogonal flat, according to
+// the given control vector.
+//
+//----------------------------------------------------------------------
+const double CO_FLAG = 999; // special flag value
+
+static void genOrthFlat( // generate points on an orthog flat
+ ANNpointArray pa, // point array
+ int n, // number of points
+ int dim, // dimension
+ double *control, // control vector
+ double std_dev) // standard deviation
+{
+ for (int i = 0; i < n; i++) { // generate each point
+ for (int d = 0; d < dim; d++) { // generate each coord
+ if (control[d] == CO_FLAG) // dimension on flat
+ pa[i][d] = (ANNcoord) annRanUnif(-1,1);
+ else // dimension off flat
+ pa[i][d] = (ANNcoord) (std_dev*annRanGauss() + control[d]);
+ }
+ }
+}
+
+void annClusOrthFlats( // clustered along orthogonal flats
+ ANNpointArray pa, // point array (modified)
+ int n, // number of points
+ int dim, // dimension
+ int n_clus, // number of colors
+ ANNbool new_clust, // generate new clusters.
+ double std_dev, // standard deviation within clusters
+ int max_dim) // maximum dimension of the flats
+{
+ static ANNpointArray control = NULL; // control vectors
+
+ if (control == NULL || new_clust) { // need new cluster centers
+ if (control != NULL) { // clusters already exist
+ annDeallocPts(control); // get rid of them
+ }
+ control = annAllocPts(n_clus, dim);
+
+ for (int c = 0; c < n_clus; c++) { // generate clusters
+ int n_dim = 1 + annRanInt(max_dim); // number of dimensions in flat
+ for (int d = 0; d < dim; d++) { // generate side locations
+ // prob. of picking next dim
+ double Prob = ((double) n_dim)/((double) (dim-d));
+ if (annRan0() < Prob) { // add this one to flat
+ control[c][d] = CO_FLAG; // flag this entry
+ n_dim--; // one fewer dim to fill
+ }
+ else { // don't take this one
+ control[c][d] = annRanUnif(-1,1);// random value in [-1,1]
+ }
+ }
+ }
+ }
+ int offset = 0; // offset in pa array
+ for (int c = 0; c < n_clus; c++) { // generate clusters
+ int pick = (n+c)/n_clus; // number of points to pick
+ // generate the points
+ genOrthFlat(pa+offset, pick, dim, control[c], std_dev);
+ offset += pick; // increment offset
+ }
+}
+
+//----------------------------------------------------------------------
+// annClusEllipsoids - points clustered around axis-aligned ellipsoids
+//
+// This distribution consists of a collection points clustered
+// among a collection of low dimensional ellipsoids whose axes
+// are alligned with the coordinate axes in the hypercube [-1,1]^d.
+// The objective is to model distributions in which the points are
+// distributed in lower dimensional subspaces, and within this
+// lower dimensional space the points are distributed with a
+// Gaussian distribution (with no correlation between the
+// dimensions).
+//
+// The distribution is given the number of clusters or "colors"
+// (n_clus), maximum number of dimensions (max_dim) of the lower
+// dimensional subspace, a "small" standard deviation
+// (std_dev_small), and a "large" standard deviation range
+// (std_dev_lo, std_dev_hi).
+//
+// The algorithm generates n_clus cluster centers uniformly from
+// the hypercube [-1,1]^d. For each cluster, it selects the
+// dimension of the subspace as a random number r between 1 and
+// max_dim. These are the dimensions of the ellipsoid. Then it
+// generates a d-element std dev vector whose entries are the
+// standard deviation for the coordinates of each cluster in the
+// distribution. Among the d-element control vector, r randomly
+// chosen values are chosen uniformly from the range [std_dev_lo,
+// std_dev_hi]. The remaining values are set to std_dev_small.
+//
+// Note that annClusGaussPts is a special case of this in which
+// max_dim = 0, and std_dev = std_dev_small.
+//
+// If the flag new_clust is set, then new cluster centers are
+// generated.
+//
+// This procedure makes use of the utility procedure genGauss
+// which generates points distributed according to a Gaussian
+// distribution.
+//
+//----------------------------------------------------------------------
+
+static void genGauss( // generate points on a general Gaussian
+ ANNpointArray pa, // point array
+ int n, // number of points
+ int dim, // dimension
+ double *center, // center vector
+ double *std_dev) // standard deviation vector
+{
+ for (int i = 0; i < n; i++) {
+ for (int d = 0; d < dim; d++) {
+ pa[i][d] = (ANNcoord) (std_dev[d]*annRanGauss() + center[d]);
+ }
+ }
+}
+
+void annClusEllipsoids( // clustered around ellipsoids
+ ANNpointArray pa, // point array (modified)
+ int n, // number of points
+ int dim, // dimension
+ int n_clus, // number of colors
+ ANNbool new_clust, // generate new clusters.
+ double std_dev_small, // small standard deviation
+ double std_dev_lo, // low standard deviation for ellipses
+ double std_dev_hi, // high standard deviation for ellipses
+ int max_dim) // maximum dimension of the flats
+{
+ static ANNpointArray centers = NULL; // cluster centers
+ static ANNpointArray std_dev = NULL; // standard deviations
+
+ if (centers == NULL || new_clust) { // need new cluster centers
+ if (centers != NULL) // clusters already exist
+ annDeallocPts(centers); // get rid of them
+ if (std_dev != NULL) // std deviations already exist
+ annDeallocPts(std_dev); // get rid of them
+
+ centers = annAllocPts(n_clus, dim); // alloc new clusters and devs
+ std_dev = annAllocPts(n_clus, dim);
+
+ for (int i = 0; i < n_clus; i++) { // gen cluster center coords
+ for (int d = 0; d < dim; d++) {
+ centers[i][d] = (ANNcoord) annRanUnif(-1,1);
+ }
+ }
+ for (int c = 0; c < n_clus; c++) { // generate cluster std dev
+ int n_dim = 1 + annRanInt(max_dim); // number of dimensions in flat
+ for (int d = 0; d < dim; d++) { // generate std dev's
+ // prob. of picking next dim
+ double Prob = ((double) n_dim)/((double) (dim-d));
+ if (annRan0() < Prob) { // add this one to ellipse
+ // generate random std dev
+ std_dev[c][d] = annRanUnif(std_dev_lo, std_dev_hi);
+ n_dim--; // one fewer dim to fill
+ }
+ else { // don't take this one
+ std_dev[c][d] = std_dev_small;// use small std dev
+ }
+ }
+ }
+ }
+
+ int offset = 0; // next slot to fill
+ for (int c = 0; c < n_clus; c++) { // generate clusters
+ int pick = (n+c)/n_clus; // number of points to pick
+ // generate the points
+ genGauss(pa+offset, pick, dim, centers[c], std_dev[c]);
+ offset += pick; // increment offset in array
+ }
+}
+
+//----------------------------------------------------------------------
+// annPlanted - Generates points from a "planted" distribution
+// In high dimensional spaces, interpoint distances tend to be
+// highly clustered around the mean value. Approximate nearest
+// neighbor searching makes little sense in this context, unless it
+// is the case that each query point is significantly closer to its
+// nearest neighbor than to other points. Thus, the query points
+// should be planted close to the data points. Given a source data
+// set, this procedure generates a set of query points having this
+// property.
+//
+// We are given a source data array and a standard deviation. We
+// generate points as follows. We select a random point from the
+// source data set, and we generate a Gaussian point centered about
+// this random point and perturbed by a normal distributed random
+// variable with mean zero and the given standard deviation along
+// each coordinate.
+//
+// Note that this essentially the same a clustered Gaussian
+// distribution, but where the cluster centers are given by the
+// source data set.
+//----------------------------------------------------------------------
+
+void annPlanted( // planted nearest neighbors
+ ANNpointArray pa, // point array (modified)
+ int n, // number of points
+ int dim, // dimension
+ ANNpointArray src, // source point array
+ int n_src, // source size
+ double std_dev) // standard deviation about source
+{
+ for (int i = 0; i < n; i++) {
+ int c = annRanInt(n_src); // generate source index
+ for (int d = 0; d < dim; d++) {
+ pa[i][d] = (ANNcoord) (std_dev*annRanGauss() + src[c][d]);
+ }
+ }
+}
diff --git a/debian/tests/rand.h b/debian/tests/rand.h
new file mode 100644
index 0000000..b87cb41
--- /dev/null
+++ b/debian/tests/rand.h
@@ -0,0 +1,131 @@
+//----------------------------------------------------------------------
+// File: rand.h
+// Programmer: Sunil Arya and David Mount
+// Description: Basic include file for random point generators
+// Last modified: 08/04/06 (Version 1.1.1)
+//----------------------------------------------------------------------
+// Copyright (c) 1997-2005 University of Maryland and Sunil Arya and
+// David Mount. All Rights Reserved.
+//
+// This software and related documentation is part of the Approximate
+// Nearest Neighbor Library (ANN). This software is provided under
+// the provisions of the Lesser GNU Public License (LGPL). See the
+// file ../ReadMe.txt for further information.
+//
+// The University of Maryland (U.M.) and the authors make no
+// representations about the suitability or fitness of this software for
+// any purpose. It is provided "as is" without express or implied
+// warranty.
+//----------------------------------------------------------------------
+// History:
+// Revision 0.1 03/04/98
+// Initial release
+// Revision 1.0 04/01/05
+// Added annClusOrthFlats distribution
+// Changed procedure names to avoid namespace conflicts
+// Added annClusFlats distribution
+// Revision 1.1.1 08/04/06
+// Added planted distribution
+//----------------------------------------------------------------------
+
+#ifndef rand_H
+#define rand_H
+
+//----------------------------------------------------------------------
+// Basic includes
+//----------------------------------------------------------------------
+#include <cstdlib> // standard includes (rand/random)
+#include <cmath> // math routines
+#include <ANN/ANN.h> // basic ANN includes
+
+//----------------------------------------------------------------------
+// Although random/srandom is a more reliable random number generator,
+// many systems do not have it. If it is not available, set the
+// preprocessor symbol ANN_NO_RANDOM, and this will substitute the use
+// of rand/srand for them.
+//----------------------------------------------------------------------
+#ifdef ANN_NO_RANDOM // for systems not having random()
+ #define ANN_RAND rand
+ #define ANN_SRAND srand
+ #define ANN_RAND_MAX RAND_MAX
+#else // otherwise use rand()
+ #define ANN_RAND random
+ #define ANN_SRAND srandom
+ #define ANN_RAND_MAX 2147483647UL // 2**{31} - 1
+ // #define ANN_RAND_MAX 1073741824UL // 2**{30}
+#endif
+
+//----------------------------------------------------------------------
+// Globals
+//----------------------------------------------------------------------
+extern int annIdum; // random number seed
+
+//----------------------------------------------------------------------
+// External entry points
+//----------------------------------------------------------------------
+
+void annUniformPts( // uniform distribution
+ ANNpointArray pa, // point array (modified)
+ int n, // number of points
+ int dim); // dimension
+
+void annGaussPts( // Gaussian distribution
+ ANNpointArray pa, // point array (modified)
+ int n, // number of points
+ int dim, // dimension
+ double std_dev); // standard deviation
+
+void annCoGaussPts( // correlated-Gaussian distribution
+ ANNpointArray pa, // point array (modified)
+ int n, // number of points
+ int dim, // dimension
+ double correlation); // correlation
+
+void annLaplacePts( // Laplacian distribution
+ ANNpointArray pa, // point array (modified)
+ int n, // number of points
+ int dim); // dimension
+
+void annCoLaplacePts( // correlated-Laplacian distribution
+ ANNpointArray pa, // point array (modified)
+ int n, // number of points
+ int dim, // dimension
+ double correlation); // correlation
+
+void annClusGaussPts( // clustered-Gaussian distribution
+ ANNpointArray pa, // point array (modified)
+ int n, // number of points
+ int dim, // dimension
+ int n_clus, // number of colors (clusters)
+ ANNbool new_clust, // generate new cluster centers
+ double std_dev); // standard deviation within clusters
+
+void annClusOrthFlats( // clustered along orthogonal flats
+ ANNpointArray pa, // point array (modified)
+ int n, // number of points
+ int dim, // dimension
+ int n_clus, // number of colors
+ ANNbool new_clust, // generate new clusters.
+ double std_dev, // standard deviation within clusters
+ int max_dim); // maximum dimension of the flats
+
+void annClusEllipsoids( // clustered around ellipsoids
+ ANNpointArray pa, // point array (modified)
+ int n, // number of points
+ int dim, // dimension
+ int n_clus, // number of colors
+ ANNbool new_clust, // generate new clusters.
+ double std_dev_small, // small standard deviation
+ double std_dev_lo, // low standard deviation for ellipses
+ double std_dev_hi, // high standard deviation for ellipses
+ int max_dim); // maximum dimension of the flats
+
+void annPlanted( // planted nearest neighbors
+ ANNpointArray pa, // point array (modified)
+ int n, // number of points
+ int dim, // dimension
+ ANNpointArray src, // source point array
+ int n_src, // source size
+ double std_dev); // standard deviation about source
+
+#endif
--
Alioth's /usr/local/bin/git-commit-notice on /srv/git.debian.org/git/debian-science/packages/ann.git
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