From 0820b707fe47acf03faf6a7a23f22ac269e8d8de Mon Sep 17 00:00:00 2001 From: Francois Fleuret Date: Wed, 3 Dec 2014 14:50:01 +0100 Subject: [PATCH] Added a new "non-absolute" criterion that enforces the proportions of classes in each cluster. --- clueless-kmeans.cc | 4 ++- clusterer.cc | 65 +++++++++++++++++++++++++---------- clusterer.h | 12 +++++-- result-clueless-absolute.png | Bin 0 -> 175653 bytes result-clueless.png | Bin 175653 -> 175958 bytes test.sh | 6 ++++ 6 files changed, 66 insertions(+), 21 deletions(-) create mode 100644 result-clueless-absolute.png diff --git a/clueless-kmeans.cc b/clueless-kmeans.cc index 14e5b92..d71a3cf 100644 --- a/clueless-kmeans.cc +++ b/clueless-kmeans.cc @@ -83,12 +83,14 @@ int main(int argc, char **argv) { mode = Clusterer::STANDARD_LP_ASSOCIATION; } else if(strcmp(argv[1], "clueless") == 0) { mode = Clusterer::UNINFORMATIVE_LP_ASSOCIATION; + } else if(strcmp(argv[1], "clueless-absolute") == 0) { + mode = Clusterer::UNINFORMATIVE_LP_ASSOCIATION_ABSOLUTE; } else { cerr << "Unknown association mode " << argv[1] << endl; exit(EXIT_FAILURE); } } else { - cerr << "Usage: " << argv[0] << " standard|clueless" << endl; + cerr << "Usage: " << argv[0] << " standard|clueless|clueless-absolute" << endl; exit(EXIT_FAILURE); } diff --git a/clusterer.cc b/clusterer.cc index 5bf04aa..965b7ba 100644 --- a/clusterer.cc +++ b/clusterer.cc @@ -154,7 +154,8 @@ scalar_t Clusterer::baseline_lp_cluster_association(int nb_points, scalar_t **po scalar_t Clusterer::uninformative_lp_cluster_association(int nb_points, scalar_t **points, int nb_classes, int *labels, - scalar_t **gamma) { + scalar_t **gamma, + int absolute_proportion) { // N points // K clusters // dist(n,k) distance of samples n to cluster k @@ -170,8 +171,8 @@ scalar_t Clusterer::uninformative_lp_cluster_association(int nb_points, scalar_t // under // // (A) \forall n, k, \gamma(n, k) >= 0 - // (B) \forall n, \sum_k \gamma(n,k) = 1 - // (C) \forall k, \sum_n \gamma(n,k) = N/K + // (B) \forall n, \sum_k \gamma(n, k) = 1 + // (C) \forall k, \sum_n \gamma(n, k) = N/K glp_prob *lp; @@ -180,7 +181,13 @@ scalar_t Clusterer::uninformative_lp_cluster_association(int nb_points, scalar_t // ** GLPK USES INDEXES STARTING AT 1, NOT 0. ** - int nb_coeffs = nb_points * _nb_clusters + nb_points * _nb_clusters; + int nb_coeffs; + + if(absolute_proportion) { + nb_coeffs = nb_points * _nb_clusters + nb_points * _nb_clusters; + } else { + nb_coeffs = nb_points * _nb_clusters + nb_points * nb_classes * _nb_clusters; + } int *coeff_row = new int[nb_coeffs + 1]; int *coeff_col = new int[nb_coeffs + 1]; @@ -253,16 +260,33 @@ scalar_t Clusterer::uninformative_lp_cluster_association(int nb_points, scalar_t // gammas for this cluster and this class is equal to the number of // sample of that class, divided by the number of clusters - for(int k = 1; k <= _nb_clusters; k++) { - for(int c = 1; c <= nb_classes; c++) { - int row = nb_points + (k - 1) * nb_classes + c; - scalar_t tau = nb_samples_per_class[c-1] / scalar_t(_nb_clusters); - glp_set_row_bnds(lp, row, GLP_FX, tau, tau); - for(int n = 1; n <= nb_points; n++) { - if(labels[n-1] == c - 1) { + if(absolute_proportion) { + for(int k = 1; k <= _nb_clusters; k++) { + for(int c = 1; c <= nb_classes; c++) { + int row = nb_points + (k - 1) * nb_classes + c; + scalar_t tau = nb_samples_per_class[c-1] / scalar_t(_nb_clusters); + glp_set_row_bnds(lp, row, GLP_FX, tau, tau); + for(int n = 1; n <= nb_points; n++) { + if(labels[n-1] == c - 1) { + coeff_row[n_coeff] = row; + coeff_col[n_coeff] = (k-1) * nb_points + n; + coeff_wgt[n_coeff] = 1.0; + n_coeff++; + } + } + } + } + } else { + for(int k = 1; k <= _nb_clusters; k++) { + for(int c = 1; c <= nb_classes; c++) { + int row = nb_points + (k - 1) * nb_classes + c; + glp_set_row_bnds(lp, row, GLP_FX, 0.0, 0.0); + for(int n = 1; n <= nb_points; n++) { coeff_row[n_coeff] = row; coeff_col[n_coeff] = (k-1) * nb_points + n; - coeff_wgt[n_coeff] = 1.0; + coeff_wgt[n_coeff] = + (labels[n-1] == c - 1 ? 1.0 : 0.0) + - scalar_t(nb_samples_per_class[c-1]) / scalar_t(nb_points); n_coeff++; } } @@ -357,18 +381,23 @@ void Clusterer::train(int mode, switch(mode) { case STANDARD_ASSOCIATION: - total_distance = - baseline_cluster_association(nb_points, points, nb_classes, labels, gammas); + total_distance = + baseline_cluster_association(nb_points, points, nb_classes, labels, gammas); break; case STANDARD_LP_ASSOCIATION: - total_distance = - baseline_lp_cluster_association(nb_points, points, nb_classes, labels, gammas); + total_distance = + baseline_lp_cluster_association(nb_points, points, nb_classes, labels, gammas); break; case UNINFORMATIVE_LP_ASSOCIATION: - total_distance = - uninformative_lp_cluster_association(nb_points, points, nb_classes, labels, gammas); + total_distance = + uninformative_lp_cluster_association(nb_points, points, nb_classes, labels, gammas, 0); + break; + + case UNINFORMATIVE_LP_ASSOCIATION_ABSOLUTE: + total_distance = + uninformative_lp_cluster_association(nb_points, points, nb_classes, labels, gammas, 1); break; default: diff --git a/clusterer.h b/clusterer.h index 584828e..6fa5382 100644 --- a/clusterer.h +++ b/clusterer.h @@ -31,9 +31,16 @@ class Clusterer { public: enum { + // Standard k-mean STANDARD_ASSOCIATION, + // Same, implemented as a LP problem for sanity check STANDARD_LP_ASSOCIATION, - UNINFORMATIVE_LP_ASSOCIATION + // Criterion forcing to have the same distribution of classes in + // all clusters + UNINFORMATIVE_LP_ASSOCIATION, + // Criterion forcing to have the same number of samples of each + // class in all clusters + UNINFORMATIVE_LP_ASSOCIATION_ABSOLUTE }; const static int max_nb_iterations = 10; @@ -66,7 +73,8 @@ public: scalar_t uninformative_lp_cluster_association(int nb_points, scalar_t **points, int nb_classes, int *labels, - scalar_t **gamma); + scalar_t **gamma, + int absolute_proportion); void update_clusters(int 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./clueless-kmeans clueless make_graph result-clueless.png + +echo "Clueless-absolute k-mean" +./clueless-kmeans clueless-absolute +make_graph result-clueless-absolute.png -- 2.39.5