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-rw-r--r--ml/dlib/dlib/test/clustering.cpp410
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diff --git a/ml/dlib/dlib/test/clustering.cpp b/ml/dlib/dlib/test/clustering.cpp
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--- a/ml/dlib/dlib/test/clustering.cpp
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@@ -1,410 +0,0 @@
-// Copyright (C) 2012 Davis E. King (davis@dlib.net)
-// License: Boost Software License See LICENSE.txt for the full license.
-
-#include <dlib/clustering.h>
-
-#include "tester.h"
-
-namespace
-{
- using namespace test;
- using namespace dlib;
- using namespace std;
-
- logger dlog("test.clustering");
-
-// ----------------------------------------------------------------------------------------
-
- void make_test_graph(
- dlib::rand& rnd,
- std::vector<sample_pair>& edges,
- std::vector<unsigned long>& labels,
- const int groups,
- const int group_size,
- const int noise_level,
- const double missed_edges
- )
- {
- labels.resize(groups*group_size);
-
- for (unsigned long i = 0; i < labels.size(); ++i)
- {
- labels[i] = i/group_size;
- }
-
- edges.clear();
- for (int i = 0; i < groups; ++i)
- {
- for (int j = 0; j < group_size; ++j)
- {
- for (int k = 0; k < group_size; ++k)
- {
- if (j == k)
- continue;
-
- if (rnd.get_random_double() < missed_edges)
- continue;
-
- edges.push_back(sample_pair(j+group_size*i, k+group_size*i, 1));
- }
- }
- }
-
- for (int k = 0; k < groups*noise_level; ++k)
- {
- const int i = rnd.get_random_32bit_number()%labels.size();
- const int j = rnd.get_random_32bit_number()%labels.size();
- edges.push_back(sample_pair(i,j,1));
- }
-
- }
-
-// ----------------------------------------------------------------------------------------
-
- void make_modularity_matrices (
- const std::vector<sample_pair>& edges,
- matrix<double>& A,
- matrix<double>& P,
- double& m
- )
- {
- const unsigned long num_nodes = max_index_plus_one(edges);
- A.set_size(num_nodes, num_nodes);
- P.set_size(num_nodes, num_nodes);
- A = 0;
- P = 0;
- std::vector<double> k(num_nodes,0);
-
- for (unsigned long i = 0; i < edges.size(); ++i)
- {
- const unsigned long n1 = edges[i].index1();
- const unsigned long n2 = edges[i].index2();
- k[n1] += edges[i].distance();
- if (n1 != n2)
- {
- k[n2] += edges[i].distance();
- A(n2,n1) += edges[i].distance();
- }
-
- A(n1,n2) += edges[i].distance();
- }
-
- m = sum(A)/2;
-
- for (long r = 0; r < P.nr(); ++r)
- {
- for (long c = 0; c < P.nc(); ++c)
- {
- P(r,c) = k[r]*k[c]/(2*m);
- }
- }
-
- }
-
- double compute_modularity_simple (
- const std::vector<sample_pair>& edges,
- std::vector<unsigned long> labels
- )
- {
- double m;
- matrix<double> A,P;
- make_modularity_matrices(edges, A, P, m);
- matrix<double> B = A - P;
-
- double Q = 0;
- for (long r = 0; r < B.nr(); ++r)
- {
- for (long c = 0; c < B.nc(); ++c)
- {
- if (labels[r] == labels[c])
- {
- Q += B(r,c);
- }
- }
- }
- return 1.0/(2*m) * Q;
- }
-
-// ----------------------------------------------------------------------------------------
-
- void test_modularity(dlib::rand& rnd)
- {
- print_spinner();
- std::vector<sample_pair> edges;
- std::vector<ordered_sample_pair> oedges;
- std::vector<unsigned long> labels;
-
- make_test_graph(rnd, edges, labels, 10, 30, 3, 0.10);
- if (rnd.get_random_double() < 0.5)
- remove_duplicate_edges(edges);
- convert_unordered_to_ordered(edges, oedges);
-
-
- const double m1 = modularity(edges, labels);
- const double m2 = compute_modularity_simple(edges, labels);
- const double m3 = modularity(oedges, labels);
-
- DLIB_TEST(std::abs(m1-m2) < 1e-12);
- DLIB_TEST(std::abs(m2-m3) < 1e-12);
- DLIB_TEST(std::abs(m3-m1) < 1e-12);
- }
-
- void test_newman_clustering(dlib::rand& rnd)
- {
- print_spinner();
- std::vector<sample_pair> edges;
- std::vector<unsigned long> labels;
-
- make_test_graph(rnd, edges, labels, 5, 30, 3, 0.10);
- if (rnd.get_random_double() < 0.5)
- remove_duplicate_edges(edges);
-
-
- std::vector<unsigned long> labels2;
-
- unsigned long num_clusters = newman_cluster(edges, labels2);
- DLIB_TEST(labels.size() == labels2.size());
- DLIB_TEST(num_clusters == 5);
-
- for (unsigned long i = 0; i < labels.size(); ++i)
- {
- for (unsigned long j = 0; j < labels.size(); ++j)
- {
- if (labels[i] == labels[j])
- {
- DLIB_TEST(labels2[i] == labels2[j]);
- }
- else
- {
- DLIB_TEST(labels2[i] != labels2[j]);
- }
- }
- }
- }
-
- void test_chinese_whispers(dlib::rand& rnd)
- {
- print_spinner();
- std::vector<sample_pair> edges;
- std::vector<unsigned long> labels;
-
- make_test_graph(rnd, edges, labels, 5, 30, 3, 0.10);
- if (rnd.get_random_double() < 0.5)
- remove_duplicate_edges(edges);
-
-
- std::vector<unsigned long> labels2;
-
- unsigned long num_clusters;
- if (rnd.get_random_double() < 0.5)
- num_clusters = chinese_whispers(edges, labels2, 200, rnd);
- else
- num_clusters = chinese_whispers(edges, labels2);
-
- DLIB_TEST(labels.size() == labels2.size());
- DLIB_TEST(num_clusters == 5);
-
- for (unsigned long i = 0; i < labels.size(); ++i)
- {
- for (unsigned long j = 0; j < labels.size(); ++j)
- {
- if (labels[i] == labels[j])
- {
- DLIB_TEST(labels2[i] == labels2[j]);
- }
- else
- {
- DLIB_TEST(labels2[i] != labels2[j]);
- }
- }
- }
- }
-
- void test_bottom_up_clustering()
- {
- std::vector<dpoint> pts;
- pts.push_back(dpoint(0.0,0.0));
- pts.push_back(dpoint(0.5,0.0));
- pts.push_back(dpoint(0.5,0.5));
- pts.push_back(dpoint(0.0,0.5));
-
- pts.push_back(dpoint(3.0,3.0));
- pts.push_back(dpoint(3.5,3.0));
- pts.push_back(dpoint(3.5,3.5));
- pts.push_back(dpoint(3.0,3.5));
-
- pts.push_back(dpoint(7.0,7.0));
- pts.push_back(dpoint(7.5,7.0));
- pts.push_back(dpoint(7.5,7.5));
- pts.push_back(dpoint(7.0,7.5));
-
- matrix<double> dists(pts.size(), pts.size());
- for (long r = 0; r < dists.nr(); ++r)
- for (long c = 0; c < dists.nc(); ++c)
- dists(r,c) = length(pts[r]-pts[c]);
-
-
- matrix<unsigned long,0,1> truth(12);
- truth = 0, 0, 0, 0,
- 1, 1, 1, 1,
- 2, 2, 2, 2;
-
- std::vector<unsigned long> labels;
- DLIB_TEST(bottom_up_cluster(dists, labels, 3) == 3);
- DLIB_TEST(mat(labels) == truth);
- DLIB_TEST(bottom_up_cluster(dists, labels, 1, 4.0) == 3);
- DLIB_TEST(mat(labels) == truth);
- DLIB_TEST(bottom_up_cluster(dists, labels, 1, 4.95) == 2);
- truth = 0, 0, 0, 0,
- 0, 0, 0, 0,
- 1, 1, 1, 1;
- DLIB_TEST(mat(labels) == truth);
- DLIB_TEST(bottom_up_cluster(dists, labels, 1) == 1);
- truth = 0, 0, 0, 0,
- 0, 0, 0, 0,
- 0, 0, 0, 0;
- DLIB_TEST(mat(labels) == truth);
-
- dists.set_size(0,0);
- DLIB_TEST(bottom_up_cluster(dists, labels, 3) == 0);
- DLIB_TEST(labels.size() == 0);
- DLIB_TEST(bottom_up_cluster(dists, labels, 1) == 0);
- DLIB_TEST(labels.size() == 0);
-
- dists.set_size(1,1);
- dists = 1;
- DLIB_TEST(bottom_up_cluster(dists, labels, 3) == 1);
- DLIB_TEST(labels.size() == 1);
- DLIB_TEST(labels[0] == 0);
- DLIB_TEST(bottom_up_cluster(dists, labels, 1) == 1);
- DLIB_TEST(labels.size() == 1);
- DLIB_TEST(labels[0] == 0);
- DLIB_TEST(bottom_up_cluster(dists, labels, 1, 0) == 1);
- DLIB_TEST(labels.size() == 1);
- DLIB_TEST(labels[0] == 0);
-
- dists.set_size(2,2);
- dists = 1;
- DLIB_TEST(bottom_up_cluster(dists, labels, 3) == 2);
- DLIB_TEST(labels.size() == 2);
- DLIB_TEST(labels[0] == 0);
- DLIB_TEST(labels[1] == 1);
- DLIB_TEST(bottom_up_cluster(dists, labels, 1) == 1);
- DLIB_TEST(labels.size() == 2);
- DLIB_TEST(labels[0] == 0);
- DLIB_TEST(labels[1] == 0);
- DLIB_TEST(bottom_up_cluster(dists, labels, 1, 1) == 1);
- DLIB_TEST(labels.size() == 2);
- DLIB_TEST(labels[0] == 0);
- DLIB_TEST(labels[1] == 0);
- DLIB_TEST(bottom_up_cluster(dists, labels, 1, 0.999) == 2);
- DLIB_TEST(labels.size() == 2);
- DLIB_TEST(labels[0] == 0);
- DLIB_TEST(labels[1] == 1);
- }
-
- void test_segment_number_line()
- {
- dlib::rand rnd;
-
-
- std::vector<double> x;
- for (int i = 0; i < 5000; ++i)
- {
- x.push_back(rnd.get_double_in_range(-1.5, -1.01));
- x.push_back(rnd.get_double_in_range(-0.99, -0.01));
- x.push_back(rnd.get_double_in_range(0.01, 1));
- }
-
- auto r = segment_number_line(x,1);
- std::sort(r.begin(), r.end());
- DLIB_TEST(r.size() == 3);
- DLIB_TEST(-1.5 <= r[0].lower && r[0].lower < r[0].upper && r[0].upper <= -1.01);
- DLIB_TEST(-0.99 <= r[1].lower && r[1].lower < r[1].upper && r[1].upper <= -0.01);
- DLIB_TEST(0.01 <= r[2].lower && r[2].lower < r[2].upper && r[2].upper <= 1);
-
- x.clear();
- for (int i = 0; i < 5000; ++i)
- {
- x.push_back(rnd.get_double_in_range(-2, 1));
- x.push_back(rnd.get_double_in_range(-2, 1));
- x.push_back(rnd.get_double_in_range(-2, 1));
- }
-
- r = segment_number_line(x,1);
- DLIB_TEST(r.size() == 3);
- r = segment_number_line(x,1.5);
- DLIB_TEST(r.size() == 2);
- r = segment_number_line(x,10.5);
- DLIB_TEST(r.size() == 1);
- DLIB_TEST(-2 <= r[0].lower && r[0].lower < r[0].upper && r[0].upper <= 1);
- }
-
- class test_clustering : public tester
- {
- public:
- test_clustering (
- ) :
- tester ("test_clustering",
- "Runs tests on the clustering routines.")
- {}
-
- void perform_test (
- )
- {
- test_bottom_up_clustering();
- test_segment_number_line();
-
- dlib::rand rnd;
-
- std::vector<sample_pair> edges;
- std::vector<unsigned long> labels;
- DLIB_TEST(newman_cluster(edges, labels) == 0);
- DLIB_TEST(chinese_whispers(edges, labels) == 0);
-
- edges.push_back(sample_pair(0,1,1));
- DLIB_TEST(newman_cluster(edges, labels) == 1);
- DLIB_TEST(labels.size() == 2);
- DLIB_TEST(chinese_whispers(edges, labels) == 1);
- DLIB_TEST(labels.size() == 2);
-
- edges.clear();
- edges.push_back(sample_pair(0,0,1));
- DLIB_TEST(newman_cluster(edges, labels) == 1);
- DLIB_TEST(labels.size() == 1);
- DLIB_TEST(chinese_whispers(edges, labels) == 1);
- DLIB_TEST(labels.size() == 1);
-
- edges.clear();
- edges.push_back(sample_pair(1,1,1));
- DLIB_TEST(newman_cluster(edges, labels) == 1);
- DLIB_TEST(labels.size() == 2);
- DLIB_TEST(chinese_whispers(edges, labels) == 2);
- DLIB_TEST(labels.size() == 2);
-
- edges.push_back(sample_pair(0,0,1));
- DLIB_TEST(newman_cluster(edges, labels) == 2);
- DLIB_TEST(labels.size() == 2);
- DLIB_TEST(chinese_whispers(edges, labels) == 2);
- DLIB_TEST(labels.size() == 2);
-
-
- for (int i = 0; i < 10; ++i)
- test_modularity(rnd);
-
- for (int i = 0; i < 10; ++i)
- test_newman_clustering(rnd);
-
- for (int i = 0; i < 10; ++i)
- test_chinese_whispers(rnd);
-
-
- }
- } a;
-
-
-
-}
-
-
-
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