diff options
author | mcarrier <mcarrier@636b058d-ea47-450e-bf9e-a15bfbe3eedb> | 2018-06-11 02:50:07 +0000 |
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committer | mcarrier <mcarrier@636b058d-ea47-450e-bf9e-a15bfbe3eedb> | 2018-06-11 02:50:07 +0000 |
commit | 68b93015eaf44d45a3a85747b4f3c53bb755e8af (patch) | |
tree | 3ff9f7e934a80b1dc0c7c06c1292f1a803c094a9 /src/Nerve_GIC | |
parent | 2528ddff5d820020374ece89228006409c224e78 (diff) |
small change in bootstrap code
git-svn-id: svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/Nerve_GIC@3577 636b058d-ea47-450e-bf9e-a15bfbe3eedb
Former-commit-id: da8a421316d545b400f82eca3f426912b4767a8d
Diffstat (limited to 'src/Nerve_GIC')
-rw-r--r-- | src/Nerve_GIC/include/gudhi/GIC.h | 9 |
1 files changed, 6 insertions, 3 deletions
diff --git a/src/Nerve_GIC/include/gudhi/GIC.h b/src/Nerve_GIC/include/gudhi/GIC.h index 4bd2c849..2a50acd7 100644 --- a/src/Nerve_GIC/include/gudhi/GIC.h +++ b/src/Nerve_GIC/include/gudhi/GIC.h @@ -1184,8 +1184,8 @@ class Cover_complex { } Cboot.set_graph_from_automatic_rips(Gudhi::Euclidean_distance()); - Cboot.set_automatic_resolution(); Cboot.set_gain(); + Cboot.set_automatic_resolution(); Cboot.set_cover_from_function(); Cboot.find_simplices(); Cboot.compute_PD(); @@ -1206,7 +1206,9 @@ class Cover_complex { */ double compute_distance_from_confidence_level(double alpha) { unsigned int N = distribution.size(); - return distribution[std::floor(alpha * N)]; + double d = distribution[std::floor(alpha * N)]; + if (verbose) std::cout << "Distance corresponding to confidence " << alpha << " is " << d << std::endl; + return d; } public: @@ -1220,6 +1222,7 @@ class Cover_complex { double level = 1; for (unsigned int i = 0; i < N; i++) if (distribution[i] > d){ level = i * 1.0 / N; break; } + if (verbose) std::cout << "Confidence level of distance " << d << " is " << level << std::endl; return level; } @@ -1231,7 +1234,7 @@ class Cover_complex { double compute_p_value() { double distancemin = -std::numeric_limits<double>::lowest(); int N = PD.size(); - for (int i = 0; i < N; i++) distancemin = std::min(distancemin, 0.5 * (PD[i].second - PD[i].first)); + for (int i = 0; i < N; i++) distancemin = std::min(distancemin, 0.5 * std::abs(PD[i].second - PD[i].first)); double p_value = 1 - compute_confidence_level_from_distance(distancemin); if (verbose) std::cout << "p value = " << p_value << std::endl; return p_value; |