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00015 #include <vcl_iostream.h>
00016 #include <mbl/mbl_stats_1d.h>
00017 #include <vnl/vnl_vector.h>
00018 #include <pdf1d/pdf1d_compare_to_pdf_bhat.h>
00019 #include <pdf1d/pdf1d_compare_to_pdf_ks.h>
00020 #include <pdf1d/pdf1d_sampler.h>
00021 #include <pdf1d/pdf1d_exponential.h>
00022 #include <pdf1d/pdf1d_gaussian_kernel_pdf_builder.h>
00023
00024
00025 void test_comparison(vcl_vector<mbl_stats_1d>& B_stats,
00026 const vnl_vector<double>& x,
00027 const pdf1d_pdf& true_pdf,
00028 vcl_vector<pdf1d_compare_to_pdf*> comparator)
00029 {
00030 int n = comparator.size();
00031 B_stats.resize(n);
00032
00033 for (int i=0;i<n;++i)
00034 {
00035 double B = comparator[i]->compare(x.data_block(),x.size(),true_pdf);
00036 B_stats[i].obs(B);
00037 }
00038 }
00039
00040
00041 void test_comparison(vcl_vector<mbl_stats_1d>& B_stats,
00042 int n_samples, int n_repeats,
00043 const pdf1d_pdf& true_pdf,
00044 vcl_vector<pdf1d_compare_to_pdf*> comparator)
00045 {
00046 vnl_vector<double> x(n_samples);
00047 pdf1d_sampler *sampler = true_pdf.new_sampler();
00048
00049 for (int i=0;i<n_repeats;++i)
00050 {
00051 sampler->get_samples(x);
00052 test_comparison(B_stats,x,true_pdf,comparator);
00053 }
00054
00055 delete sampler;
00056 }
00057
00058 void test_comparison(int n_samples, int n_trials,
00059 const pdf1d_pdf& true_pdf,
00060 vcl_vector<pdf1d_compare_to_pdf*> comparator,
00061 const vcl_vector<vcl_string>& name)
00062 {
00063 vcl_vector<mbl_stats_1d> B_stats;
00064
00065 test_comparison(B_stats,n_samples,n_trials,true_pdf,comparator);
00066
00067 vcl_cout<<"PDF: "<<true_pdf<<vcl_endl
00068 <<"Sampling "<<n_samples
00069 <<" values from pdf and computing overlap with kernel estimate.\n"
00070 <<"Averaging over "<<n_trials<<" trials."<<vcl_endl;
00071 for (unsigned int i=0;i<B_stats.size();++i)
00072 {
00073 vcl_cout<<name[i]<<" :\n"
00074 <<"Mean: "<<B_stats[i].mean()
00075 <<" Std.Err: "<<B_stats[i].stdError()<<vcl_endl;
00076 }
00077 }
00078
00079 int main()
00080 {
00081 vcl_vector<pdf1d_compare_to_pdf*> comparator;
00082 vcl_vector<vcl_string> name;
00083
00084 #if 0
00085
00086 pdf1d_compare_to_pdf_bhat comp1;
00087 comp1.set_builder(pdf1d_gaussian_builder());
00088 comparator.push_back(&comp1);
00089 name.push_back("Single Gaussian estimate");
00090
00091
00092
00093 pdf1d_compare_to_pdf_bhat comp2;
00094 pdf1d_gaussian_kernel_pdf_builder k_builder2;
00095 k_builder2.set_use_fixed_width(0.1);
00096 comp2.set_builder(k_builder2);
00097 comparator.push_back(&comp2);
00098 name.push_back("Gaussian kernel estimate, width=0.1");
00099 #endif
00100
00101
00102 pdf1d_compare_to_pdf_bhat comp3;
00103 pdf1d_gaussian_kernel_pdf_builder k_builder3;
00104 k_builder3.set_use_equal_width();
00105 comp3.set_builder(k_builder3);
00106 comparator.push_back(&comp3);
00107 name.push_back("Bhat. using Gaussian kernel estimate, width depends on n.samples");
00108
00109
00110 pdf1d_compare_to_pdf_bhat comp4;
00111 pdf1d_gaussian_kernel_pdf_builder k_builder4;
00112 k_builder4.set_use_width_from_separation();
00113 comp4.set_builder(k_builder4);
00114 comparator.push_back(&comp4);
00115 name.push_back("Bhat. using Gaussian kernel estimate, width depends on local sample separation");
00116
00117
00118 pdf1d_compare_to_pdf_bhat comp5;
00119 pdf1d_gaussian_kernel_pdf_builder k_builder5;
00120 k_builder5.set_use_adaptive();
00121 comp5.set_builder(k_builder5);
00122 comparator.push_back(&comp5);
00123 name.push_back("Bhat. using Adaptive Gaussian kernel estimate");
00124
00125
00126 pdf1d_compare_to_pdf_ks comp_ks;
00127 comparator.push_back(&comp_ks);
00128 name.push_back("KS Statistic");
00129
00130 int n_samples = 100;
00131 int n_trials = 1000;
00132
00133 pdf1d_exponential true_pdf(1.0);
00134 #if 0
00135 pdf1d_gaussian true_pdf(0,1);
00136 pdf1d_flat true_pdf(0,1);
00137
00138 pdf1d_gaussian_kernel_pdf true_pdf(2,1,0.25);
00139 #endif // 0
00140 test_comparison(n_samples,n_trials,true_pdf,comparator,name);
00141
00142 return 0;
00143 }