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PDF statistics: 8356 PDF objects out of 8907 (max. 8388607) 7715 compressed objects within 78 object streams diff --git a/latex/refman.pdf b/latex/refman.pdf index 9d5d7834..8b468ece 100644 Binary files a/latex/refman.pdf and b/latex/refman.pdf differ diff --git a/latex/struct_event__coll__graph.pdf b/latex/struct_event__coll__graph.pdf index e4e06ed6..147a6173 100644 Binary files a/latex/struct_event__coll__graph.pdf and b/latex/struct_event__coll__graph.pdf differ diff --git a/lfmcmc-meat-print_8hpp_source.html b/lfmcmc-meat-print_8hpp_source.html index 5982a507..15f262de 100644 --- a/lfmcmc-meat-print_8hpp_source.html +++ b/lfmcmc-meat-print_8hpp_source.html @@ -77,222 +77,223 @@
5 inline void LFMCMC<TData>::print(size_t burnin) const
6 {
7 
-
8  std::vector< epiworld_double > summ_params(n_parameters * 3, 0.0);
-
9  std::vector< epiworld_double > summ_stats(n_statistics * 3, 0.0);
-
10 
-
11  size_t n_samples_print = n_samples;
-
12  if (burnin > 0)
-
13  {
-
14  if (burnin >= n_samples)
-
15  throw std::length_error(
-
16  "The burnin is greater than the number of samples."
-
17  );
-
18 
-
19  n_samples_print = n_samples - burnin;
+
8  // The summary statistics will have three values: mean, the 2.5%
+
9  // quantile, and the 97.5% quantile
+
10  std::vector< epiworld_double > summ_params(n_parameters * 3, 0.0);
+
11  std::vector< epiworld_double > summ_stats(n_statistics * 3, 0.0);
+
12 
+
13  size_t n_samples_print = n_samples;
+
14  if (burnin > 0)
+
15  {
+
16  if (burnin >= n_samples)
+
17  throw std::length_error(
+
18  "The burnin is greater than the number of samples."
+
19  );
20 
-
21  }
+
21  n_samples_print = n_samples - burnin;
22 
-
23  epiworld_double n_samples_dbl = static_cast< epiworld_double >(
-
24  n_samples_print
-
25  );
-
26 
-
27  for (size_t k = 0u; k < n_parameters; ++k)
-
28  {
-
29 
-
30  // Retrieving the relevant parameter
-
31  std::vector< epiworld_double > par_i(n_samples_print);
-
32  for (size_t i = burnin; i < n_samples; ++i)
-
33  {
-
34  par_i[i] = accepted_params[i * n_parameters + k];
-
35  summ_params[k * 3] += par_i[i]/n_samples_dbl;
-
36  }
-
37 
-
38  // Computing the 95% Credible interval
-
39  std::sort(par_i.begin(), par_i.end());
-
40 
-
41  summ_params[k * 3 + 1u] =
-
42  par_i[std::floor(.025 * n_samples_dbl)];
-
43  summ_params[k * 3 + 2u] =
-
44  par_i[std::floor(.975 * n_samples_dbl)];
-
45 
-
46  }
+
23  }
+
24 
+
25  epiworld_double n_samples_dbl = static_cast< epiworld_double >(
+
26  n_samples_print
+
27  );
+
28 
+
29  for (size_t k = 0u; k < n_parameters; ++k)
+
30  {
+
31 
+
32  // Retrieving the relevant parameter
+
33  std::vector< epiworld_double > par_i(n_samples_print);
+
34  for (size_t i = burnin; i < n_samples; ++i)
+
35  {
+
36  par_i[i] = accepted_params[i * n_parameters + k];
+
37  summ_params[k * 3] += par_i[i]/n_samples_dbl;
+
38  }
+
39 
+
40  // Computing the 95% Credible interval
+
41  std::sort(par_i.begin(), par_i.end());
+
42 
+
43  summ_params[k * 3 + 1u] =
+
44  par_i[std::floor(.025 * n_samples_dbl)];
+
45  summ_params[k * 3 + 2u] =
+
46  par_i[std::floor(.975 * n_samples_dbl)];
47 
-
48  for (size_t k = 0u; k < n_statistics; ++k)
-
49  {
-
50 
-
51  // Retrieving the relevant parameter
-
52  std::vector< epiworld_double > stat_k(n_samples_print);
-
53  for (size_t i = burnin; i < n_samples; ++i)
-
54  {
-
55  stat_k[i] = accepted_stats[i * n_statistics + k];
-
56  summ_stats[k * 3] += stat_k[i]/n_samples_dbl;
-
57  }
-
58 
-
59  // Computing the 95% Credible interval
-
60  std::sort(stat_k.begin(), stat_k.end());
-
61 
-
62  summ_stats[k * 3 + 1u] =
-
63  stat_k[std::floor(.025 * n_samples_dbl)];
-
64  summ_stats[k * 3 + 2u] =
-
65  stat_k[std::floor(.975 * n_samples_dbl)];
-
66 
-
67  }
-
68 
-
69  printf_epiworld("___________________________________________\n\n");
-
70  printf_epiworld("LIKELIHOOD-FREE MARKOV CHAIN MONTE CARLO\n\n");
-
71 
-
72  printf_epiworld("N Samples : %zu\n", n_samples);
-
73 
-
74  std::string abbr;
-
75  epiworld_double elapsed;
-
76  get_elapsed("auto", &elapsed, &abbr, false);
-
77  printf_epiworld("Elapsed t : %.2f%s\n\n", elapsed, abbr.c_str());
-
78 
-
80  // PARAMETERS
-
82  printf_epiworld("Parameters:\n");
-
83 
-
84  // Figuring out format
-
85  std::string fmt_params;
-
86 
-
87  int nchar_par_num = 0;
-
88  for (auto & n : summ_params)
-
89  {
-
90 
-
91  int tmp_nchar = std::floor(std::log10(std::abs(n)));
-
92  if (nchar_par_num < tmp_nchar)
-
93  nchar_par_num = tmp_nchar;
-
94  }
-
95  nchar_par_num += 5; // 1 for neg padd, 2 for decimals, 1 the decimal point, and one b/c log(<10) < 1.
-
96  std::string charlen = std::to_string(nchar_par_num);
-
97 
-
98  if (names_parameters.size() != 0u)
-
99  {
-
100  int nchar_par = 0;
-
101  for (auto & n : names_parameters)
-
102  {
-
103  int tmp_nchar = n.length();
-
104  if (nchar_par < tmp_nchar)
-
105  nchar_par = tmp_nchar;
-
106  }
-
107 
-
108  fmt_params = std::string(" -%-") +
-
109  std::to_string(nchar_par) +
-
110  std::string("s : % ") + charlen +
-
111  std::string(".2f [% ") + charlen +
-
112  std::string(".2f, % ") + charlen +
-
113  std::string(".2f] (initial : % ") +
-
114  charlen + std::string(".2f)\n");
-
115 
-
116  for (size_t k = 0u; k < n_parameters; ++k)
-
117  {
-
118  printf_epiworld(
-
119  fmt_params.c_str(),
-
120  names_parameters[k].c_str(),
-
121  summ_params[k * 3],
-
122  summ_params[k * 3 + 1u],
-
123  summ_params[k * 3 + 2u],
-
124  params_init[k]
-
125  );
-
126  }
-
127 
-
128 
-
129  } else {
-
130 
-
131  fmt_params = std::string(" [%-2ld]: % ") + charlen +
-
132  std::string(".2f [% ") + charlen +
-
133  std::string(".2f, % ") + charlen +
-
134  std::string(".2f] (initial : % ") + charlen +
-
135  std::string(".2f)\n");
-
136 
-
137  for (size_t k = 0u; k < n_parameters; ++k)
-
138  {
-
139 
-
140  printf_epiworld(
-
141  fmt_params.c_str(),
-
142  k,
-
143  summ_params[k * 3],
-
144  summ_params[k * 3 + 1u],
-
145  summ_params[k * 3 + 2u],
-
146  params_init[k]
-
147  );
-
148  }
-
149 
-
150  }
-
151 
-
153  // Statistics
-
155  printf_epiworld("\nStatistics:\n");
-
156  int nchar = 0;
-
157  for (auto & s : summ_stats)
-
158  {
-
159  int tmp_nchar = std::floor(std::log10(std::abs(s)));
-
160  if (nchar < tmp_nchar)
-
161  nchar = tmp_nchar;
-
162  }
-
163 
-
164  nchar += 5; // See above
-
165 
-
166  std::string nchar_char = std::to_string(nchar);
-
167 
-
168  // Figuring out format
-
169  std::string fmt_stats;
-
170  if (names_statistics.size() != 0u)
-
171  {
-
172  int nchar_stats = 0;
-
173  for (auto & n : names_statistics)
-
174  {
-
175  int tmp_nchar = n.length();
-
176  if (nchar_stats < tmp_nchar)
-
177  nchar_stats = tmp_nchar;
-
178  }
-
179 
-
180  fmt_stats = std::string(" -%-") +
-
181  std::to_string(nchar_stats) +
-
182  std::string("s : % ") + nchar_char +
-
183  std::string(".2f [% ") + nchar_char +
-
184  std::string(".2f, % ") + nchar_char +
-
185  std::string(".2f] (Observed: % ") + nchar_char +
-
186  std::string(".2f)\n");
-
187 
-
188  for (size_t k = 0u; k < n_statistics; ++k)
-
189  {
-
190  printf_epiworld(
-
191  fmt_stats.c_str(),
-
192  names_statistics[k].c_str(),
-
193  summ_stats[k * 3],
-
194  summ_stats[k * 3 + 1u],
-
195  summ_stats[k * 3 + 2u],
-
196  observed_stats[k]
-
197  );
-
198  }
-
199 
-
200 
-
201  } else {
-
202 
-
203  fmt_stats = std::string(" [%-2ld] : % ") +
-
204  nchar_char +
-
205  std::string(".2f [% ") + nchar_char +
-
206  std::string(".2f, % ") + nchar_char +
-
207  std::string(".2f] (Observed: % ") + nchar_char +
-
208  std::string(".2f)\n");
-
209 
-
210  for (size_t k = 0u; k < n_statistics; ++k)
-
211  {
-
212  printf_epiworld(
-
213  fmt_stats.c_str(),
-
214  k,
-
215  summ_stats[k * 3],
-
216  summ_stats[k * 3 + 1u],
-
217  summ_stats[k * 3 + 2u],
-
218  observed_stats[k]
-
219  );
-
220  }
-
221 
-
222  }
-
223 
-
224  printf_epiworld("___________________________________________\n\n");
-
225 }
-
226 
-
227 #endif
+
48  }
+
49 
+
50  for (size_t k = 0u; k < n_statistics; ++k)
+
51  {
+
52 
+
53  // Retrieving the relevant parameter
+
54  std::vector< epiworld_double > stat_k(n_samples_print);
+
55  for (size_t i = burnin; i < n_samples; ++i)
+
56  {
+
57  stat_k[i] = accepted_stats[i * n_statistics + k];
+
58  summ_stats[k * 3] += stat_k[i]/n_samples_dbl;
+
59  }
+
60 
+
61  // Computing the 95% Credible interval
+
62  std::sort(stat_k.begin(), stat_k.end());
+
63  summ_stats[k * 3 + 1u] =
+
64  stat_k[std::floor(.025 * n_samples_dbl)];
+
65  summ_stats[k * 3 + 2u] =
+
66  stat_k[std::floor(.975 * n_samples_dbl)];
+
67 
+
68  }
+
69 
+
70  printf_epiworld("___________________________________________\n\n");
+
71  printf_epiworld("LIKELIHOOD-FREE MARKOV CHAIN MONTE CARLO\n\n");
+
72 
+
73  printf_epiworld("N Samples : %zu\n", n_samples);
+
74 
+
75  std::string abbr;
+
76  epiworld_double elapsed;
+
77  get_elapsed("auto", &elapsed, &abbr, false);
+
78  printf_epiworld("Elapsed t : %.2f%s\n\n", elapsed, abbr.c_str());
+
79 
+
81  // PARAMETERS
+
83  printf_epiworld("Parameters:\n");
+
84 
+
85  // Figuring out format
+
86  std::string fmt_params;
+
87 
+
88  int nchar_par_num = 0;
+
89  for (auto & n : summ_params)
+
90  {
+
91 
+
92  int tmp_nchar = std::floor(std::log10(std::abs(n)));
+
93  if (nchar_par_num < tmp_nchar)
+
94  nchar_par_num = tmp_nchar;
+
95  }
+
96  nchar_par_num += 5; // 1 for neg padd, 2 for decimals, 1 the decimal point, and one b/c log(<10) < 1.
+
97  std::string charlen = std::to_string(nchar_par_num);
+
98 
+
99  if (names_parameters.size() != 0u)
+
100  {
+
101  int nchar_par = 0;
+
102  for (auto & n : names_parameters)
+
103  {
+
104  int tmp_nchar = n.length();
+
105  if (nchar_par < tmp_nchar)
+
106  nchar_par = tmp_nchar;
+
107  }
+
108 
+
109  fmt_params = std::string(" -%-") +
+
110  std::to_string(nchar_par) +
+
111  std::string("s : % ") + charlen +
+
112  std::string(".2f [% ") + charlen +
+
113  std::string(".2f, % ") + charlen +
+
114  std::string(".2f] (initial : % ") +
+
115  charlen + std::string(".2f)\n");
+
116 
+
117  for (size_t k = 0u; k < n_parameters; ++k)
+
118  {
+
119  printf_epiworld(
+
120  fmt_params.c_str(),
+
121  names_parameters[k].c_str(),
+
122  summ_params[k * 3],
+
123  summ_params[k * 3 + 1u],
+
124  summ_params[k * 3 + 2u],
+
125  params_init[k]
+
126  );
+
127  }
+
128 
+
129 
+
130  } else {
+
131 
+
132  fmt_params = std::string(" [%-2ld]: % ") + charlen +
+
133  std::string(".2f [% ") + charlen +
+
134  std::string(".2f, % ") + charlen +
+
135  std::string(".2f] (initial : % ") + charlen +
+
136  std::string(".2f)\n");
+
137 
+
138  for (size_t k = 0u; k < n_parameters; ++k)
+
139  {
+
140 
+
141  printf_epiworld(
+
142  fmt_params.c_str(),
+
143  k,
+
144  summ_params[k * 3],
+
145  summ_params[k * 3 + 1u],
+
146  summ_params[k * 3 + 2u],
+
147  params_init[k]
+
148  );
+
149  }
+
150 
+
151  }
+
152 
+
154  // Statistics
+
156  printf_epiworld("\nStatistics:\n");
+
157  int nchar = 0;
+
158  for (auto & s : summ_stats)
+
159  {
+
160  int tmp_nchar = std::floor(std::log10(std::abs(s)));
+
161  if (nchar < tmp_nchar)
+
162  nchar = tmp_nchar;
+
163  }
+
164 
+
165  nchar += 5; // See above
+
166 
+
167  std::string nchar_char = std::to_string(nchar);
+
168 
+
169  // Figuring out format
+
170  std::string fmt_stats;
+
171  if (names_statistics.size() != 0u)
+
172  {
+
173  int nchar_stats = 0;
+
174  for (auto & n : names_statistics)
+
175  {
+
176  int tmp_nchar = n.length();
+
177  if (nchar_stats < tmp_nchar)
+
178  nchar_stats = tmp_nchar;
+
179  }
+
180 
+
181  fmt_stats = std::string(" -%-") +
+
182  std::to_string(nchar_stats) +
+
183  std::string("s : % ") + nchar_char +
+
184  std::string(".2f [% ") + nchar_char +
+
185  std::string(".2f, % ") + nchar_char +
+
186  std::string(".2f] (Observed: % ") + nchar_char +
+
187  std::string(".2f)\n");
+
188 
+
189  for (size_t k = 0u; k < n_statistics; ++k)
+
190  {
+
191  printf_epiworld(
+
192  fmt_stats.c_str(),
+
193  names_statistics[k].c_str(),
+
194  summ_stats[k * 3],
+
195  summ_stats[k * 3 + 1u],
+
196  summ_stats[k * 3 + 2u],
+
197  observed_stats[k]
+
198  );
+
199  }
+
200 
+
201 
+
202  } else {
+
203 
+
204  fmt_stats = std::string(" [%-2ld] : % ") +
+
205  nchar_char +
+
206  std::string(".2f [% ") + nchar_char +
+
207  std::string(".2f, % ") + nchar_char +
+
208  std::string(".2f] (Observed: % ") + nchar_char +
+
209  std::string(".2f)\n");
+
210 
+
211  for (size_t k = 0u; k < n_statistics; ++k)
+
212  {
+
213  printf_epiworld(
+
214  fmt_stats.c_str(),
+
215  k,
+
216  summ_stats[k * 3],
+
217  summ_stats[k * 3 + 1u],
+
218  summ_stats[k * 3 + 2u],
+
219  observed_stats[k]
+
220  );
+
221  }
+
222 
+
223  }
+
224 
+
225  printf_epiworld("___________________________________________\n\n");
+
226 }
+
227 
+
228 #endif
LFMCMC
Likelihood-Free Markov Chain Monte Carlo.
Definition: lfmcmc-bones.hpp:115