apop_model.c 26.1 KB
 Jerome Benoit committed Jul 10, 2015 1   Jerome Benoit committed Aug 15, 2014 2 /** \file apop_model.c sets up the estimate structure which outputs from the various regressions and MLEs.*/  Jerome Benoit committed Jul 10, 2015 3 /* Copyright (c) 2006--2011 by Ben Klemens. Licensed under the GPLv2; see COPYING. */  Jerome Benoit committed Aug 15, 2014 4 5 6 7  #define Declare_type_checking_fns #include "apop_internal.h"  Jerome Benoit committed Jul 10, 2015 8 /** Set up the \c parameters and \c info elements of the \c apop_model:  Jerome Benoit committed Aug 15, 2014 9 10 11  At close, the input model has parameters of the correct size.  Jerome Benoit committed Jul 10, 2015 12 13 14 \li This is the default action for \ref apop_prep, and many models with a custom prep routine call \ref apop_model_clear at the end. Also, \ref apop_estimate calls this function internally, which means that you robably never have to call this function directly. \li If the model has already been prepped, this function should be a no-op.  Jerome Benoit committed Aug 15, 2014 15 16 17 18 19  \param data If your params vary with the size of the data set, then the function needs a data set to calibrate against. Otherwise, it's OK to set this to \c NULL. \param model The model whose output elements will be modified. \return A pointer to the same model, should you need it. \exception outmodel->error=='d' dimension error.  Jerome Benoit committed Jul 10, 2015 20 */  Jerome Benoit committed Aug 15, 2014 21 22 23 24 25 26 27 28 29 30 31 32 apop_model * apop_model_clear(apop_data * data, apop_model *model){ Get_vmsizes(data) int width = msize2 ? msize2 : -firstcol;//use the vector only if there's no matrix. Apop_stopif(model->dsize==-1 && !width, model->error='d', 0, "The model's dsize==-1, meaning size=data width, but the input data has NULL vector and matrix."); Apop_stopif(model->vsize==-1 && !width, model->error='d', 0, "The model's vsize==-1, meaning size=data width, but the input data has NULL vector and matrix."); Apop_stopif(model->msize1==-1 && !width, model->error='d', 0, "The model's msize1==-1, meaning size=data width, but the input data has NULL vector and matrix."); Apop_stopif(model->msize2==-1 && !width, model->error='d', 0, "The model's msize2==-1, meaning size=data width, but the input data has NULL vector and matrix."); model->dsize = (model->dsize == -1 ? width : model->dsize); vsize = model->vsize == -1 ? width : model->vsize; msize1 = model->msize1 == -1 ? width : model->msize1 ; msize2 = model->msize2 == -1 ? width : model->msize2 ;  Jerome Benoit committed Jul 10, 2015 33 34  if (!model->parameters && (vsize || msize1*msize2)) model->parameters = apop_data_alloc(vsize, msize1, msize2);  Jerome Benoit committed Aug 15, 2014 35  if (!model->info) model->info = apop_data_alloc();  Jerome Benoit committed Jul 10, 2015 36 37  if (model->info->names->title && !strlen(model->info->names->title)) free(model->info->names->title);  Jerome Benoit committed Aug 15, 2014 38  Asprintf(&model->info->names->title, "");  Jerome Benoit committed Jul 10, 2015 39  if (!model->data) model->data = data;  Jerome Benoit committed Aug 15, 2014 40 41 42 43 44  return model; } /** Free an \ref apop_model structure.  Jerome Benoit committed Jul 10, 2015 45 46 47 48  \li The \c parameters and \c settings are freed. These are the elements that are copied by \c apop_model_copy. \li The \c data element is not freed, because the odds are you still need it. \li If free_me->more_size is positive, the function runs  Jerome Benoit committed Aug 15, 2014 49 50 51 free(free_me->more). But it has no idea what the \c more element contains; if it points to other structures (like an \ref apop_data set), you need to free them before calling this function.  Jerome Benoit committed Jul 10, 2015 52  \li If \c free_me is \c NULL, this does nothing.  Jerome Benoit committed Aug 15, 2014 53 54  \param free_me A pointer to the model to be freed.  Jerome Benoit committed Jul 10, 2015 55 */  Jerome Benoit committed Aug 15, 2014 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 void apop_model_free (apop_model * free_me){ if (!free_me) return; apop_data_free(free_me->parameters); if (free_me->settings){ int i=0; while (free_me->settings[i].name[0]){ if (free_me->settings[i].free) ((void (*)(void*))(free_me->settings[i].free))(free_me->settings[i].setting_group); i++; } free(free_me->settings); } if (free_me->more_size) free(free_me->more); if (free_me->info) apop_data_free(free_me->info); free(free_me); } /** Print the results of an estimation for a human to look over.  Jerome Benoit committed Jul 10, 2015 77 78 \param model The model whose information should be displayed (No default. If \c NULL, print NULL) \param output_pipe The output stream. Default: \c stdout. If you'd like something else, use \c fopen. E.g.:  Jerome Benoit committed Aug 15, 2014 79 80 81 \code FILE *out =fopen("outfile.txt", "w"); //or "a" to append. apop_model_print(the_model, out);  Jerome Benoit committed Jul 10, 2015 82 fclose(out); //optional in many cases.  Jerome Benoit committed Aug 15, 2014 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 \endcode \li The default prints the name, parameters, info, &c. but I check a vtable for alternate methods you define; see \ref vtables for details. The typedef new functions must conform to and the hash used for lookups are: \code typedef void (*apop_model_print_type)(apop_model *params, FILE *out); #define apop_model_print_hash(m1) ((m1)->log_likelihood ? (size_t)(m1)->log_likelihood : \ (m1)->p ? (size_t)(m1)->p*33 : \ (m1)->estimate ? (size_t)(m1)->estimate*33*33 : \ (m1)->draw ? (size_t)(m1)->draw*33*27 : \ (m1)->cdf ? (size_t)(m1)->cdf*27*27 : 27) \endcode  Jerome Benoit committed Jul 10, 2015 98 When building a special print method, all output should \c fprintf to the input \c FILE* handle.  Jerome Benoit committed Aug 15, 2014 99 100 101 102 103  Apophenia's output routines also accept a file handle; e.g., if the file handle is named \c out, then if the \c thismodel print method uses \c apop_data_print to print the parameters, it must do so via a form like apop_data_print(thismodel->parameters, .output_pipe=ap).  Jerome Benoit committed Jul 10, 2015 104 Your \c print method can use both by masking itself for a few lines:  Jerome Benoit committed Aug 15, 2014 105 106 107 108 109 110 111 112 113 114 115 116 117  \code void print_method(apop_model *in, FILE* ap){ void *temp = in->estimate; in->estimate = NULL; apop_model_print(in, ap); in->estimate = temp; printf("Additional info:\n"); ... } \endcode \li Print methods are intended for human consumption and are subject to change.  Jerome Benoit committed Jul 10, 2015 118 119 120 121 122 123 124 125 126 127 128 \li This function uses the \ref designated syntax for inputs. */ #ifdef APOP_NO_VARIADIC void apop_model_print(apop_model * model, FILE *output_pipe){ #else apop_varad_head(void, apop_model_print){ FILE * apop_varad_var(output_pipe, stdout); apop_model* apop_varad_var(model, NULL); if (!model) {fprintf(output_pipe, "NULL\n"); return;} apop_model_print_base(model, output_pipe); }  Jerome Benoit committed Aug 15, 2014 129   Jerome Benoit committed Jul 10, 2015 130 131 132  void apop_model_print_base(apop_model * model, FILE *output_pipe){ #endif apop_model_print_type mpf = apop_model_print_vtable_get(model);  Jerome Benoit committed Aug 15, 2014 133  if (mpf){  Jerome Benoit committed Jul 10, 2015 134  mpf(model, output_pipe);  Jerome Benoit committed Aug 15, 2014 135 136  return; }  Jerome Benoit committed Jul 10, 2015 137 138 139 140 141  if (strlen(model->name)) fprintf (output_pipe, "%s", model->name); fprintf(output_pipe, "\n\n"); if (model->parameters) apop_data_print(model->parameters, .output_pipe=output_pipe); Get_vmsizes(model->info); //maxsize if (model->info && maxsize) apop_data_print(model->info, .output_pipe=output_pipe);  Jerome Benoit committed Aug 15, 2014 142 143 144 145 146 147 148 149 } /* Alias for \ref apop_model_print. Use that one. */ void apop_model_show (apop_model * print_me){ apop_model_print(print_me, NULL); } /** Outputs a copy of the \ref apop_model input.  Jerome Benoit committed Jul 10, 2015 150   Jerome Benoit committed Aug 15, 2014 151 \param in The model to be copied  Jerome Benoit committed Jul 10, 2015 152 153 154 \return A copy of the original. Includes copies of all settings groups, and the \c parameters (if not \c NULL, copied via \ref apop_data_copy).  Jerome Benoit committed Aug 15, 2014 155 156  \li If in.more_size > 0 I memcpy the \c more pointer from the original data set.  Jerome Benoit committed Jul 10, 2015 157 \li The data set at \c in->data is not copied, but is also pointed to.  Jerome Benoit committed Aug 15, 2014 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194  \exception out->error=='a' Allocation error. In extreme cases, where there aren't even a few hundred bytes available, I will return \c NULL. \exception out->error=='s' Error copying settings groups. \exception out->error=='p' Error copying parameters or info page; the given \ref apop_data struct may be \c NULL or may have its own ->error element. */ apop_model * apop_model_copy(apop_model *in){ Apop_stopif(!in, return NULL, 1, "Copying a NULL input; returning NULL."); apop_model * out = malloc(sizeof(apop_model)); Apop_stopif(!out, return NULL, 0, "Serious allocation error; returning NULL."); memcpy(out, in, sizeof(apop_model)); if (in->more_size){ out->more = malloc(in->more_size); Apop_stopif(!out->more, out->error='a'; return out, 0, "Allocation error setting up the ->more pointer."); memcpy(out->more, in->more, in->more_size); } int i=0; out->settings = NULL; if (in->settings) do apop_settings_copy_group(out, in, in->settings[i].name); while (strlen(in->settings[i++].name)); out->parameters = apop_data_copy(in->parameters); Apop_stopif(in->parameters && (!out->parameters || out->parameters->error), out->error='p'; return out, 0, "Error copying the model parameters."); out->info = apop_data_copy(in->info); Apop_stopif(in->info && (!out->info || out->info->error), out->error='p'; return out, 0, "Error copying the info segment."); return out; } /** \def apop_model_set_parameters(in, ...) Take in an unparameterized \c apop_model and return a new \c apop_model with the given parameters. For example, if you need a N(0,1) quickly: \code apop_model *std_normal = apop_model_set_parameters(apop_normal, 0, 1); \endcode  Jerome Benoit committed Jul 10, 2015 195 196 197 198 199 200 201 202 This doesn't take in data, so it won't work with models that take the number of parameters from the data, and it will only set the vector of the model's parameter \ref apop_data set. This is most standard models. If you have a situation where these options are out, you could \li manually set Set \c .vsize and/or \c .msize1 and \c .msize2 first, then call this function, or \li prep the model via something like apop_model *new = apop_model_copy(in); apop_prep(your_data, new); (because \ref apop_prep is required to correctly allocate \c new->parameters to conform to your data).  Jerome Benoit committed Aug 15, 2014 203 204 205 206  \param in An unparameterized model, like \ref apop_normal or \ref apop_poisson. \param ... The list of parameters. \return A copy of the input model, with parameters set.  Jerome Benoit committed Jul 10, 2015 207 208 209 210 211 212 \exception out->error=='d' dimension error: you gave me a model with an indeterminate number of parameters. See notes above. Set \c .vsize or \c .msize1 and \c .msize2 first, then call this function, or use apop_model *new = apop_model_copy(in); apop_prep(your_data, new); and then call this . \see apop_data_fill  Jerome Benoit committed Aug 15, 2014 213 214 215 216 217 218 219 220 221 222 223 224 225 \hideinitializer */ apop_model *apop_model_set_parameters_base(apop_model *in, double ap[]){ apop_model *out = apop_model_copy(in); apop_prep(NULL, out); Apop_stopif((in->vsize == -1) || (in->msize1 == -1) || (in->msize2 == -1), out->error='d', 0, "This function only works with models whose number of params does not " "depend on data size. You'll have to use apop_model *new = apop_model_copy(in); " " apop_model_clear(your_data, in); and then set in->parameters using your data."); apop_data_fill_base(out->parameters, ap); return out; }  Jerome Benoit committed Jul 10, 2015 226 /** Estimate the parameters of a model given data.  Jerome Benoit committed Aug 15, 2014 227   Jerome Benoit committed Jul 10, 2015 228 229 230 231 This function copies the input model, preps it (see \ref apop_prep), and calls \c m.estimate(d, m) (which users are encouraged to never call directly). If your model has no \c estimate method, then call \c apop_maximum_likelihood(d, m), with the default MLE settings.  Jerome Benoit committed Aug 15, 2014 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276  \param d The data \param m The model \return A pointer to an output model, which typically matches the input model but has its \c parameters element filled in. */ apop_model *apop_estimate(apop_data *d, apop_model *m){ apop_model *out = apop_model_copy(m); apop_prep(d, out); if (out->estimate) out->estimate(d, out); else apop_maximum_likelihood(d, out); return out; } /** Find the probability of a data/parametrized model pair. \param d The data \param m The parametrized model, which must have either a \c log_likelihood or a \c p method. */ double apop_p(apop_data *d, apop_model *m){ Nullcheck_m(m, GSL_NAN); if (m->p) return m->p(d, m); else if (m->log_likelihood) return exp(m->log_likelihood(d, m)); Apop_stopif(0, , 0, "You asked for the probability of a model that has neither p nor log_likelihood methods."); return GSL_NAN; } /** Find the log likelihood of a data/parametrized model pair. \param d The data \param m The parametrized model, which must have either a \c log_likelihood or a \c p method. */ double apop_log_likelihood(apop_data *d, apop_model *m){ Nullcheck_m(m, GSL_NAN); //Nullcheck_p(m); //Too many models don't use the params. if (m->log_likelihood) return m->log_likelihood(d, m); else if (m->p) return log(m->p(d, m)); Apop_stopif(0, , 0, "You asked for the log likelihood of a model that has neither p nor log_likelihood methods."); return GSL_NAN; } /** Find the vector of first derivatives (aka the gradient) of the log likelihood of a data/parametrized model pair.  Jerome Benoit committed Jul 10, 2015 277 278 279 280 281 282 283 284 285 On input, the model \c m must already be sufficiently prepped that the log likelihood can be evaluated; see \ref psubsection for details. On output, the \c gsl_vector input to the function will be filled with the gradients (or NaNs on errors). If the model parameters have a more complex shape than a simple vector, then the vector will be in \c apop_data_pack order; use \c apop_data_unpack to reformat to the preferred shape. \param d The \ref apop_data set at which the score is being evaluated.  Jerome Benoit committed Aug 15, 2014 286 287 288 289 290 291 292 293 294 295 296 297 298 299 \param out The score to be returned. I expect you to have allocated this already. \param m The parametrized model, which must have either a \c log_likelihood or a \c p method. \li The default is to use \ref apop_numerical_gradient, but special-case calculations for certain models are held in a vtable; see \ref vtables for details. The typedef new functions must conform to and the hash used for lookups are: \code typedef void (*apop_score_type)(apop_data *d, gsl_vector *gradient, apop_model *m); #define apop_score_hash(m1) ((size_t)((m1).log_likelihood ? (m1).log_likelihood : (m1).p)) \endcode */ void apop_score(apop_data *d, gsl_vector *out, apop_model *m){ Nullcheck_m(m, );  Jerome Benoit committed Jul 10, 2015 300  Apop_stopif(!out, return, 0, "out vector is NULL. It must be pre-allocated to the correct size. E.g., gsl_vector *out = gsl_vector_alloc(m->vsize + m->size1*m->size2))).");  Jerome Benoit committed Aug 15, 2014 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327  apop_score_type ms = apop_score_vtable_get(m); if (ms){ ms(d, out, m); return; } gsl_vector * numeric_default = apop_numerical_gradient(d, m); gsl_vector_memcpy(out, numeric_default); gsl_vector_free(numeric_default); } Apop_settings_init(apop_pm, //defaults include base=NULL, index=0, own_rng=0 Apop_varad_set(rng, NULL); Apop_varad_set(draws, 1e4); ) Apop_settings_copy(apop_pm,) Apop_settings_free(apop_pm, ) void distract_doxygen(){/*Doxygen gets thrown by the settings macros. This decoy function is a workaround. */} /** Get a model describing the distribution of the given parameter estimates. For many models, the parameter estimates are well-known, such as the \f$t\f$-distribution of the parameters for OLS.  Jerome Benoit committed Jul 10, 2015 328 329 For models where the distribution of \f$\hat{p}\f$ is not known, if you give me data, I will return an \ref apop_normal or \ref apop_multivariate_normal model, using the parameter estimates as mean and \ref apop_bootstrap_cov for the variances.  Jerome Benoit committed Aug 15, 2014 330 331 332 333 334 335  If you don't give me data, then I will assume that this is a stochastic model where re-running the model will produce different parameter estimates each time. In this case, I will run the model 1e4 times and return a \ref apop_pmf model with the resulting parameter distributions.  Jerome Benoit committed Jul 10, 2015 336 Before calling this, I expect that you have already run \ref apop_estimate to produce \f$\hat{p}\f$.  Jerome Benoit committed Aug 15, 2014 337 338 339 340 341 342 343 344 345 346  The \ref apop_pm_settings structure dictates details of how the model is generated. For example, if you want only the distribution of the third parameter, and you know the distribution will be a PMF generated via random draws, then set settings and call the model via: \code apop_model_group_add(your_model, apop_pm, .index =3, .draws=3e5); apop_model *dist = apop_parameter_model(your_data, your_model); \endcode  Jerome Benoit committed Jul 10, 2015 347 Some useful parts of \ref apop_pm_settings:  Jerome Benoit committed Aug 15, 2014 348 349 350 351 352 353 354 \li \c index gives the position of the parameter (in \ref apop_data_pack order) in which you are interested. Thus, if this is zero or more, then you will get a univariate output distribution describing a single parameter. If index == -1, then I will give you the multivariate distribution across all parameters. The default is zero (i.e. the univariate distribution of the zeroth parameter). \li \c draws If there is no closed-form solution and bootstrap is inappropriate, then the last resort is a large numbr of random draws of the model, summarized into a PMF. Default: 1,000 draws.  Jerome Benoit committed Jul 10, 2015 355 356 357 \li \c rng If the method requires random draws, then use this. If you provide \c NULL and one is needed, I provide one for you via \ref apop_rng_get_thread. The default is via resampling as above, but special-case calculations for certain models are held in a vtable; see \ref vtables for details. The typedef new functions must conform to and the hash used for lookups are:  Jerome Benoit committed Aug 15, 2014 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409  \code typedef apop_model* (*apop_parameter_model_type)(apop_data *, apop_model *); #define apop_parameter_model_hash(m1) ((size_t)((m1).log_likelihood ? (m1).log_likelihood : (m1).p)*33 + (m1).estimate ? (size_t)(m1).estimate: 27) \endcode */ apop_model *apop_parameter_model(apop_data *d, apop_model *m){ apop_pm_settings *settings = apop_settings_get_group(m, apop_pm); if (!settings) settings = Apop_settings_add_group(m, apop_pm, .base= m); apop_parameter_model_type pm = apop_parameter_model_vtable_get(m); if (pm) return pm(d, m); else if (d){ Get_vmsizes(m->parameters);//vsize, msize1, msize2 apop_model *out = apop_model_copy(apop_multivariate_normal); out->msize1 = out->vsize = out->msize2 = out->dsize = vsize+msize1+msize2; out->parameters = apop_bootstrap_cov(d, m, settings->rng, settings->draws); out->parameters->vector = apop_data_pack(m->parameters); if (settings->index == -1) return out; else { apop_model *out2 = apop_model_set_parameters(apop_normal, apop_data_get(out->parameters, settings->index, -1), //mean apop_data_get(out->parameters, settings->index, settings->index)//var ); apop_model_free(out); return out2; } } //else Get_vmsizes(m->parameters);//vsize, msize1, msize2 apop_data *param_draws = apop_data_alloc(0, settings->draws, vsize+msize1+msize2); for (int i=0; i < settings->draws; i++){ apop_model *mm = apop_estimate (NULL, m);//If you're here, d==NULL. apop_data_pack(mm->parameters, Apop_rv(param_draws, i)); apop_model_free(mm); } if (settings->index == -1) return apop_estimate(param_draws, apop_pmf); else { apop_data *param_draws1 = apop_data_alloc(settings->draws, 0,0); gsl_vector *the_draws = Apop_cv(param_draws, settings->index); gsl_vector_memcpy(param_draws1->vector, the_draws); apop_data_free(param_draws); return apop_estimate(param_draws1, apop_pmf); } } extern apop_model *apop_swap_model; //apop_missing_data.c int apop_model_metropolis_draw(double *out, gsl_rng* rng, apop_model *params);//apop_update.c /** Draw from a model.  Jerome Benoit committed Jul 10, 2015 410 \param out An already-allocated array of doubles to be filled by the draw method. It must have size m->dsize.  Jerome Benoit committed Aug 15, 2014 411 412 413 \param r A \c gsl_rng, probably allocated via \ref apop_rng_alloc. Optional; if \c NULL, then I will call \ref apop_rng_get_thread for an RNG. \param m The model from which to make draws.  Jerome Benoit committed Jul 10, 2015 414 415 416 \li If the model has its own \c draw method, then this function will call it. \li Else, if the model is univariate, use \ref apop_arms_draw to generate random draws. \li Else, if the model is multivariate, use \ref apop_model_metropolis to generate random draws.  Jerome Benoit committed Aug 15, 2014 417 418 419 420 421 \li This makes a single draw of the given size. See \ref apop_model_draws to fill a matrix with draws. \return Zero on success; nozero on failure. out[0] is probably \c NAN on failure. */ int apop_draw(double *out, gsl_rng *r, apop_model *m){  Jerome Benoit committed Jul 10, 2015 422  if (!r) r = apop_rng_get_thread(-1);  Jerome Benoit committed Aug 15, 2014 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444  if (m->draw) return m->draw(out, r, m); else if (m->dsize == 1) return apop_arms_draw(out, r, m); //Else, MCMC, possibly setting it up first. //generate a model with data/params reversed //estimate mcmc. Swapped model will be stored as settings->base_model. OMP_critical (apop_draw) if (!Apop_settings_get_group(m, apop_mcmc)){ apop_model *swapped = apop_model_copy(apop_swap_model); swapped->more = m; swapped->msize1 = 1; swapped->msize2 = m->dsize; swapped->data = m->parameters; Apop_settings_add_group(swapped, apop_mcmc, .burnin=0.999, .periods=1000); apop_model *est = apop_model_metropolis(m->parameters, r, swapped); //leak. m->draw = apop_model_metropolis_draw; apop_settings_copy_group(m, est, "apop_mcmc"); } return apop_draw(out, r, m); }  Jerome Benoit committed Jul 10, 2015 445 446 447 /** Allocate and initialize the \c parameters, \c info, and other requisite parts of a \ref apop_model. Some models have associated prep routines that also attach settings groups to the model, and set up additional special-case functions in vtables.  Jerome Benoit committed Aug 15, 2014 448   Jerome Benoit committed Jul 10, 2015 449 450 451 452 453 454 \li The input model is modified in place. \li If called repeatedly, subsequent calls to \ref apop_prep are no-ops. Thus, a model can not be re-prepped using a new data set or other conditions. \li The default prep is to simply call \ref apop_model_clear. If the input \ref apop_model has a prep method, then that gets called instead. */  Jerome Benoit committed Aug 15, 2014 455 456 457 458 459 460 461 462 463 void apop_prep(apop_data *d, apop_model *m){ if (m->prep) m->prep(d, m); else apop_model_clear(d, m); } static double disnan(double in) {return gsl_isnan(in);} /** A prediction supplies E(a missing value | original data, already-estimated parameters, and other supplied data elements ).  Jerome Benoit committed Jul 10, 2015 464 For a regression, one would first estimate the parameters of the model, then supply a row of predictors X. The value of the dependent variable \f$y\f$ is unknown, so the system would predict that value.  Jerome Benoit committed Aug 15, 2014 465   Jerome Benoit committed Jul 10, 2015 466 For a univariate model (i.e. a model in one-dimensional data space), there is only one variable to omit and fill in, so the prediction problem reduces to the expected value: E(a missing value | original data, already-estimated parameters). [In some models, this may not be the expected value, but is a best value for the missing item using some other meaning of best'.]  Jerome Benoit committed Aug 15, 2014 467   Jerome Benoit committed Jul 10, 2015 468 469 470 In other cases, prediction is the missing data problem: for three-dimensional data, you may supply the input (34, \c NaN, 12), and the parameterized model provides the most likely value of the middle parameter given the parameters and known data.  Jerome Benoit committed Aug 15, 2014 471   Jerome Benoit committed Jul 10, 2015 472 \li If you give me a \c NULL data set, I will assume you want all values filled in, for most models with the expected value.  Jerome Benoit committed Aug 15, 2014 473 474 475 476  \li If you give me data with \c NaNs, I will take those as the points to be predicted given the provided data.  Jerome Benoit committed Jul 10, 2015 477 If the model has no \c predict method, the default is to use the \ref apop_ml_impute function to do the work. That function does a maximum-likelihood search for the best parameters.  Jerome Benoit committed Aug 15, 2014 478   Jerome Benoit committed Jul 10, 2015 479 \return If you gave me a non-\c NULL data set, I will return that, with the \c NaNs filled in. If \c NULL input, I will allocate an \ref apop_data set and fill it with the expected values.  Jerome Benoit committed Aug 15, 2014 480 481 482  There may be a second page (i.e., a \ref apop_data set attached to the ->more pointer of the main) listing confidence and standard error information. See your specific model documentation for details.  Jerome Benoit committed Jul 10, 2015 483 \li Special-case calculations for certain models are held in a vtable; see \ref vtables for details. The typedef new functions must conform to and the hash used for lookups are:  Jerome Benoit committed Aug 15, 2014 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498  \code typedef apop_data * (*apop_predict_type)(apop_data *d, apop_model *params); #define apop_predict_hash(m1) ((size_t)((m1).log_likelihood ? (m1).log_likelihood : (m1).p)*33 + (m1).estimate ? (size_t)(m1).estimate: 27) \endcode */ apop_data *apop_predict(apop_data *d, apop_model *m){ apop_data *prediction = NULL; apop_data *out = d ? d : apop_data_alloc(0, 1, m->dsize); if (!d) gsl_matrix_set_all(out->matrix, GSL_NAN); apop_predict_type mp = apop_predict_vtable_get(m); if (mp) prediction = mp(out, m); if (prediction) return prediction; if (!apop_map_sum(out, disnan)) return out; //default:  Jerome Benoit committed Jul 10, 2015 499  apop_model *f = apop_ml_impute(out, m);  Jerome Benoit committed Aug 15, 2014 500 501 502 503 504 505 506 507 508 509 510 511  apop_model_free(f); return out; } /* Are all the elements of v less than or equal to the corresponding elements of the reference vector? */ static int lte(gsl_vector *v, gsl_vector *ref){ for (int i=0; i< v->size; i++) if(v->data[i] > gsl_vector_get(ref, i)) return 0; return 1; }  Jerome Benoit committed Jul 10, 2015 512 /** Input a one-row data point/vector and a model; returns the area of the model's PDF beneath the given point.  Jerome Benoit committed Aug 15, 2014 513   Jerome Benoit committed Jul 10, 2015 514 515 516 By default, make random draws from the PDF and return the percentage of those draws beneath or equal to the given point. Many models have closed-form solutions that make no use of random draws.  Jerome Benoit committed Aug 15, 2014 517   Jerome Benoit committed Jul 10, 2015 518 519 520 521 522 See also \ref apop_cdf_settings, which is the structure used to store draws already made (which means the second, third, ... calls to this function will take much less time than the first), the \c gsl_rng, and the number of draws to be made. These are handled without your involvement, but if you would like to change the number of draws from the default, add this group before calling \ref apop_cdf :  Jerome Benoit committed Aug 15, 2014 523 524 525 526 527 528  \code Apop_model_add_group(your_model, apop_cdf, .draws=1e5, .rng=my_rng); double cdf_value = apop_cdf(your_data_point, your_model); \endcode  Jerome Benoit committed Jul 10, 2015 529 530 \li Only the first row of the input \ref apop_data set is used. Note that if you need to view row 20 of a data set as a one-row data set, use \ref Apop_r.  Jerome Benoit committed Aug 15, 2014 531 532 533 Here are many examples using common, mostly symmetric distributions. \include some_cdfs.c  Jerome Benoit committed Jul 10, 2015 534 */  Jerome Benoit committed Aug 15, 2014 535 536 537 538 539 540 541 542 543 544 545 double apop_cdf(apop_data *d, apop_model *m){ if (m->cdf) return m->cdf(d, m); apop_cdf_settings *cs = Apop_settings_get_group(m, apop_cdf); if (!cs) cs = Apop_model_add_group(m, apop_cdf); long int tally = 0; gsl_vector *ref = apop_data_pack(Apop_r(d, 0)); if (!cs->draws_made){ if (m->dsize == -1) apop_prep(d, m); Apop_stopif(m->dsize==0, return GSL_NAN, 0, "I need to make random draws from your model, but it has dsize==0. Returning NaN"); cs->draws_made = gsl_matrix_alloc(cs->draws, m->dsize);  Jerome Benoit committed Jul 10, 2015 546 547  for (int i=0; i< cs->draws; i++) apop_draw((Apop_mrv(cs->draws_made, i))->data, cs->rng, m);  Jerome Benoit committed Aug 15, 2014 548  }  Jerome Benoit committed Jul 10, 2015 549 550  for (int i=0; i< cs->draws_made->size1; i++) tally += lte(Apop_mrv(cs->draws_made, i), ref);  Jerome Benoit committed Aug 15, 2014 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569  gsl_vector_free(ref); return tally/(double)cs->draws_made->size1; } Apop_settings_init(apop_cdf, Apop_varad_set(draws, 1e4); Apop_varad_set(rng, NULL); out->draws_refcount = malloc(sizeof(int)); *out->draws_refcount = 1; ) Apop_settings_free(apop_cdf, if (in->draws_made && !--*in->draws_refcount) gsl_matrix_free(in->draws_made); ) Apop_settings_copy(apop_cdf, ++*out->draws_refcount; )`