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#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include <math.h>
#include "autoclass.h"
#include "minmax.h"
#include "globals.h"
/* SUPRESS CODECENTER WARNING MESSSAGES */
/* empty body for 'while' statement */
/*SUPPRESS 570*/
/* formal parameter '<---->' was not used */
/*SUPPRESS 761*/
/* automatic variable '<---->' was not used */
/*SUPPRESS 762*/
/* automatic variable '<---->' was set but not used */
/*SUPPRESS 765*/
/* SN_CN_PARAMS_INFLUENCE_FN
01feb95 wmt: 2.0 * global_variance (from ac-x)
*/
void sn_cn_params_influence_fn( model_DS model, tparm_DS tparm, int term_index, int n_att,
float *v, float *class_mean, float *class_sigma,
float *global_mean, float *global_sigma)
{
struct sn_cn_param *param;
tparm_DS *p;
float global_variance;
param = &(tparm->ptype.sn_cn);
*class_mean = param->mean;
*class_sigma = param->sigma;
p = model_global_tparms(model);
*global_mean = p[term_index]->ptype.sn_cn.mean;
*global_sigma = p[term_index]->ptype.sn_cn.sigma;
global_variance = p[term_index]->ptype.sn_cn.variance;
*v = (float) log ((double) (*global_sigma / *class_sigma)) +
((((square(*class_mean - *global_mean) +
(param->variance - global_variance))) / 2.0) / global_variance);
}
/* BUILD_SN_CN_PRIORS
30jul95 wmt: change log calls to safe_log to prevent "log: SING error"
error messages.
Builds an SN-CN prior from the information in a fully instantiated att
structure of the real type.
*/
static priors_DS build_sn_cn_priors( att_DS att)
{
int n;
float range, sigma_min, sigma_max;
priors_DS priors;
real_stats_DS statistics = att->r_statistics;/* Attribute range information */
range = statistics->mx - statistics->mn;
sigma_min = SN_CM_SIGMA_SAFETY_FACTOR * att->error;
/* The max sigma for values in range. */
sigma_max = max(sigma_min, range / 2.0);
if (sigma_min == sigma_max) {
n = att->warnings_and_errors->num_expander_errors;
att->warnings_and_errors->num_expander_errors += 1;
if (n == 0)
att->warnings_and_errors->model_expander_errors =
(fxlstr *) malloc(sizeof(fxlstr));
else att->warnings_and_errors->model_expander_errors =
(fxlstr *) realloc(att->warnings_and_errors->model_expander_errors,
(n+1) * sizeof(fxlstr));
strcpy(att->warnings_and_errors->model_expander_errors[n],
" single_normal_cn is faulty due to large error-to-range\n"
" ratio on sigma priors.\n");
}
priors = (priors_DS) malloc(sizeof(struct priors));
priors->sigma_min = sigma_min;
priors->sigma_max = sigma_max;
priors->known_prior = 0.0; /* not used, but needed for priors struct init */
priors->mean_mean = statistics->mean;
priors->mean_sigma =
max((range / 2.0),
(SN_CM_SIGMA_SAFETY_FACTOR /
(float) sqrt( ABSOLUTE_MIN_CLASS_WT)) * sigma_min);
priors->mean_var = square(priors->mean_sigma);
priors->minus_log_log_sigmas_ratio =
- (float) safe_log( max( safe_log( 1.0 / (1.0 - SINGLE_FLOAT_EPSILON)),
max( safe_log((double) (priors->sigma_max / priors->sigma_min)),
LEAST_POSITIVE_SHORT_FLOAT)));
priors->minus_log_mean_sigma = - (float) safe_log( (double) priors->mean_sigma);
return(priors);
}
/* SINGLE_NORMAL_CN_MODEL_TERM_BUILDER
21nov94 wmt: initialize all slots in tparm
18dec94 wmt: finish "missing" error msg
30jul95 wmt: change log calls to safe_log to prevent "log: SING error"
error messages.
Funcalled from Expand-Model-Terms. This constructs parameter, prior, and
intermediate results structures appropriate to a single-normal likelihood
term, and places them in the model. Constructs corresponding log-likelihood
and parameter update function elements and saves them on the model for later
compilation.
*/
void single_normal_cn_model_term_builder( model_DS model, term_DS term, int n_term)
/* model_DS model; The model-DS to which this term will contribute. */
/* term_DS term; The singleton term-DS definig this attributes use. */
/* int n_term; The term index for various model-DS substructures. */
{
void ***att_trans_data;
int n, n_att, n_att_trans_data;
float error, log_att_delta, log_delta_div_root_2pi;
database_DS data_base;
att_DS att;
priors_DS prior_set;
tparm_DS tparm;
struct sn_cn_param *sn;
n_att = term->att_list[0]; /* index for this SN attribute */
data_base = model->database;
att = data_base->att_info[n_att]; /* Attribute description */
error = att->error;
att_trans_data = (void ***) get("single_normal_cn", "att_trans_data");
n_att_trans_data = ((int *) get("single_normal_cn", "n_att_trans_data"))[0];
if (getf(att_trans_data, att->type, n_att_trans_data) == NULL)
fprintf( stderr, "Attribute %d not one allowed for single_normal_cn terms\n",
n_att);
if ( att->missing) {
if( (n = att->warnings_and_errors->num_expander_errors ++) == 0)
att->warnings_and_errors->model_expander_errors =
(fxlstr *) malloc(sizeof(fxlstr));
else
att->warnings_and_errors->model_expander_errors =
(fxlstr *) realloc(att->warnings_and_errors->model_expander_errors,
(n+1) * sizeof(fxlstr));
strcpy(att->warnings_and_errors->model_expander_errors[n],
" using single_normal_cn model on attribute with missing values\n");
}
if (term->n_atts != 1)
fprintf( stderr,
"Attribute %d using single_normal_cn model in non-singleton set\n",
n_att);
if (error <= 0.0)
fprintf( stderr,
"Attribute %d using single_normal_cn model with non-positive error\n",
n_att);
log_att_delta = (float) safe_log((double) error);
log_delta_div_root_2pi = LN_1_DIV_ROOT_2PI + log_att_delta;
/* Allocate parameters struct */
term->tparm = tparm = (tparm_DS) malloc(sizeof(struct new_term_params));
tparm->tppt=SN_CN;
tparm->n_atts = term->n_atts;
tparm->n_term = n_term;
tparm->n_att = n_att;
tparm->n_att_indices = tparm->n_datum = tparm->n_data = 0;
tparm->wts = tparm->datum = tparm->att_indices = NULL;
tparm->data = NULL;
tparm->w_j = tparm->ranges = tparm->class_wt = 0.0;
tparm->disc_scale = 0.0;
tparm->wt_m = tparm->log_marginal = 0.0;
tparm->log_delta = log_delta_div_root_2pi;
tparm->log_att_delta = log_att_delta;
tparm->log_pi = -1.0 * (float) safe_log( M_PI);
sn = &(tparm->ptype.sn_cn);
/* Allocate & SET priors. */
prior_set = model->priors[n_term] = build_sn_cn_priors(att);
sn->weighted_mean = sn->weighted_var = sn->mean = sn->sigma =0.0;
sn->log_sigma = sn->variance = sn->log_variance = sn->inv_variance= 0.0;
sn->ll_min_diff = sn->skewness = sn->kurtosis = 0.0;
sn->prior_mean_mean = prior_set->mean_mean;
sn->prior_mean_var = prior_set->mean_var;
sn->prior_mean_sigma = prior_set->mean_sigma;
sn->prior_sigmas_term = (-1.5 * (float) safe_log( 2.0)) +
prior_set->minus_log_log_sigmas_ratio +
(-1.0 * (float) safe_log((double) prior_set->mean_sigma));
/* Generate function elements: */
sn->prior_sigma_min_2 = square(prior_set->sigma_min);
sn->prior_sigma_max_2 = square(prior_set->sigma_max);
/* function elements are no longer genereated. preprocessed constands are
stored in the params and calls are made with parameter lists in call*/
}
/* SINGLE_NORMAL_CN_LOG_LIKELIHOOD
20dec94 wmt: return type to double
When called within the environment of Log-Likelihood-fn, this calculates the
probability of a Single-Normal-cn term in 'datum given 'params. */
double single_normal_cn_log_likelihood( tparm_DS tparm)
{
int n_att = tparm->n_att;
float log_delta = tparm->log_delta, *datum = tparm->datum;
struct sn_cn_param *sn = &(tparm->ptype.sn_cn);
return (log_delta + (-0.5 * (sn->log_variance +
(square(sn->mean - datum[n_att]) *
sn->inv_variance))));
}
/* SINGLE_NORMAL_CN_UPDATE_L_APPROX
20dec94 wmt: return type to double
When called within the environment of Update-L-Approx-fn, this calculates the
approximate log marginal likelihood log-a<w_j.S_j/H_j>_k of observing the
weighted statistics given the class hypothesis alone. See
Single-Normal-cn-Update-M-approx-term-caller.
A LOG-LINEAR APPROXIMATION IS USED IN THE REGION WHERE
0 <= w_j-known <= (* .75 *absolute-min-class-wt*)
*/
double single_normal_cn_update_l_approx( tparm_DS tparm)
{
float log_delta = tparm->log_delta, diff, t1, t2, w_j = tparm->w_j;
struct sn_cn_param *sn = &(tparm->ptype.sn_cn);
diff = sn->weighted_mean - sn->mean;
t1 = ( fabs((double) diff) <= sqrt( LEAST_POSITIVE_SINGLE_FLOAT))
? 0.0 : square(diff);
t2 = w_j * (log_delta - sn->log_sigma) +
(-0.5 * w_j * (sn->weighted_var + t1) / sn->variance);
return (t2);
}
/* SINGLE_NORMAL_CN_UPDATE_M_APPROX
20dec94 wmt: return type to double
29mar95 wmt: calculation to double
When called within the environment of Update-M-Approx-fn, this calculates
the approximate log marginal likelihood log-a<w_j.S_j/H_j>_k of observing
the weighted statistics given the class hypothesis alone. See
Single-Normal-cn-Update-M-approx-term-caller.
*/
double single_normal_cn_update_m_approx( tparm_DS tparm)
{
struct sn_cn_param *sn = &(tparm->ptype.sn_cn);
float log_att_delta = tparm->log_att_delta;
float prior_mean_mean = sn->prior_mean_mean;
float prior_mean_sigma = sn->prior_mean_sigma, diff, t1;
float prior_sigmas_term = sn->prior_sigmas_term, w_j = tparm->w_j;
double t2;
diff = sn->weighted_mean - prior_mean_mean;
t1 = ( fabs((double) diff) <=
(prior_mean_sigma * sqrt( 2.0 * LEAST_POSITIVE_SINGLE_FLOAT)))
? 0.0 : -0.5 * square( diff / prior_mean_sigma);
t2 = log_gamma( (double) (0.5 * (w_j - 1.0)), FALSE) +
(double) t1 +
(double) (w_j * log_att_delta) +
(-0.5 * (double) w_j * safe_log((double) (M_PI * w_j))) +
(-0.5 * (double) (w_j - 1.0) *
safe_log( (double) max( LEAST_POSITIVE_SINGLE_FLOAT,
sn->weighted_var))) +
(double) prior_sigmas_term;
return (t2);
}
/* SINGLE_NORMAL_CN_UPDATE_PARAMS
27nov94 wmt: use percent_equal for float tests
20dec94 wmt: return type to void
*/
void single_normal_cn_update_params( tparm_DS tparm, int known_parms_p)
{
int n_att = tparm->n_att, n_data = tparm->n_data;
struct sn_cn_param *sn=&(tparm->ptype.sn_cn);
float prior_sigma_min_2 = sn->prior_sigma_min_2;
float prior_sigma_max_2 = sn->prior_sigma_max_2;
float prior_mean_mean = sn->prior_mean_mean;
float prior_mean_var = sn->prior_mean_var;
float **data = tparm->data, *wts = tparm->wts;
float class_wt = tparm->class_wt, class_wt_1;
float ignore1, ignore2, mean, variance, skewness, kurtosis, var_ratio;
class_wt_1 = class_wt + 1;
if (class_wt > 0.0) { /* Zero class-wt implies null class */
if ( tparm->collect == TRUE) {
/* If not collect?, we proceed with the previous values. */
central_measures_x(data, n_data, n_att, wts,
percent_equal( (double) sn->mean, FLOAT_UNKNOWN,
REL_ERROR) ?
(double) prior_mean_mean : (double) sn->mean,
&ignore1, &ignore2, &mean, &variance, &skewness, &kurtosis);
sn->weighted_mean = mean;
/* Limit the weighted variance, rather than sigma, to get
consistent results: */
sn->weighted_var =
max(prior_sigma_min_2,
min(prior_sigma_max_2,
min(
class_wt * SN_CN_SIGMA_SAFETY_FACTOR * prior_mean_var,
variance)));
sn->skewness = skewness;
sn->kurtosis = kurtosis;
}
else {
sn->weighted_mean = prior_mean_mean;
sn->weighted_var = prior_mean_var;
}
}
if (known_parms_p != TRUE) { /* Update class parameters */
var_ratio = sn->weighted_var / (class_wt_1 * prior_mean_var);
sn->mean = (sn->weighted_mean * (1.0 - var_ratio)) +
(prior_mean_mean * var_ratio);
sn->variance = max(sn->weighted_var * (class_wt / class_wt_1),
(float) sqrt( LEAST_POSITIVE_SINGLE_FLOAT));
sn->sigma = (float) sqrt((double) sn->variance);
sn->log_variance = (float) safe_log((double) sn->variance);
sn->log_sigma = 0.5 * sn->log_variance;
sn->inv_variance = 1.0 / sn->variance;
sn->ll_min_diff =
max((float) sqrt( LEAST_POSITIVE_SINGLE_FLOAT),
(sn->variance *
(float) sqrt( LEAST_POSITIVE_SINGLE_FLOAT)));
}
/* return(class_wt); */
}
/* When called within the environment of Class-Equivalence-fn, this tests for a
difference of means less than sigma=ratio times MIN of sigmas. */
int single_normal_cn_class_equivalence( tparm_DS tparm1,tparm_DS tparm2,
double sigma_ratio)
{
struct sn_cn_param *sn1 = &(tparm1->ptype.sn_cn), *sn2 = &(tparm2->ptype.sn_cn);
if (fabs((double) (sn1->mean - sn2->mean)) <
(sigma_ratio * (double) min(sn1->sigma, sn2->sigma)))
return(TRUE);
else
return(FALSE);
}
/* SINGLE_NORMAL_CN_CLASS_MERGED_MARGINAL
20dec94 wmt: return type to void
When called within the environment of Class-Merged-Marginal-fn, this
generates the sufficient statistics of Single-Normal-cn term equivalent
to the weighted merging of params-0 and params-1, storing same in
params-m.
dont know what wt1 and wt0 and wtm are supposed to be. should snm be
returned maybe and let caller get snm->...
*/
void single_normal_cn_class_merged_marginal( tparm_DS tparm0, tparm_DS tparm1,
tparm_DS tparmm)
{
float wt_0=.5, wt_1=.5, wt_m=1.0;
struct sn_cn_param *sn0=&(tparm0->ptype.sn_cn),
*sn1=&(tparm1->ptype.sn_cn),
*snm=&(tparmm->ptype.sn_cn);
float prior_sigma_min_2 = sn0->prior_sigma_min_2;
snm->weighted_mean =
((wt_0 * sn0->weighted_mean) + (wt_1 * sn1->weighted_mean)) / wt_m;
snm->weighted_var =
max(prior_sigma_min_2,
(((wt_0 * (square(sn0->weighted_mean) + sn0->weighted_var)) +
(wt_1 * (square(sn1->weighted_mean) + sn1->weighted_var))) /
wt_m) -
square(snm->weighted_mean));
/* return (snm->weighted_var); */
}
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