X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=src%2Fmath%2Flinreg%2Flinreg.c;h=9465875e81b3c697d58fa27112bccc466188ece3;hb=b9f6480f1b451cb7873bb0591369504ff1b92595;hp=c717e4ada4bd690ea7b0938f6c1289525cb24542;hpb=945fd370f9fb880b310fb88f868ad47c2ae84533;p=pspp-builds.git diff --git a/src/math/linreg/linreg.c b/src/math/linreg/linreg.c index c717e4ad..9465875e 100644 --- a/src/math/linreg/linreg.c +++ b/src/math/linreg/linreg.c @@ -1,23 +1,20 @@ -/* - lib/linreg/linreg.c - - Copyright (C) 2005 Free Software Foundation, Inc. Written by Jason H. Stover. - - This program is free software; you can redistribute it and/or modify it under - the terms of the GNU General Public License as published by the Free - Software Foundation; either version 2 of the License, or (at your option) - any later version. - - This program is distributed in the hope that it will be useful, but WITHOUT - ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or - FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for - more details. - - You should have received a copy of the GNU General Public License along with - this program; if not, write to the Free Software Foundation, Inc., 51 - Franklin Street, Fifth Floor, Boston, MA 02111-1307, USA. - */ +/* PSPP - a program for statistical analysis. + Copyright (C) 2005 Free Software Foundation, Inc. + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + You should have received a copy of the GNU General Public License + along with this program. If not, see . */ + +#include #include #include @@ -94,12 +91,12 @@ linreg_mean_std (gsl_vector_const_view v, double *mp, double *sp, double *ssp) The return value is the number of distinct variables found. */ int -pspp_linreg_get_vars (const void *c_, struct variable **v) +pspp_linreg_get_vars (const void *c_, const struct variable **v) { const pspp_linreg_cache *c = c_; - struct pspp_coeff *coef = NULL; const struct variable *tmp; int i; + int j; int result = 0; /* @@ -113,23 +110,20 @@ pspp_linreg_get_vars (const void *c_, struct variable **v) /* Start at c->coeff[1] to avoid the intercept. */ - v[result] = (struct variable *) pspp_coeff_get_var (c->coeff[1], 0); - result = (v[result] == NULL) ? 0 : 1; - - for (coef = c->coeff[2]; coef < c->coeff[c->n_coeffs]; coef++) + for (j = 1; j < c->n_coeffs; j++) { - tmp = pspp_coeff_get_var (coef, 0); + tmp = pspp_coeff_get_var (c->coeff[j], 0); assert (tmp != NULL); /* Repeated variables are likely to bunch together, at the end of the array. */ i = result - 1; - while (i >= 0 && (v[i]->index != tmp->index)) + while (i >= 0 && v[i] != tmp) { i--; } if (i < 0 && result < c->n_coeffs) { - v[result] = (struct variable *) tmp; + v[result] = tmp; result++; } } @@ -151,10 +145,10 @@ pspp_linreg_cache_alloc (size_t n, size_t p) c->indep_means = gsl_vector_alloc (p); c->indep_std = gsl_vector_alloc (p); c->ssx = gsl_vector_alloc (p); /* Sums of squares for the - independent variables. + independent variables. */ c->ss_indeps = gsl_vector_alloc (p); /* Sums of squares for the - model parameters. + model parameters. */ c->cov = gsl_matrix_alloc (p + 1, p + 1); /* Covariance matrix. */ c->n_obs = n; @@ -181,15 +175,20 @@ pspp_linreg_cache_free (void *m) int i; pspp_linreg_cache *c = m; - gsl_vector_free (c->indep_means); - gsl_vector_free (c->indep_std); - gsl_vector_free (c->ss_indeps); - gsl_matrix_free (c->cov); - for (i = 0; i < c->n_coeffs; i++) + if (c != NULL) { - pspp_coeff_free (c->coeff[i]); + gsl_vector_free (c->indep_means); + gsl_vector_free (c->indep_std); + gsl_vector_free (c->ss_indeps); + gsl_matrix_free (c->cov); + gsl_vector_free (c->ssx); + for (i = 0; i < c->n_coeffs; i++) + { + pspp_coeff_free (c->coeff[i]); + } + free (c->coeff); + free (c); } - free (c); return true; } @@ -203,7 +202,7 @@ pspp_linreg (const gsl_vector * Y, const gsl_matrix * X, const pspp_linreg_opts * opts, pspp_linreg_cache * cache) { int rc; - gsl_matrix *design; + gsl_matrix *design = NULL; gsl_matrix_view xtx; gsl_matrix_view xm; gsl_matrix_view xmxtx; @@ -245,7 +244,7 @@ pspp_linreg (const gsl_vector * Y, const gsl_matrix * X, cache->dfm = cache->n_indeps; cache->dfe = cache->dft - cache->dfm; cache->n_coeffs = X->size2 + 1; /* Adjust this later to allow for - regression through the origin. + regression through the origin. */ if (cache->method == PSPP_LINREG_SWEEP) { @@ -366,6 +365,18 @@ pspp_linreg (const gsl_vector * Y, const gsl_matrix * X, } gsl_matrix_free (sw); } + else if (cache->method == PSPP_LINREG_CONDITIONAL_INVERSE) + { + /* + Use the SVD of X^T X to find a conditional inverse of X^TX. If + the SVD is X^T X = U D V^T, then set the conditional inverse + to (X^T X)^c = V D^- U^T. D^- is defined as follows: If entry + (i, i) has value sigma_i, then entry (i, i) of D^- is 1 / + sigma_i if sigma_i > 0, and 0 otherwise. Then solve the normal + equations by setting the estimated parameter vector to + (X^TX)^c X^T Y. + */ + } else { gsl_multifit_linear_workspace *wk;