X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=src%2Fmath%2Flinreg.c;h=baf9cb737e8250b59a8814dac93dc5ca5b768390;hb=d342031c6d0e00840575fb01ab2ea136e674d600;hp=7e7d4a5504a88bbf8924e2d04ec450bab8cf3669;hpb=4aa40ed36fcdb13f73520945d804e6d3d8d52738;p=pspp-builds.git diff --git a/src/math/linreg.c b/src/math/linreg.c index 7e7d4a55..baf9cb73 100644 --- a/src/math/linreg.c +++ b/src/math/linreg.c @@ -137,12 +137,15 @@ pspp_linreg_get_vars (const void *c_, const struct variable **v) independent variables. */ pspp_linreg_cache * -pspp_linreg_cache_alloc (size_t n, size_t p) +pspp_linreg_cache_alloc (const struct variable *depvar, const struct variable **indep_vars, + size_t n, size_t p) { + size_t i; pspp_linreg_cache *c; c = (pspp_linreg_cache *) malloc (sizeof (pspp_linreg_cache)); - c->depvar = NULL; + c->depvar = depvar; + c->indep_vars = indep_vars; 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 @@ -151,9 +154,22 @@ pspp_linreg_cache_alloc (size_t n, size_t p) c->ss_indeps = gsl_vector_alloc (p); /* Sums of squares for the model parameters. */ - c->cov = gsl_matrix_alloc (p + 1, p + 1); /* Covariance matrix. */ c->n_obs = n; c->n_indeps = p; + c->n_coeffs = 0; + for (i = 0; i < p; i++) + { + if (var_is_numeric (indep_vars[i])) + { + c->n_coeffs++; + } + else + { + c->n_coeffs += cat_get_n_categories (indep_vars[i]) - 1; + } + } + + c->cov = gsl_matrix_alloc (c->n_coeffs + 1, c->n_coeffs + 1); /* Default settings. */ @@ -192,7 +208,93 @@ pspp_linreg_cache_free (void *m) } return true; } +static void +cache_init (pspp_linreg_cache *cache) +{ + assert (cache != NULL); + cache->dft = cache->n_obs - 1; + cache->dfm = cache->n_indeps; + cache->dfe = cache->dft - cache->dfm; + cache->intercept = 0.0; +} +static void +post_sweep_computations (pspp_linreg_cache *cache, const struct design_matrix *dm, + gsl_matrix *sw) +{ + gsl_matrix *xm; + gsl_matrix_view xtx; + gsl_matrix_view xmxtx; + double m; + double tmp; + size_t i; + size_t j; + int rc; + + assert (sw != NULL); + assert (cache != NULL); + + cache->sse = gsl_matrix_get (sw, cache->n_indeps, cache->n_indeps); + cache->mse = cache->sse / cache->dfe; + /* + Get the intercept. + */ + m = cache->depvar_mean; + for (i = 0; i < cache->n_indeps; i++) + { + tmp = gsl_matrix_get (sw, i, cache->n_indeps); + cache->coeff[i]->estimate = tmp; + m -= tmp * pspp_linreg_get_indep_variable_mean (cache, design_matrix_col_to_var (dm, i)); + } + /* + Get the covariance matrix of the parameter estimates. + Only the upper triangle is necessary. + */ + + /* + The loops below do not compute the entries related + to the estimated intercept. + */ + for (i = 0; i < cache->n_indeps; i++) + for (j = i; j < cache->n_indeps; j++) + { + tmp = -1.0 * cache->mse * gsl_matrix_get (sw, i, j); + gsl_matrix_set (cache->cov, i + 1, j + 1, tmp); + } + /* + Get the covariances related to the intercept. + */ + xtx = gsl_matrix_submatrix (sw, 0, 0, cache->n_indeps, cache->n_indeps); + xmxtx = gsl_matrix_submatrix (cache->cov, 0, 1, 1, cache->n_indeps); + xm = gsl_matrix_calloc (1, cache->n_indeps); + for (i = 0; i < xm->size2; i++) + { + gsl_matrix_set (xm, 0, i, + pspp_linreg_get_indep_variable_mean (cache, design_matrix_col_to_var (dm, i))); + } + rc = gsl_blas_dsymm (CblasRight, CblasUpper, cache->mse, + &xtx.matrix, xm, 0.0, &xmxtx.matrix); + gsl_matrix_free (xm); + if (rc == GSL_SUCCESS) + { + tmp = cache->mse / cache->n_obs; + for (i = 1; i < 1 + cache->n_indeps; i++) + { + tmp -= gsl_matrix_get (cache->cov, 0, i) + * pspp_linreg_get_indep_variable_mean (cache, design_matrix_col_to_var (dm, i - 1)); + } + gsl_matrix_set (cache->cov, 0, 0, tmp); + + cache->intercept = m; + } + else + { + fprintf (stderr, "%s:%d:gsl_blas_dsymm: %s\n", + __FILE__, __LINE__, gsl_strerror (rc)); + exit (rc); + } +} + /* Fit the linear model via least squares. All pointers passed to pspp_linreg are assumed to be allocated to the correct size and initialized to the @@ -205,8 +307,6 @@ pspp_linreg (const gsl_vector * Y, const struct design_matrix *dm, int rc; gsl_matrix *design = NULL; gsl_matrix_view xtx; - gsl_matrix *xm; - gsl_matrix_view xmxtx; gsl_vector_view xty; gsl_vector_view xi; gsl_vector_view xj; @@ -234,13 +334,8 @@ pspp_linreg (const gsl_vector * Y, const struct design_matrix *dm, cache->depvar_std = s; cache->sst = ss; } - - cache->dft = cache->n_obs - 1; - cache->dfm = cache->n_indeps; - cache->dfe = cache->dft - cache->dfm; + cache_init (cache); cache->n_coeffs = dm->m->size2; - cache->intercept = 0.0; - for (i = 0; i < dm->m->size2; i++) { if (opts->get_indep_mean_std[i]) @@ -326,65 +421,7 @@ pspp_linreg (const gsl_vector * Y, const struct design_matrix *dm, Sweep on the matrix sw, which contains XtX, XtY and YtY. */ reg_sweep (sw); - cache->sse = gsl_matrix_get (sw, cache->n_indeps, cache->n_indeps); - cache->mse = cache->sse / cache->dfe; - /* - Get the intercept. - */ - m = cache->depvar_mean; - for (i = 0; i < cache->n_indeps; i++) - { - tmp = gsl_matrix_get (sw, i, cache->n_indeps); - cache->coeff[i]->estimate = tmp; - m -= tmp * pspp_linreg_get_indep_variable_mean (cache, design_matrix_col_to_var (dm, i)); - } - /* - Get the covariance matrix of the parameter estimates. - Only the upper triangle is necessary. - */ - - /* - The loops below do not compute the entries related - to the estimated intercept. - */ - for (i = 0; i < cache->n_indeps; i++) - for (j = i; j < cache->n_indeps; j++) - { - tmp = -1.0 * cache->mse * gsl_matrix_get (sw, i, j); - gsl_matrix_set (cache->cov, i + 1, j + 1, tmp); - } - /* - Get the covariances related to the intercept. - */ - xtx = gsl_matrix_submatrix (sw, 0, 0, cache->n_indeps, cache->n_indeps); - xmxtx = gsl_matrix_submatrix (cache->cov, 0, 1, 1, cache->n_indeps); - xm = gsl_matrix_calloc (1, cache->n_indeps); - for (i = 0; i < xm->size2; i++) - { - gsl_matrix_set (xm, 0, i, - pspp_linreg_get_indep_variable_mean (cache, design_matrix_col_to_var (dm, i))); - } - rc = gsl_blas_dsymm (CblasRight, CblasUpper, cache->mse, - &xtx.matrix, xm, 0.0, &xmxtx.matrix); - gsl_matrix_free (xm); - if (rc == GSL_SUCCESS) - { - tmp = cache->mse / cache->n_obs; - for (i = 1; i < 1 + cache->n_indeps; i++) - { - tmp -= gsl_matrix_get (cache->cov, 0, i) - * pspp_linreg_get_indep_variable_mean (cache, design_matrix_col_to_var (dm, i - 1)); - } - gsl_matrix_set (cache->cov, 0, 0, tmp); - - cache->intercept = m; - } - else - { - fprintf (stderr, "%s:%d:gsl_blas_dsymm: %s\n", - __FILE__, __LINE__, gsl_strerror (rc)); - exit (rc); - } + post_sweep_computations (cache, dm, sw); gsl_matrix_free (sw); } else if (cache->method == PSPP_LINREG_CONDITIONAL_INVERSE) @@ -604,7 +641,7 @@ double pspp_linreg_get_indep_variable_mean (pspp_linreg_cache *c, const struct v coef = pspp_linreg_get_coeff (c, v, NULL); return pspp_coeff_get_mean (coef); } - return GSL_NAN; + return 0.0; } void pspp_linreg_set_indep_variable_mean (pspp_linreg_cache *c, const struct variable *v, @@ -617,3 +654,94 @@ void pspp_linreg_set_indep_variable_mean (pspp_linreg_cache *c, const struct var pspp_coeff_set_mean (coef, m); } } + +/* + Make sure the dependent variable is at the last column, and that + only variables in the model are in the covariance matrix. + */ +static struct design_matrix * +rearrange_covariance_matrix (const struct covariance_matrix *cm, pspp_linreg_cache *c) +{ + const struct variable **model_vars; + struct design_matrix *cov; + struct design_matrix *result; + size_t *permutation; + size_t i; + size_t j; + size_t k; + size_t n_coeffs = 0; + + assert (cm != NULL); + cov = covariance_to_design (cm); + assert (cov != NULL); + assert (c != NULL); + assert (cov->m->size1 > 0); + assert (cov->m->size2 == cov->m->size1); + model_vars = xnmalloc (1 + c->n_indeps, sizeof (*model_vars)); + + /* + Put the model variables in the right order in MODEL_VARS. + Count the number of coefficients. + */ + for (i = 0; i < c->n_indeps; i++) + { + model_vars[i] = c->indep_vars[i]; + } + model_vars[i] = c->depvar; + result = covariance_matrix_create (1 + c->n_indeps, model_vars); + permutation = xnmalloc (design_matrix_get_n_cols (result), sizeof (*permutation)); + + for (j = 0; j < cov->m->size2; j++) + { + k = 0; + while (k < result->m->size2) + { + if (design_matrix_col_to_var (cov, j) == design_matrix_col_to_var (result, k)) + { + permutation[k] = j; + } + k++; + } + } + for (i = 0; i < result->m->size1; i++) + for (j = 0; j < result->m->size2; j++) + { + gsl_matrix_set (result->m, i, j, gsl_matrix_get (cov->m, permutation[i], permutation[j])); + } + free (permutation); + free (model_vars); + return result; +} +/* + Estimate the model parameters from the covariance matrix only. This + method uses less memory than PSPP_LINREG, which requires the entire + data set to be stored in memory. + + The function assumes FULL_COV may contain columns corresponding to + variables that are not in the model. It fixes this in + REARRANG_COVARIANCE_MATRIX. This allows the caller to compute a + large covariance matrix once before, then pass it to this without + having to alter it. The problem is that this means the caller must + set CACHE->N_COEFFS. +*/ +void +pspp_linreg_with_cov (const struct covariance_matrix *full_cov, + pspp_linreg_cache * cache) +{ + struct design_matrix *cov; + + assert (full_cov != NULL); + assert (cache != NULL); + + cov = rearrange_covariance_matrix (full_cov, cache); + cache_init (cache); + reg_sweep (cov->m); + post_sweep_computations (cache, cov, cov->m); + design_matrix_destroy (cov); +} + +double pspp_linreg_mse (const pspp_linreg_cache *c) +{ + assert (c != NULL); + return (c->sse / c->dfe); +}