X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=lib%2Flinreg%2Flinreg.c;h=dc7f1d0172933a581b017a830a489890120ec5cf;hb=ebfa4a33ff1d00563689d49d4f548af37b544a1c;hp=f8f023a3a3fc7dab8bc67a7512d92e8df4e970c2;hpb=be82eb03f4f7ca027eed579edbc2d39a5286d4f5;p=pspp

diff --git a/lib/linreg/linreg.c b/lib/linreg/linreg.c
index f8f023a3a3..dc7f1d0172 100644
--- a/lib/linreg/linreg.c
+++ b/lib/linreg/linreg.c
@@ -87,38 +87,39 @@ linreg_mean_std (gsl_vector_const_view v, double *mp, double *sp, double *ssp)
 pspp_linreg_cache *
 pspp_linreg_cache_alloc (size_t n, size_t p)
 {
-  pspp_linreg_cache *cache;
+  pspp_linreg_cache *c;
 
-  cache = (pspp_linreg_cache *) malloc (sizeof (pspp_linreg_cache));
-  cache->param_estimates = gsl_vector_alloc (p + 1);
-  cache->indep_means = gsl_vector_alloc (p);
-  cache->indep_std = gsl_vector_alloc (p);
-  cache->ssx = gsl_vector_alloc (p);	/* Sums of squares for the independent
+  c = (pspp_linreg_cache *) malloc (sizeof (pspp_linreg_cache));
+  c->param_estimates = gsl_vector_alloc (p + 1);
+  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.
 					 */
-  cache->ss_indeps = gsl_vector_alloc (p);	/* Sums of squares for the model 
-						   parameters. 
-						 */
-  cache->cov = gsl_matrix_alloc (p + 1, p + 1);	/* Covariance matrix. */
-  cache->n_obs = n;
-  cache->n_indeps = 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;
   /*
      Default settings.
    */
-  cache->method = PSPP_LINREG_SWEEP;
+  c->method = PSPP_LINREG_SWEEP;
 
-  return cache;
+  return c;
 }
 
 void
-pspp_linreg_cache_free (pspp_linreg_cache * cache)
+pspp_linreg_cache_free (pspp_linreg_cache * c)
 {
-  gsl_vector_free (cache->param_estimates);
-  gsl_vector_free (cache->indep_means);
-  gsl_vector_free (cache->indep_std);
-  gsl_vector_free (cache->ss_indeps);
-  gsl_matrix_free (cache->cov);
-  free (cache);
+  gsl_vector_free (c->param_estimates);
+  gsl_vector_free (c->indep_means);
+  gsl_vector_free (c->indep_std);
+  gsl_vector_free (c->ss_indeps);
+  gsl_matrix_free (c->cov);
+  free (c->coeff);
+  free (c);
 }
 
 /*
@@ -138,6 +139,7 @@ pspp_linreg (const gsl_vector * Y, const gsl_matrix * X,
   gsl_vector_view xty;
   gsl_vector_view xi;
   gsl_vector_view xj;
+  gsl_vector *param_estimates;
 
   size_t i;
   size_t j;
@@ -145,7 +147,6 @@ pspp_linreg (const gsl_vector * Y, const gsl_matrix * X,
   double m;
   double s;
   double ss;
-  double mse;
 
   if (cache == NULL)
     {
@@ -172,6 +173,9 @@ pspp_linreg (const gsl_vector * Y, const gsl_matrix * X,
   cache->dft = cache->n_obs - 1;
   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.
+				  */
   if (cache->method == PSPP_LINREG_SWEEP)
     {
       gsl_matrix *sw;
@@ -181,9 +185,9 @@ pspp_linreg (const gsl_vector * Y, const gsl_matrix * X,
          standard deviations of the independent variables here since doing
          so would cause a miscalculation of the residual sums of
          squares. Dividing by the standard deviation is done GSL's linear
-         regression functions, so if the design matrix has a very poor
+         regression functions, so if the design matrix has a poor
          condition, use QR decomposition.
-         *
+
          The design matrix here does not include a column for the intercept
          (i.e., a column of 1's). If using PSPP_LINREG_QR, we need that column,
          so design is allocated here when sweeping, or below if using QR.
@@ -245,6 +249,7 @@ pspp_linreg (const gsl_vector * Y, const gsl_matrix * X,
       for (i = 0; i < cache->n_indeps; i++)
 	{
 	  tmp = gsl_matrix_get (sw, i, cache->n_indeps);
+	  cache->coeff[i + 1].estimate = tmp;
 	  gsl_vector_set (cache->param_estimates, i + 1, tmp);
 	  m -= tmp * gsl_vector_get (cache->indep_means, i);
 	}
@@ -282,6 +287,7 @@ pspp_linreg (const gsl_vector * Y, const gsl_matrix * X,
 	  gsl_matrix_set (cache->cov, 0, 0, tmp);
 
 	  gsl_vector_set (cache->param_estimates, 0, m);
+	  cache->coeff[0].estimate = m;
 	}
       else
 	{
@@ -296,6 +302,8 @@ pspp_linreg (const gsl_vector * Y, const gsl_matrix * X,
       /*
          Use QR decomposition via GSL.
        */
+
+      param_estimates = gsl_vector_alloc (1 + X->size2);
       design = gsl_matrix_alloc (X->size1, 1 + X->size2);
 
       for (j = 0; j < X->size1; j++)
@@ -309,11 +317,16 @@ pspp_linreg (const gsl_vector * Y, const gsl_matrix * X,
 	}
       gsl_multifit_linear_workspace *wk =
 	gsl_multifit_linear_alloc (design->size1, design->size2);
-      rc = gsl_multifit_linear (design, Y, cache->param_estimates,
+      rc = gsl_multifit_linear (design, Y, param_estimates,
 				cache->cov, &(cache->sse), wk);
+      for (i = 0; i < cache->n_coeffs; i++)
+	{
+	  cache->coeff[i].estimate = gsl_vector_get (param_estimates, i);
+	}
       if (rc == GSL_SUCCESS)
 	{
 	  gsl_multifit_linear_free (wk);
+	  gsl_vector_free (param_estimates);
 	}
       else
 	{