X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=src%2Fmath%2Flinreg%2Flinreg.c;h=659d6ca48306440a22e5e99e8a355241ab1fd319;hb=b5b474193e450bba97610065df0518c08074a7fb;hp=08d620b8c271d4a90290577dffe507365a3660a7;hpb=a19b858e0ac3c69e4a28c0ca6d8674427268a863;p=pspp-builds.git

diff --git a/src/math/linreg/linreg.c b/src/math/linreg/linreg.c
index 08d620b8..659d6ca4 100644
--- a/src/math/linreg/linreg.c
+++ b/src/math/linreg/linreg.c
@@ -1,24 +1,20 @@
-/* lib/linreg/linreg.c
+/* PSPP - a program for statistical analysis.
+   Copyright (C) 2005 Free Software Foundation, Inc. 
 
- 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 3 of the License, or
+   (at your option) any later version.
 
- 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.
 
- 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.
-*/
+   You should have received a copy of the GNU General Public License
+   along with this program.  If not, see <http://www.gnu.org/licenses/>. */
 
+#include <config.h>
 #include <gsl/gsl_fit.h>
 #include <gsl/gsl_multifit.h>
 
@@ -32,7 +28,7 @@
 
   Y = Xb + Z
 
-  where Y is an n-by-1 column vector, X is an n-by-p matrix of 
+  where Y is an n-by-1 column vector, X is an n-by-p matrix of
   independent variables, b is a p-by-1 vector of regression coefficients,
   and Z is an n-by-1 normally-distributed random vector with independent
   identically distributed components with mean 0.
@@ -54,7 +50,7 @@
 */
 
 #include <math/linreg/linreg.h>
-#include <math/linreg/coefficient.h>
+#include <math/coefficient.h>
 #include <gsl/gsl_errno.h>
 #include <linreg/sweep.h>
 /*
@@ -89,9 +85,51 @@ linreg_mean_std (gsl_vector_const_view v, double *mp, double *sp, double *ssp)
   return GSL_SUCCESS;
 }
 
+/*
+  Set V to contain an array of pointers to the variables
+  used in the model. V must be at least C->N_COEFFS in length.
+  The return value is the number of distinct variables found.
+ */
+int
+pspp_linreg_get_vars (const void *c_, const struct variable **v)
+{
+  const pspp_linreg_cache *c = c_;
+  const struct variable *tmp;
+  int i;
+  int j;
+  int result = 0;
+
+  /*
+     Make sure the caller doesn't try to sneak a variable
+     into V that is not in the model.
+   */
+  for (i = 0; i < c->n_coeffs; i++)
+    {
+      v[i] = NULL;
+    }
+  for (j = 0; j < c->n_coeffs; j++)
+    {
+      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] != tmp)
+	{
+	  i--;
+	}
+      if (i < 0 && result < c->n_coeffs)
+	{
+	  v[result] = tmp;
+	  result++;
+	}
+    }
+  return result;
+}
+
 /*
   Allocate a pspp_linreg_cache and return a pointer
-  to it. n is the number of cases, p is the number of 
+  to it. n is the number of cases, p is the number of
   independent variables.
  */
 pspp_linreg_cache *
@@ -100,13 +138,14 @@ pspp_linreg_cache_alloc (size_t n, size_t p)
   pspp_linreg_cache *c;
 
   c = (pspp_linreg_cache *) malloc (sizeof (pspp_linreg_cache));
+  c->depvar = NULL;
   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.
+  c->ssx = gsl_vector_alloc (p);	/* Sums of squares for the
+					   independent variables.
 					 */
-  c->ss_indeps = gsl_vector_alloc (p);	/* Sums of squares for the model 
-					   parameters. 
+  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;
@@ -115,32 +154,52 @@ pspp_linreg_cache_alloc (size_t n, size_t p)
      Default settings.
    */
   c->method = PSPP_LINREG_SWEEP;
+  c->predict = pspp_linreg_predict;
+  c->residual = pspp_linreg_residual;	/* The procedure to compute my
+					   residuals. */
+  c->get_vars = pspp_linreg_get_vars;	/* The procedure that returns
+					   pointers to model
+					   variables. */
+  c->resid = NULL;		/* The variable storing my residuals. */
+  c->pred = NULL;		/* The variable storing my predicted values. */
 
   return c;
 }
 
-void
-pspp_linreg_cache_free (pspp_linreg_cache * c)
+bool
+pspp_linreg_cache_free (void *m)
 {
-  gsl_vector_free (c->indep_means);
-  gsl_vector_free (c->indep_std);
-  gsl_vector_free (c->ss_indeps);
-  gsl_matrix_free (c->cov);
-  pspp_linreg_coeff_free (c->coeff);
-  free (c);
+  int i;
+
+  pspp_linreg_cache *c = m;
+  if (c != NULL)
+    {
+      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);
+    }
+  return true;
 }
 
 /*
   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
-  values as indicated by opts. 
+  values as indicated by opts.
  */
 int
 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;
@@ -181,9 +240,9 @@ 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.
-				  */
+  cache->n_coeffs = X->size2;
+  cache->intercept = 0.0;
+
   if (cache->method == PSPP_LINREG_SWEEP)
     {
       gsl_matrix *sw;
@@ -257,12 +316,12 @@ 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;
+	  cache->coeff[i]->estimate = tmp;
 	  m -= tmp * gsl_vector_get (cache->indep_means, i);
 	}
       /*
          Get the covariance matrix of the parameter estimates.
-         Only the upper triangle is necessary. 
+         Only the upper triangle is necessary.
        */
 
       /*
@@ -293,7 +352,7 @@ pspp_linreg (const gsl_vector * Y, const gsl_matrix * X,
 	    }
 	  gsl_matrix_set (cache->cov, 0, 0, tmp);
 
-	  cache->coeff[0].estimate = m;
+	  cache->intercept = m;
 	}
       else
 	{
@@ -303,8 +362,21 @@ 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;
       /*
          Use QR decomposition via GSL.
        */
@@ -321,14 +393,15 @@ pspp_linreg (const gsl_vector * Y, const gsl_matrix * X,
 	      gsl_matrix_set (design, j, i + 1, tmp);
 	    }
 	}
-      gsl_multifit_linear_workspace *wk =
-	gsl_multifit_linear_alloc (design->size1, design->size2);
+
+      wk = gsl_multifit_linear_alloc (design->size1, design->size2);
       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);
+	  cache->coeff[i]->estimate = gsl_vector_get (param_estimates, i + 1);
 	}
+      cache->intercept = gsl_vector_get (param_estimates, 0);
       if (rc == GSL_SUCCESS)
 	{
 	  gsl_multifit_linear_free (wk);