Renamed time-series directory ts

diff --git a/src/math/time-series/innovations.c b/src/math/time-series/innovations.c
deleted file mode 100644 (file)
index b9a9dde..0000000
--- a/src/math/time-series/innovations.c
+++ /dev/null
@@ -1,214 +0,0 @@
-/*
-  src/math/time-series/arma/innovations.c
-  
-  Copyright (C) 2006 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.
- */
-/*
-  Find preliminary ARMA coefficients via the innovations algorithm.
-  Also compute the sample mean and covariance matrix for each series.
-
-  Reference:
-
-  P. J. Brockwell and R. A. Davis. Time Series: Theory and
-  Methods. Second edition. Springer. New York. 1991. ISBN
-  0-387-97429-6. Sections 5.2, 8.3 and 8.4.
- */
-
-#include <gsl/gsl_matrix.h>
-#include <gsl/gsl_vector.h>
-#include <math.h>
-#include <stdlib.h>
-#include <data/case.h>
-#include <data/casefile.h>
-#include <libpspp/alloc.h>
-#include <libpspp/compiler.h>
-#include <libpspp/message.h>
-#include <math/coefficient.h>
-#include <gettext.h>
-#define _(msgid) gettext (msgid)
-
-static void
-get_mean_variance (size_t n_vars, const struct casefile *cf,
-                  struct innovations_estimate **est)
-                  
-{
-  struct casereader *r;
-  struct ccase *c;
-  struct ccase *c2;
-  size_t n;
-  double *x;
-  double d;
-  double tmp;
-  double variance;
-
-  for (n = 0; n < n_vars; n++)
-    {
-      est[n]->n_obs = 2.0;
-      est[n]->mean = 0.0;
-      est[n]->variance = 0.0;
-    }
-  for (r = casefile_get_reader (cf); casereader_read (r, &c);
-       case_destroy (&c))
-    {
-      for (n = 0; n < n_vars; n++)
-       {
-         if (!mv_is_value_missing (&v->miss, val))
-           {
-             tmp = case_data (&c, est[n]->variable->fv);
-             d = (tmp - est[n]->mean) / est[n]->n_obs;
-             est[n]->mean += d;
-             est[n]->variance += est[n]->n_obs * est[n]->n_obs * d * d;
-             est[n]->n_obs += 1.0;
-           }
-       }
-    }
-  for (n = 0; n < n_vars; n++)
-    {
-      /* Maximum likelihood estimate of the variance. */
-      est[n]->variance /= est[n]->n_obs;
-    }
-}
-
-/*
-  Read the first MAX_LAG cases.
- */
-static bool
-innovations_init_cases (struct casereader *r, struct ccase **c, size_t max_lag)
-{
-  bool value = true;
-  size_t lag = 0;
-
-  while (value)
-    {
-      lag++;
-      value = casereader_read (r, c + lag);
-    }
-  return value;
-}
-
-/*
-  Read one case and update C, which contains the last MAX_LAG cases.
- */
-static bool
-innovations_update_cases (struct casereader *r, struct ccase **c, size_t max_lag)
-{
-  size_t lag;
-  bool value = false;
-  
-  for (lag = 0; lag < max_lag - 1; lag++)
-    {
-      c[lag] = c[lag+1];
-    }
-  value = casereader_read (r, c + lag);
-  return value;
-}
-static void
-get_covariance (size_t n_vars, const struct casefile *cf, 
-               struct innovations **est, size_t max_lag)
-{
-  struct casereader *r;
-  struct ccase **c;
-  struct ccase *cur_case;
-  size_t lag;
-  size_t n_vars;
-  bool read_case = false;
-  double d;
-  double tmp;
-
-  c = xnmalloc (max_lag, sizeof (*c));
-  
-  for (lag = 0; lag < max_lag; lag++)
-    {
-      c[lag] = xmalloc (sizeof *c[i]);
-    }
-
-  r = casefile_get_reader (cf);
-  read_case = innovations_init_cases (r, c, max_lag);
-
-  while (read_case)
-    {
-      for (n = 0; n < n_vars; n++)
-       {
-         cur_case = case_data (c[0], est[n]->variable->fv);
-         if (!mv_is_value_missing (&est[n]->variable->miss, cur_case))
-           {
-             cur_case -= est[n]->mean;
-             for (lag = 1; lag <= max_lag; lag++)
-               {
-                 tmp = case_data (c[lag], est[n]->variable->fv);
-                 if (!mv_is_value_missing (&est[n]->variable->miss, tmp))
-                   {
-                     d = (tmp - est[n]->mean);
-                     *(est[n]->cov + lag) += d * cur_case;
-                   }
-               }
-           }
-       }
-      read_case = innovations_update_cases (r, c, max_lag);
-    }
-  for (lag = 0; lag <= max_lag; lag++)
-    {
-      for (n = 0; n < n_vars; n++)
-       {
-         *(est[n]->cov + lag) /= (est[n]->n_obs - lag);
-       }
-    }
-  for (lag = 0; lag < max_lag; lag++)
-    {
-      free (c[lag]);
-    }
-  free (c);
-}
-
-struct innovations_estimate ** pspp_innovations (const struct variable **vars, size_t *n_vars,
-                                                size_t lag, const struct casefile *cf)
-{
-  struct innovations_estimate **est;
-  struct casereader *r;
-  struct ccase *c;
-  size_t i;
-
-  est = xnmalloc (*n_vars, sizeof *est);
-  for (i = 0; i < *n_vars; i++)
-    {
-      if (vars[i]->type == NUMERIC)
-       {
-         est[i] = xmalloc (sizeof **est);
-         est[i]->variable = vars[i];
-         est[i]->mean = 0.0;
-         est[i]->variance = 0.0;
-         est[i]->cov = gsl_matrix_calloc (lag, lag);
-         est[i]->coeff = xnmalloc (lag, sizeof (*est[i]->coeff));
-         for (j = 0; j < lag; j++)
-           {
-             est[i]->coeff + j = xmalloc (sizeof (*(est[i]->coeff + j)));
-           }
-       }
-      else
-       {
-         *n_vars--;
-         msg (MW, _("Cannot compute autocovariance for a non-numeric variable %s"),
-                    var_to_string (vars[i]));
-       }
-    }
-
-  /*
-    First data pass to get the mean and variance.
-   */
-  get_mean_variance (*n_vars, cf, est);
-  get_covariance (*n_vars, cf, est, lag);
-}

diff --git a/src/math/ts/innovations.c b/src/math/ts/innovations.c
new file mode 100644 (file)
index 0000000..dba4484
--- /dev/null
+++ b/src/math/ts/innovations.c
@@ -0,0 +1,215 @@
+/*
+  src/math/time-series/arma/innovations.c
+  
+  Copyright (C) 2006 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.
+ */
+/*
+  Find preliminary ARMA coefficients via the innovations algorithm.
+  Also compute the sample mean and covariance matrix for each series.
+
+  Reference:
+
+  P. J. Brockwell and R. A. Davis. Time Series: Theory and
+  Methods. Second edition. Springer. New York. 1991. ISBN
+  0-387-97429-6. Sections 5.2, 8.3 and 8.4.
+ */
+
+#include <gsl/gsl_matrix.h>
+#include <gsl/gsl_vector.h>
+#include <math.h>
+#include <stdlib.h>
+#include <data/case.h>
+#include <data/casefile.h>
+#include <libpspp/alloc.h>
+#include <libpspp/compiler.h>
+#include <libpspp/message.h>
+#include <math/coefficient.h>
+#include <math/innovations.h>
+#include <gettext.h>
+#define _(msgid) gettext (msgid)
+
+static void
+get_mean_variance (size_t n_vars, const struct casefile *cf,
+                  struct innovations_estimate **est)
+                  
+{
+  struct casereader *r;
+  struct ccase *c;
+  struct ccase *c2;
+  size_t n;
+  double *x;
+  double d;
+  double tmp;
+  double variance;
+
+  for (n = 0; n < n_vars; n++)
+    {
+      est[n]->n_obs = 2.0;
+      est[n]->mean = 0.0;
+      est[n]->variance = 0.0;
+    }
+  for (r = casefile_get_reader (cf); casereader_read (r, &c);
+       case_destroy (&c))
+    {
+      for (n = 0; n < n_vars; n++)
+       {
+         if (!mv_is_value_missing (&v->miss, val))
+           {
+             tmp = case_data (&c, est[n]->variable->fv);
+             d = (tmp - est[n]->mean) / est[n]->n_obs;
+             est[n]->mean += d;
+             est[n]->variance += est[n]->n_obs * est[n]->n_obs * d * d;
+             est[n]->n_obs += 1.0;
+           }
+       }
+    }
+  for (n = 0; n < n_vars; n++)
+    {
+      /* Maximum likelihood estimate of the variance. */
+      est[n]->variance /= est[n]->n_obs;
+    }
+}
+
+/*
+  Read the first MAX_LAG cases.
+ */
+static bool
+innovations_init_cases (struct casereader *r, struct ccase **c, size_t max_lag)
+{
+  bool value = true;
+  size_t lag = 0;
+
+  while (value)
+    {
+      lag++;
+      value = casereader_read (r, c + lag);
+    }
+  return value;
+}
+
+/*
+  Read one case and update C, which contains the last MAX_LAG cases.
+ */
+static bool
+innovations_update_cases (struct casereader *r, struct ccase **c, size_t max_lag)
+{
+  size_t lag;
+  bool value = false;
+  
+  for (lag = 0; lag < max_lag - 1; lag++)
+    {
+      c[lag] = c[lag+1];
+    }
+  value = casereader_read (r, c + lag);
+  return value;
+}
+static void
+get_covariance (size_t n_vars, const struct casefile *cf, 
+               struct innovations **est, size_t max_lag)
+{
+  struct casereader *r;
+  struct ccase **c;
+  struct ccase *cur_case;
+  size_t lag;
+  size_t n_vars;
+  bool read_case = false;
+  double d;
+  double tmp;
+
+  c = xnmalloc (max_lag, sizeof (*c));
+  
+  for (lag = 0; lag < max_lag; lag++)
+    {
+      c[lag] = xmalloc (sizeof *c[i]);
+    }
+
+  r = casefile_get_reader (cf);
+  read_case = innovations_init_cases (r, c, max_lag);
+
+  while (read_case)
+    {
+      for (n = 0; n < n_vars; n++)
+       {
+         cur_case = case_data (c[0], est[n]->variable->fv);
+         if (!mv_is_value_missing (&est[n]->variable->miss, cur_case))
+           {
+             cur_case -= est[n]->mean;
+             for (lag = 1; lag <= max_lag; lag++)
+               {
+                 tmp = case_data (c[lag], est[n]->variable->fv);
+                 if (!mv_is_value_missing (&est[n]->variable->miss, tmp))
+                   {
+                     d = (tmp - est[n]->mean);
+                     *(est[n]->cov + lag) += d * cur_case;
+                   }
+               }
+           }
+       }
+      read_case = innovations_update_cases (r, c, max_lag);
+    }
+  for (lag = 0; lag <= max_lag; lag++)
+    {
+      for (n = 0; n < n_vars; n++)
+       {
+         *(est[n]->cov + lag) /= (est[n]->n_obs - lag);
+       }
+    }
+  for (lag = 0; lag < max_lag; lag++)
+    {
+      free (c[lag]);
+    }
+  free (c);
+}
+
+struct innovations_estimate ** pspp_innovations (const struct variable **vars, size_t *n_vars,
+                                                size_t lag, const struct casefile *cf)
+{
+  struct innovations_estimate **est;
+  struct casereader *r;
+  struct ccase *c;
+  size_t i;
+
+  est = xnmalloc (*n_vars, sizeof *est);
+  for (i = 0; i < *n_vars; i++)
+    {
+      if (vars[i]->type == NUMERIC)
+       {
+         est[i] = xmalloc (sizeof **est);
+         est[i]->variable = vars[i];
+         est[i]->mean = 0.0;
+         est[i]->variance = 0.0;
+         est[i]->cov = xnmalloc (lag, sizeof (est[i]->cov));
+         est[i]->coeff = xnmalloc (lag, sizeof (*est[i]->coeff));
+         for (j = 0; j < lag; j++)
+           {
+             est[i]->coeff + j = xmalloc (sizeof (*(est[i]->coeff + j)));
+           }
+       }
+      else
+       {
+         *n_vars--;
+         msg (MW, _("Cannot compute autocovariance for a non-numeric variable %s"),
+                    var_to_string (vars[i]));
+       }
+    }
+
+  /*
+    First data pass to get the mean and variance.
+   */
+  get_mean_variance (*n_vars, cf, est);
+  get_covariance (*n_vars, cf, est, lag);
+}