/* PSPP - a program for statistical analysis.
- Copyright (C) 2011 Free Software Foundation, Inc.
+ Copyright (C) 2011, 2012 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
#include <config.h>
-#include <math.h>
-
-#include <libpspp/misc.h>
-
-#include <libpspp/str.h>
-#include <libpspp/message.h>
-
-
-#include <data/dataset.h>
-#include <data/missing-values.h>
-#include <data/casereader.h>
-#include <data/casewriter.h>
-#include <data/casegrouper.h>
-#include <data/dictionary.h>
-#include <data/format.h>
-#include <data/case.h>
-
-#include <language/lexer/variable-parser.h>
-#include <language/command.h>
-#include <language/lexer/lexer.h>
-
-#include <output/tab.h>
-#include <output/text-item.h>
-
-#include <stdio.h>
-#include <stdlib.h>
-
#include <gsl/gsl_matrix.h>
-#include <gsl/gsl_statistics.h>
#include <gsl/gsl_permutation.h>
#include <gsl/gsl_sort_vector.h>
+#include <gsl/gsl_statistics.h>
+#include <math.h>
+#include <stdio.h>
+#include <stdlib.h>
-#include <math/random.h>
+#include "data/case.h"
+#include "data/casegrouper.h"
+#include "data/casereader.h"
+#include "data/casewriter.h"
+#include "data/dataset.h"
+#include "data/dictionary.h"
+#include "data/format.h"
+#include "data/missing-values.h"
+#include "language/command.h"
+#include "language/lexer/lexer.h"
+#include "language/lexer/variable-parser.h"
+#include "libpspp/message.h"
+#include "libpspp/misc.h"
+#include "libpspp/str.h"
+#include "math/random.h"
+#include "output/tab.h"
+#include "output/text-item.h"
#include "gettext.h"
#define _(msgid) gettext (msgid)
#define N_(msgid) msgid
-#include "quick-cluster.h"
+enum missing_type
+ {
+ MISS_LISTWISE,
+ MISS_PAIRWISE,
+ };
+
+
+struct qc
+{
+ const struct variable **vars;
+ size_t n_vars;
+
+ int ngroups; /* Number of group. (Given by the user) */
+ int maxiter; /* Maximum iterations (Given by the user) */
+
+ const struct variable *wv; /* Weighting variable. */
-/*
-Struct KMeans:
-Holds all of the information for the functions.
-int n, holds the number of observation and its default value is -1.
-We set it in kmeans_recalculate_centers in first invocation.
-*/
+ enum missing_type missing_type;
+ enum mv_class exclude;
+};
+
+/* Holds all of the information for the functions. int n, holds the number of
+ observation and its default value is -1. We set it in
+ kmeans_recalculate_centers in first invocation. */
struct Kmeans
{
- gsl_matrix *centers; //Centers for groups
+ gsl_matrix *centers; /* Centers for groups. */
gsl_vector_long *num_elements_groups;
- int ngroups; //Number of group. (Given by the user)
- casenumber n; //Number of observations. By default it is -1.
- int m; //Number of variables. (Given by the user)
- int maxiter; //Maximum number of iterations (Given by the user)
- int lastiter; //Show at which iteration it found the solution.
- int trials; //If not convergence, how many times has clustering done.
- gsl_matrix *initial_centers; //Initial random centers
- const struct variable **variables; //Variables
- gsl_permutation *group_order; //Handles group order for reporting
- struct casereader *original_casereader; //Casereader
+
+ casenumber n; /* Number of observations (default -1). */
+
+ int lastiter; /* Iteration where it found the solution. */
+ int trials; /* If not convergence, how many times has
+ clustering done. */
+ gsl_matrix *initial_centers; /* Initial random centers. */
+
+ gsl_permutation *group_order; /* Group order for reporting. */
struct caseproto *proto;
- struct casereader *index_rdr; //We hold the group id's for each case in this structure
- const struct variable *wv; //Weighting variable
+ struct casereader *index_rdr; /* Group ids for each case. */
};
+static struct Kmeans *kmeans_create (const struct qc *qc);
+
+static void kmeans_randomize_centers (struct Kmeans *kmeans, const struct qc *qc);
+
+static int kmeans_get_nearest_group (struct Kmeans *kmeans, struct ccase *c, const struct qc *);
+
+static void kmeans_recalculate_centers (struct Kmeans *kmeans, const struct casereader *reader, const struct qc *);
+
+static int
+kmeans_calculate_indexes_and_check_convergence (struct Kmeans *kmeans, const struct casereader *reader, const struct qc *);
+
+static void kmeans_order_groups (struct Kmeans *kmeans, const struct qc *);
+
+static void kmeans_cluster (struct Kmeans *kmeans, struct casereader *reader, const struct qc *);
+
+static void quick_cluster_show_centers (struct Kmeans *kmeans, bool initial, const struct qc *);
+
+static void quick_cluster_show_number_cases (struct Kmeans *kmeans, const struct qc *);
+
+static void quick_cluster_show_results (struct Kmeans *kmeans, const struct qc *);
-/*
-Creates and returns a struct of Kmeans with given casereader 'cs', parsed variables 'variables',
-number of cases 'n', number of variables 'm', number of clusters and amount of maximum iterations.
-*/
+int cmd_quick_cluster (struct lexer *lexer, struct dataset *ds);
+
+static void kmeans_destroy (struct Kmeans *kmeans);
+
+/* Creates and returns a struct of Kmeans with given casereader 'cs', parsed
+ variables 'variables', number of cases 'n', number of variables 'm', number
+ of clusters and amount of maximum iterations. */
static struct Kmeans *
-kmeans_create (struct casereader *cs, const struct variable **variables,
- int m, int ngroups, int maxiter)
+kmeans_create (const struct qc *qc)
{
struct Kmeans *kmeans = xmalloc (sizeof (struct Kmeans));
- kmeans->centers = gsl_matrix_alloc (ngroups, m);
- kmeans->num_elements_groups = gsl_vector_long_alloc (ngroups);
- kmeans->ngroups = ngroups;
+ kmeans->centers = gsl_matrix_alloc (qc->ngroups, qc->n_vars);
+ kmeans->num_elements_groups = gsl_vector_long_alloc (qc->ngroups);
kmeans->n = 0;
- kmeans->m = m;
- kmeans->maxiter = maxiter;
kmeans->lastiter = 0;
kmeans->trials = 0;
- kmeans->variables = variables;
kmeans->group_order = gsl_permutation_alloc (kmeans->centers->size1);
- kmeans->original_casereader = cs;
kmeans->initial_centers = NULL;
kmeans->proto = caseproto_create ();
return (kmeans);
}
-
static void
kmeans_destroy (struct Kmeans *kmeans)
{
caseproto_unref (kmeans->proto);
- /*
- These reader and writer were already destroyed.
- free (kmeans->original_casereader);
- free (kmeans->index_rdr);
- */
+ casereader_destroy (kmeans->index_rdr);
free (kmeans);
}
-
-
-/*
-Creates random centers using randomly selected cases from the data.
-*/
+/* Creates random centers using randomly selected cases from the data. */
static void
-kmeans_randomize_centers (struct Kmeans *kmeans)
+kmeans_randomize_centers (struct Kmeans *kmeans, const struct qc *qc)
{
int i, j;
- for (i = 0; i < kmeans->ngroups; i++)
+ for (i = 0; i < qc->ngroups; i++)
{
- for (j = 0; j < kmeans->m; j++)
+ for (j = 0; j < qc->n_vars; j++)
{
- //gsl_matrix_set(kmeans->centers,i,j, gsl_rng_uniform (kmeans->rng));
if (i == j)
{
gsl_matrix_set (kmeans->centers, i, j, 1);
}
}
}
-/*
-If it is the first iteration, the variable kmeans->initial_centers is NULL and
-it is created once for reporting issues. In SPSS, initial centers are shown in the reports
-but in PSPP it is not shown now. I am leaving it here.
-*/
+ /* If it is the first iteration, the variable kmeans->initial_centers is NULL
+ and it is created once for reporting issues. In SPSS, initial centers are
+ shown in the reports but in PSPP it is not shown now. I am leaving it
+ here. */
if (!kmeans->initial_centers)
{
- kmeans->initial_centers = gsl_matrix_alloc (kmeans->ngroups, kmeans->m);
+ kmeans->initial_centers = gsl_matrix_alloc (qc->ngroups, qc->n_vars);
gsl_matrix_memcpy (kmeans->initial_centers, kmeans->centers);
}
}
-
static int
-kmeans_get_nearest_group (struct Kmeans *kmeans, struct ccase *c)
+kmeans_get_nearest_group (struct Kmeans *kmeans, struct ccase *c, const struct qc *qc)
{
int result = -1;
- double x;
int i, j;
- double dist;
- double mindist;
- mindist = INFINITY;
- for (i = 0; i < kmeans->ngroups; i++)
+ double mindist = INFINITY;
+ for (i = 0; i < qc->ngroups; i++)
{
- dist = 0;
- for (j = 0; j < kmeans->m; j++)
+ double dist = 0;
+ for (j = 0; j < qc->n_vars; j++)
{
- x = case_data (c, kmeans->variables[j])->f;
- dist += pow2 (gsl_matrix_get (kmeans->centers, i, j) - x);
+ const union value *val = case_data (c, qc->vars[j]);
+ if ( var_is_value_missing (qc->vars[j], val, qc->exclude))
+ continue;
+
+ dist += pow2 (gsl_matrix_get (kmeans->centers, i, j) - val->f);
}
if (dist < mindist)
{
return (result);
}
-
-
-
-/*
-Re-calculates the cluster centers
-*/
+/* Re-calculate the cluster centers. */
static void
-kmeans_recalculate_centers (struct Kmeans *kmeans)
+kmeans_recalculate_centers (struct Kmeans *kmeans, const struct casereader *reader, const struct qc *qc)
{
- casenumber i;
+ casenumber i = 0;
int v, j;
- double x, curval;
struct ccase *c;
- struct ccase *c_index;
- struct casereader *cs;
- struct casereader *cs_index;
- int index;
- double weight;
- i = 0;
- cs = casereader_clone (kmeans->original_casereader);
- cs_index = casereader_clone (kmeans->index_rdr);
+ struct casereader *cs = casereader_clone (reader);
+ struct casereader *cs_index = casereader_clone (kmeans->index_rdr);
gsl_matrix_set_all (kmeans->centers, 0.0);
for (; (c = casereader_read (cs)) != NULL; case_unref (c))
{
- c_index = casereader_read (cs_index);
- index = case_data_idx (c_index, 0)->f;
- for (v = 0; v < kmeans->m; ++v)
+ double weight = qc->wv ? case_data (c, qc->wv)->f : 1.0;
+ struct ccase *c_index = casereader_read (cs_index);
+ int index = case_data_idx (c_index, 0)->f;
+ for (v = 0; v < qc->n_vars; ++v)
{
- if (kmeans->wv)
- {
- weight = case_data (c, kmeans->wv)->f;
- }
- else
- {
- weight = 1.0;
- }
- x = case_data (c, kmeans->variables[v])->f * weight;
+ const union value *val = case_data (c, qc->vars[v]);
+ double x = val->f * weight;
+ double curval;
+
+ if ( var_is_value_missing (qc->vars[v], val, qc->exclude))
+ continue;
+
curval = gsl_matrix_get (kmeans->centers, index, v);
gsl_matrix_set (kmeans->centers, index, v, curval + x);
}
if (kmeans->n == 0)
kmeans->n = i;
- //We got sum of each center but we need averages.
- //We are dividing centers to numobs. This may be inefficient and
- //we should check it again.
- for (i = 0; i < kmeans->ngroups; i++)
+ /* We got sum of each center but we need averages.
+ We are dividing centers to numobs. This may be inefficient and
+ we should check it again. */
+ for (i = 0; i < qc->ngroups; i++)
{
casenumber numobs = kmeans->num_elements_groups->data[i];
- for (j = 0; j < kmeans->m; j++)
+ for (j = 0; j < qc->n_vars; j++)
{
if (numobs > 0)
{
}
}
-
-/*
-The variable index in struct Kmeans holds integer values that represents the current groups of cases.
-index[n]=a shows the nth case is belong to ath cluster.
-This function calculates these indexes and returns the number of different cases of the new and old
-index variables. If last two index variables are equal, there is no any enhancement of clustering.
-*/
+/* The variable index in struct Kmeans holds integer values that represents the
+ current groups of cases. index[n]=a shows the nth case is belong to ath
+ cluster. This function calculates these indexes and returns the number of
+ different cases of the new and old index variables. If last two index
+ variables are equal, there is no any enhancement of clustering. */
static int
-kmeans_calculate_indexes_and_check_convergence (struct Kmeans *kmeans)
+kmeans_calculate_indexes_and_check_convergence (struct Kmeans *kmeans, const struct casereader *reader, const struct qc *qc)
{
int totaldiff = 0;
- double weight;
struct ccase *c;
- struct casereader *cs = casereader_clone (kmeans->original_casereader);
-
+ struct casereader *cs = casereader_clone (reader);
- /* A casewriter into which we will write the indexes */
+ /* A casewriter into which we will write the indexes. */
struct casewriter *index_wtr = autopaging_writer_create (kmeans->proto);
gsl_vector_long_set_all (kmeans->num_elements_groups, 0);
for (; (c = casereader_read (cs)) != NULL; case_unref (c))
{
- /* A case to hold the new index */
+ /* A case to hold the new index. */
struct ccase *index_case_new = case_create (kmeans->proto);
- int bestindex = kmeans_get_nearest_group (kmeans, c);
- if (kmeans->wv)
- {
- weight = (casenumber) case_data (c, kmeans->wv)->f;
- }
- else
- {
- weight = 1.0;
- }
+ int bestindex = kmeans_get_nearest_group (kmeans, c, qc);
+ double weight = qc->wv ? case_data (c, qc->wv)->f : 1.0;
+ assert (bestindex < kmeans->num_elements_groups->size);
kmeans->num_elements_groups->data[bestindex] += weight;
if (kmeans->index_rdr)
{
- /* A case from which the old index will be read */
+ /* A case from which the old index will be read. */
struct ccase *index_case_old = NULL;
- /* Read the case from the index casereader */
+ /* Read the case from the index casereader. */
index_case_old = casereader_read (kmeans->index_rdr);
- /* Set totaldiff, using the old_index */
+ /* Set totaldiff, using the old_index. */
totaldiff += abs (case_data_idx (index_case_old, 0)->f - bestindex);
- /* We have no use for the old case anymore, so unref it */
+ /* We have no use for the old case anymore, so unref it. */
case_unref (index_case_old);
}
else
{
- /* If this is the first run, then assume index is zero */
+ /* If this is the first run, then assume index is zero. */
totaldiff += bestindex;
}
- /* Set the value of the new index */
+ /* Set the value of the new inde.x */
case_data_rw_idx (index_case_new, 0)->f = bestindex;
/* and write the new index to the casewriter */
casewriter_write (index_wtr, index_case_new);
}
casereader_destroy (cs);
- /* We have now read through the entire index_rdr, so it's
- of no use anymore */
+ /* We have now read through the entire index_rdr, so it's of no use
+ anymore. */
casereader_destroy (kmeans->index_rdr);
/* Convert the writer into a reader, ready for the next iteration to read */
return (totaldiff);
}
-
static void
-kmeans_order_groups (struct Kmeans *kmeans)
+kmeans_order_groups (struct Kmeans *kmeans, const struct qc *qc)
{
- gsl_vector *v = gsl_vector_alloc (kmeans->ngroups);
+ gsl_vector *v = gsl_vector_alloc (qc->ngroups);
gsl_matrix_get_col (v, kmeans->centers, 0);
gsl_sort_vector_index (kmeans->group_order, v);
+ gsl_vector_free (v);
}
-/*
-Main algorithm.
-Does iterations, checks convergency
-*/
+/* Main algorithm.
+ Does iterations, checks convergency. */
static void
-kmeans_cluster (struct Kmeans *kmeans)
+kmeans_cluster (struct Kmeans *kmeans, struct casereader *reader, const struct qc *qc)
{
int i;
bool redo;
show_warning1 = true;
cluster:
redo = false;
- kmeans_randomize_centers (kmeans);
- for (kmeans->lastiter = 0; kmeans->lastiter < kmeans->maxiter;
+ kmeans_randomize_centers (kmeans, qc);
+ for (kmeans->lastiter = 0; kmeans->lastiter < qc->maxiter;
kmeans->lastiter++)
{
- diffs = kmeans_calculate_indexes_and_check_convergence (kmeans);
- kmeans_recalculate_centers (kmeans);
- if (show_warning1 && kmeans->ngroups > kmeans->n)
+ diffs = kmeans_calculate_indexes_and_check_convergence (kmeans, reader, qc);
+ kmeans_recalculate_centers (kmeans, reader, qc);
+ if (show_warning1 && qc->ngroups > kmeans->n)
{
- msg (MW,
- _
- ("Number of clusters may not be larger than the number of cases."));
+ msg (MW, _("Number of clusters may not be larger than the number "
+ "of cases."));
show_warning1 = false;
}
if (diffs == 0)
break;
}
- for (i = 0; i < kmeans->ngroups; i++)
+ for (i = 0; i < qc->ngroups; i++)
{
if (kmeans->num_elements_groups->data[i] == 0)
{
}
-
-/*
-Reports centers of clusters.
-initial parameter is optional for future use.
-if initial is true, initial cluster centers are reported. Otherwise, resulted centers are reported.
-*/
+/* Reports centers of clusters.
+ Initial parameter is optional for future use.
+ If initial is true, initial cluster centers are reported. Otherwise,
+ resulted centers are reported. */
static void
-quick_cluster_show_centers (struct Kmeans *kmeans, bool initial)
+quick_cluster_show_centers (struct Kmeans *kmeans, bool initial, const struct qc *qc)
{
struct tab_table *t;
- int nc, nr, heading_columns, currow;
+ int nc, nr, currow;
int i, j;
- nc = kmeans->ngroups + 1;
- nr = kmeans->m + 4;
- heading_columns = 1;
+ nc = qc->ngroups + 1;
+ nr = qc->n_vars + 4;
t = tab_create (nc, nr);
tab_headers (t, 0, nc - 1, 0, 1);
currow = 0;
tab_hline (t, TAL_1, 1, nc - 1, 2);
currow += 2;
- for (i = 0; i < kmeans->ngroups; i++)
+ for (i = 0; i < qc->ngroups; i++)
{
tab_text_format (t, (i + 1), currow, TAB_CENTER, "%d", (i + 1));
}
currow++;
tab_hline (t, TAL_1, 1, nc - 1, currow);
currow++;
- for (i = 0; i < kmeans->m; i++)
+ for (i = 0; i < qc->n_vars; i++)
{
tab_text (t, 0, currow + i, TAB_LEFT,
- var_to_string (kmeans->variables[i]));
+ var_to_string (qc->vars[i]));
}
- for (i = 0; i < kmeans->ngroups; i++)
+ for (i = 0; i < qc->ngroups; i++)
{
- for (j = 0; j < kmeans->m; j++)
+ for (j = 0; j < qc->n_vars; j++)
{
if (!initial)
{
tab_double (t, i + 1, j + 4, TAB_CENTER,
gsl_matrix_get (kmeans->centers,
kmeans->group_order->data[i], j),
- var_get_print_format (kmeans->variables[j]));
+ var_get_print_format (qc->vars[j]), RC_OTHER);
}
else
{
tab_double (t, i + 1, j + 4, TAB_CENTER,
gsl_matrix_get (kmeans->initial_centers,
kmeans->group_order->data[i], j),
- var_get_print_format (kmeans->variables[j]));
+ var_get_print_format (qc->vars[j]), RC_OTHER);
}
}
}
tab_submit (t);
}
-
-/*
-Reports number of cases of each single cluster.
-*/
+/* Reports number of cases of each single cluster. */
static void
-quick_cluster_show_number_cases (struct Kmeans *kmeans)
+quick_cluster_show_number_cases (struct Kmeans *kmeans, const struct qc *qc)
{
struct tab_table *t;
int nc, nr;
int i, numelem;
long int total;
nc = 3;
- nr = kmeans->ngroups + 1;
+ nr = qc->ngroups + 1;
t = tab_create (nc, nr);
tab_headers (t, 0, nc - 1, 0, 0);
tab_title (t, _("Number of Cases in each Cluster"));
tab_text (t, 0, 0, TAB_LEFT, _("Cluster"));
total = 0;
- for (i = 0; i < kmeans->ngroups; i++)
+ for (i = 0; i < qc->ngroups; i++)
{
tab_text_format (t, 1, i, TAB_CENTER, "%d", (i + 1));
numelem =
total += numelem;
}
- tab_text (t, 0, kmeans->ngroups, TAB_LEFT, _("Valid"));
- tab_text_format (t, 2, kmeans->ngroups, TAB_LEFT, "%ld", total);
+ tab_text (t, 0, qc->ngroups, TAB_LEFT, _("Valid"));
+ tab_text_format (t, 2, qc->ngroups, TAB_LEFT, "%ld", total);
tab_submit (t);
}
-/*
-Reports
-*/
+/* Reports. */
static void
-quick_cluster_show_results (struct Kmeans *kmeans)
+quick_cluster_show_results (struct Kmeans *kmeans, const struct qc *qc)
{
- kmeans_order_groups (kmeans);
- //uncomment the line above for reporting initial centers
- //quick_cluster_show_centers (kmeans, true);
- quick_cluster_show_centers (kmeans, false);
- quick_cluster_show_number_cases (kmeans);
+ kmeans_order_groups (kmeans, qc);
+ /* Uncomment the line below for reporting initial centers. */
+ /* quick_cluster_show_centers (kmeans, true); */
+ quick_cluster_show_centers (kmeans, false, qc);
+ quick_cluster_show_number_cases (kmeans, qc);
}
-
int
cmd_quick_cluster (struct lexer *lexer, struct dataset *ds)
{
+ struct qc qc;
struct Kmeans *kmeans;
bool ok;
const struct dictionary *dict = dataset_dict (ds);
- const struct variable **variables;
- struct casereader *cs;
- int groups = 2;
- int maxiter = 2;
- size_t p;
+ qc.ngroups = 2;
+ qc.maxiter = 2;
+ qc.missing_type = MISS_LISTWISE;
+ qc.exclude = MV_ANY;
-
-
- if (!parse_variables_const (lexer, dict, &variables, &p,
+ if (!parse_variables_const (lexer, dict, &qc.vars, &qc.n_vars,
PV_NO_DUPLICATE | PV_NUMERIC))
{
- msg (ME, _("Variables cannot be parsed"));
return (CMD_FAILURE);
}
-
-
- if (lex_match (lexer, T_SLASH))
+ while (lex_token (lexer) != T_ENDCMD)
{
- if (lex_match_id (lexer, "CRITERIA"))
+ lex_match (lexer, T_SLASH);
+
+ if (lex_match_id (lexer, "MISSING"))
+ {
+ lex_match (lexer, T_EQUALS);
+ while (lex_token (lexer) != T_ENDCMD
+ && lex_token (lexer) != T_SLASH)
+ {
+ if (lex_match_id (lexer, "LISTWISE") || lex_match_id (lexer, "DEFAULT"))
+ {
+ qc.missing_type = MISS_LISTWISE;
+ }
+ else if (lex_match_id (lexer, "PAIRWISE"))
+ {
+ qc.missing_type = MISS_PAIRWISE;
+ }
+ else if (lex_match_id (lexer, "INCLUDE"))
+ {
+ qc.exclude = MV_SYSTEM;
+ }
+ else if (lex_match_id (lexer, "EXCLUDE"))
+ {
+ qc.exclude = MV_ANY;
+ }
+ else
+ goto error;
+ }
+ }
+ else if (lex_match_id (lexer, "CRITERIA"))
{
lex_match (lexer, T_EQUALS);
while (lex_token (lexer) != T_ENDCMD
if (lex_force_match (lexer, T_LPAREN))
{
lex_force_int (lexer);
- groups = lex_integer (lexer);
+ qc.ngroups = lex_integer (lexer);
+ if (qc.ngroups <= 0)
+ {
+ lex_error (lexer, _("The number of clusters must be positive"));
+ goto error;
+ }
lex_get (lexer);
lex_force_match (lexer, T_RPAREN);
}
if (lex_force_match (lexer, T_LPAREN))
{
lex_force_int (lexer);
- maxiter = lex_integer (lexer);
+ qc.maxiter = lex_integer (lexer);
+ if (qc.maxiter <= 0)
+ {
+ lex_error (lexer, _("The number of iterations must be positive"));
+ goto error;
+ }
lex_get (lexer);
lex_force_match (lexer, T_RPAREN);
}
}
else
- {
- //further command set
- return (CMD_FAILURE);
- }
+ goto error;
}
}
}
-
- cs = proc_open (ds);
-
-
- kmeans = kmeans_create (cs, variables, p, groups, maxiter);
-
- kmeans->wv = dict_get_weight (dict);
- kmeans_cluster (kmeans);
- quick_cluster_show_results (kmeans);
- ok = proc_commit (ds);
-
- kmeans_destroy (kmeans);
+ qc.wv = dict_get_weight (dict);
+
+ {
+ struct casereader *group;
+ struct casegrouper *grouper = casegrouper_create_splits (proc_open (ds), dict);
+
+ while (casegrouper_get_next_group (grouper, &group))
+ {
+ if ( qc.missing_type == MISS_LISTWISE )
+ {
+ group = casereader_create_filter_missing (group, qc.vars, qc.n_vars,
+ qc.exclude,
+ NULL, NULL);
+ }
+
+ kmeans = kmeans_create (&qc);
+ kmeans_cluster (kmeans, group, &qc);
+ quick_cluster_show_results (kmeans, &qc);
+ kmeans_destroy (kmeans);
+ casereader_destroy (group);
+ }
+ ok = casegrouper_destroy (grouper);
+ }
+ ok = proc_commit (ds) && ok;
+
+ free (qc.vars);
return (ok);
+
+ error:
+ free (qc.vars);
+ return CMD_FAILURE;
}
projects / pspp / blobdiff