/* PSPP - a program for statistical analysis.
- Copyright (C) 2011 Free Software Foundation, Inc.
+ Copyright (C) 2011, 2012, 2015 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 <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 "language/lexer/variable-parser.h"
#include "libpspp/message.h"
#include "libpspp/misc.h"
+#include "libpspp/assertion.h"
#include "libpspp/str.h"
#include "math/random.h"
#include "output/tab.h"
#define _(msgid) gettext (msgid)
#define N_(msgid) msgid
+enum missing_type
+ {
+ MISS_LISTWISE,
+ MISS_PAIRWISE,
+ };
+
+
struct qc
{
const struct variable **vars;
size_t n_vars;
+ double epsilon; /* The convergence criterium */
+
int ngroups; /* Number of group. (Given by the user) */
int maxiter; /* Maximum iterations (Given by the user) */
+ bool print_cluster_membership; /* true => print membership */
+ bool print_initial_clusters; /* true => print initial cluster */
+ bool no_initial; /* true => simplified initial cluster selection */
+ bool no_update; /* true => do not iterate */
const struct variable *wv; /* Weighting variable. */
+
+ enum missing_type missing_type;
+ enum mv_class exclude;
};
/* Holds all of the information for the functions. int n, holds the number of
struct Kmeans
{
gsl_matrix *centers; /* Centers for groups. */
- gsl_vector_long *num_elements_groups;
+ gsl_matrix *updated_centers;
+ casenumber n;
- casenumber n; /* Number of observations (default -1). */
+ gsl_vector_long *num_elements_groups;
- 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. */
-
+ double convergence_criteria;
gsl_permutation *group_order; /* Group order for reporting. */
- struct caseproto *proto;
- 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_get_nearest_group (const struct Kmeans *kmeans, struct ccase *c, const struct qc *, int *, double *, int *, double *);
static void kmeans_order_groups (struct Kmeans *kmeans, const struct qc *);
static void quick_cluster_show_centers (struct Kmeans *kmeans, bool initial, const struct qc *);
+static void quick_cluster_show_membership (struct Kmeans *kmeans, const struct casereader *reader, 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 *);
+static void quick_cluster_show_results (struct Kmeans *kmeans, const struct casereader *reader, const struct qc *);
int cmd_quick_cluster (struct lexer *lexer, struct dataset *ds);
{
struct Kmeans *kmeans = xmalloc (sizeof (struct Kmeans));
kmeans->centers = gsl_matrix_alloc (qc->ngroups, qc->n_vars);
+ kmeans->updated_centers = gsl_matrix_alloc (qc->ngroups, qc->n_vars);
kmeans->num_elements_groups = gsl_vector_long_alloc (qc->ngroups);
- kmeans->n = 0;
- kmeans->lastiter = 0;
- kmeans->trials = 0;
kmeans->group_order = gsl_permutation_alloc (kmeans->centers->size1);
kmeans->initial_centers = NULL;
- kmeans->proto = caseproto_create ();
- kmeans->proto = caseproto_add_width (kmeans->proto, 0);
- kmeans->index_rdr = NULL;
return (kmeans);
}
kmeans_destroy (struct Kmeans *kmeans)
{
gsl_matrix_free (kmeans->centers);
+ gsl_matrix_free (kmeans->updated_centers);
gsl_matrix_free (kmeans->initial_centers);
gsl_vector_long_free (kmeans->num_elements_groups);
gsl_permutation_free (kmeans->group_order);
- caseproto_unref (kmeans->proto);
+ free (kmeans);
+}
- casereader_destroy (kmeans->index_rdr);
+static double
+diff_matrix (const gsl_matrix *m1, const gsl_matrix *m2)
+{
+ int i,j;
+ double max_diff = -INFINITY;
+ for (i = 0; i < m1->size1; ++i)
+ {
+ double diff = 0;
+ for (j = 0; j < m1->size2; ++j)
+ {
+ diff += pow2 (gsl_matrix_get (m1,i,j) - gsl_matrix_get (m2,i,j) );
+ }
+ if (diff > max_diff)
+ max_diff = diff;
+ }
- free (kmeans);
+ return max_diff;
}
-/* Creates random centers using randomly selected cases from the data. */
-static void
-kmeans_randomize_centers (struct Kmeans *kmeans, const struct qc *qc)
+
+
+static double
+matrix_mindist (const gsl_matrix *m, int *mn, int *mm)
{
int i, j;
- for (i = 0; i < qc->ngroups; i++)
+ double mindist = INFINITY;
+ for (i = 0; i < m->size1 - 1; ++i)
{
- for (j = 0; j < qc->n_vars; j++)
+ for (j = i + 1; j < m->size1; ++j)
{
- if (i == j)
+ int k;
+ double diff_sq = 0;
+ for (k = 0; k < m->size2; ++k)
{
- gsl_matrix_set (kmeans->centers, i, j, 1);
+ diff_sq += pow2 (gsl_matrix_get (m, j, k) - gsl_matrix_get (m, i, k));
}
- else
+ if (diff_sq < mindist)
{
- gsl_matrix_set (kmeans->centers, i, j, 0);
+ mindist = diff_sq;
+ if (mn)
+ *mn = i;
+ if (mm)
+ *mm = j;
}
}
}
- /* 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)
+
+ return mindist;
+}
+
+
+/* Return the distance of C from the group whose index is WHICH */
+static double
+dist_from_case (const struct Kmeans *kmeans, const struct ccase *c, const struct qc *qc, int which)
+{
+ int j;
+ double dist = 0;
+ for (j = 0; j < qc->n_vars; j++)
{
- kmeans->initial_centers = gsl_matrix_alloc (qc->ngroups, qc->n_vars);
- gsl_matrix_memcpy (kmeans->initial_centers, kmeans->centers);
+ const union value *val = case_data (c, qc->vars[j]);
+ if ( var_is_value_missing (qc->vars[j], val, qc->exclude))
+ NOT_REACHED ();
+
+ dist += pow2 (gsl_matrix_get (kmeans->centers, which, j) - val->f);
}
+
+ return dist;
}
-static int
-kmeans_get_nearest_group (struct Kmeans *kmeans, struct ccase *c, const struct qc *qc)
+/* Return the minimum distance of the group WHICH and all other groups */
+static double
+min_dist_from (const struct Kmeans *kmeans, const struct qc *qc, int which)
{
- int result = -1;
- double x;
- int i, j;
- double dist;
- double mindist;
- mindist = INFINITY;
+ int j, i;
+
+ double mindist = INFINITY;
for (i = 0; i < qc->ngroups; i++)
{
- dist = 0;
+ if (i == which)
+ continue;
+
+ double dist = 0;
for (j = 0; j < qc->n_vars; j++)
{
- x = case_data (c, qc->vars[j])->f;
- dist += pow2 (gsl_matrix_get (kmeans->centers, i, j) - x);
+ dist += pow2 (gsl_matrix_get (kmeans->centers, i, j) - gsl_matrix_get (kmeans->centers, which, j));
}
+
if (dist < mindist)
{
mindist = dist;
- result = i;
}
}
- return (result);
+
+ return mindist;
}
-/* Re-calculate the cluster centers. */
+
+
+/* Calculate the initial cluster centers. */
static void
-kmeans_recalculate_centers (struct Kmeans *kmeans, const struct casereader *reader, const struct qc *qc)
+kmeans_initial_centers (struct Kmeans *kmeans, const struct casereader *reader, const struct qc *qc)
{
- casenumber i;
- int v, j;
- double x, curval;
struct ccase *c;
- struct ccase *c_index;
- struct casereader *cs;
- struct casereader *cs_index;
- int index;
-
- i = 0;
- cs = casereader_clone (reader);
- cs_index = casereader_clone (kmeans->index_rdr);
+ int nc = 0, j;
- gsl_matrix_set_all (kmeans->centers, 0.0);
+ struct casereader *cs = casereader_clone (reader);
for (; (c = casereader_read (cs)) != NULL; case_unref (c))
{
- double weight = qc->wv ? case_data (c, qc->wv)->f : 1.0;
- c_index = casereader_read (cs_index);
- index = case_data_idx (c_index, 0)->f;
- for (v = 0; v < qc->n_vars; ++v)
+ bool missing = false;
+ for (j = 0; j < qc->n_vars; ++j)
{
- x = case_data (c, qc->vars[v])->f * weight;
- curval = gsl_matrix_get (kmeans->centers, index, v);
- gsl_matrix_set (kmeans->centers, index, v, curval + x);
+ const union value *val = case_data (c, qc->vars[j]);
+ if ( var_is_value_missing (qc->vars[j], val, qc->exclude))
+ {
+ missing = true;
+ break;
+ }
+
+ if (nc < qc->ngroups)
+ gsl_matrix_set (kmeans->centers, nc, j, val->f);
}
- i++;
- case_unref (c_index);
- }
- casereader_destroy (cs);
- casereader_destroy (cs_index);
- /* Getting number of cases */
- if (kmeans->n == 0)
- kmeans->n = i;
+ if (missing)
+ continue;
- /* 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 < qc->n_vars; j++)
+ if (nc++ < qc->ngroups)
+ continue;
+
+ if (!qc->no_initial)
{
- if (numobs > 0)
+ int mq, mp;
+ double delta;
+
+ int mn, mm;
+ double m = matrix_mindist (kmeans->centers, &mn, &mm);
+
+ kmeans_get_nearest_group (kmeans, c, qc, &mq, &delta, &mp, NULL);
+ if (delta > m)
+ /* If the distance between C and the nearest group, is greater than the distance
+ between the two groups which are clostest to each other, then one group must be replaced */
{
- double *x = gsl_matrix_ptr (kmeans->centers, i, j);
- *x /= numobs;
+ /* Out of mn and mm, which is the clostest of the two groups to C ? */
+ int which = (dist_from_case (kmeans, c, qc, mn) > dist_from_case (kmeans, c, qc, mm)) ? mm : mn;
+
+ for (j = 0; j < qc->n_vars; ++j)
+ {
+ const union value *val = case_data (c, qc->vars[j]);
+ gsl_matrix_set (kmeans->centers, which, j, val->f);
+ }
}
- else
+ else if (dist_from_case (kmeans, c, qc, mp) > min_dist_from (kmeans, qc, mq))
+ /* If the distance between C and the second nearest group (MP) is greater than the
+ smallest distance between the nearest group (MQ) and any other group, then replace
+ MQ with C */
{
- gsl_matrix_set (kmeans->centers, i, j, 0);
+ for (j = 0; j < qc->n_vars; ++j)
+ {
+ const union value *val = case_data (c, qc->vars[j]);
+ gsl_matrix_set (kmeans->centers, mq, j, val->f);
+ }
}
}
}
-}
-/* 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, const struct casereader *reader, const struct qc *qc)
-{
- int totaldiff = 0;
- struct ccase *c;
- struct casereader *cs = casereader_clone (reader);
+ casereader_destroy (cs);
- /* A casewriter into which we will write the indexes. */
- struct casewriter *index_wtr = autopaging_writer_create (kmeans->proto);
+ kmeans->convergence_criteria = qc->epsilon * matrix_mindist (kmeans->centers, NULL, NULL);
- gsl_vector_long_set_all (kmeans->num_elements_groups, 0);
+ /* As it is the first iteration, the variable kmeans->initial_centers is NULL
+ and it is created once for reporting issues. */
+ kmeans->initial_centers = gsl_matrix_alloc (qc->ngroups, qc->n_vars);
+ gsl_matrix_memcpy (kmeans->initial_centers, kmeans->centers);
+}
- for (; (c = casereader_read (cs)) != NULL; case_unref (c))
+
+/* Return the index of the group which is nearest to the case C */
+static void
+kmeans_get_nearest_group (const struct Kmeans *kmeans, struct ccase *c, const struct qc *qc, int *g_q, double *delta_q, int *g_p, double *delta_p)
+{
+ int result0 = -1;
+ int result1 = -1;
+ int i, j;
+ double mindist0 = INFINITY;
+ double mindist1 = INFINITY;
+ for (i = 0; i < qc->ngroups; i++)
{
- /* A case to hold the new index. */
- struct ccase *index_case_new = case_create (kmeans->proto);
- int bestindex = kmeans_get_nearest_group (kmeans, c, qc);
- double weight = qc->wv ? case_data (c, qc->wv)->f : 1.0;
- kmeans->num_elements_groups->data[bestindex] += weight;
- if (kmeans->index_rdr)
+ double dist = 0;
+ for (j = 0; j < qc->n_vars; j++)
{
- /* A case from which the old index will be read. */
- struct ccase *index_case_old = NULL;
+ const union value *val = case_data (c, qc->vars[j]);
+ if ( var_is_value_missing (qc->vars[j], val, qc->exclude))
+ continue;
- /* Read the case from the index casereader. */
- index_case_old = casereader_read (kmeans->index_rdr);
+ dist += pow2 (gsl_matrix_get (kmeans->centers, i, j) - val->f);
+ }
- /* Set totaldiff, using the old_index. */
- totaldiff += abs (case_data_idx (index_case_old, 0)->f - bestindex);
+ if (dist < mindist0)
+ {
+ mindist1 = mindist0;
+ result1 = result0;
- /* We have no use for the old case anymore, so unref it. */
- case_unref (index_case_old);
+ mindist0 = dist;
+ result0 = i;
}
- else
+ else if (dist < mindist1)
{
- /* If this is the first run, then assume index is zero. */
- totaldiff += bestindex;
+ mindist1 = dist;
+ result1 = i;
}
+ }
- /* Set the value of the new inde.x */
- case_data_rw_idx (index_case_new, 0)->f = bestindex;
+ if (delta_q)
+ *delta_q = mindist0;
- /* 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. */
- casereader_destroy (kmeans->index_rdr);
+ if (g_q)
+ *g_q = result0;
- /* Convert the writer into a reader, ready for the next iteration to read */
- kmeans->index_rdr = casewriter_make_reader (index_wtr);
- return (totaldiff);
+ if (delta_p)
+ *delta_p = mindist1;
+
+ if (g_p)
+ *g_p = result1;
}
+
+
static void
kmeans_order_groups (struct Kmeans *kmeans, const struct qc *qc)
{
static void
kmeans_cluster (struct Kmeans *kmeans, struct casereader *reader, const struct qc *qc)
{
- int i;
- bool redo;
- int diffs;
- bool show_warning1;
-
- show_warning1 = true;
-cluster:
- redo = false;
- kmeans_randomize_centers (kmeans, qc);
- for (kmeans->lastiter = 0; kmeans->lastiter < qc->maxiter;
- kmeans->lastiter++)
+ int j;
+
+ kmeans_initial_centers (kmeans, reader, qc);
+
+ gsl_matrix_memcpy (kmeans->updated_centers, kmeans->centers);
+
+
+ for (int xx = 0 ; xx < qc->maxiter ; ++xx)
{
- diffs = kmeans_calculate_indexes_and_check_convergence (kmeans, reader, qc);
- kmeans_recalculate_centers (kmeans, reader, qc);
- if (show_warning1 && qc->ngroups > kmeans->n)
+ gsl_vector_long_set_all (kmeans->num_elements_groups, 0.0);
+
+ kmeans->n = 0;
+ if (!qc->no_update)
{
- msg (MW, _("Number of clusters may not be larger than the number "
- "of cases."));
- show_warning1 = false;
+ struct casereader *r = casereader_clone (reader);
+ struct ccase *c;
+ for (; (c = casereader_read (r)) != NULL; case_unref (c))
+ {
+ int group = -1;
+ int g;
+ bool missing = false;
+
+ for (j = 0; j < qc->n_vars; j++)
+ {
+ const union value *val = case_data (c, qc->vars[j]);
+ if ( var_is_value_missing (qc->vars[j], val, qc->exclude))
+ missing = true;
+ }
+
+ if (missing)
+ continue;
+
+ double mindist = INFINITY;
+ for (g = 0; g < qc->ngroups; ++g)
+ {
+ double d = dist_from_case (kmeans, c, qc, g);
+
+ if (d < mindist)
+ {
+ mindist = d;
+ group = g;
+ }
+ }
+
+ long *n = gsl_vector_long_ptr (kmeans->num_elements_groups, group);
+ *n += qc->wv ? case_data (c, qc->wv)->f : 1.0;
+ kmeans->n++;
+
+ for (j = 0; j < qc->n_vars; ++j)
+ {
+ const union value *val = case_data (c, qc->vars[j]);
+ if ( var_is_value_missing (qc->vars[j], val, qc->exclude))
+ continue;
+ double *x = gsl_matrix_ptr (kmeans->updated_centers, group, j);
+ *x += val->f * (qc->wv ? case_data (c, qc->wv)->f : 1.0);
+ }
+ }
+
+ casereader_destroy (r);
}
- if (diffs == 0)
- break;
- }
- for (i = 0; i < qc->ngroups; i++)
- {
- if (kmeans->num_elements_groups->data[i] == 0)
+ int g;
+
+ /* Divide the cluster sums by the number of items in each cluster */
+ for (g = 0; g < qc->ngroups; ++g)
{
- kmeans->trials++;
- if (kmeans->trials >= 3)
- break;
- redo = true;
- break;
+ for (j = 0; j < qc->n_vars; ++j)
+ {
+ long n = gsl_vector_long_get (kmeans->num_elements_groups, g);
+ double *x = gsl_matrix_ptr (kmeans->updated_centers, g, j);
+ *x /= n + 1; // Plus 1 for the initial centers
+ }
}
- }
- if (redo)
- goto cluster;
+
+ gsl_matrix_memcpy (kmeans->centers, kmeans->updated_centers);
+
+ {
+ kmeans->n = 0;
+ /* Step 3 */
+ gsl_vector_long_set_all (kmeans->num_elements_groups, 0.0);
+ gsl_matrix_set_all (kmeans->updated_centers, 0.0);
+ struct ccase *c;
+ struct casereader *cs = casereader_clone (reader);
+ for (; (c = casereader_read (cs)) != NULL; case_unref (c))
+ {
+ int group = -1;
+ kmeans_get_nearest_group (kmeans, c, qc, &group, NULL, NULL, NULL);
+
+ for (j = 0; j < qc->n_vars; ++j)
+ {
+ const union value *val = case_data (c, qc->vars[j]);
+ if ( var_is_value_missing (qc->vars[j], val, qc->exclude))
+ continue;
+
+ double *x = gsl_matrix_ptr (kmeans->updated_centers, group, j);
+ *x += val->f;
+ }
+
+ long *n = gsl_vector_long_ptr (kmeans->num_elements_groups, group);
+ *n += qc->wv ? case_data (c, qc->wv)->f : 1.0;
+ kmeans->n++;
+ }
+ casereader_destroy (cs);
+
+
+ /* Divide the cluster sums by the number of items in each cluster */
+ for (g = 0; g < qc->ngroups; ++g)
+ {
+ for (j = 0; j < qc->n_vars; ++j)
+ {
+ long n = gsl_vector_long_get (kmeans->num_elements_groups, g);
+ double *x = gsl_matrix_ptr (kmeans->updated_centers, g, j);
+ *x /= n ;
+ }
+ }
+
+ double d = diff_matrix (kmeans->updated_centers, kmeans->centers);
+ if (d < kmeans->convergence_criteria)
+ break;
+ }
+
+ if (qc->no_update)
+ break;
+ }
}
/* Reports centers of clusters.
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 = qc->ngroups + 1;
nr = qc->n_vars + 4;
- heading_columns = 1;
t = tab_create (nc, nr);
tab_headers (t, 0, nc - 1, 0, 1);
currow = 0;
tab_double (t, i + 1, j + 4, TAB_CENTER,
gsl_matrix_get (kmeans->centers,
kmeans->group_order->data[i], j),
- var_get_print_format (qc->vars[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 (qc->vars[j]));
+ var_get_print_format (qc->vars[j]), RC_OTHER);
}
}
}
tab_submit (t);
}
+/* Reports cluster membership for each case. */
+static void
+quick_cluster_show_membership (struct Kmeans *kmeans, const struct casereader *reader, const struct qc *qc)
+{
+ struct tab_table *t;
+ int nc, nr, i;
+
+ struct ccase *c;
+ struct casereader *cs = casereader_clone (reader);
+ nc = 2;
+ nr = kmeans->n + 1;
+ t = tab_create (nc, nr);
+ tab_headers (t, 0, nc - 1, 0, 0);
+ tab_title (t, _("Cluster Membership"));
+ tab_text (t, 0, 0, TAB_CENTER, _("Case Number"));
+ tab_text (t, 1, 0, TAB_CENTER, _("Cluster"));
+ tab_box (t, TAL_2, TAL_2, TAL_0, TAL_1, 0, 0, nc - 1, nr - 1);
+ tab_hline (t, TAL_1, 0, nc - 1, 1);
+
+ gsl_permutation *ip = gsl_permutation_alloc (qc->ngroups);
+ gsl_permutation_inverse (ip, kmeans->group_order);
+
+ for (i = 0; (c = casereader_read (cs)) != NULL; i++, case_unref (c))
+ {
+ int clust = -1;
+ assert (i < kmeans->n);
+ kmeans_get_nearest_group (kmeans, c, qc, &clust, NULL, NULL, NULL);
+ clust = ip->data[clust];
+ tab_text_format (t, 0, i+1, TAB_CENTER, "%d", (i + 1));
+ tab_text_format (t, 1, i+1, TAB_CENTER, "%d", (clust + 1));
+ }
+ gsl_permutation_free (ip);
+ assert (i == kmeans->n);
+ tab_submit (t);
+ casereader_destroy (cs);
+}
+
+
/* Reports number of cases of each single cluster. */
static void
quick_cluster_show_number_cases (struct Kmeans *kmeans, const struct qc *qc)
/* Reports. */
static void
-quick_cluster_show_results (struct Kmeans *kmeans, const struct qc *qc)
+quick_cluster_show_results (struct Kmeans *kmeans, const struct casereader *reader, const struct qc *qc)
{
- kmeans_order_groups (kmeans, qc);
- /* Uncomment the line below for reporting initial centers. */
- /* quick_cluster_show_centers (kmeans, true); */
+ kmeans_order_groups (kmeans, qc); /* what does this do? */
+
+ if( qc->print_initial_clusters )
+ quick_cluster_show_centers (kmeans, true, qc);
quick_cluster_show_centers (kmeans, false, qc);
quick_cluster_show_number_cases (kmeans, qc);
+ if( qc->print_cluster_membership )
+ quick_cluster_show_membership(kmeans, reader, qc);
}
int
bool ok;
const struct dictionary *dict = dataset_dict (ds);
qc.ngroups = 2;
- qc.maxiter = 2;
+ qc.maxiter = 10;
+ qc.epsilon = DBL_EPSILON;
+ qc.missing_type = MISS_LISTWISE;
+ qc.exclude = MV_ANY;
+ qc.print_cluster_membership = false; /* default = do not output case cluster membership */
+ qc.print_initial_clusters = false; /* default = do not print initial clusters */
+ qc.no_initial = false; /* default = use well separated initial clusters */
+ qc.no_update = false; /* default = iterate until convergence or max iterations */
if (!parse_variables_const (lexer, dict, &qc.vars, &qc.n_vars,
PV_NO_DUPLICATE | PV_NUMERIC))
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
+ {
+ lex_error (lexer, NULL);
+ goto error;
+ }
+ }
+ }
+ else if (lex_match_id (lexer, "PRINT"))
+ {
+ lex_match (lexer, T_EQUALS);
+ while (lex_token (lexer) != T_ENDCMD
+ && lex_token (lexer) != T_SLASH)
+ {
+ if (lex_match_id (lexer, "CLUSTER"))
+ qc.print_cluster_membership = true;
+ else if (lex_match_id (lexer, "INITIAL"))
+ qc.print_initial_clusters = true;
+ else
+ {
+ lex_error (lexer, NULL);
+ goto error;
+ }
+ }
+ }
+ else if (lex_match_id (lexer, "CRITERIA"))
{
lex_match (lexer, T_EQUALS);
while (lex_token (lexer) != T_ENDCMD
{
if (lex_match_id (lexer, "CLUSTERS"))
{
- if (lex_force_match (lexer, T_LPAREN))
+ if (lex_force_match (lexer, T_LPAREN) &&
+ lex_force_int (lexer))
{
- lex_force_int (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);
+ if (!lex_force_match (lexer, T_RPAREN))
+ goto error;
+ }
+ }
+ else if (lex_match_id (lexer, "CONVERGE"))
+ {
+ if (lex_force_match (lexer, T_LPAREN) &&
+ lex_force_num (lexer))
+ {
+ qc.epsilon = lex_number (lexer);
+ if (qc.epsilon <= 0)
+ {
+ lex_error (lexer, _("The convergence criterium must be positive"));
+ goto error;
+ }
lex_get (lexer);
- lex_force_match (lexer, T_RPAREN);
+ if (!lex_force_match (lexer, T_RPAREN))
+ goto error;
}
}
else if (lex_match_id (lexer, "MXITER"))
{
- if (lex_force_match (lexer, T_LPAREN))
+ if (lex_force_match (lexer, T_LPAREN) &&
+ lex_force_int (lexer))
{
- lex_force_int (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);
+ if (!lex_force_match (lexer, T_RPAREN))
+ goto error;
}
}
+ else if (lex_match_id (lexer, "NOINITIAL"))
+ {
+ qc.no_initial = true;
+ }
+ else if (lex_match_id (lexer, "NOUPDATE"))
+ {
+ qc.no_update = true;
+ }
else
- goto error;
+ {
+ lex_error (lexer, NULL);
+ goto error;
+ }
}
}
+ else
+ {
+ lex_error (lexer, NULL);
+ goto error;
+ }
}
qc.wv = dict_get_weight (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);
+ quick_cluster_show_results (kmeans, group, &qc);
kmeans_destroy (kmeans);
casereader_destroy (group);
}
projects / pspp / blobdiff