/* PSPP - a program for statistical analysis.
- Copyright (C) 2011, 2012, 2015 Free Software Foundation, Inc.
+ Copyright (C) 2011, 2012, 2015, 2019 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 "libpspp/assertion.h"
#include "libpspp/str.h"
#include "math/random.h"
-#include "output/tab.h"
-#include "output/text-item.h"
+#include "output/pivot-table.h"
+#include "output/output-item.h"
#include "gettext.h"
#define _(msgid) gettext (msgid)
};
+struct save_trans_data
+ {
+ /* A writer which contains the values (if any) to be appended to
+ each case in the active dataset */
+ struct casewriter *writer;
+
+ /* A reader created from the writer above. */
+ struct casereader *appending_reader;
+
+ /* The indices to be used to access values in the above,
+ reader/writer */
+ int membership_case_idx;
+ int distance_case_idx;
+
+ /* The variables created to hold the values appended to the dataset */
+ struct variable *membership;
+ struct variable *distance;
+ };
+
+
struct qc
-{
- const struct variable **vars;
- size_t n_vars;
+ {
+ struct dataset *dataset;
+ struct dictionary *dict;
+
+ const struct variable **vars;
+ size_t n_vars;
+
+ double epsilon; /* The convergence criterion */
+
+ 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 initial; /* false => simplified initial cluster selection */
+ bool update; /* false => do not iterate */
+
+ const struct variable *wv; /* Weighting variable. */
+
+ enum missing_type missing_type;
+ enum mv_class exclude;
- double epsilon; /* The convergence criterium */
+ /* Which values are to be saved? */
+ bool save_membership;
+ bool save_distance;
- 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 */
+ /* The name of the new variable to contain the cluster of each case. */
+ char *var_membership;
- const struct variable *wv; /* Weighting variable. */
+ /* The name of the new variable to contain the distance of each case
+ from its cluster centre. */
+ char *var_distance;
- enum missing_type missing_type;
- enum mv_class exclude;
-};
+ struct save_trans_data *save_trans_data;
+ };
/* 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 *updated_centers;
- casenumber n;
+ {
+ gsl_matrix *centers; /* Centers for groups. */
+ gsl_matrix *updated_centers;
+ casenumber n;
- gsl_vector_long *num_elements_groups;
+ gsl_vector_long *num_elements_groups;
- gsl_matrix *initial_centers; /* Initial random centers. */
- double convergence_criteria;
- gsl_permutation *group_order; /* Group order for reporting. */
-};
+ gsl_matrix *initial_centers; /* Initial random centers. */
+ double convergence_criteria;
+ gsl_permutation *group_order; /* Group order for reporting. */
+ };
-static struct Kmeans *kmeans_create (const struct qc *qc);
+static struct Kmeans *kmeans_create (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_get_nearest_group (const struct Kmeans *,
+ struct ccase *, const struct qc *,
+ int *, double *, int *, double *);
-static void kmeans_order_groups (struct Kmeans *kmeans, const struct qc *);
+static void kmeans_order_groups (struct Kmeans *, const struct qc *);
-static void kmeans_cluster (struct Kmeans *kmeans, struct casereader *reader, const struct qc *);
+static void kmeans_cluster (struct Kmeans *, struct casereader *,
+ const struct qc *);
-static void quick_cluster_show_centers (struct Kmeans *kmeans, bool initial, const struct qc *);
+static void quick_cluster_show_centers (struct 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_membership (struct Kmeans *,
+ const struct casereader *,
+ struct qc *);
-static void quick_cluster_show_number_cases (struct Kmeans *kmeans, const struct qc *);
+static void quick_cluster_show_number_cases (struct Kmeans *,
+ const struct qc *);
-static void quick_cluster_show_results (struct Kmeans *kmeans, const struct casereader *reader, const struct qc *);
+static void quick_cluster_show_results (struct Kmeans *,
+ const struct casereader *,
+ struct qc *);
-int cmd_quick_cluster (struct lexer *lexer, struct dataset *ds);
+int cmd_quick_cluster (struct lexer *, struct dataset *);
-static void kmeans_destroy (struct Kmeans *kmeans);
+static void kmeans_destroy (struct Kmeans *);
/* Creates and returns a struct of Kmeans with given casereader 'cs', parsed
variables 'variables', number of cases 'n', number of variables 'm', number
static struct Kmeans *
kmeans_create (const struct qc *qc)
{
- 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->group_order = gsl_permutation_alloc (kmeans->centers->size1);
- kmeans->initial_centers = NULL;
-
- return (kmeans);
+ struct Kmeans *kmeans = xmalloc (sizeof *kmeans);
+ *kmeans = (struct Kmeans) {
+ .centers = gsl_matrix_alloc (qc->ngroups, qc->n_vars),
+ .updated_centers = gsl_matrix_alloc (qc->ngroups, qc->n_vars),
+ .num_elements_groups = gsl_vector_long_alloc (qc->ngroups),
+ .group_order = gsl_permutation_alloc (qc->ngroups),
+ };
+ return kmeans;
}
static void
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)
+ for (size_t 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) );
- }
+ for (size_t 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;
}
static double
matrix_mindist (const gsl_matrix *m, int *mn, int *mm)
{
- int i, j;
double mindist = INFINITY;
- for (i = 0; i < m->size1 - 1; ++i)
- {
- for (j = i + 1; j < m->size1; ++j)
- {
- int k;
- double diff_sq = 0;
- for (k = 0; k < m->size2; ++k)
- {
- diff_sq += pow2 (gsl_matrix_get (m, j, k) - gsl_matrix_get (m, i, k));
- }
- if (diff_sq < mindist)
- {
- mindist = diff_sq;
- if (mn)
- *mn = i;
- if (mm)
- *mm = j;
- }
- }
- }
-
+ for (size_t i = 0; i + 1 < m->size1; ++i)
+ for (size_t j = i + 1; j < m->size1; ++j)
+ {
+ double diff_sq = 0;
+ for (size_t k = 0; k < m->size2; ++k)
+ diff_sq += pow2 (gsl_matrix_get (m, j, k) - gsl_matrix_get (m, i, k));
+ if (diff_sq < mindist)
+ {
+ mindist = diff_sq;
+ if (mn)
+ *mn = i;
+ if (mm)
+ *mm = j;
+ }
+ }
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)
+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++)
+ for (size_t 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))
- NOT_REACHED ();
-
+ assert (!(var_is_value_missing (qc->vars[j], val) & qc->exclude));
dist += pow2 (gsl_matrix_get (kmeans->centers, which, j) - val->f);
}
static double
min_dist_from (const struct Kmeans *kmeans, const struct qc *qc, int which)
{
- int j, i;
-
- double mindist = INFINITY;
- for (i = 0; i < qc->ngroups; i++)
+ double mindist = INFINITY;
+ for (size_t i = 0; i < qc->ngroups; i++)
{
if (i == which)
continue;
double dist = 0;
- for (j = 0; j < qc->n_vars; j++)
- {
- dist += pow2 (gsl_matrix_get (kmeans->centers, i, j) - gsl_matrix_get (kmeans->centers, which, j));
- }
+ for (size_t j = 0; j < qc->n_vars; j++)
+ dist += pow2 (gsl_matrix_get (kmeans->centers, i, j)
+ - gsl_matrix_get (kmeans->centers, which, j));
if (dist < mindist)
- {
- mindist = dist;
- }
+ mindist = dist;
}
return mindist;
}
-
-
-/* Calculate the intial cluster centers. */
+/* Calculate the initial cluster centers. */
static void
-kmeans_initial_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)
{
- struct ccase *c;
- int nc = 0, j;
+ int nc = 0;
struct casereader *cs = casereader_clone (reader);
+ struct ccase *c;
for (; (c = casereader_read (cs)) != NULL; case_unref (c))
{
bool missing = false;
- for (j = 0; j < qc->n_vars; ++j)
+ for (size_t 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))
+ 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);
}
-
if (missing)
continue;
if (nc++ < qc->ngroups)
continue;
- if (!qc->no_initial)
+ if (qc->initial)
{
- int mq, mp;
- double delta;
-
int mn, mm;
double m = matrix_mindist (kmeans->centers, &mn, &mm);
+ int mq, mp;
+ double delta;
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 */
+ between the two groups which are clostest to each
+ other, then one group must be replaced. */
{
/* 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;
+ 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)
+ for (size_t 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 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 */
+ /* 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. */
{
- for (j = 0; j < qc->n_vars; ++j)
+ for (size_t 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);
gsl_matrix_memcpy (kmeans->initial_centers, kmeans->centers);
}
-
/* 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)
+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++)
+ for (size_t i = 0; i < qc->ngroups; i++)
{
double dist = 0;
- for (j = 0; j < qc->n_vars; j++)
+ for (size_t 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))
+ if (var_is_value_missing (qc->vars[j], val) & qc->exclude)
continue;
dist += pow2 (gsl_matrix_get (kmeans->centers, i, j) - val->f);
if (g_q)
*g_q = result0;
-
if (delta_p)
*delta_p = mindist1;
*g_p = result1;
}
-
-
static void
kmeans_order_groups (struct Kmeans *kmeans, const struct qc *qc)
{
/* Main algorithm.
Does iterations, checks convergency. */
static void
-kmeans_cluster (struct Kmeans *kmeans, struct casereader *reader, const struct qc *qc)
+kmeans_cluster (struct Kmeans *kmeans, struct casereader *reader,
+ const struct qc *qc)
{
- int j;
-
kmeans_initial_centers (kmeans, reader, qc);
gsl_matrix_memcpy (kmeans->updated_centers, kmeans->centers);
-
-
- for (int xx = 0 ; xx < qc->maxiter ; ++xx)
+ for (int xx = 0; xx < qc->maxiter; ++xx)
{
gsl_vector_long_set_all (kmeans->num_elements_groups, 0.0);
kmeans->n = 0;
- if (!qc->no_update)
+ if (qc->update)
{
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++)
+ for (size_t 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))
+ 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)
+ int group = -1;
+ for (size_t g = 0; g < qc->ngroups; ++g)
{
double d = dist_from_case (kmeans, c, qc, g);
}
long *n = gsl_vector_long_ptr (kmeans->num_elements_groups, group);
- *n += qc->wv ? case_data (c, qc->wv)->f : 1.0;
+ *n += qc->wv ? case_num (c, qc->wv) : 1.0;
kmeans->n++;
- for (j = 0; j < qc->n_vars; ++j)
+ for (size_t 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))
+ 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);
+ *x += val->f * (qc->wv ? case_num (c, qc->wv) : 1.0);
}
}
casereader_destroy (r);
}
- int g;
-
/* 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 + 1; // Plus 1 for the initial centers
- }
- }
-
-
+ for (size_t g = 0; g < qc->ngroups; ++g)
+ for (size_t 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
+ }
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;
- }
+ 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 (size_t 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_num (c, qc->wv) : 1.0;
+ kmeans->n++;
+ }
+ casereader_destroy (cs);
- if (qc->no_update)
+ /* Divide the cluster sums by the number of items in each cluster */
+ for (size_t g = 0; g < qc->ngroups; ++g)
+ for (size_t 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->update)
break;
}
}
static void
quick_cluster_show_centers (struct Kmeans *kmeans, bool initial, const struct qc *qc)
{
- struct tab_table *t;
- int nc, nr, currow;
- int i, j;
- nc = qc->ngroups + 1;
- nr = qc->n_vars + 4;
- t = tab_create (nc, nr);
- tab_headers (t, 0, nc - 1, 0, 1);
- currow = 0;
- if (!initial)
- {
- tab_title (t, _("Final Cluster Centers"));
- }
- else
- {
- tab_title (t, _("Initial Cluster Centers"));
- }
- tab_box (t, TAL_2, TAL_2, TAL_0, TAL_1, 0, 0, nc - 1, nr - 1);
- tab_joint_text (t, 1, 0, nc - 1, 0, TAB_CENTER, _("Cluster"));
- tab_hline (t, TAL_1, 1, nc - 1, 2);
- currow += 2;
+ struct pivot_table *table
+ = pivot_table_create (initial
+ ? N_("Initial Cluster Centers")
+ : N_("Final Cluster Centers"));
+
+ struct pivot_dimension *clusters
+ = pivot_dimension_create (table, PIVOT_AXIS_COLUMN, N_("Cluster"));
+
+ clusters->root->show_label = true;
+ for (size_t i = 0; i < qc->ngroups; i++)
+ pivot_category_create_leaf (clusters->root,
+ pivot_value_new_integer (i + 1));
+
+ struct pivot_dimension *variables
+ = pivot_dimension_create (table, PIVOT_AXIS_ROW, N_("Variable"));
+
+ for (size_t i = 0; i < qc->n_vars; i++)
+ pivot_category_create_leaf (variables->root,
+ pivot_value_new_variable (qc->vars[i]));
+
+ const gsl_matrix *matrix = (initial
+ ? kmeans->initial_centers
+ : kmeans->centers);
+ for (size_t i = 0; i < qc->ngroups; i++)
+ for (size_t j = 0; j < qc->n_vars; j++)
+ {
+ double x = gsl_matrix_get (matrix, kmeans->group_order->data[i], j);
+ union value v = { .f = x };
+ pivot_table_put2 (table, i, j,
+ pivot_value_new_var_value (qc->vars[j], &v));
+ }
- 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 < qc->n_vars; i++)
- {
- tab_text (t, 0, currow + i, TAB_LEFT,
- var_to_string (qc->vars[i]));
- }
+ pivot_table_submit (table);
+}
- for (i = 0; i < qc->ngroups; i++)
- {
- 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 (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]), RC_OTHER);
- }
- }
- }
- tab_submit (t);
+
+/* A transformation function which juxtaposes the dataset with the
+ (pre-prepared) dataset containing membership and/or distance
+ values. */
+static enum trns_result
+save_trans_func (void *aux, struct ccase **c, casenumber x UNUSED)
+{
+ const struct save_trans_data *std = aux;
+ struct ccase *ca = casereader_read (std->appending_reader);
+ if (ca == NULL)
+ return TRNS_CONTINUE;
+
+ *c = case_unshare (*c);
+
+ if (std->membership_case_idx >= 0)
+ *case_num_rw (*c, std->membership) = case_num_idx (ca, std->membership_case_idx);
+
+ if (std->distance_case_idx >= 0)
+ *case_num_rw (*c, std->distance) = case_num_idx (ca, std->distance_case_idx);
+
+ case_unref (ca);
+
+ return TRNS_CONTINUE;
}
-/* Reports cluster membership for each case. */
+/* Free the resources of the transformation. */
+static bool
+save_trans_destroy (void *aux)
+{
+ struct save_trans_data *std = aux;
+ casereader_destroy (std->appending_reader);
+ free (std);
+ return true;
+}
+
+/* Reports cluster membership for each case, and is requested saves the
+ membership and the distance of the case from the cluster centre. */
static void
-quick_cluster_show_membership (struct Kmeans *kmeans, const struct casereader *reader, const struct qc *qc)
+quick_cluster_show_membership (struct Kmeans *kmeans,
+ const struct casereader *reader,
+ struct qc *qc)
{
- struct tab_table *t;
- int nc, nr, i;
+ struct pivot_table *table = NULL;
+ struct pivot_dimension *cases = NULL;
+ if (qc->print_cluster_membership)
+ {
+ table = pivot_table_create (N_("Cluster Membership"));
- 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);
+ pivot_dimension_create (table, PIVOT_AXIS_COLUMN, N_("Cluster"),
+ N_("Cluster"));
+
+ cases
+ = pivot_dimension_create (table, PIVOT_AXIS_ROW, N_("Case Number"));
+
+ cases->root->show_label = true;
+ }
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))
+ struct caseproto *proto = caseproto_create ();
+ if (qc->save_membership || qc->save_distance)
+ {
+ /* Prepare data which may potentially be used in a
+ transformation appending new variables to the active
+ dataset. */
+ int idx = 0;
+ int membership_case_idx = -1;
+ if (qc->save_membership)
+ {
+ proto = caseproto_add_width (proto, 0);
+ membership_case_idx = idx++;
+ }
+
+ int distance_case_idx = -1;
+ if (qc->save_distance)
+ {
+ proto = caseproto_add_width (proto, 0);
+ distance_case_idx = idx++;
+ }
+
+ qc->save_trans_data = xmalloc (sizeof *qc->save_trans_data);
+ *qc->save_trans_data = (struct save_trans_data) {
+ .membership_case_idx = membership_case_idx,
+ .distance_case_idx = distance_case_idx,
+ .writer = autopaging_writer_create (proto),
+ };
+ }
+
+ struct casereader *cs = casereader_clone (reader);
+ struct ccase *c;
+ for (int i = 0; (c = casereader_read (cs)) != NULL; i++, case_unref (c))
{
- int clust = -1;
assert (i < kmeans->n);
+ int clust;
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));
+ int cluster = ip->data[clust];
+
+ if (qc->save_trans_data)
+ {
+ /* Calculate the membership and distance values. */
+ struct ccase *outc = case_create (proto);
+ if (qc->save_membership)
+ *case_num_rw_idx (outc, qc->save_trans_data->membership_case_idx) = cluster + 1;
+
+ if (qc->save_distance)
+ *case_num_rw_idx (outc, qc->save_trans_data->distance_case_idx)
+ = sqrt (dist_from_case (kmeans, c, qc, clust));
+
+ casewriter_write (qc->save_trans_data->writer, outc);
+ }
+
+ if (qc->print_cluster_membership)
+ {
+ /* Print the cluster membership to the table. */
+ int case_idx = pivot_category_create_leaf (cases->root,
+ pivot_value_new_integer (i + 1));
+ pivot_table_put2 (table, 0, case_idx,
+ pivot_value_new_integer (cluster + 1));
+ }
}
+
+ caseproto_unref (proto);
gsl_permutation_free (ip);
- assert (i == kmeans->n);
- tab_submit (t);
+
+ if (qc->print_cluster_membership)
+ pivot_table_submit (table);
casereader_destroy (cs);
}
static void
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 = 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_box (t, TAL_2, TAL_2, TAL_0, TAL_1, 0, 0, nc - 1, nr - 1);
- tab_text (t, 0, 0, TAB_LEFT, _("Cluster"));
-
- total = 0;
- for (i = 0; i < qc->ngroups; i++)
+ struct pivot_table *table
+ = pivot_table_create (N_("Number of Cases in each Cluster"));
+
+ pivot_dimension_create (table, PIVOT_AXIS_COLUMN, N_("Statistics"),
+ N_("Count"));
+
+ struct pivot_dimension *clusters
+ = pivot_dimension_create (table, PIVOT_AXIS_ROW, N_("Clusters"));
+
+ struct pivot_category *group
+ = pivot_category_create_group (clusters->root, N_("Cluster"));
+
+ long int total = 0;
+ for (int i = 0; i < qc->ngroups; i++)
{
- tab_text_format (t, 1, i, TAB_CENTER, "%d", (i + 1));
- numelem =
- kmeans->num_elements_groups->data[kmeans->group_order->data[i]];
- tab_text_format (t, 2, i, TAB_CENTER, "%d", numelem);
- total += numelem;
+ int cluster_idx
+ = pivot_category_create_leaf (group, pivot_value_new_integer (i + 1));
+ int count = kmeans->num_elements_groups->data [kmeans->group_order->data[i]];
+ pivot_table_put2 (table, 0, cluster_idx, pivot_value_new_integer (count));
+ total += count;
}
- tab_text (t, 0, qc->ngroups, TAB_LEFT, _("Valid"));
- tab_text_format (t, 2, qc->ngroups, TAB_LEFT, "%ld", total);
- tab_submit (t);
+ int cluster_idx = pivot_category_create_leaf (clusters->root,
+ pivot_value_new_text (N_("Valid")));
+ pivot_table_put2 (table, 0, cluster_idx, pivot_value_new_integer (total));
+ pivot_table_submit (table);
}
/* Reports. */
static void
-quick_cluster_show_results (struct Kmeans *kmeans, const struct casereader *reader, const struct qc *qc)
+quick_cluster_show_results (struct Kmeans *kmeans, const struct casereader *reader,
+ struct qc *qc)
{
kmeans_order_groups (kmeans, qc); /* what does this do? */
- if( qc->print_initial_clusters )
+ 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);
+
+ quick_cluster_show_membership (kmeans, reader, qc);
}
-int
-cmd_quick_cluster (struct lexer *lexer, struct dataset *ds)
+/* Parse the QUICK CLUSTER command and populate QC accordingly.
+ Returns false on error. */
+static bool
+quick_cluster_parse (struct lexer *lexer, struct qc *qc)
{
- struct qc qc;
- struct Kmeans *kmeans;
- bool ok;
- const struct dictionary *dict = dataset_dict (ds);
- qc.ngroups = 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,
+ if (!parse_variables_const (lexer, qc->dict, &qc->vars, &qc->n_vars,
PV_NO_DUPLICATE | PV_NUMERIC))
- {
- return (CMD_FAILURE);
- }
+ return false;
while (lex_token (lexer) != T_ENDCMD)
{
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;
- }
+ 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;
- }
+ qc->missing_type = MISS_PAIRWISE;
else if (lex_match_id (lexer, "INCLUDE"))
- {
- qc.exclude = MV_SYSTEM;
- }
+ qc->exclude = MV_SYSTEM;
else if (lex_match_id (lexer, "EXCLUDE"))
- {
- qc.exclude = MV_ANY;
- }
+ qc->exclude = MV_ANY;
else
{
- lex_error (lexer, NULL);
- goto error;
+ lex_error_expecting (lexer, "LISTWISE", "DEFAULT",
+ "PAIRWISE", "INCLUDE", "EXCLUDE");
+ return false;
}
}
}
&& lex_token (lexer) != T_SLASH)
{
if (lex_match_id (lexer, "CLUSTER"))
- qc.print_cluster_membership = true;
+ qc->print_cluster_membership = true;
else if (lex_match_id (lexer, "INITIAL"))
- qc.print_initial_clusters = true;
+ qc->print_initial_clusters = true;
else
{
- lex_error (lexer, NULL);
- goto error;
+ lex_error_expecting (lexer, "CLUSTER", "INITIAL");
+ return false;
}
}
}
- else if (lex_match_id (lexer, "CRITERIA"))
+ else if (lex_match_id (lexer, "SAVE"))
{
lex_match (lexer, T_EQUALS);
while (lex_token (lexer) != T_ENDCMD
&& lex_token (lexer) != T_SLASH)
{
- if (lex_match_id (lexer, "CLUSTERS"))
+ if (lex_match_id (lexer, "CLUSTER"))
{
- if (lex_force_match (lexer, T_LPAREN) &&
- lex_force_int (lexer))
+ qc->save_membership = true;
+ if (lex_match (lexer, T_LPAREN))
{
- qc.ngroups = lex_integer (lexer);
- if (qc.ngroups <= 0)
+ if (!lex_force_id (lexer))
+ return false;
+
+ free (qc->var_membership);
+ qc->var_membership = xstrdup (lex_tokcstr (lexer));
+ if (NULL != dict_lookup_var (qc->dict, qc->var_membership))
{
- lex_error (lexer, _("The number of clusters must be positive"));
- goto error;
+ lex_error (lexer,
+ _("A variable called `%s' already exists."),
+ qc->var_membership);
+ free (qc->var_membership);
+ qc->var_membership = NULL;
+ return false;
}
+
lex_get (lexer);
+
if (!lex_force_match (lexer, T_RPAREN))
- goto error;
+ return false;
}
}
- else if (lex_match_id (lexer, "CONVERGE"))
+ else if (lex_match_id (lexer, "DISTANCE"))
{
- if (lex_force_match (lexer, T_LPAREN) &&
- lex_force_num (lexer))
+ qc->save_distance = true;
+ if (lex_match (lexer, T_LPAREN))
{
- qc.epsilon = lex_number (lexer);
- if (qc.epsilon <= 0)
+ if (!lex_force_id (lexer))
+ return false;
+
+ free (qc->var_distance);
+ qc->var_distance = xstrdup (lex_tokcstr (lexer));
+ if (NULL != dict_lookup_var (qc->dict, qc->var_distance))
{
- lex_error (lexer, _("The convergence criterium must be positive"));
- goto error;
+ lex_error (lexer,
+ _("A variable called `%s' already exists."),
+ qc->var_distance);
+ free (qc->var_distance);
+ qc->var_distance = NULL;
+ return false;
}
+
lex_get (lexer);
+
if (!lex_force_match (lexer, T_RPAREN))
- goto error;
+ return false;
}
}
- else if (lex_match_id (lexer, "MXITER"))
+ else
{
- if (lex_force_match (lexer, T_LPAREN) &&
- 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);
- if (!lex_force_match (lexer, T_RPAREN))
- goto error;
- }
+ lex_error_expecting (lexer, "CLUSTER", "DISTANCE");
+ return false;
}
- else if (lex_match_id (lexer, "NOINITIAL"))
+ }
+ }
+ else if (lex_match_id (lexer, "CRITERIA"))
+ {
+ lex_match (lexer, T_EQUALS);
+ while (lex_token (lexer) != T_ENDCMD
+ && lex_token (lexer) != T_SLASH)
+ {
+ if (lex_match_id (lexer, "CLUSTERS"))
{
- qc.no_initial = true;
+ if (!lex_force_match (lexer, T_LPAREN)
+ || !lex_force_int_range (lexer, "CLUSTERS", 1, INT_MAX))
+ return false;
+ qc->ngroups = lex_integer (lexer);
+ lex_get (lexer);
+ if (!lex_force_match (lexer, T_RPAREN))
+ return false;
}
- else if (lex_match_id (lexer, "NOUPDATE"))
+ else if (lex_match_id (lexer, "CONVERGE"))
{
- qc.no_update = true;
+ if (!lex_force_match (lexer, T_LPAREN)
+ || !lex_force_num_range_open (lexer, "CONVERGE",
+ 0, DBL_MAX))
+ return false;
+ qc->epsilon = lex_number (lexer);
+ lex_get (lexer);
+ if (!lex_force_match (lexer, T_RPAREN))
+ return false;
}
+ else if (lex_match_id (lexer, "MXITER"))
+ {
+ if (!lex_force_match (lexer, T_LPAREN)
+ || !lex_force_int_range (lexer, "MXITER", 1, INT_MAX))
+ return false;
+ qc->maxiter = lex_integer (lexer);
+ lex_get (lexer);
+ if (!lex_force_match (lexer, T_RPAREN))
+ return false;
+ }
+ else if (lex_match_id (lexer, "NOINITIAL"))
+ qc->initial = false;
+ else if (lex_match_id (lexer, "NOUPDATE"))
+ qc->update = false;
else
{
- lex_error (lexer, NULL);
- goto error;
+ lex_error_expecting (lexer, "CLUSTERS", "CONVERGE", "MXITER",
+ "NOINITIAL", "NOUPDATE");
+ return false;
}
}
}
else
{
- lex_error (lexer, NULL);
- goto error;
+ lex_error_expecting (lexer, "MISSING", "PRINT", "SAVE", "CRITERIA");
+ return false;
}
}
+ return true;
+}
- qc.wv = dict_get_weight (dict);
+int
+cmd_quick_cluster (struct lexer *lexer, struct dataset *ds)
+{
+ struct qc qc = {
+ .dataset = ds,
+ .dict = dataset_dict (ds),
+ .ngroups = 2,
+ .maxiter = 10,
+ .epsilon = DBL_EPSILON,
+ .missing_type = MISS_LISTWISE,
+ .exclude = MV_ANY,
+ .initial = true,
+ .update = true,
+ };
- {
- struct casereader *group;
- struct casegrouper *grouper = casegrouper_create_splits (proc_open (ds), dict);
+ if (!quick_cluster_parse (lexer, &qc))
+ goto error;
- 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, group, &qc);
- kmeans_destroy (kmeans);
- casereader_destroy (group);
- }
- ok = casegrouper_destroy (grouper);
- }
+ qc.wv = dict_get_weight (qc.dict);
+
+ struct casegrouper *grouper = casegrouper_create_splits (proc_open (ds), qc.dict);
+ struct casereader *group;
+ 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);
+
+ struct Kmeans *kmeans = kmeans_create (&qc);
+ kmeans_cluster (kmeans, group, &qc);
+ quick_cluster_show_results (kmeans, group, &qc);
+ kmeans_destroy (kmeans);
+ casereader_destroy (group);
+ }
+ bool ok = casegrouper_destroy (grouper);
ok = proc_commit (ds) && ok;
- free (qc.vars);
+ /* If requested, set a transformation to append the cluster and
+ distance values to the current dataset. */
+ if (qc.save_trans_data)
+ {
+ struct save_trans_data *std = qc.save_trans_data;
+
+ std->appending_reader = casewriter_make_reader (std->writer);
+
+ if (qc.save_membership)
+ {
+ /* Invent a variable name if necessary. */
+ int idx = 0;
+ struct string name;
+ ds_init_empty (&name);
+ while (qc.var_membership == NULL)
+ {
+ ds_clear (&name);
+ ds_put_format (&name, "QCL_%d", idx++);
- return (ok);
+ if (!dict_lookup_var (qc.dict, ds_cstr (&name)))
+ {
+ qc.var_membership = strdup (ds_cstr (&name));
+ break;
+ }
+ }
+ ds_destroy (&name);
+
+ std->membership = dict_create_var_assert (qc.dict, qc.var_membership, 0);
+ }
+
+ if (qc.save_distance)
+ {
+ /* Invent a variable name if necessary. */
+ int idx = 0;
+ struct string name;
+ ds_init_empty (&name);
+ while (qc.var_distance == NULL)
+ {
+ ds_clear (&name);
+ ds_put_format (&name, "QCL_%d", idx++);
+
+ if (!dict_lookup_var (qc.dict, ds_cstr (&name)))
+ {
+ qc.var_distance = strdup (ds_cstr (&name));
+ break;
+ }
+ }
+ ds_destroy (&name);
+
+ std->distance = dict_create_var_assert (qc.dict, qc.var_distance, 0);
+ }
+
+ static const struct trns_class trns_class = {
+ .name = "QUICK CLUSTER",
+ .execute = save_trans_func,
+ .destroy = save_trans_destroy,
+ };
+ add_transformation (qc.dataset, &trns_class, std);
+ }
+
+ free (qc.var_distance);
+ free (qc.var_membership);
+ free (qc.vars);
+ return ok;
error:
+ free (qc.var_distance);
+ free (qc.var_membership);
free (qc.vars);
return CMD_FAILURE;
}
projects / pspp / blobdiff