/* 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
#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;
+
+ int ngroups; /* Number of group. (Given by the user) */
+ int maxiter; /* Maximum iterations (Given by the user) */
+
+ 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
observation and its default value is -1. We set it in
kmeans_recalculate_centers in first invocation. */
{
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 (default -1). */
- int m; /* Number of variables. (Given by the user) */
- int maxiter; /* Maximum iterations (Given by the user) */
+
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. */
- const struct variable **variables;
+
gsl_permutation *group_order; /* Group order for reporting. */
- struct casereader *original_casereader;
struct caseproto *proto;
struct casereader *index_rdr; /* Group ids for each case. */
- const struct variable *wv; /* Weighting variable. */
};
-static struct Kmeans *kmeans_create (struct casereader *cs,
- const struct variable **variables,
- int m, int ngroups, int maxiter);
+static struct Kmeans *kmeans_create (const struct qc *qc);
-static void kmeans_randomize_centers (struct Kmeans *kmeans);
+static void kmeans_randomize_centers (struct Kmeans *kmeans, const struct qc *qc);
-static int kmeans_get_nearest_group (struct Kmeans *kmeans, struct ccase *c);
+static int kmeans_get_nearest_group (struct Kmeans *kmeans, struct ccase *c, const struct qc *);
-static void kmeans_recalculate_centers (struct Kmeans *kmeans);
+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);
+kmeans_calculate_indexes_and_check_convergence (struct Kmeans *kmeans, const struct casereader *reader, const struct qc *);
-static void kmeans_order_groups (struct Kmeans *kmeans);
+static void kmeans_order_groups (struct Kmeans *kmeans, const struct qc *);
-static void kmeans_cluster (struct Kmeans *kmeans);
+static void kmeans_cluster (struct Kmeans *kmeans, struct casereader *reader, const struct qc *);
-static void quick_cluster_show_centers (struct Kmeans *kmeans, bool initial);
+static void quick_cluster_show_centers (struct Kmeans *kmeans, bool initial, const struct qc *);
-static void quick_cluster_show_number_cases (struct Kmeans *kmeans);
+static void quick_cluster_show_number_cases (struct Kmeans *kmeans, const struct qc *);
-static void quick_cluster_show_results (struct Kmeans *kmeans);
+static void quick_cluster_show_results (struct Kmeans *kmeans, const struct qc *);
int cmd_quick_cluster (struct lexer *lexer, struct dataset *ds);
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 ();
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. */
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++)
{
if (i == j)
{
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)
{
/* 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);
}
/* 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++)
+ 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)
{
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. */
struct casewriter *index_wtr = autopaging_writer_create (kmeans->proto);
{
/* 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)
{
}
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. */
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."));
break;
}
- for (i = 0; i < kmeans->ngroups; i++)
+ for (i = 0; i < qc->ngroups; i++)
{
if (kmeans->num_elements_groups->data[i] == 0)
{
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);
}
}
}
/* 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. */
static void
-quick_cluster_show_results (struct Kmeans *kmeans)
+quick_cluster_show_results (struct Kmeans *kmeans, const struct qc *qc)
{
- kmeans_order_groups (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);
- quick_cluster_show_number_cases (kmeans);
+ 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
- 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