/* 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/str.h"
#include "math/random.h"
#include "output/pivot-table.h"
-#include "output/text-item.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 CASE_IDX_MEMBERSHIP;
+ int CASE_IDX_DISTANCE;
+
+ /* The variables created to hold the values appended to the dataset */
+ struct variable *membership;
+ struct variable *distance;
+};
+
+
+#define SAVE_MEMBERSHIP 0x1
+#define SAVE_DISTANCE 0x2
+
struct qc
{
+ struct dataset *dataset;
+ struct dictionary *dict;
+
const struct variable **vars;
size_t n_vars;
enum missing_type missing_type;
enum mv_class exclude;
+
+ /* Which values are to be saved? */
+ int save_values;
+
+ /* The name of the new variable to contain the cluster of each case. */
+ char *var_membership;
+
+ /* The name of the new variable to contain the distance of each case
+ from its cluster centre. */
+ char *var_distance;
+
+ struct save_trans_data *save_trans_data;
};
/* Holds all of the information for the functions. int n, holds the number of
static void quick_cluster_show_membership (struct Kmeans *kmeans,
const struct casereader *reader,
- const struct qc *);
+ 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 casereader *reader,
- const struct qc *);
+ struct qc *);
int cmd_quick_cluster (struct lexer *lexer, struct dataset *ds);
double diff = 0;
for (j = 0; j < m1->size2; ++j)
{
- diff += pow2 (gsl_matrix_get (m1,i,j) - gsl_matrix_get (m2,i,j) );
+ diff += pow2 (gsl_matrix_get (m1,i,j) - gsl_matrix_get (m2,i,j));
}
if (diff > max_diff)
max_diff = diff;
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))
+ if (var_is_value_missing (qc->vars[j], val) & qc->exclude)
NOT_REACHED ();
dist += pow2 (gsl_matrix_get (kmeans->centers, which, j) - val->f);
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))
+ if (var_is_value_missing (qc->vars[j], val) & qc->exclude)
{
missing = true;
break;
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))
+ if (var_is_value_missing (qc->vars[j], val) & qc->exclude)
continue;
dist += pow2 (gsl_matrix_get (kmeans->centers, i, j) - val->f);
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))
+ if (var_is_value_missing (qc->vars[j], val) & qc->exclude)
missing = true;
}
}
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)
{
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);
}
}
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))
+ if (var_is_value_missing (qc->vars[j], val) & qc->exclude)
continue;
double *x = gsl_matrix_ptr (kmeans->updated_centers, group, j);
}
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++;
}
casereader_destroy (cs);
pivot_table_submit (table);
}
-/* Reports cluster membership for each case. */
+
+/* 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->CASE_IDX_MEMBERSHIP >= 0)
+ *case_num_rw (*c, std->membership) = case_num_idx (ca, std->CASE_IDX_MEMBERSHIP);
+
+ if (std->CASE_IDX_DISTANCE >= 0)
+ *case_num_rw (*c, std->distance) = case_num_idx (ca, std->CASE_IDX_DISTANCE);
+
+ case_unref (ca);
+
+ return TRNS_CONTINUE;
+}
+
+
+/* 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)
+ struct qc *qc)
{
- struct pivot_table *table = pivot_table_create (N_("Cluster Membership"));
+ struct pivot_table *table = NULL;
+ struct pivot_dimension *cases = NULL;
+ if (qc->print_cluster_membership)
+ {
+ table = pivot_table_create (N_("Cluster Membership"));
- pivot_dimension_create (table, PIVOT_AXIS_COLUMN, N_("Cluster"),
- N_("Cluster"));
+ pivot_dimension_create (table, PIVOT_AXIS_COLUMN, N_("Cluster"),
+ N_("Cluster"));
- struct pivot_dimension *cases
- = pivot_dimension_create (table, PIVOT_AXIS_ROW, N_("Case Number"));
+ cases
+ = pivot_dimension_create (table, PIVOT_AXIS_ROW, N_("Case Number"));
- cases->root->show_label = true;
+ cases->root->show_label = true;
+ }
gsl_permutation *ip = gsl_permutation_alloc (qc->ngroups);
gsl_permutation_inverse (ip, kmeans->group_order);
+ struct caseproto *proto = caseproto_create ();
+ if (qc->save_values)
+ {
+ /* Prepare data which may potentially be used in a
+ transformation appending new variables to the active
+ dataset. */
+ qc->save_trans_data = xzalloc (sizeof *qc->save_trans_data);
+ qc->save_trans_data->CASE_IDX_MEMBERSHIP = -1;
+ qc->save_trans_data->CASE_IDX_DISTANCE = -1;
+ qc->save_trans_data->writer = autopaging_writer_create (proto);
+
+ int idx = 0;
+ if (qc->save_values & SAVE_MEMBERSHIP)
+ {
+ proto = caseproto_add_width (proto, 0);
+ qc->save_trans_data->CASE_IDX_MEMBERSHIP = idx++;
+ }
+
+ if (qc->save_values & SAVE_DISTANCE)
+ {
+ proto = caseproto_add_width (proto, 0);
+ qc->save_trans_data->CASE_IDX_DISTANCE = idx++;
+ }
+ }
+
struct casereader *cs = casereader_clone (reader);
struct ccase *c;
for (int i = 0; (c = casereader_read (cs)) != NULL; i++, case_unref (c))
kmeans_get_nearest_group (kmeans, c, qc, &clust, NULL, NULL, NULL);
int cluster = ip->data[clust];
- int case_idx = pivot_category_create_leaf (cases->root,
+ if (qc->save_trans_data)
+ {
+ /* Calculate the membership and distance values. */
+ struct ccase *outc = case_create (proto);
+ if (qc->save_values & SAVE_MEMBERSHIP)
+ *case_num_rw_idx (outc, qc->save_trans_data->CASE_IDX_MEMBERSHIP) = cluster + 1;
+
+ if (qc->save_values & SAVE_DISTANCE)
+ *case_num_rw_idx (outc, qc->save_trans_data->CASE_IDX_DISTANCE)
+ = 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));
+ pivot_table_put2 (table, 0, case_idx,
+ pivot_value_new_integer (cluster + 1));
+ }
}
+ caseproto_unref (proto);
gsl_permutation_free (ip);
- pivot_table_submit (table);
+
+ if (qc->print_cluster_membership)
+ pivot_table_submit (table);
casereader_destroy (cs);
}
/* Reports. */
static void
quick_cluster_show_results (struct Kmeans *kmeans, const struct casereader *reader,
- const struct qc *qc)
+ struct qc *qc)
{
kmeans_order_groups (kmeans, qc); /* what does this do? */
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);
if (lex_match_id (lexer, "LISTWISE")
|| lex_match_id (lexer, "DEFAULT"))
{
- qc.missing_type = MISS_LISTWISE;
+ 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;
+ 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;
+ return false;
+ }
+ }
+ }
+ 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, "CLUSTER"))
+ {
+ qc->save_values |= SAVE_MEMBERSHIP;
+ if (lex_match (lexer, T_LPAREN))
+ {
+ 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,
+ _("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))
+ return false;
+ }
+ }
+ else if (lex_match_id (lexer, "DISTANCE"))
+ {
+ qc->save_values |= SAVE_DISTANCE;
+ if (lex_match (lexer, T_LPAREN))
+ {
+ 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,
+ _("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))
+ return false;
+ }
+ }
+ else
+ {
+ lex_error (lexer, _("Expecting %s or %s."),
+ "CLUSTER", "DISTANCE");
+ return false;
}
}
}
if (lex_match_id (lexer, "CLUSTERS"))
{
if (lex_force_match (lexer, T_LPAREN) &&
- lex_force_int (lexer))
+ lex_force_int_range (lexer, "CLUSTERS", 1, INT_MAX))
{
- qc.ngroups = lex_integer (lexer);
- if (qc.ngroups <= 0)
- {
- lex_error (lexer, _("The number of clusters must be positive"));
- goto error;
- }
+ qc->ngroups = lex_integer (lexer);
lex_get (lexer);
if (!lex_force_match (lexer, T_RPAREN))
- goto error;
+ return false;
}
}
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)
+ qc->epsilon = lex_number (lexer);
+ if (qc->epsilon <= 0)
{
lex_error (lexer, _("The convergence criterion must be positive"));
- goto error;
+ return false;
}
lex_get (lexer);
if (!lex_force_match (lexer, T_RPAREN))
- goto error;
+ return false;
}
}
else if (lex_match_id (lexer, "MXITER"))
{
if (lex_force_match (lexer, T_LPAREN) &&
- lex_force_int (lexer))
+ lex_force_int_range (lexer, "MXITER", 1, INT_MAX))
{
- qc.maxiter = lex_integer (lexer);
- if (qc.maxiter <= 0)
- {
- lex_error (lexer, _("The number of iterations must be positive"));
- goto error;
- }
+ qc->maxiter = lex_integer (lexer);
lex_get (lexer);
if (!lex_force_match (lexer, T_RPAREN))
- goto error;
+ return false;
}
}
else if (lex_match_id (lexer, "NOINITIAL"))
{
- qc.no_initial = true;
+ qc->no_initial = true;
}
else if (lex_match_id (lexer, "NOUPDATE"))
{
- qc.no_update = true;
+ qc->no_update = true;
}
else
{
lex_error (lexer, NULL);
- goto error;
+ return false;
}
}
}
else
{
lex_error (lexer, NULL);
- goto error;
+ return false;
}
}
+ return true;
+}
- qc.wv = dict_get_weight (dict);
+int
+cmd_quick_cluster (struct lexer *lexer, struct dataset *ds)
+{
+ struct qc qc;
+ struct Kmeans *kmeans;
+ bool ok;
+ memset (&qc, 0, sizeof qc);
+ qc.dataset = ds;
+ qc.dict = dataset_dict (ds);
+ qc.ngroups = 2;
+ qc.maxiter = 10;
+ qc.epsilon = DBL_EPSILON;
+ qc.missing_type = MISS_LISTWISE;
+ qc.exclude = MV_ANY;
+
+
+ if (!quick_cluster_parse (lexer, &qc))
+ goto error;
+
+ qc.wv = dict_get_weight (qc.dict);
{
struct casereader *group;
- struct casegrouper *grouper = casegrouper_create_splits (proc_open (ds), dict);
+ struct casegrouper *grouper = casegrouper_create_splits (proc_open (ds), qc.dict);
while (casegrouper_get_next_group (grouper, &group))
{
- if ( qc.missing_type == MISS_LISTWISE )
+ if (qc.missing_type == MISS_LISTWISE)
{
group = casereader_create_filter_missing (group, qc.vars, qc.n_vars,
qc.exclude,
}
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);
+ std->writer = NULL;
+
+ if (qc.save_values & 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++);
+
+ 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_values & 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