#define _(msgid) gettext (msgid)
#define N_(msgid) msgid
-/*
-Struct KMeans:
-Holds all of the information for the functions.
-int n, holds the number of observation and its default value is -1.
-We set it in kmeans_recalculate_centers in first invocation.
-*/
+/* Holds all of the information for the functions. int n, holds the number of
+ observation and its default value is -1. We set it in
+ kmeans_recalculate_centers in first invocation. */
struct Kmeans
{
- gsl_matrix *centers; //Centers for groups
+ gsl_matrix *centers; /* Centers for groups. */
gsl_vector_long *num_elements_groups;
- int ngroups; //Number of group. (Given by the user)
- casenumber n; //Number of observations. By default it is -1.
- int m; //Number of variables. (Given by the user)
- int maxiter; //Maximum number of iterations (Given by the user)
- int lastiter; //Show at which iteration it found the solution.
- int trials; //If not convergence, how many times has clustering done.
- gsl_matrix *initial_centers; //Initial random centers
- const struct variable **variables; //Variables
- gsl_permutation *group_order; //Handles group order for reporting
- struct casereader *original_casereader; //Casereader
+ 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; //We hold the group id's for each case in this structure
- const struct variable *wv; //Weighting variable
+ struct casereader *index_rdr; /* Group ids for each case. */
+ const struct variable *wv; /* Weighting variable. */
};
static struct Kmeans *kmeans_create (struct casereader *cs,
static void kmeans_destroy (struct Kmeans *kmeans);
-/*
-Creates and returns a struct of Kmeans with given casereader 'cs', parsed variables 'variables',
-number of cases 'n', number of variables 'm', number of clusters and amount of maximum iterations.
-*/
+/* Creates and returns a struct of Kmeans with given casereader 'cs', parsed
+ variables 'variables', number of cases 'n', number of variables 'm', number
+ of clusters and amount of maximum iterations. */
static struct Kmeans *
kmeans_create (struct casereader *cs, const struct variable **variables,
int m, int ngroups, int maxiter)
return (kmeans);
}
-
static void
kmeans_destroy (struct Kmeans *kmeans)
{
free (kmeans);
}
-
-
-/*
-Creates random centers using randomly selected cases from the data.
-*/
+/* Creates random centers using randomly selected cases from the data. */
static void
kmeans_randomize_centers (struct Kmeans *kmeans)
{
{
for (j = 0; j < kmeans->m; j++)
{
- //gsl_matrix_set(kmeans->centers,i,j, gsl_rng_uniform (kmeans->rng));
if (i == j)
{
gsl_matrix_set (kmeans->centers, i, j, 1);
}
}
}
-/*
-If it is the first iteration, the variable kmeans->initial_centers is NULL and
-it is created once for reporting issues. In SPSS, initial centers are shown in the reports
-but in PSPP it is not shown now. I am leaving it here.
-*/
+ /* If it is the first iteration, the variable kmeans->initial_centers is NULL
+ and it is created once for reporting issues. In SPSS, initial centers are
+ shown in the reports but in PSPP it is not shown now. I am leaving it
+ here. */
if (!kmeans->initial_centers)
{
kmeans->initial_centers = gsl_matrix_alloc (kmeans->ngroups, kmeans->m);
}
}
-
static int
kmeans_get_nearest_group (struct Kmeans *kmeans, struct ccase *c)
{
return (result);
}
-
-
-
-/*
-Re-calculates the cluster centers
-*/
+/* Re-calculate the cluster centers. */
static void
kmeans_recalculate_centers (struct Kmeans *kmeans)
{
if (kmeans->n == 0)
kmeans->n = i;
- //We got sum of each center but we need averages.
- //We are dividing centers to numobs. This may be inefficient and
- //we should check it again.
+ /* 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++)
{
casenumber numobs = kmeans->num_elements_groups->data[i];
}
}
-
-/*
-The variable index in struct Kmeans holds integer values that represents the current groups of cases.
-index[n]=a shows the nth case is belong to ath cluster.
-This function calculates these indexes and returns the number of different cases of the new and old
-index variables. If last two index variables are equal, there is no any enhancement of clustering.
-*/
+/* The variable index in struct Kmeans holds integer values that represents the
+ current groups of cases. index[n]=a shows the nth case is belong to ath
+ cluster. This function calculates these indexes and returns the number of
+ different cases of the new and old index variables. If last two index
+ variables are equal, there is no any enhancement of clustering. */
static int
kmeans_calculate_indexes_and_check_convergence (struct Kmeans *kmeans)
{
struct ccase *c;
struct casereader *cs = casereader_clone (kmeans->original_casereader);
-
- /* A casewriter into which we will write the indexes */
+ /* A casewriter into which we will write the indexes. */
struct casewriter *index_wtr = autopaging_writer_create (kmeans->proto);
gsl_vector_long_set_all (kmeans->num_elements_groups, 0);
for (; (c = casereader_read (cs)) != NULL; case_unref (c))
{
- /* A case to hold the new index */
+ /* A case to hold the new index. */
struct ccase *index_case_new = case_create (kmeans->proto);
int bestindex = kmeans_get_nearest_group (kmeans, c);
if (kmeans->wv)
kmeans->num_elements_groups->data[bestindex] += weight;
if (kmeans->index_rdr)
{
- /* A case from which the old index will be read */
+ /* A case from which the old index will be read. */
struct ccase *index_case_old = NULL;
- /* Read the case from the index casereader */
+ /* Read the case from the index casereader. */
index_case_old = casereader_read (kmeans->index_rdr);
- /* Set totaldiff, using the old_index */
+ /* Set totaldiff, using the old_index. */
totaldiff += abs (case_data_idx (index_case_old, 0)->f - bestindex);
- /* We have no use for the old case anymore, so unref it */
+ /* We have no use for the old case anymore, so unref it. */
case_unref (index_case_old);
}
else
{
- /* If this is the first run, then assume index is zero */
+ /* If this is the first run, then assume index is zero. */
totaldiff += bestindex;
}
- /* Set the value of the new index */
+ /* Set the value of the new inde.x */
case_data_rw_idx (index_case_new, 0)->f = bestindex;
/* and write the new index to the casewriter */
casewriter_write (index_wtr, index_case_new);
}
casereader_destroy (cs);
- /* We have now read through the entire index_rdr, so it's
- of no use anymore */
+ /* We have now read through the entire index_rdr, so it's of no use
+ anymore. */
casereader_destroy (kmeans->index_rdr);
/* Convert the writer into a reader, ready for the next iteration to read */
return (totaldiff);
}
-
static void
kmeans_order_groups (struct Kmeans *kmeans)
{
gsl_sort_vector_index (kmeans->group_order, v);
}
-/*
-Main algorithm.
-Does iterations, checks convergency
-*/
+/* Main algorithm.
+ Does iterations, checks convergency. */
static void
kmeans_cluster (struct Kmeans *kmeans)
{
kmeans_recalculate_centers (kmeans);
if (show_warning1 && kmeans->ngroups > kmeans->n)
{
- msg (MW,
- _
- ("Number of clusters may not be larger than the number of cases."));
+ msg (MW, _("Number of clusters may not be larger than the number "
+ "of cases."));
show_warning1 = false;
}
if (diffs == 0)
}
-
-/*
-Reports centers of clusters.
-initial parameter is optional for future use.
-if initial is true, initial cluster centers are reported. Otherwise, resulted centers are reported.
-*/
+/* Reports centers of clusters.
+ Initial parameter is optional for future use.
+ If initial is true, initial cluster centers are reported. Otherwise,
+ resulted centers are reported. */
static void
quick_cluster_show_centers (struct Kmeans *kmeans, bool initial)
{
tab_submit (t);
}
-
-/*
-Reports number of cases of each single cluster.
-*/
+/* Reports number of cases of each single cluster. */
static void
quick_cluster_show_number_cases (struct Kmeans *kmeans)
{
tab_submit (t);
}
-/*
-Reports
-*/
+/* Reports. */
static void
quick_cluster_show_results (struct Kmeans *kmeans)
{
kmeans_order_groups (kmeans);
- //uncomment the line above for reporting initial centers
- //quick_cluster_show_centers (kmeans, true);
+ /* 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);
}
-
int
cmd_quick_cluster (struct lexer *lexer, struct dataset *ds)
{
int maxiter = 2;
size_t p;
-
-
if (!parse_variables_const (lexer, dict, &variables, &p,
PV_NO_DUPLICATE | PV_NUMERIC))
{
return (CMD_FAILURE);
}
-
-
if (lex_match (lexer, T_SLASH))
{
if (lex_match_id (lexer, "CRITERIA"))
}
}
else
- {
- //further command set
- return (CMD_FAILURE);
- }
+ return CMD_FAILURE;
}
}
}
-
cs = proc_open (ds);
-
kmeans = kmeans_create (cs, variables, p, groups, maxiter);
kmeans->wv = dict_get_weight (dict);
projects / pspp-builds.git / blobdiff