No more winnow.

4 files changed, 1 insertions, 732 deletions

diff --git a/src/libstat/CMakeLists.txt b/src/libstat/CMakeLists.txt
index 6254a41a6..810570f20 100644
--- a/src/libstat/CMakeLists.txt
+++ b/src/libstat/CMakeLists.txt
@@ -5,8 +5,7 @@ SET(TOKENIZERSSRC  tokenizers/tokenizers.c
 				tokenizers/osb.c)
 
 SET(CLASSIFIERSSRC classifiers/classifiers.c
-                classifiers/bayes.c
-				classifiers/winnow.c)
+                classifiers/bayes.c)
 				
 ADD_LIBRARY(rspamd-stat ${LINK_TYPE} ${LIBSTATSRC} ${TOKENIZERSSRC} ${CLASSIFIERSSRC})
 IF(NOT DEBIAN_BUILD)
diff --git a/src/libstat/classifiers.h b/src/libstat/classifiers.h
index fd1b63bcf..d13178486 100644
--- a/src/libstat/classifiers.h
+++ b/src/libstat/classifiers.h
@@ -47,34 +47,6 @@ struct classifier {
 /* Get classifier structure by name or return NULL if this name is not found */
 struct classifier * get_classifier (const char *name);
 
-/* Winnow algorithm */
-struct classifier_ctx * winnow_init (rspamd_mempool_t *pool,
-	struct rspamd_classifier_config *cf);
-gboolean winnow_classify (struct classifier_ctx * ctx,
-	statfile_pool_t *pool,
-	GTree *input,
-	struct rspamd_task *task,
-	lua_State *L);
-gboolean winnow_learn (struct classifier_ctx * ctx,
-	statfile_pool_t *pool,
-	const char *symbol,
-	GTree *input,
-	gboolean in_class,
-	double *sum,
-	double multiplier,
-	GError **err);
-gboolean winnow_learn_spam (struct classifier_ctx * ctx,
-	statfile_pool_t *pool,
-	GTree *input,
-	struct rspamd_task *task,
-	gboolean is_spam,
-	lua_State *L,
-	GError **err);
-GList * winnow_weights (struct classifier_ctx * ctx,
-	statfile_pool_t *pool,
-	GTree *input,
-	struct rspamd_task *task);
-
 /* Bayes algorithm */
 struct classifier_ctx * bayes_init (rspamd_mempool_t *pool,
 	struct rspamd_classifier_config *cf);
diff --git a/src/libstat/classifiers/classifiers.c b/src/libstat/classifiers/classifiers.c
index 95dd52c44..6af7d2dc8 100644
--- a/src/libstat/classifiers/classifiers.c
+++ b/src/libstat/classifiers/classifiers.c
@@ -30,14 +30,6 @@
 
 struct classifier classifiers[] = {
 	{
-		.name = "winnow",
-		.init_func = winnow_init,
-		.classify_func = winnow_classify,
-		.learn_func = winnow_learn,
-		.learn_spam_func = winnow_learn_spam,
-		.weights_func = winnow_weights
-	},
-	{
 		.name = "bayes",
 		.init_func = bayes_init,
 		.classify_func = bayes_classify,
diff --git a/src/libstat/classifiers/winnow.c b/src/libstat/classifiers/winnow.c
deleted file mode 100644
index 68d456968..000000000
--- a/src/libstat/classifiers/winnow.c
+++ /dev/null
@@ -1,694 +0,0 @@
-/*
- * Copyright (c) 2009-2012, Vsevolod Stakhov
- * All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright
- *       notice, this list of conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright
- *       notice, this list of conditions and the following disclaimer in the
- *       documentation and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY AUTHOR ''AS IS'' AND ANY
- * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL AUTHOR BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- */
-
-/*
- * Winnow classifier
- */
-
-#include "classifiers.h"
-#include "tokenizers.h"
-#include "main.h"
-#include "filter.h"
-#include "cfg_file.h"
-#include "lua/lua_common.h"
-
-#define WINNOW_PROMOTION 1.23
-#define WINNOW_DEMOTION 0.83
-
-#define MEDIAN_WINDOW_SIZE 5
-
-#define MAX_WEIGHT G_MAXDOUBLE / 2.
-
-
-
-#define MAX_LEARN_ITERATIONS 100
-
-static inline GQuark
-winnow_error_quark (void)
-{
-	return g_quark_from_static_string ("winnow-error");
-}
-
-struct winnow_callback_data {
-	statfile_pool_t *pool;
-	struct classifier_ctx *ctx;
-	stat_file_t *file;
-	stat_file_t *learn_file;
-	long double sum;
-	long double start;
-	double multiplier;
-	guint32 count;
-	guint32 new_blocks;
-	gboolean in_class;
-	gboolean do_demote;
-	gboolean fresh_run;
-	time_t now;
-};
-
-static const double max_common_weight = MAX_WEIGHT * WINNOW_DEMOTION;
-
-
-
-static gboolean
-winnow_classify_callback (gpointer key, gpointer value, gpointer data)
-{
-	token_node_t *node = key;
-	struct winnow_callback_data *cd = data;
-	double v;
-
-	/* Consider that not found blocks have value 1 */
-	v =
-		statfile_pool_get_block (cd->pool, cd->file, node->h1, node->h2,
-			cd->now);
-	if (fabs (v) > ALPHA) {
-		cd->sum += v;
-	}
-	else {
-		cd->sum += 1.0;
-		cd->new_blocks++;
-	}
-
-	cd->count++;
-
-	return FALSE;
-}
-
-static gboolean
-winnow_learn_callback (gpointer key, gpointer value, gpointer data)
-{
-	token_node_t *node = key;
-	struct winnow_callback_data *cd = data;
-	double v, c;
-
-	c = (cd->in_class) ? WINNOW_PROMOTION * cd->multiplier : WINNOW_DEMOTION /
-		cd->multiplier;
-
-	/* Consider that not found blocks have value 1 */
-	v =
-		statfile_pool_get_block (cd->pool, cd->file, node->h1, node->h2,
-			cd->now);
-	if (fabs (v) < ALPHA) {
-		/* Block not found, insert new */
-		cd->start += 1;
-		if (cd->file == cd->learn_file) {
-			statfile_pool_set_block (cd->pool,
-				cd->file,
-				node->h1,
-				node->h2,
-				cd->now,
-				c);
-			node->value = c;
-			cd->new_blocks++;
-		}
-	}
-	else {
-		cd->start += v;
-		/* Here we just increase the extra value of block */
-		if (cd->fresh_run) {
-			node->extra = 0;
-		}
-		else {
-			node->extra++;
-		}
-		node->value = v;
-
-		if (node->extra > 1) {
-			/*
-			 * Assume that this node is common for several statfiles, so
-			 * decrease its weight proportianally
-			 */
-			if (node->value > max_common_weight) {
-				/* Static fluctuation */
-				statfile_pool_set_block (cd->pool,
-					cd->file,
-					node->h1,
-					node->h2,
-					cd->now,
-					0.);
-				node->value = 0.;
-			}
-			else if (node->value > WINNOW_PROMOTION * cd->multiplier) {
-				/* Try to decrease its value */
-				/* XXX: it is more intelligent to add some adaptive filter here */
-				if (cd->file == cd->learn_file) {
-					if (node->value > max_common_weight / 2.) {
-						node->value *= c;
-					}
-					else {
-						/*
-						 * Too high token value that exists also in other
-						 * statfiles, may be statistic error, so decrease it
-						 * slightly
-						 */
-						node->value *= WINNOW_DEMOTION;
-					}
-				}
-				else {
-					node->value = WINNOW_DEMOTION / cd->multiplier;
-				}
-				statfile_pool_set_block (cd->pool,
-					cd->file,
-					node->h1,
-					node->h2,
-					cd->now,
-					node->value);
-			}
-		}
-		else if (cd->file == cd->learn_file) {
-			/* New block or block that is in only one statfile */
-			/* Set some limit on growing */
-			if (v > MAX_WEIGHT) {
-				node->value = v;
-			}
-			else {
-				node->value *= c;
-			}
-			statfile_pool_set_block (cd->pool,
-				cd->file,
-				node->h1,
-				node->h2,
-				cd->now,
-				node->value);
-		}
-		else if (cd->do_demote) {
-			/* Demote blocks in file */
-			node->value *= WINNOW_DEMOTION / cd->multiplier;
-			statfile_pool_set_block (cd->pool,
-				cd->file,
-				node->h1,
-				node->h2,
-				cd->now,
-				node->value);
-		}
-	}
-
-
-	cd->sum += node->value;
-
-	cd->count++;
-
-	return FALSE;
-}
-
-struct classifier_ctx *
-winnow_init (rspamd_mempool_t * pool, struct rspamd_classifier_config *cfg)
-{
-	struct classifier_ctx *ctx =
-		rspamd_mempool_alloc (pool, sizeof (struct classifier_ctx));
-
-	ctx->pool = pool;
-	ctx->cfg = cfg;
-
-	return ctx;
-}
-
-gboolean
-winnow_classify (struct classifier_ctx *ctx,
-	statfile_pool_t * pool,
-	GTree * input,
-	struct rspamd_task *task,
-	lua_State *L)
-{
-	struct winnow_callback_data data;
-	char *sumbuf, *value;
-	long double res = 0., max = 0.;
-	GList *cur;
-	struct rspamd_statfile_config *st, *sel = NULL;
-	int nodes, minnodes;
-
-	g_assert (pool != NULL);
-	g_assert (ctx != NULL);
-
-	data.pool = pool;
-	data.now = time (NULL);
-	data.ctx = ctx;
-
-	if (ctx->cfg->opts &&
-		(value = g_hash_table_lookup (ctx->cfg->opts, "min_tokens")) != NULL) {
-		minnodes = strtol (value, NULL, 10);
-		nodes = g_tree_nnodes (input);
-		if (nodes > FEATURE_WINDOW_SIZE) {
-			nodes = nodes / FEATURE_WINDOW_SIZE + FEATURE_WINDOW_SIZE;
-		}
-		if (nodes < minnodes) {
-			msg_info (
-				"do not classify message as it has too few tokens: %d, while %d min",
-				nodes,
-				minnodes);
-			return FALSE;
-		}
-	}
-
-	cur = rspamd_lua_call_cls_pre_callbacks (ctx->cfg, task, FALSE, FALSE, L);
-	if (cur) {
-		rspamd_mempool_add_destructor (task->task_pool,
-			(rspamd_mempool_destruct_t)g_list_free, cur);
-	}
-	else {
-		cur = ctx->cfg->statfiles;
-	}
-
-	while (cur) {
-		st = cur->data;
-		data.sum = 0;
-		data.count = 0;
-		data.new_blocks = 0;
-		if ((data.file = statfile_pool_is_open (pool, st->path)) == NULL) {
-			if ((data.file =
-				statfile_pool_open (pool, st->path, st->size, FALSE)) == NULL) {
-				msg_warn ("cannot open %s, skip it", st->path);
-				cur = g_list_next (cur);
-				continue;
-			}
-		}
-
-		if (data.file != NULL) {
-			g_tree_foreach (input, winnow_classify_callback, &data);
-		}
-
-		if (data.count != 0) {
-			res = data.sum / (double)data.count;
-		}
-		else {
-			res = 0;
-		}
-		if (res > max) {
-			max = res;
-			sel = st;
-		}
-		cur = g_list_next (cur);
-	}
-
-	if (sel != NULL) {
-#ifdef WITH_LUA
-		max = rspamd_lua_call_cls_post_callbacks (ctx->cfg, task, max, L);
-#endif
-#ifdef HAVE_TANHL
-		max = tanhl (max);
-#else
-		/*
-		 * As some implementations of libm does not support tanhl, try to use
-		 * tanh
-		 */
-		max = tanh ((double) max);
-#endif
-		sumbuf = rspamd_mempool_alloc (task->task_pool, 32);
-		rspamd_snprintf (sumbuf, 32, "%.2F", max);
-		cur = g_list_prepend (NULL, sumbuf);
-		rspamd_task_insert_result (task, sel->symbol, max, cur);
-	}
-
-	return TRUE;
-}
-
-GList *
-winnow_weights (struct classifier_ctx *ctx,
-	statfile_pool_t * pool,
-	GTree * input,
-	struct rspamd_task *task)
-{
-	struct winnow_callback_data data;
-	long double res = 0.;
-	GList *cur, *resl = NULL;
-	struct rspamd_statfile_config *st;
-	struct classify_weight *w;
-	char *value;
-	int nodes, minnodes;
-
-	g_assert (pool != NULL);
-	g_assert (ctx != NULL);
-
-	data.pool = pool;
-	data.now = time (NULL);
-	data.ctx = ctx;
-
-	if (ctx->cfg->opts &&
-		(value = g_hash_table_lookup (ctx->cfg->opts, "min_tokens")) != NULL) {
-		minnodes = strtol (value, NULL, 10);
-		nodes = g_tree_nnodes (input);
-		if (nodes > FEATURE_WINDOW_SIZE) {
-			nodes = nodes / FEATURE_WINDOW_SIZE + FEATURE_WINDOW_SIZE;
-		}
-		if (nodes < minnodes) {
-			msg_info (
-				"do not classify message as it has too few tokens: %d, while %d min",
-				nodes,
-				minnodes);
-			return NULL;
-		}
-	}
-
-	cur = ctx->cfg->statfiles;
-	while (cur) {
-		st = cur->data;
-		data.sum = 0;
-		data.count = 0;
-		if ((data.file = statfile_pool_is_open (pool, st->path)) == NULL) {
-			if ((data.file =
-				statfile_pool_open (pool, st->path, st->size, FALSE)) == NULL) {
-				msg_warn ("cannot open %s, skip it", st->path);
-				cur = g_list_next (cur);
-				continue;
-			}
-		}
-
-		if (data.file != NULL) {
-			g_tree_foreach (input, winnow_classify_callback, &data);
-		}
-
-		w =
-			rspamd_mempool_alloc0 (task->task_pool,
-				sizeof (struct classify_weight));
-		if (data.count != 0) {
-			res = data.sum / (double)data.count;
-		}
-		else {
-			res = 0;
-		}
-		w->name = st->symbol;
-		w->weight = res;
-		resl = g_list_prepend (resl, w);
-		cur = g_list_next (cur);
-	}
-
-	if (resl != NULL) {
-		rspamd_mempool_add_destructor (task->task_pool,
-			(rspamd_mempool_destruct_t)g_list_free, resl);
-	}
-
-	return resl;
-
-}
-
-
-gboolean
-winnow_learn (struct classifier_ctx *ctx,
-	statfile_pool_t *pool,
-	const char *symbol,
-	GTree * input,
-	int in_class,
-	double *sum,
-	double multiplier,
-	GError **err)
-{
-	struct winnow_callback_data data = {
-		.file = NULL,
-		.multiplier = multiplier
-	};
-	char *value;
-	int nodes, minnodes, iterations = 0;
-	struct rspamd_statfile_config *st, *sel_st = NULL;
-	stat_file_t *sel = NULL, *to_learn;
-	long double res = 0., max = 0., start_value = 0., end_value = 0.;
-	double learn_threshold = 0.0;
-	GList *cur, *to_demote = NULL;
-	gboolean force_learn = FALSE;
-
-	g_assert (pool != NULL);
-	g_assert (ctx != NULL);
-
-	data.pool = pool;
-	data.in_class = in_class;
-	data.now = time (NULL);
-	data.ctx = ctx;
-
-
-	if (ctx->cfg->opts &&
-		(value = g_hash_table_lookup (ctx->cfg->opts, "min_tokens")) != NULL) {
-		minnodes = strtol (value, NULL, 10);
-		nodes = g_tree_nnodes (input);
-		if (nodes > FEATURE_WINDOW_SIZE) {
-			nodes = nodes / FEATURE_WINDOW_SIZE + FEATURE_WINDOW_SIZE;
-		}
-		if (nodes < minnodes) {
-			msg_info (
-				"do not learn message as it has too few tokens: %d, while %d min",
-				nodes,
-				minnodes);
-			if (sum != NULL) {
-				*sum = 0;
-			}
-			g_set_error (err,
-				winnow_error_quark (),              /* error domain */
-				1,                                  /* error code */
-				"message contains too few tokens: %d, while min is %d",
-				nodes, minnodes);
-			return FALSE;
-		}
-	}
-	if (ctx->cfg->opts &&
-		(value =
-		g_hash_table_lookup (ctx->cfg->opts, "learn_threshold")) != NULL) {
-		learn_threshold = strtod (value, NULL);
-	}
-
-	if (learn_threshold <= 1.0 && learn_threshold >= 0) {
-		/* Classify message and check target statfile score */
-		cur = ctx->cfg->statfiles;
-		while (cur) {
-			/* Open or create all statfiles inside classifier */
-			st = cur->data;
-			if (statfile_pool_is_open (pool, st->path) == NULL) {
-				if (statfile_pool_open (pool, st->path, st->size,
-					FALSE) == NULL) {
-					msg_warn ("cannot open %s", st->path);
-					if (statfile_pool_create (pool, st->path, st->size) == -1) {
-						msg_err ("cannot create statfile %s", st->path);
-						g_set_error (err,
-							winnow_error_quark (),          /* error domain */
-							1,                              /* error code */
-							"cannot create statfile: %s",
-							st->path);
-						return FALSE;
-					}
-					if (statfile_pool_open (pool, st->path, st->size,
-						FALSE) == NULL) {
-						g_set_error (err,
-							winnow_error_quark (),          /* error domain */
-							1,                              /* error code */
-							"open statfile %s after creation",
-							st->path);
-						msg_err ("cannot open statfile %s after creation",
-							st->path);
-						return FALSE;
-					}
-				}
-			}
-			if (strcmp (st->symbol, symbol) == 0) {
-				sel_st = st;
-
-			}
-			cur = g_list_next (cur);
-		}
-
-		if (sel_st == NULL) {
-			g_set_error (err,
-				winnow_error_quark (),          /* error domain */
-				1,                              /* error code */
-				"cannot find statfile for symbol %s",
-				symbol);
-			msg_err ("cannot find statfile for symbol %s", symbol);
-			return FALSE;
-		}
-
-		to_learn = statfile_pool_is_open (pool, sel_st->path);
-		if (to_learn == NULL) {
-			g_set_error (err,
-				winnow_error_quark (),          /* error domain */
-				1,                              /* error code */
-				"statfile %s is not opened this maybe if your statfile pool is too small to handle all statfiles",
-				sel_st->path);
-			return FALSE;
-		}
-		/* Check target statfile */
-		data.file = to_learn;
-		data.sum = 0;
-		data.count = 0;
-		data.new_blocks = 0;
-		g_tree_foreach (input, winnow_classify_callback, &data);
-		if (data.count > 0) {
-			max = data.sum / (double)data.count;
-		}
-		else {
-			max = 0;
-		}
-		/* If most of blocks are not presented in targeted statfile do forced learn */
-		if (max < 1 + learn_threshold) {
-			force_learn = TRUE;
-		}
-		/* Check other statfiles */
-		while (cur) {
-			st = cur->data;
-			data.sum = 0;
-			data.count = 0;
-			if ((data.file = statfile_pool_is_open (pool, st->path)) == NULL) {
-				g_set_error (err,
-					winnow_error_quark (),          /* error domain */
-					1,                              /* error code */
-					"statfile %s is not opened this maybe if your statfile pool is too small to handle all statfiles",
-					st->path);
-				return FALSE;
-			}
-			g_tree_foreach (input, winnow_classify_callback, &data);
-			if (data.count != 0) {
-				res = data.sum / data.count;
-			}
-			else {
-				res = 0;
-			}
-			if (to_learn != data.file && res - max > 1 - learn_threshold) {
-				/* Demote tokens in this statfile */
-				to_demote = g_list_prepend (to_demote, data.file);
-			}
-			cur = g_list_next (cur);
-		}
-	}
-	else {
-		msg_err (
-			"learn threshold is more than 1 or less than 0, so cannot do learn, please check your configuration");
-		g_set_error (err,
-			winnow_error_quark (),          /* error domain */
-			1,                              /* error code */
-			"bad learn_threshold setting: %.2f",
-			learn_threshold);
-		return FALSE;
-	}
-	/* If to_demote list is empty this message is already classified correctly */
-	if (max > WINNOW_PROMOTION && to_demote == NULL && !force_learn) {
-		msg_info (
-			"this message is already of class %s with threshold %.2f and weight %.2F",
-			sel_st->symbol,
-			learn_threshold,
-			max);
-		goto end;
-	}
-	data.learn_file = to_learn;
-	end_value = max;
-	do {
-		cur = ctx->cfg->statfiles;
-		data.fresh_run = TRUE;
-		while (cur) {
-			st = cur->data;
-			data.sum = 0;
-			data.count = 0;
-			data.new_blocks = 0;
-			data.start = 0;
-			if ((data.file = statfile_pool_is_open (pool, st->path)) == NULL) {
-				return FALSE;
-			}
-			if (to_demote != NULL &&
-				g_list_find (to_demote, data.file) != NULL) {
-				data.do_demote = TRUE;
-			}
-			else {
-				data.do_demote = FALSE;
-			}
-
-			statfile_pool_lock_file (pool, data.file);
-			g_tree_foreach (input, winnow_learn_callback, &data);
-			statfile_pool_unlock_file (pool, data.file);
-			if (data.count != 0) {
-				res = data.sum / data.count;
-			}
-			else {
-				res = 0;
-			}
-			if (res > max) {
-				max = res;
-				sel = data.file;
-			}
-			if (data.file == to_learn) {
-				if (data.count > 0) {
-					start_value = data.start / data.count;
-				}
-				end_value = res;
-			}
-			cur = g_list_next (cur);
-			data.fresh_run = FALSE;
-		}
-
-		data.multiplier *= WINNOW_PROMOTION;
-		msg_info (
-			"learn iteration %d for statfile %s: %G -> %G, multiplier: %.2f",
-			iterations + 1,
-			symbol,
-			start_value,
-			end_value,
-			data.multiplier);
-	} while ((in_class ? sel != to_learn : sel ==
-		to_learn)  && iterations++ < MAX_LEARN_ITERATIONS);
-
-	if (iterations >= MAX_LEARN_ITERATIONS) {
-		msg_warn (
-			"learning statfile %s  was not fully successfull: iterations count is limited to %d, final sum is %G",
-			sel_st->symbol,
-			MAX_LEARN_ITERATIONS,
-			max);
-		g_set_error (err,
-			winnow_error_quark (),          /* error domain */
-			1,                              /* error code */
-			"learning statfile %s  was not fully successfull: iterations count is limited to %d",
-			sel_st->symbol, MAX_LEARN_ITERATIONS);
-		return FALSE;
-	}
-	else {
-		msg_info (
-			"learned statfile %s successfully with %d iterations and sum %G",
-			sel_st->symbol,
-			iterations + 1,
-			max);
-	}
-
-
-end:
-	if (sum) {
-#ifdef HAVE_TANHL
-		*sum = (double)tanhl (max);
-#else
-		/*
-		 * As some implementations of libm does not support tanhl, try to use
-		 * tanh
-		 */
-		*sum = tanh ((double) max);
-#endif
-	}
-	return TRUE;
-}
-
-gboolean
-winnow_learn_spam (struct classifier_ctx * ctx,
-	statfile_pool_t *pool,
-	GTree *input,
-	struct rspamd_task *task,
-	gboolean is_spam,
-	lua_State *L,
-	GError **err)
-{
-	g_set_error (err,
-		winnow_error_quark (),                  /* error domain */
-		1,                                      /* error code */
-		"learn spam is not supported for winnow"
-		);
-	return FALSE;
-}