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-rw-r--r--src/lua/lua_fann.c1032
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diff --git a/src/lua/lua_fann.c b/src/lua/lua_fann.c
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index 7eb493740..000000000
--- a/src/lua/lua_fann.c
+++ /dev/null
@@ -1,1032 +0,0 @@
-/*-
- * Copyright 2016 Vsevolod Stakhov
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#include "lua_common.h"
-
-#ifdef WITH_FANN
-#include <fann.h>
-#endif
-
-#include "unix-std.h"
-
-/***
- * @module rspamd_fann
- * This module enables [fann](http://libfann.github.io) interaction in rspamd
- * Please note, that this module works merely if you have `ENABLE_FANN=ON` option
- * definition when building rspamd
- */
-
-/*
- * Fann functions
- */
-LUA_FUNCTION_DEF (fann, is_enabled);
-LUA_FUNCTION_DEF (fann, create);
-LUA_FUNCTION_DEF (fann, create_full);
-LUA_FUNCTION_DEF (fann, load_file);
-LUA_FUNCTION_DEF (fann, load_data);
-
-/*
- * Fann methods
- */
-LUA_FUNCTION_DEF (fann, train);
-LUA_FUNCTION_DEF (fann, train_threaded);
-LUA_FUNCTION_DEF (fann, test);
-LUA_FUNCTION_DEF (fann, save);
-LUA_FUNCTION_DEF (fann, data);
-LUA_FUNCTION_DEF (fann, get_inputs);
-LUA_FUNCTION_DEF (fann, get_outputs);
-LUA_FUNCTION_DEF (fann, get_layers);
-LUA_FUNCTION_DEF (fann, get_mse);
-LUA_FUNCTION_DEF (fann, dtor);
-
-static const struct luaL_reg fannlib_f[] = {
- LUA_INTERFACE_DEF (fann, is_enabled),
- LUA_INTERFACE_DEF (fann, create),
- LUA_INTERFACE_DEF (fann, create_full),
- LUA_INTERFACE_DEF (fann, load_file),
- {"load", lua_fann_load_file},
- LUA_INTERFACE_DEF (fann, load_data),
- {NULL, NULL}
-};
-
-static const struct luaL_reg fannlib_m[] = {
- LUA_INTERFACE_DEF (fann, train),
- LUA_INTERFACE_DEF (fann, train_threaded),
- LUA_INTERFACE_DEF (fann, test),
- LUA_INTERFACE_DEF (fann, save),
- LUA_INTERFACE_DEF (fann, data),
- LUA_INTERFACE_DEF (fann, get_inputs),
- LUA_INTERFACE_DEF (fann, get_outputs),
- LUA_INTERFACE_DEF (fann, get_layers),
- LUA_INTERFACE_DEF (fann, get_mse),
- {"__gc", lua_fann_dtor},
- {"__tostring", rspamd_lua_class_tostring},
- {NULL, NULL}
-};
-
-#ifdef WITH_FANN
-struct fann *
-rspamd_lua_check_fann (lua_State *L, gint pos)
-{
- void *ud = rspamd_lua_check_udata (L, pos, "rspamd{fann}");
- luaL_argcheck (L, ud != NULL, pos, "'fann' expected");
- return ud ? *((struct fann **) ud) : NULL;
-}
-#endif
-
-/***
- * @function rspamd_fann.is_enabled()
- * Checks if fann is enabled for this rspamd build
- * @return {boolean} true if fann is enabled
- */
-static gint
-lua_fann_is_enabled (lua_State *L)
-{
-#ifdef WITH_FANN
- lua_pushboolean (L, true);
-#else
- lua_pushboolean (L, false);
-#endif
- return 1;
-}
-
-/***
- * @function rspamd_fann.create(nlayers, [layer1, ... layern])
- * Creates new neural network with `nlayers` that contains `layer1`...`layern`
- * neurons in each layer
- * @param {number} nlayers number of layers
- * @param {number} layerI number of neurons in each layer
- * @return {fann} fann object
- */
-static gint
-lua_fann_create (lua_State *L)
-{
-#ifndef WITH_FANN
- return 0;
-#else
- struct fann *f, **pfann;
- guint nlayers, *layers, i;
-
- nlayers = luaL_checknumber (L, 1);
-
- if (nlayers > 0) {
- layers = g_malloc (nlayers * sizeof (layers[0]));
-
- if (lua_type (L, 2) == LUA_TNUMBER) {
- for (i = 0; i < nlayers; i ++) {
- layers[i] = luaL_checknumber (L, i + 2);
- }
- }
- else if (lua_type (L, 2) == LUA_TTABLE) {
- for (i = 0; i < nlayers; i ++) {
- lua_rawgeti (L, 2, i + 1);
- layers[i] = luaL_checknumber (L, -1);
- lua_pop (L, 1);
- }
- }
-
- f = fann_create_standard_array (nlayers, layers);
- fann_set_activation_function_hidden (f, FANN_SIGMOID_SYMMETRIC);
- fann_set_activation_function_output (f, FANN_SIGMOID_SYMMETRIC);
- fann_set_training_algorithm (f, FANN_TRAIN_INCREMENTAL);
- fann_randomize_weights (f, 0, 1);
-
- if (f != NULL) {
- pfann = lua_newuserdata (L, sizeof (gpointer));
- *pfann = f;
- rspamd_lua_setclass (L, "rspamd{fann}", -1);
- }
- else {
- lua_pushnil (L);
- }
-
- g_free (layers);
- }
- else {
- lua_pushnil (L);
- }
-
- return 1;
-#endif
-}
-
-#ifdef WITH_FANN
-static enum fann_activationfunc_enum
-string_to_activation_func (const gchar *str)
-{
- if (str == NULL) {
- return FANN_SIGMOID_SYMMETRIC;
- }
- if (strcmp (str, "sigmoid") == 0) {
- return FANN_SIGMOID;
- }
- else if (strcmp (str, "elliot") == 0) {
- return FANN_ELLIOT;
- }
- else if (strcmp (str, "elliot_symmetric") == 0) {
- return FANN_ELLIOT_SYMMETRIC;
- }
- else if (strcmp (str, "linear") == 0) {
- return FANN_LINEAR;
- }
-
- return FANN_SIGMOID_SYMMETRIC;
-}
-
-static enum fann_train_enum
-string_to_learn_alg (const gchar *str)
-{
- if (str == NULL) {
- return FANN_TRAIN_INCREMENTAL;
- }
- if (strcmp (str, "rprop") == 0) {
- return FANN_TRAIN_RPROP;
- }
- else if (strcmp (str, "qprop") == 0) {
- return FANN_TRAIN_QUICKPROP;
- }
- else if (strcmp (str, "batch") == 0) {
- return FANN_TRAIN_BATCH;
- }
-
- return FANN_TRAIN_INCREMENTAL;
-}
-/*
- * This is needed since libfann provides no versioning macros...
- */
-static struct fann_train_data *
-rspamd_fann_create_train (guint num_data, guint num_input, guint num_output)
-{
- struct fann_train_data *t;
- fann_type *inp, *outp;
- guint i;
-
- g_assert (num_data > 0 && num_input > 0 && num_output > 0);
-
- t = calloc (1, sizeof (*t));
- g_assert (t != NULL);
-
- t->num_data = num_data;
- t->num_input = num_input;
- t->num_output = num_output;
-
- t->input = calloc (num_data, sizeof (fann_type *));
- g_assert (t->input != NULL);
-
- t->output = calloc (num_data, sizeof (fann_type *));
- g_assert (t->output != NULL);
-
- inp = calloc (num_data * num_input, sizeof (fann_type));
- g_assert (inp != NULL);
-
- outp = calloc (num_data * num_output, sizeof (fann_type));
- g_assert (outp != NULL);
-
- for (i = 0; i < num_data; i ++) {
- t->input[i] = inp;
- inp += num_input;
- t->output[i] = outp;
- outp += num_output;
- }
-
- return t;
-}
-
-
-#endif
-
-/***
- * @function rspamd_fann.create_full(params)
- * Creates new neural network with parameters:
- * - `layers` {table/numbers}: table of layers in form: {N1, N2, N3 ... Nn} where N is number of neurons in a layer
- * - `activation_hidden` {string}: activation function type for hidden layers (`tanh` by default)
- * - `activation_output` {string}: activation function type for output layer (`tanh` by default)
- * - `sparsed` {float}: create sparsed ANN, where number is a coefficient for sparsing
- * - `learn` {string}: learning algorithm (quickprop, rprop or incremental)
- * - `randomize` {boolean}: randomize weights (true by default)
- * @return {fann} fann object
- */
-static gint
-lua_fann_create_full (lua_State *L)
-{
-#ifndef WITH_FANN
- return 0;
-#else
- struct fann *f, **pfann;
- guint nlayers, *layers, i;
- const gchar *activation_hidden = NULL, *activation_output, *learn_alg = NULL;
- gdouble sparsed = 0.0;
- gboolean randomize_ann = TRUE;
- GError *err = NULL;
-
- if (lua_type (L, 1) == LUA_TTABLE) {
- lua_pushstring (L, "layers");
- lua_gettable (L, 1);
-
- if (lua_type (L, -1) != LUA_TTABLE) {
- return luaL_error (L, "bad layers attribute");
- }
-
- nlayers = rspamd_lua_table_size (L, -1);
- if (nlayers < 2) {
- return luaL_error (L, "bad layers attribute");
- }
-
- layers = g_new0 (guint, nlayers);
-
- for (i = 0; i < nlayers; i ++) {
- lua_rawgeti (L, -1, i + 1);
- layers[i] = luaL_checknumber (L, -1);
- lua_pop (L, 1);
- }
-
- lua_pop (L, 1); /* Table */
-
- if (!rspamd_lua_parse_table_arguments (L, 1, &err,
- "sparsed=N;randomize=B;learn=S;activation_hidden=S;activation_output=S",
- &sparsed, &randomize_ann, &learn_alg, &activation_hidden, &activation_output)) {
- g_free (layers);
-
- if (err) {
- gint r;
-
- r = luaL_error (L, "invalid arguments: %s", err->message);
- g_error_free (err);
- return r;
- }
- else {
- return luaL_error (L, "invalid arguments");
- }
- }
-
- if (sparsed != 0.0) {
- f = fann_create_standard_array (nlayers, layers);
- }
- else {
- f = fann_create_sparse_array (sparsed, nlayers, layers);
- }
-
- if (f != NULL) {
- pfann = lua_newuserdata (L, sizeof (gpointer));
- *pfann = f;
- rspamd_lua_setclass (L, "rspamd{fann}", -1);
- }
- else {
- g_free (layers);
- return luaL_error (L, "cannot create fann");
- }
-
- fann_set_activation_function_hidden (f,
- string_to_activation_func (activation_hidden));
- fann_set_activation_function_output (f,
- string_to_activation_func (activation_output));
- fann_set_training_algorithm (f, string_to_learn_alg (learn_alg));
-
- if (randomize_ann) {
- fann_randomize_weights (f, 0, 1);
- }
-
- g_free (layers);
- }
- else {
- return luaL_error (L, "bad arguments");
- }
-
- return 1;
-#endif
-}
-
-/***
- * @function rspamd_fann.load(file)
- * Loads neural network from the file
- * @param {string} file filename where fann is stored
- * @return {fann} fann object
- */
-static gint
-lua_fann_load_file (lua_State *L)
-{
-#ifndef WITH_FANN
- return 0;
-#else
- struct fann *f, **pfann;
- const gchar *fname;
-
- fname = luaL_checkstring (L, 1);
-
- if (fname != NULL) {
- f = fann_create_from_file (fname);
-
- if (f != NULL) {
- pfann = lua_newuserdata (L, sizeof (gpointer));
- *pfann = f;
- rspamd_lua_setclass (L, "rspamd{fann}", -1);
- }
- else {
- lua_pushnil (L);
- }
- }
- else {
- lua_pushnil (L);
- }
-
- return 1;
-#endif
-}
-
-/***
- * @function rspamd_fann.load_data(data)
- * Loads neural network from the data
- * @param {string} file filename where fann is stored
- * @return {fann} fann object
- */
-static gint
-lua_fann_load_data (lua_State *L)
-{
-#ifndef WITH_FANN
- return 0;
-#else
- struct fann *f, **pfann;
- gint fd;
- struct rspamd_lua_text *t;
- gchar fpath[PATH_MAX];
-
- if (lua_type (L, 1) == LUA_TUSERDATA) {
- t = lua_check_text (L, 1);
-
- if (!t) {
- return luaL_error (L, "text required");
- }
- }
- else {
- t = g_alloca (sizeof (*t));
- t->start = lua_tolstring (L, 1, (gsize *)&t->len);
- t->flags = 0;
- }
-
- /* We need to save data to file because of libfann stupidity */
- rspamd_strlcpy (fpath, "/tmp/rspamd-fannXXXXXXXXXX", sizeof (fpath));
- fd = mkstemp (fpath);
-
- if (fd == -1) {
- msg_warn ("cannot create tempfile: %s", strerror (errno));
- lua_pushnil (L);
- }
- else {
- if (write (fd, t->start, t->len) == -1) {
- msg_warn ("cannot write tempfile: %s", strerror (errno));
- lua_pushnil (L);
- unlink (fpath);
- close (fd);
-
- return 1;
- }
-
- f = fann_create_from_file (fpath);
- unlink (fpath);
- close (fd);
-
- if (f != NULL) {
- pfann = lua_newuserdata (L, sizeof (gpointer));
- *pfann = f;
- rspamd_lua_setclass (L, "rspamd{fann}", -1);
- }
- else {
- lua_pushnil (L);
- }
- }
-
- return 1;
-#endif
-}
-
-/***
- * @function rspamd_fann:data()
- * Returns serialized neural network
- * @return {rspamd_text} fann data
- */
-static gint
-lua_fann_data (lua_State *L)
-{
-#ifndef WITH_FANN
- return 0;
-#else
- struct fann *f = rspamd_lua_check_fann (L, 1);
- gint fd;
- struct rspamd_lua_text *res;
- gchar fpath[PATH_MAX];
- gpointer map;
- gsize sz;
-
- if (f == NULL) {
- return luaL_error (L, "invalid arguments");
- }
-
- /* We need to save data to file because of libfann stupidity */
- rspamd_strlcpy (fpath, "/tmp/rspamd-fannXXXXXXXXXX", sizeof (fpath));
- fd = mkstemp (fpath);
-
- if (fd == -1) {
- msg_warn ("cannot create tempfile: %s", strerror (errno));
- lua_pushnil (L);
- }
- else {
- if (fann_save (f, fpath) == -1) {
- msg_warn ("cannot write tempfile: %s", strerror (errno));
- lua_pushnil (L);
- unlink (fpath);
- close (fd);
-
- return 1;
- }
-
-
- (void)lseek (fd, 0, SEEK_SET);
- map = rspamd_file_xmap (fpath, PROT_READ, &sz, TRUE);
- unlink (fpath);
- close (fd);
-
- if (map != NULL) {
- res = lua_newuserdata (L, sizeof (*res));
- res->len = sz;
- res->start = map;
- res->flags = RSPAMD_TEXT_FLAG_OWN|RSPAMD_TEXT_FLAG_MMAPED;
- rspamd_lua_setclass (L, "rspamd{text}", -1);
- }
- else {
- lua_pushnil (L);
- }
-
- }
-
- return 1;
-#endif
-}
-
-
-/**
- * @method rspamd_fann:train(inputs, outputs)
- * Trains neural network with samples. Inputs and outputs should be tables of
- * equal size, each row in table should be N inputs and M outputs, e.g.
- * {0, 1, 1} -> {0}
- * @param {table} inputs input samples
- * @param {table} outputs output samples
- * @return {number} number of samples learned
- */
-static gint
-lua_fann_train (lua_State *L)
-{
-#ifndef WITH_FANN
- return 0;
-#else
- struct fann *f = rspamd_lua_check_fann (L, 1);
- guint ninputs, noutputs, j;
- fann_type *cur_input, *cur_output;
- gboolean ret = FALSE;
-
- if (f != NULL) {
- /* First check sanity, call for table.getn for that */
- ninputs = rspamd_lua_table_size (L, 2);
- noutputs = rspamd_lua_table_size (L, 3);
-
- if (ninputs != fann_get_num_input (f) ||
- noutputs != fann_get_num_output (f)) {
- msg_err ("bad number of inputs(%d, expected %d) and "
- "output(%d, expected %d) args for train",
- ninputs, fann_get_num_input (f),
- noutputs, fann_get_num_output (f));
- }
- else {
- cur_input = g_malloc (ninputs * sizeof (fann_type));
-
- for (j = 0; j < ninputs; j ++) {
- lua_rawgeti (L, 2, j + 1);
- cur_input[j] = lua_tonumber (L, -1);
- lua_pop (L, 1);
- }
-
- cur_output = g_malloc (noutputs * sizeof (fann_type));
-
- for (j = 0; j < noutputs; j++) {
- lua_rawgeti (L, 3, j + 1);
- cur_output[j] = lua_tonumber (L, -1);
- lua_pop (L, 1);
- }
-
- fann_train (f, cur_input, cur_output);
- g_free (cur_input);
- g_free (cur_output);
-
- ret = TRUE;
- }
- }
-
- lua_pushboolean (L, ret);
-
- return 1;
-#endif
-}
-
-#ifdef WITH_FANN
-struct lua_fann_train_cbdata {
- lua_State *L;
- gint pair[2];
- struct fann_train_data *train;
- struct fann *f;
- gint cbref;
- gdouble desired_mse;
- guint max_epochs;
- GThread *t;
- struct event io;
-};
-
-struct lua_fann_train_reply {
- gint errcode;
- float mse;
- gchar errmsg[128];
-};
-
-static void
-lua_fann_push_train_result (struct lua_fann_train_cbdata *cbdata,
- gint errcode, float mse, const gchar *errmsg)
-{
- lua_rawgeti (cbdata->L, LUA_REGISTRYINDEX, cbdata->cbref);
- lua_pushnumber (cbdata->L, errcode);
- lua_pushstring (cbdata->L, errmsg);
- lua_pushnumber (cbdata->L, mse);
-
- if (lua_pcall (cbdata->L, 3, 0, 0) != 0) {
- msg_err ("call to train callback failed: %s", lua_tostring (cbdata->L, -1));
- lua_pop (cbdata->L, 1);
- }
-}
-
-static void
-lua_fann_thread_notify (gint fd, short what, gpointer ud)
-{
- struct lua_fann_train_cbdata *cbdata = ud;
- struct lua_fann_train_reply rep;
-
- if (read (cbdata->pair[0], &rep, sizeof (rep)) == -1) {
- if (errno == EAGAIN || errno == EINTR) {
- event_add (&cbdata->io, NULL);
- return;
- }
-
- lua_fann_push_train_result (cbdata, errno, 0.0, strerror (errno));
- }
- else {
- lua_fann_push_train_result (cbdata, rep.errcode, rep.mse, rep.errmsg);
- }
-
- g_assert (write (cbdata->pair[0], "", 1) == 1);
- g_thread_join (cbdata->t);
- close (cbdata->pair[0]);
- close (cbdata->pair[1]);
-
- fann_destroy_train (cbdata->train);
- luaL_unref (cbdata->L, LUA_REGISTRYINDEX, cbdata->cbref);
- g_free (cbdata);
-}
-
-static void *
-lua_fann_train_thread (void *ud)
-{
- struct lua_fann_train_cbdata *cbdata = ud;
- struct lua_fann_train_reply rep;
- gchar repbuf[1];
-
- msg_info ("start learning ANN, %d epochs are possible",
- cbdata->max_epochs);
- rspamd_socket_blocking (cbdata->pair[1]);
- fann_train_on_data (cbdata->f, cbdata->train, cbdata->max_epochs, 0,
- cbdata->desired_mse);
- rep.errcode = 0;
- rspamd_strlcpy (rep.errmsg, "OK", sizeof (rep.errmsg));
- rep.mse = fann_get_MSE (cbdata->f);
-
- if (write (cbdata->pair[1], &rep, sizeof (rep)) == -1) {
- msg_err ("cannot write to socketpair: %s", strerror (errno));
-
- return NULL;
- }
-
- if (read (cbdata->pair[1], repbuf, sizeof (repbuf)) == -1) {
- msg_err ("cannot read from socketpair: %s", strerror (errno));
-
- return NULL;
- }
-
- return NULL;
-}
-#endif
-/**
- * @method rspamd_fann:train_threaded(inputs, outputs, callback, event_base, {params})
- * Trains neural network with batch of samples. Inputs and outputs should be tables of
- * equal size, each row in table should be N inputs and M outputs, e.g.
- * {{0, 1, 1}, ...} -> {{0}, {1} ...}
- * @param {table} inputs input samples
- * @param {table} outputs output samples
- * @param {callback} function that is called when train is completed
- */
-static gint
-lua_fann_train_threaded (lua_State *L)
-{
-#ifndef WITH_FANN
- return 0;
-#else
- struct fann *f = rspamd_lua_check_fann (L, 1);
- guint ninputs, noutputs, ndata, i, j;
- struct lua_fann_train_cbdata *cbdata;
- struct ev_loop *ev_base = lua_check_ev_base (L, 5);
- GError *err = NULL;
- const guint max_epochs_default = 1000;
- const gdouble desired_mse_default = 0.0001;
-
- if (f != NULL && lua_type (L, 2) == LUA_TTABLE &&
- lua_type (L, 3) == LUA_TTABLE && lua_type (L, 4) == LUA_TFUNCTION &&
- ev_base != NULL) {
- /* First check sanity, call for table.getn for that */
- ndata = rspamd_lua_table_size (L, 2);
- ninputs = fann_get_num_input (f);
- noutputs = fann_get_num_output (f);
- cbdata = g_malloc0 (sizeof (*cbdata));
- cbdata->L = L;
- cbdata->f = f;
- cbdata->train = rspamd_fann_create_train (ndata, ninputs, noutputs);
- lua_pushvalue (L, 4);
- cbdata->cbref = luaL_ref (L, LUA_REGISTRYINDEX);
-
- if (rspamd_socketpair (cbdata->pair, 0) == -1) {
- msg_err ("cannot open socketpair: %s", strerror (errno));
- cbdata->pair[0] = -1;
- cbdata->pair[1] = -1;
- goto err;
- }
-
- for (i = 0; i < ndata; i ++) {
- lua_rawgeti (L, 2, i + 1);
-
- if (rspamd_lua_table_size (L, -1) != ninputs) {
- msg_err ("invalid number of inputs: %d, %d expected",
- rspamd_lua_table_size (L, -1), ninputs);
- goto err;
- }
-
- for (j = 0; j < ninputs; j ++) {
- lua_rawgeti (L, -1, j + 1);
- cbdata->train->input[i][j] = lua_tonumber (L, -1);
- lua_pop (L, 1);
- }
-
- lua_pop (L, 1);
- lua_rawgeti (L, 3, i + 1);
-
- if (rspamd_lua_table_size (L, -1) != noutputs) {
- msg_err ("invalid number of outputs: %d, %d expected",
- rspamd_lua_table_size (L, -1), noutputs);
- goto err;
- }
-
- for (j = 0; j < noutputs; j++) {
- lua_rawgeti (L, -1, j + 1);
- cbdata->train->output[i][j] = lua_tonumber (L, -1);
- lua_pop (L, 1);
- }
- }
-
- cbdata->max_epochs = max_epochs_default;
- cbdata->desired_mse = desired_mse_default;
-
- if (lua_type (L, 5) == LUA_TTABLE) {
- rspamd_lua_parse_table_arguments (L, 5, NULL,
- "max_epochs=I;desired_mse=N",
- &cbdata->max_epochs, &cbdata->desired_mse);
- }
-
- /* Now we can call training in a separate thread */
- rspamd_socket_nonblocking (cbdata->pair[0]);
- event_set (&cbdata->io, cbdata->pair[0], EV_READ, lua_fann_thread_notify,
- cbdata);
- event_base_set (ev_base, &cbdata->io);
- /* TODO: add timeout */
- event_add (&cbdata->io, NULL);
- cbdata->t = rspamd_create_thread ("fann train", lua_fann_train_thread,
- cbdata, &err);
-
- if (cbdata->t == NULL) {
- msg_err ("cannot create training thread: %e", err);
-
- if (err) {
- g_error_free (err);
- }
-
- goto err;
- }
- }
- else {
- return luaL_error (L, "invalid arguments");
- }
-
- return 0;
-
-err:
- if (cbdata->pair[0] != -1) {
- close (cbdata->pair[0]);
- }
- if (cbdata->pair[1] != -1) {
- close (cbdata->pair[1]);
- }
-
- fann_destroy_train (cbdata->train);
- luaL_unref (L, LUA_REGISTRYINDEX, cbdata->cbref);
- g_free (cbdata);
- return luaL_error (L, "invalid arguments");
-#endif
-}
-
-/**
- * @method rspamd_fann:test(inputs)
- * Tests neural network with samples. Inputs is a single sample of input data.
- * The function returns table of results, e.g.:
- * {0, 1, 1} -> {0}
- * @param {table} inputs input sample
- * @return {table/number} outputs values
- */
-static gint
-lua_fann_test (lua_State *L)
-{
-#ifndef WITH_FANN
- return 0;
-#else
- struct fann *f = rspamd_lua_check_fann (L, 1);
- guint ninputs, noutputs, i, tbl_idx = 2;
- fann_type *cur_input, *cur_output;
-
- if (f != NULL) {
- /* First check sanity, call for table.getn for that */
- if (lua_isnumber (L, 2)) {
- ninputs = lua_tonumber (L, 2);
- tbl_idx = 3;
- }
- else {
- ninputs = rspamd_lua_table_size (L, 2);
-
- if (ninputs == 0) {
- msg_err ("empty inputs number");
- lua_pushnil (L);
-
- return 1;
- }
- }
-
- cur_input = g_malloc0 (ninputs * sizeof (fann_type));
-
- for (i = 0; i < ninputs; i++) {
- lua_rawgeti (L, tbl_idx, i + 1);
- cur_input[i] = lua_tonumber (L, -1);
- lua_pop (L, 1);
- }
-
- cur_output = fann_run (f, cur_input);
- noutputs = fann_get_num_output (f);
- lua_createtable (L, noutputs, 0);
-
- for (i = 0; i < noutputs; i ++) {
- lua_pushnumber (L, cur_output[i]);
- lua_rawseti (L, -2, i + 1);
- }
-
- g_free (cur_input);
- }
- else {
- lua_pushnil (L);
- }
-
- return 1;
-#endif
-}
-
-/***
- * @method rspamd_fann:get_inputs()
- * Returns number of inputs for neural network
- * @return {number} number of inputs
- */
-static gint
-lua_fann_get_inputs (lua_State *L)
-{
-#ifndef WITH_FANN
- return 0;
-#else
- struct fann *f = rspamd_lua_check_fann (L, 1);
-
- if (f != NULL) {
- lua_pushnumber (L, fann_get_num_input (f));
- }
- else {
- lua_pushnil (L);
- }
-
- return 1;
-#endif
-}
-
-/***
- * @method rspamd_fann:get_outputs()
- * Returns number of outputs for neural network
- * @return {number} number of outputs
- */
-static gint
-lua_fann_get_outputs (lua_State *L)
-{
-#ifndef WITH_FANN
- return 0;
-#else
- struct fann *f = rspamd_lua_check_fann (L, 1);
-
- if (f != NULL) {
- lua_pushnumber (L, fann_get_num_output (f));
- }
- else {
- lua_pushnil (L);
- }
-
- return 1;
-#endif
-}
-
-/***
- * @method rspamd_fann:get_mse()
- * Returns mean square error for ANN
- * @return {number} MSE value
- */
-static gint
-lua_fann_get_mse (lua_State *L)
-{
-#ifndef WITH_FANN
- return 0;
-#else
- struct fann *f = rspamd_lua_check_fann (L, 1);
-
- if (f != NULL) {
- lua_pushnumber (L, fann_get_MSE (f));
- }
- else {
- lua_pushnil (L);
- }
-
- return 1;
-#endif
-}
-
-/***
- * @method rspamd_fann:get_layers()
- * Returns array of neurons count for each layer
- * @return {table/number} table with number ofr neurons in each layer
- */
-static gint
-lua_fann_get_layers (lua_State *L)
-{
-#ifndef WITH_FANN
- return 0;
-#else
- struct fann *f = rspamd_lua_check_fann (L, 1);
- guint nlayers, i, *layers;
-
- if (f != NULL) {
- nlayers = fann_get_num_layers (f);
- layers = g_new (guint, nlayers);
- fann_get_layer_array (f, layers);
- lua_createtable (L, nlayers, 0);
-
- for (i = 0; i < nlayers; i ++) {
- lua_pushnumber (L, layers[i]);
- lua_rawseti (L, -2, i + 1);
- }
-
- g_free (layers);
- }
- else {
- lua_pushnil (L);
- }
-
- return 1;
-#endif
-}
-
-/***
- * @method rspamd_fann:save(fname)
- * Save fann to file named 'fname'
- * @param {string} fname filename to save fann into
- * @return {boolean} true if ann has been saved
- */
-static gint
-lua_fann_save (lua_State *L)
-{
-#ifndef WITH_FANN
- return 0;
-#else
- struct fann *f = rspamd_lua_check_fann (L, 1);
- const gchar *fname = luaL_checkstring (L, 2);
-
- if (f != NULL && fname != NULL) {
- if (fann_save (f, fname) == 0) {
- lua_pushboolean (L, true);
- }
- else {
- msg_err ("cannot save ANN to %s: %s", fname, strerror (errno));
- lua_pushboolean (L, false);
- }
- }
- else {
- lua_pushnil (L);
- }
-
- return 1;
-#endif
-}
-
-static gint
-lua_fann_dtor (lua_State *L)
-{
-#ifndef WITH_FANN
- return 0;
-#else
- struct fann *f = rspamd_lua_check_fann (L, 1);
-
- if (f) {
- fann_destroy (f);
- }
-
- return 0;
-#endif
-}
-
-static gint
-lua_load_fann (lua_State * L)
-{
- lua_newtable (L);
- luaL_register (L, NULL, fannlib_f);
-
- return 1;
-}
-
-void
-luaopen_fann (lua_State * L)
-{
- rspamd_lua_new_class (L, "rspamd{fann}", fannlib_m);
- lua_pop (L, 1);
-
- rspamd_lua_add_preload (L, "rspamd_fann", lua_load_fann);
-}
generated by cgit v1.2.3 (git 2.39.1) at 2025-01-23 19:55:48 +0000