/**
* @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
}
template<bool msg> AnnDef *Ann_GetAnnDef( int i, unsigned int inc, unsigned int outc )
{
AnnDef *def = getAnnDef(i);
if(!def) {
if(msg) Print("Could not get ANN at index %i\n", i);
return 0;
}
struct fann *ann = def->_ann;
if( inc != fann_get_num_input(ann) ) {
if(msg) Print( "Error: Input count mismatch ugen: %i / ann: %i\n", inc, fann_get_num_input(ann) );
return 0;
}
if( outc != fann_get_num_output(ann) ) {
if(msg) Print( "Error: Output count mismatch ugen: %i / ann: %i\n", outc, fann_get_num_output(ann) );
return 0;
}
return def;
}
/*! ann:__tostring()
*# Converts a neural net to a string for Lua's virtual machine
*x print(ann)
*-
*/
static int ann_tostring(lua_State *L)
{
struct fann **ann;
ann = luaL_checkudata(L, 1, FANN_METATABLE);
luaL_argcheck(L, ann != NULL, 1, "'neural net' expected");
lua_pushfstring(L, "[[FANN neural network: %d %d %d]]", fann_get_num_input(*ann),
fann_get_num_output(*ann), fann_get_total_neurons(*ann));
return 1;
}
/*! ann:run(input1, input2, ..., inputn)
*# Evaluates the neural network for the given inputs.
*x xor = ann:run(-1, 1)
*-
*/
static int ann_run(lua_State *L)
{
struct fann **ann;
int nin, nout, i;
fann_type *input, *output;
ann = luaL_checkudata(L, 1, FANN_METATABLE);
luaL_argcheck(L, ann != NULL, 1, "'neural net' expected");
nin = lua_gettop(L) - 1;
if(nin != fann_get_num_input(*ann))
luaL_error(L, "wrong number of inputs: expected %d, got %d", fann_get_num_input(*ann), nin);
nout = fann_get_num_output(*ann);
#ifdef FANN_VERBOSE
printf("Evaluating neural net: %d inputs, %d outputs\n", nin, nout);
#endif
input = lua_newuserdata(L, nin*(sizeof *input));
for(i = 0; i < nin; i++)
{
input[i] = luaL_checknumber(L, i + 2);
#ifdef FANN_VERBOSE
printf("Input %d's value is %f\n", i, input[i]);
#endif
}
output = fann_run(*ann, input);
for(i = 0; i < nout; i++)
{
#ifdef FANN_VERBOSE
printf("Output %d's value is %f\n", i, output[i]);
#endif
lua_pushnumber(L, output[i]);
}
return nout;
}
void vTrainThread::run(){
results.clear();
const unsigned int num_input = fann_get_num_input(neural);
//Готовим выборку для шага обучения
float *data =new float[num_input];
for(int i = 0;i<steps; i++ ){
struct train_result step;
memset(&step, 0, sizeof(train_result));
signal->logMessage(DEBUG, QString("Step%1").arg(i));
memset(data, 0, num_input*sizeof(float));
float desired_output = 0.0;
QByteArray input = buffer->getBuffer(num_input);
QByteArray diff = buffer->getDiff(num_input);
for(int i = 0; i<input.size(); i++){
data[i] = static_cast<float>(input.at(i));
desired_output += abs(static_cast<float>(diff.at(i)));
}
signal->logMessage(DEBUG, QString(" Diff Sum: %1").arg(desired_output));
desired_output /=255;
step.need_result = desired_output;
signal->logMessage(DEBUG, QString(" need output: %1").arg(desired_output));
float* var = fann_run(neural, data);
step.output_before_train = *var;
signal->logMessage(DEBUG, QString(" value before: %1").arg(*var));
fann_train(neural, data, &desired_output);
var = fann_run(neural, data);
step.output_after_train = desired_output;
signal->logMessage(DEBUG, QString(" value after: %1").arg(*var));
step.error1 = (step.need_result == 0 && step.output_before_train != 0) ? true : false;
step.error2 = (step.need_result != 0 && step.output_before_train == 0) ? 1 : 0;
results.append(step);
}
}
/***
* @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: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 event_base *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
}