fann_create_train_from_callback

This code can be used to read training data from MySQL rather than a text file.<?php// MySQL for This Example:/*CREATE TABLE `TrainingSets` (  `ID` int(11) NOT NULL,  `Name` varchar(150) COLLATE utf8mb4_unicode_ci NOT NULL,  `TrainingData` text COLLATE utf8mb4_unicode_ci NOT NULL) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;ALTER TABLE `TrainingSets` ADD PRIMARY KEY (`ID`);  INSERT INTO `TrainingSets` (`ID`, `Name`, `TrainingData`) VALUES(1, 'XOR', '-1 -1\n-1\n-1 1\n1\n1 -1\n1\n1 1\n-1');ALTER TABLE `TrainingSets` MODIFY `ID` int(11) NOT NULL AUTO_INCREMENT, AUTO_INCREMENT=2;*/// This function calls pulls the TrainingData from MySQLfunction get_training_data_from_db($id) {    $table_name = "TrainingSets";    $field = "TrainingData";    $connection=mysqli_connect("host","username","password","database"); // change to your DB credentials    $result=mysqli_query($connection,"SELECT $field FROM $table_name");    $data=mysqli_fetch_assoc($result);    mysqli_close($connection);    return $data[$field];}// This function prepares the newline delimited data to be handed off to FANN/*Example of "newline delimited data" (like XOR in a Plain Text File) stored in MySQL:-1 -1-1-1 111 1-11 -11*/function prepare_data_from_db($training_data) {    $training_data = explode( "\n", $training_data ); // convert training data rows to array    $num_data = count($training_data);        // Sift the data and split inputs and outputs     for($i=0;$i<$num_data;$i++) {      if($i % 2) { // $training_data[$i] is Output       $training_data['outputs'][] = explode( " ", $training_data[$i]);      }else{ // $training_data[$i] is Input       $training_data['inputs'][] = explode( " ", $training_data[$i]);      }    }    // remove the unsifted data    foreach ($training_data as $key => $value) {        if (is_numeric($key)) {            unset($training_data[$key]);        }    }    return $training_data; // returned the prepaired associative array}// This function hands the prepared data over to FANNfunction create_train_callback($num_data, $num_input, $num_output) {    global $training_data;    global $current_dataset;       $dataset = array("input" => $training_data['inputs'][$current_dataset],                    "output" => $training_data['outputs'][$current_dataset]);    $current_dataset++;    return $dataset;}// Initialize the program variables$record_id = 1; // the 'ID' for the training data in MySQL$current_dataset = 0;$num_input = 2;$num_output = 1;$num_layers = 3;$num_neurons = 3;$desired_error = 0.001;$max_epochs = 500000;$epochs_between_reports = 1000;$training_data = get_training_data_from_db($record_id); // Get the Training Data from MySQL$training_data = prepare_data_from_db($training_data); // Prepare the data$num_data = count($training_data["input"]); // How many sets are there?// Hand the data over to FANN$train_data = fann_create_train_from_callback($num_data, $num_input, $num_output, "create_train_callback");// Test for $train_data if ($train_data) {        // Create $ann    $ann = fann_create_standard($num_layers, $num_input, $num_neurons, $num_output);    // Test for $ann    if ($ann) {        fann_set_activation_function_hidden($ann, FANN_SIGMOID_SYMMETRIC);        fann_set_activation_function_output($ann, FANN_SIGMOID_SYMMETRIC);        // Train XOR ANN with training data obtainied from MySQL        if (fann_train_on_data($ann, $train_data, $max_epochs, $epochs_between_reports, $desired_error)){           print('XOR trained.<br>' . PHP_EOL);           // Test $ann           $input = array(-1, 1);           $calc_out = fann_run($ann, $input);           printf("xor test (%f,%f) -> %f\n", $input[0], $input[1], $calc_out[0]);                      // destore $ann            fann_destroy($ann);        }    }}?>