&reftitle.examples;

## XOR training

This example shows how to train data for XOR function

### Simple train

    <?php
    $num_input = 2;
    $num_output = 1;
    $num_layers = 3;
    $num_neurons_hidden = 3;
    $desired_error = 0.001;
    $max_epochs = 500000;
    $epochs_between_reports = 1000;

    $ann = fann_create_standard($num_layers, $num_input, $num_neurons_hidden, $num_output);

    if ($ann) {
        fann_set_activation_function_hidden($ann, FANN_SIGMOID_SYMMETRIC);
        fann_set_activation_function_output($ann, FANN_SIGMOID_SYMMETRIC);

        $filename = dirname(__FILE__) . "/xor.data";
        if (fann_train_on_file($ann, $filename, $max_epochs, $epochs_between_reports, $desired_error))
            fann_save($ann, dirname(__FILE__) . "/xor_float.net");

        fann_destroy($ann);
    }
    ?>

This example shows how to read and run data for XOR function

### Simple test

    <?php
    $train_file = (dirname(__FILE__) . "/xor_float.net");
    if (!is_file($train_file))
        die("The file xor_float.net has not been created! Please run simple_train.php to generate it");

    $ann = fann_create_from_file($train_file);
    if (!$ann)
        die("ANN could not be created");

    $input = array(-1, 1);
    $calc_out = fann_run($ann, $input);
    printf("xor test (%f,%f) -> %f\n", $input[0], $input[1], $calc_out[0]);
    fann_destroy($ann);
    ?>