// ChucK Brain
// Copyright 2008 Les Hall
// This software is protected by the GNU General Public License


// parameters
10 => int num_delays;  // number of delay UGens
4 => int num_layers;  // number of layers (input + hidden + output)
1::second => dur total_delay;  // the total delay duration


// variables
num_delays => int num_width;  // width of neural net
total_delay / num_delays => dur dly;  // the delay amount


// define the patch
// define and hook up the delays
Delay delay[num_delays];  // instantiate the delays
adc => delay[0];  // hook the adc to the input
for (1 => int i; i < num_delays; i++) {
    delay[i-1] => delay[i];  // chain them together in series
}
for (0 => int i; i < num_delays; i++) {
    delay[i].max (dly);  // set the max delays
    delay[i].delay (dly);  // set the delays
}
delay[num_delays-1] => blackhole;  // suck out samples
// define and hook up the neurons
Gain nn_weights[num_layers][num_width][num_width];  // define the neural net weights
Dyno nn_outputs[num_layers][num_width];  // define the neural net outputs
for (0 => int neuron; neuron < num_width; neuron++ ) {
    delay[neuron] => nn_outputs[0][neuron];  // connect inputs to delay lines
}
for (1 => int layer; layer < num_layers; layer++) {
    for (0 => int neuron; neuron < num_width; neuron++ ) {
        for (0 => int weight; weight < num_width; weight++) {
            nn_outputs[layer-1][neuron] => nn_weights[layer][neuron][weight];  // connect weight input
            nn_weights[layer][neuron][weight] => nn_outputs[layer][neuron];  // connect weight output
        }
    }
}
for (0 => int layer; layer < num_layers; layer++) {
    for (0 => int neuron; neuron < num_width; neuron++ ) {
        nn_outputs[layer][neuron].limit ();  // the activation function
    }
}
// define the output node and hook it up
Dyno out => dac;
for (0 => int neuron; neuron < num_width; neuron++ ) {
    nn_outputs[num_layers-1][neuron] => out;  // connect all neural net outputs to out
}
out.limit ();  // set output as limiter


// seed the neural net weights
for (1 => int layer; layer < num_layers; layer++) {
    for (0 => int neuron; neuron < num_width; neuron++ ) {
        for (0 => int weight; weight < num_width; weight++) {
            nn_weights[layer][neuron][weight].gain (Std.randf ());
        }
    }
}


// time loop
while (true) {
    // time delay
    1::ms => now;
}