public void ask(float inOne, float inTwo) {
brain.setInput(0, inOne);
brain.setInput(1, inTwo);
brain.feedForward();
// p.println(id + ": " + "output 0 " + brain.getOutput(0));
// p.println(id + ": " + "output 1 " + brain.getOutput(1));
}
/*
* void teach(float getIn, float getOut, int i) { for (int i = 0;
* i<repeat_teaching; i++) { brain.setInput(i, getIn); //recebe input
* brain.setOutput(i.getOut);// e output brain.feedForward();
* brain.backPropagate(); // e "ensina" } }
*/
public void teach(float in, int i) {
brain.setDesiredOutput(i, in);
brain.feedForward();
brain.backPropagate();
}
// /////////////////////////////////////////////////////////////////////////////////////////
// BRAIN FUNCTIONS
// TODO: implementar esses métodos em brainActions().
public void train() {
for (int i = 0; i < repeat_teaching; i++) {
float r = p.random(1);
if (r < 0.25f) {
brain.setInput(0, p.random(0.5f, 1f));
brain.setInput(1, p.random(0.5f, 1f));
brain.setDesiredOutput(0, 1f);
brain.setDesiredOutput(1, 0f);
brain.feedForward();
brain.backPropagate();
} else if (r >= 0.25f && r < 0.5f) {
brain.setInput(0, p.random(0f, 0.5f));
brain.setInput(1, p.random(0f, 0.5f));
brain.setDesiredOutput(0, 0.5f);
brain.setDesiredOutput(1, 0.5f);
brain.feedForward();
brain.backPropagate();
} else if (r >= 0.5f && r < 0.75f) {
brain.setInput(0, p.random(0.5f, 1f));
brain.setInput(1, p.random(0f, 0.5f));
brain.setDesiredOutput(0, 0.5f);
brain.setDesiredOutput(1, 0.5f);
brain.feedForward();
brain.backPropagate();
} else {
brain.setInput(0, p.random(0f, 0.5f));
brain.setInput(1, p.random(0f, 0.5f));
brain.setDesiredOutput(0, 0f);
brain.setDesiredOutput(1, 0f);
brain.feedForward();
brain.backPropagate();
}
}
}
// TODO: acções baseadas nos outputs da rede neural.
void brainAction() {
if (brain.getOutput(0) > 0.5) {
// walkTogheter();
}
}