//training step
//the raw data has the form {x1, x2, t}
//convert to form x={x0, x1, x2} and t=t
double updateWeights(Neuron* neuron, const double* rawData) {
int t = rawData[N]; //desired class (-1, +1)
double x[DIM]; //actual input vector
x[0] = 1; //dummy input for w0
for (int i = 1; i <= N; i++) {
x[i] = rawData[i - 1]; //copy feature vector from from rawData
}
//perceptron update step: w(k+1) = w(k) + eta*x*t
//memcpy(neuron->w, sumVec(neuron->w, multiplyVec(multiplyVec(x, t), neuron->eta)), DIM*sizeof(double));
//neuron update step: delta rule (w(k+1) = w(k) + (error*w(k) * (fdx(w(k)*in) * in)
double out = f(neuron, x);
double e = error(out, t);
double f_dx = fdx(neuron, &x);
double* a = vectorProd(multiplyVec(neuron->w, e, N), multiplyVec(neuron->w, f_dx, N), N);
memcpy(neuron->w, sumVec(neuron->w, (a), N) , DIM*sizeof(double));
return e;
}
bool insideTriangle(int x, int y, Vec3i t0, Vec3i t1, Vec3i t2) {
Vec2i a(t0.x - x, t0.y - y), b(t1.x - x, t1.y - y), c(t2.x - x, t2.y - y);
return vectorProd(a, b) * vectorProd(b, c) >= 0 &&
vectorProd(b, c) * vectorProd(c, a) >= 0 &&
vectorProd(c, a) * vectorProd(a, b) >= 0;
}
