void NeuralNetworkModel::CreateInputs(InputVector& v1, InputVector& v2, float* inputs) {
const unsigned int vlength = v1.Length();
//these hold onto the previous derivatives to calculate the second derivatives each time
double lastdx1 = v1.X(1) - v1.X(0);
double lastdx2 = v2.X(1) - v2.X(0);
double lastdy1 = v1.Y(1) - v1.Y(0);
double lastdy2 = v2.Y(1) - v2.Y(0);
//these will accumulate the lengths squared
double length1 = sqrt(lastdx1*lastdx1 + lastdy1*lastdy1);
double length2 = sqrt(lastdx2*lastdx2 + lastdy2*lastdy2);
//the rest we'll accumulate in place
inputs[1] = pow(v2.X(0)-v1.X(0), 2) + pow(v2.X(1)-v1.X(1), 2);
inputs[2] = pow(v2.Y(0)-v1.Y(0), 2) + pow(v2.Y(1)-v1.Y(1), 2);
inputs[3] = pow(lastdx2 - lastdx1, 2);
inputs[4] = pow(lastdy2 - lastdy1, 2);
inputs[5] = 0;
inputs[6] = 0;
for(unsigned int i = 2; i < vlength; i++) {
const double newdx1 = v1.X(i) - v1.X(i-1);
const double newdx2 = v2.X(i) - v2.X(i-1);
const double newdy1 = v1.Y(i) - v1.Y(i-1);
const double newdy2 = v2.Y(i) - v2.Y(i-1);
//accumulate lengths
length1 += sqrt(newdx1*newdx1 + newdy1*newdy1);
length2 += sqrt(newdx2*newdx2 + newdy2*newdy2);
//and the others
inputs[1] += pow(v2.X(i)-v1.X(i), 2);
inputs[2] += pow(v2.Y(i)-v1.Y(i), 2);
inputs[3] += pow(newdx2 - newdx1, 2);
inputs[4] += pow(newdy2 - newdy1, 2);
inputs[5] += pow( (newdx2-lastdx2) - (newdx1-lastdx1), 2);
inputs[6] += pow( (newdy2-lastdy2) - (newdy1-lastdy1), 2);
lastdx1 = newdx1;
lastdx2 = newdx2;
lastdy1 = newdy1;
lastdy2 = newdy2;
}
//the length requires scaling
inputs[0] = fabs(length2 - length1);
if( length1 > 0 || length2 > 0 ) { inputs[0] /= max(length1, length2); }
//2-6 are already complete, these can be computed in one shot
inputs[7] = pow(v2.X(0) - v1.X(0), 2);
inputs[8] = pow(v2.Y(0) - v1.Y(0), 2);
inputs[9] = pow(v2.X(-1) - v1.X(-1), 2);
inputs[10] = pow(v2.Y(-1) - v1.Y(-1), 2);
}
