void Ghost::calcPath(Point ninman) {
int dist, shortest = -1;
int x = this->x;
int y = this->y;
Point bestMove;
while (shortest != 0 && shortest != INT_MAX) {
shortest = INT_MAX;
if (map->isValidCoordinate(x,y+1)) {
bestMove.x = x;
bestMove.y = y + 1;
if (!this->alreadyInList(Point(x, y + 1))){
shortest = ManhattanDist(Point(x, y + 1),ninman);
}
}
if (map->isValidCoordinate(x+1,y)) {
dist = ManhattanDist(Point(x + 1, y),ninman);
if (dist < shortest && !this->alreadyInList(Point(x + 1, y))) {
bestMove.x = x + 1;
bestMove.y = y;
shortest = dist;
}
}
if (map->isValidCoordinate(x,y-1)) {
dist = ManhattanDist(Point(x, y - 1),ninman);
if (dist < shortest && !this->alreadyInList(Point(x, y-1))) {
bestMove.x = x;
bestMove.y = y - 1;
shortest = dist;
}
}
if (map->isValidCoordinate(x-1,y)) {
dist = ManhattanDist(Point(x - 1, y),ninman);
if (dist < shortest && !this->alreadyInList(Point(x -1, y))) {
bestMove.x = x - 1;
bestMove.y = y;
shortest = dist;
}
}
ghost_path.push_back(bestMove);
x = bestMove.x;
y = bestMove.y;
printf("%d %d %d\n",x,y,dist);
}
}
float WorleyNoise::Noise(Vector3 pos, int seed, int distanceFunc, vector<float> distances, int combine) {
unsigned int lRand, numberfeatPts;
Vector3 rDiff, featPt;
float cubeX, cubeY, cubeZ;
//Initialize values in distance array to large values
for (int i = 0; i < distances.size(); i++) {
distances[i] = 6666;
}
//1. Determine which cube the evaluation point is in
int evalCubeX = (int)floor(pos.x);
int evalCubeY = (int)floor(pos.y);
int evalCubeZ = (int)floor(pos.z);
for (int i = -1; i < 2; ++i)
for (int j = -1; j < 2; ++j)
for (int k = -1; k < 2; ++k) {
cubeX = evalCubeX + i;
cubeY = evalCubeY + j;
cubeZ = evalCubeZ + k;
//2. Generate a reproducible random number generator for the cube
lRand = lcgRandom(hash((unsigned int)(cubeX+seed), (unsigned int)(cubeY), (unsigned int)(cubeZ)));
//3. Determine how many feature points are in the cube
numberfeatPts = probLookup(lRand);
//4. Randomly place the feature points in the cube
for (unsigned int l = 0; l < numberfeatPts; ++l) {
lRand = lcgRandom(lRand);
rDiff.x = (float)lRand / 0x100000000;
lRand = lcgRandom(lRand);
rDiff.y = (float)lRand / 0x100000000;
lRand = lcgRandom(lRand);
rDiff.z = (float)lRand / 0x100000000;
featPt = Vector3(rDiff.x+cubeX, rDiff.y+cubeY, rDiff.z+cubeZ);
//5. Find the feature point closest to the evaluation point.
//This is done by inserting the distances to the feature points into a sorted list
float dist = 0;
if (distanceFunc == EUCLIDIAN) dist = EuclidianDist(pos, featPt);
if (distanceFunc == MANHATTAN) dist = ManhattanDist(pos, featPt);
if (distanceFunc == CHEBYSHEV) dist = ChebyshevDist(pos, featPt);
float temp;
//sorted insertion
for (int i = (int)distances.size()-1; i >= 0; i--) {
if (dist > distances[i]) break;
temp = distances[i];
distances[i] = dist;
if (i+1 < (int)distances.size()) distances[i+1] = temp;
}
}
//6. Check the neighboring cubes to ensure their are no closer evaluation points.
// This is done by repeating steps 1 through 5 above for each neighboring cube
}
float color = 0.0f;
if (combine == D1) color = distances[0];
if (combine == D2) color = distances[1] - distances[0];
if (combine == D3) color = distances[2] - distances[0];
if(color < 0.0f) color = 0.0f;
if(color > 1.0f) color = 1.0f;
return color;
}
