Map::Map()
{
loadLevels();
current = new Level;
*current = *all_levels[0];
next = new Level;
*next = *all_levels[1];
flag = true;
count = 0;
}
void KHangMan::setCurrentLanguage(int index)
{
if (index >= 0 && index < m_languages.size()) {
Prefs::setSelectedLanguage(m_languages[index]);
m_currentLanguage = index;
Prefs::self()->save();
loadLevels();
loadLanguageSpecialCharacters();
setLevel();
emit currentLanguageChanged();
}
}
int doInit(Uint32 vidFlags, int doWipe) {
int i;
BrInitError error;
if(br_init(&error, "kuri2d") == 0 && error != BR_INIT_ERROR_DISABLED) {
printf("Warning: BinReloc failed to initialize (error code %d)\n", error);
printf("Will fallback to hardcoded default path.\n");
}
if(SDL_Init((Uint32)(SDL_INIT_VIDEO | SDL_INIT_AUDIO))) {
printf("Unable to init SDL: %s\n", SDL_GetError());
return 0;
}
(void)atexit(SDL_Quit);
(void)atexit(TTF_Quit);
/* malloc some defaults - they *should* be reloaded later */
level = (KuriLevel *)malloc(sizeof(KuriLevel));
startState = malloc(sizeof(State));
state = malloc(sizeof(State));
strcpy(state->name, "Player");
state->level = (Uint8)0;
state->score = (Uint8)0;
state->lives = (Uint8)3;
for(i=0; i<MAX_HISCORES; i++) {
strcpy(hiscores[i].name, "nobody");
hiscores[i].score = 0;
hiscores[i].level = 0;
}
if(level && startState && state &&
(doWipe ? 1 : loadHiScores()) &&
loadBackgrounds() &&
loadBlocks() &&
loadLevels() &&
loadSounds() &&
loadFonts() &&
loadTexts() &&
openFrame(vidFlags)) {
return 1;
}
return 0;
}
LevelMenu::LevelMenu() :
_currentLevel(NULL),
_currentItemIndex(0)
{
_menuPat = new osg::PositionAttitudeTransform();
_menuPat->getOrCreateStateSet()->setMode(GL_LIGHTING, osg::StateAttribute::OFF);
_keyboardHandler = new MenuKeyboardHandler(this);
viewer.addEventHandler(_keyboardHandler);
initializeCamera();
initializeHeader();
initializeBackgroundAnimation();
initializeSelector();
loadLevels();
updateDetails();
viewer.getCamera()->setUpdateCallback(new LevelMenuUpdater(this));
Sound::getInstance()->playInLoop(MENU_MUSIC_FILE);
}
int main() {
std::mt19937 generator(time(nullptr));
sys::ComputeSystem cs;
cs.create(sys::ComputeSystem::_gpu);
sys::ComputeProgram prog;
prog.loadFromFile("resources/neoKernels.cl", cs);
std::vector<Level> levels;
std::unordered_set<char> tileDataCharset;
std::unordered_set<char> objectDataCharset;
loadLevels("userlevels.txt", levels, tileDataCharset, objectDataCharset);
// --------------------------- Create the Sparse Coder ---------------------------
const int numTiles = levels.front()._tileData.length();
const int charsetSize = 128 + 2; // + 2 indicating the portion it is on (01 for tiles, 10 for objects)
const cl::size_type visDim = std::ceil(std::sqrt(static_cast<float>(charsetSize)));
const int visArea = visDim * visDim;
const int maxObjects = 1000;
cl::Image2D input = cl::Image2D(cs.getContext(), CL_MEM_READ_WRITE, cl::ImageFormat(CL_R, CL_FLOAT), visDim, visDim);
std::vector<float> inputData(visArea, 0.0f);
std::vector<float> predData(visArea, 0.0f);
std::vector<neo::PredictiveHierarchy::LayerDesc> layerDescs(3);
layerDescs[0]._size = { 16, 16 };
layerDescs[1]._size = { 16, 16 };
layerDescs[2]._size = { 16, 16 };
neo::PredictiveHierarchy ph;
ph.createRandom(cs, prog, { static_cast<int>(visDim), static_cast<int>(visDim) }, layerDescs, { -0.01f, 0.01f }, generator);
ph._whiteningKernelRadius = 1;
// Learn levels
std::uniform_int_distribution<int> levelDist(0, levels.size() - 1);
for (int iter = 0; iter < 20; iter++) {
// Choose random level
int index = levelDist(generator);
const Level &l = levels[index];
// Set mode for tiles
inputData[charsetSize - 2] = 0.0f;
inputData[charsetSize - 1] = 1.0f;
// Run through once to get PH ready
inputData['#'] = 1.0f;
cs.getQueue().enqueueWriteImage(input, CL_TRUE, cl::array<cl::size_type, 3> { 0, 0, 0 }, cl::array<cl::size_type, 3> { visDim, visDim, 1 }, 0, 0, inputData.data());
ph.simStep(cs, input);
inputData['#'] = 0.0f;
// Run through tile data
for (int i = 0; i < l._tileData.length(); i++) {
// Set character to 1
inputData[l._tileData[i]] = 1.0f;
cs.getQueue().enqueueWriteImage(input, CL_TRUE, cl::array<cl::size_type, 3> { 0, 0, 0 }, cl::array<cl::size_type, 3> { visDim, visDim, 1 }, 0, 0, inputData.data());
ph.simStep(cs, input);
// Unset character
inputData[l._tileData[i]] = 0.0f;
}
// Set mode for objects
inputData[charsetSize - 2] = 1.0f;
inputData[charsetSize - 1] = 0.0f;
// Run through once to get PH ready
inputData['#'] = 1.0f;
cs.getQueue().enqueueWriteImage(input, CL_TRUE, { 0, 0, 0 }, { visDim, visDim, 1 }, 0, 0, inputData.data());
ph.simStep(cs, input);
inputData['#'] = 0.0f;
// Run through object data
for (int i = 0; i < l._objectData.length(); i++) {
// Set character to 1
inputData[l._objectData[i]] = 1.0f;
cs.getQueue().enqueueWriteImage(input, CL_TRUE, { 0, 0, 0 }, { visDim, visDim, 1 }, 0, 0, inputData.data());
ph.simStep(cs, input);
// Unset character
inputData[l._objectData[i]] = 0.0f;
}
std::cout << "Went over level #" << (index + 1) << " \"" << l._name << "\"" << std::endl;
}
// Generate new maps
std::ofstream toFile("generatedNLevels.txt");
std::normal_distribution<float> noiseDist(0.0f, 1.0f);
for (int i = 0; i < 10; i++) {
toFile << "$" << "Generated Level " << (i + 1) << "#NeoRL#Experimental#";
// Generated level data
// Set mode for tiles
inputData[charsetSize - 2] = 0.0f;
inputData[charsetSize - 1] = 1.0f;
// Run through once to get PH ready
//cs.getQueue().enqueueWriteImage(input, CL_TRUE, cl::array<cl::size_type, 3> { 0, 0, 0 }, cl::array<cl::size_type, 3> { visDim, visDim, 1 }, 0, 0, inputData.data());
//ph.simStep(cs, input);
char prevChar = 0;
for (int i = 0; i < numTiles; i++) {
// Set character to 1
inputData[prevChar] = 1.0f;
cs.getQueue().enqueueWriteImage(input, CL_TRUE, cl::array<cl::size_type, 3> { 0, 0, 0 }, cl::array<cl::size_type, 3> { visDim, visDim, 1 }, 0, 0, inputData.data());
ph.simStep(cs, input, false);
// Unset character
inputData[prevChar] = 0.0f;
char newChar = 0;
cs.getQueue().enqueueReadImage(ph.getPrediction(), CL_TRUE, cl::array<cl::size_type, 3> { 0, 0, 0 }, cl::array<cl::size_type, 3> { visDim, visDim, 1 }, 0, 0, predData.data());
for (int j = 1; j < charsetSize - 2; j++)
if (predData[j] > predData[newChar])
newChar = j;
// Add new character
toFile << newChar;
prevChar = newChar;
}
toFile << "|";
// Set mode for objects
inputData[charsetSize - 2] = 1.0f;
inputData[charsetSize - 1] = 0.0f;
// Run through once to get PH ready
//cs.getQueue().enqueueWriteImage(input, CL_TRUE, cl::array<cl::size_type, 3> { 0, 0, 0 }, cl::array<cl::size_type, 3> { visDim, visDim, 1 }, 0, 0, inputData.data());
//ph.simStep(cs, input);
prevChar = 0;
for (int i = 0; i < maxObjects; i++) {
// Set character to 1
inputData[prevChar] = 1.0f;
std::vector<float> noisyInputData = inputData;
for (int j = 0; j < noisyInputData.size(); j++) {
noisyInputData[j] += noiseDist(generator) * 0.1f;
}
cs.getQueue().enqueueWriteImage(input, CL_TRUE, cl::array<cl::size_type, 3> { 0, 0, 0 }, cl::array<cl::size_type, 3> { visDim, visDim, 1 }, 0, 0, inputData.data());
ph.simStep(cs, input, false);
// Unset character
inputData[prevChar] = 0.0f;
char newChar = 0;
cs.getQueue().enqueueReadImage(ph.getPrediction(), CL_TRUE, cl::array<cl::size_type, 3> { 0, 0, 0 }, cl::array<cl::size_type, 3> { visDim, visDim, 1 }, 0, 0, predData.data());
for (int j = 1; j < charsetSize - 2; j++)
if (predData[j] > predData[newChar])
newChar = j;
// If is delimiter, break
if (newChar == '#')
break;
// Add new character
toFile << newChar;
prevChar = newChar;
}
toFile << "#" << std::endl << std::endl;
}
return 0;
}
