void test2()
{
for(int i = 0; i < 2500; i++)
{
//std::unique_lock<std::mutex> mlock(this->bqMutex);
std::cout<<"odtwarzam"<<i;
Pa_WriteStream(outputStream, bq.pop().getSample(), FRAMES_PER_BUFFER);
//mlock.unlock();
}
}
int testBlockingQueue(void)
{
BlockingQueue<int> testQueue;
testQueue.push(1);
testQueue.push(2);
testQueue.push(3);
int val = testQueue.pop();
printf("First value is %d\n", val);
val = testQueue.pop();
printf("Second value is %d\n", val);
val = testQueue.pop();
printf("Third value is %d\n", val);
bool isEmpty = testQueue.empty();
if(isEmpty){
printf("Queue is empty.\n");
} else {
printf("Queue is not empty.\n");
}
return 0;
}
/* Test to see if the queue is sorted within reasonable limits */
bool is_sorted(BlockingQueue<timeout_t>& bq) {
LogContext& log = LogManager::instance().getLogContext("Test", "Scheduler");
timeout_t last = 0;
while(!bq.empty()) {
timeout_t cur = bq.front();
bq.pop();
if(cur < last && (last - cur) > 5 MILLIS ) {
/* we give a 5 ms grace period */
log.printfln(ERROR, "%ld is less than %ld", cur, last);
return false;
}
last = cur;
}
return true;
}
// Running test
bool TwitchPlaysBlockingQueueTest::RunTest(const FString& Parameters)
{
BlockingQueue<int>* q = new BlockingQueue<int>();
int32 val = 42;
q->push(val);
int32 test = q->pop();
//Test val afther a pop
TWITCH_CHECK(test == val, "Error on pop()");
//Test IsEmpty
TWITCH_CHECK(q->IsEmpty(), "Error on IsEmpty()");
//Test clear
q->clear();
TWITCH_CHECK(q->IsEmpty(), "Error on clear()");
return true;
}
void multiGameWorker() {
cerr << "Thread #" << this_thread::get_id() << " starting up." << endl;
while(true) {
// Blocking call.
Job j = jobQueue.pop();
if(j.shutdown) {
cerr << "Thread #" << this_thread::get_id() << " shutting down." << endl;
return;
}
cerr << "Thread #" << this_thread::get_id() << " starting job." << endl;
double score = runMultiGame(j.config);
double scoreDiff = -(score - j.currentScore);
double flip = (float) drand48() / RAND_MAX;
double acc = exp(-scoreDiff / j.temp);
bool accepted = scoreDiff < 0 || acc > flip;
Result res = {
j.config,
score,
accepted};
resultQueue.push(res);
cerr << "Thread #" << this_thread::get_id() << " finished job. Score: " << score << endl;
}
}
ScoreFactors optimize() {
// Start up the workers.
vector<thread> workers;
for(int i = 0; i < WORKER_COUNT; i++) {
workers.push_back(thread(multiGameWorker));
}
ScoreFactors current = AnnealingBot().sFactors;//startingSFs();
double currentScore = runMultiGame(current);
double bestScore = currentScore;
ScoreFactors bestFactors = current;
double prevTemp = initialTemp;
double temp = initialTemp;
double sdPrev = initialSD;
for(int i = 0; i < tempReductions; i++) {
int acceptCount = 0;
int count = 1;
double mean = currentScore;
double delta = currentScore - 0.0;
double M2 = 0.0 + delta*(currentScore-mean);
vector<Result> accepted;
for(int j = 0; j < neighorhoodSize;) {
// Feed the workers.
for(int y = 0; y < WORKER_COUNT; y++) {
// ScoreFactors altered = randomAlter(current);
ScoreFactors altered = randomAlter2(current);
Job job = {false, altered, temp, currentScore};
jobQueue.push(job);
}
for(int z = 0; z < WORKER_COUNT && j < neighorhoodSize; z++) {
Result res = resultQueue.pop();
if(res.accepted) {
acceptCount++;
cerr << "Master accepted update." << endl;
accepted.push_back(res);
}
// Start up more jobs until one is accepted.
if(accepted.empty()) {
// ScoreFactors altered = randomAlter(current);
ScoreFactors altered = randomAlter2(current);
Job job = {false, altered, temp, currentScore};
jobQueue.push(job);
}
// Online mean & variance.
count++;
delta = res.score - mean;
mean += delta/count;
M2 += delta*(res.score - mean);
// Increment trial counter.
j++;
}
// Choose random success, if any. Random produces better results than first in.
if(!accepted.empty()) {
int randomIdx = (int) (drand48() * accepted.size());
current = accepted[randomIdx].config;
currentScore = accepted[randomIdx].score;
if(currentScore > bestScore) {
bestScore = currentScore;
bestFactors = current;
}
accepted.clear();
}
cerr << "Round " << j << " of " << i << endl;
}
cerr << "M2: " << M2 << " count: " << count << endl;
double sd = sqrt(M2) / (count - 1);
// Minus 1 from count when calculating accept ratio as the count includes the starting score/config.
cerr << "Accept %:" << (double)acceptCount / (double)(count-1) << " at temperature: " << temp << " and SD: " << sd << endl;
sd = (1-smoothingFactor) * sd + smoothingFactor * sdPrev * (temp / prevTemp);
sdPrev = sd;
prevTemp = temp;
temp = nextTemperature(temp, sd, lambda);
cerr << "Current score: " << currentScore << endl;
cerr << "Current score factors: " << endl << printScoreFactors(current) << endl;
cerr << "Best score: " << bestScore << endl;
cerr << "Best factors: " << endl << printScoreFactors(bestFactors) << endl;
}
for(int i = 0; i < WORKER_COUNT; i++) {
jobQueue.push({true, 0, 0, 0});
}
for(int i = 0; i < WORKER_COUNT; i++) {
workers[i].join();
}
finalScore = currentScore;
return current;
}
