private void findBestOption()
{
for (int e = 0; e < trialGroupSize; e++)
{
if (performances[e].occupancyPerformance > bestPerformanceNumber)
{
bestPerformanceNumber = performances[e].occupancyPerformance;
bestPerformance = performances[e];
bestOption = options[e];
}
}
}
public PredictionPerformances execute()
{
float iterationOccupancy;
List <float> trusts;
List <float> validity;
float FinalX;
float ParkingCondition;
float cso;
float certainty;
float C;
int iterationsPerHour;
float totalHoursOfUpdates = 0;
float sectionOccupancy;
int user_id;
int update_id;
float user_tag;
// here we have the research data, now we should proceed on fusing all these updates
// at first we need to initialize some elements
predictedSectionOccupancy = new List <List <float> >();
predictedUsersTrust = new List <float>();
List <float> predictedUsersScore = new List <float>();
int nSections = data.nSections;
int nUsers = data.UserTrusts.Count;
int nIterations = data.Updates[0].Count;
for (int u = 0; u < nUsers; u++)
{
predictedUsersTrust.Add((float)0.5);
predictedUsersScore.Add(0);
}
List <List <float> > predictedIterationOccupancy = new List <List <float> >(nSections);
List <float> lastUpdateTime = new List <float>(nSections);
List <float> currentSectionOccupancy = new List <float>(nSections);
for (int s = 0; s < nSections; s++)
{
lastUpdateTime.Add(0);
currentSectionOccupancy.Add(options.I);
}
List <Boolean> processed = new List <Boolean>(10000000);
for (int s = 0; s < processed.Capacity; s++)
{
processed.Add(false);
}
float currentTime;
float interval = 5;
for (int section = 0; section < nSections; section++)
{
predictedIterationOccupancy.Add(new List <float>(nIterations));
currentTime = 7200;
for (int iteration = 0; iteration < nIterations; iteration++)
{
iterationOccupancy = options.I;
currentTime += interval;
List <UserUpdate> updates = data.Updates[section][iteration];
int nUpdates = updates.Count;
if (nUpdates == 0)
{
}
else
{
///////////// get trust of Users
//T = [tags(:).trust];
trusts = new List <float>(nUpdates);
for (int i = 0; i < nUpdates; i++)
{
user_id = updates[i].user_id - 1;
trusts.Add(predictedUsersTrust[user_id]);
}
////////////////////////////////////// make validity vector
//n = length(T);
//validity = zeros(1,n);
//for i=n:-1:1
// validity(i)=T(i);
// for j=i+1:n
// validity(i) = validity(i) * (1 - T(j));
// end
//end
validity = new List <float>(nUpdates);
for (int i = 0; i < nUpdates; i++)
{
validity.Add(0);
}
for (int i = nUpdates - 1; i >= 0; i--)
{
validity[i] = trusts[i];
for (int j = i + 1; j < nUpdates; j++)
{
validity[i] *= 1 - trusts[j];
}
}
///////////// fuse based on validity
//FinalX = 0;
//for j=1:length(validity)
// X = X_influence(tags(j),currentTime,I);
// FinalX = FinalX + validity(j)*X;
//end
//newSectionOccupancy = I + FinalX; % real between 1 and 5
//lastUpdateTimes(section) = currentTime;
FinalX = 0;
for (int i = 0; i < nUpdates; i++)
{
//FinalX += validity[i] * X_influence(updates[i].tag, updates[i].timestamp, currentTime);
FinalX += validity[i] * (updates[i].tag - options.I) / ((currentTime - updates[i].timestamp) * options.decay + 1);
}
lastUpdateTime[section] = updates[updates.Count - 1].timestamp;
iterationOccupancy = options.I + FinalX;
/////////////////// update peoples trust
//ParkingCondition = ceil((currentSectionOccupancy-1)/4 * 5);
//if ParkingCondition == 0, ParkingCondition = 1; end
//if ~isempty(processed)
// for p = find(processed == 0)
// submittedTag = tags(p).value; % r
// certainty = simOptions.certainty_coeff / (currentTime - lastUpdateTime); % coce
// % calculate C
// if ParkingCondition == submittedTag
// C = simOptions.lambda_promote * certainty;
// else
// C = simOptions.lambda_punish * certainty * -abs(ParkingCondition - submittedTag);
// end
// tags(p).score = tags(p).score + C;
// tags(p).trust = (tanh(tags(p).score/simOptions.score_coeff)+1)/2;
// tagid = tags(p).tagID;
// uid = tags(p).userID;
// trust = tags(p).trust;
// score = tags(p).score;
//% rank = floor(trust * 5) + 1; % rank = 1,2,3,4,5
// predictedTrust(uid) = trust;
// predictedScores(uid) = score;
// uus(tagid,6) = 1; % flag processed
// end
//end
cso = currentSectionOccupancy[section];
ParkingCondition = (float)(Math.Ceiling((iterationOccupancy - 1) / 4 * 5)); // {1 2 3 4 5}
if (ParkingCondition == 0)
{
ParkingCondition = 1;
}
for (int i = 0; i < nUpdates; i++)
{
update_id = updates[i].update_id - 1;
user_id = updates[i].user_id - 1;
user_tag = updates[i].tag;
if (!processed[update_id])
{
certainty = options.certainty_coeff / (currentTime - updates[i].timestamp);
if (ParkingCondition == user_tag)
{
C = options.lambda_promote * certainty;
}
else
{
C = options.lambda_punish * certainty * -1 * Math.Abs(ParkingCondition - user_tag);
}
predictedUsersScore[user_id] += C;
predictedUsersTrust[user_id] = (float)((Math.Tanh(predictedUsersScore[user_id] / options.score_coeff) + 1) / 2);
processed[update_id] = true;
}
}
}
predictedIterationOccupancy[section].Add(iterationOccupancy);
currentSectionOccupancy[section] = iterationOccupancy;
}
//iterationsPerHour = (int)(60 / interval);
//totalHoursOfUpdates = (nIterations + 1) / iterationsPerHour;
//predictedSectionOccupancy.Add(new List<float>());
//for (int h = 0; h < totalHoursOfUpdates; h++)
//{
// sectionOccupancy = 0;
// for (int iter = 0; iter < iterationsPerHour; iter++)
// {
// try
// {
// iterationOccupancy = predictedIterationOccupancy[section][h * iterationsPerHour + iter];
// }
// catch
// {
// iterationOccupancy = options.I;
// }
// sectionOccupancy += iterationOccupancy;
// }
// sectionOccupancy = sectionOccupancy / iterationsPerHour;
// sectionOccupancy = (sectionOccupancy - 1) / 4;
// predictedSectionOccupancy[section].Add(sectionOccupancy);
//}
iterationsPerHour = (int)(60 / interval);
totalHoursOfUpdates = (nIterations + 1) / iterationsPerHour;
predictedSectionOccupancy.Add(new List <float>());
for (int iter = 0; iter < predictedIterationOccupancy[section].Count; iter += iterationsPerHour)
{
predictedSectionOccupancy[section].Add((predictedIterationOccupancy[section][iter] - 1) / 4);
}
}
float error1 = 0, error2 = 0, error3 = 0, error4 = 0, temp1, temp2;
int counter = 0;
Random rnd = new Random();
// find Performances
PredictionPerformances performance = new PredictionPerformances();
for (int section = 0; section < nSections; section++)
{
for (int h = 0; h < totalHoursOfUpdates; h++)
{
counter++;
temp1 = (float)Math.Floor(predictedSectionOccupancy[section][h] * 4) + 1;
temp2 = (float)Math.Floor(data.GroundTruth[section][h] * 4) + 1;
error1 += Math.Abs(temp1 - temp2);
temp1 = (float)Math.Floor(rnd.NextDouble() * 4) + 1;
error2 += Math.Abs(temp1 - temp2);
}
}
performance.occupancyPerformance = 1 - error1 / (4 * counter);
performance.occupancyPerformanceRandom = 1 - error2 / (4 * counter);
for (int u = 0; u < nUsers; u++)
{
error3 += Math.Abs(predictedUsersTrust[u] - data.UserTrusts[u]);
error4 += Math.Abs((float)rnd.NextDouble() - data.UserTrusts[u]);
}
performance.trustPerformance = 1 - error3 / nUsers;
performance.trustPerformanceRandom = 1 - error4 / nUsers;
return(performance);
}
public PredictionPerformances execute()
{
float iterationOccupancy;
List <float> trusts;
List <float> validity;
float FinalX;
float ParkingCondition;
float cso;
float certainty;
float C;
int iterationsPerHour;
float totalHoursOfUpdates = 0;
int user_id;
int update_id;
float user_tag;
// here we have the research data, now we should proceed on fusing all these updates
// at first we need to initialize some elements
predictedSectionOccupancy = new List <List <float> >();
predictedUsersTrust = new List <float>();
List <float> predictedUsersScore = new List <float>();
int nSections = _researchData.nSections;
int nUsers = _researchData.UserTrusts.Count;
int nIterations = _researchData.Updates[0].Count;
for (int u = 0; u < nUsers; u++)
{
predictedUsersTrust.Add((float)0.5);
predictedUsersScore.Add(0);
}
List <List <float> > predictedIterationOccupancy = new List <List <float> >(nSections);
List <float> lastUpdateTime = new List <float>(nSections);
List <float> currentSectionOccupancy = new List <float>(nSections);
for (int s = 0; s < nSections; s++)
{
lastUpdateTime.Add(0);
currentSectionOccupancy.Add(options.I);
}
List <Boolean> processed = new List <Boolean>(10000000);
for (int s = 0; s < processed.Capacity; s++)
{
processed.Add(false);
}
float currentTime, error;
const float interval = 5; // every 5 minutes, standard
for (int section = 0; section < nSections; section++)
{
predictedIterationOccupancy.Add(new List <float>(nIterations));
currentTime = 7200;
for (int iteration = 0; iteration < nIterations; iteration++)
{
iterationOccupancy = options.I;
currentTime += interval;
List <UserUpdate> updates = _researchData.Updates[section][iteration];
int nUpdates = updates.Count;
if (nUpdates == 0)
{
}
else
{
trusts = new List <float>(nUpdates);
for (int i = 0; i < nUpdates; i++)
{
user_id = updates[i].user_id - 1;
trusts.Add(predictedUsersTrust[user_id]);
}
validity = new List <float>(nUpdates);
for (int i = 0; i < nUpdates; i++)
{
validity.Add(0);
}
for (int i = nUpdates - 1; i >= 0; i--)
{
validity[i] = trusts[i];
for (int j = i + 1; j < nUpdates; j++)
{
validity[i] *= 1 - trusts[j];
}
}
FinalX = 0;
for (int i = 0; i < nUpdates; i++)
{
FinalX += validity[i] * (updates[i].tag - options.I) / ((currentTime - updates[i].timestamp) * options.decayOfInteration[iteration] + 1);
}
lastUpdateTime[section] = updates[updates.Count - 1].timestamp;
iterationOccupancy = options.I + FinalX;
cso = currentSectionOccupancy[section];
ParkingCondition = (float)(Math.Ceiling((iterationOccupancy - 1) / 4 * 5)); // {1 2 3 4 5}
if (ParkingCondition == 0)
{
ParkingCondition = 1;
}
for (int i = 0; i < nUpdates; i++)
{
update_id = updates[i].update_id - 1;
user_id = updates[i].user_id - 1;
user_tag = updates[i].tag;
if (!processed[update_id])
{
certainty = options.certainty_coeff / (currentTime - updates[i].timestamp);
if (ParkingCondition == user_tag)
{
C = options.lambda_promote * certainty;
}
else
{
C = options.lambda_punish * certainty * -1 * Math.Abs(ParkingCondition - user_tag);
}
predictedUsersScore[user_id] += C;
predictedUsersTrust[user_id] = (float)((Math.Tanh(predictedUsersScore[user_id] / options.score_coeff) + 1) / 2);
processed[update_id] = true;
}
}
}
predictedIterationOccupancy[section].Add(iterationOccupancy);
currentSectionOccupancy[section] = iterationOccupancy;
}
iterationsPerHour = (int)(60 / interval);
totalHoursOfUpdates = (nIterations + 1) / iterationsPerHour;
predictedSectionOccupancy.Add(new List <float>());
for (int iter = 0; iter < predictedIterationOccupancy[section].Count; iter += iterationsPerHour)
{
predictedSectionOccupancy[section].Add((predictedIterationOccupancy[section][iter] - 1) / 4);
}
}
float error1 = 0, error2 = 0, error3 = 0, error4 = 0, temp1, temp2;
int counter = 0;
Random rnd = new Random();
// find Performances
PredictionPerformances performance = new PredictionPerformances();
for (int section = 0; section < nSections; section++)
{
for (int h = 0; h < totalHoursOfUpdates; h++)
{
counter++;
temp1 = (float)Math.Floor(predictedSectionOccupancy[section][h] * 4) + 1;
temp2 = (float)Math.Floor(_researchData.GroundTruth[section][h] * 4) + 1;
error1 += Math.Abs(temp1 - temp2);
temp1 = (float)Math.Floor(rnd.NextDouble() * 4) + 1;
error2 += Math.Abs(temp1 - temp2);
}
}
performance.occupancyPerformance = 1 - error1 / (4 * counter);
performance.occupancyPerformanceRandom = 1 - error2 / (4 * counter);
for (int u = 0; u < nUsers; u++)
{
error3 += Math.Abs(predictedUsersTrust[u] - _researchData.UserTrusts[u]);
error4 += Math.Abs((float)rnd.NextDouble() - _researchData.UserTrusts[u]);
}
performance.trustPerformance = 1 - error3 / nUsers;
performance.trustPerformanceRandom = 1 - error4 / nUsers;
return(performance);
}
