public double MeanCompare(WordData word, List <WordData> wordList)
{
double answer = 0;
int[] timing = new int[word.GetTiming().Length];
for (int i = 0; i < word.GetTiming().Length; ++i)
{
int mean = 0;
foreach (WordData listWord in wordList)
{
mean += listWord.GetTiming()[i];
}
mean /= wordList.Count;
timing[i] = mean;
}
WordData meanWord = new WordData(word.GetWord(), timing, -1);
answer = Compare(word, meanWord);
return(answer);
}
public double MedianCompare(WordData word, List <WordData> wordList)
{
double answer = 0;
int[] timing = new int[word.GetTiming().Length];
for (int i = 0; i < word.GetTiming().Length; ++i)
{
List <int> medianList = new List <int>();
int median = 0;
foreach (WordData listWord in wordList)
{
medianList.Add(listWord.GetTiming()[i]);
}
medianList.Sort();
median = medianList[(wordList.Count / 2)];
timing[i] = median;
}
WordData meanWord = new WordData(word.GetWord(), timing, -1);
answer = Compare(word, meanWord);
return(answer);
}
private KeyValuePair <int, double> predict(UserClient client)
{
KeyValuePair <int, double> answer;
List <KeyValuePair <int, double> > answerlist = new List <KeyValuePair <int, double> >(), meanAnswerList = new List <KeyValuePair <int, double> >(),
medianAnswerList = new List <KeyValuePair <int, double> >();
double likeness = 0, meanLikeness = 0, medianLikeness = 0;
List <WordData> predictionData = ToWords(client.GetPredictionData());
if (predictionData.Count > 0)
{
UserDBDataSetWorkerTableAdapters.UserClientTableAdapter userAdapt = new UserDBDataSetWorkerTableAdapters.UserClientTableAdapter();
UserDBDataSetWorker.UserClientDataTable userTable = userAdapt.GetUserClientData();
foreach (UserDBDataSetWorker.UserClientRow userRow in userTable)
{
int count = 0;
foreach (WordData word in predictionData)
{
UserDBDataSetWorkerTableAdapters.WordDataTableAdapter wordAdapt = new UserDBDataSetWorkerTableAdapters.WordDataTableAdapter();
UserDBDataSetWorker.WordDataDataTable wordTable = wordAdapt.GetDataByUserIDandWord(userRow.UserID, word.GetWord());
if (wordTable.Count > 0)
{
List <WordData> wordList = new List <WordData>();
foreach (UserDBDataSetWorker.WordDataRow wordRow in wordTable)
{
UserDBDataSetWorkerTableAdapters.TimingTableAdapter timeAdapt = new UserDBDataSetWorkerTableAdapters.TimingTableAdapter();
UserDBDataSetWorker.TimingDataTable timeTable = timeAdapt.GetDataByWordID(wordRow.WordID);
if (timeTable.Count > 0)
{
int[] timing = new int[timeTable.Count];
for (int i = 0; i < timeTable.Count; i++)
{
timing[i] = timeTable[i].Timing;
}
WordData learnedWord = new WordData(wordRow.Word, timing, userRow.UserID);
double comp = Compare(word, learnedWord);
if (comp > 0)
{
likeness += Compare(word, learnedWord);
count++;
}
wordList.Add(learnedWord);
}
}
if (word.GetTiming().Length > 0)
{
meanLikeness = MeanCompare(word, wordList);
medianLikeness = MedianCompare(word, wordList);
}
//meanaverage
}
}
likeness /= count;
answerlist.Add(new KeyValuePair <int, double>(userRow.UserID, likeness));
meanAnswerList.Add(new KeyValuePair <int, double>(userRow.UserID, meanLikeness));
medianAnswerList.Add(new KeyValuePair <int, double>(userRow.UserID, medianLikeness));
meanAList = meanAnswerList;
medianAList = medianAnswerList;
bool problem;
if (likeness > 1)
{
problem = true;
}
likeness = 0;
count = 0;
}
}
if (answerlist.Count > 1)
{
switch (predictionMode)
{
case 0: //compare
answer = MaxValue(answerlist); //most probable prediction
break;
case 1: //mean
answer = MaxValue(meanAnswerList); //most probable prediction
break;
default:
case 2: //median
answer = MaxValue(medianAnswerList); //most probable prediction
break;
}
}
else
{
answer = new KeyValuePair <int, double>(0, 0);
}
return(answer);
}
