public Dictionary <string, BigFloat> GetClassScores(int i, bool passZeroProbs)
{
// Github Link: https://github.com/Osinko/BigFloat
// This method uses BigFloat library for high precision probability calculations
// When multiplication of probability values is performed consecutively, the value gets smaller and smaller
List <string> uniqueClasses = trainFeatures[columns.Count - 1].Distinct().ToList();// Gets unique classes
string classColumn = columns.Keys.Last();
Dictionary <string, BigFloat> classScores = new Dictionary <string, BigFloat>();
BigFloat numerator;
BigFloat denominator = new BigFloat(0);
bool denominatorFlag = false;
double eps = 0.00001;// if passZeroProbs is selected, use this value as a probability value
// CPT's consist of (part1|part2, prob_value) like strings. Each part has (rule$value) like elements. (rule$value) equals to (rule=value)
// Example CPT element: (credit_history$all paid|own_telephone$none, class$bad)
foreach (string uqClass in uniqueClasses)// Calculates probability values for each class
{
string queryP1;
string part1, part2;
// Creates query according to bayes formula;
// P(class=x|test_sample)=(P(test_sample|class=x)P(class=x))/P(test_sample)
numerator = new BigFloat(1);
foreach (string str in columns.Keys)
{
if (str == classColumn)// P(class=x) part
{
part1 = String.Format("{0}${1}", str, uqClass);
part2 = "";
}
else // P(test_sample|class=x) part
{ // Creates query from test data infos like '(part1|part2_element1,part2_element2)'
part1 = String.Format("{0}${1}|", str, testFeatures[columns[str]][i]);
part2 = "";
string[] part2Split = bayesNetStructure[str].Split(',');
string splitter = "";
for (int j = 0; j < part2Split.Length; j++)
{
if (part2Split[j] == classColumn)
{
part2 += String.Format("{0}{1}${2}", splitter, part2Split[j], uqClass);
}
else
{
part2 += String.Format("{0}{1}${2}", splitter, part2Split[j], testFeatures[columns[part2Split[j]]][i]);
}
splitter = ",";
}
}
queryP1 = part1 + part2;
double prob = CPT[str][queryP1];// Gets query's probability values from CPT
if (prob == 0 && passZeroProbs)
{
prob = eps;
}
numerator = numerator.Multiply(new BigFloat(prob));
}
if (!denominatorFlag)// P(test_sample) part
{
denominatorFlag = true;
denominator = new BigFloat(0);
foreach (string uqInnerClass in uniqueClasses)// Same calculation process with numerator, one difference is this loop calculates each possibilities for classes
{
BigFloat tempDenominator = new BigFloat(1);
foreach (string str in columns.Keys)
{
if (str == classColumn)// P(class=x) part
{
part1 = String.Format("{0}${1}", str, uqInnerClass);
part2 = "";
}
else // P(test_sample|class=x) part
{ // Creates query from test data infos like '(part1|part2_element1,part2_element2)'
part1 = String.Format("{0}${1}|", str, testFeatures[columns[str]][i]);
part2 = "";
string[] part2Split = bayesNetStructure[str].Split(',');
string splitter = "";
for (int j = 0; j < part2Split.Length; j++)
{
if (part2Split[j] == classColumn)
{
part2 += String.Format("{0}{1}${2}", splitter, part2Split[j], uqInnerClass);
}
else
{
part2 += String.Format("{0}{1}${2}", splitter, part2Split[j], testFeatures[columns[part2Split[j]]][i]);
}
splitter = ",";
}
}
queryP1 = part1 + part2;
double prob = CPT[str][queryP1];// Gets query's probability values from CPT
if (prob == 0 && passZeroProbs)
{
prob = eps;
}
tempDenominator = tempDenominator.Multiply(new BigFloat(prob));
}
denominator = denominator.Add(tempDenominator);
}
}
if (denominator == 0)
{
classScores[uqClass] = 0;
}
else
{
classScores[uqClass] = numerator.Divide(denominator);
}
}
return(classScores);
}
protected override BigFloat Add(
BigFloat leftAddend,
BigFloat rightAddend)
{
return(leftAddend.Add(rightAddend));
}
