//Converts an Algo Rational to an Algo Float.
public static BigFloat RationalToFloat(AlgoValue rational)
{
BigInteger numerator = ((BigRational)rational.Value).FractionalPart.Numerator;
BigInteger denominator = ((BigRational)rational.Value).FractionalPart.Denominator;
return(BigFloat.Divide(new BigFloat(numerator), new BigFloat(denominator)));
}
private static flo SumOfDigits(flo num)
{
flo sum = 0;
while (num > 0)
{
sum += num % 10;
num = num.Divide(10).Floor();
}
return(sum);
}
public XY(BigInteger p, BigInteger q, BigInteger n, BigInteger totient, int x, int y)
{
X = x;
Y = y;
N = n;
P = p;
Q = q;
Totient = totient;
Ratio = BigFloat.Divide(Totient, N);
Diff = double.Parse(BigInteger.Subtract(N, Totient).ToString());
NDynamic = BigInteger.Parse(N.ToString().Substring(Y));
TotDynamic = BigInteger.Parse(Totient.ToString().Substring(Y));
NDouble = double.Parse(NDynamic.ToString());
TotDouble = double.Parse(TotDynamic.ToString());
}
public static void WriteToTxt(List <BalancedNumber> numbers, bool extended)
{
StreamWriter writer = new StreamWriter(File.Open(basePath + (extended ? "Balanced Numbers Extended.txt" : "Balanced Numbers.txt"), FileMode.OpenOrCreate));
int i = 0;
foreach (BalancedNumber bn in numbers)
{
writer.WriteLine("Balanced number #{0}", i + 1);
writer.WriteLine("\tNumber: {0}", bn.number.ToString());
if (extended)
{
writer.WriteLine("\t Digit Count: {0}", GetDigits(bn.number).ToString());
WritePrimeFactors(bn.PrimeFactors.number, writer);
}
writer.WriteLine("\tSideNum: {0}", bn.sideSum.ToString());
if (extended)
{
writer.WriteLine("\t Digit Count: {0}", GetDigits(bn.sideSum).ToString());
WritePrimeFactors(bn.PrimeFactors.sideSum, writer);
}
writer.WriteLine("\tK: {0}", bn.k.ToString());
if (extended)
{
writer.WriteLine("\t Digit Count: {0}", GetDigits(bn.k).ToString());
WritePrimeFactors(bn.PrimeFactors.k, writer);
}
writer.WriteLine("\tKfactor: {0}", bn.KFactor.ToString());
if (extended)
{
TestRational(bn.k, bn.PrimeFactors.kFactorNumerator, bn.number, bn.PrimeFactors.kFactorDenominator, writer);
}
if (i < numbers.Count - 1)
{
BigFloat floatn = bn.number;
BigFloat quoitent = BigFloat.Divide(numbers[i + 1].number, floatn, AccuracyGoal.Absolute(floatn.BinaryPrecision), RoundingMode.TowardsNearest);
writer.WriteLine("\tnFactor to next: {0}", quoitent.ToString());
writer.WriteLine();
}
else
{
writer.WriteLine("\tnFactor to next: N/A\n");
}
i++;
}
writer.Flush();
}
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);
}
public override BigFloat Divide(
BigFloat dividend,
BigFloat divisor)
{
return(BigFloat.Divide(dividend, divisor));
}
private void GetKFactor()
{
kFactor = BigFloat.Divide(kFactorRational.Numerator, kFactorRational.Denominator, AccuracyGoal.Absolute(kFactorRational.Numerator.BitCount + 10), RoundingMode.TowardsNegativeInfinity);
}
