public void TestProteinMassComparerWithBinning()
{
var comparer2 = new FilteredProteinMassBinning(new AminoAcidSet(), 50001);
for (var i = 9999d; i < 10010; i++)
{
Console.WriteLine("{0}, {1}",i, comparer2.GetBinNumber(i));
}
//var comparer = new ProteinMassBinning(50, 50001, true);
/*
Console.WriteLine(Constants.GetBinNumHighPrecision(50000));
Console.WriteLine(comparer.NumberOfBins);
Console.WriteLine(comparer2.NumberOfBins);
var rnd = new Random();
var mass = 0d;
for (var i = 0; i < 450; i ++)
{
if (i > 0)
{
var j = rnd.Next(aaSet.Length);
mass += aaSet[j].Mass;
}
if (mass > comparer.MaxMass) break;
var binNum = Constants.GetBinNumHighPrecision(mass);
var binNum1 = comparer.GetBinNumber(mass);
var binNum2 = comparer2.GetBinNumber(mass);
Console.WriteLine("{0}\t{1}\t{2}\t{3}\t{4}", i, mass, binNum, binNum1, binNum2);
}*/
}
public double PearsonCorrelation(Peak[] spectrum1, Peak[] spectrum2,FilteredProteinMassBinning comparer)
{
var spec1Bar = 0d;
var spec2Bar = 0d;
spectrum1 = GuassianFilter(spectrum1, .5);
spectrum2 = GuassianFilter(spectrum2, .5);
var vectorLength = comparer.GetBinNumber(10000.0);
spec1Bar = spectrum1.Sum(x => x.Intensity)/vectorLength;
spec2Bar = spectrum1.Sum(y => y.Intensity)/vectorLength;
var intensityVector1 = ConvertToFullIntensityVector(spectrum1, vectorLength, comparer);
var intensityVector2 = ConvertToFullIntensityVector(spectrum2, vectorLength, comparer);
var cov = 0.0;
var s1 = 0.0;
var s2 = 0.0;
for (var i = 0; i < vectorLength; i++)
{
var d1 = intensityVector1[i] - spec1Bar;
var d2 = intensityVector2[i] - spec2Bar;
cov += d1*d2;
s1 += d1*d1;
s2 += d2*d2;
}
if (s1 <= 0 || s2 <= 0) return 0;
return cov < 0 ? 0f : cov/Math.Sqrt(s1*s2);
}
public double RootMeanSquareDeviation(Peak[] spectrum1, Peak[] spectrum2, FilteredProteinMassBinning comparer)
{
spectrum1 = GuassianFilter(spectrum1, .5);
spectrum2 = GuassianFilter(spectrum2, .5);
var vectorLength = comparer.GetBinNumber(10000.0);
var intensityVector1 = ConvertToFullIntensityVector(spectrum1, vectorLength, comparer);
var intensityVector2 = ConvertToFullIntensityVector(spectrum2, vectorLength, comparer);
var mean1 = spectrum1.Sum(p => p.Intensity)/spectrum1.Length;
var mean2 = spectrum1.Sum(p => p.Intensity)/spectrum2.Length;
var sum = 0d;
for (int i = 0; i < vectorLength; i++)
{
var diff = intensityVector1[i] - intensityVector2[i];
sum += diff*diff;
}
return Math.Sqrt(sum/vectorLength);
}
public double DotProduct(Peak[] spectrum1, Peak[] spectrum2, FilteredProteinMassBinning comparer)
{
spectrum1 = GuassianFilter(spectrum1,.5);
spectrum2 = GuassianFilter(spectrum2,.5);
var vectorLength = comparer.GetBinNumber(10000.0);
var featureVector1 = ConvertToFullIntensityVector(spectrum1,vectorLength,comparer);
var featureVector2 = ConvertToFullIntensityVector(spectrum2, vectorLength,comparer);
var sum = 0d;
for (int i = 0; i < vectorLength; i++)
{
sum += featureVector1[i]*featureVector2[i];
}
var norm1 = featureVector1.Sum(x => x*x);
var norm2 = featureVector2.Sum(x => x * x);
return sum/Math.Sqrt(norm1*norm2);
}
public double[] ConvertToFullIntensityVector(Peak[] spectrum, int length, FilteredProteinMassBinning comparer)
{
var intensityVector = new double[length];
Array.Clear(intensityVector,0,length);
spectrum = RemovePeaks(10000.0, spectrum);
for (var i = 0; i < spectrum.Length; i++)
{
var binNumber = comparer.GetBinNumber(spectrum[i].Mz);
if (binNumber >= 0)
{
intensityVector[binNumber - 1] = spectrum[i].Intensity;
}
}
return intensityVector;
}
