public static double WeightUsingVariance(IFeatureCollectionWrapper <FeatureBase> featureValues)
{
if (featureValues.Count() < 2)
{
return(1);
}
var variance = featureValues.Variance(feature => feature.FeatureValue);
if (variance.Equals(0))
{
return((double)1 / featureValues.Count());
}
return(1 / variance);
}
public static double First3BEqualMetric(FeatureBase value, IFeatureCollectionWrapper <FeatureBase> featureValues)
{
if (value.FeatureValue.Equals(-1.0) || !featureValues.Any())
{
return(0);
}
var tmpTrue = featureValues.Count(x => x.FeatureValue.Equals(1.0));
var tmpFalse = featureValues.Count(x => x.FeatureValue.Equals(0.0));
Console.WriteLine("3Bequal " + tmpTrue + " " + tmpFalse + " FeatureValue: " + value.FeatureValue);
if ((tmpTrue > tmpFalse && value.FeatureValue.Equals(1.0)) || (tmpTrue < tmpFalse && value.FeatureValue.Equals(0.0)))
{
return(0.0);
}
return(1.0);
}
public override void ComputeFeatureForProtocolModel(IFeatureCollectionWrapper <FeatureBase> featureValues)
{
var trueValues = featureValues.Count(feature => feature.FeatureValue.Equals(1.0));
var falseValues = featureValues.Count(feature => feature.FeatureValue.Equals(0.0));
this.FeatureValue = (trueValues > falseValues)? 1 : 0;
if (trueValues == 0 || falseValues == 0)
{
this.Weight = 1;
} // /(double)Math.Sqrt(trueValues+falseValues); }
else if (trueValues < falseValues)
{
this.Weight = 1 - (trueValues / (double)falseValues);
}
else
{
this.Weight = 1 - (falseValues / (double)trueValues);
}
}
public static double WeightProbabilityMetric(IFeatureCollectionWrapper <FeatureBase> trainingFeatureVector)
{
var mostVal = (from i in trainingFeatureVector
group i by i.FeatureValue
into grp
orderby grp.Count() descending
select grp).First();
var result = (double)mostVal.Count() / trainingFeatureVector.Count();
//Console.WriteLine("Weight: " + result + "\n" + "Count " + trainingFeatureVector.Count());
return(result);
}