public double GetAccStdError(double confidenceLevel = 0.95)
{
double z = StatsUtils.GetZScore(confidenceLevel);
double acc = GetAccuracy();
return(z * Math.Sqrt(acc * (1 - acc) / GetSumAll()));
}
public double GetAvgStdErr(string expName, string algoName, OrdinalPerfMetric metric, IEnumerable <LblT> orderedLabels, out double stderr, double confidenceLevel = 0.95)
{
double zval = StatsUtils.GetZScore(confidenceLevel);
double stddev;
double avg = GetAvg(expName, algoName, metric, orderedLabels, out stddev);
stderr = zval * stddev / Math.Sqrt(GetFoldCount(expName, algoName));
return(avg);
}
public double GetAvgStdErr(string expName, string algoName, ClassPerfMetric metric, LblT lbl, out double stderr, double confidenceLevel = 0.95)
{
double zval = StatsUtils.GetZScore(confidenceLevel);
double stddev;
double avg = GetAvg(expName, algoName, metric, lbl, out stddev);
stderr = zval * stddev / Math.Sqrt(GetFoldCount(expName, algoName));
return(avg);
}
// correction from wikipedia Krippendorff's Alpha
public double GetKrippendorffsAlpha(IEnumerable <LblT> orderedLabels, Dictionary <LblT, Dictionary <LblT, double> > weights)
{
Preconditions.CheckNotNull(weights);
LblT[] labels = orderedLabels as LblT[] ?? Preconditions.CheckNotNull(orderedLabels).ToArray();
Preconditions.CheckArgument(!mLabels.Except(labels).Any());
Preconditions.CheckArgument(!labels.Except(mLabels).Any());
Preconditions.CheckArgument(StatsUtils.IsMatrixSymetrical(this));
double denom = 0, numer = 0;
for (int i = 0; i < labels.Length; i++)
{
for (int j = i + 1; j < labels.Length; j++)
{
numer += Get(labels[i], labels[j]) * weights[labels[i]][labels[j]];
denom += SumRow(labels[i]) * SumCol(labels[j]) * weights[labels[i]][labels[j]];
}
}
return(1 - (GetSumAll() - 1) * numer / denom);
}