public static double ComputeROCAUCTrapeziod <RealType>(IList <bool> labels, IList <RealType> sample)
where RealType : IComparable <RealType>
{
int postive_count = ToolsCollection.CountOccurance(labels, true);
double true_positive_increment = 1.0 / postive_count;
double false_positive_increment = 1.0 / (labels.Count - postive_count);
double[] true_positive_rates = new double[labels.Count + 1];
double[] false_positive_rates = new double[labels.Count + 1];
List <int> ordering = ToolsMathCollection.Ordering(sample);
for (int index = 1; index < labels.Count + 1; index++)
{
if (labels[ordering[index - 1]])
{
true_positive_rates[index] = true_positive_rates[index - 1] + true_positive_increment;
false_positive_rates[index] = false_positive_rates[index - 1];
}
else
{
true_positive_rates[index] = true_positive_rates[index - 1];
false_positive_rates[index] = false_positive_rates[index - 1] + false_positive_increment;
}
}
return(IntegralEvaluatorTrapezoid.EvaluateStaticValue(false_positive_rates, true_positive_rates));
}
public static int [] select_index_of_value(int [] array, int value)
{
int count = ToolsCollection.CountOccurance(array, value);
int [] indexes_with_value = new int [count];
int index_indexes_with_value = 0;
for (int index = 0; index < array.Length; index++)
{
if (array[index] == value)
{
indexes_with_value[index_indexes_with_value] = index;
index_indexes_with_value++;
}
}
return(indexes_with_value);
}
public static double TestStatic(IList <bool> labels, IList <double> sample_0)
{
if (sample_0.Count != labels.Count)
{
throw new Exception("Labels not of equal size");
}
//U = AUC∗nP∗nN
int positive_count = ToolsCollection.CountOccurance(labels, true);
int negative_count = sample_0.Count - positive_count;
double auc = ToolsMathStatistics.ComputeROCAUCTrapeziod(labels, sample_0);
double u_statistic = positive_count * negative_count * auc;
//Z transform
double expected_value = (positive_count * negative_count) / 2.0;
double variance = (positive_count * negative_count * (positive_count + negative_count + 1)) / 12.0;
double z_value = (u_statistic - expected_value) / Math.Sqrt(variance);
return(1 - Normal.CDF(0.0, 1.0, z_value));
}
