public void AverageCalculationTest()
{
int[] input = new int[] { 1, 2, 3, 4, 5 };
var result = StatFunctions.CalculateAverage(input);
Assert.IsTrue(result == 3);
}
public void ModeCalculationTest()
{
int[] input = new int[] { 1, 2, 11, 11, 88, 99 };
var result = StatFunctions.CalculateModeValue(input);
Assert.IsTrue(result[0] == 11);
}
public void MedianEvenCalculationTest()
{
int[] input = new int[] { 1, 2, 11, 24, 88, 99 };
var result = StatFunctions.CalculateMedian(input);
Assert.IsTrue(result == 17.5);
}
public void MedianOddCalculationTest()
{
int[] input = new int[] { 1, 2, 11, 25, 88 };
var result = StatFunctions.CalculateMedian(input);
Assert.IsTrue(result == 11);
}
public void WeightedAverageCalculationTest()
{
int[] input = new int[] { 1, 2, 11, 25, 88 };
int[] weights = new int[] { 10, 20, 15, 18, 22 };
var result = StatFunctions.CalculateWeightedAverage(input, weights);
Assert.IsTrue(result == 30.6);
}
/** Method: Constructor
* maxClasses - maximum of classes allowed for histogram
* maxCombTerms - maximum of convolution terms for combinatory calculation
* maxFftTerms - maximum of convolution terms for fast fourier calculation
* nThreads - number of threads */
internal Convolution(int maxClasses, int maxCombTerms, int maxFftTerms, int nThreads)
{
this.comb = new Combinatory();
stat = new StatFunctions();
normal = new NormalDistrib();
fft = new FFT();
this.maxClasses = maxClasses;
this.maxCombTerms = maxCombTerms;
this.maxFftTerms = maxFftTerms;
this.nThreads = nThreads;
}
/// <summary>
/// return calculated weighted average for student
/// </summary>
/// <param name="student"></param>
private double CalculateStudentWeightedAverage(Student student)
{
int[] weights = new int[_SubjectWeights.Count];
int[] values = new int[_SubjectWeights.Count];
for (int i = 0; i < _SubjectWeights.Count; i++)
{
weights[i] = _SubjectWeights[i].WeightPercentage;
var subject = student.Subjects.FirstOrDefault(p => p.Name == _SubjectWeights[i].Name);
int SubjectPercentage = 0;
if (subject != null)
{
SubjectPercentage = subject.SucessRate;
}
values[i] = SubjectPercentage;
}
return(StatFunctions.CalculateWeightedAverage(values, weights));
}
/** Method: Constructor */
internal NormalConv()
{
data = new List <double>();
stat = new StatFunctions();
nd = new NormalDistrib();
}
public bool Process(string outputFolder = null, string outputType = null)
{
foreach (var group in _Groups)
{
List <string> subjects = new List <string>();
foreach (var std in group.Students)
{
//calculate weighted average for each student in each group
std.WeightedAverage = CalculateStudentWeightedAverage(std);
//need to fill in list of subjects I assume that each group can have different subjects,
//if at least one person have subject in group then subject is relevant for group
foreach (var subject in std.Subjects)
{
if (!subjects.Contains(subject.Name))
{
subjects.Add(subject.Name);
}
}
}
//now when I know all the subjects relevant for group, can start calculation
foreach (var subj in subjects)
{
//get all values for this subject in group
var values = group.Students.Select(p => p.Subjects.Where(x => x.Name == subj).DefaultIfEmpty(new Subject()
{
Name = subj, SucessRate = 0
}).First().SucessRate).ToArray();
SubjectResult result = new SubjectResult();
result.Name = subj;
result.Average = StatFunctions.CalculateAverage(values);
result.Median = StatFunctions.CalculateMedian(values);
result.Modus = StatFunctions.CalculateModeValue(values);
group.SubjectResults.Add(result);
}
}
//now calculate overall results
//first build of list of relevant subjects
List <string> subjectsOverall = new List <string>();
foreach (var grp in _Groups)
{
foreach (var item in grp.SubjectResults)
{
if (!subjectsOverall.Contains(item.Name))
{
subjectsOverall.Add(item.Name);
}
}
}
//now start calculation
foreach (var subj in subjectsOverall)
{
List <int> totalvalues = new List <int>();
var valuesInGroups = _Groups.Select(u => u.Students.Select(p => p.Subjects.Where(x => x.Name == subj).DefaultIfEmpty(new Subject()
{
Name = subj, SucessRate = 0
}).First().SucessRate).ToList());
foreach (var item in valuesInGroups)
{
totalvalues.AddRange(item);
}
int[] totalValuesArray = totalvalues.ToArray();
SubjectResult newResult = new SubjectResult();
newResult.Name = subj;
newResult.Average = StatFunctions.CalculateAverage(totalValuesArray);
newResult.Median = StatFunctions.CalculateMedian(totalValuesArray);
newResult.Modus = StatFunctions.CalculateModeValue(totalValuesArray);
_TotalResults.Add(newResult);
}
//everything calculated at this point
return(true);
}
