private void CalculateDecisionFunctions()
{
bool IsClassification = true;
int iteration = 0;
while (IsClassification && (iteration < MAX_ITERATIONS_COUNT))
{
for (int i = 0; i < classes.Count; i++)
{
PerceptronClass currentClass = classes[i];
PerceptronObject currentWeigth = weigths[i];
for (int j = 0; j < currentClass.objects.Count; j++)
{
PerceptronObject currentObject = currentClass.objects[j];
IsClassification = CorrectWeigth(currentObject, currentWeigth, i);
}
}
iteration++;
}
if (iteration == MAX_ITERATIONS_COUNT)
{
MessageBox.Show("Количество итераций превысило 1000." + "\n" +
"Решаюшие функции, возможно, найдены неправильно.");
}
decisionFunctions = weigths;
}
private void ChangeWeigth(PerceptronObject weigth, PerceptronObject perceptronObject, int sign)
{
for (int i = 0; i < weigth.attributes.Count; i++)
{
weigth.attributes[i] += sign * perceptronObject.attributes[i];
}
}
private bool CorrectWeigth(PerceptronObject currentObject,
PerceptronObject currentWeigth, int classNumber)
{
bool result = false;
int objectDecision = ObjectMultiplication(currentWeigth, currentObject);
for (int i = 0; i < weigths.Count; i++)
{
decisions[i] = ObjectMultiplication(weigths[i], currentObject);
if (i != classNumber)
{
int currentDecision = decisions[i];
if (objectDecision <= currentDecision)
{
ChangeWeigth(weigths[i], currentObject, -1);
result = true;
}
}
}
if (result)
{
ChangeWeigth(currentWeigth, currentObject, 1);
}
return(result);
}
private int ObjectMultiplication(PerceptronObject weigth, PerceptronObject obj)
{
int result = 0;
for (int i = 0; i < weigth.attributes.Count; i++)
{
result += weigth.attributes[i] * obj.attributes[i];
}
return(result);
}
private void CreateRandomClasses()
{
var rand = new Random();
for (int i = 0; i < classesCount; i++)
{
var currentClass = new PerceptronClass();
for (int j = 0; j < objectsInClassCount; j++)
{
var currentObject = new PerceptronObject();
for (int k = 0; k < attributesCount; k++)
{
currentObject.attributes.Add(rand.Next(RANDOM) - RANDOM / 2);
}
currentClass.objects.Add(currentObject);
}
classes.Add(currentClass);
}
foreach (PerceptronClass perceptronClass in classes)
{
var weigth = new PerceptronObject();
for (int i = 0; i <= attributesCount; i++)
{
weigth.attributes.Add(0);
}
weigths.Add(weigth);
decisions.Add(0);
}
foreach (PerceptronClass perceptronClass in classes)
{
foreach (PerceptronObject perceptronObject in perceptronClass.objects)
{
perceptronObject.attributes.Add(1);
}
}
}
public int FindClass(PerceptronObject perceptronObject)
{
int resultClass = 0;
int decisionMax;
perceptronObject.attributes.Add(1);
decisionMax = ObjectMultiplication(weigths[0], perceptronObject);
for (int i = 1; i < weigths.Count; i++)
{
PerceptronObject weigth = weigths[i];
if (ObjectMultiplication(weigth, perceptronObject) > decisionMax)
{
decisionMax = ObjectMultiplication(weigth, perceptronObject);
resultClass = i;
}
}
return(resultClass + 1);
}
