public double GlobalLikelihood(ActivationBinaryTree tree,
List <ParametrizedTemplate> model, ActivationNode conflictResolution)
{
double result = 0;
ActivationTreeBuilder treeBuilder = new();
var kOfConflictActivations = GetConflict(tree);
var constraints = model
.Where(x => x.TemplateDescription.TemplateParametersType == TemplateTypes.BiTemplate)
.SelectMany(x => x.TemplateInstances).Cast <BiTemplate>().ToList();
foreach (var resolution in kOfConflictActivations)
{
var gama = 0;
foreach (var constraint in constraints)
{
var tempTree = treeBuilder.BuildTree(conflictResolution.Subtrace, constraint);
if (GetFulfillment(tempTree).Contains(resolution))
{
gama += 1;
}
else if (GetViolation(tempTree).Contains(resolution))
{
gama += 1;
}
}
result += gama;
}
return(result / kOfConflictActivations.Count);
}
public double LocalLikelihood(ActivationBinaryTree tree,
ActivationNode conflictResolution)
{
var trace = tree.Leaves.Where(n => n.MaxFulfilling)
.SelectMany(n => n.Subtrace)
.Distinct(new EventEqualityComparer())
.ToList();
var nf = conflictResolution.Subtrace.Count(tree.Constraint.IsActivation);
var na = trace.Count(tree.Constraint.IsActivation);
return(nf / (double)na);
}
private static GaussianRandom grnd = new GaussianRandom(); //gausova raspodela za random vrednosti
/// <summary>
/// https://en.wikipedia.org/wiki/Artificial_neuron
/// ulaz1*tezina1
/// ulaz1*tezina2
/// bias(+1*tezina)
/// sve se sabere
/// provuce se kroz aktivacionu funkciju
/// </summary>
/// <param name="n_inputs"></param>
/// <param name="activation"> String naziv aktivacione funkcije </param>
public NeuronNode(int n_inputs, string activation)
{
this.n_inputs = n_inputs;
this.multiplyNodes = new List <MultiplyNode>(); //for inputs and weights
this.sumNode = new SumNode(); //for sum of inputs*weights
this.previous_deltas = new List <double>(); //vrednost delti u prosloj iteraciji
this.gradients = new List <List <double> >(); //lokalni gradijenti
MultiplyNode mulNode;
//init inputs
//collect inputs and corresponding weights
for (int i = 0; i < this.n_inputs; i++)
{
mulNode = new MultiplyNode();
double b = grnd.NextGaussian(0.3, 0.5);
mulNode.x = new List <double>()
{
1.0, b
};
this.multiplyNodes.Add(mulNode);
previous_deltas.Add(0.0);
}
//init bias node and weight
mulNode = new MultiplyNode();
double m = grnd.NextGaussian(0.0, 0.01);
mulNode.x = new List <double>()
{
1.0, m
};
this.multiplyNodes.Add(mulNode);
previous_deltas.Add(0.0);
// TODO: Ovde dodajem ako dodajem neku drugu aktivacionu funkciju
/*
* if else(activation.Equals("TangesHiperbolic").....
*/
if (activation.Equals("sigmoid"))
{
this.activation_node = new SigmoidNode();
}
else if (activation.Equals("tanh"))
{
this.activation_node = new TanhNode();
}
else
{
throw new NotImplementedException("Activation function is not supported");
}
}
private double LocalLikelihoodNode(ActivationNode node, int na, BiTemplate constraint)
{
return(node.Subtrace.Count(constraint.IsActivation) / (double)na);
}
