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Showing content from http://accord-framework.net/docs/html/T_Accord_Math_Optimization_Losses_LogLikelihoodLoss.htm below:

Negative log-likelihood loss.

SystemObject
  Accord.Math.Optimization.LossesLogLikelihoodLoss

Accord.Math (in Accord.Math.dll) Version: 3.8.0

[SerializableAttribute]
public class LogLikelihoodLoss : ILoss<double[][]>, 
	ILoss<double[][], double>, ILoss<double[]>, 
	ILoss<double[], double>

<SerializableAttribute>
Public Class LogLikelihoodLoss
	Implements ILoss(Of Double()()), ILoss(Of Double()(), Double), 
	ILoss(Of Double()), ILoss(Of Double(), Double)

The LogLikelihoodLoss type exposes the following members.

Determines whether the specified object is equal to the current object.

Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.

Serves as the default hash function.

Gets the Type of the current instance.

Computes the loss between the expected values (ground truth) and the given actual values that have been predicted.

Computes the loss between the expected values (ground truth) and the given actual values that have been predicted.

Creates a shallow copy of the current Object.

Returns a string that represents the current object.

Checks whether an object implements a method with the given name.

Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.

Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.

Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.

The log-likelihood loss can be used to measure the performance of unsupervised model fitting algorithms. It simply computes the sum of all log-likelihood values produced by the model.

If you would like to measure the performance of a supervised classification model based on their probability predictions, please refer to the BinaryCrossEntropyLoss and CategoryCrossEntropyLoss for binary and multi-class decision problems, respectively.

The following example shows how to learn an one-class SVM and measure its performance using the log-likelihood loss class.

Accord.Math.Random.Generator.Seed = 0;


double[][] inputs =
{
    new double[] { +1.0312479734420776  },
    new double[] { +0.99444115161895752 },
    new double[] { +0.21835240721702576 },
    new double[] { +0.47197291254997253 },
    new double[] { +0.68701112270355225 },
    new double[] { -0.58556461334228516 },
    new double[] { -0.64154046773910522 },
    new double[] { -0.66485315561294556 },
    new double[] { +0.37940266728401184 },
    new double[] { -0.61046308279037476 }
};



var teacher = new OneclassSupportVectorLearning<Linear>()
{
    Kernel = new Linear(), 
    Nu = 0.1
};


var svm = teacher.Learn(inputs);


double[] prediction = svm.Score(inputs);


double ll = new LogLikelihoodLoss().Loss(prediction);

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