/// <summary>
/// Evidence message for EP.
/// </summary>
/// <param name="allTrue">Constant value for 'allTrue'.</param>
/// <param name="array">Incoming message from 'array'. Must be a proper distribution. If any element is uniform, the result will be uniform.</param>
/// <returns>Logarithm of the factor's average value across the given argument distributions.</returns>
/// <remarks><para>
/// The formula for the result is <c>log(sum_(array) p(array) factor(allTrue,array))</c>.
/// </para></remarks>
/// <exception cref="ImproperMessageException"><paramref name="array"/> is not a proper distribution</exception>
public static double LogAverageFactor(bool allTrue, [SkipIfAnyUniform] IList <Bernoulli> array)
{
Bernoulli to_allTrue = AllTrueAverageConditional(array);
return(to_allTrue.GetLogProb(allTrue));
}
/// <summary>
/// Evidence message for EP
/// </summary>
/// <param name="isGreaterThan">Incoming message from 'isGreaterThan'.</param>
/// <param name="a">Constant value for 'a'.</param>
/// <param name="b">Constant value for 'b'.</param>
/// <returns>Logarithm of the factor's average value across the given argument distributions</returns>
/// <remarks><para>
/// The formula for the result is <c>log(sum_(isGreaterThan) p(isGreaterThan) factor(isGreaterThan,a,b))</c>.
/// </para></remarks>
public static double LogAverageFactor(Bernoulli isGreaterThan, int a, int b)
{
return isGreaterThan.GetLogProb(Factor.IsGreaterThan(a, b));
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="BooleanAndOp"]/message_doc[@name="LogAverageFactor(bool, Bernoulli, bool)"]/*'/>
public static double LogAverageFactor(bool and, Bernoulli a, bool b)
{
Bernoulli to_and = AndAverageConditional(a, b);
return(to_and.GetLogProb(and));
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="BooleanAndOp"]/message_doc[@name="LogAverageFactor(Bernoulli, bool, bool)"]/*'/>
public static double LogAverageFactor(Bernoulli and, bool a, bool b)
{
return(and.GetLogProb(Factor.And(a, b)));
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="DiscreteAreEqualOp"]/message_doc[@name="LogAverageFactor(bool, Discrete, int)"]/*'/>
public static double LogAverageFactor(bool areEqual, Discrete A, int B)
{
Bernoulli to_areEqual = AreEqualAverageConditional(A, B);
return(to_areEqual.GetLogProb(areEqual));
}
/// <summary>
/// Evidence message for EP.
/// </summary>
/// <param name="and">Incoming message from 'and'.</param>
/// <param name="a">Constant value for 'a'.</param>
/// <param name="b">Constant value for 'b'.</param>
/// <returns>Logarithm of the factor's average value across the given argument distributions.</returns>
/// <remarks><para>
/// The formula for the result is <c>log(sum_(and) p(and) factor(and,a,b))</c>.
/// </para></remarks>
public static double LogAverageFactor(Bernoulli and, bool a, bool b)
{
return and.GetLogProb(Factor.And(a, b));
}
/// <summary>
/// Evidence message for EP.
/// </summary>
/// <param name="areEqual">Incoming message from 'areEqual'.</param>
/// <param name="a">Constant value for 'a'.</param>
/// <param name="b">Constant value for 'b'.</param>
/// <returns>Logarithm of the factor's average value across the given argument distributions.</returns>
/// <remarks><para>
/// The formula for the result is <c>log(sum_(areEqual) p(areEqual) factor(areEqual,a,b))</c>.
/// </para></remarks>
public static double LogAverageFactor(Bernoulli areEqual, bool a, bool b)
{
return areEqual.GetLogProb(Factor.AreEqual(a, b));
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="BooleanAreEqualOp"]/message_doc[@name="LogAverageFactor(Bernoulli, bool, bool)"]/*'/>
public static double LogAverageFactor(Bernoulli areEqual, bool a, bool b)
{
return(areEqual.GetLogProb(Factor.AreEqual(a, b)));
}
/// <summary>
/// Evidence message for EP
/// </summary>
/// <param name="or">Constant value for 'or'.</param>
/// <param name="a">Incoming message from 'a'.</param>
/// <param name="b">Constant value for 'b'.</param>
/// <returns>Logarithm of the factor's average value across the given argument distributions</returns>
/// <remarks><para>
/// The formula for the result is <c>log(sum_(a) p(a) factor(or,a,b))</c>.
/// </para></remarks>
public static double LogAverageFactor(bool or, Bernoulli a, bool b)
{
Bernoulli to_or = OrAverageConditional(a, b);
return(to_or.GetLogProb(or));
}
/// <summary>
/// Evidence message for EP
/// </summary>
/// <param name="or">Incoming message from 'or'.</param>
/// <param name="a">Constant value for 'a'.</param>
/// <param name="b">Constant value for 'b'.</param>
/// <returns>Logarithm of the factor's average value across the given argument distributions</returns>
/// <remarks><para>
/// The formula for the result is <c>log(sum_(or) p(or) factor(or,a,b))</c>.
/// </para></remarks>
public static double LogAverageFactor(Bernoulli or, bool a, bool b)
{
return(or.GetLogProb(Factor.Or(a, b)));
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="StringsAreEqualOp"]/message_doc[@name="LogAverageFactor(bool, StringDistribution, StringDistribution)"]/*'/>
public static double LogAverageFactor(bool areEqual, StringDistribution str1, StringDistribution str2)
{
Bernoulli messageToAreEqual = AreEqualAverageConditional(str1, str2);
return(messageToAreEqual.GetLogProb(areEqual));
}
/// <summary>Evidence message for EP.</summary>
/// <param name="sample">Constant value for <c>sample</c>.</param>
/// <param name="probTrue">Incoming message from <c>probTrue</c>.</param>
/// <returns>Logarithm of the factor's average value across the given argument distributions.</returns>
/// <remarks>
/// <para>The formula for the result is <c>log(sum_(probTrue) p(probTrue) factor(sample,probTrue))</c>.</para>
/// </remarks>
public static double LogAverageFactor(bool sample, Beta probTrue)
{
Bernoulli to_sample = SampleAverageConditional(probTrue);
return(to_sample.GetLogProb(sample));
}
/// <summary>
/// Returns the negative natural logarithm of the probability of ground truth for a given predictive distribution.
/// </summary>
/// <param name="groundTruth">The ground truth.</param>
/// <param name="prediction">The prediction as a <see cref="Discrete"/> distribution.</param>
/// <returns>The negative natural logarithm of the probability of <paramref name="groundTruth"/>.</returns>
public static double NegativeLogProbability(bool groundTruth, Bernoulli prediction)
{
return(-prediction.GetLogProb(groundTruth));
}
/// <summary>
/// Evidence message for EP
/// </summary>
/// <param name="or">Incoming message from 'or'.</param>
/// <param name="a">Constant value for 'a'.</param>
/// <param name="b">Constant value for 'b'.</param>
/// <returns>Logarithm of the factor's average value across the given argument distributions</returns>
/// <remarks><para>
/// The formula for the result is <c>log(sum_(or) p(or) factor(or,a,b))</c>.
/// </para></remarks>
public static double LogAverageFactor(Bernoulli or, bool a, bool b)
{
return or.GetLogProb(Factor.Or(a, b));
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="IsGreaterThanOp"]/message_doc[@name="LogAverageFactor(Bernoulli, int, int)"]/*'/>
public static double LogAverageFactor(Bernoulli isGreaterThan, int a, int b)
{
return(isGreaterThan.GetLogProb(Factor.IsGreaterThan(a, b)));
}