/// <summary>
/// Creates a uniform unnormalized discrete distribution over the values from 0 to numValues-1
/// </summary>
/// <returns></returns>
public static UnnormalizedDiscrete Uniform(int numValues)
{
UnnormalizedDiscrete d = new UnnormalizedDiscrete();
d.logProb = DenseVector.Zero(numValues);
return(d);
}
/// <summary>
/// Creates an unnormalized discrete distribution from a vector of log probabilities.
/// </summary>
/// <param name="logProb"></param>
/// <returns></returns>
public static UnnormalizedDiscrete FromLogProbs(DenseVector logProb)
{
UnnormalizedDiscrete ud = new UnnormalizedDiscrete();
ud.logProb = logProb;
return(ud);
}
/// <summary>
/// Creates an unnormalized discrete distribution from a normal discrete distribution.
/// </summary>
/// <param name="d">The discrete distribution</param>
/// <returns></returns>
public static UnnormalizedDiscrete FromDiscrete(Discrete d)
{
UnnormalizedDiscrete ud = new UnnormalizedDiscrete();
ud.logProb = (DenseVector)d.GetLogProbs();
return(ud);
}
public void SetToSum(double weight1, UnnormalizedDiscrete value1, double weight2, UnnormalizedDiscrete value2)
{
Discrete result = Discrete.Uniform(Dimension);
result.SetToSum(weight1, value1.ToDiscrete(), weight2, value2.ToDiscrete());
SetLogProbs((DenseVector)result.GetLogProbs());
}
/// <summary>
/// Creates an unnormalized discrete distribution which is the ratio of two unnormalized discrete distributions
/// </summary>
/// <param name="a">The first distribution</param>
/// <param name="b">The second distribution</param>
/// <returns>The resulting unnormalized discrete distribution</returns>
public static UnnormalizedDiscrete operator /(UnnormalizedDiscrete a, UnnormalizedDiscrete b)
{
UnnormalizedDiscrete result = UnnormalizedDiscrete.Uniform(a.Dimension);
result.SetToRatio(a, b);
return(result);
}
public static UnnormalizedDiscrete operator ^(UnnormalizedDiscrete dist, double exponent)
{
UnnormalizedDiscrete result = UnnormalizedDiscrete.Uniform(dist.Dimension);
result.SetToPower(dist, exponent);
return(result);
}
/// <summary>
/// Sets the parameters to represent the ratio of two unnormalized discrete distributions.
/// </summary>
/// <param name="numerator">The first unnormalized discrete distribution</param>
/// <param name="denominator">The second unnormalized discrete distribution</param>
/// <param name="forceProper">Ignored</param>
public void SetToRatio(UnnormalizedDiscrete numerator, UnnormalizedDiscrete denominator, bool forceProper = false)
{
if (numerator.IsPointMass)
{
if (denominator.IsPointMass)
{
if (numerator.Point == denominator.Point)
{
SetToUniform();
}
else
{
throw new DivideByZeroException();
}
}
else
{
SetTo(numerator);
}
}
else if (denominator.IsPointMass)
{
throw new DivideByZeroException();
}
else
{
logProb.SetToDifference(numerator.logProb, denominator.logProb);
}
}
/// <summary>
/// The maximum difference between the parameters of this discrete and that discrete
/// </summary>
/// <param name="that">That discrete</param>
/// <returns>The maximum difference</returns>
public double MaxDiff(object that)
{
UnnormalizedDiscrete thatd = that as UnnormalizedDiscrete;
if (thatd == null)
{
return(Double.PositiveInfinity);
}
return(logProb.MaxDiff(thatd.logProb));
}
/// <summary>
/// Sets the parameters of this instance to the parameters of that instance
/// </summary>
/// <param name="value">That instance</param>
public void SetTo(UnnormalizedDiscrete value)
{
if (object.ReferenceEquals(value, null))
{
SetToUniform();
}
else
{
logProb.SetTo(value.logProb);
}
}
/// <summary>
/// Sets the parameters to represent the product of two unnormalized discrete distributions.
/// </summary>
/// <param name="a">The first unnormalized discrete distribution</param>
/// <param name="b">The second unnormalized discrete distribution</param>
public void SetToProduct(UnnormalizedDiscrete a, UnnormalizedDiscrete b)
{
if (a.IsPointMass)
{
if (b.IsPointMass && (a.Point != b.Point))
{
throw new AllZeroException();
}
SetTo(a);
}
else if (b.IsPointMass)
{
SetTo(b);
}
else
{
logProb.SetToSum(a.logProb, b.logProb);
}
}
public void SetToPower(UnnormalizedDiscrete dist, double exponent)
{
if (dist.IsPointMass)
{
if (exponent == 0)
{
SetToUniform();
}
else if (exponent < 0)
{
throw new DivideByZeroException("The exponent is negative and the distribution is a point mass");
}
else
{
Point = dist.Point;
}
return;
}
logProb.Scale(exponent);
}
public double GetAverageLog(UnnormalizedDiscrete that)
{
throw new NotImplementedException();
}
