/// <summary>
/// Normalizes the probabilities associated with an array of objects,
/// then converts them into continuing sums.
/// This prepares them for being usable in pickFromDistribution.
/// If the probabilities are all 0, then selection is uniform, unless allowAllZeros
/// is false, in which case an ArithmeticException is thrown. If any of them are negative,
/// or if the distribution is empty, then an ArithmeticException is thrown.
/// For example,
/// {0.6, 0.4, 0.2, 0.8} -> {0.3, 0.2, 0.1, 0.4} -> {0.3, 0.5, 0.6, 1.0}
/// The probabilities are retrieved and set using chooser.
/// </summary>
public static void OrganizeDistribution(Object[] objs, IRandomChoiceChooserD chooser, bool allowAllZeros)
{
// first normalize
var sum = 0.0;
if (objs.Length == 0)
{
throw new ArithmeticException("Distribution has no elements");
}
foreach (var t in objs)
{
if (chooser.GetProbability(t) < 0.0)
{
throw new ArithmeticException("Distribution has negative probabilities");
}
sum += chooser.GetProbability(t);
}
if (sum == 0.0)
{
if (!allowAllZeros)
{
throw new ArithmeticException("Distribution has all zero probabilities");
}
else
{
foreach (var t in objs)
{
chooser.SetProbability(t, 1.0);
}
sum = objs.Length;
}
}
foreach (var t in objs)
{
chooser.SetProbability(t, chooser.GetProbability(t) / sum);
}
// now sum
sum = 0.0;
foreach (var t in objs)
{
sum += chooser.GetProbability(t);
chooser.SetProbability(t, sum);
}
// now we need to work backwards setting 0 values
int x2;
for (x2 = objs.Length - 1; x2 > 0; x2--)
{
if (chooser.GetProbability(objs[x2]) == chooser.GetProbability(objs[x2 - 1]))
{
// we're 0.0
chooser.SetProbability(objs[x2], 1.0);
}
else
{
break;
}
}
chooser.SetProbability(objs[x2], 1.0);
}
