None>A function that takes an index and value and returns a value.  See  and  for examples. 8Constraint type. A function and the constant it equals. Think of it as the pair (f, c) in the constraint     p  f(x ) = c +such that we are summing over all values . +For example, for a variance constraint the f would be (\ x -> x*x) and c would be the variance. Most general solver E This will solve the langrangian by added the constraint that the & probabilities must add up to zero. 2 This is the slowest but most flexible method. $This is for the linear case Ax = b  x2 in this situation is the vector of probablities.  For example.    maxentLinear ([1,1,1], ([[0.85, 0.1, 0.05], [0.25, 0.5, 0.25], [0.05, 0.1, 0.85]], [0.29, 0.42, 0.29])) Right [0.1, 0.8, 0.1] $To be honest I am not sure why I can' t use the  version to solve # this type of problem, but it doesn' t work. I'm still learning KThe main entry point for computing discrete maximum entropy distributions. 6 Where the constraints are all moment constraints.  DA pair of values that the distributions is over and the constraints GEither the a discription of what wrong or the probability distribution .The values and a matrix A and column vector b HEither the a discription of what wrong or the probability distribution DA pair of values that the distributions is over and the constraints HEither the a discription of what wrong or the probability distribution   None     maxent-0.3.0.1MaxEntMaxEnt.InternalExpectationFunction Constraint constraintaveragevariance maxentLinearmaxent generalMaxentGeneralConstraintsumMapsumWithpOfK pOfKLinearprobs partitionFunc objectiveFuncdotpartitionFuncLinearobjectiveFuncLinearlinProbsentropy lagrangian squaredGrad toFunction toGradient toDoubleF