Interface WeightFunctions<TSequence, TElement, TElementDistribution, TSequenceManipulator, TAutomaton>.IWeightFunction<TThis>

Interface for a function that maps arbitrary sequences of elements to real values, that can, but not necessarily has to be, represented as a weighted finite state automaton.

Inherited Members
Namespace: Microsoft.ML.Probabilistic.Distributions.Automata
Assembly: Microsoft.ML.Probabilistic.dll
Syntax
[Quality(QualityBand.Experimental)]
public interface IWeightFunction<TThis> : WeightFunctions<TSequence, TElement, TElementDistribution, TSequenceManipulator, TAutomaton>.IWeightFunction, IEquatable<TThis> where TThis : WeightFunctions<TSequence, TElement, TElementDistribution, TSequenceManipulator, TAutomaton>.IWeightFunction<TThis>, new()
Type Parameters
Name Description
TThis

The type of a concrete weight function class.
Remarks

Types implementing this interface must be constant and thread-safe.

Methods

Append(TSequence, Int32)

Creates a weight function f'(st) = f(s), where f(s) is the current weight function and t is the given sequence.

Declaration
TThis Append(TSequence sequence, int group = 0)
Parameters
Type Name Description
TSequence sequence

The sequence.
Int32 group

The group.
Returns
Type Description
TThis

The created weight function.

Append(TThis, Int32)

Creates a weight function f'(s) = sum_{tu=s} f(t)g(u), where f(t) is the current weight function and g(u) is the given weight function.

Declaration
TThis Append(TThis weightFunction, int group = 0)
Parameters
Type Name Description
TThis weightFunction

The weight function to append.
Int32 group

The group.
Returns
Type Description
TThis

The created weight function.

GetGroups()

If the current weight function uses groups, returns a dictionary [group index] -> group. Otherwise, returns an empty dictionary.

Declaration
Dictionary<int, TThis> GetGroups()
Returns
Type Description
Dictionary<Int32, TThis>

A possibly empty dictionary [group index] -> group.

MaxDiff(TThis)

Gets a value indicating how close this weight function is to a given one in terms of weights they assign to sequences.

Declaration
double MaxDiff(TThis that)
Parameters
Type Name Description
TThis that

The other weight function.
Returns
Type Description
Double

A non-negative value, which is close to zero if the two weight functions assign similar values to all sequences.

NormalizeStructure()

Returns the weight function converted to the normalized form e.g. using special case structures for point masses and functions with small support.

Declaration
TThis NormalizeStructure()
Returns
Type Description
TThis

Product(TThis)

Computes the product of the current weight function and a given one.

Declaration
TThis Product(TThis weightFunction)
Parameters
Type Name Description
TThis weightFunction

The weight function to compute the product with.
Returns
Type Description
TThis

The computed product.

Repeat(Int32, Nullable<Int32>)

Creates a weight function g(s) = sum_{k=Kmin}^{Kmax} sum_{t1 t2 ... tk = s} f(t1)f(t2)...f(tk), where f(t) is the current weight function, and Kmin and Kmax are the minimum and the maximum number of factors in a sum term.

Declaration
TThis Repeat(int minTimes = 1, int? maxTimes = null)
Parameters
Type Name Description
Int32 minTimes

The minimum number of factors in a sum term. Defaults to 1.
Nullable<Int32> maxTimes

An optional maximum number of factors in a sum term.
Returns
Type Description
TThis

The created weight function.

ScaleLog(Double)

Scales the weight function and returns the result.

Declaration
TThis ScaleLog(double logScale)
Parameters
Type Name Description
Double logScale

The logarithm of the scale.
Returns
Type Description
TThis

The scaled weight function.

Sum(TThis)

Computes the sum of the current weight function and a given weight function.

Declaration
TThis Sum(TThis weightFunction)
Parameters
Type Name Description
TThis weightFunction

The weight function to compute the sum with.
Returns
Type Description
TThis

The computed sum.

Sum(Double, TThis, Double)

Computes the weighted sum of the current weight function and a given weight function.

Declaration
TThis Sum(double weight1, TThis weightFunction, double weight2)
Parameters
Type Name Description
Double weight1

The weight of the current weight function.
TThis weightFunction

The weight function to compute the sum with.
Double weight2

The weight of the weightFunction.
Returns
Type Description
TThis

SumLog(Double, TThis, Double)

Computes the weighted sum of the current weight function and a given weight function.

Declaration
TThis SumLog(double logWeight1, TThis weightFunction, double logWeight2)
Parameters
Type Name Description
Double logWeight1

The logarithm of the weight of the current weight function.
TThis weightFunction

The weight function to compute the sum with.
Double logWeight2

The logarithm of the weight of the weightFunction.
Returns
Type Description
TThis

TryNormalizeValues(out TThis, out Double)

Attempts to normalize the weight function so that sum of its values on all possible sequences equals to one (if it is possible) and returns the result in an out parameter. If successful, calls to AsAutomaton() methods of the resulting function produce a stochastic automaton, i.e. an automaton, in which the sum of weights of all the outgoing transitions and the ending weight is 1 for every node.

Declaration
bool TryNormalizeValues(out TThis normalizedFunction, out double logNormalizer)
Parameters
Type Name Description
TThis normalizedFunction

Result of the normaliztion attempt.
Double logNormalizer

When the function returns, contains the logarithm of the normalizer.
Returns
Type Description
Boolean

true if the weight function was successfully normalized, false otherwise.