alex.ml package¶

Submodules¶

alex.ml.exceptions module¶

exception alex.ml.exceptions.FFNNException[source]¶: Bases: alex.AlexException

exception alex.ml.exceptions.NBListException[source]¶: Bases: alex.AlexException

alex.ml.features module¶

alex.ml.ffnn module¶

alex.ml.hypothesis module¶

This module collects classes representing the uncertainty about the actual value of a base type instance.

class alex.ml.hypothesis.ConfusionNetwork[source]¶

Bases: alex.ml.hypothesis.Hypothesis

Confusion network. In this representation, each fact breaks down into a sequence of elementary acts.

add(probability, fact)[source]¶: Append a fact to the confusion network.

add_merge(p, fact, combine=u'max')[source]¶: Add a fact and if it exists merge it according to the given combine strategy.

extend(conf_net)[source]¶

classmethod from_fact(fact)[source]¶: Constructs a deterministic confusion network that asserts the given `fact’. Note that `fact’ has to be an iterable of elementary acts.

get_prob(fact)[source]¶: Get the probability of the fact.

merge(conf_net, combine=u'max')[source]¶

Merges facts in the current and the given confusion networks.

Arguments:

combine – can be one of {‘new’, ‘max’, ‘add’, ‘arit’, ‘harm’}, and: determines how two probabilities should be merged (default: ‘max’)

XXX As of now, we know that different values for the same slot are contradictory (and in general, the set of contradicting attr-value pairs could be larger). We should therefore consider them alternatives to each other.

normalise()[source]¶: Makes sure that all probabilities add up to one. They should implicitly sum to one: p + (1-p) == 1.0

prune(prune_prob=0.005)[source]¶: Prune all low probability dialogue act items.

remove(fact_to_remove)[source]¶

sort(reverse=True)[source]¶

update_prob(probability, fact)[source]¶: Update the probability of a fact.

exception alex.ml.hypothesis.ConfusionNetworkException[source]¶: Bases: exceptions.Exception

class alex.ml.hypothesis.Hypothesis[source]¶

Bases: object

This is the base class for all forms of probabilistic hypotheses representations.

classmethod from_fact(fact)[source]¶: Constructs a deterministic hypothesis that asserts the given `fact’.

class alex.ml.hypothesis.NBList[source]¶

Bases: alex.ml.hypothesis.Hypothesis

This class represents the uncertainty using an n-best list.

When updating an N-best list, one should do the following.

add utterances or parse a confusion network
merge and normalise, in either order

add(probability, fact)[source]¶: Finds the last hypothesis with a lower probability and inserts the new item before that one. Optimised for adding objects from the highest probability ones to the lowest probability ones.

add_other(other)[source]¶

The N-best list is extended to include the other object to represent those object values that are not enumerated in the list.

Returns self.

classmethod from_fact(fact)[source]¶

get_best()[source]¶: Returns the most probable value of the object.

merge()[source]¶: Adds up probabilities for the same hypotheses. Returns self.

normalise()[source]¶: Scales the list to sum to one.

alex.ml.logarithmetic module¶

alex.ml.test_hypothesis module¶

class alex.ml.test_hypothesis.TestConfusionNetwork(methodName='runTest')[source]¶

Bases: unittest.case.TestCase

test_iter()[source]¶

test_remove()[source]¶