pyFTS.common package¶
Module contents¶
Submodules¶
pyFTS.common.Activations module¶
Activation functions for Time Series Classification
pyFTS.common.Composite module¶
Composite Fuzzy Sets
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class
pyFTS.common.Composite.
FuzzySet
(name, superset=False, **kwargs)[source]¶ Bases:
pyFTS.common.FuzzySet.FuzzySet
Composite Fuzzy Set
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append
(mf, parameters)[source]¶ Adds a new function to composition
Parameters: - mf –
- parameters –
Returns:
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pyFTS.common.FLR module¶
This module implements functions for Fuzzy Logical Relationship generation
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class
pyFTS.common.FLR.
FLR
(LHS, RHS)[source]¶ Bases:
object
Fuzzy Logical Relationship
Represents a temporal transition of the fuzzy set LHS on time t for the fuzzy set RHS on time t+1.
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LHS
= None¶ Left Hand Side fuzzy set
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RHS
= None¶ Right Hand Side fuzzy set
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class
pyFTS.common.FLR.
IndexedFLR
(index, LHS, RHS)[source]¶ Bases:
pyFTS.common.FLR.FLR
Season Indexed Fuzzy Logical Relationship
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index
= None¶ seasonal index
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pyFTS.common.FLR.
generate_high_order_recurrent_flr
(fuzzyData)[source]¶ Create a ordered FLR set from a list of fuzzy sets with recurrence
Parameters: fuzzyData – ordered list of fuzzy sets Returns: ordered list of FLR
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pyFTS.common.FLR.
generate_indexed_flrs
(sets, indexer, data, transformation=None, alpha_cut=0.0)[source]¶ Create a season-indexed ordered FLR set from a list of fuzzy sets with recurrence
Parameters: - sets – fuzzy sets
- indexer – seasonality indexer
- data – original data
Returns: ordered list of FLR
pyFTS.common.FuzzySet module¶
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class
pyFTS.common.FuzzySet.
FuzzySet
(name: str, mf, parameters: list, centroid: float, alpha: float = 1.0, **kwargs)[source]¶ Bases:
object
Fuzzy Set
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Z
= None¶ Partition function in respect to the membership function
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alpha
= None¶ The alpha cut value
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centroid
= None¶ The fuzzy set center of mass (or midpoint)
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membership
(x)[source]¶ Calculate the membership value of a given input
Parameters: x – input value Returns: membership value of x at this fuzzy set
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mf
= None¶ The membership function
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name
= None¶ The fuzzy set name
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parameters
= None¶ The parameters of the membership function
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partition_function
(uod=None, nbins=100)[source]¶ Calculate the partition function over the membership function.
Parameters: - uod –
- nbins –
Returns:
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transform
(x)[source]¶ Preprocess the data point for non native types
Parameters: x – Returns: return a native type value for the structured type
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type
= None¶ The fuzzy set type (common, composite, nonstationary, etc)
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variable
= None¶ In multivariate time series, indicate for which variable this fuzzy set belogs
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pyFTS.common.FuzzySet.
fuzzyfy
(data, partitioner, **kwargs)[source]¶ A general method for fuzzyfication.
Parameters: - data – input value to be fuzzyfied
- partitioner – a trained pyFTS.partitioners.Partitioner object
- kwargs – dict, optional arguments
- alpha_cut – the minimal membership value to be considered on fuzzyfication (only for mode=’sets’)
- method – the fuzzyfication method (fuzzy: all fuzzy memberships, maximum: only the maximum membership)
- mode – the fuzzyfication mode (sets: return the fuzzy sets names, vector: return a vector with the membership
values for all fuzzy sets, both: return a list with tuples (fuzzy set, membership value) )
:returns a list with the fuzzyfied values, depending on the mode
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pyFTS.common.FuzzySet.
fuzzyfy_instance
(inst, fuzzy_sets: dict, ordered_sets: list = None)[source]¶ Calculate the membership values for a data point given fuzzy sets
Parameters: - inst – data point
- fuzzy_sets – a dictionary where the key is the fuzzy set name and the value is the fuzzy set object.
- ordered_sets – a list with the fuzzy sets names ordered by their centroids.
Returns: array of membership values
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pyFTS.common.FuzzySet.
fuzzyfy_instances
(data: list, fuzzy_sets: dict, ordered_sets=None) → list[source]¶ Calculate the membership values for a data point given fuzzy sets
Parameters: - inst – data point
- fuzzy_sets – a dictionary where the key is the fuzzy set name and the value is the fuzzy set object.
- ordered_sets – a list with the fuzzy sets names ordered by their centroids.
Returns: array of membership values
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pyFTS.common.FuzzySet.
fuzzyfy_series
(data, fuzzy_sets, method='maximum', alpha_cut=0.0, ordered_sets=None)[source]¶
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pyFTS.common.FuzzySet.
get_fuzzysets
(inst, fuzzy_sets: dict, ordered_sets: list = None, alpha_cut: float = 0.0) → list[source]¶ Return the fuzzy sets which membership value for a inst is greater than the alpha_cut
Parameters: - inst – data point
- fuzzy_sets – a dictionary where the key is the fuzzy set name and the value is the fuzzy set object.
- ordered_sets – a list with the fuzzy sets names ordered by their centroids.
- alpha_cut – Minimal membership to be considered on fuzzyfication process
Returns: array of membership values
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pyFTS.common.FuzzySet.
get_maximum_membership_fuzzyset
(inst, fuzzy_sets, ordered_sets=None) → pyFTS.common.FuzzySet.FuzzySet[source]¶ Fuzzify a data point, returning the fuzzy set with maximum membership value
Parameters: - inst – data point
- fuzzy_sets – a dictionary where the key is the fuzzy set name and the value is the fuzzy set object.
- ordered_sets – a list with the fuzzy sets names ordered by their centroids.
Returns: fuzzy set with maximum membership
pyFTS.common.Membership module¶
Membership functions for Fuzzy Sets
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pyFTS.common.Membership.
bellmf
(x, parameters)[source]¶ Bell shaped membership function
Parameters: - x –
- parameters –
Returns:
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pyFTS.common.Membership.
gaussmf
(x, parameters)[source]¶ Gaussian fuzzy membership function
Parameters: - x – data point
- parameters – a list with 2 real values (mean and variance)
Returns: the membership value of x given the parameters
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pyFTS.common.Membership.
sigmf
(x, parameters)[source]¶ Sigmoid / Logistic membership function
Parameters: - x –
- parameters – an list with 2 real values (smoothness and midpoint)
:return
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pyFTS.common.Membership.
singleton
(x, parameters)[source]¶ Singleton membership function, a single value fuzzy function
Parameters: - x –
- parameters – a list with one real value
:returns
pyFTS.common.SortedCollection module¶
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class
pyFTS.common.SortedCollection.
SortedCollection
(iterable=(), key=None)[source]¶ Bases:
object
Sequence sorted by a key function.
SortedCollection() is much easier to work with than using bisect() directly. It supports key functions like those use in sorted(), min(), and max(). The result of the key function call is saved so that keys can be searched efficiently.
Instead of returning an insertion-point which can be hard to interpret, the five find-methods return a specific item in the sequence. They can scan for exact matches, the last item less-than-or-equal to a key, or the first item greater-than-or-equal to a key.
Once found, an item’s ordinal position can be located with the index() method. New items can be added with the insert() and insert_right() methods. Old items can be deleted with the remove() method.
The usual sequence methods are provided to support indexing, slicing, length lookup, clearing, copying, forward and reverse iteration, contains checking, item counts, item removal, and a nice looking repr.
Finding and indexing are O(log n) operations while iteration and insertion are O(n). The initial sort is O(n log n).
The key function is stored in the ‘key’ attibute for easy introspection or so that you can assign a new key function (triggering an automatic re-sort).
In short, the class was designed to handle all of the common use cases for bisect but with a simpler API and support for key functions.
>>> from pprint import pprint >>> from operator import itemgetter
>>> s = SortedCollection(key=itemgetter(2)) >>> for record in [ ... ('roger', 'young', 30), ... ('angela', 'jones', 28), ... ('bill', 'smith', 22), ... ('david', 'thomas', 32)]: ... s.insert(record)
>>> pprint(list(s)) # show records sorted by age [('bill', 'smith', 22), ('angela', 'jones', 28), ('roger', 'young', 30), ('david', 'thomas', 32)]
>>> s.find_le(29) # find oldest person aged 29 or younger ('angela', 'jones', 28) >>> s.find_lt(28) # find oldest person under 28 ('bill', 'smith', 22) >>> s.find_gt(28) # find youngest person over 28 ('roger', 'young', 30)
>>> r = s.find_ge(32) # find youngest person aged 32 or older >>> s.index(r) # get the index of their record 3 >>> s[3] # fetch the record at that index ('david', 'thomas', 32)
>>> s.key = itemgetter(0) # now sort by first name >>> pprint(list(s)) [('angela', 'jones', 28), ('bill', 'smith', 22), ('david', 'thomas', 32), ('roger', 'young', 30)]
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key
¶ key function
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pyFTS.common.Util module¶
pyFTS.common.flrg module¶
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class
pyFTS.common.flrg.
FLRG
(order, **kwargs)[source]¶ Bases:
object
Fuzzy Logical Relationship Group
Group a set of FLR’s with the same LHS. Represents the temporal patterns for time t+1 (the RHS fuzzy sets) when the LHS pattern is identified on time t.
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LHS
= None¶ Left Hand Side of the rule
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RHS
= None¶ Right Hand Side of the rule
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get_lower
(sets)[source]¶ Returns the lower bound value for the RHS fuzzy sets
Parameters: sets – fuzzy sets Returns: lower bound value
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get_membership
(data, sets)[source]¶ Returns the membership value of the FLRG for the input data
Parameters: - data – input data
- sets – fuzzy sets
Returns: the membership value
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get_midpoint
(sets)[source]¶ Returns the midpoint value for the RHS fuzzy sets
Parameters: sets – fuzzy sets Returns: the midpoint value
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get_upper
(sets)[source]¶ Returns the upper bound value for the RHS fuzzy sets
Parameters: sets – fuzzy sets Returns: upper bound value
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order
= None¶ Number of lags on LHS
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