GridCluster e CMVFTS
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@ -162,7 +162,10 @@ class HighOrderFTS(fts.FTS):
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if explain:
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print("Fuzzyfication \n")
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flrgs = self.generate_lhs_flrg(ndata[k - self.max_lag: k], explain)
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if not kwargs.get('fuzzyfied', False):
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flrgs = self.generate_lhs_flrg(ndata[k - self.max_lag: k], explain)
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else:
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flrgs = self.generate_lhs_flrg_fuzzyfied(ndata[k - self.max_lag: k], explain)
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if explain:
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print("Rules:\n")
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@ -172,7 +175,7 @@ class HighOrderFTS(fts.FTS):
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if flrg.get_key() not in self.flrgs:
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if len(flrg.LHS) > 0:
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mp = self.sets[flrg.LHS[-1]].centroid
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mp = self.partitioner.sets[flrg.LHS[-1]].centroid
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tmp.append(mp)
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if explain:
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@ -181,7 +184,7 @@ class HighOrderFTS(fts.FTS):
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else:
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flrg = self.flrgs[flrg.get_key()]
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mp = flrg.get_midpoint(self.sets)
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mp = flrg.get_midpoint(self.partitioner.sets)
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tmp.append(mp)
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if explain:
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@ -32,6 +32,15 @@ class ClusteredMVFTS(mvfts.MVFTS):
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self.lags = kwargs.get("lags", None)
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self.alpha_cut = kwargs.get('alpha_cut', 0.25)
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def fuzzyfy(self,data):
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ndata = []
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for ct in range(1, len(data.index)):
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ix = data.index[ct - 1]
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data_point = self.format_data(data.loc[ix])
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ndata.append(common.fuzzyfy_instance_clustered(data_point, self.cluster, self.alpha_cut))
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return ndata
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def train(self, data, **kwargs):
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@ -39,25 +48,30 @@ class ClusteredMVFTS(mvfts.MVFTS):
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self.model = self.fts_method(partitioner=self.cluster, **self.fts_params)
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if self.model.is_high_order:
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self.model.order = self.model = self.fts_method(partitioner=self.partitioner,
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self.model.order = self.model = self.fts_method(partitioner=self.cluster,
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order=self.order, **self.fts_params)
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ndata = []
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for ct in range(1, len(data.index)):
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ix = data.index[ct-1]
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data_point = self.format_data(data.loc[ix])
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ndata.append(common.fuzzyfy_instance_clustered(data_point, self.cluster, self.alpha_cut))
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ndata = self.fuzzyfy(data)
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self.model.train(ndata, fuzzyfied=True)
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self.shortname = self.model.shortname
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def forecast(self, ndata, **kwargs):
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ndata = self.fuzzyfy(ndata)
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return self.model.forecast(ndata, fuzzyfied=True, **kwargs)
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def __str__(self):
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"""String representation of the model"""
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return str(self.model)
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tmp = self.model.shortname + ":\n"
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for r in self.model.flrgs:
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tmp = tmp + str(self.model.flrgs[r]) + "\n"
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return tmp
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def __len__(self):
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"""
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@ -2,17 +2,19 @@ import numpy as np
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import pandas as pd
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from pyFTS.common import FuzzySet, Composite
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class MultivariateFuzzySet(Composite.FuzzySet):
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"""
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Multivariate Composite Fuzzy Set
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"""
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def __init__(self, name):
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def __init__(self, name, **kwargs):
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"""
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Create an empty composite fuzzy set
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:param name: fuzzy set name
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"""
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super(MultivariateFuzzySet, self).__init__(name)
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self.sets = {}
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self.target_variable = kwargs.get('target_variable',None)
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def append_set(self, variable, set):
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"""
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@ -23,6 +25,9 @@ class MultivariateFuzzySet(Composite.FuzzySet):
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"""
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self.sets[variable] = set
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if variable == self.target_variable.name:
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self.centroid = set.centroid
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def membership(self, x):
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mv = []
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for var in self.sets.keys():
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@ -36,9 +41,10 @@ def fuzzyfy_instance(data_point, var):
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fsets = FuzzySet.fuzzyfy(data_point, var.partitioner, mode='sets', method='fuzzy', alpha_cut=var.alpha_cut)
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return [(var.name, fs) for fs in fsets]
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def fuzzyfy_instance_clustered(data_point, cluster, alpha_cut=0.0):
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fsets = []
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for fset in cluster.sets:
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for fset in cluster.knn(data_point):
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if cluster.sets[fset].membership(data_point) > alpha_cut:
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fsets.append(fset)
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return fsets
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@ -1,6 +1,9 @@
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from pyFTS.partitioners import partitioner
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from pyFTS.models.multivariate.common import MultivariateFuzzySet
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from itertools import product
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from scipy.spatial import KDTree
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import numpy as np
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import pandas as pd
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class GridCluster(partitioner.Partitioner):
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"""
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@ -12,18 +15,42 @@ class GridCluster(partitioner.Partitioner):
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self.mvfts = kwargs.get('mvfts', None)
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self.sets = {}
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self.kdtree = None
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self.index = {}
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self.build(None)
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def build(self, data):
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fsets = [[x for x in k.partitioner.sets.values()]
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for k in self.mvfts.explanatory_variables]
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midpoints = []
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index = {}
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c = 0
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for k in product(*fsets):
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key = self.prefix+str(c)
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mvfset = MultivariateFuzzySet(name=key)
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c += 1
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#key = self.prefix+str(c)
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mvfset = MultivariateFuzzySet(name="", target_variable=self.mvfts.target_variable)
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mp = []
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_key = ""
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for fset in k:
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mvfset.append_set(fset.variable, fset)
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self.sets[key] = mvfset
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mp.append(fset.centroid)
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_key += fset.name
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mvfset.name = _key
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self.sets[_key] = mvfset
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midpoints.append(mp)
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self.index[c] = _key
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c += 1
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self.kdtree = KDTree(midpoints)
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def knn(self, data):
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tmp = [data[k.name] for k in self.mvfts.explanatory_variables]
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tmp, ix = self.kdtree.query(tmp,2)
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if not isinstance(ix, (list, np.ndarray)):
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ix = [ix]
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return [self.index[k] for k in ix]
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@ -3,6 +3,7 @@ import matplotlib.pylab as plt
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from pyFTS.data import TAIEX as tx
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from pyFTS.common import Transformations
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from pyFTS.benchmarks import Measures
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from pyFTS.partitioners import Grid, Util as pUtil
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from pyFTS.common import Transformations, Util
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from pyFTS.models.multivariate import common, variable, mvfts, wmvfts
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@ -99,5 +100,7 @@ model1.append_variable(vprice)
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model1.target_variable = vprice
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model1.fit(train_mv)
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print(model1)
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#print(model1)
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print(Measures.get_point_statistics(test_mv, model1))
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#"""
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