def train(self, ndata, **kwargs):
tmpdata = FuzzySet.fuzzyfy(ndata,
partitioner=self.partitioner,
method='maximum',
mode='sets')
flrs = FLR.generate_recurrent_flrs(tmpdata)
self.generate_FLRG(flrs)
def generate_lhs_flrg(self, sample):
lags = {}
flrgs = []
for ct, o in enumerate(self.lags):
lhs = FuzzySet.fuzzyfy(sample[o - 1],
partitioner=self.partitioner,
mode="sets",
alpha_cut=self.alpha_cut)
lags[ct] = lhs
root = tree.FLRGTreeNode(None)
tree.build_tree_without_order(root, lags, 0)
# Trace the possible paths
for p in root.paths():
flrg = ProbabilisticWeightedFLRG(self.order)
path = list(reversed(list(filter(None.__ne__, p))))
for lhs in path:
flrg.append_lhs(lhs)
flrgs.append(flrg)
return flrgs
def fuzzyfy_instance(data_point, var):
fsets = FuzzySet.fuzzyfy(data_point,
var.partitioner,
mode='sets',
method='fuzzy',
alpha_cut=var.alpha_cut)
return [(var.name, fs) for fs in fsets]
def train(self, data, **kwargs):
tmpdata = FuzzySet.fuzzyfy(data,
partitioner=self.partitioner,
method='maximum',
mode='sets')
flrs = FLR.generate_non_recurrent_flrs(tmpdata)
self.operation_matrix(flrs)
def generate_flrg(self, data):
l = len(data)
for k in np.arange(self.max_lag, l):
lags = {}
if self.dump: print("FLR: " + str(k))
sample = data[k - self.max_lag: k]
rhs = FuzzySet.fuzzyfy(data[k], partitioner=self.partitioner, mode="sets", alpha_cut=self.alpha_cut)
flrgs = self.generate_lhs_flrg(sample)
for flrg in flrgs:
if flrg.get_key() not in self.flrgs:
self.flrgs[flrg.get_key()] = flrg;
for st in rhs:
self.flrgs[flrg.get_key()].append_rhs(st)
def generate_lhs_flrg(self, sample, explain=False):
nsample = [FuzzySet.fuzzyfy(k, partitioner=self.partitioner, mode="sets", alpha_cut=self.alpha_cut)
for k in sample]
return self.generate_lhs_flrg_fuzzyfied(nsample, explain)
def fuzzyfy_instance(data_point, var):
return FuzzySet.fuzzyfy(data_point,
var.partitioner,
mode='sets',
method='fuzzy',
alpha_cut=var.alpha_cut)