def train(self, data, **kwargs):
if self.method == 'unconditional':
window_size = kwargs.get('parameters', 1)
tmpdata = common.fuzzySeries(data,
self.sets,
self.partitioner.ordered_sets,
window_size,
method='fuzzy')
else:
tmpdata = common.fuzzySeries(data,
self.sets,
self.partitioner.ordered_sets,
method='fuzzy',
const_t=0)
flrs = FLR.generate_non_recurrent_flrs(tmpdata)
self.generate_flrg(flrs)
if self.method == 'conditional':
self.forecasts = self.forecast(data, no_update=True)
self.residuals = np.array(data[1:]) - np.array(self.forecasts[:-1])
self.variance_residual = np.var(
self.residuals) # np.max(self.residuals
self.mean_residual = np.mean(self.residuals)
self.residuals = self.residuals[-self.memory_window:].tolist()
self.forecasts = self.forecasts[-self.memory_window:]
self.inputs = np.array(data[-self.memory_window:]).tolist()
def train(self, data, **kwargs):
if kwargs.get('sets', None) is not None:
self.sets = kwargs.get('sets', None)
if kwargs.get('parameters', None) is not None:
self.seasonality = kwargs.get('parameters', None)
flrs = FLR.generate_indexed_flrs(self.sets, self.indexer, data)
self.generate_flrg(flrs)
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 train(self, data, **kwargs):
if kwargs.get('sets', None) is not None:
self.sets = kwargs.get('sets', None)
if kwargs.get('parameters', None) is not None:
self.seasonality = kwargs.get('parameters', None)
#ndata = self.indexer.set_data(data,self.doTransformations(self.indexer.get_data(data)))
flrs = FLR.generate_indexed_flrs(self.sets, self.indexer, data)
self.generate_flrg(flrs)
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 train(self, data, **kwargs):
parameters = kwargs.get('parameters', 'fuzzy')
if parameters == 'monotonic':
tmpdata = FuzzySet.fuzzyfy_series_old(data, self.sets)
flrs = FLR.generate_recurrent_flrs(tmpdata)
self.generateFLRG(flrs)
else:
self.generate_flrg(data)
def train(self, data, **kwargs):
window_size = kwargs.get('parameters', 1)
tmpdata = common.fuzzySeries(data,
self.sets,
self.partitioner.ordered_sets,
window_size,
method='fuzzy')
flrs = FLR.generate_recurrent_flrs(tmpdata)
self.generate_flrg(flrs)
def train(self, ndata, **kwargs):
self.min_tx = min(ndata)
self.max_tx = max(ndata)
tmpdata = common.fuzzySeries(ndata,
self.sets,
self.partitioner.ordered_sets,
method='fuzzy',
const_t=0)
flrs = FLR.generate_non_recurrent_flrs(tmpdata)
self.generate_flrg(flrs)
def train(self, data, **kwargs):
self.configure_lags(**kwargs)
parameters = kwargs.get('parameters','fuzzy')
if parameters == 'monotonic':
tmpdata = self.partitioner.fuzzyfy(data, mode='sets', method='maximum')
flrs = FLR.generate_recurrent_flrs(tmpdata)
self.generate_flrg(flrs)
else:
self.generate_flrg(data)
def train(self, data, **kwargs):
if kwargs.get('sets', None) is not None:
self.sets = kwargs.get('sets', None)
if kwargs.get('parameters', None) is not None:
self.seasonality = kwargs.get('parameters', None)
flrs = FLR.generate_indexed_flrs(
self.sets,
self.indexer,
data,
transformation=self.partitioner.transformation,
alpha_cut=self.alpha_cut)
self.generate_flrg(flrs)
def __init__(self, trainData, parts, fuzzyMethod, fuzzyMode, order=1):
self.order = order
self.trainData = trainData
self.fs = Grid.GridPartitioner(data=self.trainData, npart=parts)
self.fuzzyfied = self.fs.fuzzyfy(self.trainData,
method=fuzzyMethod,
mode=fuzzyMode)
self.patterns = FLR.generate_non_recurrent_flrs(self.fuzzyfied)
if self.order > 1:
self.modelHO = pyFTS.models.hofts.HighOrderFTS(order=self.order,
partitioner=self.fs)
self.modelHO.fit(self.trainData)
else:
self.model = chen.ConventionalFTS(partitioner=self.fs)
self.model.fit(self.trainData)
def train(self, ndata, **kwargs):
tmpdata = common.fuzzySeries(ndata,
self.sets,
self.partitioner.ordered_sets,
method='fuzzy',
const_t=0)
flrs = FLR.generate_non_recurrent_flrs(tmpdata)
self.generate_flrg(flrs)
self.forecasts = self.forecast(ndata, no_update=True)
self.residuals = np.array(ndata[1:]) - np.array(self.forecasts[:-1])
self.variance_residual = np.var(
self.residuals) # np.max(self.residuals
self.mean_residual = np.mean(self.residuals)
self.residuals = self.residuals[-self.memory_window:].tolist()
self.forecasts = self.forecasts[-self.memory_window:]
self.inputs = np.array(ndata[-self.memory_window:]).tolist()
def train(self, data, **kwargs):
if kwargs.get('sets', None) is not None:
self.sets = kwargs.get('sets', None)
tmpdata = FuzzySet.fuzzyfy_series_old(data, self.sets)
flrs = FLR.generate_non_recurrent_flrs(tmpdata)
self.generate_flrg(flrs)
def train(self, data, **kwargs):
tmpdata = self.partitioner.fuzzyfy(data, method='maximum', mode='sets')
flrs = FLR.generate_recurrent_flrs(tmpdata)
self.generate_flrg(flrs, self.c)
def train(self, ndata, **kwargs):
tmpdata = FuzzySet.fuzzyfy_series(ndata, self.sets, method='maximum')
flrs = FLR.generate_recurrent_flrs(tmpdata)
self.generate_flrg(flrs)
data_max = max(train_data)
data_min = min(train_data)
norm_train_data = (train_data - data_min) / (data_max - data_min)
from pyFTS.partitioners import Grid
fs = Grid.GridPartitioner(data=norm_train_data, npart=fzz)
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[15, 5])
fs.plot(ax)
#Cria os conjuntos fuzzy
fuzzyfied = fs.fuzzyfy(norm_train_data, method='fuzzy', mode='sets')
print(fuzzyfied)
#Ordena os conjuntos fuzzy
from pyFTS.common import FLR
patterns = FLR.generate_non_recurrent_flrs(fuzzyfied)
print([str(k) for k in patterns])
#Treina o modelo com o conjunto fuzzy
from pyFTS.models import chen
model = chen.ConventionalFTS(partitioner=fs)
model.fit(norm_train_data)
print(model)
#carregamento do conjunto de teste
df = pd.read_csv(teste)
data_test = df['Adj Close'].values
new_data = []
for i in range(len(data_test)):
if data_test[i] == data_test[i]:
new_data.append(data_test[i])
def train(self, data, **kwargs):
tmpdata = FuzzySet.fuzzyfy_series(data, self.sets, method='maximum')
flrs = FLR.generate_non_recurrent_flrs(tmpdata)
self.operation_matrix(flrs)
def train(self, data, **kwargs):
tmpdata = self.partitioner.fuzzyfy(data, method='maximum', mode='sets')
flrs = FLR.generate_non_recurrent_flrs(tmpdata,
steps=self.standard_horizon)
self.generate_flrg(flrs)
