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):
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, 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()