Added the data.artificial.SignalEmulator
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Petrônio Cândido
parent
fc1f295150
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9125582c9c
@ -24,6 +24,8 @@ class FTS(object):
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"""A string with the model name"""
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self.detail = kwargs.get('name',"")
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"""A string with the model detailed information"""
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self.is_wrapper = False
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"""Indicates that this model is a wrapper for other(s) method(s)"""
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self.is_high_order = False
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"""A boolean value indicating if the model support orders greater than 1, default: False"""
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self.min_order = 1
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@ -313,6 +315,7 @@ class FTS(object):
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if 'partitioner' in kwargs:
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self.partitioner = kwargs.pop('partitioner')
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if not self.is_wrapper:
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if (self.sets is None or len(self.sets) == 0) and not self.benchmark_only and not self.is_multivariate:
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if self.partitioner is not None:
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self.sets = self.partitioner.sets
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@ -34,6 +34,83 @@ def generate_gaussian_linear(mu_ini, sigma_ini, mu_inc, sigma_inc, it=100, num=1
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return ret
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def generate_linear_periodic_gaussian(period, mu_min, sigma_min, mu_max, sigma_max, it=100, num=10, vmin=None, vmax=None):
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"""
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:param period:
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:param mu_min:
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:param sigma_min:
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:param mu_max:
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:param sigma_max:
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:param it:
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:param num:
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:param vmin:
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:param vmax:
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:return:
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"""
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if period > num:
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raise("The 'period' parameter must be lesser than 'it' parameter")
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mu_inc = (mu_max - mu_min)/period
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sigma_inc = (sigma_max - sigma_min) / period
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mu = mu_min
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sigma = sigma_min
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ret = []
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signal = True
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for k in np.arange(0, it):
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tmp = np.random.normal(mu, sigma, num)
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if vmin is not None:
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tmp = np.maximum(np.full(num, vmin), tmp)
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if vmax is not None:
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tmp = np.minimum(np.full(num, vmax), tmp)
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ret.extend(tmp)
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if k % period == 0:
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signal = not signal
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mu += (mu_inc if signal else -mu_inc)
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sigma += (sigma_inc if signal else -sigma_inc)
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return ret
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def generate_senoidal_periodic_gaussian(period, mu_min, sigma_min, mu_max, sigma_max, it=100, num=10, vmin=None, vmax=None):
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"""
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:param period:
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:param mu_min:
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:param sigma_min:
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:param mu_max:
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:param sigma_max:
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:param it:
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:param num:
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:param vmin:
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:param vmax:
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:return:
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"""
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mu_range = mu_max - mu_min
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sigma_range = sigma_max - sigma_min
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mu = mu_min
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sigma = sigma_min
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ret = []
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for k in np.arange(0, it):
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tmp = np.random.normal(mu, sigma, num)
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if vmin is not None:
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tmp = np.maximum(np.full(num, vmin), tmp)
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if vmax is not None:
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tmp = np.minimum(np.full(num, vmax), tmp)
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ret.extend(tmp)
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mu += mu_range * np.sin (period * k)
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sigma += mu_range * np.sin (period * k)
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return ret
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def generate_uniform_linear(min_ini, max_ini, min_inc, max_inc, it=100, num=10, vmin=None, vmax=None):
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"""
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Generate data sampled from Uniform distribution, with constant or linear changing bounds
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@ -78,3 +155,125 @@ def random_walk(n=500, type='gaussian'):
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return ret
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def _append(additive, start, before, new):
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if not additive:
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before.extend(new)
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return before
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else:
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for k in range(start):
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new.insert(0,0)
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tmp = np.array(before) + np.array(new)
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return tmp
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class SignalEmulator(object):
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def __init__(self, **kwargs):
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super(SignalEmulator, self).__init__()
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self.components = []
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def stationary_gaussian(self, mu, sigma, **kwargs):
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"""
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Creates a continuous Gaussian signal with mean mu and variance sigma.
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:param mu: mean
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:param sigma: variance
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:keyword cummulative: If False it cancels the previous signal and start this one, if True
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this signal is added to the previous one
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:keyword start: lag index to start this signal, the default value is 0
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:keyword it: Number of iterations, the default value is 1
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:keyword length: Number of samples generated on each iteration, the default value is 100
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:keyword vmin: Lower bound value of generated data, the default value is None
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:keyword vmax: Upper bound value of generated data, the default value is None
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:return: A list of it*num float values
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"""
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parameters = {'mu': mu, 'sigma': sigma}
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self.components.append({'dist': 'gaussian', 'type': 'constant',
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'parameters': parameters, 'args': kwargs})
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def incremental_gaussian(self, mu, sigma, **kwargs):
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"""
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Creates an additive gaussian interference on a previous signal
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:param mu:
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:param sigma:
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:keyword cummulative: If False it cancels the previous signal and start this one, if True
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this signal is added to the previous one
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:keyword start: lag index to start this signal, the default value is 0
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:keyword it: Number of iterations, the default value is 1
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:keyword length: Number of samples generated on each iteration, the default value is 100
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:keyword vmin: Lower bound value of generated data, the default value is None
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:keyword vmax: Upper bound value of generated data, the default value is None
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:return: A list of it*num float values
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"""
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parameters = {'mu': mu, 'sigma': sigma}
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self.components.append({'dist': 'gaussian', 'type': 'incremental',
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'parameters': parameters, 'args': kwargs})
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def periodic_gaussian(self, type, period, mu_min, sigma_min, mu_max, sigma_max, **kwargs):
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"""
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Creates an additive periodic gaussian interference on a previous signal
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:param mu:
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:param sigma:
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:keyword additive: If False it cancels the previous signal and start this one, if True
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this signal is added to the previous one
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:keyword start: lag index to start this signal, the default value is 0
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:keyword it: Number of iterations, the default value is 1
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:keyword length: Number of samples generated on each iteration, the default value is 100
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:keyword vmin: Lower bound value of generated data, the default value is None
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:keyword vmax: Upper bound value of generated data, the default value is None
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:return: A list of it*num float values
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"""
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parameters = {'type':type, 'period':period,
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'mu_min': mu_min, 'sigma_min': sigma_min, 'mu_max': mu_max, 'sigma_max': sigma_max}
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self.components.append({'dist': 'gaussian', 'type': 'periodic',
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'parameters': parameters, 'args': kwargs})
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def blip(self, intensity, **kwargs):
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"""
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:param intensity:
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:param kwargs:
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:return:
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"""
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self.components.append({'dist': 'blip', 'type': 'blip', 'parameters': [intensity, kwargs]})
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def run(self):
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signal = []
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last_it = 10
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last_num = 10
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for ct, component in enumerate(self.components):
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parameters = component['parameters']
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kwargs = component['args']
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additive = kwargs.get('additive', True)
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start = kwargs.get('start', 0)
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it = kwargs.get('it', last_it)
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num = kwargs.get('length', last_num)
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vmin = kwargs.get('vmin',None)
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vmax = kwargs.get('vmax', None)
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if component['type'] == 'constant':
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tmp = generate_gaussian_linear(parameters['mu'], parameters['sigma'], 0, 0,
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it=it, num=num, vmin=vmin, vmax=vmax)
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elif component['type'] == 'incremental':
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tmp = generate_gaussian_linear(0, 0, parameters['mu'], parameters['sigma'],
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it=num, num=1, vmin=vmin, vmax=vmax)
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elif component['type'] == 'periodic':
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period = component['period']
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mu_min, sigma_min = parameters['mu_min'],parameters['sigma_min']
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mu_max, sigma_max = parameters['mu_max'],parameters['sigma_max']
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if parameters['type'] == 'senoidal':
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tmp = generate_senoidal_periodic_gaussian(period, mu_min, sigma_min, mu_max, sigma_max,
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it=num, num=1, vmin=vmin, vmax=vmax)
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else:
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tmp = generate_linear_periodic_gaussian(period, mu_min, sigma_min, mu_max, sigma_max,
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it=num, num=1, vmin=vmin, vmax=vmax)
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last_num = num
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last_it = it
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signal = _append(additive, start, signal, tmp)
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return signal
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@ -31,6 +31,7 @@ class EnsembleFTS(fts.FTS):
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self.shortname = "EnsembleFTS"
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self.name = "Ensemble FTS"
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self.flrgs = {}
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self.is_wrapper = True
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self.has_point_forecasting = True
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self.has_interval_forecasting = True
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self.has_probability_forecasting = True
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@ -46,9 +46,10 @@ class IncrementalEnsembleFTS(ensemble.EnsembleFTS):
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partitioner = self.partitioner_method(data=data, **self.partitioner_params)
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model = self.fts_method(partitioner=partitioner, **self.fts_params)
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if self.model.is_high_order:
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self.model = self.fts_method(partitioner=partitioner, order=self.order, **self.fts_params)
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if model.is_high_order:
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model = self.fts_method(partitioner=partitioner, order=self.order, **self.fts_params)
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model.fit(data, **kwargs)
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if len(self.models) > 0:
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self.models.pop(0)
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self.models.append(model)
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@ -7,15 +7,21 @@ import numpy as np
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import pandas as pd
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from pyFTS.partitioners import Grid
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from pyFTS.common import Transformations
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from pyFTS import hofts, song, yu, cheng, ismailefendi, sadaei, hwang
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from pyFTS.models import chen
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from pyFTS.models.ensemble import ensemble
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from pyFTS.models.incremental import IncrementalEnsemble, TimeVariant
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os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
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from pyFTS.data import AirPassengers
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diff = Transformations.Differential(1)
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passengers = pd.read_csv("DataSets/AirPassengers.csv", sep=",")
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passengers = AirPassengers.get_data()
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model = IncrementalEnsemble.IncrementalEnsembleFTS(order=2, window_length=20, batch_size=5)
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model.fit(passengers[:40])
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model.predict(passengers[40:])
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'''
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passengers = np.array(passengers["Passengers"])
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e = ensemble.AllMethodEnsembleFTS(alpha=0.25, point_method="median", interval_method='quantile')
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@ -115,5 +121,6 @@ print(_normal)
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# distributions=[True,False], save=True, file="pictures/distribution_ahead_arma",
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# time_from=60, time_to=10, tam=[12,5])
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'''
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@ -14,21 +14,8 @@ from pyFTS.benchmarks import benchmarks as bchmk, Measures
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from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts
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from pyFTS.common import Transformations, Membership
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tdiff = Transformations.Differential(1)
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from pyFTS.data import artificial
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from pyFTS.data import TAIEX, SP500, NASDAQ, Malaysia, Enrollments
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#from pyFTS.data import mackey_glass
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#y = mackey_glass.get_data()
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from pyFTS.partitioners import Grid
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from pyFTS.models import pwfts, tsaur
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x = [k for k in np.arange(-2*np.pi, 2*np.pi, 0.5)]
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y = [np.sin(k) for k in x]
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part = Grid.GridPartitioner(data=y, npart=35)
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model = hofts.HighOrderFTS(order=2, partitioner=part)
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model.fit(y)
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forecasts = model.predict(y, steps_ahead=10)
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cd = artificial.SignalEmulator()
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cd.stationary_gaussian(10,3,length=100)
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cd.incremental_gaussian(0.5,0,start=100,length=200)
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