import physbo.predictor
[docs]class predictor(physbo.predictor.base_predictor):
"""Predictor using Baysean linear model
Attributes
==========
blm: physbo.blm.core.model
config: physbo.misc.set_config
configuration
"""
def __init__(self, config, model=None):
"""
Parameters
==========
config: physbo.misc.set_config
configuration
model: physbo.gp.core.model
See also
========
physbo.base_predictor
"""
super(predictor, self).__init__(config, model)
self.blm = None
[docs] def fit(self, training, num_basis=None):
"""
fit model to training dataset
Parameters
==========
training: physbo.variable
dataset for training
num_basis: int
the number of basis (default: self.config.predict.num_basis)
"""
if num_basis is None:
num_basis = self.config.predict.num_basis
if self.model.prior.cov.num_dim is None:
self.model.prior.cov.num_dim = training.X.shape[1]
self.model.fit(training.X, training.t, self.config)
self.blm = self.model.export_blm(num_basis)
self.delete_stats()
[docs] def prepare(self, training):
"""
initializes model by using training data set
Parameters
==========
training: physbo.variable
dataset for training
"""
self.blm.prepare(training.X, training.t, training.Z)
[docs] def delete_stats(self):
"""
resets model
"""
self.blm.stats = None
[docs] def get_basis(self, X):
"""
calculates feature maps Psi(X)
Parameters
==========
X: numpy.ndarray
inputs
Returns
=======
Psi: numpy.ndarray
feature maps
"""
return self.blm.lik.get_basis(X)
[docs] def get_post_fmean(self, training, test):
"""
calculates posterior mean value of model
Parameters
==========
training: physbo.variable
training dataset. If already trained, the model does not use this.
test: physbo.variable
inputs
Returns
=======
numpy.ndarray
"""
if self.blm.stats is None:
self.prepare(training)
return self.blm.get_post_fmean(test.X, test.Z)
[docs] def get_post_fcov(self, training, test):
"""
calculates posterior variance-covariance matrix of model
Parameters
==========
training: physbo.variable
training dataset. If already trained, the model does not use this.
test: physbo.variable
inputs
Returns
=======
numpy.ndarray
"""
if self.blm.stats is None:
self.prepare(training)
return self.blm.get_post_fcov(test.X, test.Z)
[docs] def get_post_params(self, training, test):
"""
calculates posterior weights
Parameters
==========
training: physbo.variable
training dataset. If already trained, the model does not use this.
test: physbo.variable
inputs (not used)
Returns
=======
numpy.ndarray
"""
if self.blm.stats is None:
self.prepare(training)
return self.blm.get_post_params_mean()
[docs] def get_post_samples(self, training, test, N=1, alpha=1.0):
"""
draws samples of mean values of model
Parameters
==========
training: physbo.variable
training dataset. If already trained, the model does not use this.
test: physbo.variable
inputs
N: int
number of samples
(default: 1)
alpha: float
noise for sampling source
(default: 1.0)
Returns
=======
numpy.ndarray
"""
if self.blm.stats is None:
self.prepare(training)
return self.blm.post_sampling(test.X, Psi=test.Z, N=N, alpha=alpha)
[docs] def get_predict_samples(self, training, test, N=1):
"""
draws samples of values of model
Parameters
==========
training: physbo.variable
training dataset. If already trained, the model does not use this.
test: physbo.variable
inputs
N: int
number of samples
(default: 1)
alpha: float
noise for sampling source
(default: 1.0)
Returns
=======
numpy.ndarray (N x len(test))
"""
if self.blm.stats is None:
self.prepare(training)
return self.blm.predict_sampling(test.X, Psi=test.Z, N=N).transpose()
[docs] def update(self, training, test):
"""
updates the model.
If not yet initialized (prepared), the model will be prepared by ``training``.
Otherwise, the model will be updated by ``test``.
Parameters
==========
training: physbo.variable
training dataset for initialization (preparation).
If already prepared, the model ignore this.
test: physbo.variable
training data for update.
If not prepared, the model ignore this.
"""
if self.model.stats is None:
self.prepare(training)
return None
if hasattr(test.t, "__len__"):
N = len(test.t)
else:
N = 1
if N == 1:
if test.Z is None:
if test.X.ndim == 1:
self.blm.update_stats(test.X, test.t)
else:
self.blm.update_stats(test.X[0, :], test.t)
else:
if test.Z.ndim == 1:
self.blm.update_stats(test.X, test.t, psi=test.Z)
else:
self.blm.update_stats(test.X[0, :], test.t, psi=test.Z[0, :])
else:
for n in range(N):
if test.Z is None:
self.blm.update_stats(test.X[n, :], test.t[n])
else:
self.blm.update_stats(test.X[n, :], test.t[n], psi=test.Z[n, :])