physbo.blm.core.model module
- class physbo.blm.core.model.model(lik, prior, options={})[ソース]
ベースクラス:
object
Baysean Linear Model
- prior
prior distribution of weights
- Type:
physbo.blm.prior.gauss
- lik
kernel
- Type:
physbo.blm.lik.gauss
- nbasis
number of features in random feature map
- Type:
int
- stats
auxially parameters for sampling
- Type:
Tuple
- method
sampling method
- Type:
str
- get_post_fcov(X, Psi=None, diag=True)[ソース]
calculates posterior covariance of model
- パラメータ:
X (numpy.ndarray) -- inputs
Psi (numpy.ndarray) -- feature maps (default: blm.lik.linear.basis.get_basis(X))
diag (bool) -- if True, returns only variances as a diagonal matrix (default: True)
- 戻り値の型:
numpy.ndarray
- get_post_fmean(X, Psi=None, w=None)[ソース]
calculates posterior mean of model (function)
- パラメータ:
X (numpy.ndarray) -- inputs
Psi (numpy.ndarray) -- feature maps
w (numpy.ndarray) -- weight
- post_sampling(Xtest, Psi=None, N=1, alpha=1.0)[ソース]
draws samples of mean value of model
- パラメータ:
Xtest (numpy.ndarray) -- inputs
Psi (numpy.ndarray) -- feature maps (default:
blm.lik.get_basis(Xtest)
)N (int) -- number of samples (default: 1)
alpha (float) -- noise for sampling source
- 戻り値の型:
numpy.ndarray
- predict_sampling(Xtest, Psi=None, N=1)[ソース]
draws samples from model
- パラメータ:
Xtest (numpy.ndarray) -- inputs
Psi (numpy.ndarray) -- feature map (default:
blm.lik.get_basis(Xtest)
)N (int) -- number of samples (default: 1)
- 戻り値の型:
numpy.ndarray
- prepare(X, t, Psi=None)[ソース]
initializes model by using the first training dataset
- パラメータ:
X (numpy.ndarray) -- inputs
t (numpy.ndarray) -- target (label)
Psi (numpy.ndarray) -- feature maps