com.github.gradientgmm.components
UpdatableGaussianComponent
Related Docs:
object UpdatableGaussianComponent
| package components
com.github.gradientgmm.components
Compute augmented parameter matrix
Compute augmented parameter matrix
data dimensionality
data dimensionality
Returns the covariance matrix' determinant
Returns the covariance matrix' determinant
Returns the g-concave reformulation's density evaluated on x
Returns the g-concave reformulation's density evaluated on x
Augmented parameter block matrix inverse [A B; C D].
Augmented parameter block matrix inverse [A B; C D]. Its blocks are:
A = sigmaInv
B = sigmaInv * mu
C = mu.t * sigmaInv
D = 1/s + mu.t * sigmaInv * mu
Returns the covariance matrix' log-determinant
Returns the covariance matrix' log-determinant
Returns the distribution's log-density function evaluated on x
Returns the distribution's log-density function evaluated on x
Returns the distribution's log-density function evaluated on x
Returns the distribution's log-density function evaluated on x
accelerated gradient ascent utilities.
accelerated gradient ascent utilities. See AcceleratedGradientUtils
Augmented parameter block matrix [A B; C D].
Augmented parameter block matrix [A B; C D]. The blocks are:
A = sigma + s * mu * mu.t
B = s * mu
C = s * mu.t
D = s
Returns the distribution's density function evaluated on x
Returns the distribution's density function evaluated on x
Returns the distribution's density function evaluated on x
Returns the distribution's density function evaluated on x
square root of the covariance matrix inverse, and the density's constant term
square root of the covariance matrix inverse, and the density's constant term
Positive scalar
Positive scalar
square root of the covariance matrix inverse, and the density's constant term
square root of the covariance matrix inverse, and the density's constant term
update parameter values deconstructing the matrix using a block structure [A B;C D] where A = sigma + s * mu * mu.t B = s * mu C = s * mu.t D = s
update parameter values deconstructing the matrix using a block structure [A B;C D] where A = sigma + s * mu * mu.t B = s * mu C = s * mu.t D = s
Gaussian distribution that implements an updating routine based on its g-concave reformulation and contains gradient ascent utilities necessary for accelerated algorithms.