def initialise(self, init):
""" Initialise the values of the random variables in this model. """
assert init in OPTIONS_INIT, \
"Unknown initialisation option: %s. Should be one of %s." % (init, OPTIONS_INIT)
self.muU, self.sigmaU = initialise_muU_sigmaU_wishart(init=init,
mu0=self.mu0,
beta0=self.beta0,
v0=self.v0,
W0=self.W0)
self.muV, self.sigmaV = initialise_muU_sigmaU_wishart(init=init,
mu0=self.mu0,
beta0=self.beta0,
v0=self.v0,
W0=self.W0)
self.U = initialise_U_gaussian_wishart(init=init,
I=self.I,
K=self.K,
muU=self.muU,
sigmaU=self.sigmaU)
self.V = initialise_U_gaussian_wishart(init=init,
I=self.J,
K=self.K,
muU=self.muU,
sigmaU=self.sigmaV)
self.tau = initialise_tau_gamma(alpha=self.alpha,
beta=self.beta,
R=self.R,
M=self.M,
U=self.U,
V=self.V)
def initialise(self,init):
""" Initialise the values of the random variables in this model. """
assert init in OPTIONS_INIT, \
"Unknown initialisation option: %s. Should be one of %s." % (init, OPTIONS_INIT)
self.U = initialise_U_volumeprior(init=init, I=self.I, K=self.K, gamma=self.gamma)
self.V = initialise_U_gaussian(init=init, I=self.J, K=self.K, lamb=self.lamb)
self.tau = initialise_tau_gamma(
alpha=self.alpha, beta=self.beta, R=self.R, M=self.M, U=self.U, V=self.V)
def initialise(self,init):
""" Initialise the values of the random variables in this model. """
assert init in OPTIONS_INIT, \
"Unknown initialisation option: %s. Should be one of %s." % (init, OPTIONS_INIT)
self.U = initialise_U_gaussian(init=init, I=self.I, K=self.K, lamb=self.lamb)
self.V = initialise_U_gaussian(init=init, I=self.J, K=self.K, lamb=self.lamb)
self.tau = initialise_tau_gamma(
alpha=self.alpha, beta=self.beta, R=self.R, M=self.M, U=self.U, V=self.V)
def initialise(self,init):
""" Initialise the values of the random variables in this model. """
assert init in OPTIONS_INIT, \
"Unknown initialisation option: %s. Should be one of %s." % (init, OPTIONS_INIT)
self.U = initialise_U_truncatednormal(
init=init, I=self.I, K=self.K, mu=self.muUV, tau=self.tauUV)
self.V = initialise_U_truncatednormal(
init=init, I=self.J, K=self.K, mu=self.muUV, tau=self.tauUV)
self.tau = initialise_tau_gamma(
alpha=self.alpha, beta=self.beta, R=self.R, M=self.M, U=self.U, V=self.V)
def initialise(self,init):
""" Initialise the values of the random variables in this model. """
assert init in OPTIONS_INIT, \
"Unknown initialisation option: %s. Should be one of %s." % (init, OPTIONS_INIT)
self.U = initialise_U_laplace(init=init, I=self.I, K=self.K, etaU=self.eta)
self.V = initialise_U_laplace(init=init, I=self.J, K=self.K, etaU=self.eta)
self.lambdaU = initialise_lambdaU_laplace(init=init, I=self.I, K=self.K, etaU=self.eta)
self.lambdaV = initialise_lambdaU_laplace(init=init, I=self.J, K=self.K, etaU=self.eta)
self.tau = initialise_tau_gamma(
alpha=self.alpha, beta=self.beta, R=self.R, M=self.M, U=self.U, V=self.V)
def initialise(self,init):
""" Initialise the values of the random variables in this model. """
assert init in OPTIONS_INIT, \
"Unknown initialisation option: %s. Should be one of %s." % (init, OPTIONS_INIT)
self.muU, self.sigmaU = initialise_muU_sigmaU_wishart(
init=init, mu0=self.mu0, beta0=self.beta0, v0=self.v0, W0=self.W0)
self.muV, self.sigmaV = initialise_muU_sigmaU_wishart(
init=init, mu0=self.mu0, beta0=self.beta0, v0=self.v0, W0=self.W0)
self.U = initialise_U_gaussian_wishart(init=init, I=self.I, K=self.K, muU=self.muU, sigmaU=self.sigmaU)
self.V = initialise_U_gaussian_wishart(init=init, I=self.J, K=self.K, muU=self.muU, sigmaU=self.sigmaV)
self.tau = initialise_tau_gamma(
alpha=self.alpha, beta=self.beta, R=self.R, M=self.M, U=self.U, V=self.V)
def initialise(self, init):
""" Initialise the values of the random variables in this model. """
assert init in OPTIONS_INIT, \
"Unknown initialisation option: %s. Should be one of %s." % (init, OPTIONS_INIT)
self.muU, self.tauU = initialise_muU_tauU_hierarchical(
init=init,
I=self.I,
K=self.K,
mu_mu=self.mu_mu,
tau_mu=self.tau_mu,
a=self.a,
b=self.b)
self.muV, self.tauV = initialise_muU_tauU_hierarchical(
init=init,
I=self.J,
K=self.K,
mu_mu=self.mu_mu,
tau_mu=self.tau_mu,
a=self.a,
b=self.b)
self.U = initialise_U_truncatednormal(init=init,
I=self.I,
K=self.K,
mu=self.muU,
tau=self.tauU)
self.V = initialise_U_truncatednormal(init=init,
I=self.J,
K=self.K,
mu=self.muV,
tau=self.tauV)
self.tau = initialise_tau_gamma(alpha=self.alpha,
beta=self.beta,
R=self.R,
M=self.M,
U=self.U,
V=self.V)
