def _update_beta_local(model, data):
##
counts_norm, nreplicas = data
counts_norm = np.hstack(counts_norm)
##
beta_ = np.r_[0, model.beta[1:] * np.exp(0.01 * rn.randn(model.lw.size-1))]
##
beta1 = np.repeat(model.beta[model.z.T], nreplicas, axis=1)
beta2 = np.repeat(beta_[model.z.T], nreplicas, axis=1)
loglik = _compute_loglik(counts_norm, model.log_phi.reshape(-1, 1), model.log_mu.reshape(-1, 1), beta1)
loglik_ = _compute_loglik(counts_norm, model.log_phi.reshape(-1, 1), model.log_mu.reshape(-1, 1), beta2)
z = np.repeat(model.z.T, nreplicas, axis=1)
loglik = np.bincount(z.ravel(), loglik.ravel(), minlength=model.lw.size)
loglik_ = np.bincount(z.ravel(), loglik_.ravel(), minlength=model.lw.size)
logprior = st.normalln(model.beta, model.m0, 1/model.t0)
logprior_ = st.normalln(beta_, model.m0, 1/model.t0)
logpost = loglik + logprior
logpost_ = loglik_ + logprior_
##
idxs = (logpost_ >= logpost) | (rn.rand(model.lw.size) < np.exp(logpost_ - logpost))
model.beta[model.iact & idxs] = beta_[model.iact & idxs]
##
model.beta[~model.iact] = rn.normal(model.m0, 1/np.sqrt(model.t0), model.lw.size-model.nact)
def _update_phi_local(model, data):
##
counts_norm, nreplicas = data
counts_norm = np.hstack(counts_norm)
## proposal
log_phi_ = model.log_phi * np.exp(0.01 * rn.randn(model.nfeatures))
## log-likelihood
beta = np.repeat(model.beta[model.z.T], nreplicas, axis=1)
loglik = _compute_loglik(counts_norm, model.log_phi.reshape(-1, 1), model.log_mu.reshape(-1, 1), beta).sum(-1)
loglik_ = _compute_loglik(counts_norm, log_phi_.reshape(-1, 1), model.log_mu.reshape(-1, 1), beta).sum(-1)
## log-prior
logprior = st.normalln(model.log_phi, model.mu, 1 / model.tau)
logprior_ = st.normalln(log_phi_, model.mu, 1 / model.tau)
## log-posterior
logpost = loglik + logprior
logpost_ = loglik_ + logprior_
## update
idxs = (logpost_ >= logpost) | (rn.rand(model.nfeatures) < np.exp(logpost_ - logpost))
model.log_phi[idxs] = log_phi_[idxs]
def _update_beta_local(model, data):
##
counts_norm, nreplicas = data
counts_norm = np.hstack(counts_norm)
##
beta_ = np.r_[0, model.beta[1:] * np.exp(0.01 * rn.randn(model.lw.size-1))]
##
beta1 = np.repeat(model.beta[model.z.T], nreplicas, axis=1)
beta2 = np.repeat(beta_[model.z.T], nreplicas, axis=1)
loglik = _compute_loglik(counts_norm, model.log_phi.reshape(-1, 1), model.log_mu.reshape(-1, 1), beta1)
loglik_ = _compute_loglik(counts_norm, model.log_phi.reshape(-1, 1), model.log_mu.reshape(-1, 1), beta2)
z = np.repeat(model.z.T, nreplicas, axis=1)
loglik = np.bincount(z.ravel(), loglik.ravel(), minlength=model.lw.size)
loglik_ = np.bincount(z.ravel(), loglik_.ravel(), minlength=model.lw.size)
logprior = st.normalln(model.beta, model.m0, 1/model.t0)
logprior_ = st.normalln(beta_, model.m0, 1/model.t0)
logpost = loglik + logprior
logpost_ = loglik_ + logprior_
##
idxs = np.log(rn.rand(model.lw.size)) < logpost_ - logpost
model.beta[model.iact & idxs] = beta_[model.iact & idxs]
##
model.beta[~model.iact] = rn.normal(model.m0, 1/np.sqrt(model.t0), model.lw.size-model.nact)
def plot_clusters(self, fig=None, npoints=100):
"""Plot LFC clusters"""
## data
beta = self.beta[self.iact]
occ = self.occ[self.iact]
x = np.linspace(beta.min()-1, beta.max()+1, npoints)
y = np.exp(st.normalln(x, self.m0, 1 / np.sqrt(self.t0)))
## plot
fig = pl.figure() if fig is None else fig
pl.figure(fig.number)
pl.plot(x, y)
pl.axvline(0, linestyle='--', color='k')
pl.vlines(beta[1:], [0], occ[1:] / occ[1:].sum(), color='r')
pl.grid()
pl.xlabel('LFC')
pl.ylabel('density')
pl.legend(['LFC prior', 'null cluster', 'non-null clusters'], loc=0)
pl.tight_layout()
def plot_clusters(self, fig=None, npoints=100):
"""Plot LFC clusters"""
# data
beta = self.beta[self.iact]
occ = self.occ[self.iact]
x = np.linspace(beta.min()-1, beta.max()+1, npoints)
y = np.exp(st.normalln(x, self.m0, 1 / np.sqrt(self.t0)))
# plot
fig = pl.figure() if fig is None else fig
pl.figure(fig.number)
pl.plot(x, y)
pl.axvline(0, linestyle='--', color='k')
pl.vlines(beta[1:], [0], occ[1:] / occ[1:].sum(), color='r')
pl.grid()
pl.xlabel('LFC')
pl.ylabel('density')
pl.legend(['LFC prior', 'null cluster', 'non-null clusters'], loc=0)
pl.tight_layout()
