def propose(self):
sig = pm.utils.value(self.stochastic.parents['sig'])
mu = pm.utils.value(self.stochastic.parents['mu'])
delta = pm.rmv_normal_chol(0*mu, sig)
beta = np.minimum(1, self.proposal_sd * self.adaptive_scale_factor)
bsig = beta*sig
sb2 = np.sqrt(1-beta**2)
self.stochastic.value = (self.stochastic.value - mu)*sb2+beta*delta+mu
xp,x = self.stochastic.value, self.stochastic.last_value
self._hastings_factor = pm.mv_normal_chol_like(x,(xp-mu)*sb2+mu,bsig) - pm.mv_normal_chol_like(xp,(x-mu)*sb2+mu,bsig)
def propose(self):
sig = pm.utils.value(self.stochastic.parents['sig'])
mu = pm.utils.value(self.stochastic.parents['mu'])
delta = pm.rmv_normal_chol(0*mu, sig)
beta = np.minimum(1, self.proposal_sd * self.adaptive_scale_factor)
bsig = beta*sig
sb2 = np.sqrt(1-beta**2)
self.stochastic.value = (self.stochastic.value - mu)*sb2+beta*delta+mu
xp,x = self.stochastic.value, self.stochastic.last_value
self._hastings_factor = pm.mv_normal_chol_like(x,(xp-mu)*sb2+mu,bsig) - pm.mv_normal_chol_like(xp,(x-mu)*sb2+mu,bsig)
def propose(self):
dev = pm.rmv_normal_chol(np.zeros(self.stochastic.value.shape), self.L.value)
dev *= self.adaptive_scale_factor
self.stochastic.value = self.stochastic.value + dev
map_utils.exportAscii2(lon_ax, lat_ax, np.ma.masked_array(cov2,mask=np.zeros((500,500))), 'cov2.asc')
# Make fake data
lon = np.random.uniform(-1,1,size=N)
lat = np.random.uniform(-1,1,size=N)
t = np.zeros(N)
x = np.vstack((lon,lat,t)).T
cov1 = gencirc(x,r1)
cov2 = gencirc(x,r2)
M = c1*cov1+c2*cov2
S = pm.gp.FullRankCovariance(pm.gp.cov_funs.exponential.aniso_geo_rad, amp=.5, scale=.08, inc=.5, ecc=.5).cholesky(x[:,:2])
y = pm.rmv_normal_chol(M,S.T)+np.random.normal(N)*.1
z = pm.flib.invlogit(y)
lo_age = np.ones(N)*2
up_age = np.ones(N)*10
n = np.random.randint(10,500,size=N)
pos = pm.rbinomial(n, z)
neg = n-pos
data_file = np.rec.fromarrays([pos,neg,lo_age,up_age,lon,lat,t,cov1,cov2],names='pos,neg,lo_age,up_age,lon,lat,t,cov1,cov2')
# where_0 = np.where(M==0)
# where_1 = np.where(M==1)
# where_n1 = np.where(M==-1)
#
