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debug hmc code

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This commit is contained in:
Zhenwen Dai 2014-08-06 11:01:08 +01:00
parent 18a1513edb
commit 0a1e7aface
1 changed files with 11 additions and 11 deletions
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22
GPy/inference/optimization/hmc.py
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@ -28,11 +28,14 @@ class HMC:
H_new = self._computeH()
k = np.exp(H_old-H_new)
print H_old,H_new
print k
if np.random.rand()<k:
a = np.random.rand()
if a<k:
thetas[i] = self.model.optimizer_array
ps[i] = self.p
else:
print '###'
thetas[i] = theta_old
ps[i] = p_old
self.model.optimizer_array = theta_old
@ -40,13 +43,10 @@ class HMC:
def _update(self, hmc_iters):
for i in xrange(hmc_iters):
g = self.p.copy()
g[:] = 1e-2
# self.p[:] += self.stepsize/2.*self.model.grad()[:,0]#*-self.model._transform_gradients(self.model.objective_function_gradients())
self.p[:] += self.stepsize/2.*-self.model._transform_gradients(self.model.objective_function_gradients())
self.model.optimizer_array[:] = self.model.optimizer_array[:] + self.stepsize*np.dot(self.Minv, self.p[:,None])[:,0]
self.p[:] += self.stepsize/2.*-self.model._transform_gradients(self.model.objective_function_gradients())
#self.model.optimizer_array = self.model.optimizer_array - self.stepsize*self.model._transform_gradients(self.model.objective_function_gradients())
self.p[:] += -self.stepsize/2.*self.model._transform_gradients(self.model.objective_function_gradients())
self.model.optimizer_array = self.model.optimizer_array + self.stepsize*np.dot(self.Minv, self.p)
print self.model.objective_function()
self.p[:] += -self.stepsize/2.*self.model._transform_gradients(self.model.objective_function_gradients())
def _computeH(self,):
return self.model.objective_function()+self.p.size*np.log(2*np.pi)/2.+np.log(np.linalg.det(self.M))/2.+np.dot(self.p, np.dot(self.Minv,self.p[:,None]))/2.
@ -54,10 +54,10 @@ class HMC:
class Gmodel:
def __init__(self,):
self.cov = np.array([[1., 0.99],[0.99, 1.]])
self.optimizer_array = np.random.rand(2)
self.param_array = np.random.rand(2)
def grad(self,):
return -np.dot(np.linalg.inv(self.cov),self.optimizer_array[:,None])
return -np.dot(np.linalg.inv(self.cov),self.param_array[:,None])
def objective_function(self,):
return np.dot(self.optimizer_array, np.dot(np.linalg.inv(self.cov),self.optimizer_array[:,None]))/2.
return np.dot(self.param_array, np.dot(np.linalg.inv(self.cov),self.param_array[:,None]))/2.