Commit b27108cd by Theo Steininger

### Fixed volume factors in EnsembleLikelihood

parent 97781aaa
 ... ... @@ -34,6 +34,8 @@ class EnsembleLikelihood(Likelihood): # B^{-1} c = (A_inv - # A_inv U (I_k + U^dagger A_inv U)^{-1} U^dagger A_inv) c weight = observable.domain[1].weight(1) k = observable.shape[0] n = observable.shape[1] ... ... @@ -48,8 +50,11 @@ class EnsembleLikelihood(Likelihood): u_val = obs_val - obs_mean # compute quantities for OAS estimator mu = np.vdot(u_val, u_val)/n mu = np.vdot(u_val, u_val)*weight/n alpha = (np.einsum(u_val, [0, 1], u_val, [2, 1])**2).sum() # correct the volume factor: one factor comes from the internal scalar # product and one from the trace alpha *= weight**2 numerator = alpha + mu**2 denominator = (k + 1) / (alpha - (mu**2)/n) ... ... @@ -83,7 +88,7 @@ class EnsembleLikelihood(Likelihood): a_u_val = a_u.val.get_full_data() middle = (np.eye(k) + np.einsum(u_val.conjugate(), [0, 1], a_u_val, [2, 1])) a_u_val, [2, 1])*weight) middle = np.linalg.inv(middle) # result_array = np.zeros(k) # for i in xrange(k): ... ... @@ -114,8 +119,8 @@ class EnsembleLikelihood(Likelihood): second_summand = first_summand.copy_empty() second_summand.val = second_summand_val result_1 = -c.dot(first_summand) result_2 = -c.dot(second_summand) result_1 = -c.vdot(first_summand) result_2 = -c.vdot(second_summand) result = result_1 + result_2 self.logger.debug("Calculated: %f + %f = %f" % (result_1, result_2, result)) ... ...
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!