Make the distinction between original Amisano and Tristani's way to calculate...

Make the distinction between original Amisano and Tristani's way to calculate particles weights and the likelihood (CPF1) and Murray's way (CPF2).

src/conditional_filter_proposal.m

@@ -103,7 +103,11 @@ if ParticleOptions.proposal_approximation.cubature || ParticleOptions.proposal_a
     PredictedObservedVarianceSquareRoot = mat(1:number_of_observed_variables,1:number_of_observed_variables);
     CovarianceObservedStateSquareRoot = mat(number_of_observed_variables+(1:number_of_state_variables),1:number_of_observed_variables);
     StateVectorVarianceSquareRoot = mat(number_of_observed_variables+(1:number_of_state_variables),number_of_observed_variables+(1:number_of_state_variables));
-    StateVectorMean = PredictedStateMean + (CovarianceObservedStateSquareRoot/PredictedObservedVarianceSquareRoot)*(obs - PredictedObservedMean);
+    Error = obs - PredictedObservedMean ;
+    StateVectorMean = PredictedStateMean + (CovarianceObservedStateSquareRoot/PredictedObservedVarianceSquareRoot)*Error ;
+    if strcmpi(options_.particle.filter_algorithm, 'cpf1')
+        Weights = SampleWeights.*probability2(zeros(number_of_observed_variables,1),PredictedObservedVarianceSquareRoot,Error) ; 
+    end
 else
     dState = bsxfun(@minus,tmp(mf0,:),PredictedStateMean);
     dObserved = bsxfun(@minus,tmp(mf1,:),PredictedObservedMean);
@@ -111,15 +115,20 @@ else
     PredictedObservedVariance = dObserved*diag(weights_c)*dObserved' + H;
     PredictedStateAndObservedCovariance = dState*diag(weights_c)*dObserved';
     KalmanFilterGain = PredictedStateAndObservedCovariance/PredictedObservedVariance ;
-    StateVectorMean = PredictedStateMean + KalmanFilterGain*(obs - PredictedObservedMean);
+    Error = obs - PredictedObservedMean ;
+    StateVectorMean = PredictedStateMean + KalmanFilterGain*Error ;
     StateVectorVariance = PredictedStateVariance - KalmanFilterGain*PredictedObservedVariance*KalmanFilterGain';
-    %StateVectorVariance = .5*(StateVectorVariance+StateVectorVariance');
     StateVectorVarianceSquareRoot = chol(StateVectorVariance + 1e-6)' ;
+    if strcmpi(options_.particle.filter_algorithm, 'cpf1')
+        Weights = SampleWeights.*probability2(zeros(number_of_observed_variables,1),chol(PredictedObservedVariance)',Error) ; 
+    end
 end
 
 PredictedStateVarianceSquareRoot = chol(PredictedStateVariance + 1e-6)'  ;
 ProposalStateVector = StateVectorVarianceSquareRoot*randn(size(StateVectorVarianceSquareRoot,2),1)+StateVectorMean ;
+if strcmpi(options_.particle.filter_algorithm, 'cpf2')
     Prior = probability2(PredictedStateMean,PredictedStateVarianceSquareRoot,ProposalStateVector) ; 
     Posterior = probability2(StateVectorMean,StateVectorVarianceSquareRoot,ProposalStateVector) ; 
     Likelihood = probability2(obs,H_lower_triangular_cholesky,measurement_equations(ProposalStateVector,ReducedForm,ThreadsOptions)) ; 
     Weights = SampleWeights.*Likelihood.*(Prior./Posterior) ;
+end
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