Hsu et al 2018 #2
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- Added Mike Croucher's Python implementation of the Alternating Projections method to finding the nearest positive definite correlation matrix. (Note: Need to move nearest_correlation.py to Julia working directory for PyCall to find it.) - Edited util.jl so that deprecation warnings wouldn't show up. - To Do: Still cannot generate valid thetas within max iterations -> look into moving normalization of radius-period probabilities into update_sim_param_from_vector.
- Serial version of updating population will now only convert covariance matrix to nearest positive definite if the error occurs when calling generate_theta.
- Added new class for diagonal covariance matrix - Adjusted alg.jl to use diagonal covariance - Removed try-catch for positive definite
- Removed print statement for original pop weights because it is equivalent to the new pop weights from the previous generation.
- Removed printing of population weights
- Now checks for array output to is_valid and normalize and for scaler output from pdf.
- Now checks for 3 consecutive generations without change in epsilon instead of counting 5 total repetitions. - Temporary disabled summary statistics storage.
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TODO: Should update generate_theta to take a filename as an optional parameter |
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Version of ABC used in paper calculations.