GitHub - LionBrm/Random-Processes-Project

Please refer to the project report for detailed context.

Use generate_snythetic_data_2.py to generate synthetic data
inference_2.py and inference_with_evaluation.py serve as recovering the model parameters using likelihood maximization (forward). At the core both scripts do the same, inference_with_evaluation.py is an extended version with evaluation metrics for the project repor
posterior_state_probabilites.py is the file for calcualating the posterior probabilities for the hidden states
plots_posterior_probabilites.py creates nice plots -viterby_algorithm_and_plot.py creates the corresponding Viterbi paths

Name		Name	Last commit message	Last commit date
Latest commit History 20 Commits
.vscode		.vscode
figures_posterior_outputs		figures_posterior_outputs
plots_viterbi_posterior		plots_viterbi_posterior
Notes_and_background.ipynb		Notes_and_background.ipynb
README.md		README.md
Random_Processes_Final_Report.pdf		Random_Processes_Final_Report.pdf
curves_roc_pr.csv		curves_roc_pr.csv
fetch_germline_data.py		fetch_germline_data.py
generate_germline_data.py		generate_germline_data.py
generate_synthetic_data.py		generate_synthetic_data.py
generate_synthetic_data_2.py		generate_synthetic_data_2.py
human_ighv_germlines.csv		human_ighv_germlines.csv
inference.py		inference.py
inference_2.py		inference_2.py
inference_with_evaluation.py		inference_with_evaluation.py
metadata.txt		metadata.txt
per_position_mean_occupancy_and_mutation_densities.csv		per_position_mean_occupancy_and_mutation_densities.csv
plots_posterior_probabilities.py		plots_posterior_probabilities.py
posterior_state_probabilities.py		posterior_state_probabilities.py
simulated_germline_mutations.csv		simulated_germline_mutations.csv
synthetic_aid_data.csv		synthetic_aid_data.csv
synthetic_aid_data_2.csv		synthetic_aid_data_2.csv
viterbi_plot.py		viterbi_plot.py
viterby_algorithm_and_plot_2.py		viterby_algorithm_and_plot_2.py

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