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Algorithmic data mining -course project on efficiently finding the nearest-neighour tweets
Project repository for the Algorithmic methods for data mining course project. Group Members: Rainer Kujala, [email protected] Olli-Pekka Koistinen, [email protected] Code usage: To obtain the figures shown in the report, the following steps need to be performed: 0. Set up the param data_dir (containing the orig. data set and future results) in settings.py 1. Run python prepro.py - This the plots in the report and computes the preprocessed data sets 2. Run brute_force.py with all input parameter combinations [0-2, 0-7]: pypy brute_force.py 0 0 pypy brute_force.py 0 1 ... pypy brute_force.py 2 7 (this takes a while) 3. Run plot_brute_force.py (using python) for plotting the results for brute force. 4. Run sh create_identity_indices.sh 5. Run sh create_speedup_indices.sh 6. Run speedups.py (using pypy) with all input parameter combinations as in step 2. 7. Run plot_speedups.py (using python) for plotting the speedup curves 8. Run approx.py (using pypy) with input parameter combinations [0-2, 0-7, 0-1] (the last one is for selecting the approximation threshold (1.5 or 1.2)) 9. Run plot_approx.py (using python) to plot the speedups and mean estimation accuracies. 10. To assert that the results are valid, run assert_results.py (with python)
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