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CSE515_MWDB_Project/Phase 2
History
pranavbrkr 4492dc6677 init
2023-11-22 09:51:59 -07:00
..
output
final submitted version for phase 2
2023-11-07 18:57:03 -07:00
CSE515 - Multimedia and Web Databases - Fall 2023 - Project Report - Phase 2.pdf
final submitted version for phase 2
2023-11-07 18:57:03 -07:00
phase2_F23.pdf
starting phase 2
2023-09-22 23:58:23 -07:00
README.md
final submitted version for phase 2
2023-11-07 18:57:03 -07:00
requirements.txt
WiP refactoring niraj's task 4, some bugfixes
2023-10-13 11:22:53 -07:00
task_0.ipynb
refactored madhura's task 2 code, some more fixes
2023-10-12 00:14:22 -07:00
task_1.ipynb
refactored pranav's code for task 1
2023-10-07 23:29:11 -07:00
task_2a.ipynb
refactored madhura's task 2 code, some more fixes
2023-10-12 00:14:22 -07:00
task_2b.ipynb
refactored madhura's task 2 code, some more fixes
2023-10-12 00:14:22 -07:00
task_3.ipynb
start kmeans task 9
2023-10-13 11:27:59 -07:00
task_4.ipynb
ls2 for task 9
2023-10-13 19:32:14 -07:00
task_5.ipynb
ls4 added
2023-10-13 18:49:13 -07:00
task_6.ipynb
ls4 added
2023-10-13 18:49:13 -07:00
task_7.ipynb
lda rounding off
2023-10-15 18:59:21 -07:00
task_8.ipynb
init
2023-11-22 09:51:59 -07:00
task_9.ipynb
lda rounding off
2023-10-15 18:59:21 -07:00
task_10.ipynb
lda rounding off
2023-10-15 18:59:21 -07:00
task_11.ipynb
final submitted version for phase 2
2023-11-07 18:57:03 -07:00
utils.py
lda rounding off
2023-10-15 18:59:21 -07:00

README.md

Phase 2

  • Refer phase2_F23.pdf for problem description

Requirements and dependencies

  • Requires MongoDB server (local or otherwise)
  • Install packages from requirements.txt

Environment variables

  • DATASET_PATH - path to the Caltech101 dataset

Data files

  • Files should be in the same folder as the source code to be accessed properly

Execution

  • For each task, simply run all cells in the corresponding notebook and enter inputs as prompted
  • Task 6 requires significant RAM and also takes a lot of time
  • Task 11 takes a lot of time if constructing the similarity matrix afresh