Custom code created for the purposes of the thesis: "Applications of statistical theory to sensor data analysis"
doi: 10.4121/d082e14d-6d92-44c9-9791-64b74dce3470
CC0
This is the custom code repository for replicating the results of the thesis. Three main routines are contained within this repository.
A new quality measure is proposed in the thesis for the purposes of assessing the quality of predictors in human activity recognition problems. The related code can be found in the file: measures.py
A postprocessing scheme is proposed in the thesis to remove unrealistically short activities from the classification given by the predictor. The related code can be found in the file: postprocessing.py
A new formulation of the null hypothesis in a permutation test for no effect is proposed in the thesis. The viability of the test is presented based on the simulation study. This simulation study can be found in the files: sim_study_lin_reg.ipynb and sim_study_nn.ipynb.
- 2024-05-23 first online, published, posted
DATA
- 8,282 bytesMD5:
5fd69955cd1cf89f559f375f51b1cdf2readme.txt - 2,134 bytesMD5:
8a8205f1c096078e1a3bbed3ba7dc58emeasures.py - 7,497 bytesMD5:
1df058af8819419cbc6d7bb6e6b977f5misc_func.py - 4,808 bytesMD5:
e9ce0c379848f7694230ddf4ba9347b2postprocessing.py - 406,774 bytesMD5:
0c6277d9aef261d36ebd37bf66e3bc77sim_study_lin_reg.ipynb - 145,421 bytesMD5:
cedc27a11d056cc9eb4e1f685fc996c8sim_study_nn.ipynb - 76,873 bytesMD5:
49250dfcd6a45e027cce1527b14b6e69simulation_study.ipynb -
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