- Modify `build_features` function to support iterative processing across nested directories, enhancing the system's ability to handle larger datasets and varied input structures.
- Replace direct usage of `FeatureExtractor` class with `ExtractTimeFeatures` function, which now acts as a wrapper to include this class, facilitating streamlined integration and maintenance of feature extraction processes.
- Implement `extract_numbers` function using regex to parse filenames and extract numeric identifiers, used for labels when training with SVM
- Switch output from `.npz` to `.csv` format in `build_features`, offering better compatibility with data analysis tools and readability.
- Update documentation and comments within the code to reflect changes in functionality and usage of the new feature extraction setup.
Closes#4
- Implement FeatureExtractor class in time_domain_features.py for calculating statistical features from dataset columns.
- Create build_features.py script to automate feature extraction from processed data and save results in a structured format.
- Adjust build_features.py to read processed data, utilize FeatureExtractor, and save feature matrix.
This update supports enhanced analysis capabilities within the thesis-project structure, allowing for more sophisticated data processing and model training stages.
Closes#1
The code changes add a new file `time_domain_features.py` that contains a `FeatureExtractor` class. This class calculates various time domain features for a given dataset. The features include mean, max, peak, peak-to-peak, RMS, variance, standard deviation, power, crest factor, form factor, pulse indicator, margin, kurtosis, and skewness.
The class takes a file path as input and reads the data from a CSV file. It assumes the data to analyze is in the first column. The calculated features are stored in a dictionary.
The commit message suggests that the purpose of the changes is to add a new class for time domain feature extraction.
