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Threshold Based Representation (TBR) encoder\n2. Bens Spiker Algorithm (BSA) encoder\n\n\u003c!-- TOC --\u003e\n\n- [Data](#data)\n- [Instillation](#instillation)\n- [Example](#example)\n- [Contribution](#contribution)\n- [Issues](#issues)\n\n\u003c!-- /TOC --\u003e\n\n## Data\n\nThe data given to the encoders are spatio-temporal. Each sample is one `csv` file. In each file, every column is a feature and the rows are time points.\n\nFor example each file given in the [Data](https://github.com/akshaybabloo/Spikes/tree/master/Data) folder had 128 rows and 14 columns, 14 columns are the features and 128 columns are the data points.\n\n## Instillation\n\n```\npip install pyspikes\n```\n\n## Example\n\n```python\nfrom spikes import encoder\nfrom spikes.utility import ReadCSV\n\ndata = ReadCSV('Data').get_samples()['samples']\n\nbsa = encoder.BSA(data)\nprint(bsa.get_spikes())\n\ntbr = encoder.TBR(data)\nprint(tbr.get_spikes())\n```\n\n## Contribution\n\nAll contributions are welcome.\n\n## Issues\n\nIssues can be opened through Github's [Issues](https://github.com/akshaybabloo/Spikes/issues) tab.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakshaybabloo%2Fspikes","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fakshaybabloo%2Fspikes","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakshaybabloo%2Fspikes/lists"}