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data science.\n\nIn addition, there are some utilities.\n\nIn `src`:\n\n- An object parser which converts JSON data to Python classes: `object_parser.py`.\n- An OAS-generator for Python classes: `oas.py`.\n- A parallelization framework for load testing: `parallel.py`.\n\n# Usage\n\n## List supported modules\n\n```sh\n./main \u003cTAB\u003e\n# this will list the support modules\necho            fft             linear_fit      random_walk     semilinear_fit\n```\n\n## Run a module\n\nUsing `main`\n\n```sh\n./main random_walk\n./main linear_fit\n./main semilinear_fit\n```\n\nOr, as Python modules\n\n```sh\npython3 src/data_science/random_walk.py\n```\n\n# Examples\n\nBelow are examples of various models, ranging from simple linear models with analytical solutions to more complex models with numerical solutions.\n\n## Random walk\n\n[src/random_walk.py](src/random_walk.py) generates datasets that behave like random walks.\n\n\u003cimg src=\"img/random_walks.png\" style=\"max-width: 10%\" alt=\"Plot of Random Walks\"\u003e\n\n## Linear Models\n\n[src/linear_fit.py](src/linear_fit.py) fits linear models. The simplicity of the models reduces overfitting, but this is not explicitly tested.\n\n1. A linear regression model using normalized input data, while assuming a specific function (e.g. quadratic or exponential).\n\n\u003cimg src=\"img/linear_fits.png\" style=\"max-width: 10%\" alt=\"Plot of Linear fits\"\u003e\n\n2. Polynomial regression. A linear model (w.r.t. the parameters) that uses non-linear basis functions.\nNote that the fit for the exponential signal on the right-most plot is poor.\n\n\u003cimg src=\"img/polynomial_fits.png\" style=\"max-width: 10%\" alt=\"Plot of polynomial regression fits\"\u003e\n\n## Semi-linear Models\n\n[src/semilinear_fit.py](src/semilinear_fit.py) fits various non-linear models.\n\n1. Bayesian ridge regression, with polynomial and sinoid basis functions.\n2. A Gaussian Process.\n\nNote that these models estimate both a mean and a standard deviation, which can be used to define a confidence interval (C.I.).\n\nThe accuracy is derived using relative mean absolute error.\nIt is an overestimation because the test-data overlaps with the training-data.\n\n\u003cimg src=\"img/bayesian_fits.png\" style=\"max-width: 10%\" alt=\"Plot of Bayesian regression and Gaussian Processes\"\u003e\n\nSampling from the Gaussian Process produces a collection of possible futures.\n\n\u003cimg src=\"img/bayesian_fits_future.png\" style=\"max-width: 10%\" alt=\"Plot of Predicted Future Possibilities\"\u003e\n\n# Setup\n\nUsing a `Makefile` for convenience.\n\n```sh\nmake install\nmake test\n```\n\n## Optional\n\nSetup completions\n\n```sh\nsource setup/setup.sh\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvoschezang%2Fdata-science-templates","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvoschezang%2Fdata-science-templates","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvoschezang%2Fdata-science-templates/lists"}