Metadata-Version: 2.1
Name: regressio
Version: 1.0.0
Summary: A python module for regression, interpolation and smoothing.
Home-page: https://github.com/brendanartley/regressio
Author: Brendan Artley
Author-email: brendanartley@gmail.com
License: MIT
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        Regressio is a python module for univariate regression, interpolation, and smoothing.
        
        The available models are:
        - Linear Regression
        - Ridge Regression
        - Linear Spline
        - Isotonic Regression
        - Bin Regression
        - Cubic Spline
        - Natural Cubic Spline
        - Exponential Smoothing
        
        There are also functions implemented to generate data samples.
        
        The available data generators are:
        - Random Walk
        - Isotonic Sample
        
        ## Installation
        
        Regressio is supported in Python 3.8+ and requires only NumPy and Matplotlib.
        
        `pip install git+https://github.com/brendanartley/Regressio`
        
        ## Example Usage
        
        Cubic spline. 
        
        ```python
        # Import modules + classes
        from regressio.models import cubic_spline
        from regressio.datagen import generate_random_walk
        import numpy as np
        import matplotlib.pyplot as plt
        
        # Set figsize and seed
        plt.rcParams['figure.figsize'] = (10, 5)
        np.random.seed(0)
        
        # Generate data sample
        x, y = generate_random_walk(150)
        
        # Fit model and plot result
        model = cubic_spline(pieces=15)
        model.fit(x, y, plot=True, confidence_interval=0.99)
        ```
        <img alt="Cubic Spline" src="https://github.com/brendanartley/Regressio/blob/main/imgs/cubic_spline.png?raw=true" width="650">
        
        Linear regression.
        
        ```python
        # Import modules + classes
        from regressio.models import linear_regression
        from regressio.datagen import generate_random_walk
        import numpy as np
        import matplotlib.pyplot as plt
        
        # Set figsize and seed
        plt.rcParams['figure.figsize'] = (10, 5)
        np.random.seed(1)
        
        # Generate data sample
        x, y = generate_random_walk(100)
        
        # Fit model and plot result
        model = linear_regression(degree=5)
        model.fit(x, y, plot=True, confidence_interval=0.95)
        ```
        <img alt="Linear Regression" src="https://github.com/brendanartley/Regressio/blob/main/imgs/linear_regression.png?raw=true" width="650">
        
        Exponential Smoothing.
        
        ```python
        # Import modules + classes
        from regressio.models import isotonic_regression
        from regressio.datagen import generate_isotonic_sample
        import numpy as np
        import matplotlib.pyplot as plt
        
        # Set figsize and seed
        plt.rcParams['figure.figsize'] = (10, 5)
        np.random.seed(6)
        
        # Generate data sample
        x, y = generate_isotonic_sample(100)
        
        # Fit model and plot result
        model = exponential_smoother(alpha=0.2)
        model.fit(x, y, plot=True, confidence_interval=0.90)
        ```
        <img alt="Exponential Smoother" src="https://github.com/brendanartley/Regressio/blob/main/imgs/exponential_smoother.png?raw=true" width="650">
        
        For more examples, navigate to the [examples.ipynb](examples.ipynb) file in this repository.
        
        ## Contributions
        
        We welcome all to contribute their expertise to the Regressio library. If you are new to open source contributions, [this guide](https://opensource.guide/how-to-contribute/) gives some great tips on how to get started.
        
        If you have a complex feature in mind or find a large bug in the code, please create a detailed issue and we will get to work on it.
        
        ## References
        
        - Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3. Accessed July 2022. 
        
        - Kong, Qingkai, et al. Python Programming and Numerical Methods: A Guide for Engineers and Scientists. Academic Press, an Imprint of Elsevier, pythonnumericalmethods.berkeley.edu, Accessed July 2022. 
        
        - Li, Bao, (2022). Stat 508: Applied Data Mining, Statistical Learning: Stat Online. PennState: Statistics Online Courses, online.stat.psu.edu/stat508, Accessed July 2022.
        
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