Metadata-Version: 2.1
Name: gaus-bin-dist
Version: 2.0
Summary: Gaussian and Binomial Distributions
Home-page: UNKNOWN
Author: Eric Kwok
Author-email: ekwok25@gmail.com
License: UNKNOWN
Description: # gaus-bin-dist
        
        This package contains modules for working with Gaussian and Binomial Distributions.
        
        ## Files
        
        * `gaus_bin_dist/`: Distributions package
          * `Binomialdistribution.py`: Binomial class
          * `Gaussiandistribution.py`: Gaussian class
          * `Generaldistribution.py`: Distribution class
          * `__init__.py`: Initialization script
        * `license.txt`: MIT license
        * `numbers.txt`: Test file for Gaussian class
        * `numbers_binomial.txt`: Test file for Binomial class
        * `setup.cfg`: Configuration file for code packaging
        * `setup.py`: Script for code packaging
        * `test.py`: Unit tests
        
        ## Installation
        
        Download on [PyPi](https://pypi.org/project/gaus-bin-dist/) or use following command:
        
        `pip install gaus-bin-dist`
        
        ## Python Interpreter Example
        
        ### Initialization
        ```python
        >>> from gaus_bin_dist import Gaussian, Binomial
        >>> Gaussian(10, 7)
        mean 10, standard deviation 7
        >>> Binomial(0.4, 25)
        mean 10.0, standard deviation 2.449489742783178, p 0.4, n 25
        ```
        
        ### Addition
        ```python
        >>> gaussian_one = Gaussian(25, 3)
        >>> gaussian_two = Gaussian(30, 4)
        >>> gaussian_one + gaussian_two
        mean 55, standard deviation 5.0
        >>> binomial_one = Binomial(0.4, 20)
        >>> binomial_two = Binomial(0.4, 60)
        >>> binomial_one + binomial_two
        mean 32.0, standard deviation 4.381780460041329, p 0.4, n 80
        ```
        
        ### Probability Density Function
        ```python
        >>> gaussian_one.pdf(25)  # gaussian_one PDF at x = 25
        0.1329807601338109
        >>> binomial_one.pdf(5)  # binomial_one PDF at x = 5
        0.07464701952887093
        ```
        
        ### Gaussian Visualizations
        ```python
        >>> gaussian = Gaussian()
        >>> gaussian.read_data_file('numbers.txt')
        >>> gaussian.replace_stats_with_data()  # returns (mean, stdev)
        (78.0909090909091, 92.87459776004906)
        >>> gaussian.plot_histogram()
        ```
        
        ![Gaussian Histogram](https://github.com/ekwok/gaus-bin-dist/blob/main/figures/gaussian/histogram.png)
        
        ```python
        >>> gaussian.plot_histogram_pdf()
        ```
        
        ![Gaussian Histogram PDF](https://github.com/ekwok/gaus-bin-dist/blob/main/figures/gaussian/histogram_pdf.png)
        
        ### Binomial Visualizations
        ```python
        >>> binomial = Binomial()
        >>> binomial.read_data_file('numbers_binomial.txt')
        >>> binomial.replace_stats_with_data()  # returns (p, n)
        (0.6153846153846154, 13)
        >>> binomial.plot_histogram()
        ```
        
        ![Binomial Histogram](https://github.com/ekwok/gaus-bin-dist/blob/main/figures/binomial/histogram.png)
        
        ```python
        >>> binomial.plot_pdf()
        ```
        
        ![Binomial PDF](https://github.com/ekwok/gaus-bin-dist/blob/main/figures/binomial/pdf.png)
        
Platform: UNKNOWN
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