Metadata-Version: 1.0
Name: typekev-mlnd-probability
Version: 1.1
Summary: Gaussian and Binomial distributions
Home-page: UNKNOWN
Author: Kevin Gonzalez
Author-email: typekev@gmail.com
License: UNKNOWN
Description-Content-Type: text/markdown
Description: # MLND Probability Classes
        
        This package exposes two probability distribution classes, Gaussian and Binomial, and was made as part of the Udacity Machine Learning Engineer Nanodegree.
        
        This package was created for educational purposes.
        
        # Use of `Gaussian` and `Binomial`
        
        The classes can be imported as so: `from typekev_mlnd_probability import Gaussian` and `from typekev_mlnd_probability import Binomial`
        
        Both classes are initialized with the following attributes:
        
        - `mean`: The mean or average
        - `stdev`: Standard deviation
        - `data`: The list of data in which to calculate the other attributes
        
        Both classes also contain a `read_data_file` method.
        
        The `Gaussian` and `Binomial` classes also contain unique attributes and methods. Though names may overlap, implementation will differ.
        
        ## `Gaussian` attributes and methods
        
        The `Gaussian` class contains the following additional methods:
        
        - `calculate_mean`
        - `calculate_stdev`
        - `plot_histogram`
        - `pdf`
        - `plot_histogram_pdf`
        
        The magic methods `__add__` and `__repr__` have also been implemented.
        
        ## `Binomial` attributes and methods
        
        The `Binomial` class contains the following additional attributes:
        
        - `n`: The size of the data set
        - `p`: The probability
        
        The `Binomial` class contains the following additional methods:
        
        - `calculate_mean`
        - `calculate_stdev`
        - `replace_stats_with_data`
        - `plot_bar`
        - `pdf`
        - `plot_bar_pdf`
        
        The magic methods `__add__` and `__repr__` have also been implemented.
        
Platform: UNKNOWN
