Metadata-Version: 2.1
Name: mnist-classifier
Version: 1.0.2
Summary: Basic mnist classifier example of a Reproducible Research Project in Python
Home-page: https://github.com/sandrich/classifying_digits_mnist
Author: Christian Sandrini
Author-email: mail@chrissandrini.ch
License: MIT
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        # Classifying digits using 28x28px images in one of 10 classes
        Small classifier for 28x28px handwritten digits based on [M-NIST](http://yann.lecun.com/exdb/mnist/) dataset 
        
        ## License
        MIT
        
        ## Requirements
        For this project to run properly you will need:
        
         - conda ([installation instructions](https://docs.conda.io/projects/conda/en/latest/user-guide/install/))
        
        ## Installation
        To use and reproduce this project, first clone this repository in the directory of your choice
        ```shell script
        cd /path/to/your/directory
        git clone https://github.com/sandrich/classifying_digits_mnist.git
        ``` 
        
        Then create a conda environment with the correct dependencies:
        ```shell script
        conda env create --file environment.yml
        ```
        Once the conda has finished installing all the dependencies, activate it:
        ```shell script
        conda activate mnist_classifier
        ```
        
        ## Usage
        
        The program can run without parameters which will take our researched value. Feel free to use different parameters to play with the data and algorithm
        
        ```bash
        $ python mnist_predict.py -h
        usage: mnist_predict.py [-h] [--trees TREES] [--depth DEPTH] [--impurity_method {entropy,gini}]
        
        Run MNIST classifier
        
        optional arguments:
          -h, --help            show this help message and exit
          --trees TREES         Number of trees
          --depth DEPTH         Maximum tree depth
          --impurity_method {entropy,gini}
                                Impurity method
        ```
        
        ### Example
        
        ```bash
        # python mnist_predict.py 
        No local fit dataset found.
        Downloading fit data
        ['================================================='>'']]
        Downloading fit labels
        ['================================================='>'']
        No local test dataset found.
        Downloading test data
        ['================================================='>'']]
        Downloading test labels
        ['================================================='>'']
        Starting training...
        Done training.
        Predicting...
        Predicting...
        Classification stats:
        -----------------
        Max tree depth: 9
        Number of trees: 20
        Impurity method: entropy
        -----------------
        Train Accuracy: 0.946
        Train Accuracy: 0.935
        ```
        
        ## Authors
        @sandrich - Christian Sandrini
        @bigskapinsky - Calixte Mayoraz
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
