Metadata-Version: 1.1
Name: ohmlr
Version: 0.0.8
Summary: One-hot multinomial logisitc regression
Home-page: http://joepatmckenna.github.io/ohmlr
Author: Joseph P. McKenna
Author-email: joepatmckenna@gmail.com
License: MIT
Download-URL: https://pypi.org/project/ohmlr
Description: One-hot multinomial logistic regression
        ========================
        
        Quick Start
        -----------
        
        Installation
        ~~~~~~~~~~~~
        
        - To install ``ohmlr`` on your computer using ``pip``, execute
        
          .. code-block:: sh
        
             pip install ohmlr
        
        - Test out ``ohmlr`` in Python:
        
          .. code-block:: python
        
             import ohmlr
             import numpy as np
        
             # create model and generate data
             n_features = 16
             n_x_classes = np.random.randint(2, 10, size=n_features)
             n_y_classes = 8
             model = ohmlr.ohmlr().random(n_features, n_x_classes, n_y_classes)
             x, y = model.generate_data(n_samples=1000)
        
             # fit and score model
             model.fit(x, y)
             print(model.score(x, y))
        
        
        Links
        -----
        
        Online documentation:
            http://joepatmckenna.github.io/ohmlr
        
        Source code repository:
            https://github.com/joepatmckenna/ohmlr
        
        Python package index:
            https://pypi.python.org/pypi/ohmlr
        
Keywords: inference,statistics,machine learning
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
