Metadata-Version: 2.1
Name: hal-x
Version: 0.86
Summary: Clustering via hierarchical agglomerative learning
Home-page: https://alexandreday.github.io/
Author: Alexandre Day
Author-email: alexandre.day1@gmail.com
License: MIT
Description: # Hierarchical Agglomerative Learning (HAL)
        Package for performing clustering for high-dimensional data. This packages uses heavily scikit-learn and fft accelerated t-SNE.
        
        # System requirement
        * Has been tested on latest version of OS X (Sierra and High Sierra) and on Linux (Ubuntu v 16)
        * (Optional) The dynamical plotting requires Chrome, Safari or Firefox (without *ad blockers* !).
        # Requirement:
        Python 3.6 or later versions.
        
        # Installing (once)
        Activate an [Anaconda](https://conda.io/docs/user-guide/tasks/manage-environments.html) Python 3 environment
        ```
        conda config --add channels conda-forge
        conda install cython numpy fftw scipy
        pip install hal-x
        ```
        # Updating
        For future versions of the package, you can upgrade using:
        ```
        pip install hal-x --upgrade
        ```
        # Small example
        ```
        from hal import HAL  # this imports the class HAL() 
        from sklearn.datasets import make_blobs
        import numpy as np
        
        # Setting random seed, in case you want to re-run example but keep saved data in info_hal/ 
        np.random.seed(0)
        
        # Generate some data. 
        X,y = make_blobs(10000,12,10) # 10 gaussians in 12 dimensions, 10000 data points
        
        # The HAL constructor has many optional parameters (documentation coming soon)
        model = HAL(clf_type='svm') # using linear SVMs (fastest) for agglomeration. Other options are 'rf' and 'nb' (random forest, and naive bayes)
        
        # builds model -> will save data in file info_hal
        model.fit(X)
        
        # rendering of results using javascript (with optional feature naming)
        feature_name = ['feat_%i'%i for i in range(12)]
        model.plot_tree(feature_name = feature_name)
        
        # Now that your model is fitted, can predict on data (either new or old), using a cross-validation score of 0.95
        ypred = model.predict(X, cv=0.95)
        
        # The fitted model information is in directory info_hal. To reload that information for later use, just:
        model.load()
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
