Metadata-Version: 2.1
Name: hyperdim
Version: 0.0.6
Summary: Hyperdimensionality computing machine learning library
Home-page: https://gitlab.com/da_doomer/hyperdim
License: UNKNOWN
Description: Hyperdimensionality computing machine learning library.
        
        Forked from https://gitlab.com/alehd/hd-lib
        
        # Installation and use
        
        Fork this repository and put it in you projects folder or use pip or pipenv:
        ```
        pip install --user hyperdim
        ```
        
        If you are using keras or sklearn it will be easy to use hyperdim.
        To use a model of dimensionality, say 10000:
        
        ```
        from hyperdim.hdmodel import HDModel
        from hyperdim.utils import to_categorical
        
        import numpy as np
        
        # Dummy datasets
        samples = 100
        features = 15
        classes = 5
        
        x = np.random.random(size = (samples, features))
        y = [ int(np.random.random()*classes) for _ in x ]
        y = to_categorical(y)
        
        print(x.shape) # (samples, features)
        print(y.shape) # (samples, classes)
        
        # Build model
        dimensions = 10000
        model = HDModel(features, classes, d = dimensions)
        
        # Fit using 30% of the data for validation, using one_shot_fit only
        history = model.fit(x, y, validation_split = 0.3, epochs = 1)
        
        print(history.history["acc"])
        print(history.history["val_acc"])
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
