pyhdc: Top-Level Module

Version and availability

pyhdc.TORCH_AVAILABLE: bool

True if import torch succeeded at PyHDC import time. False if PyTorch is not installed.

import pyhdc
if pyhdc.TORCH_AVAILABLE:
    enc = pyhdc.MAP_C(backend="torch", device="cuda")

These functions are thin wrappers that delegate to the first hypervector’s encoding. They are provided for concise one-off calls.

pyhdc.generate(encoding, size=None, use_generator=None)[source]

Generate one or more hypervectors using encoding.

Parameters:

encoding – An instantiated Encoding object.
size – None for a single vector, int for a 1-D batch, or tuple for a multi-dimensional batch.
use_generator – Override the encoding’s generator setting. True forces the custom generator; False forces NumPy’s default.

Returns:

A Hypervector.

enc = pyhdc.MAP_C(dimension=10_000)
hv  = pyhdc.generate(enc)
batch = pyhdc.generate(enc, size=100)

pyhdc.zeros(encoding, size=None)[source]

Return a zero-valued hypervector (or batch) for encoding.

Parameters:

Returns:

A Hypervector filled with zeros.

zero = pyhdc.zeros(enc)

pyhdc.bundle(*hypervectors)[source]

Bundle two or more hypervectors using the encoding of the first argument.

Parameters:: hypervectors – Two or more Hypervector objects produced by the same encoding.
Returns:: A Hypervector.

result = pyhdc.bundle(hv1, hv2, hv3)

pyhdc.bind(*hypervectors)[source]

Bind two or more hypervectors using the encoding of the first argument.

result = pyhdc.bind(key, value)

All encoding classes are imported at the top level:

See Encoding Classes for full documentation of each class.

All exception classes are imported at the top level:

See Exceptions for details.

See Generation Module for the full family listing.