The components Submodule

pyhdc.components exposes the individual building blocks that underpin every encoding. Most users never need this module: the encoding classes assemble the right components automatically. It is useful when:

You are writing a custom encoding subclass
You want to apply a specific operation (e.g., remap_to_unit) without a full encoding context
You are debugging or testing individual operations in isolation

Submodule layout

pyhdc.components
├── binding          : all binding functions
├── bundling         : all bundling functions
├── similarity       : all similarity functions + remap_to_unit
├── elements         : element generator functions (how random values are drawn)
├── thinning         : thinning functions (post-process sparse binary vectors)
└── input_formatting : internal normalisation utilities

The EncodingSpec wiring

When you define a custom encoding by subclassing Encoding and implementing _get_encoding_spec(), you return an EncodingSpec that names the component functions to use:

from pyhdc.encodings.base import Encoding, EncodingSpec
from pyhdc.components.binding   import ElementMultiplication
from pyhdc.components.bundling  import ElementAdditionCut
from pyhdc.components.similarity import CosineSimilarity
from pyhdc.components.elements  import UniformBipolar
from pyhdc.components.thinning  import NoThin
import numpy as np

class MyEncoding(Encoding):
    def _get_encoding_spec(self) -> EncodingSpec:
        return EncodingSpec(
            dtype=np.float32,
            element_generator=UniformBipolar,
            similarity_fn=CosineSimilarity,
            bundling_fn=ElementAdditionCut,
            thinning_fn=NoThin,
            binding_fn=ElementMultiplication,
            unbinding_fn=ElementMultiplication,   # self-inverse
            generator_output_type="floats",
        )

elements submodule

Element generators control how individual hypervector values are drawn.

Function	Description
`UniformBipolar`	Uniform random from {-1, +1} (Bernoulli p=0.5 then ×2−1)
`UniformAngles`	Uniform random in [0, 2π]
`NormalReal`	Normal distribution N(0, 1)
`BernoulliBinary`	Bernoulli(p=0.5) → {0, 1}
`BernoulliBiploar`	Bernoulli(p=0.5) → {-1, +1} (note: typo in source; “Biploar”)
`BernoulliSparse`	k-sparse binary: exactly k elements are 1, rest are 0
`SparseSegmented`	Per-segment sparse binary: k ones placed uniformly within each segment

thinning submodule

Thinning operations post-process a bundled binary hypervector to reduce density.

Function	Description
`NoThin`	No-op; returns the input unchanged. Used by encodings that do not thin.

similarity submodule

The similarity module exports the four metric functions and the remap utility:

CosineSimilarity(*hvs) : cosine similarity
HammingDistance(*hvs) : normalised Hamming, output in [-1, 1]
Overlap(*hvs) : normalised overlap, output in [-1, 1]
AngleDistance(*hvs) : angle-based distance, output in [-1, 1]
remap_to_unit(sim) : maps [-1, 1] → [0, 1]

Each function accepts one or two arguments in the same calling conventions as the Encoding.similarity() method.

input_formatting submodule

Internal utilities used by encoding methods to normalise inputs. These are considered private API and may change between releases:

_extract_data(hv) : extract the raw array from a Hypervector or pass through if already an array
_normalize_inputs(*hvs) : validate and normalise a sequence of inputs
_detect_batch_structure(*hvs) : determine whether inputs are 1-D or 2-D and which batched calling convention applies