from math import log, sqrt
from typing import Optional
import numpy as np
# Optional PyTorch support
try:
import torch
TORCH_AVAILABLE = True
except ImportError:
TORCH_AVAILABLE = False
torch = None
from pyhdc.components.input_formatting import _normalize_binding, _require_single_vector
# Type aliases
from pyhdc.types import ArrayLike
# ============================================================================
# Context-Dependent Thinning
# ============================================================================
[docs]
def AdditiveContextDependentThinning(
*hypervectors: ArrayLike,
probability: Optional[float] = None,
seed: Optional[int] = None,
) -> ArrayLike:
"""
Context-dependent thinning for sparse hypervectors.
Binds sparse vectors while controlling density through selective thinning
based on context. Used with BSDC encodings.
Args:
*hypervectors: Variable number of sparse binary hypervectors, or single 2D batch
probability: Probability parameter for thinning
seed: Random seed for reproducibility
Returns:
Bound and thinned hypervector
Note:
This requires the Disjunction operation from bundling module
"""
from pyhdc.components.bundling import Disjunction
hvs, is_torch, _ = _normalize_binding(*hypervectors)
_require_single_vector(hvs, "AdditiveContextDependentThinning")
z = Disjunction(*hypervectors)
S = hvs[0].shape[0]
if probability is None:
probability = 1 / sqrt(S)
K = int(log(1 - (1 / S)) / log(1 - probability))
if seed is not None:
if is_torch:
torch.manual_seed(seed)
else:
np.random.seed(seed)
if is_torch:
w = torch.zeros_like(z, dtype=torch.bool)
for _ in range(1, K + 1):
r = torch.randint(0, S, (1,)).item()
if r != 0:
z_roll = torch.roll(z, shifts=-r, dims=0)
w = torch.logical_or(w, z_roll)
result = torch.logical_xor(z.bool(), w).to(z.dtype)
else:
w = np.zeros(S, dtype=bool)
for _ in range(1, K + 1):
r = np.random.randint(S)
if r != 0:
z_roll = np.roll(z, -r)
w = np.logical_or(w, z_roll)
result = np.logical_xor(z.astype(bool), w).astype(z.dtype)
return result