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DEPRECATED: Legacy Float HDV Implementation
============================================
This module is DEPRECATED and will be removed in a future version.
Use `binary_hdv.BinaryHDV` instead for all new code.
Migration notes:
- HDV(dimension=N) -> BinaryHDV.random(dimension=N)
- hdv.bind(other) -> hdv.xor_bind(other)
- hdv.unbind(other) -> hdv.xor_bind(other) # XOR is self-inverse
- hdv.cosine_similarity(other) -> hdv.similarity(other)
- hdv.permute(shift) -> hdv.permute(shift)
This module is kept temporarily for backward compatibility.
"""
import warnings
import numpy as np
from dataclasses import dataclass
from typing import Optional
from .exceptions import DimensionMismatchError
@dataclass
class HDV:
"""Holographic Distributed Representation"""
vector: Optional[np.ndarray] = None # 10,000-dimensional vector
dimension: int = 10000
id: str = None
def __post_init__(self):
# Emit deprecation warning on instantiation so tests can catch it
warnings.warn(
"src.core.hdv.HDV is deprecated. Use src.core.binary_hdv.BinaryHDV instead. "
"This module will be removed in a future version.",
DeprecationWarning,
stacklevel=2
)
if self.vector is None:
# Initialize with random bipolar vector
self.vector = np.random.choice(
[-1, 1],
size=self.dimension
)
elif self.vector.shape[0] != self.dimension:
raise DimensionMismatchError(
expected=self.dimension,
actual=self.vector.shape[0],
operation="HDV initialization"
)
def __add__(self, other: 'HDV') -> 'HDV':
"""Superposition: v_A + v_B contains both"""
warnings.warn(
"HDV.__add__() is deprecated. Use binary_hdv.majority_bundle() instead.",
DeprecationWarning,
stacklevel=2
)
if self.dimension != other.dimension:
raise DimensionMismatchError(
expected=self.dimension,
actual=other.dimension,
operation="superposition"
)
return HDV(
vector=self.vector + other.vector,
dimension=self.dimension
)
def __xor__(self, other: 'HDV') -> 'HDV':
"""Binding: v_A ⊗ v_B (HRR circular convolution) (Deprecated: Use .bind() instead)"""
warnings.warn(
"HDV.__xor__() is deprecated. Use BinaryHDV.xor_bind() instead.",
DeprecationWarning,
stacklevel=2
)
return self.bind(other)
def bind(self, other: 'HDV') -> 'HDV':
"""Binding: v_A ⊗ v_B (HRR circular convolution)"""
warnings.warn(
"HDV.bind() is deprecated. Use BinaryHDV.xor_bind() instead.",
DeprecationWarning,
stacklevel=2
)
if self.dimension != other.dimension:
raise DimensionMismatchError(
expected=self.dimension,
actual=other.dimension,
operation="binding"
)
return HDV(
vector=self.fft_convolution(self.vector, other.vector),
dimension=self.dimension
)
def unbind(self, other: 'HDV') -> 'HDV':
"""Unbinding: v_AB ⊗ v_A* (Approximate inverse)"""
warnings.warn(
"HDV.unbind() is deprecated. Use BinaryHDV.xor_bind() instead (XOR is self-inverse).",
DeprecationWarning,
stacklevel=2
)
if self.dimension != other.dimension:
raise DimensionMismatchError(
expected=self.dimension,
actual=other.dimension,
operation="unbinding"
)
# Unbinding is convolution with involution
inv = self.involution(other.vector)
return HDV(
vector=self.fft_convolution(self.vector, inv),
dimension=self.dimension
).normalize()
def involution(self, a: np.ndarray) -> np.ndarray:
"""Involution for HRR: a_i* = a_{(-i mod N)}"""
res = np.zeros_like(a)
res[0] = a[0]
res[1:] = a[:0:-1]
return res
def permute(self, shift: int = 1) -> 'HDV':
"""Permutation for sequence/role representation"""
warnings.warn(
"HDV.permute() is deprecated. Use BinaryHDV.permute() instead.",
DeprecationWarning,
stacklevel=2
)
return HDV(
vector=np.roll(self.vector, shift),
dimension=self.dimension
)
def cosine_similarity(self, other: 'HDV') -> float:
"""Measure semantic similarity"""
warnings.warn(
"HDV.cosine_similarity() is deprecated. Use BinaryHDV.similarity() instead.",
DeprecationWarning,
stacklevel=2
)
norm_a = np.linalg.norm(self.vector)
norm_b = np.linalg.norm(other.vector)
if norm_a == 0 or norm_b == 0:
return 0.0
return np.dot(self.vector, other.vector) / (norm_a * norm_b)
def normalize(self) -> 'HDV':
"""Binarize for cleaner superposition"""
warnings.warn(
"HDV.normalize() is deprecated. BinaryHDV vectors are already binary.",
DeprecationWarning,
stacklevel=2
)
# np.sign returns 0 for 0, we want to avoid 0s in bipolar vectors generally,
# but for superposition result it's standard to threshold.
# If 0, we can map to 1 or -1, or keep 0 (tertiary).
# For strict bipolar, we usually map >=0 to 1, <0 to -1.
v = np.sign(self.vector)
v[v == 0] = 1 # Handle zero case deterministically
return HDV(
vector=v.astype(int),
dimension=self.dimension
)
@staticmethod
def fft_convolution(a: np.ndarray, b: np.ndarray) -> np.ndarray:
"""Circular convolution via FFT (HRR binding)"""
warnings.warn(
"HDV.fft_convolution() is deprecated. BinaryHDV uses XOR binding instead.",
DeprecationWarning,
stacklevel=2
)
fft_a = np.fft.fft(a)
fft_b = np.fft.fft(b)
fft_result = fft_a * fft_b
return np.real(np.fft.ifft(fft_result))
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