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HYPERDOA: Hyperdimensional Computing for Direction-of-Arrival Estimation
A lightweight, standalone implementation of HDC-based DOA estimation
for uniform linear arrays (ULA).
Key Features:
- Single-shot training (no iterative backpropagation)
- Multiple feature extraction strategies
- Multi-source DOA estimation
- Compatible with standard DOA datasets
Example:
>>> from hyperdoa import HDCAoAModel, DOAConfig, evaluate_hdc
>>> config = DOAConfig(N=8, M=2, T=100)
>>> model = HDCAoAModel(N=config.N, M=config.M, T=config.T, feature_type="lag")
>>> model.train_from_dataloader(train_loader)
>>> predictions = model.predict(test_data)
"""
from .config import DOAConfig
from .utils import set_seed, get_device, R2D, D2R
from .models import (
HDCAoAModel,
HDCFeatureEncoder,
SpatialSmoothingFeature,
LagFeature,
)
from .evaluation import (
evaluate_hdc,
compute_mspe,
compute_mspe_db,
save_checkpoint,
load_checkpoint,
)
__version__ = "1.0.0"
__author__ = "HYPERDOA Authors"
__all__ = [
# Config
"DOAConfig",
# Utils
"set_seed",
"get_device",
"R2D",
"D2R",
# Models
"HDCAoAModel",
"HDCFeatureEncoder",
"SpatialSmoothingFeature",
"LagFeature",
# Evaluation
"evaluate_hdc",
"compute_mspe",
"compute_mspe_db",
"save_checkpoint",
"load_checkpoint",
]
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