How to Wrap Existing Arrays as Hypervectors

enc.from_array() wraps a pre-existing NumPy array or PyTorch tensor as a Hypervector. The typical use cases are loading saved codebooks from disk and converting feature vectors from other libraries.

Basic usage

import pyhdc
import numpy as np

enc = pyhdc.MAP_C(dimension=10_000)

# Wrap a NumPy array
arr = np.random.uniform(-1, 1, size=10_000).astype(np.float32)
hv  = enc.from_array(arr)

print(hv.shape)    # (10000,)
print(hv.backend)  # numpy
print(hv.encoding) # MAP_C instance

The array must have the same last dimension as the encoding’s dimension:

bad_arr = np.zeros(5_000)
enc.from_array(bad_arr)   # DimensionsNotMatchingError

Load a saved codebook from disk

# Load a codebook that was saved as a NumPy .npy file
# Shape: (num_items, dimension)
data = np.load('codebook.npy')   # shape (100, 10000)

enc      = pyhdc.MAP_C(dimension=10_000)
codebook = [enc.from_array(data[i]) for i in range(len(data))]

query   = enc.generate()
best_idx = max(range(len(codebook)), key=lambda i: query.similarity(codebook[i]))

Wrap a PyTorch tensor

from_array auto-detects whether the input is a NumPy array or PyTorch tensor:

import torch

t  = torch.randn(10_000, dtype=torch.float32)
enc_torch = pyhdc.MAP_C(dimension=10_000, backend="torch")
hv = enc_torch.from_array(t)

print(hv.backend)   # torch

Extract the underlying array

Access .data to get the raw NumPy array or PyTorch tensor back:

arr_back = hv.data   # numpy.ndarray or torch.Tensor

You can use this to pass hypervectors to libraries that do not know about PyHDC, such as scikit-learn or matplotlib.

Dtype notes

The dtype of the wrapped array should match what the encoding expects. Mismatches generate a warning but do not raise an error. For example, MAP_C expects float32; wrapping float64 will still work but may incur an implicit conversion.