The Hypercube space for binary vectors and labeled unweighted graphs - #141
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…is_bearable for type-checking to work with Python 3.8 and Python 3.9
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August 11, 2024 12:58
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Nice job! I have left some comments about the code.
| The GeometricKernels space representing the d-dimensional hypercube graph | ||
| $C^d = \{0, 1\}^d$, the combinatorial space of binary vectors of length $d$. | ||
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| The elements of this space are represented by d-dimensional boolean vectors. |
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is there a particular reason for using boolean vectors, as opposed to, say, integer vectors?
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- To save memory. In the implementation of my dreams, they should be actual bit-arrays. This is much harder than what I did here though, especially if the implementation should be backend-independent.
- Interpretation. This is a space of binary vectors. Binary ≈ Boolean.
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Bool requires as much memory as int, so this argument doesn't quite hold. And everyone knows that binary means zeros and ones :)
…lper.py. For imports to work, add __init__.py throughout the tests/ folder hierarchy
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Revised. |
…ls/GeometricKernels into slava/binary_graph_space
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This PR implements a new space: the hypercube graph space$\{0,1\}^d$ of binary vectors.
It includes a new space (
HypercubeGraph), the respective notebookHypercubeGraph.ipynb, a theory pagedocs/theory/hypercube_graph.rst, and tests.