This is a Python only implementation of the Dynamic Continuous Indexing (DCI) algorithm proposed by Jitendra Malik and Ke Li for quickly solving the k-nearest neighbors problem. See also Ke Li's original implementation in C with Python and TensorFlow bindings.
- To just use
pydci:pip install . - If you'd like to modify it:
pip install --editable .
- Dependencies:
pip install -r requirements_test.txt - Running:
pytest pydci/test_pydci.py
- Instantiate a DCI index with
dci = DCI(dim, num_simple, num_composite)dim: Dimension of data pointsnum_simple: Number of simple indices (i.e., projection directions) to contain in each composite index; can be about half the dimension of data pointsnum_composite: Number of groups of simple indices; can be small, e.g., 3
- Add data set with
dci.add(X); can be called multiple times to add more dataX: Dataset with each point as a row
- Search for k-nearest neighbors of query point with
dci.query(q, k, max_retrieve_const, max_composite_visit_const)q: Query pointk: Number of nearest neighborsmax_retrieve_const: Determines computational extent of query step; higher takes longer but is more accurate, values between 1-10 work wellmax_composite_visit_const: Determines computational extent of query step; higher takes longer but is more accurate, values between 1-10 work well
See pydci/test_pydci.py for a starter example.
Written by Craig Gross ([email protected]) and Cullen Haselby ([email protected]) for a project in Michigan State University's MSIM course in spring 2022.
An important property of DCI is that it supports fast (sublinear) insertions to the underlying data structure. This functionality is not available in the original implementation. Though this pure Python implementation will be slower than the interface to the C code, we are only interested in understanding the overall scaling of DCI when applied to solve nearest neighbor problems on streaming datasets.