cupy-fft-match 0.0.0

A fast fuzzy matching algorithm based on FFT.

cupy_fft_match

A fast fuzzy matching algorithm based on FFT.

Installation

pip install cupy_fft_match

What is Fuzzy Matching

Consider $A$ and $B$; they are arrays, and $P$ is an array of the same length as $B$.

For any $0\leq k < \text{len}(A)$, we want to efficiently calculate:

$$ \text{Match}(A, B, P)k = \sum{i=0}^{\text{len}(B)-1} (A_{k + i} - B_{i})^2\cdot P_i $$

in which $\text{Match}(A, B, P)$ is a generated array with length $\text{len}(A)-\text{len}(B)+1$.

To put it in a pragmatic way, let $A$ be a string or an 2D image, $B$ and $P$ is a template string/image that you want to match in $A$. Then, (the low value position of) $\text{Match}(A, B, P)$ gives the position in $A$ which suits $B$ well.

If you don't need any wildcard, just let $P_i=1$ for any $i$. The position $i$ that satisfies $P_i=0$ is a wildcard.

The interfaces provided in this project support arbitrary high-dimensional arrays. Therefore, you can use high-dimensional arrays for $A$, $B$, and $P$, the algorithm will work correctly as long as they have the same number of dimensions and the length of $A$ in each dimension is not less than that of $B$.

Since CuPy's ndarray is inherently different from NumPy's ndarray, please ensure that the data has been converted to the cupy.ndarray type before invoking the algorithms in this project.

Usage

import cupy as cp
import cupy_fft_match as cm

vec_a = cp.array([ ... ]) # vec_a is the text string
vec_b = cp.array([ ... ]) # vec_b is the template string, vec_b is usually "smaller" then vec_a
vec_p = cp.array([ ... ]) # vec_p is the weight string, which has the same size as vec_b
match_ans = cm.match_arr(vec_a, vec_b, vec_p)