CurveSimilarities documentation

A collection of curve similarity measures.

There are tons of similar packages published on PyPI, but this one aims to be the most NumPy-friendly and the easiest to use.

API reference

To reproduce the examples, run the following code first:

import numpy as np
from curvesimilarities import *

Warning

The similarity functions do not sanitize the vertices of input polygonal curves. Make sure your curves do not have consecutive duplicate vertices.

Fréchet distance

Continuous and discrete Fréchet distances.

curvesimilarities.frechet.fd(P, Q)

(Continuous) Fréchet distance between two open polygonal curves.

Let \(f: [0, 1] \to \Omega\) and \(g: [0, 1] \to \Omega\) be curves where \(\Omega\) is a metric space. The Fréchet distance between \(f\) and \(g\) is defined as

\[\inf_{\alpha, \beta} \max_{t \in [0, 1]} \lVert f(\alpha(t)) - g(\beta(t)) \rVert,\]

where \(\alpha, \beta: [0, 1] \to [0, 1]\) are continuous non-decreasing surjections and \(\lVert \cdot \rVert\) is the underlying metric, which is the Euclidean metric in this implementation.

Parameters:

Parray_like

A \(p\) by \(n\) array of \(p\) vertices in an \(n\)-dimensional space.

Qarray_like

A \(q\) by \(n\) array of \(q\) vertices in an \(n\)-dimensional space.

Returns:

distdouble

The (continuous) Fréchet distance between P and Q, NaN if any vertice is empty.

Raises:

ValueError

If P and Q are not 2-dimensional arrays with same number of columns.

Notes

This function implements Alt and Godau’s algorithm [1].

References

[1]

Alt, H., & Godau, M. (1995). Computing the Fréchet distance between two polygonal curves. International Journal of Computational Geometry & Applications, 5(01n02), 75-91.

Examples

>>> fd([[0, 0], [0.5, 0], [1, 0]], [[0, 1], [1, 1]])
1.0...

curvesimilarities.frechet.dfd(P, Q)

Discrete Fréchet distance between two open polygonal curves.

Let \(\{P_0, P_1, ..., P_n\}\) and \(\{Q_0, Q_1, ..., Q_m\}\) be polyline vertices in metric space. The discrete Fréchet distance between two polylines is defined as

\[\min_{C} \max_{(i, j) \in C} \lVert P_i - Q_j \rVert,\]

where \(C\) is a nondecreasing coupling over \(\{0, ..., n\} \times \{0, ..., m\}\), starting from \((0, 0)\) and ending with \((n, m)\). \(\lVert \cdot \rVert\) is the underlying metric, which is the Euclidean metric in this implementation.

Parameters:

Parray_like

An \(p\) by \(n\) array of \(p\) vertices in an \(n\)-dimensional space.

Qarray_like

An \(q\) by \(n\) array of \(q\) vertices in an \(n\)-dimensional space.

Returns:

distdouble

The discrete Fréchet distance between P and Q, NaN if any vertice is empty.

Raises:

ValueError

If P and Q are not 2-dimensional arrays with same number of columns.

Notes

This function implements Eiter and Mannila’s algorithm [2].

References

[2]

Eiter, T., & Mannila, H. (1994). Computing discrete Fréchet distance.

Examples

>>> dfd([[0, 0], [1, 1], [2, 0]], [[0, 1], [2, -4]])
4.0

Dynamic time warping distance

Dynamic time warping distance.

This module implements only the basic algorithm. If you need advanced features, use dedicated packages such as dtw-python.

curvesimilarities.dtw.dtw(P, Q)

Dynamic time warping distance.

Let \(\{P_0, P_1, ..., P_n\}\) and \(\{Q_0, Q_1, ..., Q_m\}\) be polyline vertices in metric space. The dynamic time warping distance between two polylines is defined as

\[\min_{C} \sum_{(i, j) \in C} \lVert P_i - Q_j \rVert,\]

where \(C\) is a nondecreasing coupling over \(\{0, ..., n\} \times \{0, ..., m\}\), starting from \((0, 0)\) and ending with \((n, m)\). \(\lVert \cdot \rVert\) is the underlying metric, which is the Euclidean metric in this implementation.

Parameters:

Parray_like

A \(p\) by \(n\) array of \(p\) vertices in an \(n\)-dimensional space.

Qarray_like

A \(q\) by \(n\) array of \(q\) vertices in an \(n\)-dimensional space.

Returns:

distdouble

The dynamic time warping distance between P and Q, NaN if any vertice is empty.

Raises:

ValueError

If P and Q are not 2-dimensional arrays with same number of columns.

See also

dtw_owp

Dynamic time warping distance with optimal warping path.

Notes

This function implements the algorithm described by Senin [3].

References

[3]

Senin, P. (2008). Dynamic time warping algorithm review. Information and Computer Science Department University of Hawaii at Manoa Honolulu, USA, 855(1-23), 40.

Examples

>>> P = np.linspace([0, 0], [1, 0], 10)
>>> Q = np.linspace([0, 1], [1, 1], 20)
>>> dtw(P, Q)
20.0...

curvesimilarities.dtw.dtw_owp(P, Q)

Dynamic time warping distance and its optimal warping path.

Parameters:

Parray_like

A \(p\) by \(n\) array of \(p\) vertices in an \(n\)-dimensional space.

Qarray_like

A \(q\) by \(n\) array of \(q\) vertices in an \(n\)-dimensional space.

Returns:

distdouble

The dynamic time warping distance between P and Q, NaN if any vertice is empty.

owpndarray

Indices of P and Q for optimal warping path, empty if any vertice is empty.

Raises:

ValueError

If P and Q are not 2-dimensional arrays with same number of columns.

Examples

>>> P = np.linspace([0, 0], [1, 0], 10)
>>> Q = np.linspace([0, 1], [1, 1], 20)
>>> dist, path = dtw_owp(P, Q)
>>> dist / len(path)  # averaged dynamic time warping
1.00...
>>> import matplotlib.pyplot as plt 
>>> plt.plot(*path.T, "x")  

(Source code, png, hires.png, pdf)

curvesimilarities.dtw.sdtw(P, Q)

Squared dynamic time warping distance.

The squared dynamic time warping distance is defined as

\[\min_{C} \sum_{(i, j) \in C} \lVert P_i - Q_j \rVert^2,\]

with symbols explained in dtw().

Parameters:

Parray_like

A \(p\) by \(n\) array of \(p\) vertices in an \(n\)-dimensional space.

Qarray_like

A \(q\) by \(n\) array of \(q\) vertices in an \(n\)-dimensional space.

Returns:

distdouble

The squared dynamic time warping distance between P and Q, NaN if any vertice is empty.

Raises:

ValueError

If P and Q are not 2-dimensional arrays with same number of columns.

See also

dtw

Dynamic time warping distance.

sdtw_owp

Squared dynamic time warping distance with optimal warping path.

Examples

>>> P = np.linspace([0, 0], [1, 0], 10)
>>> Q = np.linspace([0, 1], [1, 1], 20)
>>> sdtw(P, Q)
20.0...

curvesimilarities.dtw.sdtw_owp(P, Q)

Squared dynamic time warping distance and its optimal warping path.

Parameters:

Parray_like

A \(p\) by \(n\) array of \(p\) vertices in an \(n\)-dimensional space.

Qarray_like

A \(q\) by \(n\) array of \(q\) vertices in an \(n\)-dimensional space.

Returns:

distdouble

The squared dynamic time warping distance between P and Q, NaN if any vertice is empty.

owpndarray

Indices of P and Q for optimal warping path, empty if any vertice is empty.

Raises:

ValueError

If P and Q are not 2-dimensional arrays with same number of columns.

Examples

>>> P = np.linspace([0, 0], [1, 0], 10)
>>> Q = np.linspace([0, 1], [1, 1], 20)
>>> dist, path = sdtw_owp(P, Q)
>>> (dist / len(path))**0.5  # quadratic mean dynamic time warping
1.00...
>>> import matplotlib.pyplot as plt 
>>> plt.plot(*path.T, "x")  

(Source code, png, hires.png, pdf)

Integral Fréchet distance

Integral Fréchet distance.

curvesimilarities.integfrechet.ifd(P, Q, delta)

Integral Fréchet distance between two open polygonal curves.

Let \(f, g: [0, 1] \to \Omega\) be curves defined in a metric space \(\Omega\). Let \(\alpha, \beta: [0, 1] \to [0, 1]\) be continuous non-decreasing surjections, and define \(\pi: [0, 1] \to [0, 1] \times [0, 1]\) such that \(\pi(t) = \left(\alpha(t), \beta(t)\right)\). The integral Fréchet distance between \(f\) and \(g\) is defined as

\[\inf_{\pi} \int_0^1 \delta\left(\pi(t)\right) \cdot \lVert \pi'(t) \rVert \mathrm{d}t,\]

where \(\delta\left(\pi(t)\right)\) is a distance between \(f\left(\alpha(t)\right)\) and \(g\left(\beta(t)\right)\) in \(\Omega\) and \(\lVert \cdot \rVert\) is a norm in \([0, 1] \times [0, 1]\). In this implementation, we use the Euclidean distance for \(\delta\) and the Manhattan norm for \(\lVert \cdot \rVert\).

Parameters:

Parray_like

A \(p\) by \(n\) array of \(p\) vertices in an \(n\)-dimensional space.

Qarray_like

A \(q\) by \(n\) array of \(q\) vertices in an \(n\)-dimensional space.

deltadouble

Maximum length of edges between Steiner points.

Returns:

distdouble

The integral Fréchet distance between P and Q, NaN if any vertice is empty or both vertices consist of a single point.

Raises:

ValueError

If P and Q are not 2-dimensional arrays with same number of columns.

See also

ifd_owp

Integral Fréchet distance with optimal warping path.

Notes

This function implements the algorithm of Brankovic et al [4].

References

[4]

Brankovic, M., et al. “(k, l)-Medians Clustering of Trajectories Using Continuous Dynamic Time Warping.” Proceedings of the 28th International Conference on Advances in Geographic Information Systems. 2020.

Examples

>>> ifd([[0, 0], [0.5, 0], [1, 0]], [[0, 1], [1, 1]], 0.1)
2.0

curvesimilarities.integfrechet.ifd_owp(P, Q, delta)

Integral Fréchet distance and its optimal warping path.

Parameters:

Parray_like

A \(p\) by \(n\) array of \(p\) vertices in an \(n\)-dimensional space.

Qarray_like

A \(q\) by \(n\) array of \(q\) vertices in an \(n\)-dimensional space.

deltadouble

Maximum length of edges between Steiner points.

Returns:

distdouble

The integral Fréchet distance between P and Q, NaN if any vertice is empty or both vertices consist of a single point.

owpndarray

Optimal warping path, empty if any vertice is empty or both vertices consist of a single point.

Raises:

ValueError

If P and Q are not 2-dimensional arrays with same number of columns.

Examples

>>> dist, path = ifd_owp([[0, 0], [0.5, 0], [1, 0]], [[0.5, 1], [1.5, 1]], 0.1)
>>> import matplotlib.pyplot as plt 
>>> plt.plot(*path.T)  

(Source code, png, hires.png, pdf)

Average Fréchet distance

Average Fréchet distance and its variants.

curvesimilarities.averagefrechet.afd(P, Q, delta)

Average Fréchet distance between two open polygonal curves.

The average Fréchet distance is defined as [5]

\[\inf_{\pi} \frac{ \int_0^1 \delta\left(\pi(t)\right) \cdot \lVert \pi'(t) \rVert \mathrm{d}t }{ \int_0^1 \lVert \pi'(t) \rVert \mathrm{d}t },\]

with symbols explained in ifd(). We use the Manhattan norm for \(\lVert \cdot \rVert\), so the formula can be reduced to

\[\frac{1}{\lvert f \rvert + \lvert g \rvert} \inf_{\pi} \int_0^1 \delta\left(\pi(t)\right) \cdot \lVert \pi'(t) \rVert \mathrm{d}t,\]

where \(\lvert \cdot \rvert\) denotes the length of a polygonal curve.

Parameters:

Parray_like

A \(p\) by \(n\) array of \(p\) vertices in an \(n\)-dimensional space.

Qarray_like

A \(q\) by \(n\) array of \(q\) vertices in an \(n\)-dimensional space.

deltadouble

Maximum length of edges between Steiner points.

Returns:

distdouble

The average Fréchet distance between P and Q, NaN if any vertice is empty or both vertices consist of a single point.

Raises:

ValueError

If P and Q are not 2-dimensional arrays with same number of columns.

See also

ifd

Integral Fréchet distance.

afd_owp

Average Fréchet distance with optimal warping path.

Notes

Using this function is marginally faster than calling ifd() and then dividing.

References

[5]

Buchin, M. E. On the computability of the Fréchet distance between triangulated surfaces. Diss. 2007.

Examples

>>> afd([[0, 0], [0.5, 0], [1, 0]], [[0, 1], [1, 1]], 0.1)
1.0

curvesimilarities.averagefrechet.afd_owp(P, Q, delta)

Average Fréchet distance and its optimal warping path.

Parameters:

Parray_like

A \(p\) by \(n\) array of \(p\) vertices in an \(n\)-dimensional space.

Qarray_like

A \(q\) by \(n\) array of \(q\) vertices in an \(n\)-dimensional space.

deltadouble

Maximum length of edges between Steiner points.

Returns:

distdouble

The average Fréchet distance between P and Q, NaN if any vertice is empty or both vertices consist of a single point.

owpndarray

Optimal warping path, empty if any vertice is empty or both vertices consist of a single point.

Raises:

ValueError

If P and Q are not 2-dimensional arrays with same number of columns.

Examples

>>> dist, path = afd_owp([[0, 0], [0.5, 0], [1, 0]], [[0.5, 1], [1.5, 1]], 0.1)
>>> import matplotlib.pyplot as plt 
>>> plt.plot(*path.T)  

(Source code, png, hires.png, pdf)

curvesimilarities.averagefrechet.qafd(P, Q, delta)

Quadratic average Fréchet distance between two open polygonal curves.

The quadratic average Fréchet distance is defined as

\[\inf_{\pi} \sqrt{ \frac{ \int_0^1 \delta\left(\pi(t)\right)^2 \cdot \lVert \pi'(t) \rVert \mathrm{d}t }{ \int_0^1 \lVert \pi'(t) \rVert \mathrm{d}t } },\]

with symbols explained in ifd(). We use the Manhattan norm for \(\lVert \cdot \rVert\), so the formula can be reduced to

\[\frac{1}{\sqrt{\lvert f \rvert + \lvert g \rvert}} \inf_{\pi} \sqrt{ \int_0^1 \delta\left(\pi(t)\right)^2 \cdot \lVert \pi'(t) \rVert \mathrm{d}t },\]

where \(\lvert \cdot \rvert\) denotes the length of a polygonal curve.

Parameters:

Parray_like

A \(p\) by \(n\) array of \(p\) vertices in an \(n\)-dimensional space.

Qarray_like

A \(q\) by \(n\) array of \(q\) vertices in an \(n\)-dimensional space.

deltadouble

Maximum length of edges between Steiner points.

Returns:

distdouble

The quadratic average Fréchet distance between P and Q, NaN if any vertice is empty or both vertices consist of a single point.

Raises:

ValueError

If P and Q are not 2-dimensional arrays with same number of columns.

See also

afd

Average Fréchet distance.

qafd_owp

Quadratic average Fréchet distance with optimal warping path.

Examples

>>> qafd([[0, 0], [0.5, 0], [1, 0]], [[0, 1], [1, 1]], 0.1)
1.0

curvesimilarities.averagefrechet.qafd_owp(P, Q, delta)

Quadratic average Fréchet distance and its optimal warping path.

Parameters:

Parray_like

A \(p\) by \(n\) array of \(p\) vertices in an \(n\)-dimensional space.

Qarray_like

A \(q\) by \(n\) array of \(q\) vertices in an \(n\)-dimensional space.

deltadouble

Maximum length of edges between Steiner points.

Returns:

distdouble

The quadratic average Fréchet distance between P and Q, NaN if any vertice is empty or both vertices consist of a single point.

owpndarray

Optimal warping path, empty if any vertice is empty or both vertices consist of a single point.

Raises:

ValueError

If P and Q are not 2-dimensional arrays with same number of columns.

Examples

>>> dist, path = qafd_owp([[0, 0], [0.5, 0], [1, 0]], [[0.5, 1], [1.5, 1]], 0.1)
>>> import matplotlib.pyplot as plt 
>>> plt.plot(*path.T)  

(Source code, png, hires.png, pdf)