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author | Marc Glisse <marc.glisse@inria.fr> | 2020-03-16 15:04:46 +0100 |
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committer | GitHub <noreply@github.com> | 2020-03-16 15:04:46 +0100 |
commit | 592d58a95dbe5851c9f4ded9e8740c1e7a9c1502 (patch) | |
tree | f37906894e6eda0efaa2253ed33688a4830735fe /src/python/gudhi | |
parent | 05b409f60132a73e47f6f58ba80a6343b5bdb1a6 (diff) | |
parent | 2c1edeb7fd241c8718a22618438b482704703b4a (diff) |
Merge pull request #214 from takeshimeonerespect/master
About adding timedelay for feature engineering
Diffstat (limited to 'src/python/gudhi')
-rw-r--r-- | src/python/gudhi/point_cloud/__init__.py | 0 | ||||
-rw-r--r-- | src/python/gudhi/point_cloud/timedelay.py | 95 |
2 files changed, 95 insertions, 0 deletions
diff --git a/src/python/gudhi/point_cloud/__init__.py b/src/python/gudhi/point_cloud/__init__.py new file mode 100644 index 00000000..e69de29b --- /dev/null +++ b/src/python/gudhi/point_cloud/__init__.py diff --git a/src/python/gudhi/point_cloud/timedelay.py b/src/python/gudhi/point_cloud/timedelay.py new file mode 100644 index 00000000..f01df442 --- /dev/null +++ b/src/python/gudhi/point_cloud/timedelay.py @@ -0,0 +1,95 @@ +# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. +# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. +# Author(s): Martin Royer, Yuichi Ike, Masatoshi Takenouchi +# +# Copyright (C) 2020 Inria, Copyright (C) 2020 Fujitsu Laboratories Ltd. +# Modification(s): +# - YYYY/MM Author: Description of the modification + +import numpy as np + + +class TimeDelayEmbedding: + """Point cloud transformation class. + Embeds time-series data in the R^d according to [Takens' Embedding Theorem] + (https://en.wikipedia.org/wiki/Takens%27s_theorem) and obtains the + coordinates of each point. + + Parameters + ---------- + dim : int, optional (default=3) + `d` of R^d to be embedded. + delay : int, optional (default=1) + Time-Delay embedding. + skip : int, optional (default=1) + How often to skip embedded points. + + Example + ------- + + Given delay=3 and skip=2, a point cloud which is obtained by embedding + a scalar time-series into R^3 is as follows:: + + time-series = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + point cloud = [[1, 4, 7], + [3, 6, 9]] + + Given delay=1 and skip=1, a point cloud which is obtained by embedding + a 2D vector time-series data into R^4 is as follows:: + + time-series = [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]] + point cloud = [[0, 1, 2, 3], + [2, 3, 4, 5], + [4, 5, 6, 7], + [6, 7, 8, 9]] + """ + + def __init__(self, dim=3, delay=1, skip=1): + self._dim = dim + self._delay = delay + self._skip = skip + + def __call__(self, ts): + """Transform method for single time-series data. + + Parameters + ---------- + ts : Iterable[float] or Iterable[Iterable[float]] + A single time-series data, with scalar or vector values. + + Returns + ------- + point cloud : n x dim numpy arrays + Makes point cloud from a single time-series data. + """ + return self._transform(np.array(ts)) + + def fit(self, ts, y=None): + return self + + def _transform(self, ts): + """Guts of transform method.""" + if ts.ndim == 1: + repeat = self._dim + else: + assert self._dim % ts.shape[1] == 0 + repeat = self._dim // ts.shape[1] + end = len(ts) - self._delay * (repeat - 1) + short = np.arange(0, end, self._skip) + vertical = np.arange(0, repeat * self._delay, self._delay) + return ts[np.add.outer(short, vertical)].reshape(len(short), -1) + + def transform(self, ts): + """Transform method for multiple time-series data. + + Parameters + ---------- + ts : Iterable[Iterable[float]] or Iterable[Iterable[Iterable[float]]] + Multiple time-series data, with scalar or vector values. + + Returns + ------- + point clouds : list of n x dim numpy arrays + Makes point cloud from each time-series data. + """ + return [self._transform(np.array(s)) for s in ts] |