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# 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
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.
Given delay=3 and skip=2, an point cloud which is obtained by embedding
a single time-series data into R^3 is as follows.
.. code-block:: none
time-series = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
point clouds = [[1, 4, 7],
[3, 6, 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 : list[float]
A single time-series data.
Returns
-------
point clouds : list of n x 2 numpy arrays
Makes point cloud every a single time-series data.
Raises
-------
TypeError
If the parameter's type does not match the desired type.
"""
ndts = np.array(ts)
if ndts.ndim == 1:
return self._transform(ndts)
else:
raise TypeError("Expects 1-dimensional array.")
def fit(self, ts, y=None):
return self
def _transform(self, ts):
"""Guts of transform method."""
return ts[
np.add.outer(
np.arange(0, len(ts)-self._delay*(self._dim-1), self._skip),
np.arange(0, self._dim*self._delay, self._delay))
]
def transform(self, ts):
"""Transform method for multiple time-series data.
Parameters
----------
ts : list[list[float]]
Multiple time-series data.
Attributes
----------
ndts :
The ndts means that all time series need to have exactly
the same size.
Returns
-------
point clouds : list of n x 3 numpy arrays
Makes point cloud every a single time-series data.
Raises
-------
TypeError
If the parameter's type does not match the desired type.
"""
ndts = np.array(ts)
if ndts.ndim == 2:
return np.apply_along_axis(self._transform, 1, ndts)
else:
raise TypeError("Expects 2-dimensional array.")
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