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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.
"""
def __init__(self, dim=3, delay=1, skip=1):
self._dim = dim
self._delay = delay
self._skip = skip
def __call__(self, *args, **kwargs):
return self.transform(*args, **kwargs)
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.
Parameters
----------
ts : list[float] or list[list[float]]
A single or multiple time-series data.
Returns
-------
point clouds : list[list[float, float, float]] or list[list[list[float, float, float]]]
Makes point cloud every a single time-series data.
"""
ndts = np.array(ts)
if ndts.ndim == 1:
# for single.
return self._transform(ndts).tolist()
else:
# for multiple.
return np.apply_along_axis(self._transform, 1, ndts).tolist()
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