Public Member Functions | |
def | __call__ (self, ts) |
def | transform (self, ts) |
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 gudhi.point_cloud.timedelay.TimeDelayEmbedding.__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.
def gudhi.point_cloud.timedelay.TimeDelayEmbedding.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.
GUDHI Version 3.3.0 - C++ library for Topological Data Analysis (TDA) and Higher Dimensional Geometry Understanding. - Copyright : MIT | Generated on Tue Aug 11 2020 11:58:59 for GUDHI by Doxygen 1.8.18 |