Public Member Functions | |
def | __init__ (self, use=False, num_pts=10, threshold=-1, location="upper") |
def | fit (self, X, y=None) |
def | transform (self, X) |
def | __call__ (self, diag) |
This is a class for removing points that are close or far from the diagonal in persistence diagrams. If persistence diagrams are n x 2 numpy arrays (i.e. persistence diagrams with ordinary features), points are ordered and thresholded by distance-to-diagonal. If persistence diagrams are n x 1 numpy arrays (i.e. persistence diagrams with essential features), points are not ordered and thresholded by first coordinate.
def gudhi.representations.preprocessing.ProminentPoints.__init__ | ( | self, | |
use = False , |
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num_pts = 10 , |
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threshold = -1 , |
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location = "upper" |
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) |
Constructor for the ProminentPoints class. Parameters: use (bool): whether to use the class or not (default False). location (string): either "upper" or "lower" (default "upper"). Whether to keep the points that are far away ("upper") or close ("lower") to the diagonal. num_pts (int): cardinality threshold (default 10). If location == "upper", keep the top **num_pts** points that are the farthest away from the diagonal. If location == "lower", keep the top **num_pts** points that are the closest to the diagonal. threshold (double): distance-to-diagonal threshold (default -1). If location == "upper", keep the points that are at least at a distance **threshold** from the diagonal. If location == "lower", keep the points that are at most at a distance **threshold** from the diagonal.
def gudhi.representations.preprocessing.ProminentPoints.__call__ | ( | self, | |
diag | |||
) |
Apply ProminentPoints on a single persistence diagram and outputs the result. Parameters: diag (n x 2 numpy array): input persistence diagram. Returns: n x 2 numpy array: thresholded persistence diagram.
def gudhi.representations.preprocessing.ProminentPoints.fit | ( | self, | |
X, | |||
y = None |
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) |
Fit the ProminentPoints class on a list of persistence diagrams (this function actually does nothing but is useful when ProminentPoints is included in a scikit-learn Pipeline). Parameters: X (list of n x 2 or n x 1 numpy arrays): input persistence diagrams. y (n x 1 array): persistence diagram labels (unused).
def gudhi.representations.preprocessing.ProminentPoints.transform | ( | self, | |
X | |||
) |
If location == "upper", first select the top **num_pts** points that are the farthest away from the diagonal, then select and return from these points the ones that are at least at distance **threshold** from the diagonal for each persistence diagram individually. If location == "lower", first select the top **num_pts** points that are the closest to the diagonal, then select and return from these points the ones that are at most at distance **threshold** from the diagonal for each persistence diagram individually. Parameters: X (list of n x 2 or n x 1 numpy arrays): input persistence diagrams. Returns: list of n x 2 or n x 1 numpy arrays: thresholded persistence diagrams.
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 |