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DataInstance.md

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DataInstance

A Data Instance stores the points it is generated from a DataDescriptor

Class Variables

classNumber (int): The number of classes in the DataInstance

nPoints (int): The number of points in the DataInstance

points ([DataPoint]): The points of the DataInstance

dimension (int): The dimension of the DataInstance. Corresponds to the dimension of the DataPoint in points

##Constructors

__init__({'classNumber': classNumber, 'nPoints': nPoints, 'points': points, 'dimension': dimension, 'orientation': orientation})

Methods

plot(noBack=False, nPoints=self.nPoints):

Plot nPointsrandomly selected of the DataInstance. If noBack, the points with class 0 are ignord

numpyify():
  return numpy.array([numpy.array, numpy.array])

Returns numpy arrays of the points and labels split

predict(model):
  return DataInstance

Returns the DataInstance predicted by the Keras + Tensorflow network when feeding the instance points

predictAndEvaluate(model):
  return float

Computes the accuracy of the Keras + Tensorflow network over the DataInstance

computeBottleNeckDistance(DataInstance, min_persistence=0, nPoints=self.nPoints, targetCluster=[1]):
  return float

Computes the bottleneckDistance of the DataInstance to another one passed in argument using up to nPoints from the instances and restricting the study to points with classes in targetCluster

bettiNumbers(targetCluster=[1], threshold=0.05, nPoints=self.nPoints, maxDim=self.dimension, maxEdge=10, fromValue=0.05, toValue=0.05):
  return [int]

Computes the betti numbers of the DataInstance using gudhi for more information go to gudhi's documentation

newBettiNumbers(targetCluster=[1], threshold=0.05, nPoints=self.nPoints, errorRate=0.005, plot=False, showProgress=False):
  return [int]

Computes the betti numbers of the DataInstance using a custom algorithm for up to nPoints evenly distributed in targetCluster