Function reference • rgudhi

Persistence diagram generation

Data structures for cell complexes

CubicalComplex: R6 Class for Cubical Complex

PeriodicCubicalComplex: R6 Class for Periodic Cubical Complex

SimplexTree: R6 Class for Simplex Tree

Filtrations and reconstructions

AlphaComplex: R6 Class for Alpha Complex

RipsComplex: R6 Class for Rips Complex

WitnessComplex: R6 Class for Witness Complex

StrongWitnessComplex: R6 Class for Strong Witness Complex

TangentialComplex: R6 Class for Tangential Complex

Persistence diagram class

as_persistence_diagram() is_persistence_diagram(): Persistence Diagram

plot(<persistence_diagram>): Plot for persistence_diagram objects

autoplot(<persistence_diagram>): Plot for persistence_diagram objects

Persistence diagram manipulation

Preprocessing

BirthPersistenceTransform: Preprocessing: Birth Persistence Transform

DiagramScaler: Preprocessing: Diagram Scaler

DiagramSelector: Preprocessing: Diagram Selector

Padding: Preprocessing: Padding

ProminentPoints: Preprocessing: Prominent Points

Vector representations

Atol: Vector Representation: Atol

BettiCurve: Vector Representation: Betti Curve

ComplexPolynomial: Vector Representation: Complex Polynomial

Entropy: Vector Representation: Entropy

Landscape: Vector Representation: Landscape

PersistenceImage: Vector Representation: Persistence Image

Silhouette: Vector Representation: Silhouette

TopologicalVector: Vector Representation: Topological Vector

Kernel representations

PersistenceFisherKernel: Kernel Representation: Persistence Fisher Kernel

PersistenceScaleSpaceKernel: Kernel Representation: Persistence Scale-Space Kernel

PersistenceWeightedGaussianKernel: Kernel Representation: Persistence Weighted Gaussian Kernel

PersistenceSlicedWassersteinKernel: Kernel Representation: Persistence Sliced Wasserstein Kernel

Metrics

BottleneckDistance: Metrics: Bottleneck Distance

PersistenceFisherDistance: Metrics: Persistence Fisher Distance

SlicedWassersteinDistance: Metrics: Sliced Wasserstein Distance

WassersteinDistance: Metrics: Wasserstein Distance

Persistence diagram sample class

as_persistence_diagram_sample() is_persistence_diagram_sample(): Persistence Diagram Sample

mean(<persistence_diagram_sample>): Persistence Diagram Sample Mean

Data Scalers

MaxAbsScaler: Scales each feature by its maximum absolute value

MinMaxScaler: Transforms features by scaling each feature to a given range

RobustScaler: Scales features using statistics that are robust to outliers

StandardScaler: Standardizes features by removing the mean and scaling to unit variance

Clustering

AffinityPropagation: Performs clustering according to the affinity propagation algorithm

AgglomerativeClustering: Performs clustering according to the agglomerative algorithm

Birch: Performs clustering according to the Birch algorithm

DBSCAN: Performs clustering according to the DBSCAN algorithm

FeatureAgglomeration: Performs clustering according to the feature agglomeration algorithm

KMeans: Performs clustering according to the k-means algorithm

BisectingKMeans: Performs clustering according to the bisecting k-means algorithm

MiniBatchKMeans: Performs clustering according to the mini-batch k-means algorithm

MeanShift: Performs clustering according to the mean shift algorithm

OPTICS: Performs clustering according to the OPTICS algorithm

SpectralClustering: Performs clustering according to the spectral clustering algorithm

SpectralBiclustering: Performs clustering according to the spectral biclustering algorithm

SpectralCoclustering: Performs clustering according to the spectral coclustering algorithm

Tomato: Clustering: Tomato

Data sets

seq_circle(): Circular Sequence Generation

sphere(): Sampling on the Sphere

torus(): Sampling on the Torus

fetch_bunny() fetch_spiral_2d() clear_data_home(): Remote Data Sets