Machine Learning and Inference for Topological Data Analysis

Documentation for package ‘TDApplied’ version 2.0.4

Help Pages

bootstrap_persistence_thresholds	Estimate persistence threshold(s) for topological features in a data set using bootstrapping.
check_PyH_setup	Make sure that python has been configured correctly for persistent homology calculations.
check_ripser	Verify an imported ripser module.
diagram_distance	Calculate distance between a pair of persistence diagrams.
diagram_kernel	Calculate persistence Fisher kernel value between a pair of persistence diagrams.
diagram_kkmeans	Cluster a group of persistence diagrams using kernel k-means.
diagram_kpca	Calculate the kernel PCA embedding of a group of persistence diagrams.
diagram_ksvm	Fit a support vector machine model where each training set instance is a persistence diagram.
diagram_mds	Dimension reduction of a group of persistence diagrams via metric multidimensional scaling.
diagram_to_df	Convert a TDA/TDAstats persistence diagram to a data frame.
distance_matrix	Compute a distance matrix from a list of persistence diagrams.
gram_matrix	Compute the gram matrix for a group of persistence diagrams.
import_ripser	Import the python module ripser.
independence_test	Independence test for two groups of persistence diagrams.
permutation_test	Permutation test for finding group differences between persistence diagrams.
plot_diagram	Plot persistence diagrams
predict_diagram_kkmeans	Predict the cluster labels for new persistence diagrams using a pre-computed clustering.
predict_diagram_kpca	Project persistence diagrams into a low-dimensional space via a pre-computed kernel PCA embedding.
predict_diagram_ksvm	Predict the outcome labels for a list of persistence diagrams using a pre-trained diagram ksvm model.
PyH	Fast persistent homology calculations with python.
TDApplied	Machine learning and inference for persistence diagrams