ripser.Rips

class ripser.Rips(maxdim=1, thresh=inf, coeff=2, do_cocycles=False, n_perm=None, verbose=True)

sklearn style class interface for ripser with fit and transform methods..

Parameters:

• maxdim (int, optional, default 1) – Maximum homology dimension computed. Will compute all dimensions lower than and equal to this value. For 1, H_0 and H_1 will be computed.

• thresh (float, default infinity) – Maximum distances considered when constructing filtration. If infinity, compute the entire filtration.

• coeff (int prime, default 2) – Compute homology with coefficients in the prime field Z/pZ for p=coeff.

• do_cocycles (bool) – Indicator of whether to compute cocycles, if so, we compute and store cocycles in the cocycles_ dictionary Rips member variable

• n_perm (int) – The number of points to subsample in a “greedy permutation,” or a furthest point sampling of the points. These points will be used in lieu of the full point cloud for a faster computation, at the expense of some accuracy, which can be bounded as a maximum bottleneck distance to all diagrams on the original point set

• verbose (boolean) – Whether to print out information about this object as it is constructed

`dgm_`

After transform, dgm_ contains computed persistence diagrams in each dimension

Type:

list of ndarray, each shape (n_pairs, 2)

cocycles_

A list of representative cocycles in each dimension. The list in each dimension is parallel to the diagram in that dimension; that is, each entry of the list is a representative cocycle of the corresponding point expressed as an ndarray(K, d+1), where K is the number of nonzero values of the cocycle and d is the dimension of the cocycle. The first d columns of each array index into the simplices of the (subsampled) point cloud, and the last column is the value of the cocycle at that simplex

Type:

list (size maxdim) of list of ndarray

dperm2all_

The distance matrix used in the computation if n_perm is none. Otherwise, the distance from all points in the permutation to all points in the dataset

Type:

ndarray(n_samples, n_samples) or ndarray (n_perm, n_samples) if n_perm

metric_

The metric to use when calculating distance between instances in a feature array. If metric is a string, it must be one of the options specified in pairwise_distances, including “euclidean”, “manhattan”, or “cosine”. Alternatively, if metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays from X as input and return a value indicating the distance between them.

Type:

string or callable

num_edges

The number of edges added during the computation

Type:

int

idx_perm

Index into the original point cloud of the points used as a subsample in the greedy permutation

Type:

ndarray(n_perm) if n_perm > 0

r_cover

Covering radius of the subsampled points. If n_perm <= 0, then the full point cloud was used and this is 0

Type:

float

Examples

from ripser import Rips
import tadasets

data = tadasets.dsphere(n=110, d=2)[0]
rips = Rips()
rips.transform(data)
rips.plot()

__init__(maxdim=1, thresh=inf, coeff=2, do_cocycles=False, n_perm=None, verbose=True)

Methods

__init__([maxdim, thresh, coeff, ...])
fit_transform(X[, distance_matrix, metric])	Compute persistence diagrams for X data array and return the diagrams.
plot([diagrams])	A helper function to plot persistence diagrams.
transform(X[, distance_matrix, metric])