"""Product of Riemannian metrics.
Define the metric of a product manifold endowed with a product metric.
"""
import joblib
import geomstats.backend as gs
import geomstats.errors
import geomstats.vectorization
from geomstats.geometry.riemannian_metric import RiemannianMetric
[docs]class ProductRiemannianMetric(RiemannianMetric):
"""Class for product of Riemannian metrics.
Parameters
----------
metrics : list
List of metrics in the product.
default_point_type : str, {'vector', 'matrix'}
Point type.
Optional, default: 'vector'.
n_jobs : int
Number of jobs for parallel computing.
Optional, default: 1.
"""
def __init__(self, metrics, default_point_type='vector', n_jobs=1):
self.n_metrics = len(metrics)
dims = [metric.dim for metric in metrics]
signatures = [metric.signature for metric in metrics]
sig_0 = sum(sig[0] for sig in signatures)
sig_1 = sum(sig[1] for sig in signatures)
sig_2 = sum(sig[2] for sig in signatures)
super(ProductRiemannianMetric, self).__init__(
dim=sum(dims),
signature=(sig_0, sig_1, sig_2),
default_point_type=default_point_type)
self.metrics = metrics
self.dims = dims
self.signatures = signatures
self.n_jobs = n_jobs
[docs] def metric_matrix(self, base_point=None, point_type=None):
"""Compute the matrix of the inner-product.
Matrix of the inner-product defined by the Riemmanian metric
at point base_point of the manifold.
Parameters
----------
base_point : array-like, shape=[..., n_metrics, dim] or
[..., dim]
Point on the manifold at which to compute the inner-product matrix.
Optional, default: None.
point_type : str, {'vector', 'matrix'}
Type of representation used for points.
Optional, default: None.
Returns
-------
matrix : array-like, shape=[..., dim, dim] or
[..., dim + n_metrics, dim + n_metrics]
Matrix of the inner-product at the base point.
"""
if point_type is None:
point_type = self.default_point_type
base_point = gs.to_ndarray(base_point, to_ndim=2)
base_point = gs.to_ndarray(base_point, to_ndim=3)
matrix = gs.zeros([
len(base_point), self.dim, self.dim])
cum_dim = 0
for i in range(self.n_metrics):
cum_dim_next = cum_dim + self.dims[i]
if point_type == 'matrix':
matrix_next = self.metrics[i].metric_matrix(base_point[:, i])
elif point_type == 'vector':
matrix_next = self.metrics[i].metric_matrix(
base_point[:, cum_dim:cum_dim_next, cum_dim:cum_dim_next])
else:
raise ValueError('invalid point_type argument: {}, expected '
'either matrix of vector'.format(point_type))
matrix[:, cum_dim:cum_dim_next, cum_dim:cum_dim_next] = matrix_next
cum_dim = cum_dim_next
return matrix[0] if len(base_point) == 1 else matrix
[docs] def is_intrinsic(self, point):
"""Test in a point is represented in intrinsic coordinates.
This method is only useful for `point_type=vector`.
Parameters
----------
point : array-like, shape=[..., dim]
Point on the product manifold.
Returns
-------
intrinsic : array-like, shape=[...,]
Whether intrinsic coordinates are used for all manifolds.
"""
if self.default_point_type != 'vector':
raise ValueError(
'Invalid default_point_type: \'vector\' expected.')
if point.shape[1] == self.dim:
intrinsic = True
elif point.shape[1] == sum(dim + 1 for dim in self.dims):
intrinsic = False
else:
raise ValueError(
'Input shape does not match the dimension of the manifold')
return intrinsic
@staticmethod
def _get_method(metric, method_name, metric_args):
return getattr(metric, method_name)(**metric_args)
def _iterate_over_metrics(
self, func, args, intrinsic=False):
cum_index = gs.cumsum(self.dims, axis=0)[:-1] if intrinsic else \
gs.cumsum(gs.array([k + 1 for k in self.dims]), axis=0)
arguments = {
key: gs.split(args[key], cum_index, axis=1) for key in args.keys()}
args_list = [{key: arguments[key][j] for key in args.keys()} for j in
range(self.n_metrics)]
pool = joblib.Parallel(n_jobs=self.n_jobs, prefer='threads')
out = pool(
joblib.delayed(self._get_method)(
self.metrics[i], func, args_list[i]) for i in range(
self.n_metrics))
return out
[docs] def inner_product(
self, tangent_vec_a, tangent_vec_b, base_point=None,
point_type=None):
"""Compute the inner-product of two tangent vectors at a base point.
Inner product defined by the Riemannian metric at point `base_point`
between tangent vectors `tangent_vec_a` and `tangent_vec_b`.
Parameters
----------
tangent_vec_a : array-like, shape=[..., dim + 1]
First tangent vector at base point.
tangent_vec_b : array-like, shape=[..., dim + 1]
Second tangent vector at base point.
base_point : array-like, shape=[..., dim + 1]
Point on the manifold.
Optional, default: None.
point_type : str, {'vector', 'matrix'}
Type of representation used for points.
Optional, default: None.
Returns
-------
inner_prod : array-like, shape=[...,]
Inner-product of the two tangent vectors.
"""
if base_point is None:
base_point = gs.empty((self.n_metrics, self.dim))
if point_type is None:
point_type = self.default_point_type
geomstats.errors.check_parameter_accepted_values(
point_type, 'point_type', ['vector', 'matrix'])
tangent_vec_a = gs.to_ndarray(tangent_vec_a, to_ndim=2)
tangent_vec_b = gs.to_ndarray(tangent_vec_b, to_ndim=2)
base_point = gs.to_ndarray(base_point, to_ndim=2)
if point_type == 'vector':
intrinsic = self.is_intrinsic(tangent_vec_b)
args = {'tangent_vec_a': tangent_vec_a,
'tangent_vec_b': tangent_vec_b,
'base_point': base_point}
inner_prod = self._iterate_over_metrics(
'inner_product', args, intrinsic)
return gs.sum(gs.stack(inner_prod, axis=1), axis=1)
inner_products = [
metric.inner_product(
tangent_vec_a[..., i, :],
tangent_vec_b[..., i, :],
base_point[..., i, :])
for i, metric in enumerate(self.metrics)]
return sum(inner_products)
[docs] def exp(self, tangent_vec, base_point=None, point_type=None):
"""Compute the Riemannian exponential of a tangent vector.
Parameters
----------
tangent_vec : array-like, shape=[..., dim]
Tangent vector at a base point.
base_point : array-like, shape=[..., dim]
Point on the manifold.
Optional, default: None.
point_type : str, {'vector', 'matrix'}
Type of representation used for points.
Optional, default: None.
Returns
-------
exp : array-like, shape=[..., dim]
Point on the manifold equal to the Riemannian exponential
of tangent_vec at the base point.
"""
if base_point is None:
base_point = [None, ] * self.n_metrics
if point_type is None:
point_type = self.default_point_type
geomstats.errors.check_parameter_accepted_values(
point_type, 'point_type', ['vector', 'matrix'])
if point_type == 'vector':
tangent_vec = gs.to_ndarray(tangent_vec, to_ndim=2)
base_point = gs.to_ndarray(base_point, to_ndim=2)
intrinsic = self.is_intrinsic(base_point)
args = {'tangent_vec': tangent_vec, 'base_point': base_point}
exp = self._iterate_over_metrics('exp', args, intrinsic)
return gs.hstack(exp)
tangent_vec = gs.to_ndarray(tangent_vec, to_ndim=3)
base_point = gs.to_ndarray(base_point, to_ndim=3)
exp = gs.stack([
self.metrics[i].exp(tangent_vec[:, i], base_point[:, i])
for i in range(self.n_metrics)], axis=1)
return exp[0] if len(tangent_vec) == 1 else exp
[docs] def log(self, point, base_point=None, point_type=None):
"""Compute the Riemannian logarithm of a point.
Parameters
----------
point : array-like, shape=[..., dim]
Point on the manifold.
base_point : array-like, shape=[..., dim]
Point on the manifold.
Optional, default: None.
point_type : str, {'vector', 'matrix'}
Type of representation used for points.
Optional, default: None.
Returns
-------
log : array-like, shape=[..., dim]
Tangent vector at the base point equal to the Riemannian logarithm
of point at the base point.
"""
if base_point is None:
base_point = [None] * self.n_metrics
if point_type is None:
point_type = self.default_point_type
geomstats.errors.check_parameter_accepted_values(
point_type, 'point_type', ['vector', 'matrix'])
if point_type == 'vector':
point = gs.to_ndarray(point, to_ndim=2)
base_point = gs.to_ndarray(base_point, to_ndim=2)
intrinsic = self.is_intrinsic(base_point)
args = {'point': point, 'base_point': base_point}
logs = self._iterate_over_metrics('log', args, intrinsic)
logs = gs.concatenate(logs, axis=1)
return logs
point = gs.to_ndarray(point, to_ndim=2, axis=0)
point = gs.to_ndarray(point, to_ndim=3, axis=0)
base_point = gs.to_ndarray(base_point, to_ndim=2, axis=0)
base_point = gs.to_ndarray(base_point, to_ndim=3, axis=0)
logs = gs.stack(
[self.metrics[i].log(point[:, i], base_point[:, i])
for i in range(self.n_metrics)], axis=1)
return logs