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-rw-r--r--src/python/gudhi/datasets/generators/_points.cc13
1 files changed, 3 insertions, 10 deletions
diff --git a/src/python/gudhi/datasets/generators/_points.cc b/src/python/gudhi/datasets/generators/_points.cc
index 70ce4925..6baed673 100644
--- a/src/python/gudhi/datasets/generators/_points.cc
+++ b/src/python/gudhi/datasets/generators/_points.cc
@@ -85,9 +85,7 @@ PYBIND11_MODULE(_points, m) {
m.def("sphere", &generate_points_on_sphere,
py::arg("n_samples"), py::arg("ambient_dim"),
py::arg("radius") = 1., py::arg("sample") = "random",
- R"pbdoc(
- Generate random i.i.d. points uniformly on a (d-1)-sphere in R^d
-
+ R"pbdoc( Generate random i.i.d. points uniformly on a (d-1)-sphere in R^d
:param n_samples: The number of points to be generated.
:type n_samples: integer
:param ambient_dim: The ambient dimension d.
@@ -102,19 +100,14 @@ PYBIND11_MODULE(_points, m) {
m.def("ctorus", &generate_points_on_torus,
py::arg("n_samples"), py::arg("dim"), py::arg("sample") = "random",
- R"pbdoc(
- Generate random i.i.d. points on a d-torus in R^2d or as a grid
-
+ R"pbdoc( Generate random i.i.d. points on a d-torus in R^2d or as a grid
:param n_samples: The number of points to be generated.
:type n_samples: integer
:param dim: The dimension of the torus on which points would be generated in R^2*dim.
:type dim: integer
:param sample: The sample type. Available values are: `"random"` and `"grid"`. Default value is `"random"`.
:type sample: string
- :rtype: numpy array of float.
- The shape of returned numpy array is :
- if sample is 'random' : (n_samples, 2*dim).
- if sample is 'grid' : (⌊n_samples**(1./dim)⌋**dim, 2*dim), where shape[0] is rounded down to the closest perfect 'dim'th power.
+ :rtype: numpy array of float. The shape of returned numpy array is: If sample is 'random': (n_samples, 2*dim). If sample is 'grid': (⌊n_samples**(1./dim)⌋**dim, 2*dim), where shape[0] is rounded down to the closest perfect 'dim'th power.
:returns: the generated points on a torus.
)pbdoc");
}