Avoid the word "guess"

1 files changed, 4 insertions, 4 deletions

diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py
index 4ebd834d..346d892d 100644
--- a/src/python/gudhi/representations/vector_methods.py
+++ b/src/python/gudhi/representations/vector_methods.py
@@ -156,7 +156,7 @@ class Landscape(BaseEstimator, TransformerMixin):
             num_landscapes (int): number of piecewise-linear functions to output (default 5).
             resolution (int): number of sample for all piecewise-linear functions (default 100).
             sample_range ([double, double]): minimum and maximum of all piecewise-linear function domains, of the form [x_min, x_max] (default [numpy.nan, numpy.nan]). It is the interval on which samples will be drawn evenly. If one of the values is numpy.nan, it can be computed from the persistence diagrams with the fit() method.
-            keep_endpoints (bool): when guessing `sample_range`, use the exact extremities (where the value is always 0). This is mostly useful for plotting, the default is to use a slightly smaller range.
+            keep_endpoints (bool): when computing `sample_range`, use the exact extremities (where the value is always 0). This is mostly useful for plotting, the default is to use a slightly smaller range.
 
         Attributes:
             grid_ (1d array): The grid on which the landscapes are computed.
@@ -230,7 +230,7 @@ class Silhouette(BaseEstimator, TransformerMixin):
             weight (function): weight function for the persistence diagram points (default constant function, ie lambda x: 1). This function must be defined on 2D points, ie on lists or numpy arrays of the form [p_x,p_y].
             resolution (int): number of samples for the weighted average (default 100).
             sample_range ([double, double]): minimum and maximum for the weighted average domain, of the form [x_min, x_max] (default [numpy.nan, numpy.nan]). It is the interval on which samples will be drawn evenly. If one of the values is numpy.nan, it can be computed from the persistence diagrams with the fit() method.
-            keep_endpoints (bool): when guessing `sample_range`, use the exact extremities (where the value is always 0). This is mostly useful for plotting, the default is to use a slightly smaller range.
+            keep_endpoints (bool): when computing `sample_range`, use the exact extremities (where the value is always 0). This is mostly useful for plotting, the default is to use a slightly smaller range.
 
         Attributes:
             grid_ (1d array): The grid on which the silhouette is computed.
@@ -299,7 +299,7 @@ class BettiCurve(BaseEstimator, TransformerMixin):
             resolution (int): number of samples for the piecewise-constant function (default 100), or None for the exact curve.
             sample_range ([double, double]): minimum and maximum of the piecewise-constant function domain, of the form [x_min, x_max] (default [numpy.nan, numpy.nan]). It is the interval on which samples will be drawn evenly. If one of the values is numpy.nan, it can be computed from the persistence diagrams with the fit() method.
             predefined_grid (1d array or None, default=None): Predefined filtration grid points at which to compute the Betti curves. Must be strictly ordered. Infinities are ok. If None (default), and resolution is given, the grid will be uniform from x_min to x_max in 'resolution' steps, otherwise a grid will be computed that captures all changes in Betti numbers in the provided data.
-            keep_endpoints (bool): when guessing `sample_range` (fixed `resolution`, no `predefined_grid`), use the exact extremities. This is mostly useful for plotting, the default is to use a slightly smaller range.
+            keep_endpoints (bool): when computing `sample_range` (fixed `resolution`, no `predefined_grid`), use the exact extremities. This is mostly useful for plotting, the default is to use a slightly smaller range.
 
         Attributes:
             grid_ (1d array): The grid on which the Betti numbers are computed. If predefined_grid was specified, `grid_` will always be that grid, independently of data. If not and resolution is None, the grid is fitted to capture all filtration values at which the Betti numbers change.
@@ -456,7 +456,7 @@ class Entropy(BaseEstimator, TransformerMixin):
             normalized (bool): whether to normalize the entropy summary function (default True). Used only if **mode** = "vector". 
             resolution (int): number of sample for the entropy summary function (default 100). Used only if **mode** = "vector".
             sample_range ([double, double]): minimum and maximum of the entropy summary function domain, of the form [x_min, x_max] (default [numpy.nan, numpy.nan]). It is the interval on which samples will be drawn evenly. If one of the values is numpy.nan, it can be computed from the persistence diagrams with the fit() method. Used only if **mode** = "vector".
-            keep_endpoints (bool): when guessing `sample_range`, use the exact extremities. This is mostly useful for plotting, the default is to use a slightly smaller range.
+            keep_endpoints (bool): when computing `sample_range`, use the exact extremities. This is mostly useful for plotting, the default is to use a slightly smaller range.
 
         Attributes:
             grid_ (1d array): In vector mode, the grid on which the entropy summary function is computed.