more documentation on python api

Author: Bluemi

python/src/deglib/graph.py

@@ -109,6 +109,11 @@ def get_entry_vertex_indices(self) -> List[int]:
     def has_path(self, entry_vertex_indices: List[int], to_vertex: int, eps: float, k: int) -> List[ObjectDistance]:
         """
         Returns a path from one of the entry vertex indices to the given to_vertex.
+
+        :param entry_vertex_indices: List of start vertices
+        :param to_vertex: The vertex to find a path to
+        :param eps: Controls how many nodes are checked during search. Lower eps values like 0.001 are faster but less
+                    accurate. Higher eps values like 0.1 are slower but more accurate. Should always be greater 0.
         """
         raise NotImplementedError()
 
@@ -126,15 +131,19 @@ def search(
         extended the search range.
 
         :param query: A feature vector for which similar feature vectors should searched.
-        :param eps: TODO
+        :param eps: Controls how many nodes are checked during search. Lower eps values like 0.001 are faster but less
+                    accurate. Higher eps values like 0.1 are slower but more accurate. Should always be greater 0.
         :param k: The number of results that will be returned
         :param max_distance_computation_count: Limit the number of distance calculations. If set to 0 this is ignored.
         :param entry_vertex_indices: Start point for exploratory search. If None, a reasonable default is used.
         :param threads: The number of threads to use for parallel processing. It should not excel the number of queries.
                         If set to 0, the minimum of the number of cores of this machine and the number of queries is
                         used.
         :param thread_batch_size: If threads != 1, the number of queries to search in the same thread.
-        :returns: TODO
+        :returns: A tuple containing (indices, distances) where indices is a numpy-array of shape [n_queries, k]
+                  containing the indices to the closest found neighbors to the queries.
+                  Distances is a numpy-array of shape [n_queries, k] containing the distances to the closest found
+                  neighbors.
         """
         # handle query shapes
         if len(query.shape) == 1:
@@ -160,11 +169,11 @@ def search(
     @abstractmethod
     def explore(self, entry_vertex_index: int, k: int, max_distance_computation_count: int) -> ResultSet:
         """
-        A exploration for similar element, limited by max_distance_computation_count
+        An exploration for similar element, limited by max_distance_computation_count
 
         :param entry_vertex_index: The start point for which similar feature vectors should be searched
         :param k: The number of similar feature vectors to return
-        :param max_distance_computation_count: TODO
+        :param max_distance_computation_count: Limit the number of distance calculations. If set to 0 this is ignored.
         """
         raise NotImplementedError()
 
@@ -217,7 +226,6 @@ def get_feature_space(self) -> FloatSpace:
         # first two parameters get ignored
         return FloatSpace(float_space_cpp=self.graph_cpp.get_feature_space())
 
-
     def get_internal_index(self, external_label: int) -> int:
         """
         Translates internal index to external label
@@ -233,7 +241,8 @@ def has_path(self, entry_vertex_indices: List[int], to_vertex: int, eps: float,
 
         :param entry_vertex_indices: List of start vertices
         :param to_vertex: The vertex to find a path to
-        :param eps: TODO
+        :param eps: Controls how many nodes are checked during search. Lower eps values like 0.001 are faster but less
+                    accurate. Higher eps values like 0.1 are slower but more accurate. Should always be greater 0.
         :param k: TODO
         """
         return [ObjectDistance(od) for od in self.graph_cpp.has_path(entry_vertex_indices, to_vertex, eps, k)]
@@ -465,7 +474,8 @@ def has_path(self, entry_vertex_indices: List[int], to_vertex: int, eps: float,
 
         :param entry_vertex_indices: List of start vertices
         :param to_vertex: The vertex to find a path to
-        :param eps: TODO
+        :param eps: Controls how many nodes are checked during search. Lower eps values like 0.001 are faster but less
+                    accurate. Higher eps values like 0.1 are slower but more accurate. Should always be greater 0.
         :param k: TODO
         """
         return [ObjectDistance(od) for od in self.graph_cpp.has_path(entry_vertex_indices, to_vertex, eps, k)]