pcst endpoints

Author: IoannisPanagiotas

doc/sphinx/algorithms.json

@@ -2810,6 +2810,54 @@
     },
     "description": "Returns an estimation of the memory consumption for that procedure."
   },
+  {
+    "function": {
+      "name": "gds.prizeSteinerTree.mutate",
+      "signature": "G: Graph, **config: Any",
+      "return_type": "DataFrame"
+    },
+    "description": "An approximation algorithm for the prize collector steiner tree problem"
+  },
+  {
+    "function": {
+      "name": "gds.prizeSteinerTree.mutate.estimate",
+      "signature": "G: Graph, **config: Any",
+      "return_type": "Series[Any]"
+    },
+    "description": "Returns an estimation of the memory consumption for that procedure."
+  },
+  {
+    "function": {
+      "name": "gds.prizeSteinerTree.stats",
+      "signature": "G: Graph, **config: Any",
+      "return_type": "DataFrame"
+    },
+    "description": "An approximation algorithm for the prize collector steiner tree problem"
+  },
+  {
+    "function": {
+      "name": "gds.prizeSteinerTree.stats.estimate",
+      "signature": "G: Graph, **config: Any",
+      "return_type": "Series[Any]"
+    },
+    "description": "Returns an estimation of the memory consumption for that procedure."
+  },
+  {
+    "function": {
+      "name": "gds.prizeSteinerTree.write",
+      "signature": "G: Graph, **config: Any",
+      "return_type": "DataFrame"
+    },
+    "description": "An approximation algorithm for the prize collector steiner tree problem"
+  },
+  {
+    "function": {
+      "name": "gds.prizeSteinerTree.write.estimate",
+      "signature": "G: Graph, **config: Any",
+      "return_type": "Series[Any]"
+    },
+    "description": "Returns an estimation of the memory consumption for that procedure."
+  },
   {
     "function": {
       "name": "gds.randomWalk.mutate",

doc/sphinx/source/algorithms.rst

@@ -1568,6 +1568,30 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g
 
     Returns an estimation of the memory consumption for that procedure.
 
+.. py:function:: gds.prizeSteinerTree.mutate(G: Graph, **config: Any) -> DataFrame
+
+    An approximation algorithm for the prize collector steiner tree problem
+
+.. py:function:: gds.prizeSteinerTree.mutate.estimate(G: Graph, **config: Any) -> Series[Any]
+
+    Returns an estimation of the memory consumption for that procedure.
+
+.. py:function:: gds.prizeSteinerTree.stats(G: Graph, **config: Any) -> DataFrame
+
+    An approximation algorithm for the prize collector steiner tree problem
+
+.. py:function:: gds.prizeSteinerTree.stats.estimate(G: Graph, **config: Any) -> Series[Any]
+
+    Returns an estimation of the memory consumption for that procedure.
+
+.. py:function:: gds.prizeSteinerTree.write(G: Graph, **config: Any) -> DataFrame
+
+    An approximation algorithm for the prize collector steiner tree problem
+
+.. py:function:: gds.prizeSteinerTree.write.estimate(G: Graph, **config: Any) -> Series[Any]
+
+    Returns an estimation of the memory consumption for that procedure.
+
 .. py:function:: gds.randomWalk.mutate(G: Graph, **config: Any) -> Series[Any]
 
     Random Walk is an algorithm that provides random paths in a graph. It’s similar to how a drunk person traverses a city. The mutate procedure produces the count a node occurs on a random walk.