From 707809b0006a76210bc3d0e4312ff5c73ff68300 Mon Sep 17 00:00:00 2001 From: AHTESHAM ZAIDI Date: Mon, 3 Oct 2022 03:25:24 +0530 Subject: [PATCH] Update astar.py (#6456) * Update astar.py Improved comments added punctuations. * Update astar.py * Update machine_learning/astar.py Co-authored-by: Caeden * Update astar.py Co-authored-by: Christian Clauss Co-authored-by: Caeden --- machine_learning/astar.py | 55 ++++++++++++++++++--------------------- 1 file changed, 26 insertions(+), 29 deletions(-) diff --git a/machine_learning/astar.py b/machine_learning/astar.py index ee3fcff0b..7a60ed225 100644 --- a/machine_learning/astar.py +++ b/machine_learning/astar.py @@ -1,41 +1,38 @@ """ -The A* algorithm combines features of uniform-cost search and pure -heuristic search to efficiently compute optimal solutions. -A* algorithm is a best-first search algorithm in which the cost -associated with a node is f(n) = g(n) + h(n), -where g(n) is the cost of the path from the initial state to node n and -h(n) is the heuristic estimate or the cost or a path -from node n to a goal.A* algorithm introduces a heuristic into a -regular graph-searching algorithm, -essentially planning ahead at each step so a more optimal decision -is made.A* also known as the algorithm with brains +The A* algorithm combines features of uniform-cost search and pure heuristic search to +efficiently compute optimal solutions. + +The A* algorithm is a best-first search algorithm in which the cost associated with a +node is f(n) = g(n) + h(n), where g(n) is the cost of the path from the initial state to +node n and h(n) is the heuristic estimate or the cost or a path from node n to a goal. + +The A* algorithm introduces a heuristic into a regular graph-searching algorithm, +essentially planning ahead at each step so a more optimal decision is made. For this +reason, A* is known as an algorithm with brains. + +https://en.wikipedia.org/wiki/A*_search_algorithm """ import numpy as np class Cell: """ - Class cell represents a cell in the world which have the property - position : The position of the represented by tupleof x and y - coordinates initially set to (0,0) - parent : This contains the parent cell object which we visited - before arrinving this cell - g,h,f : The parameters for constructing the heuristic function - which can be any function. for simplicity used line - distance + Class cell represents a cell in the world which have the properties: + position: represented by tuple of x and y coordinates initially set to (0,0). + parent: Contains the parent cell object visited before we arrived at this cell. + g, h, f: Parameters used when calling our heuristic function. """ def __init__(self): self.position = (0, 0) self.parent = None - self.g = 0 self.h = 0 self.f = 0 """ - overrides equals method because otherwise cell assign will give - wrong results + Overrides equals method because otherwise cell assign will give + wrong results. """ def __eq__(self, cell): @@ -48,8 +45,8 @@ class Cell: class Gridworld: """ Gridworld class represents the external world here a grid M*M - matrix - world_size: create a numpy array with the given world_size default is 5 + matrix. + world_size: create a numpy array with the given world_size default is 5. """ def __init__(self, world_size=(5, 5)): @@ -90,10 +87,10 @@ class Gridworld: def astar(world, start, goal): """ - Implementation of a start algorithm - world : Object of the world object - start : Object of the cell as start position - stop : Object of the cell as goal position + Implementation of a start algorithm. + world : Object of the world object. + start : Object of the cell as start position. + stop : Object of the cell as goal position. >>> p = Gridworld() >>> start = Cell() @@ -137,14 +134,14 @@ def astar(world, start, goal): if __name__ == "__main__": world = Gridworld() - # stat position and Goal + # Start position and goal start = Cell() start.position = (0, 0) goal = Cell() goal.position = (4, 4) print(f"path from {start.position} to {goal.position}") s = astar(world, start, goal) - # Just for visual reasons + # Just for visual reasons. for i in s: world.w[i] = 1 print(world.w)