AStar changes optimalisation
This commit is contained in:
parent
4ee1e5af4f
commit
e0a0e4f456
204
UI/Apath.py
204
UI/Apath.py
@ -2,70 +2,182 @@ import numpy as np
|
||||
from heapq import * # pylint: disable=unused-wildcard-import
|
||||
|
||||
|
||||
def heuristic(a, b):
|
||||
|
||||
x = abs(a[0]-b[0])
|
||||
y = abs(a[1]-b[1])
|
||||
def astar(table, start, end):
|
||||
"""Returns a list of tuples as a path from the given start to the given end in the given table"""
|
||||
|
||||
if x > y:
|
||||
return 14*y + 10*(x - y)
|
||||
else:
|
||||
return 14*x + 10*(y - x)
|
||||
# Create start and end node
|
||||
start_node = table[start[0]][start[1]]
|
||||
start_node.g = start_node.h = start_node.f = 0
|
||||
end_node = table[end[0]][end[1]]
|
||||
end_node.g = end_node.h = end_node.f = 0
|
||||
|
||||
# Initialize both open and closed list
|
||||
open_list = []
|
||||
closed_list = []
|
||||
|
||||
# Add the start node
|
||||
open_list.append(start_node)
|
||||
|
||||
def Astar(array, start, goal):
|
||||
# Loop until you find the end
|
||||
while len(open_list) > 0:
|
||||
|
||||
# Get the current node
|
||||
current_node = open_list[0]
|
||||
current_index = 0
|
||||
for index, item in enumerate(open_list):
|
||||
if item.f < current_node.f:
|
||||
current_node = item
|
||||
current_index = index
|
||||
|
||||
neighbors = [(0, 1), (0, -1), (1, 0), (-1, 0), (1, 1), (1, -1), (-1, 1), (-1, -1)]
|
||||
# Pop current off open list, add to closed list
|
||||
open_list.pop(current_index)
|
||||
closed_list.append(current_node)
|
||||
|
||||
# Found the goal
|
||||
if current_node == end_node:
|
||||
path = []
|
||||
current = current_node
|
||||
while current is not None:
|
||||
path.append((current.row,current.col))
|
||||
current = current.parent
|
||||
return path[::-1] # Return reversed path
|
||||
|
||||
came_from = {}
|
||||
gscore = {start: 0}
|
||||
fscore = {start: heuristic(start, goal)}
|
||||
oheap = []
|
||||
checked = []
|
||||
# Generate children
|
||||
children = []
|
||||
for new_position in [(0, -1), (0, 1), (-1, 0), (1, 0), (-1, -1), (-1, 1), (1, -1), (1, 1)]: # Adjacent squares
|
||||
|
||||
heappush(oheap, (fscore[start], start))
|
||||
# Get node position #check
|
||||
node_position = (current_node.row + new_position[0], current_node.col + new_position[1])
|
||||
|
||||
while oheap:
|
||||
# Make sure within range
|
||||
if node_position[0] > (len(table) - 1) or node_position[0] < 0 or node_position[1] > (len(table[len(table)-1]) -1) or node_position[1] < 0:
|
||||
continue
|
||||
|
||||
current = heappop(oheap)[1]
|
||||
checked.append(current)
|
||||
# Make sure walkable terrain
|
||||
if table[node_position[0]][node_position[1]].field_type == 3:
|
||||
continue
|
||||
|
||||
if current == goal:
|
||||
data = []
|
||||
while current in came_from:
|
||||
data.append(current)
|
||||
current = came_from[current]
|
||||
# Create new node
|
||||
table[node_position[0]][node_position[1]].parent = current_node
|
||||
#new_node = table[node_position[0]][node_position[1]]
|
||||
print("Dla :",node_position[0],node_position[1],"rodzicem jest:",current_node.row,current_node.col)
|
||||
|
||||
return list(reversed(data)), checked
|
||||
print("array current",array[current[0],current[1]])
|
||||
array[current[0], current[1]]=2
|
||||
# Append
|
||||
children.append(table[node_position[0]][node_position[1]])
|
||||
|
||||
for i, j in neighbors:
|
||||
# Loop through children
|
||||
for child in children:
|
||||
|
||||
neighbor = current[0] + i, current[1] + j
|
||||
|
||||
tentative_g_score = gscore[current] + heuristic(current, neighbor)
|
||||
|
||||
if 0 <= neighbor[0] < array.shape[0]:
|
||||
if 0 <= neighbor[1] < array.shape[1]:
|
||||
if array.flat[array.shape[1] * neighbor[0]+neighbor[1]] == 1:
|
||||
continue
|
||||
else:
|
||||
# array bound y walls
|
||||
# Child is on the closed list
|
||||
for closed_child in closed_list:
|
||||
if child == closed_child:
|
||||
continue
|
||||
else:
|
||||
# array bound x walls
|
||||
|
||||
# Create the f, g, and h values
|
||||
child.g = current_node.g + 1
|
||||
child.h = ((child.row - end_node.row) ** 2) + ((child.col - end_node.col) ** 2)
|
||||
child.f = child.g + child.h
|
||||
|
||||
# Child is already in the open list
|
||||
for open_node in open_list:
|
||||
if child == open_node and child.g > open_node.g:
|
||||
continue
|
||||
|
||||
# Add the child to the open list
|
||||
open_list.append(child)
|
||||
|
||||
class AStarNode():
|
||||
|
||||
def __init__(self, parent=None, position=None):
|
||||
self.parent = parent
|
||||
self.position = position
|
||||
self.g = 0
|
||||
self.h = 0
|
||||
self.f = 0
|
||||
|
||||
def __eq__(self, other):
|
||||
return self.position == other.position
|
||||
|
||||
|
||||
def APath(table, start, end):
|
||||
"""Returns a list of tuples as a path from the given start to the given end in the given table"""
|
||||
|
||||
# Create start and end node
|
||||
start_node = AStarNode(None, start)
|
||||
start_node.g = start_node.h = start_node.f = 0
|
||||
end_node = AStarNode(None, end)
|
||||
end_node.g = end_node.h = end_node.f = 0
|
||||
|
||||
# Initialize both open and closed list
|
||||
open_list = []
|
||||
closed_list = []
|
||||
|
||||
# Add the start node
|
||||
open_list.append(start_node)
|
||||
|
||||
# Loop until you find the end
|
||||
while len(open_list) > 0:
|
||||
|
||||
# Get the current node
|
||||
current_node = open_list[0]
|
||||
current_index = 0
|
||||
for index, item in enumerate(open_list):
|
||||
if item.f < current_node.f:
|
||||
current_node = item
|
||||
current_index = index
|
||||
|
||||
# Pop current off open list, add to closed list
|
||||
open_list.pop(current_index)
|
||||
closed_list.append(current_node)
|
||||
|
||||
# Found the goal
|
||||
if current_node == end_node:
|
||||
path = []
|
||||
current = current_node
|
||||
while current is not None:
|
||||
path.append(current.position)
|
||||
current = current.parent
|
||||
return path[::-1] # Return reversed path
|
||||
|
||||
# Generate children
|
||||
children = []
|
||||
for new_position in [(0, -1), (0, 1), (-1, 0), (1, 0), (-1, -1), (-1, 1), (1, -1), (1, 1)]: # Adjacent squares
|
||||
|
||||
# Get node position
|
||||
node_position = (current_node.position[0] + new_position[0], current_node.position[1] + new_position[1])
|
||||
|
||||
# Make sure within range
|
||||
if node_position[0] > (len(table) - 1) or node_position[0] < 0 or node_position[1] > (len(table[len(table)-1]) -1) or node_position[1] < 0:
|
||||
continue
|
||||
|
||||
if array[neighbor[0]][neighbor[1]] == 2 and tentative_g_score >= gscore.get(neighbor, 0):
|
||||
# Make sure walkable terrain
|
||||
if table[node_position[0]][node_position[1]].field_type == 3:
|
||||
continue
|
||||
|
||||
if tentative_g_score < gscore.get(neighbor, 0) or neighbor not in [i[1]for i in oheap]:
|
||||
came_from[neighbor] = current
|
||||
gscore[neighbor] = tentative_g_score
|
||||
fscore[neighbor] = tentative_g_score + heuristic(neighbor, goal)
|
||||
heappush(oheap, (fscore[neighbor], neighbor))
|
||||
return False
|
||||
# Create new node
|
||||
new_node = AStarNode(current_node, node_position)
|
||||
|
||||
# Append
|
||||
children.append(new_node)
|
||||
|
||||
# Loop through children
|
||||
for child in children:
|
||||
|
||||
# Child is on the closed list
|
||||
for closed_child in closed_list:
|
||||
if child == closed_child:
|
||||
continue
|
||||
|
||||
# Create the f, g, and h values
|
||||
child.g = current_node.g + 1
|
||||
child.h = ((child.position[0] - end_node.position[0]) ** 2) + ((child.position[1] - end_node.position[1]) ** 2)
|
||||
child.f = child.g + child.h
|
||||
|
||||
# Child is already in the open list
|
||||
for open_node in open_list:
|
||||
if child == open_node and child.g > open_node.g:
|
||||
continue
|
||||
|
||||
# Add the child to the open list
|
||||
open_list.append(child)
|
||||
|
||||
@ -41,15 +41,12 @@ class Node:
|
||||
ORANGE = (255, 165, 0)
|
||||
|
||||
def __init__(self, row: int, col: int,
|
||||
field_type: int = 0, reachable: bool = True):
|
||||
field_type: int = 0):
|
||||
self.row = row
|
||||
self.col = col
|
||||
self.field_type = field_type
|
||||
self.reachable = reachable
|
||||
self.visited = False
|
||||
|
||||
def visit(self):
|
||||
self.visited = True
|
||||
|
||||
|
||||
def draw(self, screen):
|
||||
color = self.get_field_color()
|
||||
|
||||
21
UI/window.py
21
UI/window.py
@ -2,7 +2,7 @@ import pygame as pg
|
||||
import numpy as np
|
||||
import random
|
||||
from UI.grid import Grid, Node
|
||||
from UI.Apath import Astar
|
||||
from UI.Apath import APath, astar
|
||||
|
||||
|
||||
|
||||
@ -32,7 +32,7 @@ class Window():
|
||||
grid.change_field(19, 19, 2)
|
||||
|
||||
#random obsticle
|
||||
for x in range(40):
|
||||
for x in range(70):
|
||||
grid.change_field(random.randint(1,18),random.randint(1,18),3)
|
||||
|
||||
#path
|
||||
@ -41,17 +41,20 @@ class Window():
|
||||
|
||||
#convert table to support Apath algoritm
|
||||
array = [[self.grid.table[col][row] for row in range(cols)] for col in range(rows)]
|
||||
for i,x in enumerate(array):
|
||||
for j,y in enumerate(x):
|
||||
if y.field_type == 3:
|
||||
array[i][j] = None
|
||||
|
||||
nodes_array = np.array(array)
|
||||
#for i,x in enumerate(array):
|
||||
# for j,y in enumerate(x):
|
||||
# if y.field_type == 3:
|
||||
# array[i][j] = None
|
||||
|
||||
#nodes_array = np.array(array)
|
||||
|
||||
#Run A star
|
||||
|
||||
path, check = Astar(nodes_array, (0,0), (19, 19))
|
||||
print(path,"\n\n",check,"\n\n")
|
||||
#path, check = Astar(nodes_array, (0,0), (19, 19))
|
||||
#print(path,"\n\n",check,"\n\n")
|
||||
path = APath(array,(0,0),(19,19))
|
||||
print(path,"\n\n")
|
||||
|
||||
|
||||
for t in path:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user