def h(cls, state: TileBoard):
manhattan_sum = 0
# Populates Solved State
solved = [[None for c in range(state.cols)] for r in range(state.rows)]
index = 1
for row in range(state.boardsize):
for col in range(state.boardsize):
if row == state.rows // 2 and col == state.cols // 2:
solved[row][col] = None
else:
solved[row][col] = index
index += 1
# Calculate Displacements
for row in range(state.boardsize):
for col in range(state.boardsize):
item = state.get(row, col)
# Find location for solved state
for solved_row in range(len(solved)):
if item in solved[solved_row]:
# Adds manhattan distance to manhattan_sum
# If item is at [1, 2] in state and [0, 0] in solved then distance is 1 + 2 = 3
manhattan_sum += abs(solved_row - row) + abs(solved[solved_row].index(item) - col)
return manhattan_sum
def h(cls, state: TileBoard):
manhattan_distance = 0
goal_state = list(state.goals[0])
size = state.boardsize
# calculate misplacement tiles
# h = |x1 − x2| + |y1 − y2|
# for index above board mod board_size will get cols
# for index above board divide board_size will get rows
for row in range(size):
for col in range(size):
# not count the blank for misplacement
if state.get(row, col) is not None:
val_rc = state.get(row, col)
idx = goal_state.index(val_rc)
if idx > size - 1:
manhattan_distance += abs(idx // size - row) + abs(idx % size - col)
else:
manhattan_distance += row + abs(idx - col)
return manhattan_distance
def h(cls, state: TileBoard):
manhattan_solution = 0
solved = [[None for c in range(state.cols)] for r in range(state.rows)]
index = 1
for row in range(state.boardsize):
for col in range(state.boardsize):
if row == state.rows // 2 and col == state.cols // 2:
solved[row][col] = None
else:
solved[row][col] = index
index += 1
# Calculate distance to solution
for row in range(state.boardsize):
for col in range(state.boardsize):
item = state.get(row, col)
for solved_row in range(len(solved)):
manhattan_solution += abs(solved_row - row) + \
abs(solved[solved_row].index(item) - col)
return manhattan_solution
