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Particle_Swarm_Optimization.py
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Particle_Swarm_Optimization.py
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import numpy as np
import random
from past.builtins import range
random.seed()
Nd = 9 # Number of digits (in the case of standard Sudoku puzzles, this is 9x9).
class Particle(object):
def __init__(self, valid, given, weights, lazy_threshold):
self.weights = weights
self.valid = valid
self.lazy_threshold = lazy_threshold
self.given = given
self.position = np.zeros((Nd, Nd))
self.rand_position()
self.valid = np.zeros((Nd, Nd))
self.fitness = 0
self.personal_best_fitness = 0
self.global_best_fitness = 0
self.lazy = 0
self.personal_best_position = self.position.copy()
self.global_best_position = np.zeros((Nd, Nd))
self.velocity = np.zeros((Nd, Nd))
for row in range(Nd):
for column in range(Nd):
if given[row][column] == 0:
self.velocity[row][column] = random.uniform(-2, 2)
self.update_fitness()
def mutate(self, mutate_rate):
for row in range(len(self.position)):
if random.uniform(0, 1) < mutate_rate:
from_column = random.randint(0, 8)
to_column = random.randint(0, 8)
while from_column == to_column or self.given[row][from_column] != 0 or self.given[row][to_column] != 0:
from_column = random.randint(0, 8)
to_column = random.randint(0, 8)
self.position[row][from_column], self.position[row][to_column] = self.position[row][to_column], \
self.position[row][from_column]
def rand_position(self):
for i in range(0, Nd): # New row in candidate.
# Fill in the givens.
for j in range(0, Nd): # New column j value in row i.
# If value is already given, don't change it.
if self.given[i][j] != 0:
self.position[i][j] = self.given[i][j]
# Fill in the gaps using the helper board.
elif self.given[i][j] == 0:
self.position[i][j] = self.valid[i][j][random.randint(0, len(self.valid[i][j]) - 1)]
def update_lazy(self):
if self.fitness == self.global_best_fitness:
self.lazy += 1
if self.lazy >= self.lazy_threshold:
self.rand_position()
self.personal_best_position = self.position.copy()
self.update_fitness()
else:
self.lazy = 0
def update_fitness(self):
# Calculate overall fitness.
column_count = np.zeros(Nd)
block_count = np.zeros(Nd)
column_sum = 0
block_sum = 0
self.position = self.position.astype(int)
for j in range(0, Nd):
for i in range(0, Nd):
column_count[self.position[i][j] - 1] += 1
for k in range(len(column_count)):
if column_count[k] == 1:
column_sum += (1 / Nd) / Nd
column_count = np.zeros(Nd)
# For each block...
for i in range(0, Nd, 3):
for j in range(0, Nd, 3):
block_count[self.position[i][j] - 1] += 1
block_count[self.position[i][j + 1] - 1] += 1
block_count[self.position[i][j + 2] - 1] += 1
block_count[self.position[i + 1][j] - 1] += 1
block_count[self.position[i + 1][j + 1] - 1] += 1
block_count[self.position[i + 1][j + 2] - 1] += 1
block_count[self.position[i + 2][j] - 1] += 1
block_count[self.position[i + 2][j + 1] - 1] += 1
block_count[self.position[i + 2][j + 2] - 1] += 1
for k in range(len(block_count)):
if block_count[k] == 1:
block_sum += (1 / Nd) / Nd
block_count = np.zeros(Nd)
if int(column_sum) == 1 and int(block_sum) == 1:
fitness = 1.0
else:
fitness = column_sum * block_sum
self.fitness = fitness
# Sync best position and fitness.
if self.fitness > self.personal_best_fitness:
self.personal_best_fitness = self.fitness
self.personal_best_position = self.position.copy()
if self.fitness > self.global_best_fitness:
self.global_best_fitness = self.fitness
self.global_best_position = self.position.copy()
return
def update_velocity(self):
for i in range(Nd):
for j in range(Nd):
t1 = self.weights[0] * self.velocity[i][j]
t2 = self.weights[1] * random.uniform(0, 1) * (self.personal_best_position[i][j] - self.given[i][j])
t3 = self.weights[2] * random.uniform(0, 1) * (self.global_best_position[i][j] - self.given[i][j])
self.velocity[i][j] = t1 + t2 + t3
if self.velocity[i][j] > 3:
self.velocity[i][j] = -3
if self.velocity[i][j] < -3:
self.velocity[i][j] = 3
def update_position(self):
for i in range(Nd):
for j in range(Nd):
self.position[i][j] = self.position[i][j] + self.velocity[i][j]
if self.position[i][j] >= 9.5:
self.position[i][j] = round(self.position[i][j]) - 9
if self.position[i][j] < .5:
self.position[i][j] = round(self.position[i][j]) + 9
self.mutate(0.5)
class Swarm(object):
globalbestvalid = None
def __init__(self, given, weights, lazy_threshold):
self.nparticles = 500
self.particles = []
self.global_best_position = None
self.global_best_fitness = 0
self.step_best = 0
valid = self.get_valid(given)
for _ in range(self.nparticles):
p = Particle(valid, given, weights, lazy_threshold)
self.particles.append(p)
self.update_global_best()
def get_valid(self, given):
# Determine the legal values that each square can take.
helper = [[[] for j in range(0, Nd)] for i in range(0, Nd)]
for row in range(0, Nd):
for column in range(0, Nd):
for value in range(1, 10):
if given[row][column] == 0 and not (
self.is_column_duplicate(given, column, value) or self.is_block_duplicate(given, row,
column,
value) or self.is_row_duplicate(
given, row, value)):
# Value is available.
helper[row][column].append(value)
elif given[row][column] != 0:
# Given/known value from file.
helper[row][column].append(given[row][column])
break
return helper
def is_row_duplicate(self, values, row, value):
""" Check duplicate in a row. """
for column in range(0, Nd):
if values[row][column] == value:
return True
return False
def is_column_duplicate(self, values, column, value):
""" Check duplicate in a column. """
for row in range(0, Nd):
if values[row][column] == value:
return True
return False
def is_block_duplicate(self, values, row, column, value):
""" Check duplicate in a 3 x 3 block. """
i = 3 * (int(row / 3))
j = 3 * (int(column / 3))
if ((values[i][j] == value)
or (values[i][j + 1] == value)
or (values[i][j + 2] == value)
or (values[i + 1][j] == value)
or (values[i + 1][j + 1] == value)
or (values[i + 1][j + 2] == value)
or (values[i + 2][j] == value)
or (values[i + 2][j + 1] == value)
or (values[i + 2][j + 2] == value)):
return True
else:
return False
def update_global_best(self):
best = self.global_best_fitness
self.step_best = 0
for p in self.particles:
if p.fitness > self.step_best:
self.step_best = p.fitness
if p.global_best_fitness > best:
best = p.global_best_fitness
self.global_best_position = p.global_best_position
self.global_best_fitness = best
for p in self.particles:
p.global_best_fitness = best
p.global_best_position = self.global_best_position
def optimize(self):
for p in self.particles:
p.update_velocity()
p.update_position()
p.update_fitness()
p.update_lazy()
self.update_global_best()
class Sudoku(object):
""" Solves a given Sudoku puzzle using a Particle Swarm Optimization Algorithm. """
def __init__(self):
self.given = None
return
def load(self, given):
self.given = given
return
def solve(self):
Ns = 2000 # Number of the moving steps
s = Swarm(self.given, [.3, .7, .5], 10)
print("Generate particle swarm.")
for step in range(Ns):
if s.global_best_fitness == 1:
return step, s
else:
s.optimize()
print("Step:", step, " Best fitness:", s.step_best)
return -1, 1