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local_search.py
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82 lines (58 loc) · 2.34 KB
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import math
import random
import matplotlib.pyplot as plt
class HillClimbing:
def __init__(self):
self.history = []
def search(self, state, verbose=0):
current = state
while True:
if verbose == 1: print(current)
elif verbose == 2: current.plot(show_conflicts=False)
elif verbose == 3: current.plot(show_conflicts=True)
self.history.append(current)
neighbor = current.best_neighbor()
if neighbor >= current: return current
current = neighbor
def __call__(self, state, verbose=0):
self.search(state, verbose)
def plot_history(self):
plt.figure(figsize=(12, 4))
conflicts = [state.conflicts() for state in self.history]
plt.plot(range(len(conflicts)), conflicts)
plt.xlabel('Iteration')
plt.ylabel('Conflicts')
plt.show()
class SimulatedAnnealing:
def __init__(self):
self.history = []
self.T = None
def search(self, state=None, T0=10, alpha=0.99, tol=1e-8, verbose=0):
self.T = T0
current = state
while True:
clear_output(wait=True)
if verbose == 0: print(f'T = {self.T:.8f}, Conflicts = {current.conflicts():d}')
elif verbose == 1: print(current)
elif verbose == 2: current.plot(show_conflicts=False)
elif verbose == 3: current.plot(show_conflicts=True)
self.history.append(current)
if self.T < tol or current.conflicts() == 0:
return current
neighbor = current.random_neighbor()
delta_E = current.conflicts() - neighbor.conflicts()
if delta_E >= 0:
current = neighbor
elif random.random() < math.exp(delta_E / self.T):
current = neighbor
self.T = alpha * self.T
return current
def __call__(self, state=None, T0=10, alpha=0.99, tol=1e-8, verbose=0):
return self.search(state, T0, alpha, tol, verbose)
def plot_history(self):
plt.figure(figsize=(12, 4))
conflicts = [state.conflicts() for state in self.history]
plt.plot(range(len(conflicts)), conflicts)
plt.xlabel('Iteration')
plt.ylabel('Conflicts')
plt.show()