Created
December 2, 2012 00:02
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hill_climb_params.py
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import numpy as np | |
import copy | |
n_params = 5 | |
upper_bound = 5 | |
lower_bound = 0 | |
def get_neighbors(solution): | |
neighbors = [] | |
for i in range(n_params): | |
x = copy.deepcopy(solution) | |
if x[i] < upper_bound: | |
x[i] += 1 | |
neighbors.append(x) | |
x = copy.deepcopy(solution) | |
if x[i] > lower_bound: | |
x[i] -= 1 | |
neighbors.append(x) | |
return neighbors | |
def get_cost(solution): | |
cost = 0 | |
for i,param in enumerate(solution): | |
cost += (-1.)**i * param**(i+1) | |
return cost | |
def hill_climb(): | |
initial_solution = np.random.randint(lower_bound, upper_bound, n_params) | |
current_solution = initial_solution | |
print 'initial solution', initial_solution | |
current_cost = get_cost(initial_solution) | |
step = 1 | |
while True: | |
#try to replace each single component w/ its neighbors | |
lowest_cost = current_cost | |
lowest_solution = current_solution | |
print 'hill-climbing cost at step %6d: %d' % (step, lowest_cost) | |
neighbors = get_neighbors(current_solution) | |
for new_solution in neighbors: | |
neighbor_cost = get_cost(new_solution) | |
if neighbor_cost < lowest_cost: | |
lowest_cost = neighbor_cost | |
lowest_solution = new_solution | |
if lowest_cost >= current_cost: | |
break | |
else: | |
current_solution= lowest_solution | |
current_cost = lowest_cost | |
step += 1 | |
return current_solution | |
print hill_climb() |
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