def _anneal(self, objfunc, schedule, probfunc):
''' our actual annealing method
'''
k_current = 1
# get our initial state
dim = len(objfunc.maxs)
params = [(objfunc.mins[i], func.maxs[i] - func.mins[i]) for i in xrange(dim)]
seeds = np.array(lhs([uniform]*dim, params, 1, True, np.identity(dim))).T[0]
state = self._get_state(objfunc, [0.0]*dim, seeds)
state = [s + objfunc.mins[i] for i, s in enumerate(state)]
best = np.array(state)
best_val = objfunc.eval(best)
t_current = self.t_initial
while t_current > self.tfinal:
# run until temperature is low enough
k_current += 1
t_current = schedule(k_current)
state = self._get_state(objfunc, state, [1.0]*dim)
current_val = objfunc.eval(state)
if uniform.rvs() < probfunc(t_current, current_val, best_val, dim):
best = state
best_val = current_val
return best
def _test_anneal(self, objfunc, schedule, probfunc, iterations):
''' our actual annealing method
'''
bests = []
k_current = 1
# get our initial state
dim = len(objfunc.maxs)
params = [(objfunc.mins[i], objfunc.maxs[i] - objfunc.mins[i]) for i in xrange(dim)]
seeds = np.array(lhs([uniform]*dim, params, 1, True, np.identity(dim))).T[0]
state = self._get_state(objfunc, [0.0]*dim, seeds)
state = [s + objfunc.mins[i] for i, s in enumerate(state)]
best = np.array(state)
start_val = best_val = objfunc.eval(best)
t_current = self.t_initial
bests.append(best_val)
for _ in range(iterations):
k_current += 1
t_current = schedule(k_current)
state = self._get_state(objfunc, state, [1.0]*dim)
current_val = objfunc.eval(state)
if uniform.rvs() < probfunc(t_current, current_val, best_val, dim):
best = state
best_val = current_val
bests.append(best_val)
return np.array(bests)
def _generate_pop(self, size, func):
''' initializes our population
'''
dim = len(func.maxs)
params = [(func.mins[i], func.maxs[i] - func.mins[i]) for i in xrange(dim)]
seeds = np.array(lhs([uniform]*dim, params, size, True, np.identity(dim))).T
seeds.astype(np.float32)
flies = [FireFly(func, self, np.array(seeds[i])) for i in xrange(size)]
return flies
def _generate_pop(self, size, func):
''' initializes our population
'''
dim = len(func.maxs)
params = [(func.mins[i], func.maxs[i] - func.mins[i])
for i in xrange(dim)]
seeds = np.array(
lhs([uniform] * dim, params, size, True, np.identity(dim))).T
seeds.astype(np.float32)
flies = [FireFly(func, self, np.array(seeds[i])) for i in xrange(size)]
return flies
def _generate_pop(self, psize, func):
''' initializes our population
'''
mins = np.array(func.mins)
maxs = np.array(func.maxs)
lows = -np.abs(maxs - mins)
hihs = np.abs(maxs - mins)
# calculate our initial positions
dim = len(func.maxs)
params = [(mins[i], maxs[i] - mins[i]) for i in xrange(dim)]
seeds = np.array(lhs([uniform]*dim, params, psize, True, np.identity(dim))).T
seeds.astype(np.float32)
# calculate our initial velocities
v_seeds = np.array([uniform.rvs(lows[i], hihs[i], size=psize) for i in range(dim)]).T
return [Particle(func, np.array(seeds[i]), np.array(v_seeds[i])) for i in xrange(psize)]
def _generate_pop(self, psize, func):
''' initializes our population
'''
mins = np.array(func.mins)
maxs = np.array(func.maxs)
lows = -np.abs(maxs - mins)
hihs = np.abs(maxs - mins)
# calculate our initial positions
dim = len(func.maxs)
params = [(mins[i], maxs[i] - mins[i]) for i in xrange(dim)]
seeds = np.array(
lhs([uniform] * dim, params, psize, True, np.identity(dim))).T
seeds.astype(np.float32)
# calculate our initial velocities
v_seeds = np.array(
[uniform.rvs(lows[i], hihs[i], size=psize) for i in range(dim)]).T
return [
Particle(func, np.array(seeds[i]), np.array(v_seeds[i]))
for i in xrange(psize)
]
def test_correlation_structure(self):
cm = numpy.array([[1,.8],[.8,1]])
c=lhs.lhs([st.norm,st.beta], [(50,1),(10,2)],2000,False, cm)
r,p = st.spearmanr(c[0],c[1])
assert_almost_equal(r,0.8,1)
assert p < 0.05
def test_correlation_structure(self):
cm = numpy.array([[1, .8], [.8, 1]])
c = lhs.lhs([st.norm, st.beta], [(50, 1), (10, 2)], 2000, False, cm)
r, p = st.spearmanr(c[0], c[1])
assert_almost_equal(r, 0.8, 1)
assert p < 0.05
