def generate_random_point(self):
co_od = []
for _ in xrange(self.dim):
co_od.append(np.random.uniform(self.range_lower_limit, self.range_upper_limit))
self.coords = copy.deepcopy(co_od)
self.z = evaluate(self.coords)
def generate_random_point(self):
co_od = []
for _ in xrange(self.dim):
co_od.append(
np.random.uniform(self.range_lower_limit,
self.range_upper_limit))
self.coords = copy.deepcopy(co_od)
self.z = evaluate(self.coords)
def generate_neighbour(self):
for ix in xrange(self.dim):
offset = (2 * np.random.random() - 1.0) * 0.5
self.coords[ix] += offset
if self.coords[ix] < self.range_lower_limit:
self.coords[ix] = self.range_lower_limit
elif self.coords[ix] > self.range_upper_limit:
self.coords[ix] = self.range_upper_limit
self.z = evaluate(self.coords)
def generate_neighbour(self):
for ix in xrange(self.dim):
offset = (2 * np.random.random() - 1.0) * 0.5
self.coords[ix] += offset
if self.coords[ix] < self.range_lower_limit:
self.coords[ix] = self.range_lower_limit
elif self.coords[ix] > self.range_upper_limit:
self.coords[ix] = self.range_upper_limit
self.z = evaluate(self.coords)
def evaluate_point(self):
self.z = evaluate(self.coords)
def evaluate_point(self):
self.z = evaluate(self.coords)