def step(self):
"""Take a step in the optimization"""
rnd_cross = _random((self.npop, self.ndim))
for i in xrange(self.npop):
t0, t1, t2 = i, i, i
while t0 == i:
t0 = _randint(self.npop)
while t1 == i or t1 == t0:
t1 = _randint(self.npop)
while t2 == i or t2 == t0 or t2 == t1:
t2 = _randint(self.npop)
v = self.population[t0, :] + self.F * (self.population[t1, :] -
self.population[t2, :])
crossover = rnd_cross[i] <= self.C
u = np.where(crossover, v, self.population[i, :])
ri = _randint(self.ndim)
u[ri] = v[ri]
ufit = self.m * self.fun(u)
if ufit < self.fitness[i]:
self.population[i, :] = u
self.fitness[i] = ufit
def bootstrap_indexes(data, n_samples=10000):
"""
Given data points data, where axis 0 is considered to delineate points, return
an generator for sets of bootstrap indexes. This can be used as a list
of bootstrap indexes (with list(bootstrap_indexes(data))) as well.
"""
for _ in range(n_samples):
yield _randint(data.shape[0], size=(data.shape[0], ))
def bootstrap_indexes(data, n_samples=10000):
"""
Given data points data, where axis 0 is considered to delineate points, return
an generator for sets of bootstrap indexes. This can be used as a list
of bootstrap indexes (with list(bootstrap_indexes(data))) as well.
"""
for _ in range(n_samples):
yield _randint(data.shape[0], size=(data.shape[0],))
def sample2():
return _randint(low=0, high=10)
def integer_rewards(rbound, N):
"""
Returns an array of N integer rewards with absolute value <= rbound
"""
return _randint(-rbound,rbound,N)