def _gset_tail_settings(self):
"""Compute settings relevant to tail selection
"""
tails_to_anal = []
if self.anal_right:
tails_to_anal.append(Tail.right)
if self.anal_left:
tails_to_anal.append(Tail.left)
self.tails_to_anal = tuple(tails_to_anal)
if bool(self.tails_to_anal):
self.analyze_tails = True
nd = NormalDist()
self.alpha_qntl = nd.inv_cdf(1 - len(self.tails_to_anal) / 2 *
self.alpha_signif)
else:
self.analyze_tails = False
self._enforce_null_tail_analysis_opts()
def one_one(low, high, n, p, f, it, fit):
dec = round(mt.pow(1.5, -0.25), 15)
np.set_printoptions(precision=p)
#initialization
values = np.random.uniform(low, high, n)
sigma = 0.2
print("Init population")
print(values, sigma, '\n')
#first aptitude
ap = f(values)
print("Aptitude of the population")
print(values, sigma, ap, '\n')
ii = 1
it += 1
while (ii < it):
print("*****Iteration ", ii, "*****")
print(values, sigma, ap)
al_g = np.zeros((n, ))
N = NormalDist(0, sigma)
for i in range(0, n):
al = rdm.uniform(0, 1)
al_g[i] = N.inv_cdf(al)
print_al(i + 1, al, al_g[i])
new_values = np.copy(values)
for i in range(0, n):
new_values[i] += al_g[i]
if f(new_values) <= f(values):
values = np.copy(new_values)
sigma *= 1.5
else:
sigma *= dec
sigma = round(sigma, 30)
print(values, sigma, f(values), '\n')
ii += 1
def mu_lam(low, high, n, mu, lam, p, f, it, fit):
dec = round(mt.pow(1.5, -0.25), 15)
det = round(mt.sqrt(2 * mt.sqrt(n)), 15)
np.set_printoptions(precision=p)
#initialization
values = np.random.uniform(low, high, (mu, n))
sigma = np.full((mu, n), 0.2)
print("Init population")
print_po(values, sigma, mu)
#first aptitude
ap = calc(values, f, mu)
print("Aptitude of the population")
print_ap(values, sigma, ap, mu)
ii = 1
it += 1
while (ii < it):
values_n = np.zeros((lam, n))
sigma_n = np.zeros((lam, n))
f_n = np.zeros((lam, 1))
print("*****Iteration ", ii, "*****")
for i in range(0, lam):
#Select the parents
print("Descendent N", i + 1)
print("Cross")
mu_ = mu - 1
indx1 = rdm.randint(0, mu_)
indx2 = rdm.randint(0, mu_)
if f(values[indx1]) < f(values[indx2]):
p1 = indx1
else:
p1 = indx2
print(indx1, '-', indx2, ' => ', p1, ' => ', values[p1], sigma[p1])
indx1 = rdm.randint(0, mu_)
indx2 = rdm.randint(0, mu_)
if f(values[indx1]) < f(values[indx2]):
p2 = indx1
else:
p2 = indx2
print(indx1, '-', indx2, ' => ', p2, ' => ', values[p2], sigma[p2])
child = np.zeros(n)
sigma_ = np.zeros(n)
for j in range(0, n):
child[j] = np.mean([values[p1][j], values[p2][j]])
sigma_[j] = mt.sqrt(sigma[p1][j] * sigma[p2][j])
al_g = np.zeros(n)
N1 = NormalDist(0, det)
print("Mutation")
for j in range(0, n):
al = rdm.uniform(0, 1)
sigma_[j] *= mt.exp(N1.inv_cdf(al))
N2 = NormalDist(0, sigma_[j])
al = rdm.uniform(0, 1)
child[j] += N2.inv_cdf(al)
print(child, sigma_, '\n')
values_n[i] = child
sigma_n[i] = sigma_
f_n[i] = f(child)
print("Aptitude of the all population")
print_ap(values, sigma, ap, mu)
L = np.hstack([values_n, sigma_n, f_n.reshape(1, lam).T])
L = L[L[:, 2 * n].argsort(axis=0)]
L = np.delete(L, slice(mu, lam), axis=0)
values = np.copy(L[:, [0, n - 1]])
sigma = np.copy(L[:, [n, (2 * n) - 1]])
ap = calc(values, f, mu)
print("Aptitude of the new population")
print_ap(values, sigma, ap, mu)
#print(values,sigma,f(values),'\n')
ii += 1