def generate_initial_population(pop_size=10, number_of_gd_steps=50):
p1 = GradientDescentAdam(R, lr=alpha)
initial_pop = []
meta_g = None
vec_len = None
for x in range(pop_size):
_, _, _, _, G, S = p1.optimize(number_of_gd_steps, ks=ks)
v, meta = roll(G, S)
meta_g = meta
vec_len = len(v)
initial_pop.append(v.tolist())
problem = Problem(vec_len, 1)
problem.types[:] = Real(0, 1000)
problem.function = partial(fit, p1, meta_g)
generator = RandomGenerator()
population = []
for i in range(pop_size):
p = generator.generate(problem)
p.variables = initial_pop[i]
problem.evaluate(p)
population.append(p)
return problem, population, meta_g, generator
def generate_initial_population(number_of_gd_steps=50):
p1 = GradientDescentAdam(R, lr=alpha)
initial_pop = []
scale = 0.01
noise_scale=0.0000001
Gv, Sv = p1.construct_starting_point(ks, scale=scale)
_, _, _, _, G, S = p1.optimize(G=Gv.copy(), S=Sv.copy(), steps=number_of_gd_steps, ks=ks)
v, meta = roll(G, S)
initial_pop.append(v.tolist())
GvNoise = []
for x in Gv:
GvNoise.append(x+np.random.randn(*x.shape)*noise_scale)
SvNoise = []
for x in Sv:
n=[]
for y in x:
n.append(y+np.random.randn(*y.shape)*noise_scale)
SvNoise.append(n)
_, _, _, _, G, S = p1.optimize(G=GvNoise.copy(), S=SvNoise.copy(), steps=number_of_gd_steps, ks=ks)
v, meta = roll(G, S)
initial_pop.append(v.tolist())
np.random.rand()
_, _, _, _, G, S = p1.optimize(number_of_gd_steps, ks=ks)
v, meta = roll(G, S)
meta_g = meta
vec_len = len(v)
initial_pop.append(v.tolist())
problem = Problem(vec_len, 1)
problem.types[:] = Real(0, 1000)
problem.function = partial(fit, p1, meta_g)
generator = RandomGenerator()
population = []
for i in range(3):
p = generator.generate(problem)
p.variables = initial_pop[i]
problem.evaluate(p)
population.append(p)
return problem, population, meta_g, p1
