def _add_transform_genes(self):
"""Sets up for evolution of the DSHW model."""
self._alleles.add(pu.make_real_gene(1, 0, 1, .1), weight=1) # alpha
self._alleles.add(pu.make_real_gene(1, 0, 1, .1), weight=1) # beta
self._alleles.add(pu.make_real_gene(1, 0, 1, .1), weight=1) # gamma
self._alleles.add(pu.make_real_gene(1, 0, 1, .1), weight=1) # omega
self._alleles.add(pu.make_real_gene(1, 0, 1, .1), weight=1) # phi
self._loci_list += ['alpha', 'beta', 'gamma', 'omega', 'phi']
def _add_transform_genes(self):
"""Sets up for evolution of the DSHW model."""
self._alleles.add(pu.make_real_gene(1, 0, 1, .1), weight=1) # alpha
self._alleles.add(pu.make_real_gene(1, 0, 1, .1), weight=1) # beta
self._alleles.add(pu.make_real_gene(1, 0, 1, .1), weight=1) # gamma
self._alleles.add(pu.make_real_gene(1, 0, 1, .1), weight=1) # omega
self._alleles.add(pu.make_real_gene(1, 0, 1, .1), weight=1) # phi
self._loci_list += ['alpha', 'beta', 'gamma', 'omega', 'phi']
def _add_transform_genes(self):
"""Sets up for evolution of the ESN model."""
self._alleles.add(pu.make_int_gene(1, 10, 500, 25), weight=1) # Network size
self._alleles.add(pu.make_real_gene(1, 0, 1, 0.05), weight=1) # Leak rate
self._alleles.add(pu.make_real_gene(1, 0.1, 0.75, 0.05), weight=1) # Input scaling
self._alleles.add(pu.make_real_gene(1, 0, 1, 0.05), weight=1) # Bias scaling
self._alleles.add(pu.make_real_gene(1, 0.5, 2, 0.05), weight=1) # Spectral radius
# We don't want too many seeds per evolutions, but we don't want to
# always evolve on the same 5 networks either:
self._alleles.add(pu.make_choice_gene(
1, np.random.random_integers(0, 2**16, 5)), weight=1) # Seed
# Grid optimization showed that for a training length of 336, with
# other params set based on previous gridopts and operating on the
# total dataset rather than single AMS'es, optimal ridge was ~5. Scaled
# thus 5/336=0.015.
self._alleles.add(pu.make_choice_gene(
1, [0.0001/self._max_hindsight_hours]), weight=1) # Scaled ridge
self._loci_list += ['size', 'leak', 'in_scale',
'bias_scale', 'spectral', 'seed', 'ridge' ]
def _add_lambda_gene(self):
self._alleles.add(pu.make_real_gene(1, 0, 9, 0.2))
def _add_transform_genes(self):
"""Sets up for evolution of the ARIMA model."""
self._alleles.add(pu.make_real_gene(
1, 0, 1, 0.1)) # Dummy to make 1D crossover work in Pyevolve
self._loci_list += ['crossover_dummy']
def _add_lambda_gene(self):
self._alleles.add(pu.make_real_gene(1, 0, 9, 0.2))
def add_genes(self, alleles, loci_list):
alleles.add(pu.make_real_gene(len(self._temp_columns), 0, 1, .1),
weight=1)
loci_list += ['temp_weights']
def add_genes(self, alleles, loci_list):
alleles.add(pu.make_real_gene(len(self._temp_columns), 0, 1, .1), weight=1)
loci_list += ['temp_weights']
def _add_transform_genes(self):
"""Sets up for evolution of the ARIMA model."""
self._alleles.add(pu.make_real_gene(1, 0, 1, 0.1)) # Dummy to make 1D crossover work in Pyevolve
self._loci_list += ['crossover_dummy']
