def train_PSO_PSO_ANN(inputs, res_ex, boundary, draw_graph=False):
dim = 9 + N_H_LAYER * 2
opso = PSO(dim,
lambda param: fitness_mean(inputs, res_ex, 50, 25,
*scale_args(param, boundary)),
max_iter=20, n_particle=10, n_neighbor=4,
inertia_start=0.5, inertia_end=0.5, comparator=minimise,
min_bound=MIN_BOUND, max_bound=MAX_BOUND)
print("\nRunning...\n")
if draw_graph:
opso.set_graph_config(inputs=inputs, res_ex=res_ex, opso=True)
opso.run()
return opso
def train_ANN_PSO(inputs,
res_ex,
max_iter,
n_particle,
n_neighbor,
nb_h_layers,
nb_neurons_layer,
min_bound,
max_bound,
cognitive_trust,
social_trust,
inertia_start,
inertia_end,
velocity_max,
activations,
draw_graph=False):
nb_neurons = set_nb_neurons(len(inputs[0]), nb_neurons_layer, nb_h_layers)
# print(nb_neurons, n_neighbor, activations)
ann = ANN(nb_neurons=nb_neurons,
nb_layers=len(nb_neurons),
activations=activations)
dim = sum(nb_neurons[i] * nb_neurons[i + 1]
for i in range(len(nb_neurons) - 1)) + len(nb_neurons) - 1
pso = PSO(dim,
lambda params: fitness_for_ann(params, ann, inputs, res_ex),
max_iter=max_iter,
n_particle=n_particle,
n_neighbor=n_neighbor,
comparator=minimise,
min_bound=min_bound,
max_bound=max_bound,
cognitive_trust=cognitive_trust,
social_trust=social_trust,
inertia_start=inertia_start,
inertia_end=inertia_end,
velocity_max=velocity_max,
endl='',
version=2007)
if draw_graph:
pso.set_graph_config(inputs=inputs, res_ex=res_ex)
pso.run()
return pso, ann
def train_PSO_PSO_ANN(inputs,
res_ex,
boundary,
opso_arg,
pso_arg,
draw_graph=False):
dim = 11
opso = PSO(dim,
lambda param: fitness_mean(inputs, res_ex, *pso_arg.values(),
*scale_args(param, boundary)),
**opso_arg,
comparator=minimise,
min_bound=train_help.MIN_BOUND,
max_bound=train_help.MAX_BOUND,
endl="11")
print("\nRunning...\n")
if draw_graph:
opso.set_graph_config(inputs=inputs, res_ex=res_ex, opso=True)
opso.run()
return opso
