cost += fit(model, loss, opt, X_train[:, start:end, :],
Y_train[start:end])
# Predicting samples from evaluating set
preds = predict(model, X_val)
# Calculates accuracy
acc = np.mean(preds == Y_val)
return 1 - acc
# Number of agents and decision variables
n_agents = 10
n_variables = 2
# Lower and upper bounds (has to be the same size as `n_variables`)
lower_bound = [0, 0]
upper_bound = [1, 1]
# Creates the space, optimizer and function
space = SearchSpace(n_agents, n_variables, lower_bound, upper_bound)
optimizer = PSO()
function = Function(lstm)
# Bundles every piece into Opytimizer class
opt = Opytimizer(space, optimizer, function)
# Runs the optimization task
opt.start(n_iterations=100)
from opytimizer.optimizers.swarm import PSO
# One should declare a hyperparameters object based
# on the desired algorithm that will be used
params = {'w': 0.7, 'c1': 1.7, 'c2': 1.7}
# Creates a PSO optimizer
o = PSO(params=params)
