def run_esn(repetition, matrix_path, idx, **kwargs):
"""run the esn with the current configuration
"""
return [
c.Run(**kwargs).load(matrix_path, idx).__enter__()()
for _ in range(repetition)
] if kwargs["transformer"] not in [
t.Transformers.threshold, t.Transformers.identity
] else [c.Run(**kwargs).load(matrix_path, idx).__enter__()()]
def run_net_load_matrix(load_param: c.Res, **kwargs) -> dict:
run_dict = core.Run(**{
**c.esn_gen,
**kwargs
}).load((c.path + load_param.value), randint(0, 9)).__enter__()()
with open(c.path_esn + "_".join(res_name(run_dict)), "wb") as f:
pickle.dump(run_dict, f)
return run_dict
def run_network(out_transf: transf.Transformer = transf.Transformers.sig_prob,
**PSO_kwargs) -> tuple:
PSO_kwargs = map_params(**PSO_kwargs)
load_param = PSO_kwargs.pop("reservoir") if PSO_kwargs.get(
"reservoir", None) else None
run_dict = (run_net_load_matrix(load_param, **PSO_kwargs)
if load_param else core.Run(**{
**c.esn_gen,
**PSO_kwargs
}).__enter__()())
pprint(f"dumped conf : {PSO_kwargs}")
return ((raw_out := run_dict["output"]),
out_transf.value(0.8,
transf._identity)(raw_out), run_dict["desired"])
def test():
data = np.loadtxt('/home/vimmoos/NN/testESN/MackeyGlass_t17.txt')
with c.Run(
**{
"data": data,
"in_out": 1,
"reservoir": 100,
"train_len": 2000,
"test_len": 2000,
"init_len": 100,
"leaking_rate": 0.3,
"spectral_radius": 1.25,
"density": .5,
"reg": 1e-8
}) as gen:
return gen()
def test_midi():
train_len = test_len = 970
init_len = 100
music = it.repeat(
cmidi.exec_proc_dict(emidi.example_proc_dict)["matrixs"][0], 20)
data = c.Data(np.array(list(music)), None, init_len, train_len, test_len)
with c.Run(
**{
"data": data,
"in_out": 6,
"reservoir": 500,
"error_len": 500,
"leaking_rate": 0.3,
"spectral_radius": 0.8,
"density": .5,
"reg": 1e-8,
"transformer": ta.user_threshold(0.5),
}) as gen:
return gen()
def test_randomMatrix():
import random
train_len = test_len = 2000
data = np.array([
np.array([random.randint(0, 10) / 10])
for _ in range(train_len + test_len + 1)
])
with c.Run(
**{
"data": data,
"in_out": 1,
"reservoir": 100,
"train_len": train_len,
"test_len": test_len,
"init_len": 100,
"leaking_rate": 0.3,
"spectral_radius": 1.25,
"density": .5,
"reg": 1e-8
}) as gen:
return gen()
def test_generated():
train_len = test_len = 1200
init_len = 200
music = (tgen.test_patterns[2] * 300)
data = c.Data(np.array(list(~music)), music.tempo, init_len, train_len,
test_len)
with c.Run(
**
{
"data": data,
"reservoir": 300,
"in_out": 9,
"leaking_rate": 0.3,
"reg": 1e-8,
"transformer": ta.Transformers.pow_prob,
"t_param": 1,
"t_squeeze": np.tanh,
}).load(
"/home/vimmoos/NN/resources/reservoir/0.18333333333333335_0.04_2000",
0) as gen:
return gen()