def __init__(self, app, config):
super().__init__(config)
self.app = app
self.app.from_json(config["app_config"])
self.obs_space = self.app.observation_space().as_json().to_python()
self.action_space = self.app.action_space().as_json().to_python()
# Setup encoder
if ("encoder_array" in config.keys()):
self.encoder = neuro.EncoderArray(config["encoder_array"])
else:
ea_json = {}
if ("encoders" in config.keys()):
ea_json["encoders"] = config["encoders"]
elif ("named_encoders" in config.keys()):
ea_json["named_encoders"] = config["named_encoders"]
ea_json["use_encoders"] = config["use_encoders"]
else:
ea_json["encoders"] = [config["encoder"]]
ea_json["dmin"] = self.obs_space["low"]
ea_json["dmax"] = self.obs_space["high"]
ea_json["interval"] = config["encoder_interval"]
self.encoder = neuro.EncoderArray(ea_json)
if ("decoder_array" in config.keys()):
self.decoder = neuro.DecoderArray(config["decoder_array"])
else:
da_json = {}
if ("decoders" in config.keys()):
da_json["decoders"] = config["decoders"]
elif ("named_decoders" in config.keys()):
da_json["named_decoders"] = config["named_decoders"]
da_json["use_decoders"] = config["use_decoders"]
else:
da_json["decoders"] = [config["decoder"]]
if ("low" in self.action_space.keys()):
da_json["dmin"] = self.action_space["low"]
da_json["dmax"] = self.action_space["high"]
else:
val = self.action_space["max"]
da_json["dmin"] = [0]
da_json["dmax"] = [val - 1]
self.decoder = neuro.DecoderArray(da_json)
self.n_inputs = self.encoder.get_num_neurons()
self.n_outputs = self.decoder.get_num_neurons()
def __init__(self, config, X, y):
split_test_size = config["split_test_size"]
split_seed = config["split_seed"]
self.runtime = config["runtime"]
self.network_filename = config["network_filename"]
self.labels = sorted(np.unique(y))
self.config = config
self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(
X, y, test_size=split_test_size, random_state=split_seed)
ea_json = {}
if ("encoders" in config.keys()):
ea_json["encoders"] = config["encoders"]
elif ("named_encoders" in config.keys()):
ea_json["named_encoders"] = config["named_encoders"]
ea_json["use_encoders"] = config["use_encoders"]
else:
ea_json["encoders"] = [config["encoder"]]
ea_json["dmin"] = [min(X[:, i]) for i in range(len(X[0]))]
ea_json["dmax"] = [max(X[:, i]) for i in range(len(X[0]))]
ea_json["interval"] = config["encoder_interval"]
self.encoder = neuro.EncoderArray(ea_json)
self.n_inputs = self.encoder.get_num_neurons()
self.n_outputs = config["num_outputs"]
def __init__(self, config, X, y):
split_test_size = config["split_test_size"]
split_seed = config["split_seed"]
self.runtime = config["runtime"]
if ("app_params" not in config.keys()):
config["app_params"] = {}
config["app_params"]["split_test_size"] = config["split_test_size"]
config["app_params"]["split_seed"] = config["split_seed"]
else:
split_test_size = config["app_params"]["split_test_size"]
split_seed = config["app_params"]["split_seed"]
self.network_filename = config["network_filename"]
self.labels = sorted(np.unique(y))
self.config = config
if (config["split"] == 1):
self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(
X, y, test_size=split_test_size, random_state=split_seed)
else:
self.X_train = []
self.y_train = []
self.X_test = X
self.y_test = y
if ("encoder_array" in config.keys()):
self.encoder = neuro.EncoderArray(config["encoder_array"])
else:
ea_json = {}
if ("encoders" in config.keys()):
ea_json["encoders"] = config["encoders"]
elif ("named_encoders" in config.keys()):
ea_json["named_encoders"] = config["named_encoders"]
ea_json["use_encoders"] = config["use_encoders"]
else:
ea_json["encoders"] = [config["encoder"]]
if (config["timeseries"] == "false"):
ea_json["dmin"] = [np.min(X[:, i]) for i in range(len(X[0]))]
ea_json["dmax"] = [np.max(X[:, i]) for i in range(len(X[0]))]
elif (config["timeseries"] == "true"):
ea_json["dmin"] = [np.min(X[:, i]) for i in range(len(X[0]))]
ea_json["dmax"] = [np.max(X[:, i]) for i in range(len(X[0]))]
ea_json["interval"] = config["encoder_interval"]
self.encoder = neuro.EncoderArray(ea_json)
# TODO: Encode all of the data as spikes here and store spikes instead of/in addition to data
# TODO: Allow for multiple outputs scenarios
if ("decoder_array" in config.keys()):
self.decoder = neuro.DecoderArray(config["decoder_array"])
else:
da_json = {}
if ("decoders" in config.keys()):
da_json["decoders"] = config["decoders"]
elif ("named_decoders" in config.keys()):
da_json["named_decoders"] = config["named_decoders"]
da_json["use_decoders"] = config["use_decoders"]
else:
da_json["decoders"] = [config["decoder"]]
da_json["dmin"] = [0]
da_json["dmax"] = [len(np.unique(y)) - 1]
self.decoder = neuro.DecoderArray(da_json)
self.n_inputs = self.encoder.get_num_neurons()
self.n_outputs = self.decoder.get_num_neurons()