""",
formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument('-n', "--NRN", help="NEURON TYPE", default='LIF')
parser.add_argument('-a', "--amp",help="Amplitude of the current in pA",\
type=float, default=200.)
parser.add_argument('-d', "--duration",help="Duration of the current step in ms",\
type=float, default=400.)
parser.add_argument('-as', "--amplitudes",
help="ARRAY of Amplitude of different steps in pA",\
type=float, default=[], nargs='*')
parser.add_argument('-ds', "--durations",
help="ARRAY of durations of different steps in ms",\
type=float, default=[], nargs='*')
parser.add_argument('-dl', "--delay",help="Duration of the current step in ms",\
type=float, default=150.)
parser.add_argument('-p', "--post",help="After-Pulse duration of the step (ms)",\
type=float, default=400.)
parser.add_argument("-c", "--color", help="color of the plot", default='k')
parser.add_argument("--save",
default='',
help="save the figures with a given string")
parser.add_argument("-v", "--verbose", help="", action="store_true")
args = parser.parse_args()
from neural_network_dynamics.cells.pulse_protocols import current_pulse_sim
from graphs.my_graph import graphs
mg = graphs()
mg.response_to_current_pulse(*current_pulse_sim(vars(args)))
mg.show()
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_moons
from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier
if __name__ == '__main__':
import sys, os
# visualization module
sys.path.append('../..')
from graphs.my_graph import graphs
mg = graphs('screen')
X, y = make_moons(n_samples=400, noise=0.30, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
# Single Decision Tree Classifier
tree = DecisionTreeClassifier()
tree.fit(X_train, y_train)
# Bagging Classifier
bag_clf = BaggingClassifier(\
DecisionTreeClassifier(),
n_estimators=500, max_samples=0.5, bootstrap=True)
bag_clf.fit(X_train, y_train)
fig, AX = mg.figure(axes=(1, 2))
for ax in AX: