Alexander/P-unit_model
2
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

add routine for fitting with const refractory period

Browse Source
This commit is contained in:
alexanderott 2020-06-01 12:18:42 +02:00
parent 859faae812
commit 5a58483edb
1 changed files with 38 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

38
Fitter.py
View File

@ -96,6 +96,24 @@ class Fitter:
return fmin, self.base_model.get_parameters()
def fit_routine_const_ref_period(self, start_parameters):
self.counter = 0
# fit only v_offset, mem_tau, input_scaling, dend_tau
x0 = np.array([start_parameters["mem_tau"], start_parameters["noise_strength"],
start_parameters["input_scaling"], self.tau_a, start_parameters["delta_a"],
start_parameters["dend_tau"], start_parameters["refractory_period"]])
initial_simplex = create_init_simples(x0, search_scale=2)
self.base_model.set_variable("refractory_period", start_parameters["refractory_period"])
# error_list = [error_bf, error_vs, error_sc, error_cv,
# error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope]
error_weights = (0, 1, 1, 1, 1, 1, 1, 1, 1)
fmin = minimize(fun=self.cost_function_without_ref_period,
args=(error_weights,), x0=x0, method="Nelder-Mead",
options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 200, "maxiter": 400})
return fmin, self.base_model.get_parameters()
# similar results to fit routine 1
def fit_routine_2(self, start_parameters):
self.counter = 0
@ -143,6 +161,26 @@ class Fitter:
return sum(error_list)
def cost_function_without_ref_period(self, X, error_weights=None):
self.base_model.set_variable("mem_tau", X[0])
self.base_model.set_variable("noise_strength", X[1])
self.base_model.set_variable("input_scaling", X[2])
self.base_model.set_variable("tau_a", X[3])
self.base_model.set_variable("delta_a", X[4])
self.base_model.set_variable("dend_tau", X[5])
base_stimulus = SinusoidalStepStimulus(self.eod_freq, 0)
# find right v-offset
test_model = self.base_model.get_model_copy()
test_model.set_variable("noise_strength", 0)
v_offset = test_model.find_v_offset(self.baseline_freq, base_stimulus)
self.base_model.set_variable("v_offset", v_offset)
# [error_bf, error_vs, error_sc, error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope]
error_list = self.calculate_errors(error_weights)
return sum(error_list)
def cost_function_all_without_noise(self, X, error_weights=None):
self.base_model.set_variable("mem_tau", X[0])
self.base_model.set_variable("input_scaling", X[1])