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

code clean up

Browse Source
This commit is contained in:
a.ott
2020-07-28 10:54:02 +02:00
parent 90ccb4459d
commit c5b72bec26
3 changed files with 28 additions and 272 deletions
Download Patch File Download Diff File Expand all files Collapse all files

127
tests/old_fit_routines.py
View File

@@ -1,127 +0,0 @@
def fit_routine_1(self, cell_data=None):
global SAVE_PATH_PREFIX
SAVE_PATH_PREFIX = "fit_routine_1_"
# errors: [error_bf, error_vs, error_sc, error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope]
self.counter = 0
# fit only v_offset, mem_tau, noise_strength, input_scaling
x0 = np.array([0.02, 0.03, 70])
initial_simplex = create_init_simples(x0, search_scale=2)
error_weights = (1, 1, 1, 1, 1, 0, 0)
fmin_step1 = minimize(fun=self.cost_function_with_fixed_adaption_tau, args=(self.tau_a, self.delta_a, error_weights),
x0=x0, method="Nelder-Mead",
options={"initial_simplex": initial_simplex})
res_parameters_step1 = self.base_model.get_parameters()
if cell_data is not None:
print("##### After step 1: (fixed adaption)")
print_comparision_cell_model(cell_data, res_parameters_step1)
self.counter = 0
x0 = np.array([res_parameters_step1["mem_tau"], res_parameters_step1["noise_strength"],
res_parameters_step1["input_scaling"], res_parameters_step1["tau_a"],
res_parameters_step1["delta_a"]])
initial_simplex = create_init_simples(x0, search_scale=2)
error_weights = (1, 1, 1, 1, 1, 2, 4)
fmin_step2 = minimize(fun=self.cost_function_all, args=(error_weights), x0=x0, method="Nelder-Mead",
options={"initial_simplex": initial_simplex})
res_parameters_step2 = self.base_model.get_parameters()
if cell_data is not None:
print("##### After step 2: (Everything)")
# print_comparision_cell_model(cell_data, res_parameters_step2)
return fmin_step2, res_parameters_step2
def fit_routine_2(self, cell_data=None):
global SAVE_PATH_PREFIX
SAVE_PATH_PREFIX = "fit_routine_2_"
# errors: [error_bf, error_vs, error_sc, error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope]
self.counter = 0
# fit only v_offset, mem_tau, noise_strength, input_scaling
x0 = np.array([0.02, 0.03, 70])
initial_simplex = create_init_simples(x0, search_scale=2)
error_weights = (1, 1, 5, 1, 2, 0, 0)
fmin = minimize(fun=self.cost_function_with_fixed_adaption_tau,
args=(self.tau_a, self.delta_a, error_weights), x0=x0, method="Nelder-Mead",
options={"initial_simplex": initial_simplex})
res_parameters = self.base_model.get_parameters()
return fmin, res_parameters
def fit_routine_3(self, cell_data=None):
global SAVE_PATH_PREFIX
SAVE_PATH_PREFIX = "fit_routine_3_"
# errors: [error_bf, error_vs, error_sc, error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope]
self.counter = 0
# fit only v_offset, mem_tau, noise_strength, input_scaling, dend_tau
x0 = np.array([0.02, 0.03, 70, 0.001])
initial_simplex = create_init_simples(x0, search_scale=2)
error_weights = (1, 1, 5, 1, 2, 0, 0)
fmin = minimize(fun=self.cost_function_with_fixed_adaption_with_dend_tau,
args=(self.tau_a, self.delta_a, error_weights), x0=x0, method="Nelder-Mead",
options={"initial_simplex": initial_simplex})
res_parameters = self.base_model.get_parameters()
return fmin, res_parameters
def fit_routine_4(self, cell_data=None, start_parameters=None):
global SAVE_PATH_PREFIX
SAVE_PATH_PREFIX = "fit_routine_4_"
# errors: [error_bf, error_vs, error_sc, error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope]
self.counter = 0
# fit only v_offset, mem_tau, input_scaling, dend_tau
if start_parameters is None:
x0 = np.array([0.02, 70, 0.001])
else:
x0 = np.array([start_parameters["mem_tau"], start_parameters["noise_strength"],
start_parameters["input_scaling"], start_parameters["dend_tau"]])
initial_simplex = create_init_simples(x0, search_scale=2)
error_weights = (0, 5, 15, 1, 2, 1, 0)
fmin = minimize(fun=self.cost_function_with_fixed_adaption_with_dend_tau,
args=(self.tau_a, self.delta_a, error_weights),
x0=x0, method="Nelder-Mead",
options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 400, "maxiter": 400})
res_parameters = fmin.x
# print_comparision_cell_model(cell_data, self.base_model.get_parameters())
self.counter = 0
x0 = np.array([self.tau_a,
self.delta_a, res_parameters[0]])
initial_simplex = create_init_simples(x0, search_scale=2)
error_weights = (0, 1, 1, 2, 2, 4, 2)
fmin = minimize(fun=self.cost_function_only_adaption,
args=(error_weights,), x0=x0, method="Nelder-Mead",
options={"initial_simplex": initial_simplex, "xatol": 0.001})
print(fmin)
print_comparision_cell_model(cell_data, self.base_model.get_parameters())
#
# # self.counter = 0
# # x0 = np.array([res_parameters[0],
# # res_parameters[1], self.tau_a,
# # self.delta_a, res_parameters[2]])
# # initial_simplex = create_init_simples(x0, search_scale=2)
# # error_weights = (1, 3, 1, 2, 1, 3, 2)
# # fmin = minimize(fun=self.cost_function_all_without_noise,
# # args=(error_weights,), x0=x0, method="Nelder-Mead",
# # options={"initial_simplex": initial_simplex, "xatol": 0.001})
# # res_parameters = self.base_model.get_parameters()
# #
# # print_comparision_cell_model(cell_data, self.base_model.get_parameters())
#
# self.counter = 0
# x0 = np.array([res_parameters[0], start_parameters["noise_strength"],
# res_parameters[1], res_parameters[2],
# res_parameters[3], res_parameters[4]])
# initial_simplex = create_init_simples(x0, search_scale=2)
# error_weights = (0, 1, 2, 1, 1, 3, 2)
# fmin = minimize(fun=self.cost_function_all,
# args=(error_weights,), x0=x0, method="Nelder-Mead",
# options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxiter": 599})
# res_parameters = self.base_model.get_parameters()
return fmin, self.base_model.get_parameters()