Alexander/P-unit_model
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save and plot all error values that occured during fitting, delete unused fit routines

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alexanderott 2020-07-22 18:07:00 +02:00
parent 302aee34d6
commit ab8948ec41
1 changed files with 6 additions and 46 deletions
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52
Fitter.py
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@ -9,6 +9,7 @@ import numpy as np
from warnings import warn from warnings import warn
from scipy.optimize import minimize from scipy.optimize import minimize
import time import time
from helperFunctions import plot_errors
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
@ -53,6 +54,7 @@ class Fitter:
self.f_zero_curve_freq = np.array([]) self.f_zero_curve_freq = np.array([])
self.f_zero_curve_time = np.array([]) self.f_zero_curve_time = np.array([])
self.errors = []
# self.tau_a = 0 # self.tau_a = 0
@ -125,51 +127,9 @@ class Fitter:
error_weights = (0, 2, 2, 2, 1, 1, 1, 1, 0, 1) error_weights = (0, 2, 2, 2, 1, 1, 1, 1, 0, 1)
fmin = minimize(fun=self.cost_function_all, fmin = minimize(fun=self.cost_function_all,
args=(error_weights,), x0=x0, method="Nelder-Mead", args=(error_weights,), x0=x0, method="Nelder-Mead",
options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 600, "maxiter": 1200}) options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 600, "maxiter": 400})
return fmin, self.base_model.get_parameters() plot_errors(self.errors)
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
x0 = np.array([start_parameters["mem_tau"], start_parameters["input_scaling"], # mem_tau, input_scaling
start_parameters["delta_a"], start_parameters["dend_tau"]]) # delta_a, dend_tau
initial_simplex = create_init_simples(x0, search_scale=2)
error_weights = (0, 1, 1, 1, 1, 3, 2, 3, 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, "maxfev": 100, "maxiter": 400})
best_pars = fmin.x
x0 = np.array([best_pars[0], start_parameters["noise_strength"],
best_pars[1], self.tau_a, best_pars[2],
best_pars[3]])
initial_simplex = create_init_simples(x0, search_scale=2)
# 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, 2, 2, 2, 2, 1, 1, 1, 1)
fmin = minimize(fun=self.cost_function_all,
args=(error_weights,), x0=x0, method="Nelder-Mead",
options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 100, "maxiter": 400})
return fmin, self.base_model.get_parameters() return fmin, self.base_model.get_parameters()
@ -204,11 +164,11 @@ class Fitter:
# [error_bf, error_vs, error_sc, error_f_inf, error_f_inf_slope, error_f_zero, error_f_zero_slope] # [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) error_list = self.calculate_errors(error_weights)
print("sum: {:.2f}, ".format(sum(error_list))) # print("sum: {:.2f}, ".format(sum(error_list)))
if sum(error_list) < self.smallest_error: if sum(error_list) < self.smallest_error:
self.smallest_error = sum(error_list) self.smallest_error = sum(error_list)
self.best_parameters_found = X self.best_parameters_found = X
self.errors.append(error_list)
return sum(error_list) return sum(error_list)
def cost_function_without_ref_period(self, X, error_weights=None): def cost_function_without_ref_period(self, X, error_weights=None):