From 302aee34d6340789be4cda9f209e9e58cafae926 Mon Sep 17 00:00:00 2001 From: alexanderott Date: Wed, 22 Jul 2020 18:06:12 +0200 Subject: [PATCH 1/2] add Function to plot histograms of error values --- helperFunctions.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/helperFunctions.py b/helperFunctions.py index ba6c884..c5a3fe2 100644 --- a/helperFunctions.py +++ b/helperFunctions.py @@ -5,6 +5,25 @@ from scipy.optimize import curve_fit import functions as fu from numba import jit import matplotlib.pyplot as plt +import time + + +def plot_errors(list_errors): + names = ["error_bf", "error_vs", "error_sc", "error_f_inf", + "error_f_inf_slope", "error_f_zero", "error_f_zero_s", "f_zero_curve"] + data = np.array(list_errors) + + fig, axes = plt.subplots(2, 4, figsize=(10, 8)) + + for i in range(8): + col = i % 4 + row = int(i/4.0) + + axes[row, col].hist(data[:, i]) + axes[row, col].set_title(names[i]) + + plt.savefig("figures/error_distributions/error_distribution_{}.png".format(time.strftime("%H:%M:%S"))) + plt.close() def fit_clipped_line(x, y): From ab8948ec412afd27fca10c36f342bbd84a1250b7 Mon Sep 17 00:00:00 2001 From: alexanderott Date: Wed, 22 Jul 2020 18:07:00 +0200 Subject: [PATCH 2/2] save and plot all error values that occured during fitting, delete unused fit routines --- Fitter.py | 52 ++++++---------------------------------------------- 1 file changed, 6 insertions(+), 46 deletions(-) diff --git a/Fitter.py b/Fitter.py index f13d19e..4870170 100644 --- a/Fitter.py +++ b/Fitter.py @@ -9,6 +9,7 @@ import numpy as np from warnings import warn from scipy.optimize import minimize import time +from helperFunctions import plot_errors import matplotlib.pyplot as plt @@ -53,6 +54,7 @@ class Fitter: self.f_zero_curve_freq = np.array([]) self.f_zero_curve_time = np.array([]) + self.errors = [] # self.tau_a = 0 @@ -125,51 +127,9 @@ class Fitter: error_weights = (0, 2, 2, 2, 1, 1, 1, 1, 0, 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": 600, "maxiter": 1200}) + options={"initial_simplex": initial_simplex, "xatol": 0.001, "maxfev": 600, "maxiter": 400}) - 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 - - 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}) + plot_errors(self.errors) 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_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: self.smallest_error = sum(error_list) self.best_parameters_found = X - + self.errors.append(error_list) return sum(error_list) def cost_function_without_ref_period(self, X, error_weights=None):