78 lines
2.1 KiB
Python
78 lines
2.1 KiB
Python
|
|
from ModelFit import get_best_fit
|
|
import numpy as np
|
|
import os
|
|
import pandas
|
|
import matplotlib.pyplot as plt
|
|
import statsmodels.api as sm
|
|
|
|
|
|
def main():
|
|
|
|
|
|
folder = "results/final_1/"
|
|
# input len(cells) x len(variables) 2D array
|
|
variable_order = ['Burstiness', 'baseline_frequency', 'coefficient_of_variation',
|
|
'f_inf_slope', 'f_zero_slope', 'serial_correlation', 'vector_strength']
|
|
behaviour, error = get_variables(folder, variable_order)
|
|
|
|
|
|
df_behaviour = pandas.DataFrame(behaviour, columns=variable_order)
|
|
# print(df)
|
|
gamma_glm = sm.GLM(error, df_behaviour, sm.families.Gamma())
|
|
fitted_model = gamma_glm.fit()
|
|
|
|
params = fitted_model.params
|
|
p_values = fitted_model.pvalues
|
|
print(p_values)
|
|
predicted = fitted_model.predict()
|
|
# for i in range(len(predicted)):
|
|
# print("err: {:.2f} - {:.2f} prediction".format(error[i], predicted[i]))
|
|
print(fitted_model.summary())
|
|
|
|
|
|
pass
|
|
|
|
|
|
def get_variables(folder, order):
|
|
variables = []
|
|
error_values = []
|
|
for cell in sorted(os.listdir(folder)):
|
|
fit = get_best_fit(folder + cell)
|
|
error = fit.get_error_value()
|
|
error_values.append(error)
|
|
cell_behaviour, _ = fit.get_behaviour_values()
|
|
cell_behaviour_variables = []
|
|
|
|
for b in order:
|
|
cell_behaviour_variables.append(cell_behaviour[b])
|
|
|
|
variables.append(np.array(cell_behaviour_variables, dtype=np.float64))
|
|
|
|
return np.array(variables), np.array(error_values, dtype=np.float64)
|
|
|
|
|
|
def till_shorthand():
|
|
# logit_GLM = sm.GLM(df[ < result >], df[ < params >], family = sm.families.Binomial())
|
|
# fitted_model = logit_GLM.fit()
|
|
#
|
|
# fitted_model.predict()
|
|
# # --> gets you the predicted values for df[<results>] based on the fitted model
|
|
#
|
|
#
|
|
# fitted_model.params
|
|
# # --> coeff of params(sorted by input)
|
|
#
|
|
# fitted_model.pvalues
|
|
# # --> selbsterklärend
|
|
#
|
|
# fitted_model.summary()
|
|
# # ODER
|
|
# fitted_model.summary2()
|
|
# # --> überblick über das gefittete model
|
|
pass
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|