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def logerr_func(dblabel,X_predicted,I_sub,K_sub):
import numpy as np
# loading
# file = '/media/aneumann/Harddisk/Datenbanken/PythonTest_5/Predicted_0_2_CNN_whole.npz'
# content_predict = np.load(file)
# dblabel_matrix, dbmixed_matrix, predictionError_matrix, X_predicted_matrix = content_predict['dblabel_matrix'], content_predict['dbmixed_matrix'], content_predict['predictionError_matrix'], content_predict['X_predicted_matrix']
# o = 1
# dbmixed = dbmixed_matrix[o]
# X_predicted = X_predicted_matrix[o]
# dblabel = dblabel_matrix[o]
# predictionError = predictionError_matrix[o]
# initialize
logerr_mean = []
logerr_over = []
logerr_under = []
logerr_var = []
# reference label psd
refpsd = dblabel[0:256,0:128]
# estimated psd
estpsd = X_predicted[0:256,0:128]
# logerr mean
deltadb_mean = np.absolute(np.subtract(refpsd,estpsd))
# print(deltadb_mean)
logerr_mean = np.mean(deltadb_mean)
# print(logerr_mean)
# logerr over
deltadb = refpsd - estpsd
condition_over = deltadb > 0
arr_over = np.extract(condition_over, deltadb)
logerr_over = np.mean(arr_over)
# print(logerr_over)
# logerr under
condition_under = deltadb < 0
arr_under = np.extract(condition_under, deltadb)
logerr_under = np.mean(arr_under)
# print(logerr_under)
# logerr Var
I_sub = 4
K_sub = 4
logerr_varki = np.zeros((int(deltadb_mean.shape[0]/K_sub),int(deltadb_mean.shape[1]/I_sub)))
for i in range(0,int(deltadb_mean.shape[1]/I_sub)):
for k in range(0,int(deltadb_mean.shape[0]/K_sub)):
mu = (1/K_sub) * np.sum(deltadb_mean[k*K_sub:k*K_sub+(K_sub),:],axis=0)
logerr_varki[k,i] = 1/(K_sub*I_sub) * np.sum(np.power((deltadb_mean[k*K_sub:k*K_sub+K_sub,i*I_sub:i*I_sub+I_sub]-mu[i*I_sub:i*I_sub+I_sub]),2))
logerr_var = np.mean(logerr_varki)
# print(logerr_var)
return logerr_mean, logerr_over, logerr_under, logerr_var