file.write("Last Node :" + str(last_node) + '\n')
file.write("Units :" + str(units) + '\n')
file.write("filter_A :" + str(filter_A) + '\n')
file.write("filter_B :" + str(filter_B) + '\n')
# file.write("filter_C :" + str(filter_C) + '\n')
file.write("Optimizer :" + str(optimizer) + '\n\n')
stop_tset = clock()
elap_tset = stop_tset - start_tset
Total_time_tset = int(elap_tset / 60)
file.write("The Elapsed Time for this test set :" + str(Total_time_tset) + ' min' + '\n')
print ("The Elapsed Time for this test set :" + str(Total_time_tset) + ' min' + '\n')
file.write('===================================================================' + '\n\n')
file.close()
finish_alarm.ring('piano01')
print('Test set ' + str(tn) + ' is complete.')
score_recog = '{0:.3f}'.format(score[1])
mv_score = '{0:.3f}'.format(mv_score)
if speak ==1:
try:
speak_str('Test set. ' + str(tn) + ' is complete.')
speak_str('The recognition score is, ' + score_recog +'.' + score_recog +'.')
speak_str('and the majority vote score is, ' + mv_score +'.' + mv_score +'.')
finally:
print ()
def set_data(open_data,test_set_num, valid_set_num, speak):
'''
set_file.py
This python file divides pickle data into Train set, Valid set, Test set.
'''
#############################################################################
# open_data = 'data_j1_01010101_SR48kHz'
# open_data = 'data_j1_11111111_SR48kHz'
# open_data = 'class13_data_j1_01010101_SR48kHz'
#############################################################################
pickle_path = 'pickle_folder/'
with open(pickle_path + open_data + '.pickle') as f:
sum_mat_X_data, sum_mat_y_data, sum_mat_k_data, sum_mat_D_data, jump_num, FN, nb_classes = pickle.load(f)
print "The feature data per one inst sample is (=jump_num) :", jump_num
## Set the key for test set and validation set.
## The whole data is divided into 10 different data sets.
## You can choose a key number from 1 to 10.
# val_key = [[8],[9]]
# # val_key = []
# tst_key = [[7],[1]]
val_key = [valid_set_num]
# val_key =[]
tst_key = [test_set_num]
# print "shape of sum_mat_X_data", shape(sum_mat_X_data)
# print "shape of sum_mat_y_data", shape(sum_mat_y_data)
# print "shape of sum_mat_k_data", shape(sum_mat_k_data)
# print "shape of sum_mat_D_data", shape(sum_mat_D_data)
# print sum_mat_k_data
## Call "test_set.set_train_data" function to divide data
X_train, X_test, X_valid, y_train, y_test, y_valid, k_train, k_test, k_valid, D_train, D_test, D_valid \
= test_set_val.set_train_data(sum_mat_X_data,sum_mat_y_data,sum_mat_k_data, sum_mat_D_data, tst_key, val_key)
# print "k_test:", k_test.T
# print "y_test:", y_test.T
# print "y_train:", y_train.T
# print "shape of X_train", shape(X_train).
# print "shape of X_test", shape(X_test).
# print "shape of X_valid", shape(X_valid)
# print "shape of X_train", shape(X_train)
# print "shape of X_test", shape(X_test)
# print "shape of X_valid", shape(X_valid)
#
# print "shape of y_train", shape(y_train)
# print "shape of y_test", shape(y_test)
# print "shape of y_valid", shape(y_valid)
#
#
# print "shape of k_train", shape(k_train)
# print "shape of k_test", shape(k_test)
# print "shape of k_valid", shape(k_valid)
#
# print "shape of D_train", shape(D_train)
# print "shape of D_test", shape(D_test)
# print "shape of D_valid", shape(D_valid)
print "\n" + open_data + " : DATA set ready! \n"
# speak_str('Data seperation for test set '+ str(test_set_num) +' is complete.')
if speak == 1 :
try:
speak_str('Initiate classification for test set '+ str(test_set_num)+ '.'+'\n')
except ConnectionError as e:
print ('ConnectionError \n')
finish_alarm.ring('guitar_c3_04')
return X_train, X_test, X_valid, y_train, y_test, y_valid, D_train, D_test, D_valid, jump_num, FN, nb_classes, open_data
file.write( vars_str[k] + ":"+ str(var) +"\n")
file.write( "random seed" + ":" + str(seed_n) + "\n")
file.write( "div_margin" + ":" + str(div_margin) + "\n")
file.write( "min_margin" + ":" + str(min_margin) + "\n")
file.write( "nb_classes is :"+str(nb_classes)+"\n")
file.write( "inst_index"+str(inst_index)+"\n")
file.write( "itv_stride"+str(itv_stride)+"\n\n")
file.write( "Instruments(folder_name) : " + str(folder_name[family]) + "\n" )
file.write( data_note +'\n')
file.close()
# print sum_mat_D_data
t_stop = clock()
elapsed_time = int(int(t_stop - t_start)/60)
print 'Feature extraction process for ' + rand_animal + ' is complete'
print 'Elapsed time is :'+ str(elapsed_time) +' minutes '
print pickle_file + ' has been finished.'
if speak == 1:
try:
finish_alarm.ring('bell01')
speak_str('Feature extraction process for ' + rand_animal + ' is complete.')
speak_str('Elapsed time is :'+ str(elapsed_time) +' minutes ')
except ConnectionError as e:
print ('ConnectionError! Check the Wifi Connection. \n')
print 'sample count:', sample_count