# measure the sensitivity of gamma for the selected brains and save the text file
brain_names = brain_list.keys()
results_file_c = 'libsvm_measures_C.txt'
results_file_g = 'libsvm_measures_gamma.txt'
for brain in brain_names:
datasets = load_data(dataset_directory, brain)
resultc1, resultc2 = '', ''
gamma = brain_list[brain][1]
C = brain_list[brain][0]
brain_str = brain + '\t' + 'gamma=' + str(gamma) + ', \t'
brain_str += 'C=' + str(C) + ', \n'
for gammap in gammaps:
paramsc = [gamma, gammap, C]
dice_c, processed_timec = svm_model2(paramsc, datasets)
#if brain == 'LG_0008':
# pdb.set_trace()
resultc1 += "%.7f" % dice_c + '\t'
resultc2 += "%.4f" % processed_timec + '\t'
resultc1 += '\n'
resultc2 += '\n'
if not os.path.exists(results_path + results_file_c):
with open(results_path + results_file_c, 'w') as c:
c.write(brain_str)
c.write(resultc1)
c.write(resultc2)
else:
with open(results_path + results_file_c, 'a') as c:
# measure the sensitivity of gamma for the selected brains and save the text file
brain_names = brain_list.keys()
results_file_c = 'libsvm_measures_C.txt'
results_file_g = 'libsvm_measures_gamma.txt'
for brain in brain_names:
datasets = load_data(dataset_directory , brain)
resultc1, resultc2 = '' ,''
gamma = brain_list[brain][1]
C = brain_list[brain][0]
brain_str = brain + '\t' + 'gamma=' + str(gamma) + ', \t'
brain_str += 'C=' + str(C) + ', \n'
for gammap in gammaps:
paramsc = [ gamma, gammap,C]
dice_c , processed_timec = svm_model2( paramsc, datasets)
#if brain == 'LG_0008':
# pdb.set_trace()
resultc1 += "%.7f" % dice_c + '\t'
resultc2 += "%.4f" % processed_timec + '\t'
resultc1 += '\n'
resultc2 += '\n'
if not os.path.exists(results_path + results_file_c):
with open(results_path + results_file_c,'w') as c:
c.write(brain_str)
c.write(resultc1)
c.write(resultc2)
else:
with open(results_path + results_file_c,'a') as c: