diff --git a/lang/fr/gklearn/preimage/experiments/tools/analyze_results_of_random_edit_costs.py b/lang/fr/gklearn/preimage/experiments/tools/analyze_results_of_random_edit_costs.py
new file mode 100644
index 0000000..bbde39e
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+++ b/lang/fr/gklearn/preimage/experiments/tools/analyze_results_of_random_edit_costs.py
@@ -0,0 +1,91 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Apr 14 16:57:18 2020
+
+@author: ljia
+"""
+import pandas as pd
+import numpy as np
+import os
+import math
+
+def summarize_results_of_random_edit_costs(data_dir, ds_name, gkernel):
+	sod_sm_list = []
+	sod_gm_list = []
+	dis_k_sm_list = []
+	dis_k_gm_list = []
+	dis_k_min_gi = []
+	time_total_list = []
+	mge_dec_order_list = []
+	mge_inc_order_list = []
+	
+	# get results from .csv.
+	file_name = data_dir + 'results_summary.' + ds_name + '.' + gkernel + '.csv'
+	try:
+		df = pd.read_csv(file_name)
+	except FileNotFoundError:
+		return
+	for index, row in df.iterrows():
+		if row['target'] == 'all' and row['fit method'] == 'random':
+			if not math.isnan(float(row['SOD SM'])):
+				sod_sm_list.append(float(row['SOD SM']))
+			if not math.isnan(float(row['SOD GM'])):
+				sod_gm_list.append(float(row['SOD GM']))
+			if not math.isnan(float(row['dis_k SM'])):
+				dis_k_sm_list.append(float(row['dis_k SM']))
+			if not math.isnan(float(row['dis_k GM'])):
+				dis_k_gm_list.append(float(row['dis_k GM']))
+			if not math.isnan(float(row['min dis_k gi'])):
+				dis_k_min_gi.append(float(row['min dis_k gi']))
+			if not math.isnan(float(row['time total'])):
+				time_total_list.append(float(row['time total']))
+			if 'mge num decrease order' in row:
+				mge_dec_order_list.append(int(row['mge num decrease order']))
+			if 'mge num increase order' in row:
+				mge_inc_order_list.append(int(row['mge num increase order']))
+	# return if no results.
+	if len(sod_sm_list) == 0:
+		return
+	
+	# construct output results.
+	op = {}
+	op['measure'] = ['max', 'min', 'mean']
+	op['SOD SM'] = [np.max(sod_sm_list), np.min(sod_sm_list), np.mean(sod_sm_list)]
+	op['SOD GM'] = [np.max(sod_gm_list), np.min(sod_gm_list), np.mean(sod_gm_list)]
+	op['dis_k SM'] = [np.max(dis_k_sm_list), np.min(dis_k_sm_list), np.mean(dis_k_sm_list)]
+	op['dis_k GM'] = [np.max(dis_k_gm_list), np.min(dis_k_gm_list), np.mean(dis_k_gm_list)]
+	op['min dis_k gi'] = [np.max(dis_k_min_gi), np.min(dis_k_min_gi), np.mean(dis_k_min_gi)]
+	op['time total'] = [np.max(time_total_list), np.min(time_total_list), np.mean(time_total_list)]
+	if len(mge_dec_order_list) > 0:
+		op['mge num decrease order'] = [np.max(mge_dec_order_list), np.min(mge_dec_order_list), np.mean(mge_dec_order_list)]
+	if len(mge_inc_order_list) > 0:
+		op['mge num increase order'] = [np.max(mge_inc_order_list), np.min(mge_inc_order_list), np.mean(mge_inc_order_list)]
+	df = pd.DataFrame(data=op)
+	
+	# write results to .csv
+	df.to_csv(data_dir + 'summary_for_random_edit_costs.csv', index=False, header=True)
+	
+	
+def compute_for_all_experiments(data_dir):
+	dir_list = [i for i in os.listdir(data_dir) if os.path.isdir(data_dir + i)]
+	for dir_name in dir_list:
+		sp_tmp = dir_name.split('.')
+		ds_name = sp_tmp[0].strip('[error]')
+		gkernel = sp_tmp[1]
+		summarize_results_of_random_edit_costs(data_dir + dir_name + '/',
+										 ds_name, gkernel)
+		if os.path.exists(data_dir + dir_name + '/update_order/'):
+			summarize_results_of_random_edit_costs(data_dir + dir_name + '/update_order/',
+										 ds_name, gkernel)
+
+
+if __name__ == '__main__':
+# 	data_dir = '../results/xp_median_preimage.update_order/'
+	root_dir_tnz = '../../results/CRIANN/xp_median_preimage.init10/'
+	root_dir_ntnz = '../../results/CRIANN/xp_median_preimage.init10.no_triangle_rule/'
+	root_dir_tz = '../../results/CRIANN/xp_median_preimage.init10.triangle_rule.allow_zeros/'
+	root_dir_ntz = '../../results/CRIANN/xp_median_preimage.init10.no_triangle_rule.allow_zeros/'
+	data_dirs = [root_dir_tnz, root_dir_ntnz, root_dir_tz, root_dir_ntz]
+	for data_dir in data_dirs:
+		compute_for_all_experiments(data_dir)
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