diff --git a/inference_from_record_pairs.py b/inference_from_record_pairs.py
new file mode 100644
index 0000000..a2bd590
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+++ b/inference_from_record_pairs.py
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+import numpy as np
+import pandas as pd
+import time
+import Levenshtein
+import copy
+
+
+def my_Levenshtein_ratio(str1, str2):
+    return 1 - Levenshtein.distance(str1, str2) / max(len(str1), len(str2))
+
+
+def if_minimal(md, md_list, target_col):
+    # 假设这个md是minimal
+    minimal = True
+    for _ in md_list:
+        if _ != md:
+            # 假设列表中每一个md都使当前md不minimal
+            exist = True
+            # 如果左边任何一个大于，则假设不成立
+            for col in list(set(_.keys()) - set([target_col])):
+                if _[col] > md[col]:
+                    exist = False
+            # 如果右边小于，假设也不成立
+            if _[target_col] < md[target_col]:
+                exist = False
+            # 任何一次假设成立，当前md不minimal
+            if exist:
+                minimal = False
+                break
+    return minimal
+
+
+def satisfy_confidence(md, df, conf_thresh, target_col):
+    support = 0
+    support_plus = 0
+    for row1 in df.itertuples():
+        i = row1[0]
+        df_slice = df[i + 1:]
+        for row2 in df_slice.itertuples():
+            left_satisfy = True
+            both_satisfy = True
+            for col in df.columns.values.tolist():
+                sim = my_Levenshtein_ratio(getattr(row1, col), getattr(row2, col))
+                if col == target_col:
+                    if sim < 1:
+                        both_satisfy = False
+                else:
+                    if sim < md[col]:
+                        left_satisfy = False
+                        both_satisfy = False
+            if left_satisfy:
+                support += 1
+            if both_satisfy:
+                support_plus += 1
+    confidence = support_plus / support
+    return confidence >= conf_thresh
+
+
+def inference_from_record_pairs(path, threshold, target_col):
+    data = pd.read_csv(path, low_memory=False, encoding='ISO-8859-1')
+    data = data.astype(str)
+    columns = data.columns.values.tolist()
+
+    md_list = []
+    minimal_vio = []
+    init_md = {}
+    for col in columns:
+        init_md[col] = 1 if col == target_col else 0
+    md_list.append(init_md)
+
+    for row1 in data.itertuples():
+        # 获取当前行的索引，从后一行开始切片
+        i = row1[0]
+        data1 = data[i + 1:]
+        for row2 in data1.itertuples():
+            violated_mds = []
+            # sims是两行的相似度
+            sims = {}
+            for col in columns:
+                similarity = my_Levenshtein_ratio(getattr(row1, col), getattr(row2, col))
+                sims[col] = similarity
+
+            # 寻找violated md,从md列表中删除并加入vio列表
+            for md in md_list:
+                lhs_satis = True
+                rhs_satis = True
+                for col in list(set(columns) - set([target_col])):
+                    if sims[col] < md[col]:
+                        lhs_satis = False
+                if sims[target_col] < md[target_col]:
+                    rhs_satis = False
+                if lhs_satis == True and rhs_satis == False:
+                    md_list.remove(md)
+                    violated_mds.append(md)
+            minimal_vio.extend(violated_mds)
+
+            for vio_md in violated_mds:
+                # 特殊化右侧,我们需要右侧百分百相似，其实不需要降低右侧阈值
+                # if sims[target_col] >= threshold:
+                #     new_rhs = sims[target_col]
+                #     spec_r_md = copy.deepcopy(vio_md)
+                #     spec_r_md[target_col] = new_rhs
+                #     if if_minimal(spec_r_md, md_list, target_col):
+                #         md_list.append(spec_r_md)
+
+                # 特殊化左侧
+                for col in list(set(columns) - set([target_col])):
+                    if sims[col] + 0.001 <= 1:
+                        spec_l_md = copy.deepcopy(vio_md)
+                        spec_l_md[col] = threshold if sims[col] < threshold else sims[col] + 0.001
+                        if if_minimal(spec_l_md, md_list, target_col):
+                            md_list.append(spec_l_md)
+
+            for vio in minimal_vio:
+                if not if_minimal(vio, md_list, target_col):
+                    minimal_vio.remove(vio)
+
+    for _ in minimal_vio:
+        if not satisfy_confidence(_, data, 0.8, target_col):
+            minimal_vio.remove(_)
+
+    list1 = copy.deepcopy(minimal_vio)
+    for _ in list1:
+        if not if_minimal(_, minimal_vio, target_col):
+            minimal_vio.remove(_)
+
+    return md_list, minimal_vio
+
+
+if __name__ == '__main__':
+    # 目前可以仿照这个main函数写
+    path = "/home/w/PycharmProjects/py_entitymatching/py_entitymatching/datasets/end-to-end/Amazon-GoogleProducts/output/8.14/TP_single_tuple.csv"
+    start = time.time()
+    # 输入：csv文件路径，md左侧相似度阈值，md右侧目标字段
+    # 输出：2个md列表，列表1中md无violation,列表2中md有violation但confidence满足阈值(0.8)
+    # 例如此处输入参数要求md左侧相似度字段至少为0.7，右侧指向'id'字段
+    mds, mds_vio = inference_from_record_pairs(path, 0.7, 'id')
+
+    # 将列表1写入本地，路径需自己修改
+    md_path = '/home/w/A-New Folder/8.14/Goods Dataset/TP_md_list.txt'
+    with open(md_path, 'w') as f:
+        for _ in mds:
+            f.write(str(_)+'\n')
+
+    # 将列表2写入本地，路径需自己修改
+    vio_path = '/home/w/A-New Folder/8.14/Goods Dataset/TP_vio_list.txt'
+    with open(vio_path, 'w') as f:
+        for _ in mds_vio:
+            f.write(str(_)+'\n')
+
+    print(time.time() - start)