diff --git a/weights/all_weights_here.txt b/abl/__init__.py
similarity index 100%
rename from weights/all_weights_here.txt
rename to abl/__init__.py
diff --git a/abl/abducer/__init__.py b/abl/abducer/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/abducer/abducer_base.py b/abl/abducer/abducer_base.py
similarity index 54%
rename from abducer/abducer_base.py
rename to abl/abducer/abducer_base.py
index c0fd102..cf14bb7 100644
--- a/abducer/abducer_base.py
+++ b/abl/abducer/abducer_base.py
@@ -10,32 +10,15 @@
 #
 # ================================================================#
 
-import sys
-
-sys.path.append(".")
-sys.path.append("..")
-
 import abc
-from abducer.kb import *
 import numpy as np
 from zoopt import Dimension, Objective, Parameter, Opt
-from utils.utils import confidence_dist, flatten, hamming_dist
-
-import math
-import time
-
+from ..utils.utils import confidence_dist, flatten, reform_idx, hamming_dist
 
 class AbducerBase(abc.ABC):
-    def __init__(
-        self,
-        kb,
-        dist_func="confidence",
-        zoopt=False,
-        multiple_predictions=False,
-        cache=True,
-    ):
+    def __init__(self, kb, dist_func='confidence', zoopt=False, multiple_predictions=False, cache=True):
         self.kb = kb
-        assert dist_func == "hamming" or dist_func == "confidence"
+        assert dist_func == 'hamming' or dist_func == 'confidence'
         self.dist_func = dist_func
         self.zoopt = zoopt
         self.multiple_predictions = multiple_predictions
@@ -46,41 +29,42 @@ class AbducerBase(abc.ABC):
             self.cache_candidates = {}
 
     def _get_cost_list(self, pred_res, pred_res_prob, candidates):
-        if self.dist_func == "hamming":
+        if self.dist_func == 'hamming':
+            if self.multiple_predictions:
+                pred_res = flatten(pred_res)
+                candidates = [flatten(c) for c in candidates]
+                
             return hamming_dist(pred_res, candidates)
-        elif self.dist_func == "confidence":
-            mapping = dict(
-                zip(
-                    self.kb.pseudo_label_list,
-                    list(range(len(self.kb.pseudo_label_list))),
-                )
-            )
-            return confidence_dist(
-                pred_res_prob, [list(map(lambda x: mapping[x], c)) for c in candidates]
-            )
+        
+        elif self.dist_func == 'confidence':
+            if self.multiple_predictions:
+                pred_res_prob = flatten(pred_res_prob)
+                candidates = [flatten(c) for c in candidates]
+                
+            mapping = dict(zip(self.kb.pseudo_label_list, list(range(len(self.kb.pseudo_label_list)))))
+            candidates = [list(map(lambda x: mapping[x], c)) for c in candidates]
+            return confidence_dist(pred_res_prob, candidates)
 
     def _get_one_candidate(self, pred_res, pred_res_prob, candidates):
         if len(candidates) == 0:
             return []
         elif len(candidates) == 1 or self.zoopt:
             return candidates[0]
+        
         else:
             cost_list = self._get_cost_list(pred_res, pred_res_prob, candidates)
             min_address_num = np.min(cost_list)
             idxs = np.where(cost_list == min_address_num)[0]
-            return [candidates[idx] for idx in idxs][0]
+            candidate = [candidates[idx] for idx in idxs][0]
+            return candidate
 
     # for zoopt
     def _zoopt_score_multiple(self, pred_res, key, solution):
         all_address_flag = reform_idx(solution, pred_res)
         score = 0
         for idx in range(len(pred_res)):
-            address_idx = [
-                i for i, flag in enumerate(all_address_flag[idx]) if flag != 0
-            ]
-            candidate = self.kb.address_by_idx(
-                [pred_res[idx]], key[idx], address_idx, True
-            )
+            address_idx = [i for i, flag in enumerate(all_address_flag[idx]) if flag != 0]
+            candidate = self.address_by_idx([pred_res[idx]], key[idx], address_idx)
             if len(candidate) > 0:
                 score += 1
         return score
@@ -88,9 +72,7 @@ class AbducerBase(abc.ABC):
     def _zoopt_address_score(self, pred_res, key, sol):
         if not self.multiple_predictions:
             address_idx = [idx for idx, i in enumerate(sol.get_x()) if i != 0]
-            candidates = self.kb.address_by_idx(
-                pred_res, key, address_idx, self.multiple_predictions
-            )
+            candidates = self.address_by_idx(pred_res, key, address_idx)
             return 1 if len(candidates) > 0 else 0
         else:
             return self._zoopt_score_multiple(pred_res, key, sol.get_x())
@@ -107,7 +89,7 @@ class AbducerBase(abc.ABC):
             dim=dimension,
             constraint=lambda sol: self._constrain_address_num(sol, max_address_num),
         )
-        parameter = Parameter(budget=100, autoset=True)
+        parameter = Parameter(budget=100, intermediate_result=False, autoset=True)
         solution = Opt.min(objective, parameter).get_x()
 
         return solution
@@ -118,11 +100,7 @@ class AbducerBase(abc.ABC):
             pred_res = flatten(pred_res)
             key = tuple(key)
         if (tuple(pred_res), key) in self.cache_min_address_num:
-            address_num = min(
-                max_address_num,
-                self.cache_min_address_num[(tuple(pred_res), key)]
-                + require_more_address,
-            )
+            address_num = min(max_address_num, self.cache_min_address_num[(tuple(pred_res), key)] + require_more_address)
             if (tuple(pred_res), key, address_num) in self.cache_candidates:
                 candidates = self.cache_candidates[(tuple(pred_res), key, address_num)]
                 if self.zoopt:
@@ -137,6 +115,9 @@ class AbducerBase(abc.ABC):
             key = tuple(key)
         self.cache_min_address_num[(tuple(pred_res), key)] = min_address_num
         self.cache_candidates[(tuple(pred_res), key, address_num)] = candidates
+        
+    def address_by_idx(self, pred_res, key, address_idx):
+        return self.kb.address_by_idx(pred_res, key, address_idx, self.multiple_predictions)
 
     def abduce(self, data, max_address_num=-1, require_more_address=0):
         pred_res, pred_res_prob, key = data
@@ -151,18 +132,12 @@ class AbducerBase(abc.ABC):
         if self.zoopt:
             solution = self.zoopt_get_solution(pred_res, key, max_address_num)
             address_idx = [idx for idx, i in enumerate(solution) if i != 0]
-            candidates = self.kb.address_by_idx(
-                pred_res, key, address_idx, self.multiple_predictions
-            )
+            candidates = self.address_by_idx(pred_res, key, address_idx)
             address_num = int(solution.sum())
             min_address_num = address_num
         else:
             candidates, min_address_num, address_num = self.kb.abduce_candidates(
-                pred_res,
-                key,
-                max_address_num,
-                require_more_address,
-                self.multiple_predictions,
+                pred_res, key, max_address_num, require_more_address, self.multiple_predictions
             )
 
         candidate = self._get_one_candidate(pred_res, pred_res_prob, candidates)
@@ -176,32 +151,22 @@ class AbducerBase(abc.ABC):
         return self.kb.abduce_rules(pred_res)
 
     def batch_abduce(self, Z, Y, max_address_num=-1, require_more_address=0):
-        if self.multiple_predictions:
-            return self.abduce(
-                (Z["cls"], Z["prob"], Y), max_address_num, require_more_address
-            )
-        else:
-            return [
-                self.abduce((z, prob, y), max_address_num, require_more_address)
-                for z, prob, y in zip(Z["cls"], Z["prob"], Y)
-            ]
+        # if self.multiple_predictions:
+        return self.abduce((Z['cls'], Z['prob'], Y), max_address_num, require_more_address)
+        # else:
+            # return [self.abduce((z, prob, y), max_address_num, require_more_address) for z, prob, y in zip(Z['cls'], Z['prob'], Y)]
 
     def __call__(self, Z, Y, max_address_num=-1, require_more_address=0):
         return self.batch_abduce(Z, Y, max_address_num, require_more_address)
 
+if __name__ == '__main__':
+    from kb import add_KB, prolog_KB, HWF_KB
+    
+    prob1 = [[0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]]
+    prob2 = [[0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]]
 
-if __name__ == "__main__":
-    prob1 = [
-        [0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0],
-        [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
-    ]
-    prob2 = [
-        [0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0],
-        [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
-    ]
-
-    kb = add_KB()
-    abd = AbducerBase(kb, "confidence")
+    kb = add_KB(GKB_flag=True)
+    abd = AbducerBase(kb, 'confidence')
     res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0)
     print(res)
     res = abd.abduce(([1, 1], prob2, 8), max_address_num=2, require_more_address=0)
@@ -213,9 +178,23 @@ if __name__ == "__main__":
     res = abd.abduce(([1, 1], prob1, 20), max_address_num=2, require_more_address=0)
     print(res)
     print()
+    
+    
+    multiple_prob = [[[0, 0.99, 0.01, 0, 0, 0, 0, 0, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]],
+                     [[0, 0, 0.01, 0, 0, 0, 0, 0.99, 0, 0], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]]]
+    
+    
+    kb = add_KB()
+    abd = AbducerBase(kb, 'confidence', multiple_predictions=True)
+    res = abd.abduce(([[1, 1], [1, 2]], multiple_prob, [4, 8]), max_address_num=4, require_more_address=0)
+    print(res)
+    res = abd.abduce(([[1, 1], [1, 2]], multiple_prob, [4, 8]), max_address_num=4, require_more_address=1)
+    print(res)
+    print()
+    
 
-    kb = add_prolog_KB()
-    abd = AbducerBase(kb, "confidence")
+    kb = prolog_KB(pseudo_label_list=list(range(10)), pl_file='../examples/datasets/mnist_add/add.pl')
+    abd = AbducerBase(kb, 'confidence')
     res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0)
     print(res)
     res = abd.abduce(([1, 1], prob2, 8), max_address_num=2, require_more_address=0)
@@ -228,8 +207,8 @@ if __name__ == "__main__":
     print(res)
     print()
 
-    kb = add_prolog_KB()
-    abd = AbducerBase(kb, "confidence", zoopt=True)
+    kb = prolog_KB(pseudo_label_list=list(range(10)), pl_file='../examples/datasets/mnist_add/add.pl')
+    abd = AbducerBase(kb, 'confidence', zoopt=True)
     res = abd.abduce(([1, 1], prob1, 8), max_address_num=2, require_more_address=0)
     print(res)
     res = abd.abduce(([1, 1], prob2, 8), max_address_num=2, require_more_address=0)
@@ -242,49 +221,63 @@ if __name__ == "__main__":
     print(res)
     print()
 
-    kb = HWF_KB(len_list=[1, 3, 5])
-    abd = AbducerBase(kb, "hamming")
-    res = abd.abduce(
-        (["5", "+", "2"], None, 3), max_address_num=2, require_more_address=0
-    )
+    kb = HWF_KB(len_list=[1, 3, 5], GKB_flag=True, max_err = 0.1)
+    abd = AbducerBase(kb, 'hamming')
+    res = abd.abduce((['5', '+', '2'], None, 3), max_address_num=2, require_more_address=0)
+    print(res)
+    res = abd.abduce((['5', '+', '9'], None, 64), max_address_num=3, require_more_address=0)
+    print(res)
+    print()
+    
+    kb = HWF_KB(len_list=[1, 3, 5], GKB_flag=True, max_err = 1)
+    abd = AbducerBase(kb, 'hamming')
+    res = abd.abduce((['5', '+', '9'], None, 64), max_address_num=3, require_more_address=0)
+    print(res)
+    res = abd.abduce((['5', '+', '2'], None, 1.67), max_address_num=3, require_more_address=0)
+    print(res)
+    res = abd.abduce((['5', '8', '8', '8', '8'], None, 3.17), max_address_num=5, require_more_address=3)
+    print(res)
+    print()
+    
+    kb = HWF_KB(len_list=[1, 3, 5], max_err = 0.1)
+    abd = AbducerBase(kb, 'hamming', multiple_predictions=True)
+    res = abd.abduce(([['5', '+', '2'], ['5', '+', '9']], None, [3, 64]), max_address_num=6, require_more_address=0)
+    print(res)
+    print()
+    
+    kb = HWF_KB(len_list=[1, 3, 5], max_err = 0.1)
+    abd = AbducerBase(kb, 'hamming')
+    res = abd.abduce((['5', '+', '2'], None, 3), max_address_num=2, require_more_address=0)
+    print(res)
+    res = abd.abduce((['5', '+', '9'], None, 64), max_address_num=3, require_more_address=0)
     print(res)
-    res = abd.abduce(
-        (["5", "+", "2"], None, 64), max_address_num=3, require_more_address=0
-    )
+    
+    kb = HWF_KB(len_list=[1, 3, 5], max_err = 1)
+    abd = AbducerBase(kb, 'hamming')
+    res = abd.abduce((['5', '+', '9'], None, 64), max_address_num=3, require_more_address=0)
     print(res)
-    res = abd.abduce(
-        (["5", "+", "2"], None, 1.67), max_address_num=3, require_more_address=0
-    )
+    res = abd.abduce((['5', '+', '2'], None, 1.67), max_address_num=3, require_more_address=0)
     print(res)
-    res = abd.abduce(
-        (["5", "8", "8", "8", "8"], None, 3.17),
-        max_address_num=5,
-        require_more_address=3,
-    )
+    res = abd.abduce((['5', '8', '8', '8', '8'], None, 3.17), max_address_num=5, require_more_address=3)
     print(res)
     print()
 
-    kb = HED_prolog_KB()
+    kb = prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='../examples/datasets/hed/learn_add.pl')
     abd = AbducerBase(kb, zoopt=True, multiple_predictions=True)
-    consist_exs = [[1, "+", 0, "=", 0], [1, "+", 1, "=", 0], [0, "+", 0, "=", 1, 1]]
-    consist_exs2 = [
-        [1, "+", 0, "=", 0],
-        [1, "+", 1, "=", 0],
-        [0, "+", 1, "=", 1, 1],
-    ]  # not consistent with rules
-    inconsist_exs = [[1, "+", 0, "=", 0], [1, "=", 1, "=", 0], [0, "=", 0, "=", 1, 1]]
+    consist_exs = [[1, 1, '+', 0, '=', 1, 1], [1, '+', 1, '=', 1, 0], [0, '+', 0, '=', 0]]
+    inconsist_exs = [[1, '+', 0, '=', 0], [1, '=', 1, '=', 0], [0, '=', 0, '=', 1, 1]]
     # inconsist_exs = [[1, '+', 0, '=', 0], ['=', '=', '=', '=', 0], ['=', '=', 0, '=', '=', '=']]
-    rules = ["my_op([0], [0], [1, 1])", "my_op([1], [1], [0])", "my_op([1], [0], [0])"]
+    rules = ['my_op([0], [0], [0])', 'my_op([1], [1], [1, 0])']
 
-    print(kb.logic_forward(consist_exs), kb.logic_forward(inconsist_exs))
-    print(kb.consist_rule(consist_exs, rules), kb.consist_rule(consist_exs2, rules))
+    print(kb._logic_forward(consist_exs, True), kb._logic_forward(inconsist_exs, True))
+    print(kb.consist_rule([1, '+', 1, '=', 1, 0], rules), kb.consist_rule([1, '+', 1, '=', 1, 1], rules))
     print()
 
-    res = abd.abduce((consist_exs, None, [1] * len(consist_exs)))
+    res = abd.abduce((consist_exs, None, [None] * len(consist_exs)))
     print(res)
-    res = abd.abduce((inconsist_exs, None, [1] * len(consist_exs)))
+    res = abd.abduce((inconsist_exs, None, [None] * len(inconsist_exs)))
     print(res)
     print()
 
     abduced_rules = abd.abduce_rules(consist_exs)
-    print(abduced_rules)
+    print(abduced_rules)
\ No newline at end of file
diff --git a/abducer/kb.py b/abl/abducer/kb.py
similarity index 50%
rename from abducer/kb.py
rename to abl/abducer/kb.py
index b9beb3f..5b82f03 100644
--- a/abducer/kb.py
+++ b/abl/abducer/kb.py
@@ -15,93 +15,26 @@ import bisect
 import copy
 import numpy as np
 
-import sys
-
-sys.path.append("..")
-
 from collections import defaultdict
 from itertools import product, combinations
-from utils.utils import flatten, reform_idx, hamming_dist, check_equal
+from ..utils.utils import flatten, reform_idx, hamming_dist, check_equal
 
 from multiprocessing import Pool
 
 import pyswip
 
-
 class KBBase(ABC):
-    def __init__(self, pseudo_label_list=None):
-        pass
-
-    @abstractmethod
-    def logic_forward(self):
-        pass
-
-    @abstractmethod
-    def abduce_candidates(self):
-        pass
-    
-    @abstractmethod
-    def address_by_idx(self):
-        pass
-
-    def _address(self, address_num, pred_res, key, multiple_predictions=False):
-        new_candidates = []
-        if not multiple_predictions:
-            address_idx_list = list(combinations(list(range(len(pred_res))), address_num))
-        else:
-            address_idx_list = list(combinations(list(range(len(flatten(pred_res)))), address_num))
-
-        for address_idx in address_idx_list:
-            candidates = self.address_by_idx(pred_res, key, address_idx, multiple_predictions)
-            new_candidates += candidates
-        return new_candidates
-
-    def _abduce_by_search(self, pred_res, key, max_address_num, require_more_address=0, multiple_predictions=False):
-        candidates = []
-
-        for address_num in range(len(flatten(pred_res)) + 1):
-            if address_num == 0:
-                if check_equal(self.logic_forward(pred_res), key):
-                    candidates.append(pred_res)
-            else:
-                new_candidates = self._address(address_num, pred_res, key, multiple_predictions)
-                candidates += new_candidates
-
-            if len(candidates) > 0:
-                min_address_num = address_num
-                break
-
-            if address_num >= max_address_num:
-                return [], 0, 0
-
-        for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1):
-            if address_num > max_address_num:
-                return candidates, min_address_num, address_num - 1
-            new_candidates = self._address(address_num, pred_res, key, multiple_predictions)
-            candidates += new_candidates
-
-        return candidates, min_address_num, address_num
-
-    def __len__(self):
-        pass
-
-
-class ClsKB(KBBase):
-    def __init__(self, GKB_flag=False, pseudo_label_list=None, len_list=None):
-        super().__init__()
-        self.GKB_flag = GKB_flag
+    def __init__(self, pseudo_label_list, len_list=None, GKB_flag=False, max_err=0):
         self.pseudo_label_list = pseudo_label_list
         self.len_list = len_list
+        self.GKB_flag = GKB_flag
+        self.max_err = max_err
 
         if GKB_flag:
             self.base = {}
             X, Y = self._get_GKB()
             for x, y in zip(X, Y):
                 self.base.setdefault(len(x), defaultdict(list))[y].append(x)
-        else:
-            self.all_address_candidate_dict = {}
-            for address_num in range(max(self.len_list) + 1):
-                self.all_address_candidate_dict[address_num] = list(product(self.pseudo_label_list, repeat=address_num))
 
     # For parallel version of _get_GKB
     def _get_XY_list(self, args):
@@ -130,39 +63,80 @@ class ClsKB(KBBase):
                 part_X, part_Y = zip(*XY_list)
                 X.extend(part_X)
                 Y.extend(part_Y)
+        if type(Y[0]) in (int, float):      
+            sorted_XY = sorted(list(zip(Y, X)))
+            X = [x for y, x in sorted_XY]
+            Y = [y for y, x in sorted_XY]
         return X, Y
 
-    def logic_forward(self):
+    @abstractmethod
+    def logic_forward(self, pseudo_labels):
         pass
+    
+    def _logic_forward(self, xs, multiple_predictions=False):
+        if not multiple_predictions:
+            return self.logic_forward(xs)
+        else:
+            res = [self.logic_forward(x) for x in xs]
+            return res
 
-    def abduce_candidates(self, pred_res, key, max_address_num=-1, require_more_address=0, multiple_predictions=False):
+    def abduce_candidates(self, pred_res, key, max_address_num, require_more_address=0, multiple_predictions=False):
         if self.GKB_flag:
-            return self._abduce_by_GKB(pred_res, key, max_address_num, require_more_address)
+            return self._abduce_by_GKB(pred_res, key, max_address_num, require_more_address, multiple_predictions)
         else:
             return self._abduce_by_search(pred_res, key, max_address_num, require_more_address, multiple_predictions)
+    
+    @abstractmethod
+    def _find_candidate_GKB(self, pred_res, key):
+        pass
+    
+    def _abduce_by_GKB(self, pred_res, key, max_address_num, require_more_address, multiple_predictions):
+        if self.base == {}:
+            return [], 0, 0
 
-    def _abduce_by_GKB(self, pred_res, key, max_address_num, require_more_address):
-        if self.base == {} or len(pred_res) not in self.len_list:
-            return []
-
-        all_candidates = self.base[len(pred_res)][key]
-
-        if len(all_candidates) == 0:
-            candidates = []
-            min_address_num = 0
-            address_num = 0
+        if not multiple_predictions:
+            if len(pred_res) not in self.len_list:
+                return [], 0, 0
+            all_candidates = self._find_candidate_GKB(pred_res, key)
+            if len(all_candidates) == 0:
+                return [], 0, 0
+            else:
+                cost_list = hamming_dist(pred_res, all_candidates)
+                min_address_num = np.min(cost_list)
+                address_num = min(max_address_num, min_address_num + require_more_address)
+                idxs = np.where(cost_list <= address_num)[0]
+                candidates = [all_candidates[idx] for idx in idxs]
+                return candidates, min_address_num, address_num
+       
         else:
-            cost_list = hamming_dist(pred_res, all_candidates)
-            min_address_num = np.min(cost_list)
+            min_address_num = 0
+            all_candidates_save = []
+            cost_list_save = []
+            
+            for p_res, k in zip(pred_res, key):
+                if len(p_res) not in self.len_list:
+                    return [], 0, 0
+                all_candidates = self._find_candidate_GKB(p_res, k)
+                if len(all_candidates) == 0:
+                    return [], 0, 0
+                else:
+                    all_candidates_save.append(all_candidates)
+                    cost_list = hamming_dist(p_res, all_candidates)
+                    min_address_num += np.min(cost_list)
+                    cost_list_save.append(cost_list)
+            
+            multiple_all_candidates = [flatten(c) for c in product(*all_candidates_save)]
+            assert len(multiple_all_candidates[0]) == len(flatten(pred_res))
+            multiple_cost_list = np.array([sum(cost) for cost in product(*cost_list_save)])
+            assert len(multiple_all_candidates) == len(multiple_cost_list)
             address_num = min(max_address_num, min_address_num + require_more_address)
-            idxs = np.where(cost_list <= address_num)[0]
-            candidates = [all_candidates[idx] for idx in idxs]
-
-        return candidates, min_address_num, address_num
-
+            idxs = np.where(multiple_cost_list <= address_num)[0]
+            candidates = [reform_idx(multiple_all_candidates[idx], pred_res) for idx in idxs]
+            return candidates, min_address_num, address_num
+    
     def address_by_idx(self, pred_res, key, address_idx, multiple_predictions=False):
         candidates = []
-        abduce_c = self.all_address_candidate_dict[len(address_idx)]
+        abduce_c = product(self.pseudo_label_list, repeat=len(address_idx))
 
         if multiple_predictions:
             save_pred_res = pred_res
@@ -176,10 +150,48 @@ class ClsKB(KBBase):
             if multiple_predictions:
                 candidate = reform_idx(candidate, save_pred_res)
 
-            if self.logic_forward(candidate) == key:
+            if check_equal(self._logic_forward(candidate, multiple_predictions), key, self.max_err):
                 candidates.append(candidate)
         return candidates
 
+    def _address(self, address_num, pred_res, key, multiple_predictions):
+        new_candidates = []
+        if not multiple_predictions:
+            address_idx_list = combinations(list(range(len(pred_res))), address_num)
+        else:
+            address_idx_list = combinations(list(range(len(flatten(pred_res)))), address_num)
+
+        for address_idx in address_idx_list:
+            candidates = self.address_by_idx(pred_res, key, address_idx, multiple_predictions)
+            new_candidates += candidates
+        return new_candidates
+
+    def _abduce_by_search(self, pred_res, key, max_address_num, require_more_address, multiple_predictions):
+        candidates = []
+
+        for address_num in range(len(flatten(pred_res)) + 1):
+            if address_num == 0:
+                if check_equal(self._logic_forward(pred_res, multiple_predictions), key, self.max_err):
+                    candidates.append(pred_res)
+            else:
+                new_candidates = self._address(address_num, pred_res, key, multiple_predictions)
+                candidates += new_candidates
+
+            if len(candidates) > 0:
+                min_address_num = address_num
+                break
+
+            if address_num >= max_address_num:
+                return [], 0, 0
+
+        for address_num in range(min_address_num + 1, min_address_num + require_more_address + 1):
+            if address_num > max_address_num:
+                return candidates, min_address_num, address_num - 1
+            new_candidates = self._address(address_num, pred_res, key, multiple_predictions)
+            candidates += new_candidates
+
+        return candidates, min_address_num, address_num
+
     def _dict_len(self, dic):
         if not self.GKB_flag:
             return 0
@@ -193,130 +205,77 @@ class ClsKB(KBBase):
             return sum(self._dict_len(v) for v in self.base.values())
 
 
-class add_KB(ClsKB):
-    def __init__(self, GKB_flag=False, pseudo_label_list=list(range(10)), len_list=[2]):
-        super().__init__(GKB_flag, pseudo_label_list, len_list)
-
-    def logic_forward(self, nums):
-        return sum(nums)
+class ClsKB(KBBase):
+    def __init__(self, pseudo_label_list, len_list, GKB_flag):
+        super().__init__(pseudo_label_list, len_list, GKB_flag)
 
+    def _find_candidate_GKB(self, pred_res, key):
+        return self.base[len(pred_res)][key]
 
-class HWF_KB(ClsKB):
-    def __init__(
-        self, GKB_flag=False, pseudo_label_list=['1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', 'times', 'div'], len_list=[1, 3, 5, 7]
-    ):
-        super().__init__(GKB_flag, pseudo_label_list, len_list)
 
-    def valid_candidate(self, formula):
-        if len(formula) % 2 == 0:
-            return False
-        for i in range(len(formula)):
-            if i % 2 == 0 and formula[i] not in ['1', '2', '3', '4', '5', '6', '7', '8', '9']:
-                return False
-            if i % 2 != 0 and formula[i] not in ['+', '-', 'times', 'div']:
-                return False
-        return True
+class add_KB(ClsKB):
+    def __init__(self, pseudo_label_list=list(range(10)), len_list=[2], GKB_flag=False):
+        super().__init__(pseudo_label_list, len_list, GKB_flag)
 
-    def logic_forward(self, formula):
-        if not self.valid_candidate(formula):
-            return np.inf
-        mapping = {
-            '1': '1',
-            '2': '2',
-            '3': '3',
-            '4': '4',
-            '5': '5',
-            '6': '6',
-            '7': '7',
-            '8': '8',
-            '9': '9',
-            '+': '+',
-            '-': '-',
-            'times': '*',
-            'div': '/',
-        }
-        formula = [mapping[f] for f in formula]
-        return round(eval(''.join(formula)), 2)
+    def logic_forward(self, nums):
+        return sum(nums)
 
 
 class prolog_KB(KBBase):
-    def __init__(self, pseudo_label_list):
-        super().__init__()
-        self.pseudo_label_list = pseudo_label_list
+    def __init__(self, pseudo_label_list, pl_file):
+        super().__init__(pseudo_label_list)
         self.prolog = pyswip.Prolog()
+        self.prolog.consult(pl_file)
 
-    def logic_forward(self):
-        pass
-
-    def abduce_candidates(self, pred_res, key, max_address_num, require_more_address, multiple_predictions):
-        return self._abduce_by_search(pred_res, key, max_address_num, require_more_address, multiple_predictions)
+    def logic_forward(self, pseudo_labels):
+        result = list(self.prolog.query("logic_forward(%s, Res)." % pseudo_labels))[0]['Res']
+        if result == 'true':
+            return True
+        elif result == 'false':
+            return False
+        return result
+    
+    def _address_pred_res(self, pred_res, address_idx, multiple_predictions):
+        import re
+        address_pred_res = pred_res.copy()
+        if multiple_predictions:
+            address_pred_res = flatten(address_pred_res)
+        
+        for idx in address_idx:
+            address_pred_res[idx] = 'P' + str(idx)
+        if multiple_predictions:
+            address_pred_res = reform_idx(address_pred_res, pred_res)
+        
+        # TODO：不知道有没有更简洁的方法
+        regex = r"'P\d+'"
+        return re.sub(regex, lambda x: x.group().replace("'", ""), str(address_pred_res))
+    
+    def get_query_string(self, pred_res, key, address_idx, multiple_predictions):
+        query_string = "logic_forward("
+        query_string += self._address_pred_res(pred_res, address_idx, multiple_predictions)
+        key_is_none_flag = key is None or (type(key) == list and key[0] is None)
+        query_string += ",%s)." % key if not key_is_none_flag else ")."
+        return query_string
 
+    def _find_candidate_GKB(self, pred_res, key):
+        pass
+    
     def address_by_idx(self, pred_res, key, address_idx, multiple_predictions=False):
         candidates = []
-        # print(address_idx)
-        if not multiple_predictions:
-            query_string = self.get_query_string(pred_res, key, address_idx)
-        else:
-            query_string = self.get_query_string_need_flatten(pred_res, key, address_idx)
-
+        query_string = self.get_query_string(pred_res, key, address_idx, multiple_predictions)
         if multiple_predictions:
             save_pred_res = pred_res
             pred_res = flatten(pred_res)
-
-        abduce_c = [list(z.values()) for z in list(self.prolog.query(query_string))]
+        abduce_c = [list(z.values()) for z in self.prolog.query(query_string)]
         for c in abduce_c:
             candidate = pred_res.copy()
             for i, idx in enumerate(address_idx):
                 candidate[idx] = c[i]
-
             if multiple_predictions:
                 candidate = reform_idx(candidate, save_pred_res)
-
             candidates.append(candidate)
         return candidates
 
-
-class add_prolog_KB(prolog_KB):
-    def __init__(self, pseudo_label_list=list(range(10))):
-        super().__init__(pseudo_label_list)
-        for i in self.pseudo_label_list:
-            self.prolog.assertz("pseudo_label(%s)" % i)
-        self.prolog.assertz("addition(Z1, Z2, Res) :- pseudo_label(Z1), pseudo_label(Z2), Res is Z1+Z2")
-
-    def logic_forward(self, nums):
-        return list(self.prolog.query("addition(%s, %s, Res)." % (nums[0], nums[1])))[0]['Res']
-
-    def get_query_string(self, pred_res, key, address_idx):
-        query_string = "addition("
-        for idx, i in enumerate(pred_res):
-            tmp = 'Z' + str(idx) + ',' if idx in address_idx else str(i) + ','
-            query_string += tmp
-        query_string += "%s)." % key
-        return query_string
-
-
-class HED_prolog_KB(prolog_KB):
-    def __init__(self, pseudo_label_list=[0, 1, '+', '=']):
-        super().__init__(pseudo_label_list)
-        self.prolog.consult('./datasets/hed/learn_add.pl')
-
-    # corresponding to `con_sol is not None` in `consistent_score_mapped` within `learn_add.py`
-    def logic_forward(self, exs):
-        return len(list(self.prolog.query("abduce_consistent_insts(%s)." % exs))) != 0
-
-    def get_query_string_need_flatten(self, pred_res, key, address_idx):
-        # flatten
-        flatten_pred_res = flatten(pred_res)
-        # add variables for prolog
-        for idx in range(len(flatten_pred_res)):
-            if idx in address_idx:
-                flatten_pred_res[idx] = 'X' + str(idx)
-        # unflatten
-        new_pred_res = reform_idx(flatten_pred_res, pred_res)
-
-        query_string = "abduce_consistent_insts(%s)." % new_pred_res
-        return query_string.replace("'", "").replace("+", "'+'").replace("=", "'='")
-
     def consist_rule(self, exs, rules):
         rules = str(rules).replace("\'","")
         return len(list(self.prolog.query("eval_inst_feature(%s, %s)." % (exs, rules)))) != 0
@@ -327,92 +286,79 @@ class HED_prolog_KB(prolog_KB):
         if len(prolog_result) == 0:
             return None
         prolog_rules = prolog_result[0]['X']
-        rules = []
-        for rule in prolog_rules:
-            rules.append(rule.value)
+        rules = [rule.value for rule in prolog_rules]
         return rules
 
-    # def consist_rules(self, pred_res, rules):
-
 
 class RegKB(KBBase):
-    def __init__(self, GKB_flag=False, X=None, Y=None):
-        super().__init__()
-        tmp_dict = {}
-        for x, y in zip(X, Y):
-            tmp_dict.setdefault(len(x), defaultdict(list))[y].append(np.array(x))
-
-        self.base = {}
-        for l in tmp_dict.keys():
-            data = sorted(list(zip(tmp_dict[l].keys(), tmp_dict[l].values())))
-            X = [x for y, x in data]
-            Y = [y for y, x in data]
-            self.base[l] = (X, Y)
-
-    def valid_candidate(self):
-        pass
-
-    def logic_forward(self):
-        pass
-
-    def abduce_candidates(self, key, length=None):
-        if key is None:
-            return self.get_all_candidates()
-
-        length = self._length(length)
+    def __init__(self, pseudo_label_list=None, len_list=None, GKB_flag=False, max_err=1e-3):
+        super().__init__(pseudo_label_list, len_list, GKB_flag, max_err)
+
+    def _find_candidate_GKB(self, pred_res, key):
+        potential_candidates = self.base[len(pred_res)]
+        key_list = list(potential_candidates.keys())
+        key_idx = bisect.bisect_left(key_list, key)
+        
+        all_candidates = []
+        for idx in range(key_idx - 1, 0, -1):
+            k = key_list[idx]
+            if abs(k - key) <= self.max_err:
+                all_candidates += potential_candidates[k]
+            else:
+                break
+            
+        for idx in range(key_idx, len(key_list)):
+            k = key_list[idx]
+            if abs(k - key) <= self.max_err:
+                all_candidates += potential_candidates[k]
+            else:
+                break
+        return all_candidates
+    
 
-        min_err = 999999
-        candidates = []
-        for l in length:
-            X, Y = self.base[l]
-
-            idx = bisect.bisect_left(Y, key)
-            begin = max(0, idx - 1)
-            end = min(idx + 2, len(X))
-
-            for idx in range(begin, end):
-                err = abs(Y[idx] - key)
-                if abs(err - min_err) < 1e-9:
-                    candidates.extend(X[idx])
-                elif err < min_err:
-                    candidates = copy.deepcopy(X[idx])
-                    min_err = err
-        return candidates
+class HWF_KB(RegKB):
+    def __init__(
+        self, 
+        pseudo_label_list=['1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', 'times', 'div'], 
+        len_list=[1, 3, 5, 7],
+        GKB_flag=False,
+        max_err=1e-3
+    ):
+        super().__init__(pseudo_label_list, len_list, GKB_flag, max_err)
 
-    def get_all_candidates(self):
-        return sum([sum(D[0], []) for D in self.base.values()], [])
+    def _valid_candidate(self, formula):
+        if len(formula) % 2 == 0:
+            return False
+        for i in range(len(formula)):
+            if i % 2 == 0 and formula[i] not in ['1', '2', '3', '4', '5', '6', '7', '8', '9']:
+                return False
+            if i % 2 != 0 and formula[i] not in ['+', '-', 'times', 'div']:
+                return False
+        return True
 
-    def __len__(self):
-        return sum([sum(len(x) for x in D[0]) for D in self.base.values()])
+    def logic_forward(self, formula):
+        if not self._valid_candidate(formula):
+            return np.inf
+        mapping = {
+            '1': '1',
+            '2': '2',
+            '3': '3',
+            '4': '4',
+            '5': '5',
+            '6': '6',
+            '7': '7',
+            '8': '8',
+            '9': '9',
+            '+': '+',
+            '-': '-',
+            'times': '*',
+            'div': '/',
+        }
+        formula = [mapping[f] for f in formula]
+        return eval(''.join(formula))
 
 
 import time
 
 if __name__ == "__main__":
-    t1 = time.time()
-    kb = HWF_KB(True)
-    t2 = time.time()
-    print(t2 - t1)
-
-    # X = ["1+1", "0+1", "1+0", "2+0", "1+0+1"]
-    # Y = [2, 1, 1, 2, 2]
-    # kb = ClsKB(X, Y)
-    # print('len(kb):', len(kb))
-    # res = kb.get_candidates(2, 5)
-    # print(res)
-    # res = kb.get_candidates(2, 3)
-    # print(res)
-    # res = kb.get_candidates(None)
-    # print(res)
-    # print()
-
-    # X = ["1+1", "0+1", "1+0", "2+0", "1+0.5", "0.75+0.75"]
-    # Y = [2, 1, 1, 2, 1.5, 1.5]
-    # kb = RegKB(X, Y)
-    # print('len(kb):', len(kb))
-    # res = kb.get_candidates(1.6)
-    # print(res)
-    # res = kb.get_candidates(1.6, length = 9)
-    # print(res)
-    # res = kb.get_candidates(None)
-    # print(res)
+    pass
\ No newline at end of file
diff --git a/framework.py b/abl/framework.py
similarity index 98%
rename from framework.py
rename to abl/framework.py
index 2a1fdad..c3a5c6e 100644
--- a/framework.py
+++ b/abl/framework.py
@@ -14,7 +14,7 @@ import pickle as pk
 
 import numpy as np
 
-from utils.plog import INFO, DEBUG, clocker
+from .utils.plog import INFO, DEBUG, clocker
 
 def block_sample(X, Z, Y, sample_num, epoch_idx):
     part_num = (len(X) // sample_num)
diff --git a/framework_hed.py b/abl/framework_hed.py
similarity index 95%
rename from framework_hed.py
rename to abl/framework_hed.py
index b7439c3..3f09ff6 100644
--- a/framework_hed.py
+++ b/abl/framework_hed.py
@@ -16,12 +16,15 @@ import torch.nn as nn
 import numpy as np
 import os
 
-from utils.plog import INFO, DEBUG, clocker
-from utils.utils import flatten, reform_idx, block_sample, gen_mappings, mapping_res, remapping_res
+from .utils.plog import INFO, DEBUG, clocker
+from .utils.utils import flatten, reform_idx, block_sample, gen_mappings, mapping_res, remapping_res
 
-from models.nn import MLP, SymbolNetAutoencoder
-from models.basic_model import BasicModel, BasicDataset
-from datasets.hed.get_hed import get_pretrain_data
+from .models.nn import MLP, SymbolNetAutoencoder
+from .models.basic_model import BasicModel, BasicDataset
+
+import sys
+sys.path.append("..")
+from examples.datasets.hed.get_hed import get_pretrain_data
 
 def result_statistics(pred_Z, Z, Y, logic_forward, char_acc_flag):
     result = {}
@@ -147,7 +150,7 @@ def abduce_and_train(model, abducer, mapping, train_X_true, select_num):
     for m in mappings:
         pred_res = mapping_res(original_pred_res, m)
         max_abduce_num = 20
-        solution = abducer.zoopt_get_solution(pred_res, [1] * len(pred_res), max_abduce_num)
+        solution = abducer.zoopt_get_solution(pred_res, [None] * len(pred_res), max_abduce_num)
         all_address_flag = reform_idx(solution, pred_res)
 
         consistent_idx_tmp = []
@@ -155,7 +158,7 @@ def abduce_and_train(model, abducer, mapping, train_X_true, select_num):
         
         for idx in range(len(pred_res)):
             address_idx = [i for i, flag in enumerate(all_address_flag[idx]) if flag != 0]
-            candidate = abducer.kb.address_by_idx([pred_res[idx]], 1, address_idx, True)
+            candidate = abducer.address_by_idx([pred_res[idx]], None, address_idx)
             if len(candidate) > 0:
                 consistent_idx_tmp.append(idx)
                 consistent_pred_res_tmp.append(candidate[0][0])
@@ -211,7 +214,7 @@ def get_rules_from_data(model, abducer, mapping, train_X_true, samples_per_rule,
             consistent_idx = []
             consistent_pred_res = []
             for idx in range(len(pred_res)):
-                if abducer.kb.logic_forward([pred_res[idx]]):
+                if abducer.kb.logic_forward(pred_res[idx]):
                     consistent_idx.append(idx)
                     consistent_pred_res.append(pred_res[idx])
 
diff --git a/abl/models/__init__.py b/abl/models/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/models/basic_model.py b/abl/models/basic_model.py
similarity index 100%
rename from models/basic_model.py
rename to abl/models/basic_model.py
diff --git a/models/lenet5.py b/abl/models/lenet5.py
similarity index 100%
rename from models/lenet5.py
rename to abl/models/lenet5.py
diff --git a/models/nn.py b/abl/models/nn.py
similarity index 97%
rename from models/nn.py
rename to abl/models/nn.py
index cecbeea..7a0f560 100644
--- a/models/nn.py
+++ b/abl/models/nn.py
@@ -10,9 +10,6 @@
 #
 # ================================================================#
 
-import sys
-
-sys.path.append("..")
 
 import torchvision
 
@@ -23,8 +20,6 @@ from torch.autograd import Variable
 import torchvision.transforms as transforms
 import numpy as np
 
-from models.basic_model import BasicModel
-import utils.plog as plog
 
 
 class LeNet5(nn.Module):
diff --git a/models/wabl_models.py b/abl/models/wabl_models.py
similarity index 99%
rename from models/wabl_models.py
rename to abl/models/wabl_models.py
index 9c97bbc..3b682ee 100644
--- a/models/wabl_models.py
+++ b/abl/models/wabl_models.py
@@ -21,7 +21,6 @@ from sklearn.preprocessing import StandardScaler
 from sklearn.svm import SVC
 from sklearn.gaussian_process import GaussianProcessClassifier
 from sklearn.gaussian_process.kernels import RBF
-from models.basic_model import BasicModel
 
 import pickle as pk
 import random
diff --git a/utils/plog.py b/abl/utils/plog.py
similarity index 100%
rename from utils/plog.py
rename to abl/utils/plog.py
diff --git a/utils/utils.py b/abl/utils/utils.py
similarity index 83%
rename from utils/utils.py
rename to abl/utils/utils.py
index 5cd433d..67c3cf7 100644
--- a/utils/utils.py
+++ b/abl/utils/utils.py
@@ -1,30 +1,23 @@
 import torch
 import torch.nn as nn
 import numpy as np
-from utils.plog import INFO
+from .plog import INFO
 from collections import OrderedDict
+from itertools import chain
 
-# for multiple predictions, modify from `learn_add.py`
+# for multiple predictions
 def flatten(l):
-    return (
-        [item for sublist in l for item in flatten(sublist)]
-        if isinstance(l, list)
-        else [l]
-    )
-
-
-# for multiple predictions, modify from `learn_add.py`
+    if not isinstance(l[0], (list, tuple)):
+        return l
+    return list(chain.from_iterable(l))
+    
+# for multiple predictions
 def reform_idx(flatten_pred_res, save_pred_res):
     re = []
     i = 0
     for e in save_pred_res:
-        j = 0
-        idx = []
-        while j < len(e):
-            idx.append(flatten_pred_res[i + j])
-            j += 1
-        re.append(idx)
-        i = i + j
+        re.append(flatten_pred_res[i:i + len(e)])
+        i += len(e)
     return re
 
 
@@ -85,11 +78,10 @@ def remapping_res(pred_res, m):
         remapping[value] = key
     return [[remapping[symbol] for symbol in formula] for formula in pred_res]
 
-
-def check_equal(a, b):
+def check_equal(a, b, max_err=0):
     if isinstance(a, (int, float)) and isinstance(b, (int, float)):
-        return abs(a - b) <= 1e-3
-
+        return abs(a - b) <= max_err
+    
     if isinstance(a, list) and isinstance(b, list):
         if len(a) != len(b):
             return False
@@ -119,4 +111,4 @@ def reduce_dimension(data):
                 [extract_feature(symbol_img) for symbol_img in equation]
                 for equation in equations
             ]
-            data[truth_value][equation_len] = reduced_equations
+            data[truth_value][equation_len] = reduced_equations       
diff --git a/datasets/data_generator.py b/examples/datasets/data_generator.py
similarity index 100%
rename from datasets/data_generator.py
rename to examples/datasets/data_generator.py
diff --git a/datasets/hed/BK.pl b/examples/datasets/hed/BK.pl
similarity index 100%
rename from datasets/hed/BK.pl
rename to examples/datasets/hed/BK.pl
diff --git a/datasets/hed/README.md b/examples/datasets/hed/README.md
similarity index 100%
rename from datasets/hed/README.md
rename to examples/datasets/hed/README.md
diff --git a/datasets/hed/get_hed.py b/examples/datasets/hed/get_hed.py
similarity index 100%
rename from datasets/hed/get_hed.py
rename to examples/datasets/hed/get_hed.py
diff --git a/datasets/hed/learn_add.pl b/examples/datasets/hed/learn_add.pl
similarity index 92%
rename from datasets/hed/learn_add.pl
rename to examples/datasets/hed/learn_add.pl
index af1d6bb..fbf698f 100644
--- a/datasets/hed/learn_add.pl
+++ b/examples/datasets/hed/learn_add.pl
@@ -32,6 +32,9 @@ abduce_consistent_insts(Exs):-
 % (Experimental) Uncomment to use parallel abduction
 % abduce_consistent_exs_concurrent(Exs), !.
 
+logic_forward(Exs, X) :- abduce_consistent_insts([Exs]) -> X = true ; X = false.
+logic_forward(Exs) :- abduce_consistent_insts(Exs).
+
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %% Abduce Delta_C given pseudo-labels
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
diff --git a/datasets/hwf/README.md b/examples/datasets/hwf/README.md
similarity index 100%
rename from datasets/hwf/README.md
rename to examples/datasets/hwf/README.md
diff --git a/datasets/hwf/get_hwf.py b/examples/datasets/hwf/get_hwf.py
similarity index 100%
rename from datasets/hwf/get_hwf.py
rename to examples/datasets/hwf/get_hwf.py
diff --git a/examples/datasets/mnist_add/add.pl b/examples/datasets/mnist_add/add.pl
new file mode 100644
index 0000000..96f0869
--- /dev/null
+++ b/examples/datasets/mnist_add/add.pl
@@ -0,0 +1,2 @@
+pseudo_label(N) :- between(0, 9, N).
+logic_forward([Z1, Z2], Res) :- pseudo_label(Z1), pseudo_label(Z2), Res is Z1+Z2.
diff --git a/datasets/mnist_add/get_mnist_add.py b/examples/datasets/mnist_add/get_mnist_add.py
similarity index 100%
rename from datasets/mnist_add/get_mnist_add.py
rename to examples/datasets/mnist_add/get_mnist_add.py
diff --git a/datasets/mnist_add/test_data.txt b/examples/datasets/mnist_add/test_data.txt
similarity index 100%
rename from datasets/mnist_add/test_data.txt
rename to examples/datasets/mnist_add/test_data.txt
diff --git a/datasets/mnist_add/train_data.txt b/examples/datasets/mnist_add/train_data.txt
similarity index 100%
rename from datasets/mnist_add/train_data.txt
rename to examples/datasets/mnist_add/train_data.txt
diff --git a/example.py b/examples/example.py
similarity index 86%
rename from example.py
rename to examples/example.py
index 3751db3..0a69539 100644
--- a/example.py
+++ b/examples/example.py
@@ -10,24 +10,24 @@
 #
 # ================================================================#
 
-from utils.plog import logger, INFO
-from utils.utils import reduce_dimension
+import sys
+sys.path.append("../")
+
+from abl.utils.plog import logger, INFO
 import torch.nn as nn
 import torch
 
-from models.nn import LeNet5, SymbolNet
-from models.basic_model import BasicModel, BasicDataset
-from models.wabl_models import DecisionTree, WABLBasicModel
-from sklearn.neighbors import KNeighborsClassifier
+from abl.models.nn import LeNet5, SymbolNet
+from abl.models.basic_model import BasicModel, BasicDataset
+from abl.models.wabl_models import DecisionTree, WABLBasicModel
 
 from multiprocessing import Pool
-from abducer.abducer_base import AbducerBase
-from abducer.kb import add_KB, HWF_KB, HED_prolog_KB
+from abl.abducer.abducer_base import AbducerBase
+from abl.abducer.kb import add_KB, HWF_KB, prolog_KB
 from datasets.mnist_add.get_mnist_add import get_mnist_add
 from datasets.hwf.get_hwf import get_hwf
 from datasets.hed.get_hed import get_hed, split_equation
-import framework_hed
-import framework_hed_knn
+from abl import framework_hed
 
 
 def run_test():
@@ -36,7 +36,7 @@ def run_test():
     # kb = HWF_KB(True)
     # abducer = AbducerBase(kb)
 
-    kb = HED_prolog_KB()
+    kb = prolog_KB(pseudo_label_list=[1, 0, '+', '='], pl_file='../examples/datasets/hed/learn_add.pl')
     abducer = AbducerBase(kb, zoopt=True, multiple_predictions=True)
 
     recorder = logger()
diff --git a/nonshare_example.py b/examples/nonshare_example.py
similarity index 100%
rename from nonshare_example.py
rename to examples/nonshare_example.py
diff --git a/share_example.py b/examples/share_example.py
similarity index 100%
rename from share_example.py
rename to examples/share_example.py
diff --git a/examples/weights/all_weights_here.txt b/examples/weights/all_weights_here.txt
new file mode 100644
index 0000000..e69de29