Delete PYAS_Compress.py

PYAS_Compress.py

@@ -1,35 +0,0 @@
-import json
-
-class ListCompressor:
-    def __init__(self):
-        self.model = {'ones': [], 'zero': [], 'word': {}}
-
-    def save_model(self, file_name):
-        with open(file_name, 'w') as f:
-            json.dump(self.model, f)
-        print(f"\nModel Is Saved In {file_name}")
-
-    def load_model(self, model_data):
-        if isinstance(model_data, str):
-            with open(model_data, 'r') as f:
-                model_data = json.load(f)
-        self.model = model_data
-
-    def train_model(self, ones, zero, min_count=1, max_ratio=1.0):
-        unique_words = set([item for sublist in ones + zero for item in sublist])
-        self.model['word'] = {word: idx for idx, word in enumerate(unique_words)}
-        ones = [[self.model['word'][item] for item in lst] for lst in ones if len(lst) >= min_count and len(lst) / len(unique_words) <= max_ratio]
-        zero = [[self.model['word'][item] for item in lst] for lst in zero if len(lst) >= min_count and len(lst) / len(unique_words) <= max_ratio]
-        self.model['ones'].extend(ones)
-        self.model['zero'].extend(zero)
-
-    def simplify_lists(self, lists):
-        return [list(set(lst)) for lst in lists]
-
-    def predict(self, new_list, similarity=0.5):
-        new_list = [self.model['word'].get(item, -1) for item in new_list if item in self.model['word']]
-        if len(new_list) < 1:
-            return False
-        max_vfl = [len(set(new_list) & set(ones)) / len(set(new_list) | set(ones)) for ones in self.model['ones']]
-        max_sfl = [len(set(new_list) & set(zero)) / len(set(new_list) | set(zero)) for zero in self.model['zero']]
-        return max(max_vfl) >= max(max_sfl) and max(max_vfl) >= similarity