添加python脚本

2026-01-22 18:02:21 +08:00
parent d324e370db
commit 9c2153b05b
5 changed files with 3152 additions and 0 deletions
--- a/PyModel/predict_model_v6.py
+++ b/PyModel/predict_model_v6.py
@@ -0,0 +1,84 @@
+import json
+from datetime import datetime
+
+class LotteryPredictorV6:
+    def __init__(self, stats_path):
+        with open(stats_path, 'r', encoding='utf-8') as f:
+            self.stats = json.load(f)
+        # self.result_weights = {'time': 0.83, 'week': 0.14, 'date': 0.03}
+        self.result_weights = {'time': 0.9, 'week': 0.05, 'date': 0.05}
+        self.result_detail_weights = {'time': 0.9, 'week': 0.05, 'date': 0.05}
+        self.winner_weights = {'time': 0.9, 'week': 0.05, 'date': 0.05}
+        self.gd_weights = {'time': 0.9, 'week': 0.05, 'date': 0.05}
+        self.glh_weights = {'time': 0.9, 'week': 0.05, 'date': 0.05}
+
+    def predict(self, date_str, time_str):
+        dt = datetime.strptime(date_str, '%Y-%m-%d')
+        day_of_month = str(dt.day)
+        day_of_week = str(dt.weekday())
+
+        t_stats = self.stats['by_time'].get(time_str, {})
+        d_stats = self.stats['by_date'].get(day_of_month, {})
+        w_stats = self.stats['by_week'].get(day_of_week, {})
+
+        raw_probs = {
+            'digital_pos': {},
+            'pos_detail': {},
+            'winner_prob': {},
+            'direct_gd1': {},
+            'direct_gd2': {},
+            'glh_pos': {}
+        }
+
+        # 1. 数字位置概率
+        for i in range(10):
+            pos_key = f'pos_{i}'
+            combined = {}
+            for val in range(1, 11):
+                v = str(val)
+                p = (t_stats.get('result_pos_prob', {}).get(pos_key, {}).get(v, 0) * self.result_weights['time'] +
+                     w_stats.get('result_pos_prob', {}).get(pos_key, {}).get(v, 0) * self.result_weights['week'] +
+                     d_stats.get('result_pos_prob', {}).get(pos_key, {}).get(v, 0) * self.result_weights['date'])
+                combined[v] = p
+            raw_probs['digital_pos'][pos_key] = combined
+
+        # 2. 位置详情 (单双大小)
+        for i in range(10):
+            pos_key = f'pos_{i}'
+            raw_probs['pos_detail'][pos_key] = {'big_small': {}, 'odd_even': {}}
+            for cat in ['big_small', 'odd_even']:
+                options = ['大', '小'] if cat == 'big_small' else ['单', '双']
+                for opt in options:
+                    p = (t_stats.get('result_pos_detail_prob', {}).get(pos_key, {}).get(cat, {}).get(opt, 0) * self.result_detail_weights['time'] +
+                         w_stats.get('result_pos_detail_prob', {}).get(pos_key, {}).get(cat, {}).get(opt, 0) * self.result_detail_weights['week'] +
+                         d_stats.get('result_pos_detail_prob', {}).get(pos_key, {}).get(cat, {}).get(opt, 0) * self.result_detail_weights['date'])
+                    raw_probs['pos_detail'][pos_key][cat][opt] = p
+
+        # 3. 冠亚和
+        for val in range(3, 20):
+            v = str(val)
+            p = (t_stats.get('winner_prob', {}).get(v, 0) * self.winner_weights['time'] +
+                 w_stats.get('winner_prob', {}).get(v, 0) * self.winner_weights['week'] +
+                 d_stats.get('winner_prob', {}).get(v, 0) * self.winner_weights['date'])
+            raw_probs['winner_prob'][v] = p
+
+        # 4. GD1 & GD2
+        for cat, opts in [('direct_gd1', ['冠亚大', '冠亚小']), ('direct_gd2', ['冠亚单', '冠亚双'])]:
+            stat_key = 'GD1_prob' if cat == 'direct_gd1' else 'GD2_prob'
+            for opt in opts:
+                p = (t_stats.get(stat_key, {}).get(opt, 0) * self.gd_weights['time'] +
+                     w_stats.get(stat_key, {}).get(opt, 0) * self.gd_weights['week'] +
+                     d_stats.get(stat_key, {}).get(opt, 0) * self.gd_weights['date'])
+                raw_probs[cat][opt] = p
+
+        # 5. 龙虎
+        for i in range(5):
+            pos_key = f'pos_{i}'
+            raw_probs['glh_pos'][pos_key] = {}
+            for opt in ['龙', '虎']:
+                p = (t_stats.get('GLH_pos_prob', {}).get(pos_key, {}).get(opt, 0) * self.glh_weights['time'] +
+                     w_stats.get('GLH_pos_prob', {}).get(pos_key, {}).get(opt, 0) * self.glh_weights['week'] +
+                     d_stats.get('GLH_pos_prob', {}).get(pos_key, {}).get(opt, 0) * self.glh_weights['date'])
+                raw_probs['glh_pos'][pos_key][opt] = p
+
+        return raw_probs