From 66b50698582feaa0f95f454821e5ee6fc1ccfdb7 Mon Sep 17 00:00:00 2001
From: Chris Smith <ole088@gmail.com>
Date: Tue, 17 Mar 2026 20:47:04 -0400
Subject: [PATCH] Add AI code in comment form for review

---
 lottery_predictor/analyze.py | 84 +++++++++++++++++++++++++++++++++---
 1 file changed, 79 insertions(+), 5 deletions(-)

diff --git a/lottery_predictor/analyze.py b/lottery_predictor/analyze.py
index 76bf85f..b1759e2 100644
--- a/lottery_predictor/analyze.py
+++ b/lottery_predictor/analyze.py
@@ -1,16 +1,15 @@
-from datetime import datetime
 from collections import Counter
+from datetime import datetime
+from itertools import combinations
 
 import numpy as np
 import pandas as pd
-from sklearn.ensemble import RandomForestClassifier
+from sklearn.ensemble import RandomForestRegressor
 from sqlalchemy import select
 
-from data.database import Base, dal, project_variables
+from data.database import Base, dal
 import util.drawing as drawutil
 
-RANDOM_SEED = 42
-
 
 def load_dataframe(
         table_name: str, start_date: datetime | None = None, end_date: datetime | None = None
@@ -125,3 +124,78 @@ def get_cold_numbers(probabilities: dict[int, float], bottom: int = 5) -> list[t
     :returns list of tuples: A list of the hot numbers and their raw score
     """
     return sorted(probabilities.items(), key=lambda item: item[1])[:bottom]
+
+
+"""
+def extract_features(row):
+    balls = [row['main_ball1'], row['main_ball2'], row['main_ball3'], row['main_ball4'], row['main_ball5']]
+    pd.Series({
+        'sum': sum(balls),
+        'mean': np.mean(balls),
+        'std': np.std(balls),
+        'even_count': len([b for b in balls if b % 2 == 0]),
+        'range': max(balls) - min(balls)
+    })
+
+# Melt the 5 ball columns into one long column of numbers
+melted = df.melt(id_vars=['draw_date'], value_vars=['ball1', 'ball2', 'ball3', 'ball4', 'ball5'], value_name='number')
+
+# Find the most recent date each number appeared
+last_seen = melted.groupby('number')['draw_date'].max()
+
+# calculate days overdue relative to today
+current_date = datetime.now()
+overdue_days = (current_date - last_seen).dt.days
+
+# sort to find the "most overdue" at the top
+most_overdue = overdue_days.sort_values(ascending=False)
+
+# print the 10 most overdue
+print(most_overdue.head(10))
+
+game_ball_last_seen = df.groupby('powerball')['draw_date'].max() # or 'mega-ball'
+game_ball_overdue = (current_date - game_ball_last_seen).dt.days.soft_values(ascending=False)
+
+features = np.apply(extract_features, axis=1)
+y = np.ones(len(data_frame))
+# train a model to recognize what a "winning" set looks like
+model = RandomForestRegressor(n_estimators=100)
+model.fit(features, y)
+
+# Sample: Generate combinations from the top 15 most frequent/overdue numbers
+target_numbers = most_overdue.head(15).index.tolist()
+candidate_sets = list(combinations(target_numbers, 2))
+
+results = []
+for s in candidate_sets:
+    # Create features for this hypothetical set
+    s_feat = extract_features({'main_ball1': s[0], 'main_ball2': s[1], 'main_ball3': s[2], 'main_ball4': s[3],
+        'main_ball5': s[4]})
+
+    # Model predicts how closely this matches historical 'winning' patterns
+    score = model.predict(s_feat.values.reshape(1, -1))[0]
+    results.append({'set': s, 'score': score})
+
+# Rank by score
+ranked_sets = pd.DataFrame(results).sort_values(by='score', ascending=False)
+
+# Get frequency counts
+counts = melted['number'].value_counts(normalize=True)
+
+def calc_prob(s):
+    return np.prod([counts.get(num, 0) for num in s])
+
+ranked_sets['statistical_prob'] = ranked_sets['set'].apply(calc_prob)
+
+game_counts = df['game_ball'].value_counts(normalize=True)
+
+def calc_total_prob(row):
+    # calculate prob for the 5 main balls
+    five_ball_prob = np.prod([counts.get(num, 0) for num in row['set']])
+    # multiply the prob of the specific game ball
+    game_prob = game_counts.get(row['game_ball'], 0)
+    return five_ball_prob * game_prob
+
+ranked_sets['total_prob'] = ranked_sets.apply(calc_total_prob, axis=1)
+
+"""