Gunakan SH-256 top 50 4D dan buatkan scriptnya unt...

Berikut script Google Colab untuk membuat Top 50 angka 4D menggunakan SHA-256 dari file PRIZE.txt. File kamu berisi format tanggal/waktu dan baris angka 4D seperti 6112, 0236, dst. (PRIZE.txt)

Catatan: SHA-256 ini bukan prediksi pasti, hanya ranking deterministik berbasis data historis + hash. Hasil akan sama selama file dan parameter sama.

python
# =========================
# SHA-256 TOP 50 4D COLAB
# =========================

import re
import hashlib
import pandas as pd
from collections import Counter
from google.colab import files

# 1) Upload file PRIZE.txt
uploaded = files.upload()

# Ambil nama file pertama yang di-upload
filename = list(uploaded.keys())[0]
print("File terbaca:", filename)

# 2) Baca isi file
with open(filename, "r", encoding="utf-8", errors="ignore") as f:
    text = f.read()

# 3) Parsing angka 4D
# Format yang dicari:
# 6112  0, 3, 4, 5, 7, 8, 9
# 0236  1, 4, 5, 7, 8, 9
pattern = re.compile(r"^\s*(\d{4})\s+(?:\d\s*,?\s*)+", re.MULTILINE)
numbers = pattern.findall(text)

# Hapus duplikat parsing yang aneh, tapi tetap pertahankan urutan asli
numbers = [n.zfill(4) for n in numbers]

print("Total angka 4D terbaca:", len(numbers))
print("Contoh 10 angka terakhir:", numbers[-10:])

if len(numbers) == 0:
    raise ValueError("Tidak ada angka 4D yang berhasil dibaca. Cek format file PRIZE.txt.")

# =========================
# KONFIGURASI
# =========================

TOP_N = 50

# Berapa data terakhir yang dipakai sebagai seed SHA-256
LAST_N_SEED = 200

# Pilihan mode:
# "sha256" = ranking murni SHA-256
# "hybrid" = SHA-256 + statistik posisi digit + penalti angka yang baru keluar
MODE = "sha256"

# =========================
# MEMBUAT SEED DARI DATA HISTORIS
# =========================

recent_seed_data = numbers[-LAST_N_SEED:]
seed_text = "|".join(recent_seed_data)

seed_hash = hashlib.sha256(seed_text.encode()).hexdigest()

print("Seed SHA-256:")
print(seed_hash)

# =========================
# FUNGSI SCORE SHA-256
# =========================

def sha256_score(candidate, seed_hash):
    raw = f"{seed_hash}|{candidate}"
    digest = hashlib.sha256(raw.encode()).hexdigest()
    score = int(digest, 16)
    return score, digest

# =========================
# OPSIONAL: SCORE STATISTIK HYBRID
# =========================

def build_position_stats(data):
    pos_counts = [Counter() for _ in range(4)]

    for num in data:
        for i, d in enumerate(num):
            pos_counts[i][d] += 1

    return pos_counts

def position_score(candidate, pos_counts, total):
    score = 0
    for i, d in enumerate(candidate):
        # Laplace smoothing agar digit yang belum muncul tetap punya nilai
        score += (pos_counts[i][d] + 1) / (total + 10)
    return score / 4

recent_stats_data = numbers[-300:] if len(numbers) >= 300 else numbers
pos_counts = build_position_stats(recent_stats_data)
recent_exact = set(numbers[-100:])

# =========================
# GENERATE SEMUA ANGKA 0000 - 9999
# =========================

candidates = [f"{i:04d}" for i in range(10000)]

rows = []

for cand in candidates:
    hash_score, digest = sha256_score(cand, seed_hash)

    if MODE == "sha256":
        final_score = hash_score

    elif MODE == "hybrid":
        # Normalisasi SHA score ke 0 - 1
        hash_norm = hash_score / ((1 << 256) - 1)

        # Statistik posisi digit
        pos_norm = position_score(cand, pos_counts, len(recent_stats_data))

        # Penalti jika angka sudah muncul sangat baru
        repeat_penalty = 0.85 if cand in recent_exact else 1.0

        # Bobot hybrid
        final_score = (
            0.70 * hash_norm +
            0.30 * pos_norm
        ) * repeat_penalty

    else:
        raise ValueError("MODE harus 'sha256' atau 'hybrid'.")

    rows.append({
        "rank_score": final_score,
        "angka_4d": cand,
        "sha256": digest,
        "pernah_muncul": cand in set(numbers),
        "muncul_100_terakhir": cand in recent_exact
    })

# =========================
# SORTING TOP 50
# =========================

df = pd.DataFrame(rows)
df = df.sort_values("rank_score", ascending=False).reset_index(drop=True)

top50 = df.head(TOP_N).copy()
top50.insert(0, "rank", range(1, TOP_N + 1))

# Tampilkan hasil
top50_display = top50[[
    "rank",
    "angka_4d",
    "rank_score",
    "pernah_muncul",
    "muncul_100_terakhir",
    "sha256"
]]

pd.set_option("display.max_colwidth", 80)
display(top50_display)

# =========================
# SIMPAN KE CSV
# =========================

output_file = "top50_4d_sha256.csv"
top50_display.to_csv(output_file, index=False)

print("File hasil disimpan:", output_file)

# Download otomatis
files.download(output_file)

Untuk mode murni SHA-256, biarkan:

python
MODE = "sha256"

Kalau ingin ranking yang digabung dengan pola statistik posisi digit, ubah menjadi:

python
MODE = "hybrid"