Bertindaklah sebagai mesin kalkulator statistik ko...

python
# Google Colab Script
# Upload file: PRIZE_LEVEL_4_ELITE.csv

from google.colab import files
import pandas as pd
from collections import Counter
from decimal import Decimal, ROUND_HALF_UP

uploaded = files.upload()

filename = "PRIZE_LEVEL_4_ELITE.csv"
df = pd.read_csv(filename)

digit_cols = ["d1", "d2", "d3", "d4"]

# 1. Hot Digits: 100 baris terakhir
last_100 = df.tail(100)

hot_result = {}
hot_digits_per_col = []

for col in digit_cols:
    counts = Counter(last_100[col].astype(int))
    max_freq = max(counts.values())
    hot_digits = [d for d in range(10) if counts.get(d, 0) == max_freq]
    hot_result[col] = {
        "digits": hot_digits,
        "frequency": max_freq
    }
    hot_digits_per_col.append(hot_digits)

# Kombinasi hot digit, tie-aware
hot_combinations = []
for d1 in hot_digits_per_col[0]:
    for d2 in hot_digits_per_col[1]:
        for d3 in hot_digits_per_col[2]:
            for d4 in hot_digits_per_col[3]:
                hot_combinations.append(f"{d1}{d2}{d3}{d4}")

# 2. Cold Digits: seluruh dataset dari bawah ke atas
cold_result = {}
cold_digits = []

for col in digit_cols:
    distances = {d: None for d in range(10)}

    for distance, value in enumerate(reversed(df[col].astype(int).tolist())):
        if distances[value] is None:
            distances[value] = distance

        if all(v is not None for v in distances.values()):
            break

    distances = {
        d: len(df) if distances[d] is None else distances[d]
        for d in range(10)
    }

    max_distance = max(distances.values())
    cold_digit_list = [d for d in range(10) if distances[d] == max_distance]

    cold_result[col] = {
        "digits": cold_digit_list,
        "absence_rows": max_distance
    }

    cold_digits.append(str(cold_digit_list[0]) if len(cold_digit_list) == 1 else "/".join(map(str, cold_digit_list)))

cold_combination = "".join(cold_digits)

# 3. Moving Average sum_digits: 20 baris terakhir
last_20_sum_digits = df["sum_digits"].tail(20).astype(int)
ma20 = last_20_sum_digits.mean()
predicted_sum_digits = int(Decimal(str(ma20)).quantize(Decimal("1"), rounding=ROUND_HALF_UP))

# Output ringkasan
summary = pd.DataFrame([
    {
        "Analisis": "Hot Digits - 100 baris terakhir",
        "d1": f"{hot_result['d1']['digits']} = {hot_result['d1']['frequency']} kali",
        "d2": f"{hot_result['d2']['digits']} = {hot_result['d2']['frequency']} kali",
        "d3": f"{hot_result['d3']['digits']} = {hot_result['d3']['frequency']} kali",
        "d4": f"{hot_result['d4']['digits']} = {hot_result['d4']['frequency']} kali",
        "Output": ", ".join(hot_combinations)
    },
    {
        "Analisis": "Cold Digits - seluruh dataset",
        "d1": f"{cold_result['d1']['digits']} = absen {cold_result['d1']['absence_rows']} baris",
        "d2": f"{cold_result['d2']['digits']} = absen {cold_result['d2']['absence_rows']} baris",
        "d3": f"{cold_result['d3']['digits']} = absen {cold_result['d3']['absence_rows']} baris",
        "d4": f"{cold_result['d4']['digits']} = absen {cold_result['d4']['absence_rows']} baris",
        "Output": cold_combination
    },
    {
        "Analisis": "Moving Average sum_digits - 20 baris terakhir",
        "d1": "",
        "d2": "",
        "d3": "",
        "d4": "",
        "Output": f"Jumlah = {last_20_sum_digits.sum()}; MA20 = {ma20}; Prediksi total digit = {predicted_sum_digits}"
    }
])

display(summary)

Script Google Colab lengkap: Download colab_pattern_backtest_PRIZE_LEVEL_4.py