Säähavainnot

import random
 
def get_forecast(previous_temperatures):
    forecast = previous_temperatures[:12]
 
    total_change = previous_temperatures[-1] - previous_temperatures[0]
    forecast = (t + total_change for t in forecast)
 
    fluctuation = max(previous_temperatures) - min(previous_temperatures)
    cutoff = 0.45 * fluctuation
    forecast = [(forecast if i < cutoff else None) for i, forecast in enumerate(forecast)]
 
    return forecast
 
def load_test_data():
    parsed_data = []
    with open("test-data.txt", "r", encoding="utf-8") as file:
        for line in file:
            temperatures = [float(s) for s in line.strip().split(" ")]
            forecast_input = temperatures[:24]
            correct_forecast = temperatures[24:]
 
            parsed_data.append((forecast_input, correct_forecast))
 
    random.shuffle(parsed_data)
    return parsed_data
 
def solve_task():
    day_count = int(input(""))
 
    data = ([float(s) for s in input("").split(" ")] for _ in range(day_count))
 
    for day in data:
        forecast = get_forecast(day)
 
        print(" ".join(("?" if h is None else str(h)) for h in forecast))
 
def get_point_forecast_quality(correct_temperature, forecast_temperature):
    if forecast_temperature is None:
        return 1
 
    deviation = abs(correct_temperature - forecast_temperature)
 
    if deviation > 2.05:
        return 0
    elif deviation > 0.75:
        return 1
    else:
        return 2
 
def get_point_forecast_score(correct_temperature, forecast_temperature):
    possible_scores = [-1, 0, 1]
    return possible_scores[get_point_forecast_quality(correct_temperature, forecast_temperature)]
 
def get_random_color():
    return (random.uniform(0, 1), random.uniform(0, 1), random.uniform(0, 1))
 
def plot():
    import matplotlib.pyplot as plt
    import numpy as np
 
    color_codes = ["#F00", "#FF0", "#0F0"]
 
    fig = plt.figure()
    x = np.arange(36)
 
    test_data = load_test_data()
    for day in test_data[:20]:
        line_color = get_random_color()
        forecast = get_forecast(day[0])
 
        full_day = day[0] + day[1]
        plt.plot(x, full_day, color=line_color)
 
        points = [p for p in zip(x[24:], forecast, day[1]) if p[1] is not None]
        filtered_t = [t for t, forecast, correct in points]
        filtered_forecast = [forecast for t, forecast, correct in points]
        filtered_correct = [correct for t, forecast, correct in points]
 
        colors = [color_codes[get_point_forecast_quality(correct, forecast)] for t, forecast, correct in points]
        plt.scatter(filtered_t, filtered_forecast, c = colors)
 
        plt.plot(filtered_t, filtered_forecast, color=line_color)
 
    plt.show()
 
def test_perfomance():
    test_data = load_test_data()
 
    correct_forecasts = 0
    incorrect_forecasts = 0
    skipped_forecasts = 0
 
    for day in test_data:
        forecast = get_forecast(day[0])
 
        for f, c in zip(forecast, day[1]):
            score = get_point_forecast_score(c, f)
 
            if f is None:
                skipped_forecasts += 1
            elif score == 1:
                correct_forecasts += 1
            elif score == -1:
                incorrect_forecasts += 1
 
    final_score = 25 * (correct_forecasts - incorrect_forecasts) / len(test_data)
    print(f"{final_score = }")
    print(f"{correct_forecasts = }")
    print(f"{incorrect_forecasts = }")
    print(f"{skipped_forecasts = }")
    print("")
 
solve_task()

Test 1

Verdict: ACCEPTED

Test 2

Verdict: ACCEPTED

Test 3

Verdict: ACCEPTED

Test 4

Verdict: ACCEPTED

Test 5

Verdict: ACCEPTED

Test 6

Verdict: ACCEPTED

Test 7

Verdict: ACCEPTED

Test 8

Verdict: ACCEPTED