import csv

DATA_FILE = "data.csv"
THETAS_FILE = "thetas.csv"


def load_thetas():
    with open(THETAS_FILE) as f:
        lines = f.readlines()
        return float(lines[0]), float(lines[1])


def load_data():
    km = []
    price = []
    with open(DATA_FILE) as f:
        reader = csv.reader(f)
        next(reader)
        for row in reader:
            km.append(float(row[0]))
            price.append(float(row[1]))
    return km, price


def mean(vals):
    return sum(vals) / len(vals)


def estimate_price(mileage, theta0, theta1):
    return theta0 + theta1 * mileage


def main():
    kms, prices = load_data()
    theta0, theta1 = load_thetas()
    predicted_prices = [estimate_price(x, theta0, theta1) for x in kms]
    mean_price = mean(prices)
    r2 = 1 - sum((p - pp) ** 2 for p, pp in zip(prices, predicted_prices)) / sum(
        (p - mean_price) ** 2 for p in prices
    )
    print(f"R-squared = {r2}")


if __name__ == "__main__":
    main()