from marginal_values import calculate_expected_marginal_value
from independent_histogram import IndependentHistogram


def expected_local_bid(goods, valuation_function, price_distribution, num_iterations=100, num_samples=50):
    """
    Iteratively computes a bid vector by updating bids to be the expected marginal value for each good.

    """
    # TODO: Implement LocalBid with price sampling according to Algorithm 2
    raise NotImplementedError("Implement LocalBid with price sampling")

if __name__ == "__main__":
    # Test with a simple additive valuation function
    # This mimics how the old server generated valuations
    def test_valuation(bundle):
        """Simple additive valuation for testing."""
        base_values = {"a": 20, "b": 25, "c": 30}
        return sum(base_values.get(item, 0) for item in bundle)
    
    # Create a simple price distribution for testing
    test_histogram = IndependentHistogram(["a", "b", "c"], [5, 5, 5], [100, 100, 100])
    
    # Add some sample data to the histogram
    for _ in range(10):
        test_histogram.add_record({"a": 15, "b": 20, "c": 25})
    
    print("Testing expected_local_bid with sample data...")
    try:
        result = expected_local_bid(
            goods=["a", "b", "c"],
            valuation_function=test_valuation,
            price_distribution=test_histogram,
            num_iterations=10,
            num_samples=50
        )
        print("Result:", result)
    except NotImplementedError:
        print("expected_local_bid not yet implemented - this is expected for the stencil") 