online_attacks/experiments/hyper_search_stochastic_toy.py [53:83]:
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        comp_ratio_list.append(
            compute_competitive_ratio(
                indices[online_algorithm.name], indices[offline_algorithm.name]
            )
        )

        if knapsack:
            offline_value = sum([x[0] for x in indices[offline_algorithm.name]])
            online_knapsack_list.append(
                compute_knapsack_online_value(indices[online_algorithm.name])
            )

    # Indicator Competitive Ratio
    comp_ratio = np.sum(comp_ratio_list) / (params.K * num_perms)
    print(
        "Competitive Ratio for %s with K = %d is %f "
        % (online_algorithm.name, params.K, comp_ratio)
    )

    # Knapsack Competitive Ratio
    if knapsack:
        comp_ratio = np.sum(online_knapsack_list) / (offline_value * num_perms)
        print(
            "Knapsack Competitive Ratio for %s with K = %d is %f "
            % (online_algorithm.name, params.K, comp_ratio)
        )

    return comp_ratio


def main():
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online_attacks/experiments/toy.py [34:62]:
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        comp_ratio_list.append(
            compute_competitive_ratio(
                indices[online_algorithm.name], indices[offline_algorithm.name]
            )
        )
        if knapsack:
            offline_value = sum([x[0] for x in indices[offline_algorithm.name]])
            online_knapsack_list.append(
                compute_knapsack_online_value(indices[online_algorithm.name])
            )
    # Indicator Competitive Ratio
    comp_ratio = np.sum(comp_ratio_list) / (params.K * num_perms)
    print(
        "Competitive Ratio for %s with K = %d is %f "
        % (online_algorithm.name, params.K, comp_ratio)
    )

    # Knapsack Competitive Ratio
    if knapsack:
        comp_ratio = np.sum(online_knapsack_list) / (offline_value * num_perms)
        print(
            "Knapsack Competitive Ratio for %s with K = %d is %f "
            % (online_algorithm.name, params.K, comp_ratio)
        )

    return comp_ratio


def main():
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