def deploy_tokens_and_fund_accounts(
    token_amount: int,
    number_of_tokens: int,
    deploy_service: BlockChainService,
    participants: typing.List[typing.Address],
    contract_manager: ContractManager,
) -> typing.List[typing.TokenAddress]:
    """ Deploy `number_of_tokens` ERC20 token instances with `token_amount` minted and
    distributed among `blockchain_services`. Optionally the instances will be registered with
    the raiden registry.

    Args:
        token_amount (int): number of units that will be created per token
        number_of_tokens (int): number of token instances that will be created
        deploy_service (BlockChainService): the blockchain connection that will deploy
        participants (list(address)): participant addresses that will receive tokens
    """
    result = list()
    for _ in range(number_of_tokens):
        token_address = deploy_contract_web3(
            CONTRACT_HUMAN_STANDARD_TOKEN,
            deploy_service.client,
            contract_manager=contract_manager,
            constructor_arguments=(token_amount, 2, "raiden", "Rd"),
        )

        result.append(token_address)

        # only the creator of the token starts with a balance (deploy_service),
        # transfer from the creator to the other nodes
        for transfer_to in participants:
            deploy_service.token(token_address).transfer(
                to_address=transfer_to, amount=token_amount // len(participants)
            )

    return result
def apply_ktst(K, y, iterations=10000, subjects=False, verbose=True):
    """
    Compute MMD^2_u, its null distribution and the p-value of the
    kernel two-sample test.

    Parameters:
    ----------
    K: array-like
        Kernel matrix
    y: array_like
        class labels
    verbose: bool
        Verbosity

    Returns:
    -------
    mmd2u: float
        MMD^2_u value.
    acc_null: array
        Null distribution of the MMD^2_u
    p_value: float
         p-value
    """
    assert len(np.unique(y)) == 2, 'KTST only works on binary problems'

    # Assuming that the first m rows of the kernel matrix are from one
    # class and the other n rows from the second class.
    m = len(y[y == 0])
    n = len(y[y == 1])
    mmd2u = MMD2u(K, m, n)
    if verbose:
        print("MMD^2_u = %s" % mmd2u)
        print("Computing the null distribution.")
    if subjects:
        perms = [permutation_subjects_ktst(y) for i in range(iterations)]
        mmd2u_null = compute_null_distribution_given_permutations(K, m, n,
                                                                  perms,
                                                                  iterations)
    else:
        mmd2u_null = compute_null_distribution(K, m, n, iterations,
                                               verbose=verbose)

    p_value = max(1.0/iterations, (mmd2u_null > mmd2u).sum()
                  / float(iterations))
    if verbose:
        print("p-value ~= %s \t (resolution : %s)" % (p_value, 1.0/iterations))

    return mmd2u, mmd2u_null, p_value