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 compute_svm_score_nestedCV(K, y, n_folds,
                               scoring=balanced_accuracy_scoring,
                               random_state=None,
                               param_grid=[{'C': np.logspace(-5, 5, 25)}]):
    """Compute cross-validated score of SVM using precomputed kernel.
    """
    cv = StratifiedKFold(y, n_folds=n_folds, shuffle=True,
                         random_state=random_state)
    scores = np.zeros(n_folds)
    for i, (train, test) in enumerate(cv):
        cvclf = SVC(kernel='precomputed')
        y_train = y[train]
        cvcv = StratifiedKFold(y_train, n_folds=n_folds,
                               shuffle=True,
                               random_state=random_state)
        clf = GridSearchCV(cvclf, param_grid=param_grid, scoring=scoring,
                           cv=cvcv, n_jobs=1)
        clf.fit(K[train, :][:, train], y_train)
        # print clf.best_params_
        scores[i] = clf.score(K[test, :][:, train], y[test])

    return scores.mean()