def update(self, doc, process=None):
        if process is None:
            pk = 'id'
            id = doc.id
            doc.synced = False
            set = doc
        elif process == UPDATE_PROCESS:
            if self.db.docs.find({'sid': doc.sid}).count() == 0:
                return self.insert(doc, synced=True)
            pk = 'sid'
            id = doc.sid
            doc.synced = not doc.merged() # must be commited if was merged
            doc.merged(False)
            set = {'tid': doc.tid, 'data': doc.data, 'user': doc.user,
                   'public': doc.public, 'synced': doc.synced}
        elif process == COMMIT_PROCESS:
            pk = 'id'
            id = doc.id
            doc.synced = True
            set = {'sid': doc.sid, 'tid': doc.tid, 'synced': doc.synced}
        else:
            raise DatabaseException('Incorrect update process')

        try:
            rs = self.db.docs.update({pk: id}, {'$set': set}, safe=True)
            assert rs['n'] == 1
        except Exception as e:
            raise DatabaseException(e)
        return doc
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()