larsmans

from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.metrics.pairwise import rbf_kernel


class KMeansTransformer(BaseEstimator, TransformerMixin):
    def __init__(self, centroids):
        self.centroids = centroids

    def fit(self, X, y=None):
        return self

larsmans

cimport numpy as np


def csc_columnwise_max(np.ndarray[np.float64_t, ndim=1] data,
                       np.ndarray[int, ndim=1] indices,
                       np.ndarray[int, ndim=1] indptr,
                       np.ndarray[np.float64_t, ndim=1] out):
    cdef double mx
    cdef int n_features = indptr.shape[0] - 1
    cdef int i, j

larsmans

# Quick and dirty Brat-to-CSV conversion.

from __future__ import print_function
import csv
import io
import re
import sys

# copy server/src/{gtbtokenize,tokenise}.py from Brat
from tokenise import gtb_token_boundary_gen