ogrisel

mblondel

# (C) Mathieu Blondel, November 2013
# License: BSD 3 clause

import numpy as np


def ranking_precision_score(y_true, y_score, k=10):
    """Precision at rank k

    Parameters

skylander86

def fleiss_kappa(M):
  """
  See `Fleiss' Kappa <https://en.wikipedia.org/wiki/Fleiss%27_kappa>`_.

  :param M: a matrix of shape (:attr:`N`, :attr:`k`) where `N` is the number of subjects and `k` is the number of categories into which assignments are made. `M[i, j]` represent the number of raters who assigned the `i`th subject to the `j`th category.
  :type M: numpy matrix
  """
  N, k = M.shape  # N is # of items, k is # of categories
  n_annotators = float(np.sum(M[0, :]))  # # of annotators

udibr

import numpy as np
from keras.layers import GRU, initializations, K
from collections import OrderedDict

class GRULN(GRU):
    '''Gated Recurrent Unit with Layer Normalization
    Current impelemtation only works with consume_less = 'gpu' which is already
    set.

    # Arguments

chelsea1992

import networkx as nx
import sys
from collections import deque
import community as cm
from collections import defaultdict
import matplotlib.pyplot as plt


# calculate the betweeness
def cal_betweeness(graph):