File size: 1,586 Bytes
ddb7b62 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | # Copyright 2018 Babylon Partners. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import numpy as np
def fuzzify(s, u):
"""
Sentence fuzzifier.
Computes membership vector for the sentence S with respect to the
universe U
:param s: list of word embeddings for the sentence
:param u: the universe matrix U with shape (K, d)
:return: membership vectors for the sentence
"""
f_s = np.dot(s, u.T)
m_s = np.max(f_s, axis=0)
m_s = np.maximum(m_s, 0, m_s)
return m_s
def dynamax_jaccard(x, y):
"""
DynaMax-Jaccard similarity measure between two sentences
:param x: list of word embeddings for the first sentence
:param y: list of word embeddings for the second sentence
:return: similarity score between the two sentences
"""
u = np.vstack((x, y))
m_x = fuzzify(x, u)
m_y = fuzzify(y, u)
m_inter = np.sum(np.minimum(m_x, m_y))
m_union = np.sum(np.maximum(m_x, m_y))
return m_inter / m_union
|