# 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