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Yiming-Wange
GitHub Repository: Yiming-Wange/CS224n-2023-solution
 Path: blob/main/a2/__pycache__/word2vec.cpython-39.pyc
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rc	CsTt||�}t�|�}d||<t�||�}|||}t�|||�}|||fS)a[ Naive Softmax loss & gradient function for word2vec models

    Implement the naive softmax loss and gradients between a center word's 
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    for our word2vec models. For those unfamiliar with numpy notation, note 
    that a numpy ndarray with a shape of (x, ) is a one-dimensional array, which
    you can effectively treat as a vector with length x.

    Arguments:
    centerWordVec -- numpy ndarray, center word's embedding
                    in shape (word vector length, )
                    (v_c in the pdf handout)
    outsideWordIdx -- integer, the index of the outside word
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    outsideVectors -- outside vectors is
                    in shape (num words in vocab, word vector length) 
                    for all words in vocab (tranpose of U in the pdf handout)
    dataset -- needed for negative sampling, unused here.

    Return:
    loss -- naive softmax loss
    gradCenterVec -- the gradient with respect to the center word vector
                     in shape (word vector length, )
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    gradOutsideVecs -- the gradient with respect to all the outside word vectors
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fS)aI Negative sampling loss function for word2vec models

    Implement the negative sampling loss and gradients for a centerWordVec
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    Note: The same word may be negatively sampled multiple times. For
    example if an outside word is sampled twice, you shall have to
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fS)a Skip-gram model in word2vec

    Implement the skip-gram model in this function.

    Arguments:
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    windowSize -- integer, context window size
    outsideWords -- list of no more than 2*windowSize strings, the outside words
    word2Ind -- a dictionary that maps words to their indices in
              the word vector list
    centerWordVectors -- center word vectors (as rows) is in shape 
                        (num words in vocab, word vector length) 
                        for all words in vocab (V in pdf handout)
    outsideVectors -- outside vectors is in shape 
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                        for all words in vocab (transpose of U in the pdf handout)
    word2vecLossAndGradient -- the loss and gradient function for
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                               word vectors, could be one of the two
                               loss functions you implemented above.

    Return:
    loss -- the loss function value for the skip-gram model
            (J in the pdf handout)
    gradCenterVecs -- the gradient with respect to the center word vector
                     in shape (num words in vocab, word vector length)
                     (dJ / dv_c in the pdf handout)
    gradOutsideVecs -- the gradient with respect to all the outside word vectors
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