# chainer.functions.argmax_crf1d¶

chainer.functions.argmax_crf1d(cost, xs)[source]

Computes a state that maximizes a joint probability of the given CRF.

Parameters: cost (Variable) – A $$K \times K$$ matrix which holds transition cost between two labels, where $$K$$ is the number of labels. xs (list of Variable) – Input vector for each label. len(xs) denotes the length of the sequence, and each Variable holds a $$B \times K$$ matrix, where $$B$$ is mini-batch size, $$K$$ is the number of labels. Note that $$B$$s in all the variables are not necessary the same, i.e., it accepts the input sequences with different lengths. A tuple of Variable object s and a list ps. The shape of s is (B,), where B is the mini-batch size. i-th element of s, s[i], represents log-likelihood of i-th data. ps is a list of numpy.ndarray or cupy.ndarray, and denotes the state that maximizes the point probability. len(ps) is equal to len(xs), and shape of each ps[i] is the mini-batch size of the corresponding xs[i]. That means, ps[i].shape == xs[i].shape[0:1]. tuple