code: def binary_accuracy(y, t):
      """Computes binary classification accuracy of the minibatch.
  
      Args:
          y (:class:`~chainer.Variable` or :ref:`ndarray`):
              Array whose i-th element indicates the score of
              positive at the i-th sample.
              The prediction label :math:`\\hat t[i]` is ``1`` if
              ``y[i] >= 0``, otherwise ``0``.
  
          t (:class:`~chainer.Variable` or :ref:`ndarray`):
              Array holding a signed integer vector of ground truth labels.
              If ``t[i] == 1``, it indicates that i-th sample is positive.
              If ``t[i] == 0``, it indicates that i-th sample is negative.
              If ``t[i] == -1``, corresponding ``y[i]`` is ignored.
              Accuracy is zero if all ground truth labels are ``-1``.
  
      Returns:
          ~chainer.Variable: A variable holding a scalar array of the accuracy.
  
      .. note:: This function is non-differentiable.
  
      .. admonition:: Example
  
          We show the most common case, when ``y`` is the two dimensional array.
  
          >>> y = np.array([[-2.0, 0.0], # prediction labels are [0, 1]
          ...               [3.0, -5.0]]) # prediction labels are [1, 0]
          >>> t = np.array([[0, 1],
          ...              [1, 0]], np.int32)
          >>> F.binary_accuracy(y, t).array \
  # 100% accuracy because all samples are correct.
          array(1.)
          >>> t = np.array([[0, 0],
          ...              [1, 1]], np.int32)
          >>> F.binary_accuracy(y, t).array \
  # 50% accuracy because y[0][0] and y[1][0] are correct.
          array(0.5)
          >>> t = np.array([[0, -1],
          ...              [1, -1]], np.int32)
          >>> F.binary_accuracy(y, t).array \
  # 100% accuracy because of ignoring y[0][1] and y[1][1].
          array(1.)
      """
      return BinaryAccuracy()(y, t)
  
file_path: chainer/chainer/functions/evaluation/binary_accuracy.py
imports: from __future__ import division
  from chainer import backend
  from chainer import function
  from chainer.utils import type_check
label_desc: Use image node_id 1 for calling a global function with example usage: binary_accuracy(y, t) and returns: BinaryAccuracy