yapf

    def _realize_(x):

      assert (len(x.shape) == 2)

      inds = np.argsort(

          -(x**2).sum(axis=0)**.5 + np.random.normal(0, self.noise, x[0].shape))

      inds = np.argsort(-(x**2).sum(axis=0)**.5 +

                        np.random.normal(0, self.noise, x[0].shape))

      x = x[inds, :][:, inds] * 1

      x = x.flatten()[self.triuind]

      return x

      feed_dict = {}

      with tf.Session() as sess:

        for count in range(target_len // 100 + 1):

          feed_dict[similarity_placeholder] = similarity_xs[count * 100:min((

              count + 1) * 100, target_len), :]

          feed_dict[similarity_placeholder] = similarity_xs[count * 100:min(

              (count + 1) * 100, target_len), :]

          # generating batch of data by slicing similarity matrix

          # into 100*reference_dataset_length

          fetched_values = sess.run([value, top_label], feed_dict=feed_dict)

    n_features = X_target.shape[1]

    print('start similarity calculation')

    time1 = time.time()

    similarity = IRVTransformer.matrix_mul(X_target, np.transpose(self.X)) / (

        n_features -

        IRVTransformer.matrix_mul(1 - X_target, np.transpose(1 - self.X)))

    similarity = IRVTransformer.matrix_mul(X_target, np.transpose(

        self.X)) / (n_features - IRVTransformer.matrix_mul(

            1 - X_target, np.transpose(1 - self.X)))

    time2 = time.time()

    print('similarity calculation takes %i s' % (time2 - time1))

    for i in range(self.n_tasks):

    for X1_id in range(X1_iter):

      result = np.zeros((1,))

      for X2_id in range(X2_iter):

        partial_result = np.matmul(X1[X1_id * shard_size:min((

            X1_id + 1) * shard_size, X1_shape[0]), :],

                                   X2[:, X2_id * shard_size:min((

                                       X2_id + 1) * shard_size, X2_shape[1])])

        partial_result = np.matmul(

            X1[X1_id * shard_size:min((X1_id + 1) *

                                      shard_size, X1_shape[0]), :],

            X2[:, X2_id * shard_size:min((X2_id + 1) *

                                         shard_size, X2_shape[1])])

        # calculate matrix multiplicatin on slices

        if result.size == 1:

          result = partial_result

        end = min((start + 1) * batch_size, X.shape[0])

        X_batch = X[(start * batch_size):end]

        output = self.sess.run(

            [self.outputs], feed_dict={

                self.inputs: X_batch

            })[0]

            [self.outputs], feed_dict={self.inputs: X_batch})[0]

        X_out.append(output)

        num_transformed = num_transformed + X_batch.shape[0]

        print('%i samples transformed' % num_transformed)

    X = self.featurizer.featurize(X)

    return X, y, w

class DataTransforms():

  """Applies different data transforms to images."""

  def __init__(self, Image):

    self.Image = Image

  """ Scales the image

        Parameters:

            h - height of the images

            w - width of the images

    """

  def scale(self, h, w):

    return scipy.misc.imresize(self.Image, (h, w))

            direction - "lr" denotes left-right fliplr

                        "ud" denotes up-down flip

    """

  def flip(self, direction="lr"):

    if direction == "lr":

      return np.fliplr(self.Image)

    elif direction == "ud":

      return np.flipud(self.Image)

    else:

            raise ValueError("Invalid flip command : Enter either lr (for left to right flip) or ud (for up to down flip)")

      raise ValueError(

          "Invalid flip command : Enter either lr (for left to right flip) or ud (for up to down flip)"

      )

  """ Rotates the image

        Parameters:

            angle (default = 0 i.e no rotation) - Denotes angle by which the image should be rotated (in Degrees)

    """

  def rotate(self, angle=0):

    return scipy.ndimage.rotate(self.Image, angle)