parameters: (y)
return_statement: return linkage(y, method='median', metric='euclidean')
docstring: Perform median/WPGMC linkage.
  
      See `linkage` for more information on the return structure
      and algorithm.
  
       The following are common calling conventions:
  
       1. ``Z = median(y)``
  
          Performs median/WPGMC linkage on the condensed distance matrix
          ``y``.  See ``linkage`` for more information on the return
          structure and algorithm.
  
       2. ``Z = median(X)``
  
          Performs median/WPGMC linkage on the observation matrix ``X``
          using Euclidean distance as the distance metric. See `linkage`
          for more information on the return structure and algorithm.
  
      Parameters
      ----------
      y : ndarray
          A condensed distance matrix. A condensed
          distance matrix is a flat array containing the upper
          triangular of the distance matrix. This is the form that
          ``pdist`` returns.  Alternatively, a collection of
          m observation vectors in n dimensions may be passed as
          a m by n array.
  
      Returns
      -------
      Z : ndarray
          The hierarchical clustering encoded as a linkage matrix.
  
      See Also
      --------
      linkage: for advanced creation of hierarchical clusterings.
      scipy.spatial.distance.pdist : pairwise distance metrics
  
      Examples
      --------
      >>> from scipy.cluster.hierarchy import median, fcluster
      >>> from scipy.spatial.distance import pdist
  
      First we need a toy dataset to play with::
  
          x x    x x
          x        x
  
          x        x
          x x    x x
  
      >>> X = [[0, 0], [0, 1], [1, 0],
      ...      [0, 4], [0, 3], [1, 4],
      ...      [4, 0], [3, 0], [4, 1],
      ...      [4, 4], [3, 4], [4, 3]]
  
      Then we get a condensed distance matrix from this dataset:
  
      >>> y = pdist(X)
  
      Finally, we can perform the clustering:
  
      >>> Z = median(y)
      >>> Z
      array([[ 0.        ,  1.        ,  1.        ,  2.        ],
             [ 3.        ,  4.        ,  1.        ,  2.        ],
             [ 9.        , 10.        ,  1.        ,  2.        ],
             [ 6.        ,  7.        ,  1.        ,  2.        ],
             [ 2.        , 12.        ,  1.11803399,  3.        ],
             [ 5.        , 13.        ,  1.11803399,  3.        ],
             [ 8.        , 15.        ,  1.11803399,  3.        ],
             [11.        , 14.        ,  1.11803399,  3.        ],
             [18.        , 19.        ,  3.        ,  6.        ],
             [16.        , 17.        ,  3.5       ,  6.        ],
             [20.        , 21.        ,  3.25      , 12.        ]])
  
      The linkage matrix ``Z`` represents a dendrogram - see
      `scipy.cluster.hierarchy.linkage` for a detailed explanation of its
      contents.
  
      We can use `scipy.cluster.hierarchy.fcluster` to see to which cluster
      each initial point would belong given a distance threshold:
  
      >>> fcluster(Z, 0.9, criterion='distance')
      array([ 7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6], dtype=int32)
      >>> fcluster(Z, 1.1, criterion='distance')
      array([5, 5, 6, 7, 7, 8, 1, 1, 2, 3, 3, 4], dtype=int32)
      >>> fcluster(Z, 2, criterion='distance')
      array([3, 3, 3, 4, 4, 4, 1, 1, 1, 2, 2, 2], dtype=int32)
      >>> fcluster(Z, 4, criterion='distance')
      array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)
  
      Also `scipy.cluster.hierarchy.dendrogram` can be used to generate a
      plot of the dendrogram.
docstring_summary: Perform median/WPGMC linkage.
docstring_tokens: Perform median / WPGMC linkage .
function: def median(y):
      """
      Perform median/WPGMC linkage.
  
      See `linkage` for more information on the return structure
      and algorithm.
  
       The following are common calling conventions:
  
       1. ``Z = median(y)``
  
          Performs median/WPGMC linkage on the condensed distance matrix
          ``y``.  See ``linkage`` for more information on the return
          structure and algorithm.
  
       2. ``Z = median(X)``
  
          Performs median/WPGMC linkage on the observation matrix ``X``
          using Euclidean distance as the distance metric. See `linkage`
          for more information on the return structure and algorithm.
  
      Parameters
      ----------
      y : ndarray
          A condensed distance matrix. A condensed
          distance matrix is a flat array containing the upper
          triangular of the distance matrix. This is the form that
          ``pdist`` returns.  Alternatively, a collection of
          m observation vectors in n dimensions may be passed as
          a m by n array.
  
      Returns
      -------
      Z : ndarray
          The hierarchical clustering encoded as a linkage matrix.
  
      See Also
      --------
      linkage: for advanced creation of hierarchical clusterings.
      scipy.spatial.distance.pdist : pairwise distance metrics
  
      Examples
      --------
      >>> from scipy.cluster.hierarchy import median, fcluster
      >>> from scipy.spatial.distance import pdist
  
      First we need a toy dataset to play with::
  
          x x    x x
          x        x
  
          x        x
          x x    x x
  
      >>> X = [[0, 0], [0, 1], [1, 0],
      ...      [0, 4], [0, 3], [1, 4],
      ...      [4, 0], [3, 0], [4, 1],
      ...      [4, 4], [3, 4], [4, 3]]
  
      Then we get a condensed distance matrix from this dataset:
  
      >>> y = pdist(X)
  
      Finally, we can perform the clustering:
  
      >>> Z = median(y)
      >>> Z
      array([[ 0.        ,  1.        ,  1.        ,  2.        ],
             [ 3.        ,  4.        ,  1.        ,  2.        ],
             [ 9.        , 10.        ,  1.        ,  2.        ],
             [ 6.        ,  7.        ,  1.        ,  2.        ],
             [ 2.        , 12.        ,  1.11803399,  3.        ],
             [ 5.        , 13.        ,  1.11803399,  3.        ],
             [ 8.        , 15.        ,  1.11803399,  3.        ],
             [11.        , 14.        ,  1.11803399,  3.        ],
             [18.        , 19.        ,  3.        ,  6.        ],
             [16.        , 17.        ,  3.5       ,  6.        ],
             [20.        , 21.        ,  3.25      , 12.        ]])
  
      The linkage matrix ``Z`` represents a dendrogram - see
      `scipy.cluster.hierarchy.linkage` for a detailed explanation of its
      contents.
  
      We can use `scipy.cluster.hierarchy.fcluster` to see to which cluster
      each initial point would belong given a distance threshold:
  
      >>> fcluster(Z, 0.9, criterion='distance')
      array([ 7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6], dtype=int32)
      >>> fcluster(Z, 1.1, criterion='distance')
      array([5, 5, 6, 7, 7, 8, 1, 1, 2, 3, 3, 4], dtype=int32)
      >>> fcluster(Z, 2, criterion='distance')
      array([3, 3, 3, 4, 4, 4, 1, 1, 1, 2, 2, 2], dtype=int32)
      >>> fcluster(Z, 4, criterion='distance')
      array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)
  
      Also `scipy.cluster.hierarchy.dendrogram` can be used to generate a
      plot of the dendrogram.
  
      """
      return linkage(y, method='median', metric='euclidean')
function_tokens: def median ( y ) : return linkage ( y , method = 'median' , metric = 'euclidean' )