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scipy.cluster.hierarchy.

# is_valid_im

scipy.cluster.hierarchy.is_valid_im(R, warning=False, throw=False, name=None)

  Return True if the inconsistency matrix passed is valid.

  It must be a \(n\) by 4 array of doubles. The standard deviations `R[:,1]` must be nonnegative. The link counts `R[:,2]` must be positive and no greater than \(n-1\).

  Parameters:
    R ndarray

      The inconsistency matrix to check for validity.

    warning bool, optional

      When True, issues a Python warning if the linkage matrix passed is invalid.

    throw bool, optional

      When True, throws a Python exception if the linkage matrix passed is invalid.

    name str, optional

      This string refers to the variable name of the invalid linkage matrix.

  Returns:
    b bool

      True if the inconsistency matrix is valid.

  See also

  `linkage`

    for a description of what a linkage matrix is.

  `inconsistent`

    for the creation of a inconsistency matrix.

  Examples

      >>> from scipy.cluster.hierarchy import ward, inconsistent, is_valid_im
      >>> from scipy.spatial.distance import pdist

  Given a data set `X`, we can apply a clustering method to obtain a linkage matrix `Z`. `scipy.cluster.hierarchy.inconsistent` can be also used to obtain the inconsistency matrix `R` associated to this clustering process:

      >>> 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]]

      >>> Z = ward(pdist(X))
      >>> R = inconsistent(Z)
      >>> Z
      array([[ 0.        ,  1.        ,  1.        ,  2.        ],
             [ 3.        ,  4.        ,  1.        ,  2.        ],
             [ 6.        ,  7.        ,  1.        ,  2.        ],
             [ 9.        , 10.        ,  1.        ,  2.        ],
             [ 2.        , 12.        ,  1.29099445,  3.        ],
             [ 5.        , 13.        ,  1.29099445,  3.        ],
             [ 8.        , 14.        ,  1.29099445,  3.        ],
             [11.        , 15.        ,  1.29099445,  3.        ],
             [16.        , 17.        ,  5.77350269,  6.        ],
             [18.        , 19.        ,  5.77350269,  6.        ],
             [20.        , 21.        ,  8.16496581, 12.        ]])
      >>> R
      array([[1.        , 0.        , 1.        , 0.        ],
             [1.        , 0.        , 1.        , 0.        ],
             [1.        , 0.        , 1.        , 0.        ],
             [1.        , 0.        , 1.        , 0.        ],
             [1.14549722, 0.20576415, 2.        , 0.70710678],
             [1.14549722, 0.20576415, 2.        , 0.70710678],
             [1.14549722, 0.20576415, 2.        , 0.70710678],
             [1.14549722, 0.20576415, 2.        , 0.70710678],
             [2.78516386, 2.58797734, 3.        , 1.15470054],
             [2.78516386, 2.58797734, 3.        , 1.15470054],
             [6.57065706, 1.38071187, 3.        , 1.15470054]])

  Now we can use `scipy.cluster.hierarchy.is_valid_im` to verify that `R` is correct:

      >>> is_valid_im(R)
      True

  However, if `R` is wrongly constructed (e.g., one of the standard deviations is set to a negative value), then the check will fail:

      >>> R[-1,1] = R[-1,1] * -1
      >>> is_valid_im(R)
      False