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# scipy.stats.special_ortho_group

scipy.stats.special_ortho_group = <scipy.stats._multivariate.special_ortho_group_gen object>

  A Special Orthogonal matrix (SO(N)) random variable.

  Return a random rotation matrix, drawn from the Haar distribution (the only uniform distribution on SO(N)) with a determinant of +1.

  The dim keyword specifies the dimension N.

  Parameters:
    dim scalar

      Dimension of matrices

    seed{None, int, np.random.RandomState, np.random.Generator}, optional

      Used for drawing random variates. If seed is None, the RandomState singleton is used. If seed is an int, a new `RandomState` instance is used, seeded with seed. If seed is already a `RandomState` or `Generator` instance, then that object is used. Default is None.

  Methods

  rvs(dim=None, size=1, random_state=None) | Draw random samples from SO(N).

  See also

  `ortho_group`, `scipy.spatial.transform.Rotation.random`

  Notes

  The `rvs` method returns a random rotation matrix drawn from the Haar distribution, the only uniform distribution on SO(N). The algorithm generates a Haar-distributed orthogonal matrix in O(N) using the `rvs` method of `ortho_group`, then adjusts the matrix to ensure that the determinant is +1.

  For a random rotation in three dimensions, see `scipy.spatial.transform.Rotation.random`.

  Examples

      >>> import numpy as np
      >>> from scipy.stats import special_ortho_group
      >>> x = special_ortho_group.rvs(3)

      >>> np.dot(x, x.T)
      array([[  1.00000000e+00,   1.13231364e-17,  -2.86852790e-16],
             [  1.13231364e-17,   1.00000000e+00,  -1.46845020e-16],
             [ -2.86852790e-16,  -1.46845020e-16,   1.00000000e+00]])

      >>> import scipy.linalg
      >>> scipy.linalg.det(x)
      1.0

  This generates one random matrix from SO(3). It is orthogonal and has a determinant of 1.

  Alternatively, the object may be called (as a function) to fix the dim parameter, returning a “frozen” special_ortho_group random variable:

      >>> rv = special_ortho_group(5)
      >>> # Frozen object with the same methods but holding the
      >>> # dimension parameter fixed.