Multivariate Pattern Analysis in Python

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# mvpa2.clfs.gpr.asarray

`mvpa2.clfs.gpr.``asarray`(a, dtype=None, order=None)

  Convert the input to an array.

  Parameters:

  a : array_like

  > Input data, in any form that can be converted to an array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists and ndarrays.

  dtype : data-type, optional

  > By default, the data-type is inferred from the input data.

  order : {‘C’, ‘F’}, optional

  > Whether to use row-major (‘C’) or column-major (‘F’ for FORTRAN) memory representation. Defaults to ‘C’.

  Returns:

  out : ndarray

  > Array interpretation of `a`. No copy is performed if the input is already an ndarray. If `a` is a subclass of ndarray, a base class ndarray is returned.

  See also

  `asanyarray`
    Similar function which passes through subclasses.
  `ascontiguousarray`
    Convert input to a contiguous array.
  `asfarray`
    Convert input to a floating point ndarray.
  `asfortranarray`
    Convert input to an ndarray with column-major memory order.
  `asarray_chkfinite`
    Similar function which checks input for NaNs and Infs.
  `fromiter`
    Create an array from an iterator.
  `fromfunction`
    Construct an array by executing a function on grid positions.

  Examples

  Convert a list into an array:

      >>> a = [1, 2]
      >>> np.asarray(a)
      array([1, 2])

  Existing arrays are not copied:

      >>> a = np.array([1, 2])
      >>> np.asarray(a) is a
      True

  If `dtype` is set, array is copied only if dtype does not match:

      >>> a = np.array([1, 2], dtype=np.float32)
      >>> np.asarray(a, dtype=np.float32) is a
      True
      >>> np.asarray(a, dtype=np.float64) is a
      False

  Contrary to `asanyarray`, ndarray subclasses are not passed through:

      >>> issubclass(np.matrix, np.ndarray)
      True
      >>> a = np.matrix([[1, 2]])
      >>> np.asarray(a) is a
      False
      >>> np.asanyarray(a) is a
      True

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