(a_gpu, handle=None)
return _transpose(a_gpu, True, handle)
Hermitian (conjugate) matrix transpose.

    Conjugate transpose a matrix in device memory and return an object
    representing the transposed matrix.

    Parameters
    ----------
    a_gpu : pycuda.gpuarray.GPUArray
        Input matrix of shape `(m, n)`.
    handle : int
        CUBLAS context. If no context is specified, the default handle from
        `skcuda.misc._global_cublas_handle` is used.

    Returns
    -------
    at_gpu : pycuda.gpuarray.GPUArray
        Transposed matrix of shape `(n, m)`.

    Examples
    --------
    >>> import pycuda.autoinit
    >>> import pycuda.driver as drv
    >>> import pycuda.gpuarray as gpuarray
    >>> import numpy as np
    >>> import skcuda.linalg as linalg
    >>> linalg.init()
    >>> a = np.array([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]], np.float32)
    >>> a_gpu = gpuarray.to_gpu(a)
    >>> at_gpu = linalg.hermitian(a_gpu)
    >>> np.all(a.T == at_gpu.get())
    True
    >>> b = np.array([[1j, 2j, 3j, 4j, 5j, 6j], [7j, 8j, 9j, 10j, 11j, 12j]], np.complex64)
    >>> b_gpu = gpuarray.to_gpu(b)
    >>> bt_gpu = linalg.hermitian(b_gpu)
    >>> np.all(np.conj(b.T) == bt_gpu.get())
    True
Hermitian (conjugate) matrix transpose.
Hermitian ( conjugate ) matrix transpose .
def hermitian(a_gpu, handle=None):
    """
    Hermitian (conjugate) matrix transpose.

    Conjugate transpose a matrix in device memory and return an object
    representing the transposed matrix.

    Parameters
    ----------
    a_gpu : pycuda.gpuarray.GPUArray
        Input matrix of shape `(m, n)`.
    handle : int
        CUBLAS context. If no context is specified, the default handle from
        `skcuda.misc._global_cublas_handle` is used.

    Returns
    -------
    at_gpu : pycuda.gpuarray.GPUArray
        Transposed matrix of shape `(n, m)`.

    Examples
    --------
    >>> import pycuda.autoinit
    >>> import pycuda.driver as drv
    >>> import pycuda.gpuarray as gpuarray
    >>> import numpy as np
    >>> import skcuda.linalg as linalg
    >>> linalg.init()
    >>> a = np.array([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]], np.float32)
    >>> a_gpu = gpuarray.to_gpu(a)
    >>> at_gpu = linalg.hermitian(a_gpu)
    >>> np.all(a.T == at_gpu.get())
    True
    >>> b = np.array([[1j, 2j, 3j, 4j, 5j, 6j], [7j, 8j, 9j, 10j, 11j, 12j]], np.complex64)
    >>> b_gpu = gpuarray.to_gpu(b)
    >>> bt_gpu = linalg.hermitian(b_gpu)
    >>> np.all(np.conj(b.T) == bt_gpu.get())
    True
    """

    return _transpose(a_gpu, True, handle)
def hermitian ( a_gpu , handle = None ) : return _transpose ( a_gpu , True , handle )