merged (hard) the util from devel

GPy/util/diag.py

@@ -0,0 +1,114 @@
+'''
+.. module:: GPy.util.diag
+
+.. moduleauthor:: Max Zwiessele <ibinbei@gmail.com>
+
+'''
+__updated__ = '2013-12-03'
+
+import numpy as np
+
+def view(A, offset=0):
+    """
+    Get a view on the diagonal elements of a 2D array.
+    
+    This is actually a view (!) on the diagonal of the array, so you can 
+    in-place adjust the view.
+    
+    :param :class:`ndarray` A: 2 dimensional numpy array
+    :param int offset: view offset to give back (negative entries allowed)
+    :rtype: :class:`ndarray` view of diag(A)
+    
+    >>> import numpy as np
+    >>> X = np.arange(9).reshape(3,3)
+    >>> view(X)
+    array([0, 4, 8])
+    >>> d = view(X)
+    >>> d += 2
+    >>> view(X)
+    array([ 2,  6, 10])
+    >>> view(X, offset=-1)
+    array([3, 7])
+    >>> subtract(X, 3, offset=-1)
+    array([[ 2,  1,  2],
+           [ 0,  6,  5],
+           [ 6,  4, 10]])
+    """
+    from numpy.lib.stride_tricks import as_strided
+    assert A.ndim == 2, "only implemented for 2 dimensions"
+    assert A.shape[0] == A.shape[1], "attempting to get the view of non-square matrix?!" 
+    if offset > 0:
+        return as_strided(A[0, offset:], shape=(A.shape[0] - offset, ), strides=((A.shape[0]+1)*A.itemsize, ))
+    elif offset < 0:
+        return as_strided(A[-offset:, 0], shape=(A.shape[0] + offset, ), strides=((A.shape[0]+1)*A.itemsize, ))
+    else:
+        return as_strided(A, shape=(A.shape[0], ), strides=((A.shape[0]+1)*A.itemsize, ))
+
+def _diag_ufunc(A,b,offset,func):
+    dA = view(A, offset); func(dA,b,dA)
+    return A
+
+def times(A, b, offset=0):
+    """
+    Times the view of A with b in place (!).
+    Returns modified A 
+    Broadcasting is allowed, thus b can be scalar.
+    
+    if offset is not zero, make sure b is of right shape!
+    
+    :param ndarray A: 2 dimensional array
+    :param ndarray-like b: either one dimensional or scalar
+    :param int offset: same as in view.
+    :rtype: view of A, which is adjusted inplace
+    """
+    return _diag_ufunc(A, b, offset, np.multiply)
+multiply = times
+
+def divide(A, b, offset=0):
+    """
+    Divide the view of A by b in place (!).
+    Returns modified A 
+    Broadcasting is allowed, thus b can be scalar.
+    
+    if offset is not zero, make sure b is of right shape!
+    
+    :param ndarray A: 2 dimensional array
+    :param ndarray-like b: either one dimensional or scalar
+    :param int offset: same as in view.
+    :rtype: view of A, which is adjusted inplace
+    """
+    return _diag_ufunc(A, b, offset, np.divide)
+
+def add(A, b, offset=0):
+    """
+    Add b to the view of A in place (!).
+    Returns modified A.
+    Broadcasting is allowed, thus b can be scalar.
+    
+    if offset is not zero, make sure b is of right shape!
+    
+    :param ndarray A: 2 dimensional array
+    :param ndarray-like b: either one dimensional or scalar
+    :param int offset: same as in view.
+    :rtype: view of A, which is adjusted inplace
+    """
+    return _diag_ufunc(A, b, offset, np.add)
+
+def subtract(A, b, offset=0):
+    """
+    Subtract b from the view of A in place (!).
+    Returns modified A.
+    Broadcasting is allowed, thus b can be scalar.
+    
+    if offset is not zero, make sure b is of right shape!
+    
+    :param ndarray A: 2 dimensional array
+    :param ndarray-like b: either one dimensional or scalar
+    :param int offset: same as in view.
+    :rtype: view of A, which is adjusted inplace
+    """
+    return _diag_ufunc(A, b, offset, np.subtract)
+        
+if __name__ == '__main__':
+    import doctest
+    doctest.testmod()