Ensure that documentation for dot, vdot, inner, alterdot, restoredot is the same, independent of whether these functions come from _dotblas or multiarray/numeric.py

1 files changed, 5 insertions, 96 deletions

diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py
index ff2eb6ca9..57f36cfad 100644
--- a/numpy/core/numeric.py
+++ b/numpy/core/numeric.py
@@ -608,9 +608,6 @@ def convolve(a,v,mode='full'):
     mode = _mode_from_name(mode)
     return multiarray.correlate(a, v[::-1], mode)
 
-inner = multiarray.inner
-dot = multiarray.dot
-
 def outer(a,b):
     """
     Returns the outer product of two vectors.
@@ -654,110 +651,22 @@ def outer(a,b):
     b = asarray(b)
     return a.ravel()[:,newaxis]*b.ravel()[newaxis,:]
 
-def vdot(a, b):
-    """
-    Return the dot product of two vectors.
-
-    The vdot(`a`, `b`) function handles complex numbers differently than
-    dot(`a`, `b`).  If the first argument is complex the complex conjugate
-    of the first argument is used for the calculation of the dot product.
-
-    For 2-D arrays it is equivalent to matrix multiplication, and for 1-D
-    arrays to inner product of vectors (with complex conjugation of `a`).
-    For N dimensions it is a sum product over the last axis of `a` and
-    the second-to-last of `b`::
-
-        dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])
-
-    Parameters
-    ----------
-    a : array_like
-        If `a` is complex the complex conjugate is taken before calculation
-        of the dot product.
-    b : array_like
-        Second argument to the dot product.
-
-    Returns
-    -------
-    output : ndarray
-        Returns dot product of `a` and `b`.  Can be an int, float, or
-        complex depending on the types of `a` and `b`.
-
-    See Also
-    --------
-    dot : Return the dot product without using the complex conjugate of the
-          first argument.
-
-    Notes
-    -----
-    The dot product is the summation of element wise multiplication.
-
-    .. math::
-     a \\cdot b = \\sum_{i=1}^n a_i^*b_i = a_1^*b_1+a_2^*b_2+\\cdots+a_n^*b_n
-
-    Examples
-    --------
-    >>> a = np.array([1+2j,3+4j])
-    >>> b = np.array([5+6j,7+8j])
-    >>> np.vdot(a, b)
-    (70-8j)
-    >>> np.vdot(b, a)
-    (70+8j)
-    >>> a = np.array([[1, 4], [5, 6]])
-    >>> b = np.array([[4, 1], [2, 2]])
-    >>> np.vdot(a, b)
-    30
-    >>> np.vdot(b, a)
-    30
-
-    """
-    return dot(asarray(a).ravel().conj(), asarray(b).ravel())
-
 # try to import blas optimized dot if available
 try:
     # importing this changes the dot function for basic 4 types
     # to blas-optimized versions.
     from _dotblas import dot, vdot, inner, alterdot, restoredot
 except ImportError:
+    # docstrings are in add_newdocs.py
+    inner = multiarray.inner
+    dot = multiarray.dot
+    def vdot(a, b):
+        return dot(asarray(a).ravel().conj(), asarray(b).ravel())
     def alterdot():
-        """
-        Change `dot`, `vdot`, and `innerproduct` to use accelerated BLAS functions.
-
-        Typically, as a user of Numpy, you do not explicitly call this function. If
-        Numpy is built with an accelerated BLAS, this function is automatically
-        called when Numpy is imported.
-
-        When Numpy is built with an accelerated BLAS like ATLAS, these functions
-        are replaced to make use of the faster implementations.  The faster
-        implementations only affect float32, float64, complex64, and complex128
-        arrays. Furthermore, the BLAS API only includes matrix-matrix,
-        matrix-vector, and vector-vector products. Products of arrays with larger
-        dimensionalities use the built in functions and are not accelerated.
-
-        See Also
-        --------
-        restoredot : `restoredot` undoes the effects of `alterdot`.
-
-        """
         pass
     def restoredot():
-        """
-        Restore `dot`, `vdot`, and `innerproduct` to the default non-BLAS
-        implementations.
-
-        Typically, the user will only need to call this when troubleshooting and
-        installation problem, reproducing the conditions of a build without an
-        accelerated BLAS, or when being very careful about benchmarking linear
-        algebra operations.
-
-        See Also
-        --------
-        alterdot : `restoredot` undoes the effects of `alterdot`.
-
-        """
         pass
 
-
 def tensordot(a, b, axes=2):
     """
     Returns the tensor dot product for (ndim >= 1) arrays along an axes.