$$s = \mathbf)=0$, it implies that the dot product of any two orthogonal vectors must be $0$. The dot product is represented by a dot operator: In Euclidean geometry, the dot product between the Cartesian components of two vectors is often referred to as the inner product. That form is easily adapted to stacks of matrices and is sometimes used on vector architectures, see Golub and Van Loan.The dot product is an algebraic operation which takes two equal-sized vectors and returns a single scalar (which is why it is sometimes referred to as the scalar product). It's just matrix multipy considered as a sum of the outer products of column and row vectors, i.e., outer product of first column in first matrix and first row in second matrix plus outer product of second column in first matrix plus second row in second matrix, etc. > initializing the the c list first help? > How about using list comprehension? And setting dot = numpy.dot. > Or is there a way to do this all using weave.inline? To use this approach, we must first import Python’s numpy module. For 2-D vectors, it is the equivalent to matrix multiplication. numpy.dot () is a method that accepts two sequences as arguments, whether they are vectors or multidimensional arrays, and displays the output, which is the dot product. Numpy with Python This function returns the dot product of two arrays. > Is there a way to do this in one instruction? The numpy.dot () method of the numpy library provides an efficient way to find the dot product of two sequences. > I thought that maybe it's due to the fact that I have thousands of matrices and it's a python for loop and there's a high Python overhead. > Making a for loop that calculates the dot product of each is extremely slow, > I have a list of 4x4 transformation matrices, that I want to "dot with" another list of the same size (elementwise). Given two tensors, a and b, and an arraylike object containing two arraylike objects, (aaxes, baxes), sum the products of a ’s and b ’s elements (components) over the axes specified by aaxes and baxes. > On Thu, at 9:38 AM, Emmanuel Bengio wrote: Uses processes to parallelize a dot product is not a very solution because processes do not share memory, they need to exchange data. numpy.tensordot(a, b, axes2) source Compute tensor dot product along specified axes.
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