## Understanding tensordot

After I learned how to use `einsum`

, I am now trying to understand how `np.tensordot`

works.

After I learned how to use `einsum`

, I am now trying to understand how `np.tensordot`

works.

Sometimes it is useful to “clone” a row or column vector to a matrix. By cloning I mean converting a row vector such as

**TL;DR** how to link ATLAS/MKL to existing Numpy without rebuilding.

Are there any algorithms that will return the equation of a straight line from a set of 3D data points? I can find plenty of sources which will give the equation of a line from 2D data sets, but none in 3D.

I have a numpy 2d array [medium/large sized – say 500×500]. I want to find the eigenvalues of the element-wise exponent of it. The problem is that some of the values are quite negative (-800,-1000, etc), and their exponents underflow (meaning they are so close to zero, so that numpy treats them as zero). Is there anyway to use arbitrary precision in numpy?