Before we get into linear algebra class in NumPy, there are five vector products we will cover at the beginning of this section. Let's review them one by one, starting with the numpy.dot() product:
In [26]: x = np.array([[1, 2], [3, 4]])
In [27]: y = np.array([[10, 20], [30, 40]])
In [28]: np.dot(x, y)
Out[28]:
array([[ 70, 100],
[150, 220]])
The numpy.dot() function performs matrix multiplication, and the detailed calculation is shown here:
numpy.vdot() handles multi-dimensional arrays differently than numpy.dot(). It does not perform a matrix product, but flattens input arguments to one-dimensional vectors first:
In [29]: np.vdot(x, y) Out[29]: 300
The detailed calculation of numpy.vdot() is as follows:
The numpy.outer() function is the outer product of two vectors. It flattens the input arrays if they are not one-dimensional. Let's say that the flattened input vector A has shape (M, ) and the flattened input...