NumPy Essentials by Leo (Liang-Huan) Chin & Tanmay Dutta

Author:Leo (Liang-Huan) Chin & Tanmay Dutta [Chin, Leo (Liang-Huan)]
Language: eng
Format: azw3
Publisher: Packt Publishing
Published: 2016-04-28T04:00:00+00:00

Linear algebra in NumPy

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:

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