How to do SVD in python?
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# How to do SVD in python?

This recipe helps you do SVD in python

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## Recipe Objective.

How to do SVD in python?

The singular value decomposition (SVD) provides a alternate way to resolve a matrix, into singular vectors and singular values. The SVD permits us to get a number of constant kind of info because the eigendecomposition.

The SVD is calculated by iteration in numerical numbers.

## Step 1- Importing Libraries.

``` # Singular-value decomposition import numpy as np from scipy.linalg import svd ```

## Step 2- Creating Arrays.

We will create a 2-dimensional array of increasing digits to understand that there are no limits in SVD regarding digit size.

``` # define a matrix X = np.array([[1, 10], [100, 1000], [10000, 100000],[1000000,10000000]]) print(X) ```

## Step 3- Applying SVD

Now we will factorize the 2d matrix into singular values and singular vectors.

``` # SVD y, z, W = svd(X) print(y) print(z) print(W) ```

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