Normalize numpy ndarray data

Question

My data is numpy ndarray with shape(2,3,4) following this: I've try to normalize 0-1 scale for each column through sklearn normalization.

from sklearn.preprocessing import normalize  

x = np.array([[[1, 2, 3, 4],
      [2, 2, 3, 4],
      [3, 2, 3, 4]],
      [[4, 2, 3, 4],
      [5, 2, 3, 4],
      [6, 2, 3, 4]]])

x.shape ==> ( 2,3,4) 

x = normalize(x, norm='max', axis=0, )

However, I catch the error :

ValueError: Found array with dim 3. the normalize function expected <= 2.

How do I solve this problem?

Thank you.

Divakar · Accepted Answer · 2017-06-02 10:01:44Z

4

It seems scikit-learn expects ndarrays with at most two dims. So, to solve it would be to reshape to 2D, feed it to normalize that gives us a 2D array, which could be reshaped back to original shape -

from sklearn.preprocessing import normalize  

normalize(x.reshape(x.shape[0],-1), norm='max', axis=0).reshape(x.shape)

Alternatively, it's much simpler with NumPy that works fine with generic ndarrays -

x/np.linalg.norm(x, ord=np.inf, axis=0, keepdims=True)

edited Jun 2, 2017 at 10:01

answered Jun 2, 2017 at 9:05

Divakar

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6 Comments

Chris Joo Over a year ago

Thank you so much!! However, the code above does not apply to the column by column, but to the whole data. Which option should be applied?

Divakar Over a year ago

@ChrisJoo Not sure what you mean by column to column. Maybe you meant using it along axis=1 instead of axis=0?

Chris Joo Over a year ago

eg. 1st column [ [1, 2, 3], [4, 5, 6] ] should be [ [ 0.1667, 0.3333, 0.5000], [ 0.6667 , 0.8333, 1.0000 ] ] and 2nd column( 2, 2, 2, 2, 2, 2) should be [ 1, 1, 1, 1, 1, 1 ].

Divakar Over a year ago

That doesn't look like l2 norm, but a max norm and also along the last axis. So, use : x/np.linalg.norm(x, ord=np.inf,axis=-1,keepdims=1) or norm='max', with the sklearn version.

Chris Joo Over a year ago

I've solved the problem.. You're a great help to me.. Thank you!! normalize(x.reshape(x.shape[1],-1), norm='max', axis=0).reshape(x.shape)

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