How to normalize negative number in numpy array [closed]

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I have video features as numpy files (.npy) with the shape of (15, 2048) with positive and negative value. I want to normalize it so that all the values are positive only. The numpy array is something like this:

array([[-1.4004713 ,  0.6517762 , -0.11610898, ...,  0.3231497 ,
         0.10557604, -1.0216804 ],
       [-0.34153703,  1.2883852 ,  0.16293666, ..., -0.53560203,
        -0.067341  ,  0.00724552],
       [ 0.877613  ,  0.11527498, -0.45193946, ..., -0.16771363,
         0.38475066, -0.284884  ],
       ...,
       [-0.5748145 ,  0.08665206,  0.27134556, ..., -0.03541826,
         0.05377219, -0.62528425],
       [-0.02782645, -0.04130568, -0.0581201 , ...,  1.2714614 ,
         0.7328908 ,  0.2180524 ],
       [-0.28007308,  1.2357589 , -0.04791486, ...,  0.14003311,
         1.0041502 , -0.47158736]], dtype=float32)

I tried this code but it didn't work:

 Result = np.where(a >= 0, a/np.max(a), -a/np.min(a))

Do you mean that you want to take the absolute value and then normalize? — mkrieger1
– mkrieger1, Commented Oct 29, 2022 at 11:49
Numpy absolute will convert everything to a positive value. If you then want to normalize the array have a look at sklearn's normalizer — DrBwts
– DrBwts, Commented Oct 29, 2022 at 11:52
I do not want absolute value, I want normalization. I tried this code: Result = np.where(a >= 0, a/np.max(a), -a/np.min(a)) but it didn't work — A_B_Y
– A_B_Y, Commented Oct 29, 2022 at 11:58
Have you tried anything of your own? Usually people show their code and explain what didn't work. — Matt Hall
– Matt Hall, Commented Oct 29, 2022 at 12:01

Matt Hall · Accepted Answer · 2022-10-29 12:01:04Z

2

Assuming you want to normalize to the global min and max, and there are no NaNs, the normalized array is given by:

(arr - arr.min()) / (arr.max() - arr.min())

If you have NaNs, rephrase this with np.nanmin() and np.nanmax().

answered Oct 29, 2022 at 12:01

Matt Hall

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