Generate max likelyhood masks in numpy

Question

We have several "in_arrays" like

in_1=np.array([0.4,0.7,0.8,0.3])
in_2=np.array([0.9,0.8,0.6,0.4])

I need to create two outputs like

out_1=np.array([0,0,1,0])
out_2=np.array([1,1,0,0])

So, the given element of the output array is 1 if the value in the corresponding input array is greater than 0.5 AND the value in this position of this input array is greater than the values of other arrays in this position. What is the right way to do this?

Update. This is clearly a data science question. This is needed to run voting between several predicted segmentation masks by several different models to assign the most likely type of object on the picture.

When you say other arrays, are you only ever comparing to the other array (i.e. do you always only have two arrays) or do you have more arrays? — Oxbowerce, Sep 21 '21 at 14:39

score 1 · Accepted Answer · edited Sep 21 '21 at 16:57

1

Try this,

def max_is_greater_than_half(*args):
df      = pd.DataFrame(dict({'col_'+str(i+1): val for i, val in enumerate(args)}))
max_val = df.apply(max, axis=1)
df = df.apply(lambda x: (x &gt; 0.5) &amp; (max_val == x), axis=0).astype(int)

return [np.array(df[col].values) for col in df.columns]



out1, out2 = max_is_greater_than_half(in_1, in_2)

The code allows adding as many 'in' arrays as you want, but do not forget assigning the result to the same number of output arrays.

edited Sep 21 '21 at 16:57

Stephen Rauch

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answered Sep 21 '21 at 16:07

Arislan Makhmudov

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can you please answer this question as well? https://stackoverflow.com/questions/69283472/python-lambda-on-a-pandas-dataframe-containing-2d-numpy-arrays – sixtytrees Sep 22 '21 at 11:42
You've got the answer :) – Arislan Makhmudov Sep 22 '21 at 11:56

Generate max likelyhood masks in numpy

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