CODEWITHSUNDEEP
pythonnumpymatrixmachine-learninglogistic-regression
Mona Jalal
Mona Jalal

Reputation: 38155

numpy TypeError: ufunc 'invert' not supported for the input types, and the inputs

For the code below:

def makePrediction(mytheta, myx):
    # -----------------------------------------------------------------
    pr = sigmoid(np.dot(myx, mytheta))

    pr[pr < 0.5] =0
    pr[pr >= 0.5] = 1

    return pr

    # -----------------------------------------------------------------

# Compute the percentage of samples I got correct:
pos_correct = float(np.sum(makePrediction(theta,pos)))
neg_correct = float(np.sum(np.invert(makePrediction(theta,neg))))
tot = len(pos)+len(neg)
prcnt_correct = float(pos_correct+neg_correct)/tot
print("Fraction of training samples correctly predicted: %f." % prcnt_correct)

I get this error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-33-f0c91286cd02> in <module>()
     13 # Compute the percentage of samples I got correct:
     14 pos_correct = float(np.sum(makePrediction(theta,pos)))
---> 15 neg_correct = float(np.sum(np.invert(makePrediction(theta,neg))))
     16 tot = len(pos)+len(neg)
     17 prcnt_correct = float(pos_correct+neg_correct)/tot

TypeError: ufunc 'invert' not supported for the input types, and the inputs

Why is it happening and how can I fix it?

Upvotes: 6

Views: 45497

Answers (2)

NotANumber
NotANumber

Reputation: 499

np.invert requires ints or bools, use the method np.linalg.inv instead.

Upvotes: 8

user707650
user707650

Reputation:

From the documentation:

Parameters:
x : array_like.
Only integer and boolean types are handled."

Your original array is floating point type (the return value of sigmoid()); setting values in it to 0 and 1 won't change the type. You need to use astype(np.int):

neg_correct = float(np.sum(np.invert(makePrediction(theta,neg).astype(np.int))))

should do it (untested).

Doing that, the float() cast you have also makes more sense. Though I would just remove the cast, and rely on Python doing the right thing.
In case you are still using Python 2 (but please use Python 3), just add 

from __future__ import division

to let Python do the right thing (it won't hurt if you do it in Python 3; it just doesn't do anything). With that (or in Python 3 anyway), you can remove numerous other float() casts you have elsewhere in your code, improving readability.

Upvotes: 5

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