In terms of comparing two numpy arrays and counting the number of matches (e.g. correct class prediction in machine learning), I found the below example for two dimensions useful:
import numpy as np
result = np.random.randint(3,size=(5,2)) # 5x2 random integer array
target = np.random.randint(3,size=(5,2)) # 5x2 random integer array
res = np.equal(result,target)
print result
print target
print np.sum(res[:,0])
print np.sum(res[:,1])
which can be extended to D dimensions.
The results are:
Prediction:
[[1 2]
[2 0]
[2 0]
[1 2]
[1 2]]
Target:
[[0 1]
[1 0]
[2 0]
[0 0]
[2 1]]
Count of correct prediction for D=1: 1
Count of correct prediction for D=2: 2
boolarr.sum(axis=1 or axis=0)
axis = 1 will output number of trues in a row and axis = 0 will count number of trues in columns so
boolarr[[true,true,true],[false,false,true]]
print(boolarr.sum(axis=1))
will be (3,1)
That question solved a quite similar question for me and I thought I should share :
In raw python you can use sum()
to count True
values in a list
:
>>> sum([True,True,True,False,False])
3
But this won't work :
>>> sum([[False, False, True], [True, False, True]])
TypeError...
Source: Stackoverflow.com