Sum up indentical list elements

Question

I have an array with elements, and I want to sum up the accuracy. I want to sum the arrays that have the elements in the same order. I rather not be writing a for loop going through each element with zip and summing them up, is there an easier way to do this?

The two arrays are as follows, and currently my code is below for calculating the sum.

yp = [[0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1]]
y = [[0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1]]

sums = np.sum(yp == y)

I am getting an accuracy of zero.

`yp` and `y` are python lists. Therefore yp == y returns True (since they are identical). If you want to compute the sum, convert them to numpy array before. `yp = np.array(yp)` — Adrien Logut, Apr 24 '18 at 18:49
Oh I see, that makes sense. I will convert them to numpy and see what happens. — Jam1, Apr 24 '18 at 18:50
This is a very long list. This Is just the first part. They are not always identical. They just have the same size. — Jam1, Apr 24 '18 at 18:52
Converting them to numpy array and then doing `yp == y` will produce you an array of the same size than y and yp, full of boolean. It is a element-wise comparison. If you sum it, it will gives you how many items are identicals. — Adrien Logut, Apr 24 '18 at 18:52
which is exactly what `np.sum(yp == y)` is doing if `yp` and `y` are numpy arrays ;) — Adrien Logut, Apr 24 '18 at 18:54
@AdrienLogut the point is that `np.equal` also works with lists, etc. Also, `np.count_nonzero` should be a bit faster. — Paul Panzer, Apr 24 '18 at 18:56
Possible duplicate of [check how many elements are equal in two numpy arrays python](https://stackoverflow.com/questions/25490641/check-how-many-elements-are-equal-in-two-numpy-arrays-python) — TwistedSim, Apr 24 '18 at 19:03
`np.sum` does not work for this when both arrays are numpy arrays. I just tried it. — Jam1, Apr 24 '18 at 19:05
It definitly does, I've just tested it. Try Paul Panzer solution though. — Adrien Logut, Apr 24 '18 at 19:06
Guys I am going to use `zip` Sometimes I get 0 accuracy, sometimes I get 100%. Thank you all. — Jam1, Apr 24 '18 at 19:08

score 0 · Answer 1 · edited Jun 20 '20 at 09:12

Using your example:

yp = [[0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1]]
y = [[0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1]]

# First make the two arrays in question numpy arrays.
yp = np.array(yp)
y = np.array(y)
array_length = y.shape[1] # store length of sub arrays

equal_elements = np.array(yp) == np.array(y)  # check all equal elements
sums = np.sum(equal_elements, 1) # sum the number of equal elements in each sub array, use axis 1 as each array/sample is axis 0
equal_arrays = np.where(sums==array_length)[0] # returns a tuple, so index first element immediately

number_equal_arrays = equal_arrays.shape[0]  # What elements are equal
print('Number of equal arrays %d' % number_equal_arrays)
print('Accuracy %0.2f' % (number_equal_arrays/yp.shape[0]))

prints

Number of equal arrays 6

Accuracy 1.00

Sum up indentical list elements

1 Answers1