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To check if two NumPy arrays A and B are equal: Use a comparison operator (==) to form a comparison array. Check if all the elements in the comparison array are True.
Compare Two Arrays in Python Using the numpy. array_equiv() Method. The numpy. array_equiv(a1, a2) method takes array a1 and a2 as input and returns True if both arrays' shape and elements are the same; otherwise, returns False .
To compare each element of a NumPy array arr against the scalar x using any of the greater (>), greater equal (>=), smaller (<), smaller equal (<=), or equal (==) operators, use the broadcasting feature with the array as one operand and the scalar as another operand.
Method 1: We generally use the == operator to compare two NumPy arrays to generate a new array object. Call ndarray. all() with the new array object as ndarray to return True if the two NumPy arrays are equivalent.
check out the assert functions in numpy.testing, e.g.
assert_array_equal
for floating point arrays equality test might fail and assert_almost_equal is more reliable.
update
A few versions ago numpy obtained assert_allclose which is now my favorite since it allows us to specify both absolute and relative error and doesn't require decimal rounding as the closeness criterion.
I think (arr1 == arr2).all() looks pretty nice. But you could use:
numpy.allclose(arr1, arr2)
but it's not quite the same.
An alternative, almost the same as your example is:
numpy.alltrue(arr1 == arr2)
Note that scipy.array is actually a reference numpy.array. That makes it easier to find the documentation.
I find that using
self.assertEqual(arr1.tolist(), arr2.tolist())
is the easiest way of comparing arrays with unittest.
I agree it's not the prettiest solution and it's probably not the fastest but it's probably more uniform with the rest of your test cases, you get all the unittest error description and it's really simple to implement.
Since Python 3.2 you can use assertSequenceEqual(array1.tolist(), array2.tolist()).
This has the added value of showing you the exact items in which the arrays differ.
In my tests I use this:
numpy.testing.assert_array_equal(arr1, arr2)
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