# jax.numpy.array_equal¶

jax.numpy.array_equal(a1, a2, equal_nan=False)[source]

True if two arrays have the same shape and elements, False otherwise.

LAX-backend implementation of array_equal(). Original docstring below.

Parameters
• a2 (a1,) – Input arrays.

• equal_nan (bool) – Whether to compare NaN’s as equal. If the dtype of a1 and a2 is complex, values will be considered equal if either the real or the imaginary component of a given value is nan.

Returns

b – Returns True if the arrays are equal.

Return type

bool

allclose()

Returns True if two arrays are element-wise equal within a tolerance.

array_equiv()

Returns True if input arrays are shape consistent and all elements equal.

Examples

>>> np.array_equal([1, 2], [1, 2])
True
>>> np.array_equal(np.array([1, 2]), np.array([1, 2]))
True
>>> np.array_equal([1, 2], [1, 2, 3])
False
>>> np.array_equal([1, 2], [1, 4])
False
>>> a = np.array([1, np.nan])
>>> np.array_equal(a, a)
False
>>> np.array_equal(a, a, equal_nan=True)
True


When equal_nan is True, complex values with nan components are considered equal if either the real or the imaginary components are nan.

>>> a = np.array([1 + 1j])
>>> b = a.copy()
>>> a.real = np.nan
>>> b.imag = np.nan
>>> np.array_equal(a, b, equal_nan=True)
True