jax.numpy.arrayΒΆ
-
jax.numpy.
array
(object, dtype=None, copy=True, order='K', ndmin=0)[source]ΒΆ Create an array.
LAX-backend implementation of
array()
. Original docstring below.array(object, dtype=None, *, copy=True, order=βKβ, subok=False, ndmin=0)
- Parameters
object (array_like) β An array, any object exposing the array interface, an object whose __array__ method returns an array, or any (nested) sequence.
dtype (data-type, optional) β The desired data-type for the array. If not given, then the type will be determined as the minimum type required to hold the objects in the sequence.
copy (bool, optional) β If true (default), then the object is copied. Otherwise, a copy will only be made if __array__ returns a copy, if obj is a nested sequence, or if a copy is needed to satisfy any of the other requirements (dtype, order, etc.).
order ({'K', 'A', 'C', 'F'}, optional) β Specify the memory layout of the array. If object is not an array, the newly created array will be in C order (row major) unless βFβ is specified, in which case it will be in Fortran order (column major). If object is an array the following holds.
ndmin (int, optional) β Specifies the minimum number of dimensions that the resulting array should have. Ones will be pre-pended to the shape as needed to meet this requirement.
- Returns
out β An array object satisfying the specified requirements.
- Return type
See also
empty_like()
Return an empty array with shape and type of input.
ones_like()
Return an array of ones with shape and type of input.
zeros_like()
Return an array of zeros with shape and type of input.
full_like()
Return a new array with shape of input filled with value.
empty()
Return a new uninitialized array.
ones()
Return a new array setting values to one.
zeros()
Return a new array setting values to zero.
full()
Return a new array of given shape filled with value.
Notes
When order is βAβ and object is an array in neither βCβ nor βFβ order, and a copy is forced by a change in dtype, then the order of the result is not necessarily βCβ as expected. This is likely a bug.
Examples
>>> np.array([1, 2, 3]) array([1, 2, 3])
Upcasting:
>>> np.array([1, 2, 3.0]) array([ 1., 2., 3.])
More than one dimension:
>>> np.array([[1, 2], [3, 4]]) array([[1, 2], [3, 4]])
Minimum dimensions 2:
>>> np.array([1, 2, 3], ndmin=2) array([[1, 2, 3]])
Type provided:
>>> np.array([1, 2, 3], dtype=complex) array([ 1.+0.j, 2.+0.j, 3.+0.j])
Data-type consisting of more than one element:
>>> x = np.array([(1,2),(3,4)],dtype=[('a','<i4'),('b','<i4')]) >>> x['a'] array([1, 3])
Creating an array from sub-classes:
>>> np.array(np.mat('1 2; 3 4')) array([[1, 2], [3, 4]])
>>> np.array(np.mat('1 2; 3 4'), subok=True) matrix([[1, 2], [3, 4]])