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map#

ivy.map(fn, constant=None, unique=None, mean=False)[source]#

Apply a function on each item of an iterable x.

Parameters:

  • fn (Callable) – The function to map onto x.

  • constant (Optional[Dict[str, Any]], default: None) – keyword arguments which remain constant between each function call. Default is None.

  • unique (Optional[Dict[str, Iterable[Any]]], default: None) – keyword arguments which are unique for each function call. Default is None.

  • mean (bool, default: False) – Whether to compute the mean across the return values, and return this mean. Default is False.

Return type:

List

Returns:

ret – x following the application of fn to each of its iterated items.

Examples

With int inputs:

>>> def special_square(x : float) -> float : return np.square(x)
>>> results = ivy.map(fn = special_square,
...                   constant = None,
...                   unique = {'x' : [1,2,3]},
...                   mean = False)
>>> print(results)
[1, 4, 9]

>>> results = ivy.map(fn = special_square,
...                   constant = None,
...                   unique = {'x':[0,1,2]},
...                   mean = True)
>>> print(results)
1.6666666666666667

>>> def special_pow(x:float,y:float) ->float : return np.power(x,y)
>>> results = ivy.map(fn = special_pow,
...                   constant = {'y':[0,1]},
...                   unique = {'x':[1,2,3]},
...                   mean = False)
>>> print(results)
[array([1,1]),
array([1,2]),
array([1,3])]

>>> results = ivy.map(fn = special_pow,
...                   constant = {'y':[0,1]},
...                   unique = {'x':[1,2,3]},
...                   mean = True)
>>> print(results)
[1. 2.]

With float inputs:

>>> def linear_model(w:float, x:float, b:float) -> float: return w*x + b
>>> results = ivy.map(fn = linear_model,
...                   constant = {'w':10., 'b':1.},
...                   unique = {'x':[0.,1.,2.]},
...                   mean = False)
>>> print(results)
[1.0, 11.0, 21.0]

With ivy.Array inputs:

>>> results = ivy.map(fn = linear_model,
...    constant = {'w':ivy.array([1.,0.,1.]), 'b':ivy.array([0.,10.,100.])},
...    unique = {'x':[ivy.array([0.,1.,0.]), ivy.array([1.,1.,1.])]},
...    mean = False)
>>> print(results)
[ivy.array([0., 10., 100.]),
ivy.array([1., 10., 101.])]

>>> results = ivy.map(fn = linear_model,
...    constant = {'w':ivy.array([1.,0.,1.]), 'b':ivy.array([0.,10.,100.])},
...    unique = {'x':[ivy.array([0.,1.,0.]), ivy.array([1.,1.,1.])]},
...    mean = True)
>>> print(results)
ivy.array([  0.5,  10. , 100. ])
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