Set of lazily evaluated python functions.
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The lazyness is achivied by curried functions.
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Because this functions are being written as I think and read about some lambda calculus ideas, you will see
- functions written almost entirely using python lambda functions
- no statements on function's body, only expressions
- recursion as a tool for repetition
- some simple data structures using just functions.
- I've implemented an immutable pair by using functions.
Example:
from lambda_py.lazy_pair import *
import lambda_py.math as m
# This is how you can create a pair
pair = Pair(3)(4)
# showing the evaluation lazyness building a pair in distinct moments
partially_applied_pair_of_numbers = Pair(3)
pair = partially_applied_pair_of_numbers(4) # 'point' is a pair (3, 4)
# To get a string representation of the pair, you need send the 'show' function to the pair:
pair(show)
# You can get the first element of this pair by passing a function called 'first' to the pair:
pair(first) # -> 3
# or get the second similarly
pair(second) # -> 4
# the map_ function creates a new pair whose values is a transformation of the original pair:
pair2 = pair(map_(lambda x: x + 2)) # -> (5,6)
pair3 = pair(map_(m.double)) # -> (6,8)
times3 = m.times(3) # lazy times function
pair4 = pair(map_(lambda x: times3(x))) # -> (9, 12)
# chain of tranformations.
pair5 = pair (map_(lambda x: x + 2)) \
(map_(lambda x: x * 2)) \
(map_(times3))A tuple of 3 elements was implemented with the same api