diff --git a/readme.md b/readme.md index 1cb2850..a8ef6d6 100644 --- a/readme.md +++ b/readme.md @@ -931,7 +931,7 @@ Functional programming has its origins in mathematics and so do a lot of its ter Translating this into the language of computer science, a function is an expression with **exactly one (immutable)** parameter and **exactly one** return value. That value depends entirely on the argument which makes functions context-independant. This property is called [referential transparency](#referential-transparency) which allows for [equational reasoning](#equational-reasoning). Its mathematical origin also necessitates the absence of hidden [side effects](#side-effects) - a function is always [pure](#purity), by definition. These features make functions (as defined by mathematics) so pleasant to work with: they are entirely deterministic and therefore predictable. -Contrast this with the definition above: none of the aforementioned constrains are baked into the language constructs we need to deal with. They have been sacrificed in part for pragmatism, convenience, and expressiveness - useful features of programming languages. This mismatch the concept of a function and its concrete implementation must be accounted for by the programmer; she must appreciate the principles functional programming is grounded on in order to benefit from its promises. +Contrast this with the definition above: none of the aforementioned constrains are baked into the language constructs we need to deal with. They have been sacrificed in part for pragmatism, convenience, and expressiveness - useful features of programming languages. This mismatch between the concept of a function and its concrete implementation must be accounted for by the programmer; she must appreciate the principles functional programming is grounded on in order to benefit from its promises. ## Partial function A partial function is a function which may not be defined for all inputs - it might return an unexpected result with some inputs or it may never terminate. Partial functions add cognitive overhead, they are harder to reason about and they can lead to runtime errors. Some examples: