Week 14 - FPL Flashcards

Question

What does (sort '(3 7 5 1 9)) return in Scheme?

Answer 1

(1 3 5 7 9).

Answer 2

A function is tail recursive if its recursive call is the last operation in the function. Tail recursive functions can be converted to use iteration by the compiler, making them faster. Scheme requires that all tail recursive functions be converted to use iteration.

Answer 3

Functional composition is when one function is applied to the result of another function. If h is the composition of f and g, then h(x) = f(g(x)).

Answer 4

In Scheme, it is possible to define a function that builds Scheme code and requests its interpretation using the interpreter function EVAL.

Answer 5

A non-tail-recursive function can be converted into a tail-recursive function by using an auxiliary helper function that carries an additional parameter to accumulate the result. This ensures that the recursive call is the last operation.

Answer 6

Functional composition is the process of applying one function to the result of another. In Scheme, it can be implemented using the compose function, which takes two functions f and g and returns a new function that applies f to the result of g.

Answer 7

FPLs mimic mathematical functions, which is important because it allows for a more predictable and reliable way to manage data and behavior, as functions always produce the same output given the same input without side effects.

Answer 8

Recursion replaces loops in functional programming because it allows iteration without changing state, maintaining immutability and functional purity.

Answer 9

Macros in Common Lisp are used to extend the language by allowing the creation of new syntactic constructs. They are expanded and then evaluated, enabling complex code transformations and abstractions. Users can define their own macros using DEFMACRO.

Answer 10

Functional programming languages handle data by passing it directly to functions and returning new data without altering the original data, avoiding traditional variables and assignments.

Answer 11

Referential transparency means that a function's output depends only on its input values and produces no side effects, ensuring consistent results and making code easier to understand and debug.

Answer 12

Basic operations for lists include accessing the first element (head), retrieving all elements except the first (tail), obtaining all elements except the last (init), and accessing the last element (last).

Answer 13

List concatenation is performed using the ++ operator, such as [1, 3] ++ [5, 7], resulting in [1, 3, 5, 7].

Answer 14

Elements in lists can be accessed by index using the !! operator, for example, [1, 2, 3, 4] !! 1 retrieves the element at index 1, resulting in 2.

Answer 15

Lists are created by enclosing elements within square brackets, like directions = ["north", "south", "east", "west"].

Answer 16

Pattern parameters allow functions to be defined based on different patterns, enabling flexible function definitions like product [] = 1 and product (a:x) = a * product x for calculating the product of a list.

Answer 17

Guards in function definitions are conditions added to lines of a function to specify when that definition can be applied, providing a clear way to handle different cases within a function.

Answer 18

While some languages may require reserved words to introduce function definitions, others, like Haskell, don't, and alternative definitions of functions with different formal parameters have the same appearance.

Answer 19

List comprehensions are used to generate new lists by applying transformations to existing lists, making it easier to create lists with specific patterns or properties.

Answer 20

String comprehension is similar to list comprehension, treating strings as lists of characters, enabling operations like pairing characters with corresponding numbers or extracting substrings.

Answer 21

Higher-order functions can take other functions as parameters or return functions as results, providing powerful tools for abstraction and composition in functional programming. Examples include map, which applies a function to each element of a list, and foldr, which reduces a list to a single value by repeatedly applying a function.

Answer 22

Reduces a list to a single value by recursively applying a function.

Answer 23

By recursively counting elements in a list.

Answer 24

By appending each element to the result list in reverse order.

Answer 25

Combining functions where the output of one becomes the input of another.

Answer 26

It checks if a predicate is true for all elements in a list.

Answer 27

Delays computation until necessary, improving efficiency and enabling infinite lists.

Answer 28

Using lazy evaluation to generate elements as needed.

Answer 29

Checks if an element is in a list.

Answer 30

By ensuring it terminates correctly when searching for an element.

Answer 31

Using let mutable.

Answer 32

Functional language with imperative and OOP features.

Answer 33

A way to define values and functions.

Answer 34

The compiler deduces types automatically.

Answer 35

bool, byte, int, float, double, char, unit.

Answer 36

Group of values of different types.

Answer 37

Collections that support lazy evaluation.

Answer 38

A value type tuple stored in the stack.

Answer 39

An ordered collection of elements that cannot be changed.

Answer 40

Treating functions with multiple arguments as a series of functions.

Answer 41

Collections of elements generated lazily.

Answer 42

To define recursive functions.

Answer 43

Applies a function to each list element and returns a new list.

Answer 44

Filters list elements based on a predicate function.

Answer 45

Accumulates results from a list based on an initial value.

Answer 46

Combining functions where the output of one is the input of another using >>.

Answer 47

let inline (>>) f g x = g (f x)

Answer 48

let inline (<<) f g x = f (g x)

Answer 49

Sends the value of its left operand to the last parameter of the call on its right.

Answer 50

let inline (|>) x f = f x

Answer 51

Lack of support for higher-order functions.

Answer 52

(input-parameters) => expression

Answer 53

Complex semantics and syntax, efficient execution, concurrency designed by the programmer.

Answer 54

Simple semantics and syntax, less efficient execution, automatic concurrency.

Answer 55

Function application, conditional expressions, recursion, and functional forms.

Answer 56

Supporting lazy evaluation, infinite lists, and set comprehension.

Week 14 - FPL Flashcards

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