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One common cause, for example, is that a programmer intends to iterate over sequence of nodes in a data structure such as a linked list or tree, executing the loop code once for each node. Improperly formed links can create a reference loop in the data structure, where one node links to another that occurs earlier in the sequence.
Python uses the following syntax to express list comprehensions over finite lists: S = [ 2 * x for x in range ( 100 ) if x ** 2 > 3 ] A generator expression may be used in Python versions >= 2.4 which gives lazy evaluation over its input, and can be used with generators to iterate over 'infinite' input such as the count generator function which ...
When eager evaluation is desirable (primarily when the sequence is finite, as otherwise evaluation will never terminate), one can either convert to a list, or use a parallel construction that creates a list instead of a generator. For example, in Python a generator g can be evaluated to a list l via l = list(g), while in F# the sequence ...
Here, the list [0..] represents , x^2>3 represents the predicate, and 2*x represents the output expression.. List comprehensions give results in a defined order (unlike the members of sets); and list comprehensions may generate the members of a list in order, rather than produce the entirety of the list thus allowing, for example, the previous Haskell definition of the members of an infinite list.
The loop counter is used to decide when the loop should terminate and for the program flow to continue to the next instruction after the loop. A common identifier naming convention is for the loop counter to use the variable names i , j , and k (and so on if needed), where i would be the most outer loop, j the next inner loop, etc.
A generator call can then be used in place of a list, or other structure whose elements will be iterated over. Whenever the for loop in the example requires the next item, the generator is called, and yields the next item. Generators don't have to be infinite like the prime-number example above.
If the loop is checking something simple then it will spend most of its time asleep and will waste very little CPU time. In programs that never end (such as operating systems), infinite busy waiting can be implemented by using unconditional jumps as shown by this NASM syntax: jmp $. The CPU will unconditionally jump to its own position forever ...
A classic example of recursion is the definition of the factorial function, given here in Python code: def factorial ( n ): if n > 0 : return n * factorial ( n - 1 ) else : return 1 The function calls itself recursively on a smaller version of the input (n - 1) and multiplies the result of the recursive call by n , until reaching the base case ...