i was looking some examples of interactions with the keyboard and stumbled upon this code that i found interesting. But i'm having trouble understanding a certain part of it(it's marked down below).I don't get how all this whole ''boolean'' declaration, ''switch'' and ''CASE'' works, i tried to look in the reference but still. Could someone explain in a simple maner how these work?
float x = 300;
float y = 300;
float speed = 5;
boolean isLeft, isRight, isUp, isDown;
int i = 0;
void keyPressed() {
setMove(keyCode, true);
if (isLeft ){
x -= speed;
}
if(isRight){
x += speed;
}
}
void keyReleased() {
setMove(keyCode, false);
}
boolean setMove(int k, boolean b) {// <<<--- From this part down
switch (k) {
case UP:
return isUp = b;
case DOWN:
return isDown = b;
case LEFT:
return isLeft = b;
case RIGHT:
return isRight = b;
default:
return b; }
}
Questions like these are best answered by the reference:
Works like an if else structure, but switch() is more convenient when you need to select between three or more alternatives. Program controls jumps to the case with the same value as the expression. All remaining statements in the switch are executed unless redirected by a break. Only primitive datatypes which can convert to an integer (byte, char, and int) may be used as the expression parameter. The default is optional.
The rest of the code is setting the corresponding variable to whatever value you passed in as the b parameter, and then returning it.
You should get into the habit of debugging your code. Add print statements to figure out exactly what the code is doing.
I am working on Objective C and I want to do something like this:
if (a && !b) {
// a do something...
} else if (!a && b) {
// b do something...
}
I wondered if there is something simpler, like:
if (a XOR b) {
// the existing variable do something...
}
Thanks in advance!!
Objective C is a superset of C, use the ^ operator. Or you can think logically (since xor is only true if either is true and the other is false) and use:
// This won't work for all types, be careful
if (a != b){
if (a){
// a do something
}
if (b){
// b do something
}
}
Note this solution, expanded from the xor, is more lengthy than the one you provided.
well I'm not sure if I misunderstood, but I guess a possibly solution would be to use the ? operator.
void *aux;
if( aux = a ? (b ? NULL : a) : (b ? b : NULL) )
//working with aux here
Never the less, if the idea is to keep it simple, this is quite unreadable. Also, this would expand to something like:
void *aux;
if(a){
if(b)
aux = NULL;
else
aux = a;
}else{
if(b)
aux = b;
else
aux = NULL;
}
My suggestion is that you leave the code as is. It's more readable and in terms of performance, I don't believe you'll notice much difference between the approaches
edit for clarity:
BTW, Inside the if block, the aux var will contain the value that exists. And if aux is NULL the if block won't be entered. Also, aux doesn't have to be void * or a pointer, it only has to be compatible with a and b datatypes.
if( !a != !b ) // same as a xor b
I'm learning Rust, so I'm doing the Project Euler problems, as they are good exercises imho.
But I'm already stuck on the second problem. The idea is to find the sum of all the even numbers that are less than 4000000 in the Fibonacci sequence.
So I tried to do it a bit the functional way, and using a custom iterator :
use std::mem;
static LIMIT: uint = 4000000u;
struct Fibonacci {
current: uint,
next: uint,
limit: uint,
}
fn fibo(lim: uint) -> Fibonacci {
Fibonacci {
current: 1u, next: 1u, limit: lim
}
}
impl Iterator<uint> for Fibonacci {
fn next(&mut self) -> Option<uint> {
let nex = self.current + self.next;
let cur = mem::replace(&mut self.next, nex);
if cur >= self.limit { return None; }
Some(mem::replace(&mut self.current, cur))
}
}
fn main() {
let sum = fibo(LIMIT).filter(|&x| x%2 == 0).fold(0, |sum, x| sum + x);
println!("Sum of fibs : {}", sum);
}
It looks good, and it gives the correct Fibonacci sequence (I verified with println!s).
The problem is that it doesn't give the correct sum : it outputs 1089154 whereas it should output 4613732. To me it seems that the fold misses the last number, but I can't see why !
I'm a total beginner with Rust, so any help would be greatly appreciated, thanks !
What you should be testing in your exit branch is the value of self.current; instead, you're testing the value of self.next. In other words, you're failing to output the last number in the sequence (as Matthieu suggested).
I verified that if the iterator is implemented correctly, it should produce the correct result with this (using grabbag_macros = "0.0.1" as a Cargo dependency):
#![feature(phase)]
#[phase(plugin, link)] extern crate grabbag_macros;
static LIMIT: u64 = 4000000;
fn main() {
let sum = recurrence![f[n]: u64 = 1, 1... f[n-1] + f[n-2]]
.take_while(|&n| n <= LIMIT)
.filter(|&n| n % 2 == 0)
.fold(0, |a, b| a + b);
println!("Sum of fibs: {}", sum);
}
A few other random notes: I'd avoid uint for this, since it's platform-dependent. You don't need the u suffix unless you need to be specific about the type. You can use take_while or plain old take instead of hard-coding a limit into your iterator.
I've written a C++/CLI wrapper for a C++-DLL to use this DLL in a C# programm.
However, when I call a function, which takes a char* I get a AccessViolation
int Wrapper::Net_methodX(int a, String^ key, long v)
{
IntPtr ptr = Marshal::StringToHGlobalAnsi(key);
pin_ptr<char> cKey = static_cast<char*>(ptr.ToPointer());
int val = methodX(a,cKey, v); // AccessViolation here
Marshal::FreeHGlobal(ptr);
return val;
}
The signature of the C++-function is
int methodX(int a, char *Key, long v);
EDIT 1
Just to "pin" like the following didn't work either:
int Wrapper::Net_methodX(int a, String^ key, long v)
{
IntPtr ptr = Marshal::StringToHGlobalAnsi(key);
char* cKey = static_cast<char*>(ptr.ToPointer());
pin_ptr<char> pinned = cKey;
int val = methodX(a,cKey, v);
Marshal::FreeHGlobal(ptr);
return val;
}
EDIT 1 END
EDIT 2
I tried also PtrToStringChars the following way (Thanks Matt, found also some doc here):
int Wrapper::Net_methodX(int a, String^ key, long v)
{
pin_ptr<const wchar_t> wkey = PtrToStringChars(key);
size_t convertedChars = 0;
size_t sizeInBytes = ((key->Length + 1) * 2);
errno_t err = 0;
char * ckey = (char * ) malloc(sizeInBytes);
err = wcstombs_s(&convertedChars, ckey, sizeInBytes, wkey, sizeInBytes);
int val = methodX(A_Symbol_Table,ckey, Value);
return val;
}
AccessViolation still occurs, maybe it's an error in methodX() (which is a Third-party-DLL).
EDIT 2 END
I have read some related questions here, but did not find a solution yet.
Any hints?
Thank you.
I know this is an old question, but for anyone who stumble upon this question looking for an answer, here are some simpler solutions.
Simply use sprintf to do the conversion like this: sprintf(cStr, "%s", clrString);. See my answer to this question for a complete example.
Read KB311259 as suggested by Matt Smith. If you are using VS 2008 or higher, use marshal_as<> (Method #4 in the KB). It's much simpler than the other methods in that document.
Simon,
I tried out your example and I do not get an Access Violation. Here's my code:
using namespace System;
using namespace System::Runtime::InteropServices;
ref class Wrapper
{
public:
static int Net_methodX(int a, String^ key, long v);
};
int methodX(int a, char * pKey, long v)
{
IntPtr ptr = static_cast<IntPtr>(pKey);
String ^ pString = Marshal::PtrToStringAnsi(ptr);
System::Console::WriteLine(pString);
return a;
}
int Wrapper::Net_methodX(int a, String^ pKey, long v)
{
IntPtr ptr = Marshal::StringToHGlobalAnsi(pKey);
pin_ptr<char> cKey = static_cast<char*>(ptr.ToPointer());
int val = methodX(a,cKey, v); // AccessViolation here
Marshal::FreeHGlobal(ptr);
return val;
}
void main()
{
Wrapper wrapper;
String ^ p = gcnew String("Hello");
wrapper.Net_methodX(0, p, 0);
}
Also, I have a few comments:
Read here: http://support.microsoft.com/kb/311259
You are using a pin_ptr to native memory. The StringToHGlobalAnsi method returns native memory, so I don't think using a pin_ptr makes sense here. A pin_ptr would make sense if you were using a method that gives you back a pointer to managed memory (like PtrToStringChars). Unless you are modifying the string, you probably want to go with the PtrToStringChars approach anyways--to avoid unnecessary allocation and copying.
Would you post an example version of methodX that causes the problem? If I can reproduce the issue, I might be able to be more helpful.
Simon
I think there is a problem with the following code
pin_ptr<char> cKey = static_cast<char*>(ptr.ToPointer());
You might want to read this http://social.msdn.microsoft.com/forums/en-US/vclanguage/thread/0bd049fe-844a-4cb6-b9f6-c8f5107bc957
Let me know if it helped you.
Sujay
I'm writing a function to find triangle numbers and the natural way to write it is recursively:
function triangle (x)
if x == 0 then return 0 end
return x+triangle(x-1)
end
But attempting to calculate the first 100,000 triangle numbers fails with a stack overflow after a while. This is an ideal function to memoize, but I want a solution that will memoize any function I pass to it.
Mathematica has a particularly slick way to do memoization, relying on the fact that hashes and function calls use the same syntax:
triangle[0] = 0;
triangle[x_] := triangle[x] = x + triangle[x-1]
That's it. It works because the rules for pattern-matching function calls are such that it always uses a more specific definition before a more general definition.
Of course, as has been pointed out, this example has a closed-form solution: triangle[x_] := x*(x+1)/2. Fibonacci numbers are the classic example of how adding memoization gives a drastic speedup:
fib[0] = 1;
fib[1] = 1;
fib[n_] := fib[n] = fib[n-1] + fib[n-2]
Although that too has a closed-form equivalent, albeit messier: http://mathworld.wolfram.com/FibonacciNumber.html
I disagree with the person who suggested this was inappropriate for memoization because you could "just use a loop". The point of memoization is that any repeat function calls are O(1) time. That's a lot better than O(n). In fact, you could even concoct a scenario where the memoized implementation has better performance than the closed-form implementation!
You're also asking the wrong question for your original problem ;)
This is a better way for that case:
triangle(n) = n * (n - 1) / 2
Furthermore, supposing the formula didn't have such a neat solution, memoisation would still be a poor approach here. You'd be better off just writing a simple loop in this case. See this answer for a fuller discussion.
I bet something like this should work with variable argument lists in Lua:
local function varg_tostring(...)
local s = select(1, ...)
for n = 2, select('#', ...) do
s = s..","..select(n,...)
end
return s
end
local function memoize(f)
local cache = {}
return function (...)
local al = varg_tostring(...)
if cache[al] then
return cache[al]
else
local y = f(...)
cache[al] = y
return y
end
end
end
You could probably also do something clever with a metatables with __tostring so that the argument list could just be converted with a tostring(). Oh the possibilities.
In C# 3.0 - for recursive functions, you can do something like:
public static class Helpers
{
public static Func<A, R> Memoize<A, R>(this Func<A, Func<A,R>, R> f)
{
var map = new Dictionary<A, R>();
Func<A, R> self = null;
self = (a) =>
{
R value;
if (map.TryGetValue(a, out value))
return value;
value = f(a, self);
map.Add(a, value);
return value;
};
return self;
}
}
Then you can create a memoized Fibonacci function like this:
var memoized_fib = Helpers.Memoize<int, int>((n,fib) => n > 1 ? fib(n - 1) + fib(n - 2) : n);
Console.WriteLine(memoized_fib(40));
In Scala (untested):
def memoize[A, B](f: (A)=>B) = {
var cache = Map[A, B]()
{ x: A =>
if (cache contains x) cache(x) else {
val back = f(x)
cache += (x -> back)
back
}
}
}
Note that this only works for functions of arity 1, but with currying you could make it work. The more subtle problem is that memoize(f) != memoize(f) for any function f. One very sneaky way to fix this would be something like the following:
val correctMem = memoize(memoize _)
I don't think that this will compile, but it does illustrate the idea.
Update: Commenters have pointed out that memoization is a good way to optimize recursion. Admittedly, I hadn't considered this before, since I generally work in a language (C#) where generalized memoization isn't so trivial to build. Take the post below with that grain of salt in mind.
I think Luke likely has the most appropriate solution to this problem, but memoization is not generally the solution to any issue of stack overflow.
Stack overflow usually is caused by recursion going deeper than the platform can handle. Languages sometimes support "tail recursion", which re-uses the context of the current call, rather than creating a new context for the recursive call. But a lot of mainstream languages/platforms don't support this. C# has no inherent support for tail-recursion, for example. The 64-bit version of the .NET JITter can apply it as an optimization at the IL level, which is all but useless if you need to support 32-bit platforms.
If your language doesn't support tail recursion, your best option for avoiding stack overflows is either to convert to an explicit loop (much less elegant, but sometimes necessary), or find a non-iterative algorithm such as Luke provided for this problem.
function memoize (f)
local cache = {}
return function (x)
if cache[x] then
return cache[x]
else
local y = f(x)
cache[x] = y
return y
end
end
end
triangle = memoize(triangle);
Note that to avoid a stack overflow, triangle would still need to be seeded.
Here's something that works without converting the arguments to strings.
The only caveat is that it can't handle a nil argument. But the accepted solution can't distinguish the value nil from the string "nil", so that's probably OK.
local function m(f)
local t = { }
local function mf(x, ...) -- memoized f
assert(x ~= nil, 'nil passed to memoized function')
if select('#', ...) > 0 then
t[x] = t[x] or m(function(...) return f(x, ...) end)
return t[x](...)
else
t[x] = t[x] or f(x)
assert(t[x] ~= nil, 'memoized function returns nil')
return t[x]
end
end
return mf
end
I've been inspired by this question to implement (yet another) flexible memoize function in Lua.
https://github.com/kikito/memoize.lua
Main advantages:
Accepts a variable number of arguments
Doesn't use tostring; instead, it organizes the cache in a tree structure, using the parameters to traverse it.
Works just fine with functions that return multiple values.
Pasting the code here as reference:
local globalCache = {}
local function getFromCache(cache, args)
local node = cache
for i=1, #args do
if not node.children then return {} end
node = node.children[args[i]]
if not node then return {} end
end
return node.results
end
local function insertInCache(cache, args, results)
local arg
local node = cache
for i=1, #args do
arg = args[i]
node.children = node.children or {}
node.children[arg] = node.children[arg] or {}
node = node.children[arg]
end
node.results = results
end
-- public function
local function memoize(f)
globalCache[f] = { results = {} }
return function (...)
local results = getFromCache( globalCache[f], {...} )
if #results == 0 then
results = { f(...) }
insertInCache(globalCache[f], {...}, results)
end
return unpack(results)
end
end
return memoize
Here is a generic C# 3.0 implementation, if it could help :
public static class Memoization
{
public static Func<T, TResult> Memoize<T, TResult>(this Func<T, TResult> function)
{
var cache = new Dictionary<T, TResult>();
var nullCache = default(TResult);
var isNullCacheSet = false;
return parameter =>
{
TResult value;
if (parameter == null && isNullCacheSet)
{
return nullCache;
}
if (parameter == null)
{
nullCache = function(parameter);
isNullCacheSet = true;
return nullCache;
}
if (cache.TryGetValue(parameter, out value))
{
return value;
}
value = function(parameter);
cache.Add(parameter, value);
return value;
};
}
}
(Quoted from a french blog article)
In the vein of posting memoization in different languages, i'd like to respond to #onebyone.livejournal.com with a non-language-changing C++ example.
First, a memoizer for single arg functions:
template <class Result, class Arg, class ResultStore = std::map<Arg, Result> >
class memoizer1{
public:
template <class F>
const Result& operator()(F f, const Arg& a){
typename ResultStore::const_iterator it = memo_.find(a);
if(it == memo_.end()) {
it = memo_.insert(make_pair(a, f(a))).first;
}
return it->second;
}
private:
ResultStore memo_;
};
Just create an instance of the memoizer, feed it your function and argument. Just make sure not to share the same memo between two different functions (but you can share it between different implementations of the same function).
Next, a driver functon, and an implementation. only the driver function need be public
int fib(int); // driver
int fib_(int); // implementation
Implemented:
int fib_(int n){
++total_ops;
if(n == 0 || n == 1)
return 1;
else
return fib(n-1) + fib(n-2);
}
And the driver, to memoize
int fib(int n) {
static memoizer1<int,int> memo;
return memo(fib_, n);
}
Permalink showing output on codepad.org. Number of calls is measured to verify correctness. (insert unit test here...)
This only memoizes one input functions. Generalizing for multiple args or varying arguments left as an exercise for the reader.
In Perl generic memoization is easy to get. The Memoize module is part of the perl core and is highly reliable, flexible, and easy-to-use.
The example from it's manpage:
# This is the documentation for Memoize 1.01
use Memoize;
memoize('slow_function');
slow_function(arguments); # Is faster than it was before
You can add, remove, and customize memoization of functions at run time! You can provide callbacks for custom memento computation.
Memoize.pm even has facilities for making the memento cache persistent, so it does not need to be re-filled on each invocation of your program!
Here's the documentation: http://perldoc.perl.org/5.8.8/Memoize.html
Extending the idea, it's also possible to memoize functions with two input parameters:
function memoize2 (f)
local cache = {}
return function (x, y)
if cache[x..','..y] then
return cache[x..','..y]
else
local z = f(x,y)
cache[x..','..y] = z
return z
end
end
end
Notice that parameter order matters in the caching algorithm, so if parameter order doesn't matter in the functions to be memoized the odds of getting a cache hit would be increased by sorting the parameters before checking the cache.
But it's important to note that some functions can't be profitably memoized. I wrote memoize2 to see if the recursive Euclidean algorithm for finding the greatest common divisor could be sped up.
function gcd (a, b)
if b == 0 then return a end
return gcd(b, a%b)
end
As it turns out, gcd doesn't respond well to memoization. The calculation it does is far less expensive than the caching algorithm. Ever for large numbers, it terminates fairly quickly. After a while, the cache grows very large. This algorithm is probably as fast as it can be.
Recursion isn't necessary. The nth triangle number is n(n-1)/2, so...
public int triangle(final int n){
return n * (n - 1) / 2;
}
Please don't recurse this. Either use the x*(x+1)/2 formula or simply iterate the values and memoize as you go.
int[] memo = new int[n+1];
int sum = 0;
for(int i = 0; i <= n; ++i)
{
sum+=i;
memo[i] = sum;
}
return memo[n];