I am trying to replace an if else statement with a ternary operator
if the cost of vodka $24 return at discount price 18 (24 *.75)
This if else loop works fine and gives me the desired result but when i
try to convert it to ternary I get "expected ':'" error in xcode. What am I
doing wrong here?
ternary operator works like this
(condition) ? (executeFirst) : (executeSecond)
here is what I have:
NSUInteger cost = 24;
if (cost == 24) {
return cost *= .75);
} else {
return nil;
}
NSUInteger cost = 24;
(cost = 24) ? return cost *= .75 : return nil;
return cost;
}
Ternary operator is used to assign a value to some variable.
Use
cost = (cost == 24) ? cost * 0.75 : cost;
or:
return (cost == 24) ? cost*0.75 : cost;
Note the difference between '==' and '='. You must have made a typo or forgot about it in your code. '==' sign checks if left and right values are equal, and '=' assigns the right value to the left side (a variable).
What you want is something like:
NSUInteger cost = 24;
return (cost == 24) ? cost *= .75 : return nil;
2 things:
The first part of the ternary operator (the condition) should be a boolean, you previously had an assignment (== vs. =)
Return the result of the ternary operator.
The ternary operator has to evaluate to something (think of it as a mini function that HAS TO return a value). For the ternary condition ? one : two, you can't have statements (e.g. x = 3) at one and two; they must be expressions. So in your case it would be
return (cost == 24 ? 0.75 * cost : nil);
You should assign ternary operator value to some variable, o return it.
NSUInteger cost = 24;
cost = (cost == 24) ? cost * .75 : nil;
return cost;
Or
NSUInteger cost = 24;
return (cost == 24) ? cost * .75 : nil;
Related
So the Fibonacci number for log (N) — without matrices.
Ni // i-th Fibonacci number
= Ni-1 + Ni-2 // by definition
= (Ni-2 + Ni-3) + Ni-2 // unwrap Ni-1
= 2*Ni-2 + Ni-3 // reduce the equation
= 2*(Ni-3 + Ni-4) + Ni-3 //unwrap Ni-2
// And so on
= 3*Ni-3 + 2*Ni-4
= 5*Ni-4 + 3*Ni-5
= 8*Ni-5 + 5*Ni-6
= Nk*Ni-k + Nk-1*Ni-k-1
Now we write a recursive function, where at each step we take k~=I/2.
static long N(long i)
{
if (i < 2) return 1;
long k=i/2;
return N(k) * N(i - k) + N(k - 1) * N(i - k - 1);
}
Where is the fault?
You get a recursion formula for the effort: T(n) = 4T(n/2) + O(1). (disregarding the fact that the numbers get bigger, so the O(1) does not even hold). It's clear from this that T(n) is not in O(log(n)). Instead one gets by the master theorem T(n) is in O(n^2).
Btw, this is even slower than the trivial algorithm to calculate all Fibonacci numbers up to n.
The four N calls inside the function each have an argument of around i/2. So the length of the stack of N calls in total is roughly equal to log2N, but because each call generates four more, the bottom 'layer' of calls has 4^log2N = O(n2) Thus, the fault is that N calls itself four times. With only two calls, as in the conventional iterative method, it would be O(n). I don't know of any way to do this with only one call, which could be O(log n).
An O(n) version based on this formula would be:
static long N(long i) {
if (i<2) {
return 1;
}
long k = i/2;
long val1;
long val2;
val1 = N(k-1);
val2 = N(k);
if (i%2==0) {
return val2*val2+val1*val1;
}
return val2*(val2+val1)+val1*val2;
}
which makes 2 N calls per function, making it O(n).
public class fibonacci {
public static int count=0;
public static void main(String[] args) {
Scanner scan = new Scanner(System.in);
int i = scan.nextInt();
System.out.println("value of i ="+ i);
int result = fun(i);
System.out.println("final result is " +result);
}
public static int fun(int i) {
count++;
System.out.println("fun is called and count is "+count);
if(i < 2) {
System.out.println("function returned");
return 1;
}
int k = i/2;
int part1 = fun(k);
int part2 = fun(i-k);
int part3 = fun(k-1);
int part4 = fun(i-k-1);
return ((part1*part2) + (part3*part4)); /*RESULT WILL BE SAME FOR BOTH METHODS*/
//return ((fun(k)*fun(i-k))+(fun(k-1)*fun(i-k-1)));
}
}
I tried to code to problem defined by you in java. What i observed is that complexity of above code is not completely O(N^2) but less than that.But as per conventions and standards the worst case complexity is O(N^2) including some other factors like computation(division,multiplication) and comparison time analysis.
The output of above code gives me information about how many times the function
fun(int i) computes and is being called.
OUTPUT
So including the time taken for comparison and division, multiplication operations, the worst case time complexity is O(N^2) not O(LogN).
Ok if we use Analysis of the recursive Fibonacci program technique.Then we end up getting a simple equation
T(N) = 4* T(N/2) + O(1)
where O(1) is some constant time.
So let's apply Master's method on this equation.
According to Master's method
T(n) = aT(n/b) + f(n) where a >= 1 and b > 1
There are following three cases:
If f(n) = Θ(nc) where c < Logba then T(n) = Θ(nLogba)
If f(n) = Θ(nc) where c = Logba then T(n) = Θ(ncLog n)
If f(n) = Θ(nc) where c > Logba then T(n) = Θ(f(n))
And in our equation a=4 , b=2 & c=0.
As case 1 c < logba => 0 < 2 (which is log base 2 and equals to 2) is satisfied
hence T(n) = O(n^2).
For more information about how master's algorithm works please visit: Analysis of Algorithms
Your idea is correct, and it will perform in O(log n) provided you don't compute the same formula
over and over again. The whole point of having N(k) * N(i-k) is to have (k = i - k) so you only have to compute one instead of two. But if you only call recursively, you are performing the computation twice.
What you need is called memoization. That is, store every value that you already have computed, and
if it comes up again, then you get it in O(1).
Here's an example
const int MAX = 10000;
// memoization array
int f[MAX] = {0};
// Return nth fibonacci number using memoization
int fib(int n) {
// Base case
if (n == 0)
return 0;
if (n == 1 || n == 2)
return (f[n] = 1);
// If fib(n) is already computed
if (f[n]) return f[n];
// (n & 1) is 1 iff n is odd
int k = n/2;
// Applying your formula
f[n] = fib(k) * fib(n - k) + fib(k - 1) * fib(n - k - 1);
return f[n];
}
I am working on some assembly program analysis task using Z3. And I am trapped in simulating the semantics of x86 opcode bsf.
The semantics of bsf operand1 operand2 is defined as searches the source operand (operand1) for the least significant set bit (1 bit).
Its semantics can be simulated in C as:
if(operand1 == 0) {
ZF = 0;
operand2 = Undefined;
}
else {
ZF = 0;
Temporary = 0;
while(Bit(operand1, Temporary) == 0) {
Temporary = Temporary + 1;
operand2 = Temporary;
}
}
Right now, suppose each operand (e.g., register) maintains a symbolic expression, I am trying to simulate the above semantics in Z3Py. The code I wrote is something like this (simplified):
def aux_bsf(x): # x is operand1
if simplify(x == 0):
raise Exception("undefined in aux_bsf")
else:
n = x.size()
for i in range(n):
b = Extract(i, i, x)
if simplify(b == 1):
return BitVecVal(i, 32)
raise Exception("undefined in bsf")
However, I find that the evaluation of simplify(x==0) (e.g., x equals BitVecVal(13, 32) + BitVec("symbol1", 32),) is always equal to True. In other words, I am always trapped in the first exception!
Am I doing anything wrong here..?
====================================================
OK, so I think what I need is something like:
def aux_bsf(x):
def aux(x, i):
if i == 31:
return 31
else:
return If(Extract(i, i, x) == 1, i, aux(x, i+1))
return aux(x, 0)
simplify(x == 0) returns an expression, it does not return True/False, where False = 0. Python would treat an expression reference as a non-zero value and therefore take the first branch. Unless 'x' is a bit-vector constant, simplification would not return a definite value. The same issue is with simplify(b == 1).
You could encode such functions as a relation between operand1 and operand2, e.g., something along the lines of:
def aux_bsf(s, x, y):
for k in range(x.size()):
s.Add(Implies(lsb(k, x), y == k)
def lsb(k, x):
first0 = True
if k > 0:
first0 = Extract(x, k-1,0) == 0
return And(Extract(x,k,k) == 1, first0)
You can also use uninterpreted functions for the cases where aux_bsf is under-specified.
For example:
def aux_bsf(x):
bv = x.sort()
bsf_undef = Function('bsf-undef', bv, bv)
result = bsf_undef(x)
for k in reverse(range(bv.size()))
result = If(Extract(x, k, k) == 1), BitVecVal(k, bv), result)
return result
def reverse(xs):
....
I want to get a random number either + or -:
But what's wrong here
func randomPlusMinus(value:Float) -> Float {
return value * (arc4random() % 2 ? 1 : -1)
}
Error: Could not find an overload for '*' that accepts the supplied arguments
Try:
func randomPlusMinus(value:Float) -> Float {
let invert: Bool = arc4random_uniform(2) == 1
return value * (invert ? -1.0 : 1.0)
}
I don't think you can say if 0 or if 1. You should be using a boolean value with if and the ternary operator (cond ? v1 : v2).
Then there's the Swift numerics thing (which is really annoying, they need to add/implement more convertible protocols in the Std library :/ )
PS - I don't have an interpreter handy, but I will double check later
Having an explicit test for the result of the modulo operation works for me:
func randomPlusMinus(value:Float) -> Float {
return 0 == (arc4random() % 2) ? value : -value
}
I'm a little late to answering this, but I feel the simplest solution would be:
func randomPlusMinus(value:Float) -> Float {
return value * (arc4random_uniform(2) * 2 - 1)
}
The arc4random call will (supposedly) return 0 50% of the time and 1 50% of the time. So multiplying by 2 gives 0 or 2, then subtracting 1 gives -1 or 1. So the function returns value * -1 50% of the time and value * 1 the other 50% of the time.
I think this is what you are after if you want to random the +- of original value:
func randomPlusMinus(value:Float) -> Float {
let x = arc4random_uniform(2)
switch x {
case 0 :
return value * -1
default :
return value
}
}
For example I want to calculate (reasonably efficiently)
2^1000003 mod 12321
And finally I want to do (2^1000003 - 3) mod 12321. Is there any feasible way to do this?
Basic modulo properties tell us that
1) a + b (mod n) is (a (mod n)) + (b (mod n)) (mod n), so you can split the operation in two steps
2) a * b (mod n) is (a (mod n)) * (b (mod n)) (mod n), so you can use modulo exponentiation (pseudocode):
x = 1
for (10000003 times) {
x = (x * 2) % 12321; # x will never grow beyond 12320
}
Of course, you shouldn't do 10000003 iterations, just remember that 21000003 = 2 * 21000002 , and 21000002 = (2500001)2 and so on...
In some reasonably C- or java-like language:
def modPow(Long base, Long exponent, Long modulus) = {
if (exponent < 0) {complain or throw or whatever}
else if (exponent == 0) {
return 1;
} else if (exponent & 1 == 1) { // odd exponent
return (base * modPow(base, exponent - 1, modulus)) % modulus;
} else {
Long halfexp = modPow(base, exponent / 2, modulus);
return (halfexp * halfexp) % modulus;
}
}
This requires that modulus is small enough that both (modulus - 1) * (modulus - 1) and base * (modulus - 1) won't overflow whatever integer type you're using. If modulus is too large for that, then there are some other techniques to compensate a bit, but it's probably just easier to attack it with some arbitrary-precision integer arithmetic library.
Then, what you want is:
(modPow(2, 1000003, 12321) + (12321 - 3)) % 12321
Well in Java there's an easy way to do this:
Math.pow(2, 1000003) % 12321;
For languages without the Math.* functions built in it'd be a little harder. Can you clarify which language this is supposed to be in?
This scripting language doesn't have a % or Mod(). I do have a Fix() that chops off the decimal part of a number. I only need positive results, so don't get too robust.
Will
// mod = a % b
c = Fix(a / b)
mod = a - b * c
do? I'm assuming you can at least divide here. All bets are off on negative numbers.
a mod n = a - (n * Fix(a/n))
For posterity, BrightScript now has a modulo operator, it looks like this:
c = a mod b
If someone arrives later, here are some more actual algorithms (with errors...read carefully)
https://eprint.iacr.org/2014/755.pdf
There are actually two main kind of reduction formulae: Barett and Montgomery. The paper from eprint repeat both in different versions (algorithms 1-3) and give an "improved" version in algorithm 4.
Overview
I give now an overview of the 4. algorithm:
1.) Compute "A*B" and Store the whole product in "C" that C and the modulus $p$ is the input for that algorithm.
2.) Compute the bit-length of $p$, say: the function "Width(p)" returns exactly that value.
3.) Split the input $C$ into N "blocks" of size "Width(p)" and store each in G. Start in G[0] = lsb(p) and end in G[N-1] = msb(p). (The description is really faulty of the paper)
4.) Start the while loop:
Set N=N-1 (to reach the last element)
precompute $b:=2^{Width(p)} \bmod p$
while N>0 do:
T = G[N]
for(i=0; i<Width(p); i++) do: //Note: that counter doesn't matter, it limits the loop)
T = T << 1 //leftshift by 1 bit
while is_set( bit( T, Width(p) ) ) do // (N+1)-th bit of T is 1
unset( bit( T, Width(p) ) ) // unset the (N+1)-th bit of T (==0)
T += b
endwhile
endfor
G[N-1] += T
while is_set( bit( G[N-1], Width(p) ) ) do
unset( bit( G[N-1], Width(p) ) )
G[N-1] += b
endwhile
N -= 1
endwhile
That does alot. Not we only need to recursivly reduce G[0]:
while G[0] > p do
G[0] -= p
endwhile
return G[0]// = C mod p
The other three algorithms are well defined, but this lacks some information or present it really wrong. But it works for any size ;)
What language is it?
A basic algorithm might be:
hold the modulo in a variable (modulo);
hold the target number in a variable (target);
initialize modulus variable;
while (target > 0) {
if (target > modulo) {
target -= modulo;
}
else if(target < modulo) {
modulus = target;
break;
}
}
This may not work for you performance-wise, but:
while (num >= mod_limit)
num = num - mod_limit
In javascript:
function modulo(num1, num2) {
if (num2 === 0 || isNaN(num1) || isNaN(num2)) {
return NaN;
}
if (num1 === 0) {
return 0;
}
var remainderIsPositive = num1 >= 0;
num1 = Math.abs(num1);
num2 = Math.abs(num2);
while (num1 >= num2) {
num1 -= num2
}
return remainderIsPositive ? num1 : 0 - num1;
}