So there was a puzzle:
This equation is incomplete: 1 2 3 4 5 6 7 8 9 = 100. One way to make
it accurate is by adding seven plus and minus signs, like so: 1 + 2 +
3 – 4 + 5 + 6 + 78 + 9 = 100.
How can you do it using only 3 plus or minus signs?
I'm quite new to Prolog, solved the puzzle, but i wonder how to optimize it
makeInt(S,F,FinInt):-
getInt(S,F,0,FinInt).
getInt(Start, Finish, Acc, FinInt):-
0 =< Finish - Start,
NewAcc is Acc*10 + Start,
NewStart is Start +1,
getInt(NewStart, Finish, NewAcc, FinInt).
getInt(Start, Finish, A, A):-
0 > Finish - Start.
itCounts(X,Y,Z,Q):-
member(XLastDigit,[1,2,3,4,5,6]),
FromY is XLastDigit+1,
numlist(FromY, 7, ListYLastDigit),
member(YLastDigit, ListYLastDigit),
FromZ is YLastDigit+1,
numlist(FromZ, 8, ListZLastDigit),
member(ZLastDigit,ListZLastDigit),
FromQ is ZLastDigit+1,
member(YSign,[-1,1]),
member(ZSign,[-1,1]),
member(QSign,[-1,1]),
0 is XLastDigit + YSign*YLastDigit + ZSign*ZLastDigit + QSign*9,
makeInt(1, XLastDigit, FirstNumber),
makeInt(FromY, YLastDigit, SecondNumber),
makeInt(FromZ, ZLastDigit, ThirdNumber),
makeInt(FromQ, 9, FourthNumber),
X is FirstNumber,
Y is YSign*SecondNumber,
Z is ZSign*ThirdNumber,
Q is QSign*FourthNumber,
100 =:= X + Y + Z + Q.
Not sure this stands for an optimization. The code is just shorter:
sum_123456789_eq_100_with_3_sum_or_sub(L) :-
append([G1,G2,G3,G4], [0'1,0'2,0'3,0'4,0'5,0'6,0'7,0'8,0'9]),
maplist([X]>>(length(X,N), N>0), [G1,G2,G3,G4]),
maplist([G,F]>>(member(Op, [0'+,0'-]),F=[Op|G]), [G2,G3,G4], [F2,F3,F4]),
append([G1,F2,F3,F4], L),
read_term_from_codes(L, T, []),
100 is T.
It took me a while, but I got what your code is doing. It's something like this:
itCounts(X,Y,Z,Q) :- % generate X, Y, Z, and Q s.t. X+Y+Z+Q=100, etc.
generate X as a list of digits
do the same for Y, Z, and Q
pick the signs for Y, Z, and Q
convert all those lists of digits into numbers
verify that, with the signs, they add to 100.
The inefficiency here is that the testing is all done at the last minute. You can improve the efficiency if you can throw out some possible solutions as soon as you pick one of your numbers, that is, testing earlier.
itCounts(X,Y,Z,Q) :- % generate X, Y, Z, and Q s.t. X+Y+Z+Q=100, etc.
generate X as a list of digits, and convert it to a number
if it's so big or small the rest can't possibly bring the sum back to 100, fail
generate Y as a list of digits, convert to number, and pick it sign
if it's so big or so small the rest can't possibly bring the sum to 100, fail
do the same for Z
do the same for Q
Your function is running pretty fast already, even if I search all possible solutions. It only picks 6 X's; 42 Y's; 224 Z's; and 15 Q's. I don't think optimizing will be worth your while.
But if you really wanted to: I tested this by putting a testing function immediately after selecting an X. It reduced the 6 X's to 3 (all before finding the solution); 42 Y's to 30; 224 Z's to 184; and 15 Q's to 11. I believe we could reduce it further by testing immediately after a Y is picked, to see whether X YSign Y is already so large or small there can be no solution.
In PROLOG programs that are more computationally intensive, putting parts of the 'test' earlier in 'generate and test' algorithms can help a lot.
Related
Can you please help me to solve this problem ( Using VBA Excel )
We need to find the numbers X and Y such that:
3*X = Y
X is a 4-digit number and Y is a 5-digit number
We find all the digits (from 0 to 9) in the equation.
digits must used one time in the equation
for example : 3 * 4321 = 567890
Facts we know:
X must be in range 3334 <= X <= 9999
Y must be in range 10000 <= Y <= 29997
Y must be a multiple of 3
Thus, the sum of the digits of Y must be a multiple of three
In turn, since (1+2+4..9) mod 3 == 0, X must also be a multiple of three
The least significant digit of neither number can be 0, i.e. neither can be a multiple of 10
The least significant digit of the X cannot be 1
The least significant digit of the Y cannot be 9
Y must be a multiple of 9, so the sum of its digits must be divisible by 9
Thus, X must be congruent to 6 mod 9
In turn, Y must be congruent to 18 mod 27
The most significant digit of Y must be either 1 or 2 (assuming no leading zeros allowed)
There are 9 choose 4 == 126 ways to construct a 4-digit number, as well as a 5-digit number, without repeating digits (but not checking bounds).
With all of this in mind, your best bet is to probably brute force for Y, constructing numbers from combinations of 5 distinct digits, trying only numbers leading with 1|2 and performing a quick modulus check before checking if the equation itself is satisfied.
I currently am going through some examples of J, and am trying to do an exponential moving average.
For the simple moving average I have done as follows:
sma =: +/%[
With the following given:
5 sma 1 2 3 4 5
1.2 1.4 1.6 1.8 2
After some more digging I found an example of the exponential moving average in q.
.q.ema:{first[y]("f"$1-x)\x*y}
I have tried porting this to J with the following code:
ema =: ({. y (1 - x)/x*y)
However this results in the following error:
domain error
| ema=:({.y(1-x) /x*y)
This is with x = 20, and y an array of 20 random numbers.
A couple of things that I notice that might help you out.
1) When you declare a verb explicitly you need to use the : Explicit conjunction and in this case since you have a dyadic verb the correct form would be 4 : 'x contents of verb y' Your first definition of sma =: +/%[ is tacit, since no x or y variables are shown.
ema =: 4 : '({. y (1 - x)/x*y)'
2) I don't know q, but in J it looks as if you are trying to Insert / a noun 1 - x into a list of integers x * y. I am guessing that you really want to Divides %. You can use a gerunds as arguments to Insert but they are special nouns representing verbs and 1 - x does not represent a verb.
ema =: 4 : '({. y (1 - x)%x*y)'
3) The next issue is that you would have created a list of numbers with (1 - x) % x * y, but at that point y is a number adjacent to a list of numbers with no verb between which will be an error. Perhaps you meant to use y * (1 - x)%x*y ?
At this point I am not sure what you want exponential moving average to do and hope the clues I have provided will give you the boost that you need.
The outer loop executes n times while the inner loop executes ? So the total time is n*something.
Do i need to learn summation,if yes then any book to refer?
for(int i=1;i<=n;i++)
for(int j=1;j<=n;j+=i)
printf("*");
This question can be approached by inspection:
n = 16
i | j values | # terms
1 | 1, 2, ..., 16 | n
2 | 1, 3, 5, ..., 16 | n / 2
.. | .. | n / 3
16 | 16 | n / n
In the above table, i is the outer loop value, and j values show the iterations of the inner loop. By inspection, we can see that the loops will take n * (1 + 1/2 + 1/3 + ... + 1/n) steps. This is a bounded harmonic series. As this Math Stack Exchange article shows, there is no closed form for the above expression in terms of n. However, as this SO article shows, there is an upper bound of O(n*ln(n)).
So, the running time for your two loops is O(n*ln(n)).
I believe the time complexity of that is O(n*log(n)). Here is why:
Let us pick some arbitrary natural number i and see how many steps the inner loop takes for this given i. Well for this i, you are going from j=1 to j<=n with a jump of i in between. So basically you are doing this summation many steps:
summation = 1 + (1+i) + (1+2i) + ... (1+ki)
where k is the largest integer such that 1+ki <= n. That is, k is the number of steps and this is what we want to solve for. Well we can solve for k in the equality resulting in k <= (n-1)/i and thus k = ⌊(n-1)/i⌋. That is, k is the floor function/integer division of (n-1)/i. Since we are dealing with time complexities, this floor function doesn't matter so we will just say k = n/i for simplicity. This is the number of steps that the inner loop will take for a given i. So we basically need to add all these for i = 1 to i <= n.
So numsteps will be this addition:
numsteps = n/1 + n/2 + n/3 + ... n/n
= n(1 + 1/2 + 1/3 + ... 1+n)
So we need to find the sum of 1 + 1/2 + ... 1/n to finish this. There is actually no good closed form for this sum but it is on the order of ln(n). You can read more about this here. You can also guess this since the integral from 1 to n of 1/x is ln(n). Again, since we are dealing with time complexity, we can just use ln(n) to represent its complexity. Thus we have:
numsteps = n(ln(n))
And so the time complexity is O(n*log(n)).
Edit: My bad, i was calculating the sum :P
I am super confused what the percentage sign does in Objective C. Can someone explain to me in language that an average idiot like myself can understand?! Thanks.
% is the modulo operator, so for example 10 % 3 would result in 1.
If you have some numbers a and b, a % b gives you just the remainder of a divided by b.
So in the example 10 % 3, 10 divided by 3 is 3 with remainder 1, so the answer is 1.
If there is no remainder to a divided by b, the answer is zero, so for example, 4 % 2 = 0.
Here's a relevant SO question about modular arithmetic.
Same as what it does in C, it's "modulo" (also known as integer remainder).
% is the modulo operator. It returns the remainder of <number> / <number>. For example:
5 % 2
means 5 / 2, which equals 2 with a remainder of 1, so, 1 is the value that is returned. Here's some more examples:
3 % 3 == 0 //remainder of 3/3 is 0
6 % 3 == 0 //remainder of 6/3 is 0
5 % 3 == 2 //remainder of 5/3 is 2
15 % 4 == 3 //remainder of 15/4 is 3
99 % 30 == 9 //remainder of 99/30 is 9
The definition of modulo is:
mod·u·lo
(in number theory) with respect to or using a modulus of a specified number. Two numbers are congruent modulo a given number if they give the same remainder when divided by that number.
In Mathematics, The Percentage Sign %, Called Modulo (Or Sometimes The Remainder Operator) Is A Operator Which Will Find The Remainder Of Two Numbers x And y. Mathematically Speaking, If x/y = {(z, r) : y * z + r = x}, Where All x, y, and z Are All Integers, Then
x % y = {r: ∃z: x/y = (z, r)}. So, For Example, 10 % 3 = 1.
Some Theorems And Properties About Modulo
If x < y, Then x % y = x
x % 1 = 0
x % x = 0
If n < x, Then (x + n) % x = n
x Is Even If And Only If x % 2 = 0
x Is Odd If And Only If x % 2 = 1
And Much More!
Now, One Might Ask: How Do We Find x % y? Well, Here's A Fairly Simple Way:
Do Long Division. I Could Explain How To Do It, But Instead, Here's A Link To A Page Which Explains Long Division: https://www.mathsisfun.com/numbers/long-division-index.html
Stop At Fractions. Once We Reach The Part Where We Would Normally Write The Answer As A Fraction, We Should Stop. So, For Example, 101/2 Would Be 50.5, But, As We Said, We Would Stop At The Fractions, So Our Answer Ends Up Being 50.
Output What's Left As The Answer. Here's An Example: 103/3. First, Do Long Division. 103 - 90 = 13. 13 - 12 = 1. Now, As We Said, We Stop At The Fractions. So Instead Of Continuing The Process And Getting The Answer 34.3333333..., We Get 34. And Finally, We Output The Remainder, In This Case, 1.
NOTE: Some Mathematicians Write x mod y Instead Of x % y, But Most Programming Languages Only Understand %.
I am processing a series of points which all have the same Y value, but different X values. I go through the points by incrementing X by one. For example, I might have Y = 50 and X is the integers from -30 to 30. Part of my algorithm involves finding the distance to the origin from each point and then doing further processing.
After profiling, I've found that the sqrt call in the distance calculation is taking a significant amount of my time. Is there an iterative way to calculate the distance?
In other words:
I want to efficiently calculate: r[n] = sqrt(x[n]*x[n] + y*y)). I can save information from the previous iteration. Each iteration changes by incrementing x, so x[n] = x[n-1] + 1. I can not use sqrt or trig functions because they are too slow except at the beginning of each scanline.
I can use approximations as long as they are good enough (less than 0.l% error) and the errors introduced are smooth (I can't bin to a pre-calculated table of approximations).
Additional information:
x and y are always integers between -150 and 150
I'm going to try a couple ideas out tomorrow and mark the best answer based on which is fastest.
Results
I did some timings
Distance formula: 16 ms / iteration
Pete's interperlating solution: 8 ms / iteration
wrang-wrang pre-calculation solution: 8ms / iteration
I was hoping the test would decide between the two, because I like both answers. I'm going to go with Pete's because it uses less memory.
Just to get a feel for it, for your range y = 50, x = 0 gives r = 50 and y = 50, x = +/- 30 gives r ~= 58.3. You want an approximation good for +/- 0.1%, or +/- 0.05 absolute. That's a lot lower accuracy than most library sqrts do.
Two approximate approaches - you calculate r based on interpolating from the previous value, or use a few terms of a suitable series.
Interpolating from previous r
r = ( x2 + y2 ) 1/2
dr/dx = 1/2 . 2x . ( x2 + y2 ) -1/2 = x/r
double r = 50;
for ( int x = 0; x <= 30; ++x ) {
double r_true = Math.sqrt ( 50*50 + x*x );
System.out.printf ( "x: %d r_true: %f r_approx: %f error: %f%%\n", x, r, r_true, 100 * Math.abs ( r_true - r ) / r );
r = r + ( x + 0.5 ) / r;
}
Gives:
x: 0 r_true: 50.000000 r_approx: 50.000000 error: 0.000000%
x: 1 r_true: 50.010000 r_approx: 50.009999 error: 0.000002%
....
x: 29 r_true: 57.825065 r_approx: 57.801384 error: 0.040953%
x: 30 r_true: 58.335225 r_approx: 58.309519 error: 0.044065%
which seems to meet the requirement of 0.1% error, so I didn't bother coding the next one, as it would require quite a bit more calculation steps.
Truncated Series
The taylor series for sqrt ( 1 + x ) for x near zero is
sqrt ( 1 + x ) = 1 + 1/2 x - 1/8 x2 ... + ( - 1 / 2 )n+1 xn
Using r = y sqrt ( 1 + (x/y)2 ) then you're looking for a term t = ( - 1 / 2 )n+1 0.36n with magnitude less that a 0.001, log ( 0.002 ) > n log ( 0.18 ) or n > 3.6, so taking terms to x^4 should be Ok.
Y=10000
Y2=Y*Y
for x=0..Y2 do
D[x]=sqrt(Y2+x*x)
norm(x,y)=
if (y==0) x
else if (x>y) norm(y,x)
else {
s=Y/y
D[round(x*s)]/s
}
If your coordinates are smooth, then the idea can be extended with linear interpolation. For more precision, increase Y.
The idea is that s*(x,y) is on the line y=Y, which you've precomputed distances for. Get the distance, then divide it by s.
I assume you really do need the distance and not its square.
You may also be able to find a general sqrt implementation that sacrifices some accuracy for speed, but I have a hard time imagining that beating what the FPU can do.
By linear interpolation, I mean to change D[round(x)] to:
f=floor(x)
a=x-f
D[f]*(1-a)+D[f+1]*a
This doesn't really answer your question, but may help...
The first questions I would ask would be:
"do I need the sqrt at all?".
"If not, how can I reduce the number of sqrts?"
then yours: "Can I replace the remaining sqrts with a clever calculation?"
So I'd start with:
Do you need the exact radius, or would radius-squared be acceptable? There are fast approximatiosn to sqrt, but probably not accurate enough for your spec.
Can you process the image using mirrored quadrants or eighths? By processing all pixels at the same radius value in a batch, you can reduce the number of calculations by 8x.
Can you precalculate the radius values? You only need a table that is a quarter (or possibly an eighth) of the size of the image you are processing, and the table would only need to be precalculated once and then re-used for many runs of the algorithm.
So clever maths may not be the fastest solution.
Well there's always trying optimize your sqrt, the fastest one I've seen is the old carmack quake 3 sqrt:
http://betterexplained.com/articles/understanding-quakes-fast-inverse-square-root/
That said, since sqrt is non-linear, you're not going to be able to do simple linear interpolation along your line to get your result. The best idea is to use a table lookup since that will give you blazing fast access to the data. And, since you appear to be iterating by whole integers, a table lookup should be exceedingly accurate.
Well, you can mirror around x=0 to start with (you need only compute n>=0, and the dupe those results to corresponding n<0). After that, I'd take a look at using the derivative on sqrt(a^2+b^2) (or the corresponding sin) to take advantage of the constant dx.
If that's not accurate enough, may I point out that this is a pretty good job for SIMD, which will provide you with a reciprocal square root op on both SSE and VMX (and shader model 2).
This is sort of related to a HAKMEM item:
ITEM 149 (Minsky): CIRCLE ALGORITHM
Here is an elegant way to draw almost
circles on a point-plotting display:
NEW X = OLD X - epsilon * OLD Y
NEW Y = OLD Y + epsilon * NEW(!) X
This makes a very round ellipse
centered at the origin with its size
determined by the initial point.
epsilon determines the angular
velocity of the circulating point, and
slightly affects the eccentricity. If
epsilon is a power of 2, then we don't
even need multiplication, let alone
square roots, sines, and cosines! The
"circle" will be perfectly stable
because the points soon become
periodic.
The circle algorithm was invented by
mistake when I tried to save one
register in a display hack! Ben Gurley
had an amazing display hack using only
about six or seven instructions, and
it was a great wonder. But it was
basically line-oriented. It occurred
to me that it would be exciting to
have curves, and I was trying to get a
curve display hack with minimal
instructions.