On re-arranging digits in number 1862 next highest number is 2168
On re-arranging digits in number 22405 next highest number is 22450
What is an algorithm for finding next highest number?
Here is a summary of an algorithm that does what you want. If you want more details, code, or a proof of the correctness of the algorithm, show us more of what you have done so far.
Let's use 1862 as an example. Scan the digits of that number from the right-most digit to the left until you find a consecutive pair of digits where the left digit is smaller than the right digit. In this case that is the 18. Let's call that left digit the "pivot" position (1 here). You will now rearrange the digits in the number starting with that pivot. Replace the pivot with the next-larger digit that is anywhere to the right of it (2 in this case). Then after that digit, place all the other digits that used to be at or right of the pivot (186 in this case) in ascending order (168 here). The result is your answer 2168.
In your other example 22405 you scan back and stop at 05. You replace the 0 with the 5 then put the other digit(s), 0 in this case, after it in increasing order. So you leave the 224 alone and end up with 22450.
If, in your backwards scan, you do not find any consecutive pair of digits where the left digit is smaller than the right digit, then there is no larger number with those digits.
There is a trick to speed up the placement of the digits in increasing order, but I'll leave that to you.
Related
I'm facing a problem. when we want to subtract a number from another using 2's complement we can do that. I don't know how to subtract fractional number using 2's complement.
5 is in binary form 101 and 2 is 10. if we want to subtract 2 from 5 we need to find out 2's complement of 2
2's complement of 2-> 11111110
so if we now add with binary of 5 we can get the subtraction result. If I want to get the result of 5.5-2.125. what would be the procedure.
Fixed point numbers can be used and it is still common to find them in embedded code or hardware.
Their use is identical to integers, but you need to specify where your "point" is. For instance, assume that you want 3 bits after after the point and that your data is 8 bits, bits 7..3 are the integer part (left of "point") and bits 2..0 the fractional part. The interpretation of integer part is as usual the binary decomposition of this integer: bits 3 correspond to 20, bits 4 to 21, etc.
For the fractional part, the decomposition is in negative powers or two. bits 2 correspond to 2-1, bits 1 to 2-2 and bit 0 to 2-3.
So for you problem, 5.5=4+1+1/2=22+20+2-1 and its code is 00101(.)100. Similarly 2.125=2+1/8 and its code is 00010(.)001 (note (.) is just an help to understand the coding).
Indeed they are just integers, but you must take into account that all your numbers are multiplied by 2-3. This will have no impact for addition, but results of multiplication and division must be adjusted. Taking into account the position of point and managing over and underflows is the difficulty of arithmetic with fixed point, but it allows to do fractional computations even if your hardware does not provide floating point support (for instance with low end microcontrollers or FPGA systems).
Two complement is similar to integers and its computation is identical. If code of 2.125 is 00010(.)001, than -2.125==11101(.)111. Operations are as usual.
+5 00101(.)100
-2.125 11101(.)111
00011(.)011
and 00011(.)011=2+1+1/4+1/8=3,375
For the record, two complement first use was for fixed point fractional numbers and two complement name comes from that. If a fractional number if represented by, say 0(.)1100000 (0.75), its negative counter part will be 1(.)0100000 (-0.75 or 1.25 if interpreted as unsigned) and we always have x+(unsigned)-x=2. For this coding, the negative value of a fractional number x is the number y that must be added to x to get a 2, hence the name that y is 2's complement of x.
How do I represent integers numbers, for example, 23647 in two bytes, where one byte contains the last two digits (47) and the other contains the rest of the digits(236)?
There are several ways do to this.
One way is to try to use Binary Coded Decimal (BCD). This codes decimal digits, rather than the number as a whole into binary. The packed form puts two decimal digits into a byte. However, your example value 23647 has five decimal digits and will not fit into two bytes in BCD. This method will fit values up to 9999.
Another way is to put each of your two parts in binary and place each part into a byte. You can do integer division by 100 to get the upper part, so in Python you could use
upperbyte = 23647 // 100
Then the lower part can be gotten by the modulus operation:
lowerbyte = 23647 % 100
Python will directly convert the results into binary and store them that way. You can do all this in one step in Python and many other languages:
upperbyte, lowerbyte = divmod(23647, 100)
You are guaranteed that the lowerbyte value fits, but if the given value is too large the upperbyte value many not actually fit into a byte. All this assumes that the value is positive, since negative values would complicate things.
(This following answer was for a previous version of the question, which was to fit a floating-point number like 36.47 into two bytes, one byte for the integer part and another byte for the fractional part.)
One way to do that is to "shift" the number so you consider those two bytes to be a single integer.
Take your value (36.47), multiply it by 256 (the number of values that fit into one byte), round it to the nearest integer, convert that to binary. The bottom 8 bits of that value are the "decimal numbers" and the next 8 bits are the "integer value." If there are any other bits still remaining, your number was too large and there is an overflow condition.
This assumes you want to handle only non-negative values. Handling negatives complicates things somewhat. The final result is only an approximation to your starting value, but that is the best you can do.
Doing those calculations on 36.47 gives the binary integer
10010001111000
So the "decimal byte" is 01111000 and the "integer byte" is 100100 or 00100100 when filled out to 8 bits. This represents the float number 36.46875 exactly and your desired value 36.47 approximately.
I am not sure how to approach it but could someone help me convert the following numbers to their decimal representation:
and
The general method goes something like this:
Work from right to left, you'll want to count the positions (starting with zero) and sum up the terms according to a the following formula:
Say you're working in base x. Then, if you're at the ith position, and that digit is d, then that position will contribute a term of d times x^i to the final sum.
As a concrete example, take your first number - here, x=7 (the base). Starting from the right, the first digit is d=6 at the i=0 position. So we start with 6*(7^0) = 6(1) = 6.
Moving to the left, i=1 and d=5. So we get 5(7^1) = 5(7) = 35 for this term.
Then, moving to the last digit, i=2 and d=4. So we get 4*(7^2)=4(49)=196 for the last term.
Now, you can just add all of these up to get 35 + 6 + 196 = 237 as your final number (in base 10, that is).
The exact same algorithm works for any base, so you should be able to apply it to the binary number in the exact same way.
(Just let x=2 and work right to left, noting that i ranges from 0 to 7 here.)
If you have a randomly generated password, consisting of only alphanumeric characters, of length 12, and the comparison is case insensitive (i.e. 'A' == 'a'), what is the probability that one specific string of length 3 (e.g. 'ABC') will appear in that password?
I know the number of total possible combinations is (26+10)^12, but beyond that, I'm a little lost. An explanation of the math would also be most helpful.
The string "abc" can appear in the first position, making the string look like this:
abcXXXXXXXXX
...where the X's can be any letter or number. There are (26 + 10)^9 such strings.
It can appear in the second position, making the string look like:
XabcXXXXXXXX
And there are (26 + 10)^9 such strings also.
Since "abc" can appear at anywhere from the first through 10th positions, there are 10*36^9 such strings.
But this overcounts, because it counts (for instance) strings like this twice:
abcXXXabcXXX
So we need to count all of the strings like this and subtract them off of our total.
Since there are 6 X's in this pattern, there are 36^6 strings that match this pattern.
I get 7+6+5+4+3+2+1 = 28 patterns like this. (If the first "abc" is at the beginning, the second can be in any of 7 places. If the first "abc" is in the second place, the second can be in any of 6 places. And so on.)
So subtract off 28*36^6.
...but that subtracts off too much, because it subtracted off strings like this three times instead of just once:
abcXabcXabcX
So we have to add back in the strings like this, twice. I get 4+3+2+1 + 3+2+1 + 2+1 + 1 = 20 of these patterns, meaning we have to add back in 2*20*(36^3).
But that math counted this string four times:
abcabcabcabc
...so we have to subtract off 3.
Final answer:
10*36^9 - 28*36^6 + 2*20*(36^3) - 3
Divide that by 36^12 to get your probability.
See also the Inclusion-Exclusion Principle. And let me know if I made an error in my counting.
If A is not equal to C, the probability P(n) of ABC occuring in a string of length n (assuming every alphanumeric symbol is equally likely) is
P(n)=P(n-1)+P(3)[1-P(n-3)]
where
P(0)=P(1)=P(2)=0 and P(3)=1/(36)^3
To expand on Paul R's answer. Probability (for equally likely outcomes) is the number of possible outcomes of your event divided by the total number of possible outcomes.
There are 10 possible places where a string of length 3 can be found in a string of length 12. And there are 9 more spots that can be filled with any other alphanumeric characters, which leads to 36^9 possibilities. So the number of possible outcomes of your event is 10 * 36^9.
Divide that by your total number of outcomes 36^12. And your answer is 10 * 36^-3 = 0.000214
EDIT: This is not completely correct. In this solution, some cases are double counted. However they only form a very small contribution to the probability so this answer is still correct up to 11 decimal places. If you want the full answer, see Nemo's answer.
This is more of a maths question than programming but I figure a lot of people here are pretty good at maths! :)
My question is: Given a 9 x 9 grid (81 cells) that must contain the numbers 1 to 9 each exactly 9 times, how many different grids can be produced. The order of the numbers doesn't matter, for example the first row could contain nine 1's etc. This is related to Sudoku and we know the number of valid Sudoku grids is 6.67×10^21, so since my problem isn't constrained like Sudoku by having to have each of the 9 numbers in each row, column and box then the answer should be greater than 6.67×10^21.
My first thought was that the answer is 81! however on further reflection this assumes that the 81 numbers possible for each cell are different, distinct number. They are not, there are 81 possible numbers for each cell but only 9 possible different numbers.
My next thought was then that each of the cells in the first row can be any number between 1 and 9. If by chance the first row happened to be all the same number, say all 1s, then each cell in the second row could only have 8 possibilites, 2-9. If this continued down until the last row then number of different permutations could be calculated by 9^2 * 8^2 * 7^2 ..... * 1^2. However this doesn't work if each row doesn't contain 9 of the same number.
It's been quite a while since I studied this stuff and I can't think of a way to work it out, I'd appreciate any help anyone can offer.
Imagine taking 81 blank slips of paper and writing a number from 1 to 9 on each slip (nine of each number). Shuffle the deck, and start placing the slips on the 9x9 grid.
You'd be able to create 81! different patterns if you considered each slip to be unique.
But instead you want to consider all the 1's to be equivalent.
For any particular configuration, how many times will that configuration be repeated
due to the 1's all being equivalent? The answer is 9!, the number of ways you can permute the nine slips with 1 written on them.
So that cuts the total number of permutations down to 81!/9!. (You divide by the number of indistinguishable permutations. Instead of 9! indistinguishable permutations, imagine there were just 2 indistinguishable permutations. You would divide the count by 2, right? So the rule is, you divide by the number of indistinguishable permutations.)
Ah, but you also want the 2's to be equivalent, and the 3's, and so forth.
By the same reasoning, that cuts down the number of permutations to
81!/(9!)^9
By Stirling's approximation, that is roughly 5.8 * 10^70.
First, let's start with 81 numbers, 1 through 81. The number of permutations for that is 81P81, or 81!. Simple enough.
However, we have nine 1s, which can be arranged in 9! indistinguishable permutations. Same with 2, 3, etc.
So what we have is the total number of board permutations divided by all the indistinguishable permutations of all numbers, or 81! / (9! ** 9).
>>> reduce(operator.mul, range(1,82))/(reduce(operator.mul, range(1, 10))**9)
53130688706387569792052442448845648519471103327391407016237760000000000L