I need to do a bitwise "and" in a cypher query. It seems that cypher does not support bitwise operations. Any suggestions for alternatives?
This is what I want to detect ...
For example 268 is (2^8 + 2^3 + 2^2) and as you can see 2^3 = 8 is a part of my original number. So if I use bitwise AND it will be (100001100) & (1000) = 1000 so this way I can detect if 8 is a part of 268 or not.
How can I do this without bitwise support? any suggestions? I need to do this in cypher.
Another way to perform this type of test using cypher would be to convert your decimal values to collections of the decimals that represent the bits that are set.
// convert the binary number to a collection of decimal parts
// create an index the size of the number to convert
// create a collection of decimals that correspond to the bit locations
with '100001100' as number
, [1,2,4,8,16,32,64,128,256,512,1024,2048,4096] as decimals
with number
, range(length(number)-1,0,-1) as index
, decimals[0..length(number)] as decimals
// map the bits to decimal equivalents
unwind index as i
with number, i, (split(number,''))[i] as binary_placeholder, decimals[-i-1] as decimal_placeholder
// multiply the decimal value by the bits that are set
with collect(decimal_placeholder * toInt(binary_placeholder)) as decimal_placeholders
// filter out the zero values from the collection
with filter(d in decimal_placeholders where d > 0) as decimal_placeholders
return decimal_placeholders
Here is a sample of what this returns.
Then when you want to test whether the number is in the decimal, you can just test the actual decimal for presence in the collection.
with [4, 8, 256] as decimal_placeholders
, 8 as decimal_to_test
return
case
when decimal_to_test in decimal_placeholders then
toString(decimal_to_test) + ' value bit is set'
else
toString(decimal_to_test) + ' value bit is NOT set'
end as bit_set_test
Alternatively, if one had APOC available they could use apoc.bitwise.op which is a wrapper around the java bitwise operations.
RETURN apoc.bitwise.op(268, "&",8 ) AS `268_AND_8`
Which yields the following result
If you absolutely have to do the operation in cypher probably a better solution would be to implement something like #evan's SO solution Alternative to bitwise operation using cypher.
You could start by converting your data using cypher that looks something like this...
// convert binary to a product of prime numbers
// start with the number to conver an a collection of primes
with '100001100' as number
, [2,3,5,7,13,17,19,23,29,31,37] as primes
// create an index based on the size of the binary number to convert
// take a slice of the prime array that is the size of the number to convert
with number
, range(length(number)-1,0,-1) as index
, primes[0..length(number)] as primes, decimals[0..length(number)] as decimals
// iterate over the index and match the prime number to the bits in the number to convert
unwind index as i
with (split(number,''))[i] as binary_place_holder, primes[-i-1] as prime_place_holder, decimals[-i-1] as decimal_place_holder
// collect the primes that are set by multiplying by the set bits
with collect(toInt(binary_place_holder) * prime_place_holder) as prime_placeholders
// filter out the zero bits
with filter(p in prime_placeholders where p > 0) as prime_placeholders
// return a product of primes of the set bits
return prime_placeholders, reduce(pp = 1, p in prime_placeholders | pp * p) as prime_product
Sample of the output of the above query. The query could be adapted to update attributes with the prime product.
Here is a screen cap of how the conversion breaks down
Then when you want to use it you could use the modulus of the prime number in the location of the bit you want to test.
// test if the fourth bit is set in the decimal 268
// 268 is the equivalent of a prime product of 1015
// a modulus 7 == 0 will indicate the bit is set
with 1015 as prime_product
, [2,3,5,7,13,17,19,23,29,31,37] as primes
, 4 as bit_to_test
with bit_to_test
, prime_product
, primes[bit_to_test-1] as prime
, prime_product % primes[bit_to_test-1] as mod_remains
with
case when mod_remains = 0 then
'bit ' + toString(bit_to_test) + ' set'
else
'bit ' + toString(bit_to_test) + ' NOT set'
end as bit_set
return bit_set
It almost certainly defeats the purpose of choosing a bitwise operation in the first place but if you absolutely needed to AND the two binary numbers in cypher you could do something like this with collections.
with split('100001100', '') as bin_term_1
, split('000001000', '') as bin_term_2
, toString(1) as one
with bin_term_1, bin_term_2, one, range(0,size(bin_term_1)-1,1) as index
unwind index as i
with i, bin_term_1, bin_term_2, one,
case
when (bin_term_1[i] = one) and (bin_term_2[i] = one) then
1
else
0
end as r
return collect(r) as AND
Thanks Dave. I tried your solutions and they all worked. They were a good hint for me to find another approach. This is how I solved it. I used String comparison.
with '100001100' as number , '100000000' as sub_number
with number,sub_number,range(length (number)-1,length (number)-length(sub_number),-1) as tail,length (number)-length(sub_number) as difference
unwind tail as i
with i,sub_number,number, i - length (number) + length (sub_number) as sub_number_position
with sub_number_position, (split(number,''))[i-1] as bit_mask , (split(sub_number,''))[sub_number_position] as sub_bit
with collect(toInt(bit_mask) * toInt(sub_bit)) as result
return result
Obviously the number and sub_number can have different values.
Related
I would like to assert that the most significant digit of a number is a particular value, but I don't actually know the length of the number. If it was the least significant digit, I know I could use the python mod (%) to check for it. But with an unknown number of digits, I'm unsure of how I could check this in z3.
For example, I may know that the left most digit is a 9, such as 9x, or 9xx, or 9xxx etc.
Thanks so much in advance
The generic way to do this would be to convert to a string and check that the first character matches:
from z3 import *
s = Solver()
n = Int('n')
s.add(SubString(IntToStr(n), 0, 1) == "9")
r = s.check()
if r == sat:
m = s.model()
print("n =", m[n])
else:
print("Solver said:", r)
This prints:
n = 9
Note that IntToStr expects its argument to be non-negative, so if you need to support negative numbers, you'll have to write extra code to accommodate for that. See https://smtlib.cs.uiowa.edu/theories-UnicodeStrings.shtml for details.
Aside While this will accomplish what you want in its generality, it may not be the most efficient way to encode this constraint. Since it goes through strings, the constraints generated might cause performance issues. If you have an upper limit on your number, it might be more efficient to code it explicitly. For instance, if you know your number is less than a 1000, I'd code it as (pseudocode):
n == 9 || n >= 90 && n <= 99 || n >= 900 && n <= 999
etc. until you have the range you needed covered. This would lead to much simpler constraints and perform a lot better in general. Note that this'll work even if you don't know the exact length, but have an upper bound on it. But of course, it all depends on what you are trying to achieve and what else you know about the number itself.
def digits(n):
total=0
for i in range(0,n):
if n/(10**(i))<1 and n/(10**(i-1))=>1:
total+=i
else:
total+=0
return total
I want to find the number of digits in 13 so I do the below
print digits(13)
it gives me $\0$ for every number I input into the function.
there's nothing wrong with what I've written as far as I can see:
if a number has say 4 digits say 1234 then dividing by 10^4 will make it less than 1: 0.1234 and dividing by 10^3 will make it 1.234
and by 10^3 will make it 1.234>1. when i satisfies BOTH conditions you know you have the correct number of digits.
what's failing here? Please can you advise me on the specific method I've tried
and not a different one?
Remember for every n there can only be one i which satisfies that condition.
so when you add i to the total there will only be i added so total returning total will give you i
your loop makes no sense at all. It goes from 0 to exact number - not what you want.
It looks like python, so grab a solution that uses string:
def digits(n):
return len(str(int(n))) # make sure that it's integer, than conver to string and return number of characters == number of digits
EDIT:
If you REALLY want to use a loop to count number of digits, you can do this this way:
def digits(n):
i = 0
while (n > 1):
n = n / 10
++i
return i
EDIT2:
since you really want to make your solution work, here is your problem. Provided, that you call your function like digits(5), 5 is of type integer, so your division is integer-based. That means, that 6/100 = 0, not 0.06.
def digits(n):
for i in range(0,n):
if n/float(10**(i))<1 and n/float(10**(i-1))=>1:
return i # we don't need to check anything else, this is the solution
return null # we don't the answer. This should not happen, but still, nice to put it here. Throwing an exception would be even better
I fixed it. Thanks for your input though :)
def digits(n):
for i in range(0,n):
if n/(10**(i))<1 and n/(10**(i-1))>=1:
return i
HI I know there have been may question about this here but I wasn't able to find a detailed enough answer, Wikipedia has two examples of ISIN and how is their checksum calculated.
The part of calculation that I'm struggling with is
Multiply the group containing the rightmost character
The way I understand this statement is:
Iterate through each character from right to left
once you stumble upon a character rather than digit record its position
if the position is an even number double all numeric values in even position
if the position is an odd number double all numeric values in odd position
My understanding has to be wrong because there are at least two problems:
Every ISIN starts with two character country code so position of rightmost character is always the first character
If you omit the first two characters then there is no explanation as to what to do with ISINs that are made up of all numbers (except for first two characters)
Note
isin.org contains even less information on verifying ISINs, they even use the same example as Wikipedia.
I agree with you; the definition on Wikipedia is not the clearest I have seen.
There's a piece of text just before the two examples that explains when one or the other algorithm should be used:
Since the NSIN element can be any alpha numeric sequence (9 characters), an odd number of letters will result in an even number of digits and an even number of letters will result in an odd number of digits. For an odd number of digits, the approach in the first example is used. For an even number of digits, the approach in the second example is used
The NSIN is identical to the ISIN, excluding the first two letters and the last digit; so if the ISIN is US0378331005 the NSIN is 037833100.
So, if you want to verify the checksum digit of US0378331005, you'll have to use the "first algorithm" because there are 9 digits in the NSIN. Conversely, if you want to check AU0000XVGZA3 you're going to use the "second algorithm" because the NSIN contains 4 digits.
As to the "first" and "second" algorithms, they're identical, with the only exception that in the former you'll multiply by 2 the group of odd digits, whereas in the latter you'll multiply by 2 the group of even digits.
Now, the good news is, you can get away without this overcomplicated algorithm.
You can, instead:
Take the ISIN except the last digit (which you'll want to verify)
Convert all letters to numbers, so to obtain a list of digits
Reverse the list of digits
All the digits in an odd position are doubled and their digits summed again if the result is >= 10
All the digits in an even position are taken as they are
Sum all the digits, take the modulo, subtract the result from 0 and take the absolute value
The only tricky step is #4. Let's clarify it with a mini-example.
Suppose the digits in an odd position are 4, 0, 7.
You'll double them and get: 8, 0, 14.
8 is not >= 10, so we take it as it is. Ditto for 0. 14 is >= 10, so we sum its digits again: 1+4=5.
The result of step #4 in this mini-example is, therefore: 8, 0, 5.
A minimal, working implementation in Python could look like this:
import string
isin = 'US4581401001'
def digit_sum(n):
return (n // 10) + (n % 10)
alphabet = {letter: value for (value, letter) in
enumerate(''.join(str(n) for n in range(10)) + string.ascii_uppercase)}
isin_to_digits = ''.join(str(d) for d in (alphabet[v] for v in isin[:-1]))
isin_sum = 0
for (i, c) in enumerate(reversed(isin_to_digits), 1):
if i % 2 == 1:
isin_sum += digit_sum(2*int(c))
else:
isin_sum += int(c)
checksum_digit = abs(- isin_sum % 10)
assert int(isin[-1]) == checksum_digit
Or, more crammed, just for functional fun:
checksum_digit = abs( - sum(digit_sum(2*int(c)) if i % 2 == 1 else int(c)
for (i, c) in enumerate(
reversed(''.join(str(d) for d in (alphabet[v] for v in isin[:-1]))), 1)) % 10)
I need a base converter function for Lua. I need to convert from base 10 to base 2,3,4,5,6,7,8,9,10,11...36 how can i to this?
In the string to number direction, the function tonumber() takes an optional second argument that specifies the base to use, which may range from 2 to 36 with the obvious meaning for digits in bases greater than 10.
In the number to string direction, this can be done slightly more efficiently than Nikolaus's answer by something like this:
local floor,insert = math.floor, table.insert
function basen(n,b)
n = floor(n)
if not b or b == 10 then return tostring(n) end
local digits = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ"
local t = {}
local sign = ""
if n < 0 then
sign = "-"
n = -n
end
repeat
local d = (n % b) + 1
n = floor(n / b)
insert(t, 1, digits:sub(d,d))
until n == 0
return sign .. table.concat(t,"")
end
This creates fewer garbage strings to collect by using table.concat() instead of repeated calls to the string concatenation operator ... Although it makes little practical difference for strings this small, this idiom should be learned because otherwise building a buffer in a loop with the concatenation operator will actually tend to O(n2) performance while table.concat() has been designed to do substantially better.
There is an unanswered question as to whether it is more efficient to push the digits on a stack in the table t with calls to table.insert(t,1,digit), or to append them to the end with t[#t+1]=digit, followed by a call to string.reverse() to put the digits in the right order. I'll leave the benchmarking to the student. Note that although the code I pasted here does run and appears to get correct answers, there may other opportunities to tune it further.
For example, the common case of base 10 is culled off and handled with the built in tostring() function. But similar culls can be done for bases 8 and 16 which have conversion specifiers for string.format() ("%o" and "%x", respectively).
Also, neither Nikolaus's solution nor mine handle non-integers particularly well. I emphasize that here by forcing the value n to an integer with math.floor() at the beginning.
Correctly converting a general floating point value to any base (even base 10) is fraught with subtleties, which I leave as an exercise to the reader.
you can use a loop to convert an integer into a string containting the required base. for bases below 10 use the following code, if you need a base larger than that you need to add a line that mapps the result of x % base to a character (usign an array for example)
x = 1234
r = ""
base = 8
while x > 0 do
r = "" .. (x % base ) .. r
x = math.floor(x / base)
end
print( r );
I'm puzzling over how to map a set of sequences to consecutive integers.
All the sequences follow this rule:
A_0 = 1
A_n >= 1
A_n <= max(A_0 .. A_n-1) + 1
I'm looking for a solution that will be able to, given such a sequence, compute a integer for doing a lookup into a table and given an index into the table, generate the sequence.
Example: for length 3, there are 5 the valid sequences. A fast function for doing the following map (preferably in both direction) would be a good solution
1,1,1 0
1,1,2 1
1,2,1 2
1,2,2 3
1,2,3 4
The point of the exercise is to get a packed table with a 1-1 mapping between valid sequences and cells.
The size of the set in bounded only by the number of unique sequences possible.
I don't know now what the length of the sequence will be but it will be a small, <12, constant known in advance.
I'll get to this sooner or later, but though I'd throw it out for the community to have "fun" with in the meantime.
these are different valid sequences
1,1,2,3,2,1,4
1,1,2,3,1,2,4
1,2,3,4,5,6,7
1,1,1,1,2,3,2
these are not
1,2,2,4
2,
1,1,2,3,5
Related to this
There is a natural sequence indexing, but no so easy to calculate.
Let look for A_n for n>0, since A_0 = 1.
Indexing is done in 2 steps.
Part 1:
Group sequences by places where A_n = max(A_0 .. A_n-1) + 1. Call these places steps.
On steps are consecutive numbers (2,3,4,5,...).
On non-step places we can put numbers from 1 to number of steps with index less than k.
Each group can be represent as binary string where 1 is step and 0 non-step. E.g. 001001010 means group with 112aa3b4c, a<=2, b<=3, c<=4. Because, groups are indexed with binary number there is natural indexing of groups. From 0 to 2^length - 1. Lets call value of group binary representation group order.
Part 2:
Index sequences inside a group. Since groups define step positions, only numbers on non-step positions are variable, and they are variable in defined ranges. With that it is easy to index sequence of given group inside that group, with lexicographical order of variable places.
It is easy to calculate number of sequences in one group. It is number of form 1^i_1 * 2^i_2 * 3^i_3 * ....
Combining:
This gives a 2 part key: <Steps, Group> this then needs to be mapped to the integers. To do that we have to find how many sequences are in groups that have order less than some value. For that, lets first find how many sequences are in groups of given length. That can be computed passing through all groups and summing number of sequences or similar with recurrence. Let T(l, n) be number of sequences of length l (A_0 is omitted ) where maximal value of first element can be n+1. Than holds:
T(l,n) = n*T(l-1,n) + T(l-1,n+1)
T(1,n) = n
Because l + n <= sequence length + 1 there are ~sequence_length^2/2 T(l,n) values, which can be easily calculated.
Next is to calculate number of sequences in groups of order less or equal than given value. That can be done with summing of T(l,n) values. E.g. number of sequences in groups with order <= 1001010 binary, is equal to
T(7,1) + # for 1000000
2^2 * T(4,2) + # for 001000
2^2 * 3 * T(2,3) # for 010
Optimizations:
This will give a mapping but the direct implementation for combining the key parts is >O(1) at best. On the other hand, the Steps portion of the key is small and by computing the range of Groups for each Steps value, a lookup table can reduce this to O(1).
I'm not 100% sure about upper formula, but it should be something like it.
With these remarks and recurrence it is possible to make functions sequence -> index and index -> sequence. But not so trivial :-)
I think hash with out sorting should be the thing.
As A0 always start with 0, may be I think we can think of the sequence as an number with base 12 and use its base 10 as the key for look up. ( Still not sure about this).
This is a python function which can do the job for you assuming you got these values stored in a file and you pass the lines to the function
def valid_lines(lines):
for line in lines:
line = line.split(",")
if line[0] == 1 and line[-1] and line[-1] <= max(line)+1:
yield line
lines = (line for line in open('/tmp/numbers.txt'))
for valid_line in valid_lines(lines):
print valid_line
Given the sequence, I would sort it, then use the hash of the sorted sequence as the index of the table.