Given there are billions cookies, UUID like strings, what is the best way to test collision rate of say 32 bit hash function like murmur3 on this sample?
First of all it is hard to generate billions of unique strings as it is impossible to keep it in memory and there is no 100% precise random string generator.
Only way I can think of is :
generating them and using approx. datastructures like bloomfilter or cuckoo filter to discard possible duplicates. Then we would have say exactly 5B of unique UUIDs stored in a file.
iterate through them, hash them and repeat step 1) with the hash codes while counting how many collisions are there.
Is there any better way of doing that? This still has a drawback in that there is a certain false positive rate while testing the hash codes in 2). The hash codes would have to be written to file too, being manually checked in case of possible false positive hit.
the murmur_32 collision rate is extremely high in these magnitudes ...
Only 100M unique uuids has 1.145577 % collision rate precisely ...
Scala snippet
Choose a word at random from the English dictionary, submit to Google, then use the urls that come back as "random" data to test your hash function on.
Related
I am working on a data set of more than 22,000 records, and when I tried it with the apriori model, it's taking way too much time even for small number of records like 20. Is there a problem in my code or Is there a faster way to convert the asscocians into a list quickly? The code I used is below.
for i in range(0, 20):
transactions.append([str(dataset.values[i,j]) for j in range(0, 543)])
from apyori import apriori
associations = apriori(transactions, min_support=0.004, min_confidence=0.3, min_lift=3, min_length=2)
result = list(associations)
It's difficult to assess without your data, but the complexity of Apriori is based on a number of factors, including your support threshold, number of transactions, number of items, average/max transaction length, etc.
In cases where even a small number of transactions is taking a long time to run it's often a matter of too low of a minimum support. When support is very low (near 0) the algorithm is effectively still brute forcing, since it has to look at all possible combinations of items, of every length. This is the equivalent of a mathematical power set, which is exponential. For just 41 items you're actually trying 2^41 -1 possible combinations, which is just over 1.1 TRILLION possibilities.
I recommend starting with a "high" min_support at first (e.g. 0.20) and then working your way down slowly. It's easier to test things that take seconds at first than ones that'll take a long time.
Other important note: There is no min_length parameter in Apyori. I'm not sure where everyone's getting that from (you're not alone in thinking there is one), unless it's this one random blog post I found. The parameters are as follows (straight from the code):
Keyword arguments:
min_support -- The minimum support of relations (float).
min_confidence -- The minimum confidence of relations (float).
min_lift -- The minimum lift of relations (float).
max_length -- The maximum length of the relation (integer).
For what it's worth, I wrote unofficial docs for Apyori that can be found here.
So in Lua it's common knowledge that you can use math.randomseed but it's also obvious that math.random sets the seed as well (calling it twice does not return the same result), what does it set it to, and how can I keep track of it, and if it's impossible, please explain why that is so.
This is not a Lua question, but general question on how some RNG algorithm works.
First, Lua don't have their own RNG - they just output you (slightly mangled) value from RNG of underlying C library. Most RNG implementations do not reveal you their inner state, but sometimes you can caclulate it yourself.
For example when you use Lua on Windows, you'll be using LCG-based RNG from MS C library. The numbers you get is a slice of seed, not full value. There are two ways you can deal with that:
If you know how many times you called random, you can just take initial seed value, feed it to your copy of the same algorithm with same constants that are hardcoded in MS library and get exact value of seed.
If you don't, but you can be sure that nobody interferes in between your two calls to random, you can get two generated numbers, and reverse LCG algorithm by shifting bits back to their place. This will leave you with several missing bits (with one more bit thanks to Lua mangling) that you will need to simply bruteforce - just reiterate over all missing bits until your copy of algorithm produces exactly same two "random" numbers you've recorded before. That will be current seed stored inside library's RNG as well. Well programmed solution in Lua can bruteforce this in about 0.2-0.5s on somewhat dated PC - I did it past. Here's example on Crypto.SE talking about this task in more details: Predicting values from a Linear Congruential Generator.
First approach can be used with any other RNG algorithm that doesn't use any real entropy, second with most RNGs that don't mask too much bits in slice to make bruteforcing unreasonable.
Real answer though is: you don't need to keep track of seed at all. What you want is probably something else.
If you set a seed all numbers math.random() generates are pseudo-random (This is always the case as the system will generate a seed by itself).
math.randomseed(4)
print(math.random())
print(math.random())
math.randomseed(4)
print(math.random())
Outputs
0.50827539156303
0.75454387490399
0.50827539156303
So if you reset the seed to the same value you can predict all values that are going to come up to the maximum number of consecutive values that you already generated using that seed.
What the seed does not do is keep the output of math.random() the same. It would be the same if you kept resetting it to the same value.
An analogy as an example
Imagine the random number is an integer between 0 and 9 (instead of a double between 0 and 1).
math.random() could traverse pi's decimals from an arbitrary starting position (default could be system time).
What you do when you use set.seed() is (not literally, this is an analogy as mentioned) set the starting decimals of where in pi you are going to retrieve your numbers.
If you now reset the seed to the same starting position the numbers are going to be the same as the last time you reset the starting position.
You will know the numbers of to the last call, after that you can't be certain anymore.
I have a very small question which has been baffling me for a while. I have a dataset with interesting features, but some of them are dimensionless quantities (I've tried using z-scores) on them but they've made things worse. These are:
Timestamps (Like YYYYMMDDHHMMSSMis) I am getting the last 9 chars from this.
User IDs (Like in a Hash form) How do I extract meaning from them?
IP Addresses (You know what those are). I only extract the first 3 chars.
City (Has an ID like 1,15,72) How do I extract meaning from this?
Region (Same as city) Should I extract meaning from this or just leave it?
The rest of the things are prices, widths and heights which understand. Any help or insight would be much appreciated. Thank you.
Timestamps can be transformed into Unix Timestamps, which are reasonable natural numbers
User IF/Cities/Regions are nominal values, which has to be encoded somehow. The most common approach is to create as much "dummy" dimensions as the number of possible values. So if you have 100 ciries, than you create 100 dimensions and give "1" only on the one representing a particular city (and 0 on the others)
IPs should rather be removed, or transformed into some small group of them (based on the DNS-network identification and nominal to dummy transformation as above)
My platform here is Ruby - a webapp using Rails 3.2 in particular.
I'm trying to match objects (people) based on their ratings for certain items. People may rate all, some, or none of the same items as other people. Ratings are integers between 0 and 5. The number of items available to rate, and the number of users, can both be considered to be non-trivial.
A quick illustration -
The brute-force approach is to iterate through all people, calculating differences for each item. In Ruby-flavoured pseudo-code -
MATCHES = {}
for each (PERSON in (people except USER)) do
for each (RATING that PERSON has made) do
if (USER has rated the item that RATING refers to) do
MATCHES[PERSON's id] += difference between PERSON's rating and USER's rating
end
end
end
lowest values in MATCHES are the best matches for USER
The problem here being that as the number of items, ratings, and people increase, this code will take a very significant time to run, and ignoring caching for now, this is code that has to run a lot, since this matching is the primary function of my app.
I'm open to cleverer algorithms and cleverer databases to achieve this, but doing it algorithmically and as such allowing me to keep everything in MySQL or PostgreSQL would make my life a lot easier. The only thing I'd say is that the data does need to persist.
If any more detail would help, please feel free to ask. Any assistance greatly appreciated!
Check out the KD-Tree. It's specifically designed to speed up neighbour-finding in N-Dimensional spaces, like your rating system (Person 1 is 3 units along the X axis, 4 units along the Y axis, and so on).
You'll likely have to do this in an actual programming language. There are spatial indexes for some DBs, but they're usually designed for geographic work, like PostGIS (which uses GiST indexing), and only support two or three dimensions.
That said, I did find this tantalizing blog post on PostGIS. I was then unable to find any other references to this, but maybe your luck will be better than mine...
Hope that helps!
Technically your task is matching long strings made out of characters of a 5 letter alphabet. This kind of stuff is researched extensively in the area of computational biology. (Typically with 4 letter alphabets). If you do not know the book http://www.amazon.com/Algorithms-Strings-Trees-Sequences-Computational/dp/0521585198 then you might want to get hold of a copy. IMHO this is THE standard book on fuzzy matching / scoring of sequences.
Is your data sparse? With rating, most of the time not every user rates every object.
Naively comparing each object to every other is O(n*n*d), where d is the number of operations. However, a key trick of all the Hadoop solutions is to transpose the matrix, and work only on the non-zero values in the columns. Assuming that your sparsity is s=0.01, this reduces the runtime to O(d*n*s*n*s), i.e. by a factor of s*s. So if your sparsity is 1 out of 100, your computation will be theoretically 10000 times faster.
Note that the resulting data will still be a O(n*n) distance matrix, so strictl speaking the problem is still quadratic.
The way to beat the quadratic factor is to use index structures. The k-d-tree has already been mentioned, but I'm not aware of a version for categorical / discrete data and missing values. Indexing such data is not very well researched AFAICT.
I have an application in which I have to store a couple of millions of integers, I have to store them in a Look up table, obviously I cannot store such amount of data in memory and in my requirements I am very limited I have to store the data in an embebedded system so I am very limited in the space, so I would like to ask you about recommended methods that I can use for the reduction of the look up table. I cannot use function approximation such as neural networks, the values needs to be in a table. The range of the integers is not known at the moment. When I say integers I mean a 32 bit value.
Basically the idea is use some copmpression method to reduce the amount of memory but without losing many precision. This thing needs to run in hardware so the computation overhead cannot be very high.
In my algorithm I have to access to one value of the table do some operations with it and after update the value. In the end what I should have is a function which I pass an index to it and then I get a value, and after I have to use another function to write a value in the table.
I found one called tile coding , this one is based on several look up tables, does anyone know any other method?.
Thanks.
I'd look at the types of numbers you need to store and pull out the information that's common for many of them. For example, if they're tightly clustered, you can take the mean, store it, and store the offsets. The offsets will have fewer bits than the original numbers. Or, if they're more or less uniformly distributed, you can store the first number and then store the offset to the next number.
It would help to know what your key is to look up the numbers.
I need more detail on the problem. If you cannot store the real value of the integers but instead an approximation, that means you are going to reduce (throw away) some of the data (detail), correct? I think you are looking for a hash, which can be an artform in itself. For example say you have 32 bit values, one hash would be to take the 4 bytes and xor them together, this would result in a single 8 bit value, reducing your storage by a factor of 4 but also reducing the real value of original data. Typically you could/would go further and perhaps and only use a few of those 8 bits , say the lower 4 and reduce the value further.
I think my real problem is either you need the data or you dont, if you need the data you need to compress it or find more memory to store it. If you dont, then use a hash of some sort to reduce the number of bits until you reach the amount of memory you have for storage.
Read http://www.cs.ualberta.ca/~sutton/RL-FAQ.html
"Function approximation" refers to the
use of a parameterized functional form
to represent the value function
(and/or the policy), as opposed to a
simple table."
Perhaps that applies. Also, update your question with additional facts -- don't merely answer in the comments.
Edit.
A bit array can easily store a bit for each of your millions of numbers. Let's say you have numbers in the range of 1 to 8 million. In a single megabyte of storage you can have a 1 bit for each number in your set and a 0 for each number not in your set.
If you have numbers in the range of 1 to 32 million, you'll require 4Mb of memory for a big table of all 32M distinct numbers.
See my answer to Modern, high performance bloom filter in Python? for a Python implementation of a bit array of unlimited size.
If you are merely looking for the presence of the number in question a bloom filter, might be what you are looking for. Honestly though your question is fairly vague and confusing. It would help to explain what Q values are, and what you do with them once you find them in the table.
If your set of integers is homongenous, then you could try a hash table, because there is a trick you can use to cut the size of the stored integers, in your case, in half.
Assume the integer, n, because its set is homogenous can be the hash. Assume you have 0x10000 (16k) buckets. Each bucket index, iBucket = n&FFFF. Each item in a bucket need only store 16 bits, since the first 16 bits are the bucket index. The other thing you have to do to keep the data small is to put the count of items in the bucket, and use an array to hold the items in the bucket. Using a linked list will be too large and slow. When you iterate the array looking for a match, remember you only need to compare the 16 bits that are stored.
So assuming a bucket is a pointer to the array and a count. On a 32 bit system, this is 64 bits max. If the number of ints was small enough we might be able to do some fancy things and use 32 bits for a bucket. 16k * 8 bytes = 524k, 2 million shorts = 4mb. So this gets you a method to lookup the ints and about 40% compression.