I have some constraints of the form Sizes[i1] + Sizes[i2] + Sizes[i3]<=1, which I add by
model.add(Sizes[i1] + Sizes[i2] + Sizes[i3]<=1)
for some specific indices i1,i2,i3. Later I want to add for all other index combinations the constraints
model.add(Sizes[k1] + Sizes[k2] + Sizes[k3]>1)
Is there some nice way to do this, e.g. to check if the constraint already exists in the model?
Maybe I can store the handle which is returned by the IloModel::add function (e.g. as an ILOExtracableArray or even IloConstraintArray?) but even then I con't know how to check if the contraint already exists.
Thank you
I don't think that there is really an easy way to get this back from a cplex model. I have previously had to do something similar in several projects, so I give my two suggestions below.
(1) If you know you always have the same number of things in each constraint, then you can create a structure to hold that information, like:
class tuple{
public int index1;
public int index2;
public int index3;
}
and then you can just create one for each constraint you add, and keep them in a list or array or similar.
(2) If you know about the possible values of the indices, then maybe you can create a hash-code or similar from the indices. If done right, this can also solve the issue of symmetry due to permuting the indices - (Sizes[a] + Sizes[b] + Sizes[c]) is the same as (Sizes[b] + Sizes[a] + Sizes[c]).
Then as above, you can keep the hash codes in a list or array for the constraints that you added.
Related
I generating a bunch of hash values for the same key using MurmerHash like the following. It outputs 50 different hash values for 50 different seeds.
for(int seed=0; seed<50; seed++)
MurmurHash3_x86_32(key, strlen(key), seed, &hash);
But it is not so time efficient when I have a large number of keys i.e. 10 million keys. Is there any other way to make it faster?
It's been a while, but saw this and figured I would contribute an answer for anyone else who needs it.
NOTE -- I can't remember where I read this, or who to attribute the formula to. If someone knows, please let me know so I can properly attribute this, and so that others can verify the correctness of this solution.
You can generate n number of independent hashes by combining 2 independent hashes.
for (i = 0; i < n; i++) {
(hash1 + i * hash2) % max_hash_size;
}
Addition, multiplication, and modulation should be faster than generating an entirely new hash.
I use MurmurHash with two different seeds, and then combine them to generate the extra hash values that I need.
I wish I could remember where I read this....
I have a model that has one attribute with a list of floats:
values = ArrayField(models.FloatField(default=0), default=list, size=64, verbose_name=_('Values'))
Currently, I'm getting my entries and order them according to the sum of all diffs with another list:
def diff(l1, l2):
return sum([abs(v1-v2) for v1, v2 in zip(l1, l2)])
list2 = [0.3, 0, 1, 0.5]
entries = Model.objects.all()
entries.sort(key=lambda t: diff(t.values, list2))
This works fast if my numer of entries is very slow small. But I'm afraid with a large number of entries, the comparison and sorting of all the entries will get slow since they have to be loaded from the database. Is there a way to make this more efficient?
best way is to write it yourself, right now you are iterating over a list over 4 times!
although this approach looks pretty but it's not good.
one thing that you can do is:
have a variable called last_diff and set it to 0
iterate through all entries.
iterate though each entry.values
from i = 0 to the end of list, calculate abs(entry.values[i]-list2[i])
sum over these values in a variable called new_diff
if new_diff > last_diff break from inner loop and push the entry into its right place (it's called Insertion Sort, check it out!)
in this way, in average scenario, time complexity is much lower than what you are doing now!
and maybe you must be creative too. I'm gonna share some ideas, check them for yourself to make sure that they are fine.
assuming that:
values list elements are always positive floats.
list2 is always the same for all entries.
then you may be able to say, the bigger the sum over the elements in values, the bigger the diff value is gonna be, no matter what are the elements in list2.
then you might be able to just forget about whole diff function. (test this!)
The only way to makes this really go faster, is to move as much work as possible to the database, i.e. the calculations and the sorting. It wasn't easy, but with the help of this answer I managed to actually write a query for that in almost pure Django:
class Unnest(models.Func):
function = 'UNNEST'
class Abs(models.Func):
function = 'ABS'
class SubquerySum(models.Subquery):
template = '(SELECT sum(%(field)s) FROM (%(subquery)s) _sum)'
x = [0.3, 0, 1, 0.5]
pairdiffs = Model.objects.filter(pk=models.OuterRef('pk')).annotate(
pairdiff=Abs(Unnest('values')-Unnest(models.Value(x, ArrayField(models.FloatField())))),
).values('pairdiff')
entries = Model.objects.all().annotate(
diff=SubquerySum(pairdiffs, field='pairdiff')
).order_by('diff')
The unnest function turns each element of the values into a row. In this case it happens twice, but the two resulting columns are instantly subtracted and made positive. Still, there are as many rows per pk as there are values. These need to be summed, but that's not as easy as it sounds. The column can't be simply be aggregated. This was by far the most tricky part—even after fiddling with it for so long, I still don't quite understand why Postgres needs this indirection. Of the few options there are to make it work, I believe a subquery is the single one expressible in Django (and only as of 1.11).
Note that the above behaves exactly the same as with zip, i.e. the when one array is longer than the other, the remainder is ignored.
Further improvements
While it will be a lot faster already when you don't have to retrieve all rows anymore and loop over them in Python, it doesn't change yet that it results in a full table scan. All rows will have to be processed, every single time. You can do better, though. Have a look into the cube extension. Use it to calculate the L1 distance—at least, that seems what you're calculating—directly with the <#> operator. That will require the use of RawSQL or a custom Expression. Then add a GiST index on the SQL expression cube("values"), or directly on the field if you're able to change the type from float[] to cube. In case of the latter, you might have to implement your own CubeField too—I haven't found any package yet that provides it. In any case, with all that in place, top-N queries on the lowest distance will be fully indexed hence blazing fast.
I have an array and hash which I need to combine. Please let me know the simplest way to do this.
array1 = [:user_id, :project_id, :task_id]
entry_hash = {"User"=>1, "Project"=>[8], "Task"=>[87]}
When it is combined i want a hash like
output = {"user_id"=>1, "project_id"=>8, "task_id"=>87}
Thanks for the help!
It's a bit unclear to me what you want to achieve here. Looking at your example, the easiest solution would be to change the keys of entry_hash using .downcase and adding _id. For the value, you could check if it's an array and if so, use the first value.
output = {}
entry_hash.each do |key, value|
output[key.downcase + '_id'] = value.kind_of?(Array) ? value[0] : value
end
This assumes of course that the keys in the hash are the nouns for the column names in the array. The code above will not work if the names are more complex (e.g. CamelCaseName or snake_case_id). Rails comes with ActiveSupport that can help you there, but this is a totally different question: Converting camel case to underscore case in ruby
If array and hash don't share the same names there is no easy way to do this automatically. Hash doesn't guarantee the order of its elements, so iterating through both and mapping values like in the above snippet won't work reliably.
there is a table which grows as
1,1
1,1,2
1,1,3,3
1,1,4,4,6
1,1,5,5,10,10
1,1,6,6,15,15,20
.....and so on
If i want to find an specific element of the table like if I want to find 4th element of 6th row then the answer will be 6 but if I want to find the nth element of mth row for any n>=1 m>=1 then how to do it?
These numbers look like binomial coefficients, so this "table" could be Pascal's triangle row-wise re-ordered by size.
Though, this is just one of the infinitely many "tables" that'd start like this. If you don't name a specific production rule or another way to deduce arbitrary values of the "table", there's no way telling for sure which of those infinitely many "tables" you have here.
I assume you want to hold the values in a kind of table without wasting memory by for example giving each line more slots than necessary.
To do that I'd suggest a vector of vectors (assuming your values are integers):
std::vector< std::vector<int> > table;
Provided you are sure that a value at (m, n) exists you can get it with:
int value = table[m][n];
(Note that m and n count from 0.)
If you're not sure use the safer
int value = table.at(m).at(n);
which will throw an exception if (m, n) doesn't exist.
To add a row you could call
table.resize(table.size() + 1);
and to add a column to a row
table[m].resize(table[m].size() + 1);
I'd recommend to put the table into the protected or private section of a special class and add functions to access the elements as needed.
Here is a recursive function that I'm trying to create that finds all the subsets passed in an STL set. the two params are an STL set to search for subjects, and a number i >= 0 which specifies how big the subsets should be. If the integer is bigger then the set, return empty subset
I don't think I'm doing this correctly. Sometimes it's right, sometimes its not. The stl set gets passed in fine.
list<set<int> > findSub(set<int>& inset, int i)
{
list<set<int> > the_list;
list<set<int> >::iterator el = the_list.begin();
if(inset.size()>i)
{
set<int> tmp_set;
for(int j(0); j<=i;j++)
{
set<int>::iterator first = inset.begin();
tmp_set.insert(*(first));
the_list.push_back(tmp_set);
inset.erase(first);
}
the_list.splice(el,findSub(inset,i));
}
return the_list;
}
From what I understand you are actually trying to generate all subsets of 'i' elements from a given set right ?
Modifying the input set is going to get you into trouble, you'd be better off not modifying it.
I think that the idea is simple enough, though I would say that you got it backwards. Since it looks like homework, i won't give you a C++ algorithm ;)
generate_subsets(set, sizeOfSubsets) # I assume sizeOfSubsets cannot be negative
# use a type that enforces this for god's sake!
if sizeOfSubsets is 0 then return {}
else if sizeOfSubsets is 1 then
result = []
for each element in set do result <- result + {element}
return result
else
result = []
baseSubsets = generate_subsets(set, sizeOfSubsets - 1)
for each subset in baseSubssets
for each element in set
if no element in subset then result <- result + { subset + element }
return result
The key points are:
generate the subsets of lower rank first, as you'll have to iterate over them
don't try to insert an element in a subset if it already is, it would give you a subset of incorrect size
Now, you'll have to understand this and transpose it to 'real' code.
I have been staring at this for several minutes and I can't figure out what your train of thought is for thinking that it would work. You are permanently removing several members of the input list before exploring every possible subset that they could participate in.
Try working out the solution you intend in pseudo-code and see if you can see the problem without the stl interfering.
It seems (I'm not native English) that what you could do is to compute power set (set of all subsets) and then select only subsets matching condition from it.
You can find methods how to calculate power set on Wikipedia Power set page and on Math Is Fun (link is in External links section on that Wikipedia page named Power Set from Math Is Fun and I cannot post it here directly because spam prevention mechanism). On math is fun mainly section It's binary.
I also can't see what this is supposed to achieve.
If this isn't homework with specific restrictions i'd simply suggest testing against a temporary std::set with std::includes().