I'm looking to do some calculations and pass the resultant Jacobian NxN matrix and a right hand side vector(n) to boost's ublas and eventually ViennaCL.
The vector was no issue using copy(), however, the matrix is proving to be difficult. Any help would be greatly appreciated
// Global Variables
vector< vector<float> > Jacobian(0, vector<float>(0)); //Jacobian matrix
vector<float> delta_PQ; //rhs
//
// Set up some ublas objects
//
ublas::vector<ScalarType> rhs;
ublas::vector<ScalarType> result;
ublas::compressed_matrix<ScalarType> ublas_matrix;
using namespace boost::numeric;
typedef float ScalarType;
// Resize RHS from main program
resize_vector(rhs2, j_dimension);
ublas_matrix2.resize(j_dimension, j_dimension);
//copy content to GPU vector (recommended initialization)
copy(delta_PQ.begin(), delta_PQ.end(), rhs.begin()); //works
copy(Jacobian.begin(), Jacobian.end(), ublas_matrix); //won't compile
I have tried numerous variations and looked at the documentation:
http://ublas.sourceforge.net/refdoc/classboost_1_1numeric_1_1ublas_1_1compressed__matrix.html
Also, ViennaCL's example does not work for me:
http://viennacl.sourceforge.net/viennacl-examples-sparse-matrix.html
After a few hours of googling I decided to post on here in hopes someone else can crack it and it will be easier to find for the next person.
To close the loop on this I wanted to let everyone know what I did to solve my problem. Special thanks to Karl Rupp over at the ViennaCL project.
As an alternative, fill the ublas-matrix directly via operator(), i.e.
ublas_matrix(1,1) = value1;
ublas_matrix(7,8) = value2;
etc. Depending on the order of the values, filling ublas_matrix directly may be slower or faster than copy. As a rule of thumb, vector< map > is faster whenever the entries are written in a 'random' fashion, whereas ublas_matrix is faster if you fill row and column entries in consecutive order (and eventually supply the number of nonzero entries to the matrix constructor upfront).
Related
I thought that a cool way of using vectors could be to have one vector class template hold an two separate int variables for x/y-coordinates to graph.
example:
std::vector<int, int> *name*;
// First int. being the x-intercept on a graph
// Second int. being the y-intercept on a graph
(I also understand that I could just make every even/odd location or two separate vectors to classify each x/y-coordinate, but for me I would just like to see if this could work)
However, after making this vector type, I came across an issue with assigning which int within the vector will be written to or extracted from. Could anyone tell me how to best select and std::cout both x/y ints appropriately?
P.S. - My main goal, in using vectors this way, is to make a very basic graph output to Visual Studio terminal. While being able to change individual x/y-intercepts by 'selecting' and changing if needed. These coordinates will be outputted to the terminal via for/while loops.
Also, would anyone like to list out different ways to best make x/y-coordinates with different containers?
Your question rather broad, in other words it is asking for a bit too much. I will just try to give you some pointers from which you can work your way to what you like.
A) equidistant x
If your x values are equidistant, ie 0, 0.5, 1, 1.5 then there is no need to store them, simply use a
std::vector<int> y;
if the number of variables is not known at compile time, otherwise a
std::array<int,N> y;
B) arbitrary x
There are several options that depend on what you actually want to do. For simply storing (x,y)-pairs and printing them on the screen, they all work equally well.
map
std::map<int,int> map_x_to_y = { { 1,1}, {2,4}, {3,9}};
// print on screen
for (const auto& xy : map_x_to_y) {
std::cout << xy.first << ":" xy.second;
}
a vector of pairs
std::vector<std::pair<int,int>> vector_x_and_y = { { 1,1}, {2,4}, {3,9}};
Printing on screen is actually the same as with map. The advantage of the map is that it has its elements ordered, while this is not the case for the vector.
C) not using any container
For leightweight calculations you can consider to not store the (xy) pairs at all, but simply use a function:
int fun(int x) { return x*x; }
TL;DR / more focussed
A vector stores one type. You cannot have a std::vector<int,int>. If you look at the documentation of std::vector you will find that the second template parameter is an allocator (something you probably dont have to care about for some time). If you want to store two values as one element in a vector you either have to use std::vector<std::pair<double,double>> or a different container.
PS
I used std::pair in the examples above. However, I do consider it as good practice to name things whenever I can and leave std::pair for cases when I simply cannot give names better than first and second. In this spirit you can replace std::pair in the above examples with a
struct data_point {
int x;
int y;
};
I have a function which requires me to pass a fairly large matrix (which I created using Eigen) - and ranges from dimensions 200x200 -> 1000x1000. The function is more complex than this, but the bare bones of it are:
#include <Eigen/Dense>
int main()
{
MatrixXi mIndices = MatrixXi::Zero(1000,1000);
MatrixXi* pMatrix = &mIndices;
MatrixXi mTest;
for(int i = 0; i < 10000; i++)
{
mTest = pMatrix[0];
// Then do stuff to the copy
}
}
Is the reason that it takes much longer to run with a larger size of matrix because it takes longer to find the available space in RAM for the array when I set it equal to mTest? When I switch to a sparse array, this seems to be quite a lot quicker.
If I need to pass around large matrices, and I want to minimise the incremental effect of matrix size on runtime, then what is best practice here? At the moment, the same program is running slower in c++ than it is in Matlab, and obviously I would like to speed it up!
Best,
Ben
In the code you show, you are copying a 1,000,000 element 10,000 times. The assignment in the loop creates a copy.
Generally if you're passing an Eigen matrix to another function, it can be beneficial to accept the argument by reference.
It's not really clear from your code what you're trying to achieve however.
I noticed something strange about my code, and the amount of time it was taking to declare and initialize a 2D matrix of mine.
First way:
vector< vector<double> > gblStiff(dOF, vector<double>(dOF, 0));
dOF was some determined value earlier in the code. This first way took approximately 3 seconds when dOF = 30000!!
Second way:
double** gblStiff = new double*[dOF];
for (i=0; i < dOF; i++)
gblStiff[i] = new double[dOF];
This second way takes 0.063 seconds for the same dOF!!
Anyone able to shed light onto why this is happening? I am extremely curious.
This is really a C++ question. First, you probably didn't compile with optimisation. The vector template really needs a little compiler help to be usable. Second, vector's constructor initialises all of the elements; in your case, it initialises dOF*dOF doubles to zero. new double[dOF] does no such thing; that memory can contain anything at all.
all
I am using multidimensional STL vector to store my data in C++. What I have is a 3D vector
vector<vector<vector<double>>> vec;
What I want to retrieve from it is :
&vec[][1][]; // I need a pointer that points to a 2D matrix located at column 1 in vec
Anyone has any idea to do so? I would be extremly appreciate any help!
Regards
Long
It is best to consider vec just as a vector whose elements happen to be
vectors-of-vectors-of-double, rather than as a multi-dimensional structure.
You probably know, but just in case you don't I'll mention it,
that this datatype does not necessarily represent a rectangular cuboid.
vec will only have that "shape" if you ensure that all the vectors are
the same size at each level. The datatype is quite happy for the vector vec[j]
to be a different size from the one at vec[k] and likewise for vec[j][n]
to be a vector of different size from vec[j][m], so that your structure is "jagged".
So you want to get a pointer to the vector<vector<double>> that is at
index 1 in vec. You can do that by:
vector<vector<double>> * pmatrix = &vec[1];
However this pointer will be an unnecessarily awkward means of accessing that
vector<vector<double>>. You certainly won't be able to write the
like of:
double d = pmatrix[j][k];
and expect to get a double at coordinates (j,k) in the "matrix addressed
by a pmatrix". Because pmatrix is a pointer-to-a-vector-of-vector-of-double;
so what pmatrix[j] refers to is the vector-of-vector-of-double (not vector-of-double)
at index j from pmatrix, where the index goes in steps of
sizeof(vector<vector<double>>). The statement will reference who-knows-what
memory and very likely crash your program.
Instead, you must write the like of:
double d = (*pmatrix)[j][k];
where (*pmatrix) gives you the vector-of-vector-of-double addressed by pmatrix,
or equivalently but more confusingly:
double d = pmatrix[0][j][k];
Much simpler - and therefore, the natural C++ way - is to take a reference,
rather than pointer, to the vector<vector<double>> at index 1 in vec. You
do that simply by:
vector<vector<double>> & matrix = vec[1];
Now matrix is simply another name for the vector<vector<double>> at index 1 in vec,
and you can handle it matrix-wise just as you'd expect (always assuming
you have made sure it is a matrix, and not a jagged array).
Another thing to consider was raised in a comment by manu343726. Do you
want the code that receives this reference to vec[1] to be able to
use it to modify the contents of vec[1] - which would include changing its
size or the size of any of the vector<double>s within it?
If you allow modification, that's fine. If you don't then you want to get
a const reference. You can do that by:
vector<vector<double> > const & matrix = vec[1];
Possibly, you want the receiving code to be able to modify the doubles
but not the sizes of the vectors that contain them? In that case, std::vector
is the wrong container type for your application. If that's your position I
can update this answer to offer alternative containers.
Consider using matrix from some linear algebra library. There are some directions here
I wanted to use boost accumulators to calculate statistics of a variable that is a vector. Is there a simple way to do this. I think it's not possible to use the dumbest thing:
using namespace boost::accumulators;
//stuff...
accumulator_set<vector<double>, stats<tag::mean> > acc;
vector<double> some_vetor;
//stuff
some_vector = doStuff();
acc(some_vector);
maybe this is obvious, but I tried anyway. :P
What I wanted was to have an accumulator that would calculate a vector which is the mean of the components of many vectors. Is there an easy way out?
EDIT:
I don't know if I was thoroughly clear. I don't want this:
for_each(vec.begin(), vec.end(),acc);
This would calculate the mean of the entries of a given vector. What I need is different. I have a function that will spit vectors:
vector<double> doSomething();
// this is a monte carlo simulation;
And I need to run this many times and calculate the vectorial mean of those vectors:
for(int i = 0; i < numberOfMCSteps; i++){
vec = doSomething();
acc(vec);
}
cout << mean(acc);
And I want mean(acc) to be a vector itself, whose entry [i] would be the means of the entries [i] of the accumulated vectors.
Theres a hint about this in the docs of Boost, but nothing explicit. And I'm a bit dumb. :P
I've looked into your question a bit, and it seems to me that Boost.Accumulators already provides support for std::vector. Here is what I could find in a section of the user's guide :
Another example where the Numeric
Operators Sub-Library is useful is
when a type does not define the
operator overloads required to use it
for some statistical calculations.
For instance, std::vector<> does not overload any arithmetic operators, yet
it may be useful to use std::vector<>
as a sample or variate type. The
Numeric Operators Sub-Library defines
the necessary operator overloads in
the boost::numeric::operators
namespace, which is brought into scope
by the Accumulators Framework with a
using directive.
Indeed, after verification, the file boost/accumulators/numeric/functional/vector.hpp does contain the necessary operators for the 'naive' solution to work.
I believe you should try :
Including either
boost/accumulators/numeric/functional/vector.hpp before any other accumulators header
boost/accumulators/numeric/functional.hpp while defining BOOST_NUMERIC_FUNCTIONAL_STD_VECTOR_SUPPORT
Bringing the operators into scope with a using namespace boost::numeric::operators;.
There's only one last detail left : execution will break at runtime because the initial accumulated value is default-constructed, and an assertion will occur when trying to add a vector of size n to an empty vector. For this, it seems you should initialize the accumulator with (where n is the number of elements in your vector) :
accumulator_set<std::vector<double>, stats<tag::mean> > acc(std::vector<double>(n));
I tried the following code, mean gives me a std::vector of size 2 :
int main()
{
accumulator_set<std::vector<double>, stats<tag::mean> > acc(std::vector<double>(2));
const std::vector<double> v1 = boost::assign::list_of(1.)(2.);
const std::vector<double> v2 = boost::assign::list_of(2.)(3.);
const std::vector<double> v3 = boost::assign::list_of(3.)(4.);
acc(v1);
acc(v2);
acc(v3);
const std::vector<double> &meanVector = mean(acc);
}
I believe this is what you wanted ?
I don't have it set up to try right now, but if all boost::accumulators need is properly defined mathematical operators, then you might be able to get away with a different vector type: http://www.boost.org/doc/libs/1_37_0/libs/numeric/ublas/doc/vector.htm
And what about the documentation?
// The data for which we wish to calculate statistical properties:
std::vector< double > data( /* stuff */ );
// The accumulator set which will calculate the properties for us:
accumulator_set< double, features< tag::min, tag::mean > > acc;
// Use std::for_each to accumulate the statistical properties:
acc = std::for_each( data.begin(), data.end(), acc );