Re: [eigen] Being able to use Eigen::Map<Eigen::Matrix< ... > > everywhere you can use a Eigen::Matrix

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On Mon, 18 Feb 2013, Malcolm Reynolds wrote:

First post to this list, apologise if this is something really obvious but
I'm having a bit of trouble using Eigen::Map. As I understand, it allows me
to declare an Eigen matrix which reuses some memory already allocated (in my
cases I'm gettin the data pointer from Numpy matrices), and then use this
value everywhere I can use a regular Eigen matrix?

All Eigen functions are written such that a Map can be used in the same way as a regular Eigen matrix. If you write your own functions, you may need to do something extra if you want your own functions to accept a Map instead of a regular matrix. This is explained at

I have a function which I'd already written with the signature

predict(const MatrixXd & features, MatrixXd * const labels_out)

following the good practice (afaik) of using const refs for parameters that
don't need to be modified, and pointers otherwise. Anyway I'd like to pass
in Eigen::Maps for both arguments of this function, as I'm writing a python
interface for my library. [...]

You are calling this function like:

Map<MatrixXd> mapFeatures = ...;
Map<MatrixXd> mapLabels = ...;
predict(mapFeatures, &mapLabels);

For the first argument, the compiler converts the Map<MatrixXd> into a MatrixXd by introducing a temporary object, so it is as if you had written:

MatrixXd tempObject(mapFeatures);
predict(tempObject, &mapLabels);

This does what you want, but it induces a performance penalty because when tempObject is constructed, all the data from mapFeatures is copied into the matrix tempObject.

However, this does not work for the second argument. If the compiler were to introduce another temporary object and pass that as the second argument, then predict() would change the data in the temporary object and not in mapLabels.

The solution is to have predict() accept arguments of the template type MatrixBase, as explained in the link I included above. If you still have questions after reading that, do not hesitate to ask. Any suggestions on how we can improve the explanations would be very welcome.

Good luck,

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