Hi.
[Please avoid top-posting.]
On Sat, 30 Apr 2016 23:56:13 +0300, Artem Barger wrote:
Well, I do not have too much examples in my mind. Actually only one
practical use case, which made me to think that using "double[]" is
not
very practical. I was trying to cluster Wikipedia dataset, which is
very
sparse (a lot of zero entries) and it looked like huge waste of RAM
to
store zeros.
We'll all agree on this, I expect. ;-)
Therefore I started to wonder why not to use RealVector
instead, since it has sparse implementation so I will be able to
leverage
it.
The principle is fine; but I'm wary to use "RealVector" in new code
since it must be refactored...
Right now using kmeans++ clustering algorithm provided by
common.maths
it's not doable to cluster entire wikipedia dataset or any other huge
datasets.
Could you expand on this application? What is the data?
Another possible alternative is to implement SparseClusterable
(inherits
from Clusterable) and the sparse measure which will inherit from
DistanceMeasure and will provide metric computation for such sparse
representation.
IIUC it would require changing "Clusterable" anyways since its method
returns a "double[]".
But it may be OK if the final goal is worth it.
[One of the things to bear in mind while testing new implementations
is to not loose performance on the other classes of problems (or you'll
take heat from some of this list's observers...).]
Best regards,
Gilles
Best regards,
Artem Barger.
On Sat, Apr 30, 2016 at 11:41 PM, Gilles
<gil...@harfang.homelinux.org>
wrote:
On Mon, 25 Apr 2016 15:52:03 +0300, Artem Barger wrote:
Hi All,
I'd like to provide a solution for [MATH-1330] issue. Before
starting I
have a concerns regarding the possible design and the actual
implementation.
Currently all implementations of Clusterer interface expect to
receive
instance of DistanceMeasure class, which used to compute distance
or
metric
between two points. Switching clustering algorithms to work with
Vectors
will make this unnecessary, therefore there will be no need to
provide
DistanceMeasure, since Vector class already provides methods to
compute
vector norms.
I think that reasons for using "double[]" in the
"o.a.c.m.ml.clustering"
package were:
* simple and straightforward (fixed dimension Cartesian
coordinates)
* not couple it with the "o.a.c.m.linear" package whose
"RealVector" is
for variable size sequences of elements (and is also,
inconsistently,
used as a Cartesian vector, and also as column- and
row-matrix[1])
It is arguable adapted for a family of problems which the developer
probably had in mind when taking those design decisions.
It would be interesting to know for which class of problems, the
design
is inappropriate, in order to clarify ideas.
The main drawback of this approach is that we will loose the ability
to
control which metric to use during clustering, however the only
classes
which make an implicit use of this parameters are: Clusterer and
KmeansPlusPlusClusterer all others assumes EucledianDistance by
default.
There is a default indeed, but all "Clusterer" implementations use
whatever "DistanceMeasure" has been passed to the constructor.
Assuming that "RealVector" knows how to compute the distance means
that
users will have to implement their own subclass of "RealVector" and
override "getDistance(RealVector)" if they want another distance.
Alternatively, CM would have to define all these classes.
At first sight, it does not seem the right way to go...
One of the possible approaches is to extend DistanceMeasure
interface to be
able to compute distance between two vectors? After all it's only
sub
first
vector from the second and compute desired norm on the result.
Seems good (at first sight) but (IMHO) only if we implement a new
"CartesianVector" class unencumbered with all the cruft of
"RealVector".
Another possible solution is to make vector to return it's
coordinates,
hence it avail us to use DistanceMeasure as is. Personally I do not
think
this is good approach, since it will make no sense with sparse
vectors.
Ruled out indeed if it conflicts with your intended usage.
Last alternative this comes to my mind is to create a set of enums
to
indicate which vector norm to use to compute distances, also do no
think
this is very good solution, since sounds too intrusive and might
break
backward compatibility.
And forward compatibility (clustering code will have to be adapted
if
another distance is added later).
What do you think? Am I missing something? Is there a better
possible way
to achieve the goal?
As indicated above, a practical example might help visualize the
options.
Regards,
Gilles
[1] Cf. https://issues.apache.org/jira/browse/MATH-765
Best regards,
Artem Barger.
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