Hi Roberto,
>From the docs:
X: array [n_samples, n_samples] or [n_samples, n_features]
Array of distances between samples, or a feature array.
The array is treated as a feature array unless the metric is
given as 'precomputed'.
In most cases, X is the
When using DBSCAN as in the examples:
db = DBSCAN(eps=0.3, min_samples=10).fit(X)
I'm not sure if I understand what X is.
Is X[i][j] supposed to be some sort of measure from node i to node j?
If so, does X need to be normalized, or will DBSCAN scale the values
accordingly?
Thank you,
-
On 05/08/2012 10:41 AM, Gael Varoquaux wrote:
> On Mon, May 07, 2012 at 03:50:00PM -0400, Félix-Antoine Fortin wrote:
>> I presumed there are valid reasons for using a numpy array, and represent
>> error points as -1.
> Reasons for using a numpy array are that it is faster and more memory
> effici
On Mon, May 07, 2012 at 03:50:00PM -0400, Félix-Antoine Fortin wrote:
> I presumed there are valid reasons for using a numpy array, and represent
> error points as -1.
Reasons for using a numpy array are that it is faster and more memory
efficient than a list.
Gael
-
Hi,
I have recently used the DBSCAN implementation of scikit-learn, and I have a
"quick" question.
Currently, noise points are labelled as -1 in a numpy array.
>From my point of view, clustering labels can be used for example as index of a
>sequence.
However, in Python -1 is still a valid ind
On 14 March 2012 08:54, Lars Buitinck wrote:
> 2012/3/13 Robert Layton :
> > Lars, you are right, it should have metric='precomputed' in it.
> > However by passing the distance matrix without a metric, the features
> become
> > "distance to point i", which act as sort of meta-features anyway,
> a
2012/3/13 Robert Layton :
> Lars, you are right, it should have metric='precomputed' in it.
> However by passing the distance matrix without a metric, the features become
> "distance to point i", which act as sort of meta-features anyway, allowing
> training to happen. This means that it works with
On 14 March 2012 08:05, Andreas wrote:
> Hi Lars.
> All I can say is that it worked for me by passing X directly:
>
> http://scikit-learn.org/dev/auto_examples/cluster/plot_cluster_comparison.html
>
> I'm deadlining right now, hopefully I have time to work on Olivier's
> "quadratic_fit" (or whate
Hi Lars.
All I can say is that it worked for me by passing X directly:
http://scikit-learn.org/dev/auto_examples/cluster/plot_cluster_comparison.html
I'm deadlining right now, hopefully I have time to work on Olivier's
"quadratic_fit" (or whatever) proposal afterward.
Cheers,
Andy
On 03/13/2012
Hi all,
A colleague approached me today asking how the scikit-learn DBSCAN
algorithm should be applied and I must admit that the documentation
and example was confusing even to me. The fit docstring says
X: array [n_samples, n_samples] or [n_samples, n_features]
Array of distances bet
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