Hi Gianni.
There is a fundamental problem with what you want to do, independent of
SVMs.
In the plot, the 2d plane of the pot represents the input space.
Your input space is 6d. You can not represent 6d on a computer monitor
(that I know of).
So there is no way to plot your data.
What you could do, is plot 2d projections of you data, for example using
PCA.
That makes it somewhat harder to plot decision boundaries, though.
Hth,
Andy
On 04/25/2013 10:31 PM, Gianni Iannelli wrote:
Hi everyone!
I'm new to scikit and I'm gettin trouble with some visualization
method!!!
What I wanna do is visualize in a plot/graph, something like this:
http://scikit-learn.org/stable/auto_examples/exercises/plot_iris_exercise.html
Essentialy I would see the background color based on my training set
to see how the SVM will classify my new elements. It could be a line
or a color in the bacground.
Everything is ok if I will use just two features! I can obtain XX and
YY but, in my case, I have 6 features and I don't know hot to do the
grid!! Almost all the example use just two features from the iris
dataset...There is one that use four but essentially it applies a PCA
in order to reduce the features to two and so, let me say, I kind of
cheating in order to get the graph....
Actually my code is the following:
....
graph = pylab.scatter(X_train[:,0],X_train[:,2],c=colors, zorder= 10)
ax = grafico.axes
ax.set_xlabel('Feature_1')
ax.set_ylabel('Feature_2')
pylab.scatter(clf.support_vectors_[:,0],clf.support_vectors_[:,2],marker
= 'x', c='y',s=200,zorder= 1, label='Support Vector')
pylab.legend(loc = 'lower right')
pylab.show()
....
Where X_train contain six features and not just two!
Thanks to everyone!!!
Solimyr
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