Is there any way to visualize svm model in Opencv using matplotlib in python like this one http://scikit-learn.org/stable/auto_examples/svm/plot_iris.html .
In order to visualize decision boundary of SVM your data has to be two dimensional. If this is a case, you can just use scikit-learns code, and just substitute call to .predict with analogous predict from your own library (like opencv).
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A project I'm working on requires me to combine 2 kernels and obtain results. The first kernel is formed using the features, and the second kernel needs to be the correlation between the features. Combining these kernels and using an SVM classifier is the requirement. Can anyone help me implement this in python?
I have been exploring Scikit-learn as a tool, and I am very interested in learning if I can modify how Scikit-learn classifies a data point, more specifically, its SVM function. I am looking for a programmatic way to attack this problem.
In general, we can say that SVM classification looks something like this, and let's imagine the blue points are positive, and red points are negative:
SVM
Where the classification occurs as follows:
SVM Classification
As my understanding goes, Scikit-learn does this for us quite easily.
However, I was wondering if there are any parameters I could change to make it look something more like this:
SVM The way I want
That is, the positive point that makes the support vector is also my decision boundary.. Is there another algorithm that I am missing? Would I have to build my SVM function from the ground up?
Thank you
I am using tflearn, and I am trying to plot some curves by using mathplotlib. I am not using tf.Session (There is an example on that but I cant use that solution) and I don't want to visualise it in tensorboard.
I was wondering if there was a way. Would it be possible to get the network structure as well somehow?
thank you
I would like to know, if there is the possibility to somehow train a svm classifier using scikit in python (love this module and its documentation) and import that trained model into C++ for prediction making.
Here is how far I got:
I have written a python script which uses scikit to create a reasonable svm classifier
I can also store that model in pickle format
Now, I had a look at libSVM for C++ but I do not see how that is able to import such a model. I think that the documentation is not that good or I missed something here.
However, I also thought that instead of storing the whole model, I could just store the parameters of the SVM Classifier and load only those parameters ( I think the needed once are: Support Vectors, C, degree) for a linear SVM classifier. Unfortunately, I cannot find any documentation of libSVM on how to do that.
A last option which I would not prefer that much would be to go with OpenCV in which I could train a SVM classifier, store it and load it back all in C++. But this would introduce even more library dependencies (especially such a large one) for my program. If there is a good way to avoid that, I would love to do so.
As always I thank you in advance!
Best,
Tukk
I have already extracted image features of 3 classes using SURF (openCV) and stacked all descriptors together into a numpy array.
My next steps would be:
clustering using K-means
build feature histogram for each training case based on clusters
train a multi class linear SVM to separate cases based on feature histograms
My question is:
at what point would it be most appropriate to scale my data?
Should this be done before clustering, histograms or SVM?
I am using sklearn's standard scaler, as well as linearSVC.