Support Vector Machines (SVM).
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>>> import numpy as np
>>> import mlpy
>>> xtr = np.array([[1.0, 2.0, 3.0, 1.0], # first sample
... [1.0, 2.0, 3.0, 2.0], # second sample
... [1.0, 2.0, 3.0, 1.0]]) # third sample
>>> ytr = np.array([1, -1, 1]) # classes
>>> mysvm = mlpy.Svm() # initialize Svm class
>>> mysvm.compute(xtr, ytr) # compute SVM
1
>>> mysvm.predict(xtr) # predict SVM model on training data
array([ 1, -1, 1])
>>> xts = np.array([4.0, 5.0, 6.0, 7.0]) # test point
>>> mysvm.predict(xts) # predict SVM model on test point
-1
>>> mysvm.realpred # real-valued prediction
-5.5
>>> mysvm.weights(xtr, ytr) # compute weights on training data
array([ 0., 0., 0., 1.])
Initialize the Svm class
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Compute SVM model
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Predict svm model on a test point(s)
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Return feature weights
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Note
For tr kernel (Terminated Ramp Kernel) see [Merler06].
[Vapnik95] | V Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag, 1995. |
[Cristianini] | N Cristianini and J Shawe-Taylor. An introduction to support vector machines. Cambridge University Press. |
[Merler06] | S Merler and G Jurman. Terminated Ramp - Support Vector Machine: a nonparametric data dependent kernel. Neural Network, 19:1597-1611, 2006. |
[Nasr09] |
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