SVM-specializer

A SEJITS implementation of a support vector machine using ctree.

Examples

Iris Toy Dataset

###Iris Toy Dataset

from svm.svmtools import *
from sklearn import datasets

# iris data set from scikit-learn
iris = datasets.load_iris()
# get 1st 100 data points
X = iris.data[:100,:2]
y = iris.target[:100]
for i in range(y.shape[0]): #iris.target gives 0's and 1's. We want +1's and -1's
    if y[i] == 0:
        y[i] = -1

svm = SVMKernel()
# currently only supports float32 and int 32
X = X.astype(np.float32)
y = y.astype(np.int32)

print('Python implementation:')
svm.train(X,y,'gaussian',heuristicMethod = 0, pythonOnly= True)
plot_svm2d(X,y,svm, 'Iris Data Set Python')

print('Ocl implementation:')
svm.train(X,y,'gaussian',heuristicMethod = 0)
plot_svm2d(X,y,svm, 'Iris Data Set OpenCL')