1.使用测试向量训练出简单的SVM分类器

2.使用分类器

3.pickle存储结果

import dlib
try:
    import cPickle as pickle
except ImportError:
    import pickle

x = dlib.vectors()
y = dlib.array()

# Make a training dataset.  Here we have just two training examples.  Normally
# you would use a much larger training dataset, but for the purpose of example
# this is plenty.  For binary classification, the y labels should all be either +1 or -1.
x.append(dlib.vector([1, 2, 3, -1, -2, -3]))
y.append(+1)

x.append(dlib.vector([-1, -2, -3, 1, 2, 3]))
y.append(-1)


# Now make a training object.  This object is responsible for turning a
# training dataset into a prediction model.  This one here is a SVM trainer
# that uses a linear kernel.  If you wanted to use a RBF kernel or histogram
# intersection kernel you could change it to one of these lines:
#  svm = dlib.svm_c_trainer_histogram_intersection()
#  svm = dlib.svm_c_trainer_radial_basis()
svm = dlib.svm_c_trainer_linear()
svm.be_verbose()
svm.set_c(10)

# Now train the model.  The return value is the trained model capable of making predictions.
classifier = svm.train(x, y)

# Now run the model on our data and look at the results.
print("prediction for first sample:  {}".format(classifier(x[0])))
print("prediction for second sample: {}".format(classifier(x[1])))


# classifier models can also be pickled in the same was as any other python object.
with open('saved_model.pickle', 'wb') as handle:
    pickle.dump(classifier, handle, 2)

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作者：_iorilan
来源：CSDN
原文：https://blog.csdn.net/lan_liang/article/details/84713451
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