pysvmlight / examples /

import svmlight
#import nltk
import pickle

training_data = __import__('data').train0
test_data = __import__('data').test0

# train a model based on the data
model = svmlight.learn(training_data, type='classification', verbosity=0)

# model data can be stored in the same format SVM-Light uses, for interoperability
# with the binaries.
svmlight.write_model(model, 'my_model.dat')

#with open("model.pickle", 'wb') as f:
#    pickle.dump(model, f)

# classify the test data. this function returns a list of numbers, which represent
# the classifications.
predictions = svmlight.classify(model, test_data)
for p in predictions:
    print '%.8f' % p
Tip: Filter by directory path e.g. /media app.js to search for public/media/app.js.
Tip: Use camelCasing e.g. ProjME to search for
Tip: Filter by extension type e.g. /repo .js to search for all .js files in the /repo directory.
Tip: Separate your search with spaces e.g. /ssh pom.xml to search for src/ssh/pom.xml.
Tip: Use ↑ and ↓ arrow keys to navigate and return to view the file.
Tip: You can also navigate files with Ctrl+j (next) and Ctrl+k (previous) and view the file with Ctrl+o.
Tip: You can also navigate files with Alt+j (next) and Alt+k (previous) and view the file with Alt+o.