Trained SVM models are placed under models folders.
Scikit-learn's SVC model: The scaler and estimator are pickled into a pickle file. Suppose X_raw below is an array-like variable representing a 1x15 feature vector. Then this can be your Python snippet:
import pickle
[scaler, estimator] = pickle.load(open('sklearn_scaler_and_model.p', 'rb'))
X_scaled = scaler.transform(X_raw)
estimator.predict(X_scaled)libsvm model: The scaler file is mild_vs_viral.scaler and the model file is mild_vs_viral.model. Suppose a 15-d feature vector is placed in a file test.input in svmlight format. Then this can be your commands:
svm-scale -r mild_vs_viral.scaler test.input > test.scaled
svm-predict test.scaled mild_vs_viral.model test.prediction_output The scaler file is loaded via the -r option when scaling data in svm-scale. The model file is loaded as a mandatory argument when making predictions in svm-predict.
If you have any questions, please contact Forrest Bao at forrest dot bao @ gmail dot com, or create an issue ticket.