Does prediction for a calculated svm model
[predicted_label, accuracy, decision_values] = svmpredict(testing_label_vector, testing_instance_matrix, model) [predicted_label, accuracy, decision_values] = svmpredict(testing_label_vector, testing_instance_matrix, model, 'libsvm_options')
SVM model structure from svmtrain.
-b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); one-class SVM not supported yet
SVM prediction output vector
a vector with accuracy, mean squared error, squared correlation coefficient.
If selected, probability estimate vector
vector of predicted labels
a vector including accuracy (for classification), mean squared error, and squared correlation coefficient (for regression).
a matrix containing decision values or probability estimates (if '-b 1' is specified).
The third output is a matrix containing decision values or probability estimates (if '-b 1' is specified). If k is the number of classes in training data, for decision values, each row includes results of predicting k(k-1)/2 binary-class SVMs.
For classification, k = 1 is a special case. Decision value +1 is returned for each testing instance,instead of an empty vector.
For probabilities, each row contains k values indicating the probability that the testing instance is in each class.
Note that the order of classes here is the same as 'Label' field in the model structure.