train data_fn labels_fn will train a binary SVM classifier with the linear (dot product) kernel and at the same time find out the lambda values to test later on in order to find the best one (lambda = 1/C).
data_fn is a dense numerical matrix dumped in a tab-separated text file without any format header. Rows are observations, columns are features.
labels_fn is a vector of tab-separated "1" or "-1" integer labels in a text file, without any format header. Column
labels_fn is the corresponding label of line
val read_lambdas : ?debug:bool -> Result.t -> float list
read_lambdas train_result will extract all lambda values that need to be tested (on a test set) in order to find the best one. If this list is empty then training was not successful.
predict ~lambda:1.0 train_result to_predict_data_fn will run the previously trained SVM model on the new data stored in
to_predict_data_fn must follow the same format than
data_fn used while training. On success, a filename is returned. This text file contains the predicted decision values, one per line of
val read_predictions : ?debug:bool -> Result.t -> float list
read_predictions result will decode predicted decision values in
result, or crash if the previous call to
predict was not successful. Upon success and if
not debug, the file containing the predicted decision values is removed.