OCaml wrapper to SVM R packages e1071 and svmpath
Module Orsvm_e1071 . Svm
type gamma = float
type kernel =
| RBF of gamma
| Linear
type filename = string
type nb_columns = int
type sparsity =
| Dense
| Sparse of nb_columns
val train : ?debug:bool -> sparsity -> cost:float -> kernel -> filename -> filename -> Result.t

train ~cost Dense kernel data_fn labels_fn will train a binary SVM classifier with the given RBF or Linear kernel with parameter cost on the data in data_fn with labels in labels_fn. 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 i in labels_fn is the corresponding label of line i in data_fn.

val predict : ?debug:bool -> sparsity -> Result.t -> filename -> Result.t

predict Dense train_result to_predict_data_fn will run the previously trained SVM model on the new data stored in to_predict_data_fn. 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 to_predict_data_fn.

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.