package orsvm_e1071

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Module Orsvm_e1071.SvmSource

Sourcetype gamma = float
Sourcetype kernel =
  1. | RBF of gamma
  2. | Linear
Sourcetype filename = string
Sourcetype nb_columns = int
Sourcetype sparsity =
  1. | Dense
  2. | Sparse of nb_columns
Sourceval 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.

Sourceval 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.

Sourceval 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.