libsvm 0.9.4 · OCaml Package

type t

Type of a SVM problem (training set).

val create : x:Lacaml.D.mat -> y:Lacaml.D.vec -> t

create x y constructs a problem from a feature matrix x and target vector y. Each row of x is a feature vector of a training instance.

val create_k : k:Lacaml.D.mat -> y:Lacaml.D.vec -> t

create_k k y constructs a problem from a matrix k and target vector y. The matrix k has to be of the following form:

1 K(x1,x1) K(x1,x2) ... K(x1,xL)

2 K(x2,x1) K(x2,x2) ... K(x2,xL)

...

L K(xL,x1) K(xL,x2) ... K(xL,xL)

where L denotes the number of training instances and K(x,y) is the precomputed kernel value of the two training instances x and y.

val get_n_samples : t -> int

get_n_samples prob

val get_n_feats : t -> int

get_n_feats prob

val get_targets : t -> Lacaml.D.vec

get_targets prob

val load : string -> t

load filename loads a problem from the file filename.

val output : t -> Stdlib.out_channel -> unit

output prob oc outputs the problem prob to an output channel oc. NOTE: the function does not close the output channel.

val save : t -> string -> unit

save prob filename saves the problem prob to the file filename.

val min_max_feats : t -> [ `Min of Lacaml.D.vec ] * [ `Max of Lacaml.D.vec ]

min_max_feats prob

returns
the smallest and largest feature value for each column in the feature matrix.

val scale : 
  ?lower:float ->
  ?upper:float ->
  t ->
  min_feats:Lacaml.D.vec ->
  max_feats:Lacaml.D.vec ->
  t

scale ?lower ?upper prob min_feats max_feats

returns
a linearly scaled problem where each feature (attribute) lies in the range [lower,upper]. The default range is [-1,1].

val print : t -> unit

print prob prints the internal representation of a problem. It is mainly used for debugging purposes.

package libsvm