Type of a SVM problem (training set).

create ~x ~y constructs a problem from a sparse encoding x of training vectors and a target vector y.

create_dense ~x ~y constructs a problem from a dense encoding x of training vectors and a target vector y. This is useful in particular in conjunction with the `PRECOMPUTED type of kernel when invoking train. In that case x represents a kernel matrix 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.

get_n_samples prob

• returns

  the number of training samples.

get_n_feats prob

• returns

  the number of features (attributes).

get_targets prob

• returns

  the targets of training instances.

load filename loads a problem from the file filename.

• raises Failure

  if an error occured during parsing of filename.

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

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

min_max_feats prob

• returns

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

scale ?lower ?upper prob scales in place prob such that each feature (attribute) lies in the range [lower,upper]. The default range is [-1,1].

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