SYNOPSIS
svmpredict [ b probability_estimates ] [ q ] test_data model_file [ output_file ]DESCRIPTION
svmpredict uses a Support Vector Machine specified by a given input model_file to make predictions for each of the samples in test_dataThe format of this file is identical to the training_data file used in svm_train(1) and is just a sparse vector as follows:
 <label> <index1>:<value1> <index2>:<value2> . . .
.
.
.
 There is one sample per line. Each sample consists of a target value (label or regression target) followed by a sparse representation of the input vector. All unmentioned coordinates are assumed to be 0. For classification, <label> is an integer indicating the class label (multiclass is supported). For regression, <label> is the target value which can be any real number. For oneclass SVM, it's not used so can be any number. Except using precomputed kernels (explained in another section), <index>:<value> gives a feature (attribute) value. <index> is an integer starting from 1 and <value> is a real number. Indices must be in an ASCENDING order. If you have label data available for testing then you can enter these values in the test_data file. If they are not available you can just enter 0 and will not know real accuracy for the SVM directly, however you can still get the results of its prediction for the data point.

If output_file is given, it will be used to specify the filename to store the predicted results, one per line, in the same order as the test_data file.
OPTIONS
 b probabilityestimates
 probability_estimates is a binary value indicating whether to calculate probability estimates when training the SVC or SVR model. Values are 0 or 1 and defaults to 0 for speed.
 q
 quiet mode; suppress messages to stdout.
FILES
training_set_file must be prepared in the following simple sparse training vector format:
 <label> <index1>:<value1> <index2>:<value2> . . .
.
.
.
 There is one sample per line. Each sample consist of a target value (label or regression target) followed by a sparse representation of the input vector. All unmentioned coordinates are assumed to be 0. For classification, <label> is an integer indicating the class label (multiclass is supported). For regression, <label> is the target value which can be any real number. For oneclass SVM, it's not used so can be any number. Except using precomputed kernels (explained in another section), <index>:<value> gives a feature (attribute) value. <index> is an integer starting from 1 and <value> is a real number. Indices must be in an ASCENDING order.

ENVIRONMENT
No environment variables.