contextFold.app
Class Train
java.lang.Object
contextFold.app.Train
public class Train
- extends java.lang.Object
Obtains feature weight parameters of scoring models via a
machine-learning procedure.
Usage (from the 'ContextFold' directory):
    "java -cp bin contextFold.app.Train <arg1 name>:X1 <arg2 name>:X2
...", where Xi is the value to assign for the i-th argument.
Usage examples:
    java -cp bin contextFold.app.Train train:C:/RNAdata/trainData.txt
    java -cp bin contextFold.app.Train train:C:/RNAdata/trainData.txt
f:StMedCoHigh
    java -cp bin contextFold.app.Train train:C:/RNAdata/trainData.txt
f:trained/Baseline.model iter:6 out:trained/BaselineRetrained improve:0.5
Required argument:
    train - Path for the training data file (must contain
structures in addition to sequences).
Main optional arguments (use the 'man' argument for the complete list):
    man - Prints the usage manual.
    f - A scoring model file. Can be either a name of parameterization
java file (extending the contextFold.models.FeatureSetter) found in the
contextFold.models package, or a path to a previously trained scoring model
file (a '.model' file). Default value: 'StHighCoHigh' (using the model
defined in the StHighCoHigh.java file, in the contextFold.models package).
    out - Path for the trained model output file (if not specified,
a file with the same name as the input file and an the added '.model' extension
will be generated in the same directory as the input file).
    iter - Maximum number of iterations of the training procedure
(Default: 20).
    shuffle - If true, shuffles the training sequences after each
phase of processing the complete set (Allowed values: true (default),
false).
    log - Path to a log file. If not specified, log output is
printed to screen.
    loss - Loss metric (Allowed values: fMeasure (default),
hamming).
    keepIterationWeights - Saving intermediate weights after each
iteration (Allowed values: false (default), true).
    improve - improve:X requires that the loss estimation (based on
the training set) will improve in at least X percentage (Default: X = 1) after
each Y consecutive iterations, where Y is defined by the 'maxNI' argument.
    maxNI - maxNI:Y imposes that if there was no sufficient improvement
in the last Y iterations (according to the 'improve' parameter), the process
terminates (Default: Y = 3).
Constructor Summary |
Train()
|
Method Summary |
static void |
main(java.lang.String[] args)
|
Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Train
public Train()
main
public static void main(java.lang.String[] args)
throws java.io.IOException
- Throws:
java.io.IOException