def __init__(self, examplesFileName):
"""
Create the class by reading examples from a Matlab file. Instantiate the SVM
and create a preprocesor to standarise examples to have zero mean and unit variance.
"""
self.examplesList = ExamplesList.readFromFile(examplesFileName)
self.examplesList.setDefaultExamplesName("X")
self.examplesList.setLabelsName("y")
(freqs, items) = Util.histogram(self.examplesList.getSampledDataField("y").ravel())
logging.info("Distribution of labels: " + str((freqs, items)))
logging.info("The base error rate is " + str(float(min(freqs))/self.examplesList.getNumExamples()))
self.classifier = LibSVM()
self.errorMethod = Evaluator.balancedError
self.preprocessor = Standardiser()
X = self.preprocessor.standardiseArray(self.examplesList.getDataField(self.examplesList.getDefaultExamplesName()))
self.examplesList.overwriteDataField(self.examplesList.getDefaultExamplesName(), X)
