da.py 文件源码

def __init__(self, classData, shrinkage=0):
        """Construct a new Linear Discriminant Analysis (LDA) classifier.

        Args:
            classData:  Training data.  This is a numpy array or list of numpy
                        arrays with shape (nCls,nObs[,nIn]).  If the dimensions
                        index is missing the data is assumed to be
                        one-dimensional.

            shrinkage:  This parameter regularizes LDA by shrinking the average
                        covariance matrix toward its average eigenvalue:
                            covariance = (1-shrinkage)*covariance +
                            shrinkage*averageEigenvalue*identity
                        Behavior is undefined if shrinkage is outside [0,1].
                        This parameter has no effect if average is 0.

        Returns:
            A trained LDA classifier.
        """
        Classifier.__init__(self, util.colmat(classData[0]).shape[1],
                            len(classData))

        self.dtype = np.result_type(*[cls.dtype for cls in classData])

        self.shrinkage = shrinkage

        self.train(classData)