knn.py 文件源码

def __init__(self, classData, k=1, distMetric='euclidean', *args, **kwargs):
        Classifier.__init__(self, util.colmat(classData[0]).shape[1],
                            len(classData))

        self.k = k
        minObs = min([len(cls) for cls in classData])
        if self.k > minObs:
            raise Exception('k=%d exceeds the number of examples in smallest training class %d.' %
                            (k, minObs))

        if callable(distMetric):
            self.distFunc = lambda x1, x2: distMetric(x1, x2, *args, **kwargs)
        else:
            self.distFunc = lambda x1, x2: spdist.cdist(x1, x2, metric=distMetric)

        self.trainData = classData