I'm writing a k nearest neighbors implementation to solve multiclass classification.

import heapqimport loggingimport numpy as npfrom scipy import spatiallogging.basicConfig()class KNN(object):    similarities = {        1: lambda a, b: np.linalg.norm(a-b),        2: lambda a, b: spatial.distance.cosine(a, b),    }    def __init__(self, k, similarity_func, loglevel=logging.DEBUG):        self.k = k        self.logger = logging.getLogger(type(self).__name__)        self.logger.setLevel(loglevel)        if similarity_func not in KNN.similarities:            raise ValueError("Illegal similarity value {0}. Legal values are {1}".format(similarity_func, sorted(KNN.similarities.keys())))        self.similarity_func = KNN.similarities[similarity_func]    def train(self, X, y):        self.training_X = X        self.training_y = y        self.num_classes = len(np.unique(y))        self.logger.debug("There are %s classes", self.num_classes)        return self    def probs(self, X):        class_probs = []        for i, e in enumerate(X, 1):            votes = np.zeros((self.num_classes,))            self.logger.debug("Votes: %s", votes)            if i % 100 == 0:                self.logger.info("Example %s", i)            distance = [(self.similarity_func(e, x), y) for x, y in zip(self.training_X, self.training_y)]            for (_, label) in heapq.nsmallest(self.k, distance, lambda t: t[0]):                votes[label] += 1            class_probs.append(normalize(votes))        return class_probs    def predict(self, X):        return np.argmax(self.probs(X))

I find that this implementation'spredict is slow™ and think it could be sped up with vectorized operations in numpy, but I'm fairly inexperienced with numpy vectorization techniques.

Does anyone have some suggestions for performance boosts I can get frompredict?

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