calculate (
        knn,
        x,
        weight_fn=equal_weight,
        distance_fn=distance.euclidean,
        )

calculate(knn, x[, weight_fn][, distance_fn]) -> weight dict

Calculate the probability for each class. knn is a kNN object. x is the observed data. weight_fn is an optional function that takes x and a training example, and returns a weight. distance_fn is an optional function that takes two points and returns the distance between them. Returns a dictionary of the class to the weight given to the class.

classify (
        knn,
        x,
        weight_fn=equal_weight,
        distance_fn=distance.euclidean,
        )

classify(knn, x[, weight_fn][, distance_fn]) -> class

Classify an observation into a class. If not specified, weight_fn will give all neighbors equal weight and distance_fn will be the euclidean distance.

equal_weight ( x,  y )

equal_weight(x, y) -> 1

train (
        xs,
        ys,
        k,
        typecode=None,
        )

train(xs, ys, k) -> kNN

Train a k nearest neighbors classifier on a training set. xs is a list of observations and ys is a list of the class assignments. Thus, xs and ys should contain the same number of elements. k is the number of neighbors that should be examined when doing the classification.