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Package Bio :: Module kNN |
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This module provides code for doing k-nearest-neighbors classification. k Nearest Neighbors is a supervised learning algorithm that classifies a new observation based the classes in its surrounding neighborhood. Glossary: distance The distance between two points in the feature space. weight The importance given to each point for classification. Classes: kNN Holds information for a nearest neighbors classifier. Functions: train Train a new kNN classifier. calculate Calculate the probabilities of each class, given an observation. classify Classify an observation into a class. Weighting Functions: equal_weight Every example is given a weight of 1.
Classes | |
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kNN |
Holds information necessary to do nearest neighbors classification. |
Function Summary | |
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calculate(knn, x[, weight_fn][, distance_fn]) -> weight dict | |
classify(knn, x[, weight_fn][, distance_fn]) -> class | |
equal_weight(x, y) -> 1 | |
train(xs, ys, k) -> kNN |
Function Details |
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calculate(knn, x, weight_fn=<function equal_weight at 0x4108fbc4>, distance_fn=<function euclidean at 0x40e3f6f4>)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=<function equal_weight at 0x4108fbc4>, distance_fn=<function euclidean at 0x40e3f6f4>)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. |
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