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kNN.py
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.
Distance Functions:
euclidean_dist The euclidean distance between two points.
Weighting Functions:
equal_weight Every example is given a weight of 1.
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Functions
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calculate
classify
equal_weight
euclidean_dist
euclidean_dist_py
train
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calculate
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calculate (
knn,
x,
weight_fn=equal_weight,
distance_fn=euclidean_dist,
)
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.
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classify
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classify (
knn,
x,
weight_fn=equal_weight,
distance_fn=euclidean_dist,
)
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.
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equal_weight
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equal_weight ( x, y )
equal_weight(x, y) -> 1
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euclidean_dist
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euclidean_dist ( x, y )
euclidean_dist(x, y) -> euclidean distance between x and y
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Exceptions
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ValueError, "vectors must be same length"
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euclidean_dist_py
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euclidean_dist_py ( x, y )
euclidean_dist_py(x, y) -> euclidean distance between x and y
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Exceptions
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ValueError, "vectors must be same length"
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train
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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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Classes
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| kNN | Holds information necessary to do nearest neighbors classification.
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