Package Bio :: Module kNN
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Module kNN

source code


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 [hide private]
  kNN
Holds information necessary to do nearest neighbors classification.
Functions [hide private]
1

equal_weight(x, y) source code
kNN

train(xs, ys, k)
Train a k nearest neighbors classifier on a training set.
source code
 
calculate(knn, x, weight_fn=<function equal_weight at 0x7fa9874a58c0>, distance_fn=None)
calculate(knn, x[, weight_fn][, distance_fn]) -> weight dict
source code
 
classify(knn, x, weight_fn=<function equal_weight at 0x7fa9874a58c0>, distance_fn=None)
classify(knn, x[, weight_fn][, distance_fn]) -> class
source code
Variables [hide private]
  __package__ = 'Bio'
Function Details [hide private]

train(xs, ys, k)

source code 
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.

Returns:
kNN

calculate(knn, x, weight_fn=<function equal_weight at 0x7fa9874a58c0>, distance_fn=None)

source code 
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.  If distance_fn is None (the default), the
Euclidean distance is used.  Returns a dictionary of the class to
the weight given to the class.

classify(knn, x, weight_fn=<function equal_weight at 0x7fa9874a58c0>, distance_fn=None)

source code 
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.  distance_fn is an optional function
that takes two points and returns the distance between them.  If
distance_fn is None (the default), the Euclidean distance is used.