We start with the standard simplistic tests that will serve to get the basic wiring up for our classifier. First, the test:
import NaiveBayes def no_observations_test(): classifier = NaiveBayes.Classifier() classification = classifier.classify(observation=23.2) assert classification is None, "Should not classify observations without training examples."
And then the code:
class Classifier:
def classify(self, observation):
passAs the next step to approach a solution, let's try the case where we've only observed the data from a single class:
def given_an_observation_for_a_single_class_test(): classifier = NaiveBayes.Classifier() classifier.train(classification='a class', observation=0) classification = classifier.classify(observation=23.2) assert classification == 'a class', "Should always classify as given class if there is only one."
A very simple solution is to just set a single classification that gets set every time we train something:
class Classifier...