def extract_features(feature_vector):
"""Maps a feature vector to whether each feature is continuous or discrete."""
return [
DiscreteFeature(feature_vector[0]),
DiscreteFeature(feature_vector[1]),
ContinuousFeature(feature_vector[2])
]
def extract_features(feature_vector):
"""Maps a feature vector to whether each feature is continuous or discrete."""
return [
DiscreteFeature(feature_vector[0]),
ContinuousFeature(feature_vector[1]),
ContinuousFeature(feature_vector[2]),
ContinuousFeature(feature_vector[3]),
ContinuousFeature(feature_vector[4]),
ContinuousFeature(feature_vector[5]),
ContinuousFeature(feature_vector[6]),
ContinuousFeature(feature_vector[7]),
ContinuousFeature(feature_vector[8]),
ContinuousFeature(feature_vector[9]),
ContinuousFeature(feature_vector[10]),
ContinuousFeature(feature_vector[11]),
ContinuousFeature(feature_vector[12]),
ContinuousFeature(feature_vector[13]),
ContinuousFeature(feature_vector[14]),
ContinuousFeature(feature_vector[15]),
ContinuousFeature(feature_vector[16]),
ContinuousFeature(feature_vector[17]),
ContinuousFeature(feature_vector[18]),
ContinuousFeature(feature_vector[19]),
ContinuousFeature(feature_vector[20]),
ContinuousFeature(feature_vector[21]),
ContinuousFeature(feature_vector[22]),
ContinuousFeature(feature_vector[23]),
ContinuousFeature(feature_vector[24]),
ContinuousFeature(feature_vector[25]),
ContinuousFeature(feature_vector[26]),
ContinuousFeature(feature_vector[27]),
ContinuousFeature(feature_vector[28]),
ContinuousFeature(feature_vector[29])
]
def extract_features(example):
return [DiscreteFeature(example[0]), DiscreteFeature(example[1])]
def test_raise_not_fitted_error_if_predict_is_called_before_predict(self):
clf = NaiveBayes(lambda x: [DiscreteFeature(x[0])])
with self.assertRaises(NotFittedError):
clf.predict([0, 1])
def extract_features(feature_vector):
return [
DiscreteFeature(feature_vector[0]),
DiscreteFeature(feature_vector[1]),
ContinuousFeature(feature_vector[2])
]