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from learning import parse_csv, weighted_mode, weighted_replicate, DataSet, \
PluralityLearner, NaiveBayesLearner, NearestNeighborLearner, \
NeuralNetLearner, PerceptronLearner, DecisionTreeLearner, \
euclidean_distance
def test_euclidean():
distance = euclidean_distance([1,2], [3,4])
assert round(distance, 2) == 2.83
distance = euclidean_distance([1,2,3], [4,5,6])
assert round(distance, 2) == 5.2
distance = euclidean_distance([0,0,0], [0,0,0])
assert distance == 0
def test_exclude():
iris = DataSet(name='iris', exclude=[3])
assert iris.inputs == [0, 1, 2]
def test_parse_csv():
assert parse_csv(Iris)[0] == [5.1,3.5,1.4,0.2,'setosa']
def test_weighted_mode():
assert weighted_mode('abbaa', [1, 2, 3, 1, 2]) == 'b'
def test_weighted_replicate():
assert weighted_replicate('ABC', [1, 2, 1], 4) == ['A', 'B', 'B', 'C']
def test_plurality_learner():
zoo = DataSet(name="zoo")
pL = PluralityLearner(zoo)
assert pL([1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 4, 1, 0, 1]) == "mammal"
def test_naive_bayes():
iris = DataSet(name="iris")
# Discrete
nBD = NaiveBayesLearner(iris)
assert nBD([5,3,1,0.1]) == "setosa"
def test_k_nearest_neighbors():
iris = DataSet(name="iris")
kNN = NearestNeighborLearner(iris,k=3)
assert kNN([5,3,1,0.1]) == "setosa"
def test_decision_tree_learner():
iris = DataSet(name="iris")
dTL = DecisionTreeLearner(iris)
assert dTL([5,3,1,0.1]) == "setosa"
def test_neural_network_learner():
iris = DataSet(name="iris")
classes = ["setosa","versicolor","virginica"]
iris.classes_to_numbers(classes)
nNL = NeuralNetLearner(iris, [5], 0.15, 75)
pred1 = nNL([5,3,1,0.1])
pred2 = nNL([6,3,3,1.5])
pred3 = nNL([7.5,4,6,2])
# NeuralNetLearner might be wrong. If it is, check if prediction is in range.
assert pred1 == 0 or pred1 in range(len(classes))
assert pred2 == 1 or pred2 in range(len(classes))
assert pred3 == 2 or pred3 in range(len(classes))
def test_perceptron():
iris = DataSet(name="iris")
iris.classes_to_numbers()
classes_number = len(iris.values[iris.target])
perceptron = PerceptronLearner(iris)
pred1 = perceptron([5,3,1,0.1])
pred2 = perceptron([6,3,4,1])
pred3 = perceptron([7.5,4,6,2])
# PerceptronLearner might be wrong. If it is, check if prediction is in range.
assert pred1 == 0 or pred1 in range(classes_number)
assert pred2 == 1 or pred2 in range(classes_number)
assert pred3 == 2 or pred3 in range(classes_number)