from learning import parse_csv, weighted_mode, weighted_replicate, DataSet, \

PluralityLearner, NaiveBayesLearner, NearestNeighborLearner, \

NeuralNetLearner, PerceptronLearner, DecisionTreeLearner

from utils import DataFile

def test_exclude():

iris = DataSet(name='iris', exclude=[3])

assert iris.inputs == [0, 1, 2]

def test_parse_csv():

Iris = DataFile('iris.csv').read()

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")

nB = NaiveBayesLearner(iris)

assert nB([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")

iris.remove_examples("virginica")

classes = ["setosa","versicolor","virginica"]

iris.classes_to_numbers()

nNL = NeuralNetLearner(iris)

# NeuralNetLearner might be wrong. Just check if prediction is in range.

assert nNL([5,3,1,0.1]) in range(len(classes))

def test_perceptron():

iris = DataSet(name="iris")

iris.remove_examples("virginica")

classes = ["setosa","versicolor","virginica"]

iris.classes_to_numbers()

perceptron = PerceptronLearner(iris)

# PerceptronLearner might be wrong. Just check if prediction is in range.

assert perceptron([5,3,1,0.1]) in range(len(classes))