Update test_learning.py (#376)

antmarakis · norvig · commit efa562812698 · 2017-03-22T00:29:51.000-07:00
Add DecisionTreeLearner, NeuralNetLearner and PerceptronLearner tests
diff --git a/tests/test_learning.py b/tests/test_learning.py
@@ -1,11 +1,13 @@
 from learning import parse_csv, weighted_mode, weighted_replicate, DataSet, \
-                     PluralityLearner, NaiveBayesLearner, NearestNeighborLearner
+                     PluralityLearner, NaiveBayesLearner, NearestNeighborLearner, \
+                     NeuralNetLearner, PerceptronLearner, DecisionTreeLearner
 from utils import DataFile
 
 
+
 def test_parse_csv():
     Iris = DataFile('iris.csv').read()
-    assert parse_csv(Iris)[0] == [5.1, 3.5, 1.4, 0.2, 'setosa']
+    assert parse_csv(Iris)[0] == [5.1,3.5,1.4,0.2,'setosa']
 
 
 def test_weighted_mode():
@@ -20,18 +22,46 @@ def test_plurality_learner():
     zoo = DataSet(name="zoo")
 
     pL = PluralityLearner(zoo)
-    assert pL([]) == "mammal"
+    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"
+    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"
+    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()
+
+    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")
+    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))
