diff --git a/tests/test_learning.py b/tests/test_learning.py
index 72c0350a6..34346b7ec 100644
--- a/tests/test_learning.py
+++ b/tests/test_learning.py
@@ -1,8 +1,10 @@
-from learning import parse_csv, weighted_mode, weighted_replicate, DataSet, \
- PluralityLearner, NaiveBayesLearner, NearestNeighborLearner, \
- NeuralNetLearner, PerceptronLearner, DecisionTreeLearner, \
- euclidean_distance, grade_learner, err_ratio, random_weights
+
+import pytest
+import math
from utils import DataFile
+from learning import (parse_csv, weighted_mode, weighted_replicate, DataSet,
+ PluralityLearner, NaiveBayesLearner, NearestNeighborLearner,
+ rms_error, manhattan_distance, mean_boolean_error, mean_error)
@@ -74,16 +76,43 @@ def test_naive_bayes():
def test_k_nearest_neighbors():
iris = DataSet(name="iris")
-
kNN = NearestNeighborLearner(iris,k=3)
+ assert kNN([5,3,1,0.1]) == "setosa"
assert kNN([5, 3, 1, 0.1]) == "setosa"
assert kNN([6, 5, 3, 1.5]) == "versicolor"
assert kNN([7.5, 4, 6, 2]) == "virginica"
+def test_rms_error():
+ assert rms_error([2,2], [2,2]) == 0
+ assert rms_error((0,0), (0,1)) == math.sqrt(0.5)
+ assert rms_error((1,0), (0,1)) == 1
+ assert rms_error((0,0), (0,-1)) == math.sqrt(0.5)
+ assert rms_error((0,0.5), (0,-0.5)) == math.sqrt(0.5)
+
+def test_manhattan_distance():
+ assert manhattan_distance([2,2], [2,2]) == 0
+ assert manhattan_distance([0,0], [0,1]) == 1
+ assert manhattan_distance([1,0], [0,1]) == 2
+ assert manhattan_distance([0,0], [0,-1]) == 1
+ assert manhattan_distance([0,0.5], [0,-0.5]) == 1
+
+def test_mean_boolean_error():
+ assert mean_boolean_error([1,1], [0,0]) == 1
+ assert mean_boolean_error([0,1], [1,0]) == 1
+ assert mean_boolean_error([1,1], [0,1]) == 0.5
+ assert mean_boolean_error([0,0], [0,0]) == 0
+ assert mean_boolean_error([1,1], [1,1]) == 0
+
+def test_mean_error():
+ assert mean_error([2,2], [2,2]) == 0
+ assert mean_error([0,0], [0,1]) == 0.5
+ assert mean_error([1,0], [0,1]) == 1
+ assert mean_error([0,0], [0,-1]) == 0.5
+ assert mean_error([0,0.5], [0,-0.5]) == 0.5
+
def test_decision_tree_learner():
iris = DataSet(name="iris")
-
dTL = DecisionTreeLearner(iris)
assert dTL([5, 3, 1, 0.1]) == "setosa"
assert dTL([6, 5, 3, 1.5]) == "versicolor"
@@ -92,10 +121,8 @@ def test_decision_tree_learner():
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)
tests = [([5, 3, 1, 0.1], 0),
([5, 3.5, 1, 0], 0),
@@ -103,7 +130,6 @@ def test_neural_network_learner():
([6, 2, 3.5, 1], 1),
([7.5, 4, 6, 2], 2),
([7, 3, 6, 2.5], 2)]
-
assert grade_learner(nNL, tests) >= 2/3
assert err_ratio(nNL, iris) < 0.25
@@ -111,9 +137,7 @@ def test_neural_network_learner():
def test_perceptron():
iris = DataSet(name="iris")
iris.classes_to_numbers()
-
classes_number = len(iris.values[iris.target])
-
perceptron = PerceptronLearner(iris)
tests = [([5, 3, 1, 0.1], 0),
([5, 3.5, 1, 0], 0),
@@ -121,7 +145,6 @@ def test_perceptron():
([6, 2, 3.5, 1], 1),
([7.5, 4, 6, 2], 2),
([7, 3, 6, 2.5], 2)]
-
assert grade_learner(perceptron, tests) > 1/2
assert err_ratio(perceptron, iris) < 0.4
@@ -130,12 +153,8 @@ def test_random_weights():
min_value = -0.5
max_value = 0.5
num_weights = 10
-
test_weights = random_weights(min_value, max_value, num_weights)
-
assert len(test_weights) == num_weights
-
for weight in test_weights:
assert weight >= min_value and weight <= max_value
-
-
+
\ No newline at end of file
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