Adding BSO-related tests.

tests/opytimizer/optimizers/social/test_bso.py

@@ -0,0 +1,153 @@
+from types import new_class
+import numpy as np
+
+from opytimizer.optimizers.social import bso
+from opytimizer.spaces import search
+
+
+def test_bso_params():
+    params = {
+        'm': 5,
+        'p_replacement_cluster': 0.2,
+        'p_single_cluster': 0.8,
+        'p_single_best': 0.4,
+        'p_double_best': 0.5,
+        'k': 20
+    }
+
+    new_bso = bso.BSO(params=params)
+
+    assert new_bso.m == 5
+
+    assert new_bso.p_replacement_cluster == 0.2
+
+    assert new_bso.p_single_cluster == 0.8
+
+    assert new_bso.p_single_best == 0.4
+
+    assert new_bso.p_double_best == 0.5
+
+    assert new_bso.k == 20
+
+
+def test_bso_params_setter():
+    new_bso = bso.BSO()
+
+    try:
+        new_bso.m = 'a'
+    except:
+        new_bso.m = 5
+
+    assert new_bso.m == 5
+
+    try:
+        new_bso.m = -1
+    except:
+        new_bso.m = 5
+
+    assert new_bso.m == 5
+
+    try:
+        new_bso.p_replacement_cluster = 'b'
+    except:
+        new_bso.p_replacement_cluster = 0.2
+
+    assert new_bso.p_replacement_cluster == 0.2
+
+    try:
+        new_bso.p_replacement_cluster = -1
+    except:
+        new_bso.p_replacement_cluster = 0.2
+
+    assert new_bso.p_replacement_cluster == 0.2
+
+    try:
+        new_bso.p_single_cluster = 'c'
+    except:
+        new_bso.p_single_cluster = 0.8
+
+    assert new_bso.p_single_cluster == 0.8
+
+    try:
+        new_bso.p_single_cluster = -1
+    except:
+        new_bso.p_single_cluster = 0.8
+
+    assert new_bso.p_single_cluster == 0.8
+
+    try:
+        new_bso.p_single_best = 'd'
+    except:
+        new_bso.p_single_best = 0.4
+
+    assert new_bso.p_single_best == 0.4
+
+    try:
+        new_bso.p_single_best = -1
+    except:
+        new_bso.p_single_best = 0.4
+
+    assert new_bso.p_single_best == 0.4
+
+    try:
+        new_bso.p_double_best = 'e'
+    except:
+        new_bso.p_double_best = 0.5
+
+    assert new_bso.p_double_best == 0.5
+
+    try:
+        new_bso.p_double_best = -1
+    except:
+        new_bso.p_double_best = 0.5
+
+    assert new_bso.p_double_best == 0.5
+
+    try:
+        new_bso.k = 'f'
+    except:
+        new_bso.k = 20
+
+    assert new_bso.k == 20
+
+    try:
+        new_bso.k = -1
+    except:
+        new_bso.k = 20
+
+    assert new_bso.k == 20
+
+
+def test_bso_clusterize():
+    search_space = search.SearchSpace(n_agents=10, n_variables=2,
+                                      lower_bound=[0, 0], upper_bound=[10, 10])
+
+    new_bso = bso.BSO()
+
+    new_bso._clusterize(search_space.agents)
+
+
+def test_bso_sigmoid():
+    new_bso = bso.BSO()
+
+    x = 0.5
+
+    y = new_bso._sigmoid(x)
+
+    assert y == 0.6224593312018546
+
+
+def test_bso_update():
+    def square(x):
+        return np.sum(x**2)
+
+    search_space = search.SearchSpace(n_agents=50, n_variables=2,
+                                      lower_bound=[0, 0], upper_bound=[10, 10])
+
+    new_bso = bso.BSO()
+    new_bso.evaluate(search_space, square)
+
+    new_bso.update(search_space, square, 1, 10)
+
+    new_bso.p_replacement_cluster = 1
+    new_bso.update(search_space, square, 1, 10)