Create test_clustering.py

tests/fuzzy_set/test_clustering.py

@@ -0,0 +1,53 @@
+import sys
+
+from sklearn.datasets import load_iris
+import pytest
+
+sys.path.append(__file__ + "/../..")
+
+from softpy.fuzzy.clustering import FuzzyCMeans
+from tests.fuzzy_set.configuration import not_raises 
+
+class TestFuzzyCMeans:
+
+    @pytest.mark.parametrize(
+        "n_clusters,epsilon,iters,fuzzifier,exception_expected",
+        [
+            (2,0.01,2,1.05,None),
+            (1,0.01,2,1.05,ValueError),
+            (2,-0.01,2,1.05,ValueError),
+            (2,0.01,0,1.05,ValueError),
+            (2,0.01,2,1,ValueError),
+        ]
+    )
+    def test_creation(self,n_clusters,epsilon,iters,fuzzifier,exception_expected: Exception):
+        if exception_expected == None:
+            with not_raises() as e_info:
+                FuzzyCMeans(n_clusters=n_clusters,epsilon=epsilon,iters=iters,fuzzifier=fuzzifier)
+        else:
+            with pytest.raises(exception_expected) as e_info:
+                FuzzyCMeans(n_clusters=n_clusters,epsilon=epsilon,iters=iters,fuzzifier=fuzzifier)
+
+    @pytest.mark.parametrize(
+        "exception_expected",
+        [
+            (None),
+            (RuntimeError)
+        ]
+    )
+    def test_fit(self,exception_expected: Exception):
+        if exception_expected == None:
+            with not_raises() as e_info:
+                X, y = load_iris(return_X_y=True)
+                f = FuzzyCMeans()
+                f.fit(X,y)
+                f.predict(X)
+                f.predict_fuzzy(X)
+                f.predict_proba(X)
+        else:
+            with pytest.raises(exception_expected) as e_info:
+                X, y = load_iris(return_X_y=True)
+                f = FuzzyCMeans()
+                f.predict(X)
+                f.predict_fuzzy(X)
+                f.predict_proba(X)