feat: Added parameter c to SupportVectorMachines (#267)

Closes #169.

### Summary of Changes

Added parameter c to SupportVectorMachines

---------

Co-authored-by: alex-senger <[email protected]>
Co-authored-by: megalinter-bot <[email protected]>
Co-authored-by: Lars Reimann <[email protected]>

src/safeds/ml/classical/classification/_support_vector_machine.py

@@ -13,14 +13,30 @@
 
 
 class SupportVectorMachine(Classifier):
-    """Support vector machine."""
+    """
+    Support vector machine.
 
-    def __init__(self) -> None:
+    Parameters
+    ----------
+    c: float
+        The strength of regularization. Must be strictly positive.
+
+    Raises
+    ------
+    ValueError
+        If `c` is less than or equal to 0.
+    """
+
+    def __init__(self, c: float = 1.0) -> None:
         # Internal state
         self._wrapped_classifier: sk_SVC | None = None
         self._feature_names: list[str] | None = None
         self._target_name: str | None = None
 
+        if c <= 0:
+            raise ValueError("The strength of regularization given by the c parameter must be strictly positive.")
+        self._c = c
+
     def fit(self, training_set: TaggedTable) -> SupportVectorMachine:
         """
         Create a copy of this classifier and fit it with the given training data.
@@ -42,10 +58,10 @@ def fit(self, training_set: TaggedTable) -> SupportVectorMachine:
         LearningError
             If the training data contains invalid values or if the training failed.
         """
-        wrapped_classifier = sk_SVC()
+        wrapped_classifier = sk_SVC(C=self._c)
         fit(wrapped_classifier, training_set)
 
-        result = SupportVectorMachine()
+        result = SupportVectorMachine(self._c)
         result._wrapped_classifier = wrapped_classifier
         result._feature_names = training_set.features.column_names
         result._target_name = training_set.target.name

src/safeds/ml/classical/regression/_support_vector_machine.py

@@ -13,14 +13,30 @@
 
 
 class SupportVectorMachine(Regressor):
-    """Support vector machine."""
+    """
+    Support vector machine.
 
-    def __init__(self) -> None:
+    Parameters
+    ----------
+    c: float
+        The strength of regularization. Must be strictly positive.
+
+    Raises
+    ------
+    ValueError
+        If `c` is less than or equal to 0.
+    """
+
+    def __init__(self, c: float = 1.0) -> None:
         # Internal state
         self._wrapped_regressor: sk_SVR | None = None
         self._feature_names: list[str] | None = None
         self._target_name: str | None = None
 
+        if c <= 0:
+            raise ValueError("The strength of regularization given by the c parameter must be strictly positive.")
+        self._c = c
+
     def fit(self, training_set: TaggedTable) -> SupportVectorMachine:
         """
         Create a copy of this regressor and fit it with the given training data.
@@ -42,10 +58,10 @@ def fit(self, training_set: TaggedTable) -> SupportVectorMachine:
         LearningError
             If the training data contains invalid values or if the training failed.
         """
-        wrapped_regressor = sk_SVR()
+        wrapped_regressor = sk_SVR(C=self._c)
         fit(wrapped_regressor, training_set)
 
-        result = SupportVectorMachine()
+        result = SupportVectorMachine(self._c)
         result._wrapped_regressor = wrapped_regressor
         result._feature_names = training_set.features.column_names
         result._target_name = training_set.target.name

tests/safeds/ml/classical/classification/test_support_vector_machine.py

@@ -0,0 +1,27 @@
+import pytest
+from safeds.data.tabular.containers import Table, TaggedTable
+from safeds.ml.classical.classification import SupportVectorMachine
+
+
+@pytest.fixture()
+def training_set() -> TaggedTable:
+    table = Table.from_dict({"col1": [1, 2, 3, 4], "col2": [1, 2, 3, 4]})
+    return table.tag_columns(target_name="col1", feature_names=["col2"])
+
+
+class TestC:
+    def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
+        fitted_model = SupportVectorMachine(c=2).fit(training_set=training_set)
+        assert fitted_model._c == 2
+
+    def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
+        fitted_model = SupportVectorMachine(c=2).fit(training_set)
+        assert fitted_model._wrapped_classifier is not None
+        assert fitted_model._wrapped_classifier.C == 2
+
+    def test_should_raise_if_less_than_or_equal_to_0(self) -> None:
+        with pytest.raises(
+            ValueError,
+            match="The strength of regularization given by the c parameter must be strictly positive.",
+        ):
+            SupportVectorMachine(c=-1)

tests/safeds/ml/classical/regression/test_support_vector_machine.py

@@ -0,0 +1,27 @@
+import pytest
+from safeds.data.tabular.containers import Table, TaggedTable
+from safeds.ml.classical.regression import SupportVectorMachine
+
+
+@pytest.fixture()
+def training_set() -> TaggedTable:
+    table = Table.from_dict({"col1": [1, 2, 3, 4], "col2": [1, 2, 3, 4]})
+    return table.tag_columns(target_name="col1", feature_names=["col2"])
+
+
+class TestC:
+    def test_should_be_passed_to_fitted_model(self, training_set: TaggedTable) -> None:
+        fitted_model = SupportVectorMachine(c=2).fit(training_set=training_set)
+        assert fitted_model._c == 2
+
+    def test_should_be_passed_to_sklearn(self, training_set: TaggedTable) -> None:
+        fitted_model = SupportVectorMachine(c=2).fit(training_set)
+        assert fitted_model._wrapped_regressor is not None
+        assert fitted_model._wrapped_regressor.C == 2
+
+    def test_should_raise_if_less_than_or_equal_to_0(self) -> None:
+        with pytest.raises(
+            ValueError,
+            match="The strength of regularization given by the c parameter must be strictly positive.",
+        ):
+            SupportVectorMachine(c=-1)