diff --git a/machine_learning/support_vector_machines.py b/machine_learning/support_vector_machines.py
index d17c9044a3e9..b9a5d99a1849 100644
--- a/machine_learning/support_vector_machines.py
+++ b/machine_learning/support_vector_machines.py
@@ -49,6 +49,19 @@ class SVC:
Traceback (most recent call last):
...
ValueError: gamma must be > 0
+
+ >>> SVC(kernel="polynomial")
+ Traceback (most recent call last):
+ ...
+ ValueError: polynomial kernel requires degree
+ >>> SVC(kernel="polynomial",degree=None)
+ Traceback (most recent call last):
+ ...
+ ValueError: degree must be float or int
+ >>> SVC(kernel="polynomial",degree=-1.0)
+ Traceback (most recent call last):
+ ...
+ ValueError: degree must be > 0
"""
def __init__(
@@ -57,9 +70,13 @@ def __init__(
regularization: float = np.inf,
kernel: str = "linear",
gamma: float = 0.0,
+ degree: float = 0.0,
+ coef0: float = 0.0,
) -> None:
self.regularization = regularization
self.gamma = gamma
+ self.degree = degree
+ self.coef0 = coef0
if kernel == "linear":
self.kernel = self.__linear
elif kernel == "rbf":
@@ -73,6 +90,14 @@ def __init__(
# in the future, there could be a default value like in sklearn
# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
# previously it was 1/(n_features)
+ elif kernel == "polynomial":
+ if self.degree == 0:
+ raise ValueError("polynomial kernel requires degree")
+ if not isinstance(self.degree, (float, int)):
+ raise ValueError("degree must be float or int")
+ if not self.degree > 0:
+ raise ValueError("degree must be > 0")
+ self.kernel = self.__polynomial
else:
msg = f"Unknown kernel: {kernel}"
raise ValueError(msg)
@@ -98,6 +123,22 @@ def __rbf(self, vector1: ndarray, vector2: ndarray) -> float:
"""
return np.exp(-(self.gamma * norm_squared(vector1 - vector2)))
+ def __polynomial(self, vector1: ndarray, vector2: ndarray) -> float:
+ """
+ Polynomial kernel: (x . y + coef0)^degree
+
+ Note: for more information see:
+ https://en.wikipedia.org/wiki/Polynomial_kernel
+
+ Args:
+ vector1 (ndarray): first vector
+ vector2 (ndarray): second vector
+
+ Returns:
+ float: (vector1 . vector2 + coef0)^degree
+ """
+ return (np.dot(vector1, vector2) + self.coef0) ** self.degree
+
def fit(self, observations: list[ndarray], classes: ndarray) -> None:
"""
Fits the SVC with a set of observations.
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