Class Kernel

machine_learning/sequential_minimum_optimization.py:404–434

class Kernel:
    def __init__(self, kernel, degree=1.0, coef0=0.0, gamma=1.0):
        self.degree = np.float64(degree)
        self.coef0 = np.float64(coef0)
        self.gamma = np.float64(gamma)
        self._kernel_name = kernel
        self._kernel = self._get_kernel(kernel_name=kernel)
        self._check()

    def _polynomial(self, v1, v2):
        return (self.gamma * np.inner(v1, v2) + self.coef0) ** self.degree

    def _linear(self, v1, v2):
        return np.inner(v1, v2) + self.coef0

    def _rbf(self, v1, v2):
        return np.exp(-1 * (self.gamma * np.linalg.norm(v1 - v2) ** 2))

    def _check(self):
        if self._kernel == self._rbf and self.gamma < 0:
            raise ValueError("gamma value must be non-negative")

    def _get_kernel(self, kernel_name):
        maps = {"linear": self._linear, "poly": self._polynomial, "rbf": self._rbf}
        return maps[kernel_name]

    def __call__(self, v1, v2):
        return self._kernel(v1, v2)

    def __repr__(self):
        return self._kernel_name


def count_time(func):