gklearn.utils.kernels¶
Those who are not graph kernels. We can be kernels for nodes or edges! These kernels are defined between pairs of vectors.
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deltakernel(x, y)[source]¶ Delta kernel. Return 1 if x == y, 0 otherwise.
- x, y : any
- Two parts to compare.
- kernel : integer
- Delta kernel.
[1] H. Kashima, K. Tsuda, and A. Inokuchi. Marginalized kernels between labeled graphs. In Proceedings of the 20th International Conference on Machine Learning, Washington, DC, United States, 2003.
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gaussiankernel(x, y, gamma=None)[source]¶ Gaussian kernel. Compute the rbf (gaussian) kernel between x and y:
K(x, y) = exp(-gamma ||x-y||^2).Read more in the User Guide of scikit-learn library.
x, y : array
- gamma : float, default None
- If None, defaults to 1.0 / n_features
kernel : float
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kernelproduct(k1, k2, d11, d12, d21=None, d22=None, lamda=1)[source]¶ Product of a pair of kernels.
k = lamda * k1(d11, d12) * k2(d21, d22)
- k1, k2 : function
- A pair of kernel functions.
- d11, d12:
- Inputs of k1. If d21 or d22 is None, apply d11, d12 to both k1 and k2.
- d21, d22:
- Inputs of k2.
- lamda: float
- Coefficient of the product.
kernel : integer
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kernelsum(k1, k2, d11, d12, d21=None, d22=None, lamda1=1, lamda2=1)[source]¶ Sum of a pair of kernels.
k = lamda1 * k1(d11, d12) + lamda2 * k2(d21, d22)
- k1, k2 : function
- A pair of kernel functions.
- d11, d12:
- Inputs of k1. If d21 or d22 is None, apply d11, d12 to both k1 and k2.
- d21, d22:
- Inputs of k2.
- lamda1, lamda2: float
- Coefficients of the product.
kernel : integer