|
- """Those who are not graph kernels. We can be kernels for nodes or edges!
- """
-
-
- def deltakernel(x, y):
- """Delta kernel. Return 1 if x == y, 0 otherwise.
-
- Parameters
- ----------
- x, y : any
- Two parts to compare.
-
- Return
- ------
- kernel : integer
- Delta kernel.
-
- References
- ----------
- [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.
- """
- return x == y #(1 if condition else 0)
-
-
- def gaussiankernel(x, y):
- """Gaussian kernel. Use sklearn.metrics.pairwise.rbf_kernel instead.
- """
- pass
-
-
- def kernelsum(k1, k2, d11, d12, d21=None, d22=None, lamda1=1, lamda2=1):
- """Sum of a pair of kernels.
-
- k = lamda1 * k1(d11, d12) + lamda2 * k2(d21, d22)
-
- Parameters
- ----------
- 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.
-
- Return
- ------
- kernel : integer
-
- """
- if d21 == None or d22 == None:
- kernel = lamda1 * k1(d11, d12) + lamda2 * k2(d11, d12)
- else:
- kernel = lamda1 * k1(d11, d12) + lamda2 * k2(d21, d22)
- return kernel
-
-
- def kernelproduct(k1, k2, d11, d12, d21=None, d22=None, lamda=1):
- """Product of a pair of kernels.
-
- k = lamda * k1(d11, d12) * k2(d21, d22)
-
- Parameters
- ----------
- 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.
-
- Return
- ------
- kernel : integer
- """
- if d21 == None or d22 == None:
- kernel = lamda * k1(d11, d12) * k2(d11, d12)
- else:
- kernel = lamda * k1(d11, d12) * k2(d21, d22)
- return kernel
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