"""
.. codeauthor:: Ivano Lauriola <ivanolauriola@gmail.com>
.. codeauthor:: Mirko Polato
================
Kernel functions
================
.. currentmodule:: MKLpy.metrics.pairwise
This module contains all boolean kernel functions of MKLpy
"""
import numpy as np
from scipy.special import binom
from scipy.sparse import issparse
from sklearn.metrics.pairwise import check_pairwise_arrays
from sklearn.metrics.pairwise import linear_kernel
#----------BOOLEAN KERNELS----------
def monotone_conjunctive_kernel(X,Z=None,c=2):
L = linear_kernel(X,Z)
return binom(L,c)
def monotone_disjunctive_kernel(X,Z=None,d=2):
L = linear_kernel(X,Z)
n = X.shape[1]
XX = np.dot(X.sum(axis=1).reshape(X.shape[0],1), np.ones((1,Z.shape[0])))
TT = np.dot(Z.sum(axis=1).reshape(Z.shape[0],1), np.ones((1,X.shape[0])))
N_x = n - XX
N_t = n - TT
N_xz = N_x - TT.T + L
N_d = binom(n, d)
N_x = binom(N_x,d)
N_t = binom(N_t,d)
N_xz = binom(N_xz,d)
return (N_d - N_x - N_t.T + N_xz)
def monotone_dnf_kernel(X,Z=None,d=2,c=2):
X, Z = check_pairwise_arrays(X, Z)
n = X.shape[1]
n_c = binom(n,c)
XX = np.dot(X.sum(axis=1).reshape(X.shape[0],1), np.ones((1,Z.shape[0])))
ZZ = np.dot(T.sum(axis=1).reshape(Z.shape[0],1), np.ones((1,X.shape[0])))
XXc = binom(XX,c)
ZZc = binom(ZZ,c)
return binom(n_c,d) - binom(n_c - XXc, d) - binom(n_c - ZZc.T, d) + binom(my_mdk(X,Z,c),d)
def monotone_cnf_kernel(X,Z=None,c=2,d=2):
pass
def conjunctive_kernel(X,Z=None,c=2):
pass
def disjunctive_kernel(X,Z=None,d=2):
pass
def dnf_kernel(X,Z=None,d=2,c=2):
pass
def cnf_kernel(X,Z=None,c=2,d=2):
pass
def tanimoto_kernel(X,Z=None):#?
L = linear_kernel(X,Z)
xx = np.linalg.norm(X,axis=1)
tt = np.linalg.norm(T,axis=1)
pass
