bbobbenchmarks.py 文件源码

def _evalfull(self, x):
        fadd = self.fopt
        curshape, dim = self.shape_(x)
        # it is assumed x are row vectors

        if self.lastshape != curshape:
            self.initwithsize(curshape, dim)

        # BOUNDARY HANDLING
        xoutside = np.maximum(0, np.abs(x) - 5.) * sign(x)
        fpen = (10. / dim) * np.sum(xoutside ** 2, -1)
        fadd = fadd + fpen

        # TRANSFORMATION IN SEARCH SPACE
        x = x - self.arrxopt # cannot be replaced with x -= arrxopt!
        x = dot(x, self.rotation)
        x = monotoneTFosc(x)
        x = dot(x, self.linearTF)

        # COMPUTATION core
        if len(curshape) < 2: # popsize is one
            ftrue = np.sum(dot(self.aK, np.cos(dot(self.bK.T, 2 * np.pi * (np.reshape(x, (1, len(x))) + 0.5)))))
        else:
            ftrue = np.zeros(curshape[0]) # curshape[0] is popsize
            for k, i in enumerate(x):
                # TODO: simplify next line
                ftrue[k] = np.sum(dot(self.aK, np.cos(dot(self.bK.T, 2 * np.pi * (np.reshape(i, (1, len(i))) + 0.5)))))
        ftrue = 10. * (ftrue / dim - self.f0) ** 3
        try:
            ftrue = np.hstack(ftrue)
        except TypeError:
            pass
        fval = self.noise(ftrue)

        # FINALIZE
        ftrue += fadd
        fval += fadd
        return fval, ftrue