bounding.py 文件源码

def __init__(self, ctr, am):
        self.n = len(ctr)  # dimension
        self.ctr = np.array(ctr)  # center coordinates
        self.am = np.array(am)  # precision matrix (inverse of covariance)

        # Volume of ellipsoid is the volume of an n-sphere divided
        # by the (determinant of the) Jacobian associated with the
        # transformation, which by definition is the precision matrix.
        self.vol = vol_prefactor(self.n) / np.sqrt(linalg.det(self.am))

        # The eigenvalues (l) of `a` are (a^-2, b^-2, ...) where
        # (a, b, ...) are the lengths of principle axes.
        # The eigenvectors (v) are the normalized principle axes.
        l, v = linalg.eigh(self.am)
        if np.all((l > 0.) & (np.isfinite(l))):
            self.axlens = 1. / np.sqrt(l)
        else:
            raise ValueError("The input precision matrix defining the "
                             "ellipsoid {0} is apparently singular with "
                             "l={1} and v={2}.".format(self.am, l, v))

        # Scaled eigenvectors are the axes, where `axes[:,i]` is the
        # i-th axis.  Multiplying this matrix by a vector will transform a
        # point in the unit n-sphere to a point in the ellipsoid.
        self.axes = np.dot(v, np.diag(self.axlens))

        # Amount by which volume was increased after initialization (i.e.
        # cumulative factor from `scale_to_vol`).
        self.expand = 1.