Python linalg.slogdet方法代碼示例

# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import slogdet [as 別名]
def do(self, a, b, tags):
        d = linalg.det(a)
        (s, ld) = linalg.slogdet(a)
        if asarray(a).dtype.type in (single, double):
            ad = asarray(a).astype(double)
        else:
            ad = asarray(a).astype(cdouble)
        ev = linalg.eigvals(ad)
        assert_almost_equal(d, multiply.reduce(ev, axis=-1))
        assert_almost_equal(s * np.exp(ld), multiply.reduce(ev, axis=-1))

        s = np.atleast_1d(s)
        ld = np.atleast_1d(ld)
        m = (s != 0)
        assert_almost_equal(np.abs(s[m]), 1)
        assert_equal(ld[~m], -inf) 

# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import slogdet [as 別名]
def do(self, a, b):
        d = linalg.det(a)
        (s, ld) = linalg.slogdet(a)
        if asarray(a).dtype.type in (single, double):
            ad = asarray(a).astype(double)
        else:
            ad = asarray(a).astype(cdouble)
        ev = linalg.eigvals(ad)
        assert_almost_equal(d, multiply.reduce(ev, axis=-1))
        assert_almost_equal(s * np.exp(ld), multiply.reduce(ev, axis=-1))

        s = np.atleast_1d(s)
        ld = np.atleast_1d(ld)
        m = (s != 0)
        assert_almost_equal(np.abs(s[m]), 1)
        assert_equal(ld[~m], -inf) 

# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import slogdet [as 別名]
def test_0_size(self):
        a = np.zeros((0, 0), dtype=np.complex64)
        res = linalg.det(a)
        assert_equal(res, 1.)
        assert_(res.dtype.type is np.complex64)
        res = linalg.slogdet(a)
        assert_equal(res, (1, 0))
        assert_(res[0].dtype.type is np.complex64)
        assert_(res[1].dtype.type is np.float32)

        a = np.zeros((0, 0), dtype=np.float64)
        res = linalg.det(a)
        assert_equal(res, 1.)
        assert_(res.dtype.type is np.float64)
        res = linalg.slogdet(a)
        assert_equal(res, (1, 0))
        assert_(res[0].dtype.type is np.float64)
        assert_(res[1].dtype.type is np.float64) 

# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import slogdet [as 別名]
def initialize(self):
        """ Initialize members for faster scoring. """
        self.ct = self.cw + self.cb
        self.p = inv(self.ct * 0.5) - inv(0.5 * self.cw + self.cb)
        self.q = inv(2 * self.cw) - inv(2 * self.ct)
        k1 = reduce(operator.mul, slogdet(0.5 * self.ct))
        k2 = reduce(operator.mul, slogdet(0.5 * self.cw + self.cb))
        k3 = reduce(operator.mul, slogdet(2 * self.ct))
        k4 = reduce(operator.mul, slogdet(2 * self.cw))
        self.k = 0.5 * (k1 - k2 + k3 - k4)
        self.r = 0.5 * (0.25 * self.p - self.q)
        self.s = 0.5 * (0.25 * self.p + self.q)
        self.t = 0.25 * np.dot(self.p, self.mean.T)
        u1 = 2 * np.dot(self.mean, 0.25 * self.p)
        self.u = self.k + np.dot(u1, self.mean.T)
        self.initialized = True 

# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import slogdet [as 別名]
def error(self, start, end):
        """Return the approximation cost on the segment [start:end].

        Args:
            start (int): start of the segment
            end (int): end of the segment

        Returns:
            float: segment cost

        Raises:
            NotEnoughPoints: when the segment is too short (less than ``'min_size'`` samples).
        """
        if end - start < self.min_size:
            raise NotEnoughPoints
        sub = self.signal[start:end]

        if self.signal.shape[1] > 1:
            cov = np.cov(sub.T)
        else:
            cov = np.array([[sub.var()]])
        _, val = slogdet(cov)
        return val * (end - start) 

# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import slogdet [as 別名]
def orientation(face, origin):
    """Compute the orientation of the face with respect to a point, origin.

    Parameters
    ----------
    face : array-like, of shape (N-dim, N-dim)
        The hyperplane we want to know the orientation of
        Do notice that the order in which you provide the points is critical
    origin : array-like, point of shape (N-dim)
        The point to compute the orientation from

    Returns
    -------
    0 if the origin lies in the same hyperplane as face,
    -1 or 1 to indicate left or right orientation

    If two points lie on the same side of the face, the orientation will
    be equal, if they lie on the other side of the face, it will be negated.
    """
    vectors = array(face)
    sign, logdet = slogdet(vectors - origin)
    if logdet < -50:  # assume it to be zero when it's close to zero
        return 0
    return sign 

# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import slogdet [as 別名]
def test_zero(self):
        assert_equal(linalg.det([[0.0]]), 0.0)
        assert_equal(type(linalg.det([[0.0]])), double)
        assert_equal(linalg.det([[0.0j]]), 0.0)
        assert_equal(type(linalg.det([[0.0j]])), cdouble)

        assert_equal(linalg.slogdet([[0.0]]), (0.0, -inf))
        assert_equal(type(linalg.slogdet([[0.0]])[0]), double)
        assert_equal(type(linalg.slogdet([[0.0]])[1]), double)
        assert_equal(linalg.slogdet([[0.0j]]), (0.0j, -inf))
        assert_equal(type(linalg.slogdet([[0.0j]])[0]), cdouble)
        assert_equal(type(linalg.slogdet([[0.0j]])[1]), double)