コード例 #1
0
def test_rff_feature_map_hessian_theano_execute():
    if not theano_available:
        raise SkipTest("Theano not available.")
       
    D = 2
    x = np.random.randn(D)
    m = 10
    sigma = 1.
    omega, u = rff_sample_basis(D, m, sigma)
    
    for i in range(m):
        rff_feature_map_comp_hessian_theano(x, omega[:, i], u[i])
コード例 #2
0
def test_rff_feature_map_hessian_theano_execute():
    if not theano_available:
        raise SkipTest("Theano not available.")

    D = 2
    x = np.random.randn(D)
    m = 10
    sigma = 1.
    omega, u = rff_sample_basis(D, m, sigma)

    for i in range(m):
        rff_feature_map_comp_hessian_theano(x, omega[:, i], u[i])
コード例 #3
0
ファイル: gaussian.py プロジェクト: JHamp/kernel_exp_family 
 def hessian(self, x):
     """
     Computes the Hessian of the learned log-density function.
     
     WARNING: This implementation slow, so don't call repeatedly.
     """
     assert_array_shape(x, ndim=1, dims={0: self.D})
     
     H = np.zeros((self.D, self.D))
     for i, theta_i in enumerate(self.theta):
         H += theta_i * rff_feature_map_comp_hessian_theano(x, self.omega[:, i], self.u[i])
 
     # RFF is a monte carlo average, so have to normalise by np.sqrt(m) here
     return H / np.sqrt(self.m)
コード例 #4
0
        def hessian(self, x):
            """
            Computes the Hessian of the learned log-density function.
            
            WARNING: This implementation slow, so don't call repeatedly.
            """
            assert_array_shape(x, ndim=1, dims={0: self.D})

            H = np.zeros((self.D, self.D))
            for i, theta_i in enumerate(self.theta):
                H += theta_i * rff_feature_map_comp_hessian_theano(
                    x, self.omega[:, i], self.u[i])

            # RFF is a monte carlo average, so have to normalise by np.sqrt(m) here
            return H / np.sqrt(self.m)