<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def to_canonical_factor(self):
 u""" Returns an equivalent CanonicalDistribution object. The formulas for calculating the cannonical factor parameters for N(μ; Σ) = C(K; h; g) are as follows - K = sigma^(-1) h = sigma^(-1) * mu g = -(0.5) * mu.T * sigma^(-1) * mu - log((2*pi)^(n/2) * det(sigma)^(0.5)) where, K,h,g are the canonical factor parameters sigma is the covariance_matrix of the distribution, mu is the mean_vector of the distribution, mu.T is the transpose of the matrix mu, and det(sigma) is the determinant of the matrix sigma. Example ------- ['x1', 'x2', 'x3'] array([[0.3125, -0.125, 0.], [-0.125, 0.5833, 0.333], [ 0., 0.333, 0.333]]) array([[ 0.6875], [-0.54166], [ 0.33333]])) -6.51533 """

        from pgmpy.factors.continuous import CanonicalDistribution

        mu = self.mean
        sigma = self.covariance

        K = self.precision_matrix

        h = np.dot(K, mu)

        g = -(0.5) * np.dot(mu.T, h)[0, 0] - np.log(
            np.power(2 * np.pi, len(self.variables)/2) *
            np.power(abs(np.linalg.det(sigma)), 0.5))

        return CanonicalDistribution(self.variables, K, h, g)