def set_batch_covariance(self, Z):
Phi = feature_map(Z, self.omega, self.u)
self.mu = np.mean(Phi, axis=0)
self.C = np.cov(Phi.T)
def set_batch_covariance(self, Z):
Phi = feature_map(Z, self.omega, self.u)
self.mu = np.mean(Phi, axis=0)
self.C = np.cov(Phi.T)
np.random.seed(0) # fix basis for now D = 2 m = 1000 gamma = .3 omega, u = sample_basis(D, m, gamma) # sample points in input space N = 2000 Z = sample_banana(N, D) # fit Gaussian in feature space Phi = feature_map(Z, omega, u) # mean and covariance, batch version mu = np.mean(Phi, 0) eta = 0.01 C = np.cov(Phi.T) + eta ** 2 * np.eye(m) L = np.linalg.cholesky(C) # step size eta = 50. plt.plot(Z[:, 0], Z[:, 1], 'bx') # proposal plotting colors colors = ['y', 'r', 'g', 'm', 'black']
