Example #1
0
def run_dlssm(y, f, mask, D, K, maxiter):
    """
    Run VB inference for linear state space model with drifting dynamics.
    """

    (M, N) = np.shape(y)

    # Dynamics matrix with ARD
    # alpha : (D) x ()
    alpha = Gamma(1e-5, 1e-5, plates=(K, ), name='alpha')
    # A : (K) x (K)
    A = Gaussian(
        np.zeros(K),
        #np.identity(K),
        diagonal(alpha),
        plates=(K, ),
        name='A_S')
    A.initialize_from_value(np.identity(K))

    # rho
    ## rho = Gamma(1e-5,
    ##             1e-5,
    ##             plates=(K,),
    ##             name="rho")

    # S : () x (N-1,K)
    S = GaussianMarkovChain(np.ones(K),
                            1e-6 * np.identity(K),
                            A,
                            np.ones(K),
                            n=N - 1,
                            name='S')
    S.initialize_from_value(1 * np.ones((N - 1, K)))

    # Projection matrix of the dynamics matrix
    # beta : (K) x ()
    beta = Gamma(1e-5, 1e-5, plates=(K, ), name='beta')
    # B : (D) x (D*K)
    B = Gaussian(np.zeros(D * K),
                 diagonal(tile(beta, D)),
                 plates=(D, ),
                 name='B')
    b = np.zeros((D, D, K))
    b[np.arange(D), np.arange(D), np.zeros(D, dtype=int)] = 1
    B.initialize_from_value(np.reshape(1 * b, (D, D * K)))

    # A : (N-1,D) x (D)
    BS = MatrixDot(B, S.as_gaussian().add_plate_axis(-1), name='BS')

    # Latent states with dynamics
    # X : () x (N,D)
    X = GaussianMarkovChain(
        np.zeros(D),  # mean of x0
        1e-3 * np.identity(D),  # prec of x0
        BS,  # dynamics
        np.ones(D),  # innovation
        n=N,  # time instances
        name='X',
        initialize=False)
    X.initialize_from_value(np.random.randn(N, D))

    # Mixing matrix from latent space to observation space using ARD
    # gamma : (D) x ()
    gamma = Gamma(1e-5, 1e-5, plates=(D, ), name='gamma')
    # C : (M,1) x (D)
    C = Gaussian(np.zeros(D), diagonal(gamma), plates=(M, 1), name='C')
    C.initialize_from_value(np.random.randn(M, 1, D))

    # Observation noise
    # tau : () x ()
    tau = Gamma(1e-5, 1e-5, name='tau')

    # Observations
    # Y : (M,N) x ()
    CX = Dot(C, X.as_gaussian())
    Y = Normal(CX, tau, name='Y')

    #
    # RUN INFERENCE
    #

    # Observe data
    Y.observe(y, mask=mask)
    # Construct inference machine
    Q = VB(Y, X, S, A, alpha, B, beta, C, gamma, tau)

    #
    # Run inference with rotations.
    #

    # Rotate the D-dimensional state space (C, X)
    rotB = transformations.RotateGaussianMatrixARD(B, beta, axis='rows')
    rotX = transformations.RotateDriftingMarkovChain(X, B, S, rotB)
    rotC = transformations.RotateGaussianARD(C, gamma)
    R_X = transformations.RotationOptimizer(rotX, rotC, D)

    # Rotate the K-dimensional latent dynamics space (B, S)
    rotA = transformations.RotateGaussianARD(A, alpha)
    rotS = transformations.RotateGaussianMarkovChain(S, A, rotA)
    rotB = transformations.RotateGaussianMatrixARD(B, beta, axis='cols')
    R_S = transformations.RotationOptimizer(rotS, rotB, K)

    # Iterate
    for ind in range(int(maxiter / 5)):
        Q.update(repeat=5)
        #Q.update(X, S, A, alpha, rho, B, beta, C, gamma, tau, repeat=maxiter)
        R_X.rotate()
        R_S.rotate()
        ## R_X.rotate(
        ## check_bound=Q.compute_lowerbound,
        ## check_bound_terms=Q.compute_lowerbound_terms,
        ## check_gradient=True
        ##     )
        ## R_S.rotate(
        ## check_bound=Q.compute_lowerbound,
        ## check_bound_terms=Q.compute_lowerbound_terms,
        ## check_gradient=True
        ##     )

    #
    # SHOW RESULTS
    #

    # Mean and standard deviation of the posterior
    (f_mean, f_squared) = CX.get_moments()
    f_std = np.sqrt(f_squared - f_mean**2)

    # Plot observations space
    for m in range(M):
        plt.subplot(M, 1, m + 1)
        plt.plot(y[m, :], 'r.')
        plt.plot(f[m, :], 'b-')
        bpplt.errorplot(y=f_mean[m, :], error=2 * f_std[m, :])
Example #2
0
def run_dlssm(y, f, mask, D, K, maxiter):
    """
    Run VB inference for linear state space model with drifting dynamics.
    """
        
    (M, N) = np.shape(y)

    # Dynamics matrix with ARD
    # alpha : (D) x ()
    alpha = Gamma(1e-5,
                  1e-5,
                  plates=(K,),
                  name='alpha')
    # A : (K) x (K)
    A = Gaussian(np.zeros(K),
    #np.identity(K),
                 diagonal(alpha),
                 plates=(K,),
                 name='A_S')
    A.initialize_from_value(np.identity(K))

    # rho
    ## rho = Gamma(1e-5,
    ##             1e-5,
    ##             plates=(K,),
    ##             name="rho")

    # S : () x (N-1,K)
    S = GaussianMarkovChain(np.ones(K),
                            1e-6*np.identity(K),
                            A,
                            np.ones(K),
                            n=N-1,
                            name='S')
    S.initialize_from_value(1*np.ones((N-1,K)))

    # Projection matrix of the dynamics matrix
    # beta : (K) x ()
    beta = Gamma(1e-5,
                 1e-5,
                 plates=(K,),
                 name='beta')
    # B : (D) x (D*K)
    B = Gaussian(np.zeros(D*K),
                 diagonal(tile(beta, D)),
                 plates=(D,),
                 name='B')
    b = np.zeros((D,D,K))
    b[np.arange(D),np.arange(D),np.zeros(D,dtype=int)] = 1
    B.initialize_from_value(np.reshape(1*b, (D,D*K)))

    # A : (N-1,D) x (D)
    BS = MatrixDot(B, 
                   S.as_gaussian().add_plate_axis(-1), 
                   name='BS')

    # Latent states with dynamics
    # X : () x (N,D)
    X = GaussianMarkovChain(np.zeros(D),         # mean of x0
                            1e-3*np.identity(D), # prec of x0
                            BS,                   # dynamics
                            np.ones(D),          # innovation
                            n=N,                 # time instances
                            name='X',
                            initialize=False)
    X.initialize_from_value(np.random.randn(N,D))

    # Mixing matrix from latent space to observation space using ARD
    # gamma : (D) x ()
    gamma = Gamma(1e-5,
                  1e-5,
                  plates=(D,),
                  name='gamma')
    # C : (M,1) x (D)
    C = Gaussian(np.zeros(D),
                 diagonal(gamma),
                 plates=(M,1),
                 name='C')
    C.initialize_from_value(np.random.randn(M,1,D))

    # Observation noise
    # tau : () x ()
    tau = Gamma(1e-5,
                1e-5,
                name='tau')

    # Observations
    # Y : (M,N) x ()
    CX = Dot(C, X.as_gaussian())
    Y = Normal(CX,
               tau,
               name='Y')

    #
    # RUN INFERENCE
    #

    # Observe data
    Y.observe(y, mask=mask)
    # Construct inference machine
    Q = VB(Y, X, S, A, alpha, B, beta, C, gamma, tau)

    #
    # Run inference with rotations.
    #

    # Rotate the D-dimensional state space (C, X)
    rotB = transformations.RotateGaussianMatrixARD(B, beta, axis='rows')
    rotX = transformations.RotateDriftingMarkovChain(X, B, S, rotB)
    rotC = transformations.RotateGaussianARD(C, gamma)
    R_X = transformations.RotationOptimizer(rotX, rotC, D)

    # Rotate the K-dimensional latent dynamics space (B, S)
    rotA = transformations.RotateGaussianARD(A, alpha)
    rotS = transformations.RotateGaussianMarkovChain(S, A, rotA)
    rotB = transformations.RotateGaussianMatrixARD(B, beta, axis='cols')
    R_S = transformations.RotationOptimizer(rotS, rotB, K)

    # Iterate
    for ind in range(int(maxiter/5)):
        Q.update(repeat=5)
        #Q.update(X, S, A, alpha, rho, B, beta, C, gamma, tau, repeat=maxiter)
        R_X.rotate()
        R_S.rotate()
        ## R_X.rotate(
        ## check_bound=Q.compute_lowerbound,
        ## check_bound_terms=Q.compute_lowerbound_terms,
        ## check_gradient=True
        ##     )
        ## R_S.rotate(
        ## check_bound=Q.compute_lowerbound,
        ## check_bound_terms=Q.compute_lowerbound_terms,
        ## check_gradient=True
        ##     )

    #
    # SHOW RESULTS
    #

    # Mean and standard deviation of the posterior
    (f_mean, f_squared) = CX.get_moments()
    f_std = np.sqrt(f_squared - f_mean**2)

    # Plot observations space
    for m in range(M):
        plt.subplot(M,1,m+1)
        plt.plot(y[m,:], 'r.')
        plt.plot(f[m,:], 'b-')
        bpplt.errorplot(y=f_mean[m,:], error=2*f_std[m,:])
Example #3
0
def run_lssm(y, f, mask, D, maxiter):
    """
    Run VB inference for linear state space model.
    """

    (M, N) = np.shape(y)

    #
    # CONSTRUCT THE MODEL
    #

    # Dynamic matrix
    # alpha: (D) x ()
    alpha = Gamma(1e-5, 1e-5, plates=(D, ), name='alpha')
    # A : (D) x (D)
    A = Gaussian(np.zeros(D), diagonal(alpha), plates=(D, ), name='A')
    A.initialize_from_value(np.identity(D))

    # Latent states with dynamics
    # X : () x (N,D)
    X = GaussianMarkovChain(
        np.zeros(D),  # mean of x0
        1e-3 * np.identity(D),  # prec of x0
        A,  # dynamics
        np.ones(D),  # innovation
        n=N,  # time instances
        name='X',
        initialize=False)
    X.initialize_from_value(np.random.randn(N, D))

    # Mixing matrix from latent space to observation space using ARD
    # gamma : (D) x ()
    gamma = Gamma(1e-5, 1e-5, plates=(D, ), name='gamma')
    # C : (M,1) x (D)
    C = Gaussian(np.zeros(D), diagonal(gamma), plates=(M, 1), name='C')
    C.initialize_from_value(np.random.randn(M, 1, D))

    # Observation noise
    # tau : () x ()
    tau = Gamma(1e-5, 1e-5, name='tau')

    # Observations
    # Y : (M,N) x ()
    CX = Dot(C, X.as_gaussian())
    Y = Normal(CX, tau, name='Y')

    #
    # RUN INFERENCE
    #

    # Observe data
    Y.observe(y, mask=mask)
    # Construct inference machine
    Q = VB(Y, X, A, alpha, C, gamma, tau)
    # Iterate
    Q.update(X, A, alpha, C, gamma, tau, repeat=maxiter)

    #
    # SHOW RESULTS
    #

    # Mean and standard deviation of the posterior
    (f_mean, f_squared) = CX.get_moments()
    f_std = np.sqrt(f_squared - f_mean**2)

    # Plot observations space
    #plt.figure()
    for m in range(M):
        plt.subplot(M, 1, m + 1)
        plt.plot(y[m, :], 'r.')
        plt.plot(f[m, :], 'b-')
        bpplt.errorplot(y=f_mean[m, :], error=2 * f_std[m, :])
Example #4
0
def run_lssm(y, f, mask, D, maxiter):
    """
    Run VB inference for linear state space model.
    """

    (M, N) = np.shape(y)

    #
    # CONSTRUCT THE MODEL
    #

    # Dynamic matrix
    # alpha: (D) x ()
    alpha = Gamma(1e-5,
                  1e-5,
                  plates=(D,),
                  name='alpha')
    # A : (D) x (D)
    A = Gaussian(np.zeros(D),
                 diagonal(alpha),
                 plates=(D,),
                 name='A')
    A.initialize_from_value(np.identity(D))

    # Latent states with dynamics
    # X : () x (N,D)
    X = GaussianMarkovChain(np.zeros(D),         # mean of x0
                            1e-3*np.identity(D), # prec of x0
                            A,                   # dynamics
                            np.ones(D),          # innovation
                            n=N,                 # time instances
                            name='X',
                            initialize=False)
    X.initialize_from_value(np.random.randn(N,D))

    # Mixing matrix from latent space to observation space using ARD
    # gamma : (D) x ()
    gamma = Gamma(1e-5,
                  1e-5,
                  plates=(D,),
                  name='gamma')
    # C : (M,1) x (D)
    C = Gaussian(np.zeros(D),
                 diagonal(gamma),
                 plates=(M,1),
                 name='C')
    C.initialize_from_value(np.random.randn(M,1,D))

    # Observation noise
    # tau : () x ()
    tau = Gamma(1e-5,
                1e-5,
                name='tau')

    # Observations
    # Y : (M,N) x ()
    CX = Dot(C, X.as_gaussian())
    Y = Normal(CX,
               tau,
               name='Y')

    #
    # RUN INFERENCE
    #

    # Observe data
    Y.observe(y, mask=mask)
    # Construct inference machine
    Q = VB(Y, X, A, alpha, C, gamma, tau)
    # Iterate
    Q.update(X, A, alpha, C, gamma, tau, repeat=maxiter)

    #
    # SHOW RESULTS
    #

    # Mean and standard deviation of the posterior
    (f_mean, f_squared) = CX.get_moments()
    f_std = np.sqrt(f_squared - f_mean**2)

    # Plot observations space
    #plt.figure()
    for m in range(M):
        plt.subplot(M,1,m+1)
        plt.plot(y[m,:], 'r.')
        plt.plot(f[m,:], 'b-')
        bpplt.errorplot(y=f_mean[m,:], error=2*f_std[m,:])