Esempio n. 1
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def run(N=50, K=5, D=2):

    # Generate data
    N1 = np.floor(0.5 * N)
    N2 = N - N1
    y = np.vstack([
        np.random.normal(0, 0.5, size=(N1, D)),
        np.random.normal(10, 0.5, size=(N2, D))
    ])

    # Construct model
    Q = gaussianmix_model(N, K, D)

    # Observe data
    Q['Y'].observe(y)

    # Run inference
    Q.update(repeat=30)

    # Run predictive model
    zh = nodes.Categorical(Q['alpha'], name='zh')
    Yh = nodes.Mixture(zh, nodes.Gaussian, Q['X'], Q['Lambda'], name='Yh')
    zh.update()

    # Plot predictive pdf
    N1 = 400
    N2 = 400
    x1 = np.linspace(-3, 15, N1)
    x2 = np.linspace(-3, 15, N2)
    xh = utils.grid(x1, x2)
    lpdf = Yh.integrated_logpdf_from_parents(xh, 0)
    pdf = np.reshape(np.exp(lpdf), (N2, N1))
    plt.clf()
    plt.contourf(x1, x2, pdf, 100)
    plt.scatter(y[:, 0], y[:, 1])
    print('integrated pdf:', np.sum(pdf) * (18 * 18) / (N1 * N2))

    Q['X'].show()
    Q['alpha'].show()

    plt.show()
Esempio n. 2
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def run(N=50, K=5, D=2):

    # Generate data
    N1 = np.floor(0.5*N)
    N2 = N - N1
    y = np.vstack([np.random.normal(0, 0.5, size=(N1,D)),
                   np.random.normal(10, 0.5, size=(N2,D))])

    
    # Construct model
    Q = gaussianmix_model(N,K,D)

    # Observe data
    Q['Y'].observe(y)

    # Run inference
    Q.update(repeat=30)

    # Run predictive model
    zh = nodes.Categorical(Q['alpha'], name='zh')
    Yh = nodes.Mixture(zh, nodes.Gaussian, Q['X'], Q['Lambda'], name='Yh')
    zh.update()

    # Plot predictive pdf
    N1 = 400
    N2 = 400
    x1 = np.linspace(-3, 15, N1)
    x2 = np.linspace(-3, 15, N2)
    xh = utils.grid(x1, x2)
    lpdf = Yh.integrated_logpdf_from_parents(xh, 0)
    pdf = np.reshape(np.exp(lpdf), (N2,N1))
    plt.clf()
    plt.contourf(x1, x2, pdf, 100)
    plt.scatter(y[:,0], y[:,1])
    print('integrated pdf:', np.sum(pdf)*(18*18)/(N1*N2))

    Q['X'].show()
    Q['alpha'].show()

    plt.show()
Esempio n. 3
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def run(N=50, K=5, D=2):

    #plt.ion()
    #17,31
    #np.random.seed(31)
    
    # Generate data
    N1 = np.floor(0.5*N)
    N2 = N - N1
    y = np.vstack([np.random.normal(0, 0.5, size=(N1,D)),
                   np.random.normal(10, 0.5, size=(N2,D))])

    
    # Construct model
    (Y, X, Lambda, z, alpha) = gaussianmix_model(N,K,D)

    # Initialize nodes (from prior and randomly)
    alpha.initialize_from_prior()
    z.initialize_from_prior()
    Lambda.initialize_from_parameters(D, 10*np.identity(D))

    X.initialize_from_prior()
    X.initialize_from_parameters(X.random(), np.identity(D))

    ## X.initialize_from_parameters(np.random.permutation(y)[:K],
    ##                              0.01*np.identity(D))

    #X.initialize_from_random()
    # Initialize means by selecting random data points
    #X.initialize_from_value(np.random.permutation(y)[:K])
    #return
    #X.initialize_random_mean()
    #Y.initialize()

    ## X.show()
    ## return

    # Data with missing values
    ## mask = np.random.rand(M,N) < 0.4 # randomly missing
    ## mask[:,20:40] = False # gap missing
    # Y.observe(y, mask)
    ## alpha.show()
    ## Lambda.show()
    ## z.show()
    X.show()
    #return

    Y.observe(y)

    ## z.update()
    ## X.update()
    ## alpha.update()

    ## X.show()
    ## alpha.show()
    #Lambda.show()
    #return

    ## X.show()
    ## Lambda.show()
    ## z.update()
    ## z.show()
    ## return

    # Inference loop.
    maxiter = 30
    L_X = np.zeros(maxiter)
    L_Lambda = np.zeros(maxiter)
    L_alpha = np.zeros(maxiter)
    L_z = np.zeros(maxiter)
    L_Y = np.zeros(maxiter)
    L = np.zeros(maxiter)
    L_last = -np.inf
    for i in range(maxiter):
        t = time.clock()

        # Update nodes
        z.update()
        alpha.update()
        X.update()
        Lambda.update()

        #Y.show()

        #z.show()

        # Compute lower bound
        L_X[i] = X.lower_bound_contribution()
        L_Lambda[i] = Lambda.lower_bound_contribution()
        L_alpha[i] = alpha.lower_bound_contribution()
        L_z[i] = z.lower_bound_contribution()
        L_Y[i] = Y.lower_bound_contribution()
        L[i] = L_X[i] + L_Lambda[i] + L_alpha[i] + L_z[i] + L_Y[i]

        #print('terms:', L_X[i], L_Lambda[i], L_alpha[i], L_z[i], L_Y[i])

        # Check convergence
        print("Iteration %d: loglike=%e (%.3f seconds)" % (i+1, L[i], time.clock()-t))
        if L_last - L[i] > 1e-6:
            L_diff = (L_last - L[i])
            print("Lower bound decreased %e! Bug somewhere or numerical inaccuracy?" % L_diff)
            #raise Exception("Lower bound decreased %e! Bug somewhere or numerical inaccuracy?" % L_diff)
        if L[i] - L_last < 1e-12:
            print("Converged.")
            #break
        L_last = L[i]

    # Predictive stuff
    zh = nodes.Categorical(alpha, name='zh')
    Yh = nodes.Mixture(nodes.Gaussian)(zh, X, Lambda, name='Yh')
    # TODO/FIXME: Messages to parents should use the masks such that
    # children don't need to be initialized!
    zh.initialize_from_prior()
    Yh.initialize_from_prior()
    zh.update()
    #zh.show()
    N1 = 400
    N2 = 400
    x1 = np.linspace(-3, 15, N1)
    x2 = np.linspace(-3, 15, N2)
    xh = utils.grid(x1, x2)
    lpdf = Yh.integrated_logpdf_from_parents(xh, 0)
    pdf = np.reshape(np.exp(lpdf), (N2,N1))
    #print(pdf)
    #plt.clf()
    plt.clf()
    #plt.imshow(x1, x2, pdf)
    plt.contourf(x1, x2, pdf, 100)
    plt.scatter(y[:,0], y[:,1])
    print('integrated pdf:', np.sum(pdf)*(18*18)/(N1*N2))
    #return

    X.show()
    alpha.show()

    plt.show()
Esempio n. 4
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def run(N=50, K=5, D=2):

    #plt.ion()
    #17,31
    #np.random.seed(31)

    # Generate data
    N1 = np.floor(0.5 * N)
    N2 = N - N1
    y = np.vstack([
        np.random.normal(0, 0.5, size=(N1, D)),
        np.random.normal(10, 0.5, size=(N2, D))
    ])

    # Construct model
    (Y, X, Lambda, z, alpha) = gaussianmix_model(N, K, D)

    # Initialize nodes (from prior and randomly)
    alpha.initialize_from_prior()
    z.initialize_from_prior()
    Lambda.initialize_from_parameters(D, 10 * np.identity(D))

    X.initialize_from_prior()
    X.initialize_from_parameters(X.random(), np.identity(D))

    ## X.initialize_from_parameters(np.random.permutation(y)[:K],
    ##                              0.01*np.identity(D))

    #X.initialize_from_random()
    # Initialize means by selecting random data points
    #X.initialize_from_value(np.random.permutation(y)[:K])
    #return
    #X.initialize_random_mean()
    #Y.initialize()

    ## X.show()
    ## return

    # Data with missing values
    ## mask = np.random.rand(M,N) < 0.4 # randomly missing
    ## mask[:,20:40] = False # gap missing
    # Y.observe(y, mask)
    ## alpha.show()
    ## Lambda.show()
    ## z.show()
    X.show()
    #return

    Y.observe(y)

    ## z.update()
    ## X.update()
    ## alpha.update()

    ## X.show()
    ## alpha.show()
    #Lambda.show()
    #return

    ## X.show()
    ## Lambda.show()
    ## z.update()
    ## z.show()
    ## return

    # Inference loop.
    maxiter = 30
    L_X = np.zeros(maxiter)
    L_Lambda = np.zeros(maxiter)
    L_alpha = np.zeros(maxiter)
    L_z = np.zeros(maxiter)
    L_Y = np.zeros(maxiter)
    L = np.zeros(maxiter)
    L_last = -np.inf
    for i in range(maxiter):
        t = time.clock()

        # Update nodes
        z.update()
        alpha.update()
        X.update()
        Lambda.update()

        #Y.show()

        #z.show()

        # Compute lower bound
        L_X[i] = X.lower_bound_contribution()
        L_Lambda[i] = Lambda.lower_bound_contribution()
        L_alpha[i] = alpha.lower_bound_contribution()
        L_z[i] = z.lower_bound_contribution()
        L_Y[i] = Y.lower_bound_contribution()
        L[i] = L_X[i] + L_Lambda[i] + L_alpha[i] + L_z[i] + L_Y[i]

        #print('terms:', L_X[i], L_Lambda[i], L_alpha[i], L_z[i], L_Y[i])

        # Check convergence
        print("Iteration %d: loglike=%e (%.3f seconds)" %
              (i + 1, L[i], time.clock() - t))
        if L_last - L[i] > 1e-6:
            L_diff = (L_last - L[i])
            print(
                "Lower bound decreased %e! Bug somewhere or numerical inaccuracy?"
                % L_diff)
            #raise Exception("Lower bound decreased %e! Bug somewhere or numerical inaccuracy?" % L_diff)
        if L[i] - L_last < 1e-12:
            print("Converged.")
            #break
        L_last = L[i]

    # Predictive stuff
    zh = nodes.Categorical(alpha, name='zh')
    Yh = nodes.Mixture(nodes.Gaussian)(zh, X, Lambda, name='Yh')
    # TODO/FIXME: Messages to parents should use the masks such that
    # children don't need to be initialized!
    zh.initialize_from_prior()
    Yh.initialize_from_prior()
    zh.update()
    #zh.show()
    N1 = 400
    N2 = 400
    x1 = np.linspace(-3, 15, N1)
    x2 = np.linspace(-3, 15, N2)
    xh = utils.grid(x1, x2)
    lpdf = Yh.integrated_logpdf_from_parents(xh, 0)
    pdf = np.reshape(np.exp(lpdf), (N2, N1))
    #print(pdf)
    #plt.clf()
    plt.clf()
    #plt.imshow(x1, x2, pdf)
    plt.contourf(x1, x2, pdf, 100)
    plt.scatter(y[:, 0], y[:, 1])
    print('integrated pdf:', np.sum(pdf) * (18 * 18) / (N1 * N2))
    #return

    X.show()
    alpha.show()

    plt.show()