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2d plotting
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Max Zwiessele
parent
c2750ae719
commit
8dbb65ab50
4 changed files with 50 additions and 49 deletions
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@ -89,7 +89,7 @@ def sparse_gplvm_oil(optimize=True, verbose=0, plot=True, N=100, Q=6, num_induci
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Y = Y - Y.mean(0)
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Y /= Y.std(0)
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# Create the model
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kernel = GPy.kern.RBF(Q, ARD=True) + GPy.kern.bias(Q)
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kernel = GPy.kern.RBF(Q, ARD=True) + GPy.kern.Bias(Q)
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m = GPy.models.SparseGPLVM(Y, Q, kernel=kernel, num_inducing=num_inducing)
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m.data_labels = data['Y'][:N].argmax(axis=1)
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@ -139,7 +139,7 @@ def swiss_roll(optimize=True, verbose=1, plot=True, N=1000, num_inducing=15, Q=4
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(1 - var))) + .001
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Z = _np.random.permutation(X)[:num_inducing]
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kernel = GPy.kern.RBF(Q, ARD=True) + GPy.kern.bias(Q, _np.exp(-2)) + GPy.kern.white(Q, _np.exp(-2))
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kernel = GPy.kern.RBF(Q, ARD=True) + GPy.kern.Bias(Q, _np.exp(-2)) + GPy.kern.White(Q, _np.exp(-2))
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m = BayesianGPLVM(Y, Q, X=X, X_variance=S, num_inducing=num_inducing, Z=Z, kernel=kernel)
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m.data_colors = c
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@ -159,28 +159,26 @@ def swiss_roll(optimize=True, verbose=1, plot=True, N=1000, num_inducing=15, Q=4
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def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40, max_iters=1000, **k):
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import GPy
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from GPy.likelihoods import Gaussian
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from matplotlib import pyplot as plt
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_np.random.seed(0)
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data = GPy.util.datasets.oil()
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kernel = GPy.kern.RBF_inv(Q, 1., [.1] * Q, ARD=True) + GPy.kern.bias(Q, _np.exp(-2))
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kernel = GPy.kern.RBF(Q, 1., [.1] * Q, ARD=True)# + GPy.kern.Bias(Q, _np.exp(-2))
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Y = data['X'][:N]
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Yn = Gaussian(Y, normalize=True)
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m = GPy.models.BayesianGPLVM(Yn, Q, kernel=kernel, num_inducing=num_inducing, **k)
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m = GPy.models.BayesianGPLVM(Y, Q, kernel=kernel, num_inducing=num_inducing, **k)
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m.data_labels = data['Y'][:N].argmax(axis=1)
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m['noise'] = Yn.Y.var() / 100.
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m['.*noise.var'] = Y.var() / 100.
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if optimize:
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m.optimize('scg', messages=verbose, max_iters=max_iters, gtol=.05)
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if plot:
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y = m.likelihood.Y[0, :]
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y = m.Y[0, :]
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fig, (latent_axes, sense_axes) = plt.subplots(1, 2)
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m.plot_latent(ax=latent_axes)
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data_show = GPy.util.visualize.vector_show(y)
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lvm_visualizer = GPy.util.visualize.lvm_dimselect(m.X[0, :], # @UnusedVariable
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data_show = GPy.plotting.matplot_dep.visualize.vector_show(y)
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lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm_dimselect(m.X[0, :], # @UnusedVariable
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m, data_show, latent_axes=latent_axes, sense_axes=sense_axes)
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raw_input('Press enter to finish')
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plt.close(fig)
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@ -190,8 +188,8 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim=False):
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_np.random.seed(1234)
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x = _np.linspace(0, 4 * _np.pi, N)[:, None]
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s1 = _np.vectorize(lambda x: -_np.sin(x))
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s2 = _np.vectorize(lambda x: _np.cos(x))
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s1 = _np.vectorize(lambda x: -_np.sin(_np.exp(x)))
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s2 = _np.vectorize(lambda x: _np.cos(x)**2)
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s3 = _np.vectorize(lambda x:-_np.exp(-_np.cos(2 * x)))
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sS = _np.vectorize(lambda x: x*_np.sin(x))
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@ -328,7 +326,7 @@ def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
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_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
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likelihood_list = [Gaussian(x, normalize=True) for x in Ylist]
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k = kern.linear(Q, ARD=True) + kern.bias(Q, _np.exp(-2)) + kern.white(Q, _np.exp(-2))
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k = kern.Linear(Q, ARD=True) + kern.Bias(Q, _np.exp(-2)) + kern.White(Q, _np.exp(-2))
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m = MRD(likelihood_list, input_dim=Q, num_inducing=num_inducing, kernels=k, initx="", initz='permute', **kw)
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m.ensure_default_constraints()
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@ -355,15 +353,15 @@ def brendan_faces(optimize=True, verbose=True, plot=True):
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m = GPy.models.GPLVM(Yn, Q)
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# optimize
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m.constrain('rbf|noise|white', GPy.core.transformations.logexp_clipped())
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m.constrain('rbf|noise|white', GPy.transformations.LogexpClipped())
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if optimize: m.optimize('scg', messages=verbose, max_iters=1000)
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if plot:
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ax = m.plot_latent(which_indices=(0, 1))
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y = m.likelihood.Y[0, :]
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data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, order='F', invert=False, scale=False)
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GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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data_show = GPy.plotting.matplot_dep.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, order='F', invert=False, scale=False)
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GPy.plotting.matplot_dep.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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raw_input('Press enter to finish')
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return m
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@ -382,8 +380,8 @@ def olivetti_faces(optimize=True, verbose=True, plot=True):
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if plot:
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ax = m.plot_latent(which_indices=(0, 1))
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y = m.likelihood.Y[0, :]
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data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(112, 92), transpose=False, invert=False, scale=False)
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GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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data_show = GPy.plotting.matplot_dep.visualize.image_show(y[None, :], dimensions=(112, 92), transpose=False, invert=False, scale=False)
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GPy.plotting.matplot_dep.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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raw_input('Press enter to finish')
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return m
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@ -398,8 +396,8 @@ def stick_play(range=None, frame_rate=15, optimize=False, verbose=True, plot=Tru
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Y = data['Y'][range[0]:range[1], :].copy()
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if plot:
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y = Y[0, :]
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data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
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GPy.util.visualize.data_play(Y, data_show, frame_rate)
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data_show = GPy.plotting.matplot_dep.visualize.stick_show(y[None, :], connect=data['connect'])
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GPy.plotting.matplot_dep.visualize.data_play(Y, data_show, frame_rate)
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return Y
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def stick(kernel=None, optimize=True, verbose=True, plot=True):
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@ -410,12 +408,12 @@ def stick(kernel=None, optimize=True, verbose=True, plot=True):
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# optimize
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m = GPy.models.GPLVM(data['Y'], 2, kernel=kernel)
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if optimize: m.optimize(messages=verbose, max_f_eval=10000)
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if plot and GPy.util.visualize.visual_available:
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if plot and GPy.plotting.matplot_dep.visualize.visual_available:
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plt.clf
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ax = m.plot_latent()
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y = m.likelihood.Y[0, :]
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data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
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GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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data_show = GPy.plotting.matplot_dep.visualize.stick_show(y[None, :], connect=data['connect'])
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GPy.plotting.matplot_dep.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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raw_input('Press enter to finish')
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return m
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@ -429,12 +427,12 @@ def bcgplvm_linear_stick(kernel=None, optimize=True, verbose=True, plot=True):
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mapping = GPy.mappings.Linear(data['Y'].shape[1], 2)
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m = GPy.models.BCGPLVM(data['Y'], 2, kernel=kernel, mapping=mapping)
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if optimize: m.optimize(messages=verbose, max_f_eval=10000)
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if plot and GPy.util.visualize.visual_available:
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if plot and GPy.plotting.matplot_dep.visualize.visual_available:
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plt.clf
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ax = m.plot_latent()
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y = m.likelihood.Y[0, :]
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data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
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GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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data_show = GPy.plotting.matplot_dep.visualize.stick_show(y[None, :], connect=data['connect'])
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GPy.plotting.matplot_dep.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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raw_input('Press enter to finish')
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return m
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@ -449,12 +447,12 @@ def bcgplvm_stick(kernel=None, optimize=True, verbose=True, plot=True):
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mapping = GPy.mappings.Kernel(X=data['Y'], output_dim=2, kernel=back_kernel)
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m = GPy.models.BCGPLVM(data['Y'], 2, kernel=kernel, mapping=mapping)
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if optimize: m.optimize(messages=verbose, max_f_eval=10000)
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if plot and GPy.util.visualize.visual_available:
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if plot and GPy.plotting.matplot_dep.visualize.visual_available:
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plt.clf
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ax = m.plot_latent()
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y = m.likelihood.Y[0, :]
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data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
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GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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data_show = GPy.plotting.matplot_dep.visualize.stick_show(y[None, :], connect=data['connect'])
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GPy.plotting.matplot_dep.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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raw_input('Press enter to finish')
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return m
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@ -480,7 +478,7 @@ def stick_bgplvm(model=None, optimize=True, verbose=True, plot=True):
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data = GPy.util.datasets.osu_run1()
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Q = 6
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kernel = GPy.kern.RBF(Q, ARD=True) + GPy.kern.bias(Q, _np.exp(-2)) + GPy.kern.white(Q, _np.exp(-2))
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kernel = GPy.kern.RBF(Q, ARD=True) + GPy.kern.Bias(Q, _np.exp(-2)) + GPy.kern.White(Q, _np.exp(-2))
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m = BayesianGPLVM(data['Y'], Q, init="PCA", num_inducing=20, kernel=kernel)
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# optimize
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m.ensure_default_constraints()
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@ -491,8 +489,8 @@ def stick_bgplvm(model=None, optimize=True, verbose=True, plot=True):
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plt.sca(latent_axes)
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m.plot_latent()
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y = m.likelihood.Y[0, :].copy()
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data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
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GPy.util.visualize.lvm_dimselect(m.X[0, :].copy(), m, data_show, latent_axes=latent_axes, sense_axes=sense_axes)
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data_show = GPy.plotting.matplot_dep.visualize.stick_show(y[None, :], connect=data['connect'])
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GPy.plotting.matplot_dep.visualize.lvm_dimselect(m.X[0, :].copy(), m, data_show, latent_axes=latent_axes, sense_axes=sense_axes)
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raw_input('Press enter to finish')
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return m
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@ -511,8 +509,8 @@ def cmu_mocap(subject='35', motion=['01'], in_place=True, optimize=True, verbose
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if plot:
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ax = m.plot_latent()
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y = m.likelihood.Y[0, :]
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data_show = GPy.util.visualize.skeleton_show(y[None, :], data['skel'])
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lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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data_show = GPy.plotting.matplot_dep.visualize.skeleton_show(y[None, :], data['skel'])
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lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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raw_input('Press enter to finish')
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lvm_visualizer.close()
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