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[param_to_array] deprecated and removed param_to_array from code, use param.values instead
This commit is contained in:
Max Zwiessele
parent
c1d998e272
commit
6a260409fa
16 changed files with 349 additions and 231 deletions
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@ -23,9 +23,6 @@ def bgplvm_test_model(optimize=False, verbose=1, plot=False, output_dim=200, nan
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X = _np.random.rand(num_inputs, input_dim)
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lengthscales = _np.random.rand(input_dim)
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k = GPy.kern.RBF(input_dim, .5, lengthscales, ARD=True)
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##+ GPy.kern.white(input_dim, 0.01)
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#)
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#k = GPy.kern.Linear(input_dim, ARD=1)# + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
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K = k.K(X)
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Y = _np.random.multivariate_normal(_np.zeros(num_inputs), K, (output_dim,)).T
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@ -159,7 +156,6 @@ def swiss_roll(optimize=True, verbose=1, plot=True, N=1000, num_inducing=25, 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 matplotlib import pyplot as plt
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from ..util.misc import param_to_array
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import numpy as np
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_np.random.seed(0)
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@ -177,7 +173,7 @@ def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40,
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fig, (latent_axes, sense_axes) = plt.subplots(1, 2)
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m.plot_latent(ax=latent_axes, labels=m.data_labels)
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data_show = GPy.plotting.matplot_dep.visualize.vector_show((m.Y[0,:]))
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lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm_dimselect(param_to_array(m.X.mean)[0:1,:], # @UnusedVariable
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lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm_dimselect(m.X.mean.values[0:1,:], # @UnusedVariable
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m, data_show, latent_axes=latent_axes, sense_axes=sense_axes, labels=m.data_labels)
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raw_input('Press enter to finish')
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plt.close(fig)
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@ -186,8 +182,6 @@ def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40,
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def ssgplvm_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 matplotlib import pyplot as plt
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from ..util.misc import param_to_array
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import numpy as np
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_np.random.seed(0)
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data = GPy.util.datasets.oil()
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@ -204,7 +198,7 @@ def ssgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40
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fig, (latent_axes, sense_axes) = plt.subplots(1, 2)
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m.plot_latent(ax=latent_axes, labels=m.data_labels)
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data_show = GPy.plotting.matplot_dep.visualize.vector_show((m.Y[0,:]))
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lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm_dimselect(param_to_array(m.X.mean)[0:1,:], # @UnusedVariable
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lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm_dimselect(m.X.mean.values[0:1,:], # @UnusedVariable
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m, data_show, latent_axes=latent_axes, sense_axes=sense_axes, labels=m.data_labels)
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raw_input('Press enter to finish')
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plt.close(fig)
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@ -228,10 +222,10 @@ def _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim=False):
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Ylist = [Y1, Y2, Y3]
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if plot_sim:
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import pylab
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from matplotlib import pyplot as plt
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import matplotlib.cm as cm
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import itertools
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fig = pylab.figure("MRD Simulation Data", figsize=(8, 6))
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fig = plt.figure("MRD Simulation Data", figsize=(8, 6))
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fig.clf()
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ax = fig.add_subplot(2, 1, 1)
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labls = slist_names
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@ -242,29 +236,11 @@ def _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim=False):
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ax = fig.add_subplot(2, len(Ylist), len(Ylist) + 1 + i)
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ax.imshow(Y, aspect='auto', cmap=cm.gray) # @UndefinedVariable
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ax.set_title("Y{}".format(i + 1))
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pylab.draw()
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pylab.tight_layout()
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plt.draw()
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plt.tight_layout()
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return slist, [S1, S2, S3], Ylist
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def _generate_high_dimensional_output(D1, D2, D3, s1, s2, s3, sS):
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S1 = _np.hstack([s1, sS])
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S2 = _np.hstack([s2, s3, sS])
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S3 = _np.hstack([s3, sS])
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Y1 = S1.dot(_np.random.randn(S1.shape[1], D1))
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Y2 = S2.dot(_np.random.randn(S2.shape[1], D2))
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Y3 = S3.dot(_np.random.randn(S3.shape[1], D3))
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Y1 += .3 * _np.random.randn(*Y1.shape)
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Y2 += .2 * _np.random.randn(*Y2.shape)
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Y3 += .25 * _np.random.randn(*Y3.shape)
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Y1 -= Y1.mean(0)
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Y2 -= Y2.mean(0)
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Y3 -= Y3.mean(0)
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Y1 /= Y1.std(0)
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Y2 /= Y2.std(0)
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Y3 /= Y3.std(0)
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return Y1, Y2, Y3, S1, S2, S3
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def _simulate_sincos(D1, D2, D3, N, num_inducing, plot_sim=False):
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_np.random.seed(1234)
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@ -291,10 +267,10 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, plot_sim=False):
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Ylist = [Y1, Y2, Y3]
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if plot_sim:
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import pylab
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from matplotlib import pyplot as plt
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import matplotlib.cm as cm
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import itertools
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fig = pylab.figure("MRD Simulation Data", figsize=(8, 6))
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fig = plt.figure("MRD Simulation Data", figsize=(8, 6))
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fig.clf()
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ax = fig.add_subplot(2, 1, 1)
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labls = slist_names
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@ -305,28 +281,28 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, plot_sim=False):
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ax = fig.add_subplot(2, len(Ylist), len(Ylist) + 1 + i)
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ax.imshow(Y, aspect='auto', cmap=cm.gray) # @UndefinedVariable
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ax.set_title("Y{}".format(i + 1))
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pylab.draw()
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pylab.tight_layout()
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plt.draw()
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plt.tight_layout()
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return slist, [S1, S2, S3], Ylist
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# def bgplvm_simulation_matlab_compare():
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# from GPy.util.datasets import simulation_BGPLVM
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# from GPy import kern
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# from GPy.models import BayesianGPLVM
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#
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# sim_data = simulation_BGPLVM()
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# Y = sim_data['Y']
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# mu = sim_data['mu']
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# num_inducing, [_, Q] = 3, mu.shape
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#
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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 = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k,
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# _debug=False)
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# m.auto_scale_factor = True
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# m['noise'] = Y.var() / 100.
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# m['linear_variance'] = .01
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# return m
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def _generate_high_dimensional_output(D1, D2, D3, s1, s2, s3, sS):
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S1 = _np.hstack([s1, sS])
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S2 = _np.hstack([s2, s3, sS])
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S3 = _np.hstack([s3, sS])
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Y1 = S1.dot(_np.random.randn(S1.shape[1], D1))
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Y2 = S2.dot(_np.random.randn(S2.shape[1], D2))
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Y3 = S3.dot(_np.random.randn(S3.shape[1], D3))
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Y1 += .3 * _np.random.randn(*Y1.shape)
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Y2 += .2 * _np.random.randn(*Y2.shape)
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Y3 += .25 * _np.random.randn(*Y3.shape)
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Y1 -= Y1.mean(0)
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Y2 -= Y2.mean(0)
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Y3 -= Y3.mean(0)
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Y1 /= Y1.std(0)
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Y2 /= Y2.std(0)
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Y3 /= Y3.std(0)
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return Y1, Y2, Y3, S1, S2, S3
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def bgplvm_simulation(optimize=True, verbose=1,
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plot=True, plot_sim=False,
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