From 9e6cc7ea6eef37ba0f03c9aeb660e31d02f949d8 Mon Sep 17 00:00:00 2001 From: Alan Saul Date: Fri, 29 Nov 2013 14:45:44 +0000 Subject: [PATCH] Minor changes to naming of signitures --- GPy/examples/dimensionality_reduction.py | 58 ++++++++++++------------ 1 file changed, 29 insertions(+), 29 deletions(-) diff --git a/GPy/examples/dimensionality_reduction.py b/GPy/examples/dimensionality_reduction.py index 9120805c..65881573 100644 --- a/GPy/examples/dimensionality_reduction.py +++ b/GPy/examples/dimensionality_reduction.py @@ -3,23 +3,23 @@ import numpy as _np default_seed = _np.random.seed(123344) -def bgplvm_test_model(seed=default_seed, optimize=0, verbose=1, plot=0): +def bgplvm_test_model(seed=default_seed, optimize=False, verbose=1, plot=False): """ - model for testing purposes. Samples from a GP with rbf kernel and learns + model for testing purposes. Samples from a GP with rbf kernel and learns the samples with a new kernel. Normally not for optimization, just model cheking """ from GPy.likelihoods.gaussian import Gaussian import GPy - + num_inputs = 13 num_inducing = 5 - if plot: + if plot: output_dim = 1 input_dim = 2 - else: + else: input_dim = 2 output_dim = 25 - + # generate GPLVM-like data X = _np.random.rand(num_inputs, input_dim) lengthscales = _np.random.rand(input_dim) @@ -43,7 +43,7 @@ def bgplvm_test_model(seed=default_seed, optimize=0, verbose=1, plot=0): import matplotlib.pyplot as pb m.plot() pb.title('PCA initialisation') - + if optimize: m.optimize('scg', messages=verbose) if plot: @@ -52,7 +52,7 @@ def bgplvm_test_model(seed=default_seed, optimize=0, verbose=1, plot=0): return m -def gplvm_oil_100(optimize=1, verbose=1, plot=1): +def gplvm_oil_100(optimize=True, verbose=1, plot=True): import GPy data = GPy.util.datasets.oil_100() Y = data['X'] @@ -64,7 +64,7 @@ def gplvm_oil_100(optimize=1, verbose=1, plot=1): if plot: m.plot_latent(labels=m.data_labels) return m -def sparse_gplvm_oil(optimize=1, verbose=0, plot=1, N=100, Q=6, num_inducing=15, max_iters=50): +def sparse_gplvm_oil(optimize=True, verbose=0, plot=True, N=100, Q=6, num_inducing=15, max_iters=50): import GPy _np.random.seed(0) data = GPy.util.datasets.oil() @@ -77,12 +77,12 @@ def sparse_gplvm_oil(optimize=1, verbose=0, plot=1, N=100, Q=6, num_inducing=15, m.data_labels = data['Y'][:N].argmax(axis=1) if optimize: m.optimize('scg', messages=verbose, max_iters=max_iters) - if plot: + if plot: m.plot_latent(labels=m.data_labels) m.kern.plot_ARD() return m -def swiss_roll(optimize=1, verbose=1, plot=1, N=1000, num_inducing=15, Q=4, sigma=.2): +def swiss_roll(optimize=True, verbose=1, plot=True, N=1000, num_inducing=15, Q=4, sigma=.2): import GPy from GPy.util.datasets import swiss_roll_generated from GPy.models import BayesianGPLVM @@ -131,16 +131,16 @@ def swiss_roll(optimize=1, verbose=1, plot=1, N=1000, num_inducing=15, Q=4, sigm if optimize: m.optimize('scg', messages=verbose, max_iters=2e3) - + if plot: fig = plt.figure('fitted') ax = fig.add_subplot(111) s = m.input_sensitivity().argsort()[::-1][:2] ax.scatter(*m.X.T[s], c=c) - + return m -def bgplvm_oil(optimize=1, verbose=1, plot=1, N=200, Q=7, num_inducing=40, max_iters=1000, **k): +def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40, max_iters=1000, **k): import GPy from GPy.likelihoods import Gaussian from matplotlib import pyplot as plt @@ -164,7 +164,7 @@ def bgplvm_oil(optimize=1, verbose=1, plot=1, N=200, Q=7, num_inducing=40, max_i m.plot_latent(ax=latent_axes) data_show = GPy.util.visualize.vector_show(y) lvm_visualizer = GPy.util.visualize.lvm_dimselect(m.X[0, :], # @UnusedVariable - m, data_show, latent_axes=latent_axes, sense_axes=sense_axes) + m, data_show, latent_axes=latent_axes, sense_axes=sense_axes) raw_input('Press enter to finish') plt.close(fig) return m @@ -227,12 +227,12 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim=False): # from GPy.util.datasets import simulation_BGPLVM # from GPy import kern # from GPy.models import BayesianGPLVM -# +# # sim_data = simulation_BGPLVM() # Y = sim_data['Y'] # mu = sim_data['mu'] # num_inducing, [_, Q] = 3, mu.shape -# +# # k = kern.linear(Q, ARD=True) + kern.bias(Q, _np.exp(-2)) + kern.white(Q, _np.exp(-2)) # m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k, # _debug=False) @@ -241,8 +241,8 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim=False): # m['linear_variance'] = .01 # return m -def bgplvm_simulation(optimize=1, verbose=1, - plot=1, plot_sim=False, +def bgplvm_simulation(optimize=True, verbose=1, + plot=True, plot_sim=False, max_iters=2e4, ): from GPy import kern @@ -268,7 +268,7 @@ def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw): from GPy import kern from GPy.models import MRD from GPy.likelihoods import Gaussian - + D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5 _, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim) likelihood_list = [Gaussian(x, normalize=True) for x in Ylist] @@ -290,7 +290,7 @@ def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw): def brendan_faces(optimize=True, verbose=True, plot=True): import GPy - + data = GPy.util.datasets.brendan_faces() Q = 2 Y = data['Y'] @@ -315,7 +315,7 @@ def brendan_faces(optimize=True, verbose=True, plot=True): def olivetti_faces(optimize=True, verbose=True, plot=True): import GPy - + data = GPy.util.datasets.olivetti_faces() Q = 2 Y = data['Y'] @@ -350,7 +350,7 @@ def stick_play(range=None, frame_rate=15, optimize=False, verbose=True, plot=Tru def stick(kernel=None, optimize=True, verbose=True, plot=True): from matplotlib import pyplot as plt import GPy - + data = GPy.util.datasets.osu_run1() # optimize m = GPy.models.GPLVM(data['Y'], 2, kernel=kernel) @@ -362,13 +362,13 @@ def stick(kernel=None, optimize=True, verbose=True, plot=True): data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect']) GPy.util.visualize.lvm(m.X[0, :].copy(), m, data_show, ax) raw_input('Press enter to finish') - + return m def bcgplvm_linear_stick(kernel=None, optimize=True, verbose=True, plot=True): from matplotlib import pyplot as plt import GPy - + data = GPy.util.datasets.osu_run1() # optimize mapping = GPy.mappings.Linear(data['Y'].shape[1], 2) @@ -387,7 +387,7 @@ def bcgplvm_linear_stick(kernel=None, optimize=True, verbose=True, plot=True): def bcgplvm_stick(kernel=None, optimize=True, verbose=True, plot=True): from matplotlib import pyplot as plt import GPy - + data = GPy.util.datasets.osu_run1() # optimize back_kernel=GPy.kern.rbf(data['Y'].shape[1], lengthscale=5.) @@ -407,7 +407,7 @@ def bcgplvm_stick(kernel=None, optimize=True, verbose=True, plot=True): def robot_wireless(optimize=True, verbose=True, plot=True): from matplotlib import pyplot as plt import GPy - + data = GPy.util.datasets.robot_wireless() # optimize m = GPy.models.GPLVM(data['Y'], 2) @@ -422,7 +422,7 @@ def stick_bgplvm(model=None, optimize=True, verbose=True, plot=True): from GPy.models import BayesianGPLVM from matplotlib import pyplot as plt import GPy - + data = GPy.util.datasets.osu_run1() Q = 6 kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, _np.exp(-2)) + GPy.kern.white(Q, _np.exp(-2)) @@ -445,7 +445,7 @@ def stick_bgplvm(model=None, optimize=True, verbose=True, plot=True): def cmu_mocap(subject='35', motion=['01'], in_place=True, optimize=True, verbose=True, plot=True): import GPy - + data = GPy.util.datasets.cmu_mocap(subject, motion) if in_place: # Make figure move in place.