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changes to regression and svigp
This commit is contained in:
Andreas
commit
ffa0f3a665
3 changed files with 30 additions and 26 deletions
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@ -4,7 +4,7 @@
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import numpy as np
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import pylab as pb
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from .. import kern
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from ..util.linalg import pdinv, mdot, tdot, dpotrs, dtrtrs, jitchol, backsub_both_sides
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from ..util.linalg import linalg, pdinv, mdot, tdot, dpotrs, dtrtrs, jitchol, backsub_both_sides
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from ..likelihoods import EP
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from gp_base import GPBase
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from model import Model
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@ -269,7 +269,6 @@ class SVIGP(GPBase):
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def optimize(self, iterations, print_interval=10, callback=lambda:None, callback_interval=5):
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param_step = 0.
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#Iterate!
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for i in range(iterations):
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@ -288,6 +287,7 @@ class SVIGP(GPBase):
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#compute the steps in all parameters
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vb_step = self.vb_steplength*natgrads[0]
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if (self.epochs>=1):#only move the parameters after the first epoch
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# print "it {} ep {} par {}".format(self.iterations, self.epochs, param_step)
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param_step = self.momentum*param_step + self.param_steplength*grads
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else:
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param_step = 0.
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@ -295,8 +295,8 @@ class SVIGP(GPBase):
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self.set_vb_param(self.get_vb_param() + vb_step)
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#Note: don't recompute everything here, wait until the next iteration when we have a new batch
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self._set_params(self._untransform_params(self._get_params_transformed() + param_step), computations=False)
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#print messages if desired
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if i and (not i%print_interval):
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print i, np.mean(self._ll_trace[-print_interval:]) #, self.log_likelihood()
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print np.round(np.mean(self._grad_trace[-print_interval:],0),3)
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@ -7,26 +7,29 @@ from matplotlib import pyplot as plt, cm
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import GPy
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from GPy.core.transformations import logexp
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from GPy.models.bayesian_gplvm import BayesianGPLVM
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from GPy.likelihoods.gaussian import Gaussian
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default_seed = np.random.seed(123344)
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def BGPLVM(seed=default_seed):
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N = 10
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num_inducing = 3
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Q = 2
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D = 4
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Q = 5
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D = 10
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# generate GPLVM-like data
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X = np.random.rand(N, Q)
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k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
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lengthscales = np.random.rand(Q)
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k = GPy.kern.rbf(Q, .5, lengthscales, ARD=True) + GPy.kern.white(Q, 0.01)
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K = k.K(X)
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Y = np.random.multivariate_normal(np.zeros(N), K, Q).T
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lik = Gaussian(Y, normalize=True)
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k = GPy.kern.rbf(Q, ARD=True) + GPy.kern.linear(Q, ARD=True) + GPy.kern.rbf(Q, ARD=True) + GPy.kern.white(Q)
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# k = GPy.kern.rbf(Q) + GPy.kern.rbf(Q) + GPy.kern.white(Q)
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k = GPy.kern.rbf_inv(Q, ARD=True) + GPy.kern.bias(Q) + GPy.kern.white(Q)
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# k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
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# k = GPy.kern.rbf(Q, ARD = False) + GPy.kern.white(Q, 0.00001)
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m = GPy.models.BayesianGPLVM(Y, Q, kernel=k, num_inducing=num_inducing)
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m = GPy.models.BayesianGPLVM(lik, Q, kernel=k, num_inducing=num_inducing)
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m.lengthscales = lengthscales
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# m.constrain_positive('(rbf|bias|noise|white|S)')
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# m.constrain_fixed('S', 1)
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@ -37,8 +40,8 @@ def BGPLVM(seed=default_seed):
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# m.optimize(messages = 1)
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# m.plot()
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# pb.title('After optimisation')
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m.randomize()
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m.checkgrad(verbose=1)
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# m.randomize()
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# m.checkgrad(verbose=1)
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return m
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@ -70,16 +73,16 @@ def sparseGPLVM_oil(optimize=True, N=100, Q=6, num_inducing=15, max_iters=50):
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# create simple GP model
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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 = GPy.models.SparseGPLVM(Y, Q, kernel=kernel, num_inducing=num_inducing)
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m.data_labels = data['Y'].argmax(axis=1)
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# optimize
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if optimize:
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m.optimize('scg', messages=1, max_iters = max_iters)
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m.optimize('scg', messages=1, max_iters=max_iters)
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# plot
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print(m)
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#m.plot_latent(labels=m.data_labels)
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# m.plot_latent(labels=m.data_labels)
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return m
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def swiss_roll(optimize=True, N=1000, num_inducing=15, Q=4, sigma=.2, plot=False):
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@ -136,12 +139,13 @@ def swiss_roll(optimize=True, N=1000, num_inducing=15, Q=4, sigma=.2, plot=False
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m.optimize('scg', messages=1)
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return m
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def BGPLVM_oil(optimize=True, N=200, Q=10, num_inducing=15, max_iters=50, plot=False, **k):
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def BGPLVM_oil(optimize=True, N=200, Q=10, num_inducing=15, max_iters=150, plot=False, **k):
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np.random.seed(0)
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data = GPy.util.datasets.oil()
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# create simple GP model
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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_inv(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
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Y = data['X'][:N]
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Yn = Y - Y.mean(0)
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Yn /= Yn.std(0)
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@ -150,7 +154,7 @@ def BGPLVM_oil(optimize=True, N=200, Q=10, num_inducing=15, max_iters=50, plot=F
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m.data_labels = data['Y'][:N].argmax(axis=1)
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# m.constrain('variance|leng', logexp_clipped())
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m['.*lengt'] = 1. # 5./((np.max(m.X,0)-np.min(m.X,0))**2) # m.X.var(0).max() / m.X.var(0)
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# m['.*lengt'] = m.X.var(0).max() / m.X.var(0)
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m['noise'] = Yn.var() / 100.
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@ -208,7 +212,7 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim=False):
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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 += .1 * np.random.randn(*Y3.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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@ -263,10 +267,11 @@ def bgplvm_simulation_matlab_compare():
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def bgplvm_simulation(optimize='scg',
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plot=True,
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max_iters=2e4):
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max_iters=2e4,
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plot_sim=False):
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# from GPy.core.transformations import logexp_clipped
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D1, D2, D3, N, num_inducing, Q = 15, 8, 8, 100, 3, 5
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slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot)
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D1, D2, D3, N, num_inducing, Q = 15, 5, 8, 300, 30, 6
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slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
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from GPy.models import mrd
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from GPy import kern
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@ -280,7 +285,7 @@ def bgplvm_simulation(optimize='scg',
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# m.constrain('variance|noise', logexp_clipped())
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m['noise'] = Y.var() / 100.
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m['linear_variance'] = .01
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if optimize:
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print "Optimizing model:"
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@ -327,7 +332,6 @@ def brendan_faces():
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# Y = data['Y']
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Yn = Y - Y.mean()
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Yn /= Yn.std()
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m.plot_latent()
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m = GPy.models.GPLVM(Yn, Q)
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# m = GPy.models.BayesianGPLVM(Yn, Q, num_inducing=100)
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@ -347,7 +351,7 @@ def brendan_faces():
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def stick_play(range=None, frame_rate=15):
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data = GPy.util.datasets.stick()
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# optimize
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if range==None:
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if range == None:
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Y = data['Y'].copy()
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else:
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Y = data['Y'][range[0]:range[1], :].copy()
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@ -67,8 +67,8 @@ def toy_ARD(optim_iters=1000, kernel_type='linear', N=300, D=4):
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X4 = np.log(np.sort(np.random.rand(N,1),0))
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X = np.hstack((X1, X2, X3, X4))
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Y1 = np.asarray(2*X[:,0]+3).T
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Y2 = np.asarray(4*(X[:,2]-1.5*X[:,0])).T
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Y1 = np.asmatrix(2*X[:,0]+3).T
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Y2 = np.asmatrix(4*(X[:,2]-1.5*X[:,0])).T
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Y = np.hstack((Y1, Y2))
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Y = np.dot(Y, np.random.rand(2,D));
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