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https://github.com/SheffieldML/GPy.git
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merged with master
This commit is contained in:
Nicolo Fusi
commit
2abba8cf14
59 changed files with 2845 additions and 1608 deletions
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@ -16,8 +16,13 @@ k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
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K = k.K(X)
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Y = np.random.multivariate_normal(np.zeros(N),K,D).T
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<<<<<<< HEAD
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k = GPy.kern.rbf(Q) + GPy.kern.rbf(Q) + GPy.kern.white(Q)
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# k = GPy.kern.linear(Q, ARD = True) + GPy.kern.white(Q, 0.00001)
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=======
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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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>>>>>>> master
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m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M=M)
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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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@ -3,16 +3,15 @@
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"""
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Simple Gaussian Processes classification
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Gaussian Processes classification
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"""
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import pylab as pb
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import numpy as np
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import GPy
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default_seed=10000
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######################################
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## 2 dimensional example
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def crescent_data(model_type='Full', inducing=10, seed=default_seed):
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def crescent_data(model_type='Full', inducing=10, seed=default_seed): #FIXME
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"""Run a Gaussian process classification on the crescent data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.
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:param model_type: type of model to fit ['Full', 'FITC', 'DTC'].
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@ -21,20 +20,28 @@ def crescent_data(model_type='Full', inducing=10, seed=default_seed):
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:param inducing : number of inducing variables (only used for 'FITC' or 'DTC').
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:type inducing: int
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"""
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data = GPy.util.datasets.crescent_data(seed=seed)
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likelihood = GPy.inference.likelihoods.probit(data['Y'])
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# Kernel object
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kernel = GPy.kern.rbf(data['X'].shape[1])
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# Likelihood object
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distribution = GPy.likelihoods.likelihood_functions.probit()
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likelihood = GPy.likelihoods.EP(data['Y'],distribution)
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if model_type=='Full':
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m = GPy.models.GP_EP(data['X'],likelihood)
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m = GPy.models.GP(data['X'],likelihood,kernel)
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else:
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# create sparse GP EP model
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m = GPy.models.sparse_GP_EP(data['X'],likelihood=likelihood,inducing=inducing,ep_proxy=model_type)
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m.approximate_likelihood()
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m.update_likelihood_approximation()
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print(m)
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# optimize
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m.em()
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m.optimize()
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print(m)
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# plot
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@ -42,54 +49,67 @@ def crescent_data(model_type='Full', inducing=10, seed=default_seed):
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return m
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def oil():
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"""Run a Gaussian process classification on the oil data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood."""
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"""
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Run a Gaussian process classification on the oil data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.
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"""
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data = GPy.util.datasets.oil()
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likelihood = GPy.inference.likelihoods.probit(data['Y'][:, 0:1])
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# Kernel object
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kernel = GPy.kern.rbf(12)
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# create simple GP model
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m = GPy.models.GP_EP(data['X'],likelihood)
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# Likelihood object
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distribution = GPy.likelihoods.likelihood_functions.probit()
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likelihood = GPy.likelihoods.EP(data['Y'][:, 0:1],distribution)
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# contrain all parameters to be positive
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# Create GP model
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m = GPy.models.GP(data['X'],likelihood=likelihood,kernel=kernel)
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# Contrain all parameters to be positive
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m.constrain_positive('')
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m.tie_param('lengthscale')
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m.approximate_likelihood()
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m.update_likelihood_approximation()
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# optimize
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# Optimize
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m.optimize()
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# plot
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#m.plot()
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print(m)
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return m
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def toy_linear_1d_classification(model_type='Full', inducing=4, seed=default_seed):
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"""Simple 1D classification example.
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:param model_type: type of model to fit ['Full', 'FITC', 'DTC'].
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def toy_linear_1d_classification(seed=default_seed):
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"""
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Simple 1D classification example
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:param seed : seed value for data generation (default is 4).
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:type seed: int
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:param inducing : number of inducing variables (only used for 'FITC' or 'DTC').
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:type inducing: int
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"""
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data = GPy.util.datasets.toy_linear_1d_classification(seed=seed)
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likelihood = GPy.inference.likelihoods.probit(data['Y'][:, 0:1])
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assert model_type in ('Full','DTC','FITC')
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Y = data['Y'][:, 0:1]
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Y[Y == -1] = 0
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# create simple GP model
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if model_type=='Full':
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m = GPy.models.simple_GP_EP(data['X'],likelihood)
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else:
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# create sparse GP EP model
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m = GPy.models.sparse_GP_EP(data['X'],likelihood=likelihood,inducing=inducing,ep_proxy=model_type)
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# Kernel object
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kernel = GPy.kern.rbf(1)
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m.constrain_positive('var')
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m.constrain_positive('len')
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m.tie_param('lengthscale')
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m.approximate_likelihood()
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# Likelihood object
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distribution = GPy.likelihoods.likelihood_functions.probit()
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likelihood = GPy.likelihoods.EP(Y,distribution)
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# Optimize and plot
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m.em(plot_all=False) # EM algorithm
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# Model definition
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m = GPy.models.GP(data['X'],likelihood=likelihood,kernel=kernel)
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# Optimize
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"""
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EPEM runs a loop that consists of two steps:
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1) EP likelihood approximation:
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m.update_likelihood_approximation()
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2) Parameters optimization:
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m.optimize()
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"""
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m.EPEM()
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# Plot
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pb.subplot(211)
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m.plot_f()
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pb.subplot(212)
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m.plot()
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print(m)
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return m
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47
GPy/examples/poisson.py
Normal file
47
GPy/examples/poisson.py
Normal file
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@ -0,0 +1,47 @@
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# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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"""
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Gaussian Processes + Expectation Propagation - Poisson Likelihood
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"""
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import pylab as pb
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import numpy as np
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import GPy
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default_seed=10000
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def toy_1d(seed=default_seed):
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"""
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Simple 1D classification example
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:param seed : seed value for data generation (default is 4).
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:type seed: int
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"""
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X = np.arange(0,100,5)[:,None]
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F = np.round(np.sin(X/18.) + .1*X) + np.arange(5,25)[:,None]
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E = np.random.randint(-5,5,20)[:,None]
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Y = F + E
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kernel = GPy.kern.rbf(1)
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distribution = GPy.likelihoods.likelihood_functions.Poisson()
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likelihood = GPy.likelihoods.EP(Y,distribution)
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m = GPy.models.GP(X,likelihood,kernel)
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m.ensure_default_constraints()
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# Approximate likelihood
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m.update_likelihood_approximation()
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# Optimize and plot
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m.optimize()
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#m.EPEM FIXME
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print m
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# Plot
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pb.subplot(211)
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m.plot_f() #GP plot
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pb.subplot(212)
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m.plot() #Output plot
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return m
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60
GPy/examples/sparse_ep_fix.py
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60
GPy/examples/sparse_ep_fix.py
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@ -0,0 +1,60 @@
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# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import numpy as np
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"""
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Sparse Gaussian Processes regression with an RBF kernel
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"""
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import pylab as pb
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import numpy as np
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import GPy
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np.random.seed(2)
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pb.ion()
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N = 500
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M = 5
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pb.close('all')
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######################################
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## 1 dimensional example
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# sample inputs and outputs
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X = np.random.uniform(-3.,3.,(N,1))
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#Y = np.sin(X)+np.random.randn(N,1)*0.05
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F = np.sin(X)+np.random.randn(N,1)*0.05
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Y = np.ones([F.shape[0],1])
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Y[F<0] = -1
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likelihood = GPy.inference.likelihoods.probit(Y)
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# construct kernel
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rbf = GPy.kern.rbf(1)
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noise = GPy.kern.white(1)
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kernel = rbf + noise
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# create simple GP model
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#m = GPy.models.sparse_GP(X,Y=None, kernel=kernel, M=M,likelihood= likelihood)
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# contrain all parameters to be positive
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#m.constrain_fixed('prec',100.)
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m = GPy.models.sparse_GP(X, Y, kernel, M=M)
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m.ensure_default_constraints()
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#if not isinstance(m.likelihood,GPy.inference.likelihoods.gaussian):
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# m.approximate_likelihood()
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print m.checkgrad()
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m.optimize('tnc', messages = 1)
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m.plot(samples=3)
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print m
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n = GPy.models.sparse_GP(X,Y=None, kernel=kernel, M=M,likelihood= likelihood)
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n.ensure_default_constraints()
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if not isinstance(n.likelihood,GPy.inference.likelihoods.gaussian):
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n.approximate_likelihood()
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print n.checkgrad()
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pb.figure()
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n.plot()
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"""
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m = GPy.models.sparse_GP_regression(X, Y, kernel, M=M)
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m.ensure_default_constraints()
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print m.checkgrad()
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"""
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