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SCG printing prettyfied
This commit is contained in:
Max Zwiessele
parent
3bb42481be
commit
6b63237adf
2 changed files with 34 additions and 29 deletions
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@ -9,8 +9,8 @@ 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 crescent_data(seed=default_seed): #FIXME
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default_seed = 10000
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def crescent_data(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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@ -27,10 +27,10 @@ def crescent_data(seed=default_seed): #FIXME
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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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likelihood = GPy.likelihoods.EP(data['Y'], distribution)
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m = GPy.models.GP(data['X'],likelihood,kernel)
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m = GPy.models.GP(data['X'], likelihood, kernel)
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m.ensure_default_constraints()
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m.update_likelihood_approximation()
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@ -54,10 +54,10 @@ def oil():
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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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likelihood = GPy.likelihoods.EP(data['Y'][:, 0:1], distribution)
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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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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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@ -85,17 +85,17 @@ def toy_linear_1d_classification(seed=default_seed):
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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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likelihood = GPy.likelihoods.EP(Y, distribution)
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# Model definition
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m = GPy.models.GP(data['X'],likelihood=likelihood,kernel=kernel)
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m = GPy.models.GP(data['X'], likelihood=likelihood, kernel=kernel)
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m.ensure_default_constraints()
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# Optimize
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m.update_likelihood_approximation()
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# Parameters optimization:
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m.optimize()
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#m.pseudo_EM() #FIXME
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# m.pseudo_EM() #FIXME
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# Plot
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pb.subplot(211)
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@ -121,20 +121,20 @@ def sparse_toy_linear_1d_classification(seed=default_seed):
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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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likelihood = GPy.likelihoods.EP(Y, distribution)
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Z = np.random.uniform(data['X'].min(),data['X'].max(),(10,1))
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Z = np.random.uniform(data['X'].min(), data['X'].max(), (10, 1))
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# Model definition
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m = GPy.models.sparse_GP(data['X'],likelihood=likelihood,kernel=kernel,Z=Z,normalize_X=False)
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m.set('len',2.)
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m = GPy.models.sparse_GP(data['X'], likelihood=likelihood, kernel=kernel, Z=Z, normalize_X=False)
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m.set('len', 2.)
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m.ensure_default_constraints()
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# Optimize
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m.update_likelihood_approximation()
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# Parameters optimization:
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m.optimize()
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#m.EPEM() #FIXME
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# m.EPEM() #FIXME
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# Plot
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pb.subplot(211)
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@ -162,15 +162,15 @@ def sparse_crescent_data(inducing=10, seed=default_seed):
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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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likelihood = GPy.likelihoods.EP(data['Y'], distribution)
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sample = np.random.randint(0,data['X'].shape[0],inducing)
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Z = data['X'][sample,:]
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sample = np.random.randint(0, data['X'].shape[0], inducing)
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Z = data['X'][sample, :]
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# create sparse GP EP model
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m = GPy.models.sparse_GP(data['X'],likelihood=likelihood,kernel=kernel,Z=Z)
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m = GPy.models.sparse_GP(data['X'], likelihood=likelihood, kernel=kernel, Z=Z)
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m.ensure_default_constraints()
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m.set('len',10.)
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m.set('len', 10.)
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m.update_likelihood_approximation()
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@ -39,6 +39,9 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
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function_eval number of fn evaluations
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status: string describing convergence status
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"""
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print " SCG"
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print ' {0:{mi}s} {1:11s} {2:11s} {3:11s}'.format("I", "F", "Scale", "|g|", mi=len(str(maxiters)))
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if xtol is None:
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xtol = 1e-6
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if ftol is None:
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@ -46,18 +49,18 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
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if gtol is None:
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gtol = 1e-5
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sigma0 = 1.0e-4
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fold = f(x, *optargs) # Initial function value.
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fold = f(x, *optargs) # Initial function value.
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function_eval = 1
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fnow = fold
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gradnew = gradf(x, *optargs) # Initial gradient.
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gradnew = gradf(x, *optargs) # Initial gradient.
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current_grad = np.dot(gradnew, gradnew)
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gradold = gradnew.copy()
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d = -gradnew # Initial search direction.
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success = True # Force calculation of directional derivs.
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nsuccess = 0 # nsuccess counts number of successes.
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beta = 1.0 # Initial scale parameter.
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betamin = 1.0e-15 # Lower bound on scale.
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betamax = 1.0e100 # Upper bound on scale.
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d = -gradnew # Initial search direction.
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success = True # Force calculation of directional derivs.
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nsuccess = 0 # nsuccess counts number of successes.
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beta = 1.0 # Initial scale parameter.
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betamin = 1.0e-15 # Lower bound on scale.
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betamax = 1.0e100 # Upper bound on scale.
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status = "Not converged"
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flog = [fold]
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@ -82,6 +85,8 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
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# Increase effective curvature and evaluate step size alpha.
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delta = theta + beta * kappa
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if delta <= 0:
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if display:
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print ""
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delta = beta * kappa
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beta = beta - theta / kappa
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@ -107,12 +112,12 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
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fnow = fold
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# Store relevant variables
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flog.append(fnow) # Current function value
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flog.append(fnow) # Current function value
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iteration += 1
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if display:
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print '\r',
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print 'Iter: {0:>0{mi}g} Obj:{1:> 12e} Scale:{2:> 12e} |g|:{3:> 12e}'.format(iteration, float(fnow), float(beta), float(current_grad), mi=len(str(maxiters))),
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print '{0:>0{mi}g} {1:> 12e} {2:> 12e} {3:> 12e}'.format(iteration, float(fnow), float(beta), float(current_grad), mi=len(str(maxiters))),
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# print 'Iteration:', iteration, ' Objective:', fnow, ' Scale:', beta, '\r',
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sys.stdout.flush()
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