mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-10 04:22:38 +02:00
Renamed some things, made some small (incorrect) gradient changes,
generalised the gp regression for any likelihood, and added a place holder link function waiting for Richardos changes
This commit is contained in:
Alan Saul
parent
5b25273d2b
commit
1dd83291fe
7 changed files with 83 additions and 53 deletions
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@ -25,9 +25,9 @@ def timing():
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edited_real_sd = real_sd
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kernel1 = GPy.kern.rbf(X.shape[1])
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t_distribution = GPy.likelihoods.likelihood_functions.Student_t(deg_free, sigma2=edited_real_sd)
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t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=edited_real_sd)
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corrupt_stu_t_likelihood = GPy.likelihoods.Laplace(Yc.copy(), t_distribution, opt='rasm')
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m = GPy.models.GP(X, corrupt_stu_t_likelihood, kernel1)
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m = GPy.models.GPRegression(X, corrupt_stu_t_likelihood, kernel1)
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m.ensure_default_constraints()
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m.update_likelihood_approximation()
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m.optimize()
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@ -54,9 +54,9 @@ def v_fail_test():
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edited_real_sd = real_sd
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print "Clean student t, rasm"
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t_distribution = GPy.likelihoods.likelihood_functions.Student_t(deg_free, sigma2=edited_real_sd)
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t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=edited_real_sd)
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stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution, opt='rasm')
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m = GPy.models.GP(X, stu_t_likelihood, kernel1)
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m = GPy.models.GPRegression(X, stu_t_likelihood, kernel1)
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m.constrain_positive('')
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vs = 25
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noises = 30
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@ -94,16 +94,16 @@ def student_t_obj_plane():
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deg_free = 1000
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kernelgp = GPy.kern.rbf(X.shape[1]) # + GPy.kern.white(X.shape[1])
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mgp = GPy.models.GP_regression(X, Y, kernel=kernelgp)
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mgp = GPy.models.GPRegression(X, Y, kernel=kernelgp)
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mgp.ensure_default_constraints()
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mgp['noise'] = real_std**2
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print "Gaussian"
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print mgp
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kernelst = kernelgp.copy()
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t_distribution = GPy.likelihoods.likelihood_functions.Student_t(deg_free, sigma2=(real_std**2))
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t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=(real_std**2))
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stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution, opt='rasm')
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m = GPy.models.GP(X, stu_t_likelihood, kernelst)
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m = GPy.models.GPRegression(X, stu_t_likelihood, kernelst)
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m.ensure_default_constraints()
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m.constrain_fixed('t_no', real_std**2)
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vs = 10
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@ -144,7 +144,7 @@ def student_t_f_check():
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deg_free = 1000
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kernelgp = GPy.kern.rbf(X.shape[1]) # + GPy.kern.white(X.shape[1])
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mgp = GPy.models.GP_regression(X, Y, kernel=kernelgp)
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mgp = GPy.models.GPRegression(X, Y, kernel=kernelgp)
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mgp.ensure_default_constraints()
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mgp.randomize()
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mgp.optimize()
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@ -154,9 +154,9 @@ def student_t_f_check():
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kernelst = kernelgp.copy()
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#kernelst += GPy.kern.bias(X.shape[1])
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t_distribution = GPy.likelihoods.likelihood_functions.Student_t(deg_free, sigma2=0.05)
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t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=0.05)
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stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution, opt='rasm')
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m = GPy.models.GP(X, stu_t_likelihood, kernelst)
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m = GPy.models.GPRegression(X, stu_t_likelihood, kernelst)
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#m['rbf_v'] = mgp._get_params()[0]
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#m['rbf_l'] = mgp._get_params()[1] + 1
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m.ensure_default_constraints()
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@ -198,7 +198,7 @@ def student_t_fix_optimise_check():
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#GP
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kernelgp = GPy.kern.rbf(X.shape[1]) # + GPy.kern.white(X.shape[1])
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mgp = GPy.models.GP_regression(X, Y, kernel=kernelgp)
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mgp = GPy.models.GPRegression(X, Y, kernel=kernelgp)
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mgp.ensure_default_constraints()
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mgp.randomize()
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mgp.optimize()
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@ -206,12 +206,12 @@ def student_t_fix_optimise_check():
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kernelst = kernelgp.copy()
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real_stu_t_std2 = (real_std**2)*((deg_free - 2)/float(deg_free))
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t_distribution = GPy.likelihoods.likelihood_functions.Student_t(deg_free, sigma2=real_stu_t_std2)
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t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=real_stu_t_std2)
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stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution, opt='rasm')
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plt.figure(1)
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plt.suptitle('Student likelihood')
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m = GPy.models.GP(X, stu_t_likelihood, kernelst)
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m = GPy.models.GPRegression(X, stu_t_likelihood, kernelst)
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m.constrain_fixed('rbf_var', mgp._get_params()[0])
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m.constrain_fixed('rbf_len', mgp._get_params()[1])
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m.constrain_positive('t_noise')
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@ -331,7 +331,7 @@ def debug_student_t_noise_approx():
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print "Clean Gaussian"
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#A GP should completely break down due to the points as they get a lot of weight
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# create simple GP model
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#m = GPy.models.GP_regression(X, Y, kernel=kernel1)
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#m = GPy.models.GPRegression(X, Y, kernel=kernel1)
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## optimize
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#m.ensure_default_constraints()
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#m.optimize()
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@ -349,10 +349,10 @@ def debug_student_t_noise_approx():
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#edited_real_sd = real_sd
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print "Clean student t, rasm"
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t_distribution = GPy.likelihoods.likelihood_functions.Student_t(deg_free, sigma2=edited_real_sd)
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t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=edited_real_sd)
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stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution, opt='rasm')
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m = GPy.models.GP(X, stu_t_likelihood, kernel6)
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m = GPy.models.GPRegression(X, stu_t_likelihood, kernel6)
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#m['rbf_len'] = 1.5
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#m.constrain_fixed('rbf_v', 1.0898)
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#m.constrain_fixed('rbf_l', 0.2651)
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@ -384,9 +384,9 @@ def debug_student_t_noise_approx():
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return m
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#print "Clean student t, ncg"
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#t_distribution = GPy.likelihoods.likelihood_functions.Student_t(deg_free, sigma2=edited_real_sd)
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#t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=edited_real_sd)
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#stu_t_likelihood = GPy.likelihoods.Laplace(Y, t_distribution, opt='ncg')
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#m = GPy.models.GP(X, stu_t_likelihood, kernel3)
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#m = GPy.models.GPRegression(X, stu_t_likelihood, kernel3)
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#m.ensure_default_constraints()
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#m.update_likelihood_approximation()
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#m.optimize()
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@ -453,7 +453,7 @@ def student_t_approx():
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print "Clean Gaussian"
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#A GP should completely break down due to the points as they get a lot of weight
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# create simple GP model
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m = GPy.models.GP_regression(X, Y, kernel=kernel1)
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m = GPy.models.GPRegression(X, Y, kernel=kernel1)
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# optimize
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m.ensure_default_constraints()
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m.optimize()
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@ -466,7 +466,7 @@ def student_t_approx():
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#Corrupt
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print "Corrupt Gaussian"
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m = GPy.models.GP_regression(X, Yc, kernel=kernel2)
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m = GPy.models.GPRegression(X, Yc, kernel=kernel2)
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m.ensure_default_constraints()
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#m.optimize()
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plt.subplot(212)
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@ -480,9 +480,9 @@ def student_t_approx():
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edited_real_sd = real_std #initial_var_guess
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print "Clean student t, rasm"
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t_distribution = GPy.likelihoods.likelihood_functions.Student_t(deg_free, sigma2=edited_real_sd)
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t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=edited_real_sd)
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stu_t_likelihood = GPy.likelihoods.Laplace(Y.copy(), t_distribution, opt='rasm')
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m = GPy.models.GP(X, stu_t_likelihood, kernel6)
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m = GPy.models.GPRegression(X, Y.copy(), kernel6, stu_t_likelihood)
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m.ensure_default_constraints()
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m.constrain_positive('t_noise')
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m.randomize()
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@ -496,9 +496,9 @@ def student_t_approx():
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plt.title('Student-t rasm clean')
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print "Corrupt student t, rasm"
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t_distribution = GPy.likelihoods.likelihood_functions.Student_t(deg_free, sigma2=edited_real_sd)
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t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=edited_real_sd)
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corrupt_stu_t_likelihood = GPy.likelihoods.Laplace(Yc.copy(), t_distribution, opt='rasm')
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m = GPy.models.GP(X, corrupt_stu_t_likelihood, kernel4)
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m = GPy.models.GPRegression(X, Yc.copy(), kernel4, corrupt_stu_t_likelihood)
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m.ensure_default_constraints()
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m.constrain_positive('t_noise')
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m.randomize()
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@ -514,9 +514,9 @@ def student_t_approx():
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return m
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#print "Clean student t, ncg"
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#t_distribution = GPy.likelihoods.likelihood_functions.Student_t(deg_free, sigma2=edited_real_sd)
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#t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=edited_real_sd)
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#stu_t_likelihood = GPy.likelihoods.Laplace(Y, t_distribution, opt='ncg')
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#m = GPy.models.GP(X, stu_t_likelihood, kernel3)
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#m = GPy.models.GPRegression(X, stu_t_likelihood, kernel3)
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#m.ensure_default_constraints()
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#m.update_likelihood_approximation()
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#m.optimize()
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@ -528,9 +528,9 @@ def student_t_approx():
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#plt.title('Student-t ncg clean')
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#print "Corrupt student t, ncg"
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#t_distribution = GPy.likelihoods.likelihood_functions.Student_t(deg_free, sigma2=edited_real_sd)
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#t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=edited_real_sd)
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#corrupt_stu_t_likelihood = GPy.likelihoods.Laplace(Yc.copy(), t_distribution, opt='ncg')
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#m = GPy.models.GP(X, corrupt_stu_t_likelihood, kernel5)
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#m = GPy.models.GPRegression(X, corrupt_stu_t_likelihood, kernel5)
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#m.ensure_default_constraints()
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#m.update_likelihood_approximation()
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#m.optimize()
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@ -582,7 +582,7 @@ def noisy_laplace_approx():
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#A GP should completely break down due to the points as they get a lot of weight
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# create simple GP model
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m = GPy.models.GP_regression(X, Y)
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m = GPy.models.GPRegression(X, Y)
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# optimize
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m.ensure_default_constraints()
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@ -601,7 +601,7 @@ def gaussian_f_check():
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Y = np.sin(X*2*np.pi) + noise
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kernelgp = GPy.kern.rbf(X.shape[1]) # + GPy.kern.white(X.shape[1])
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mgp = GPy.models.GP_regression(X, Y, kernel=kernelgp)
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mgp = GPy.models.GPRegression(X, Y, kernel=kernelgp)
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mgp.ensure_default_constraints()
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mgp.randomize()
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mgp.optimize()
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@ -612,9 +612,9 @@ def gaussian_f_check():
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kernelg = kernelgp.copy()
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#kernelst += GPy.kern.bias(X.shape[1])
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N, D = X.shape
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g_distribution = GPy.likelihoods.likelihood_functions.Gaussian(variance=0.1, N=N, D=D)
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g_distribution = GPy.likelihoods.functions.Gaussian(variance=0.1, N=N, D=D)
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g_likelihood = GPy.likelihoods.Laplace(Y.copy(), g_distribution, opt='rasm')
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m = GPy.models.GP(X, g_likelihood, kernelg)
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m = GPy.models.GPRegression(X, Y, kernelg, likelihood=g_likelihood)
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#m['rbf_v'] = mgp._get_params()[0]
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#m['rbf_l'] = mgp._get_params()[1] + 1
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m.ensure_default_constraints()
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@ -624,14 +624,15 @@ def gaussian_f_check():
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#m.constrain_positive('bias')
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m.constrain_positive('noise_var')
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m.randomize()
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import ipdb; ipdb.set_trace() # XXX BREAKPOINT
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m['noise_variance'] = 0.1
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m.likelihood.X = X
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#m.likelihood.X = X
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plt.figure()
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plt.subplot(211)
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m.plot()
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plt.subplot(212)
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ax = plt.subplot(211)
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m.plot(ax=ax)
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ax = plt.subplot(212)
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m.optimize()
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m.plot()
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m.plot(ax=ax)
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print "final optimised gaussian"
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print m
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print "real GP"
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