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38 lines
1 KiB
Python
38 lines
1 KiB
Python
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import GPy
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import numpy as np
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import scipy as sp
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import scipy.stats
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import matplotlib.pyplot as plt
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def student_t_approx():
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"""
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Example of regressing with a student t likelihood
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"""
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#Start a function, any function
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X = np.sort(np.random.uniform(0, 15, 70))[:, None]
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Y = np.sin(X)
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#Add some extreme value noise to some of the datapoints
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percent_corrupted = 0.05
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corrupted_datums = int(np.round(Y.shape[0] * percent_corrupted))
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indices = np.arange(Y.shape[0])
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np.random.shuffle(indices)
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corrupted_indices = indices[:corrupted_datums]
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print corrupted_indices
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noise = np.random.uniform(-10,10,(len(corrupted_indices), 1))
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Y[corrupted_indices] += noise
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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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# optimize
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m.ensure_default_constraints()
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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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#with a student t distribution, since it has heavy tails it should work well
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