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Martin Bubel 2023-10-06 18:59:10 +02:00
parent c20a361179
commit 3935b7203e
1 changed files with 39 additions and 43 deletions
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82
GPy/testing/meanfunc_tests.py
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@ -5,91 +5,87 @@ import unittest
import numpy as np
import GPy
class MFtests(unittest.TestCase):
def test_simple_mean_function(self):
"""
The simplest possible mean function. No parameters, just a simple Sinusoid.
"""
#create simple mean function
mf = GPy.core.Mapping(1,1)
# create simple mean function
mf = GPy.core.Mapping(1, 1)
mf.f = np.sin
mf.update_gradients = lambda a,b: None
mf.update_gradients = lambda a, b: None
X = np.linspace(0,10,50).reshape(-1,1)
Y = np.sin(X) + 0.5*np.cos(3*X) + 0.1*np.random.randn(*X.shape)
X = np.linspace(0, 10, 50).reshape(-1, 1)
Y = np.sin(X) + 0.5 * np.cos(3 * X) + 0.1 * np.random.randn(*X.shape)
k =GPy.kern.RBF(1)
k = GPy.kern.RBF(1)
lik = GPy.likelihoods.Gaussian()
m = GPy.core.GP(X, Y, kernel=k, likelihood=lik, mean_function=mf)
self.assertTrue(m.checkgrad())
assert m.checkgrad()
def test_parametric_mean_function(self):
"""
A linear mean function with parameters that we'll learn alongside the kernel
"""
X = np.linspace(-1,10,50).reshape(-1,1)
Y = 3-np.abs((X-6))
Y += .5*np.cos(3*X) + 0.3*np.random.randn(*X.shape)
X = np.linspace(-1, 10, 50).reshape(-1, 1)
mf = GPy.mappings.PiecewiseLinear(1, 1, [-1,1], [9,2])
Y = 3 - np.abs((X - 6))
Y += 0.5 * np.cos(3 * X) + 0.3 * np.random.randn(*X.shape)
k =GPy.kern.RBF(1)
mf = GPy.mappings.PiecewiseLinear(1, 1, [-1, 1], [9, 2])
k = GPy.kern.RBF(1)
lik = GPy.likelihoods.Gaussian()
m = GPy.core.GP(X, Y, kernel=k, likelihood=lik, mean_function=mf)
self.assertTrue(m.checkgrad())
assert m.checkgrad()
def test_parametric_mean_function_composition(self):
"""
A linear mean function with parameters that we'll learn alongside the kernel
"""
X = np.linspace(0,10,50).reshape(-1,1)
Y = np.sin(X) + 0.5*np.cos(3*X) + 0.1*np.random.randn(*X.shape) + 3*X
X = np.linspace(0, 10, 50).reshape(-1, 1)
Y = np.sin(X) + 0.5 * np.cos(3 * X) + 0.1 * np.random.randn(*X.shape) + 3 * X
mf = GPy.mappings.Compound(GPy.mappings.Linear(1,1),
GPy.mappings.Kernel(1, 1, np.random.normal(0,1,(1,1)),
GPy.kern.RBF(1))
)
mf = GPy.mappings.Compound(
GPy.mappings.Linear(1, 1),
GPy.mappings.Kernel(1, 1, np.random.normal(0, 1, (1, 1)), GPy.kern.RBF(1)),
)
k =GPy.kern.RBF(1)
k = GPy.kern.RBF(1)
lik = GPy.likelihoods.Gaussian()
m = GPy.core.GP(X, Y, kernel=k, likelihood=lik, mean_function=mf)
self.assertTrue(m.checkgrad())
assert m.checkgrad()
def test_parametric_mean_function_additive(self):
"""
A linear mean function with parameters that we'll learn alongside the kernel
"""
X = np.linspace(0,10,50).reshape(-1,1)
Y = np.sin(X) + 0.5*np.cos(3*X) + 0.1*np.random.randn(*X.shape) + 3*X
X = np.linspace(0, 10, 50).reshape(-1, 1)
Y = np.sin(X) + 0.5 * np.cos(3 * X) + 0.1 * np.random.randn(*X.shape) + 3 * X
mf = GPy.mappings.Additive(GPy.mappings.Constant(1,1,3),
GPy.mappings.Additive(GPy.mappings.MLP(1,1),
GPy.mappings.Identity(1,1)
)
)
mf = GPy.mappings.Additive(
GPy.mappings.Constant(1, 1, 3),
GPy.mappings.Additive(GPy.mappings.MLP(1, 1), GPy.mappings.Identity(1, 1)),
)
k =GPy.kern.RBF(1)
k = GPy.kern.RBF(1)
lik = GPy.likelihoods.Gaussian()
m = GPy.core.GP(X, Y, kernel=k, likelihood=lik, mean_function=mf)
self.assertTrue(m.checkgrad())
assert m.checkgrad()
def test_svgp_mean_function(self):
# an instance of the SVIGOP with a men function
X = np.linspace(0,10,500).reshape(-1,1)
Y = np.sin(X) + 0.5*np.cos(3*X) + 0.1*np.random.randn(*X.shape)
Y = np.where(Y>0, 1,0) # make aclassificatino problem
X = np.linspace(0, 10, 500).reshape(-1, 1)
Y = np.sin(X) + 0.5 * np.cos(3 * X) + 0.1 * np.random.randn(*X.shape)
Y = np.where(Y > 0, 1, 0) # make aclassificatino problem
mf = GPy.mappings.Linear(1,1)
Z = np.linspace(0,10,50).reshape(-1,1)
mf = GPy.mappings.Linear(1, 1)
Z = np.linspace(0, 10, 50).reshape(-1, 1)
lik = GPy.likelihoods.Bernoulli()
k =GPy.kern.RBF(1) + GPy.kern.White(1, 1e-4)
m = GPy.core.SVGP(X, Y,Z=Z, kernel=k, likelihood=lik, mean_function=mf)
self.assertTrue(m.checkgrad())
k = GPy.kern.RBF(1) + GPy.kern.White(1, 1e-4)
m = GPy.core.SVGP(X, Y, Z=Z, kernel=k, likelihood=lik, mean_function=mf)
assert m.checkgrad()