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121 lines
3.6 KiB
Python
121 lines
3.6 KiB
Python
"""
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Created on 4 Sep 2015
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@author: maxz
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"""
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import numpy as np
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import GPy
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from GPy.core.parameterization.variational import NormalPosterior
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class TestGP:
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def setup(self):
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np.random.seed(12345)
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self.N = 20
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self.N_new = 50
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self.D = 1
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self.X = np.random.uniform(-3.0, 3.0, (self.N, 1))
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self.Y = np.sin(self.X) + np.random.randn(self.N, self.D) * 0.05
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self.X_new = np.random.uniform(-3.0, 3.0, (self.N_new, 1))
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def test_setxy_bgplvm(self):
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self.setup()
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k = GPy.kern.RBF(1)
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m = GPy.models.BayesianGPLVM(self.Y, 1, kernel=k)
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mu, var = m.predict(m.X)
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X = m.X
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Xnew = NormalPosterior(m.X.mean[:10].copy(), m.X.variance[:10].copy())
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m.set_XY(Xnew, m.Y[:10].copy())
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assert m.checkgrad()
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assert m.num_data == m.X.shape[0]
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assert m.input_dim == m.X.shape[1]
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m.set_XY(X, self.Y)
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mu2, var2 = m.predict(m.X)
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np.testing.assert_allclose(mu, mu2)
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np.testing.assert_allclose(var, var2)
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def test_setxy_gplvm(self):
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self.setup()
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k = GPy.kern.RBF(1)
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m = GPy.models.GPLVM(self.Y, 1, kernel=k)
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mu, var = m.predict(m.X)
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X = m.X.copy()
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Xnew = X[:10].copy()
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m.set_XY(Xnew, m.Y[:10].copy())
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assert m.checkgrad()
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assert m.num_data == m.X.shape[0]
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assert m.input_dim == m.X.shape[1]
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m.set_XY(X, self.Y)
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mu2, var2 = m.predict(m.X)
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np.testing.assert_allclose(mu, mu2)
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np.testing.assert_allclose(var, var2)
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def test_setxy_gp(self):
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self.setup()
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k = GPy.kern.RBF(1)
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m = GPy.models.GPRegression(self.X, self.Y, kernel=k)
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mu, var = m.predict(m.X)
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X = m.X.copy()
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m.set_XY(m.X[:10], m.Y[:10])
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assert m.checkgrad()
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assert m.num_data == m.X.shape[0]
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assert m.input_dim == m.X.shape[1]
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m.set_XY(X, self.Y)
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mu2, var2 = m.predict(m.X)
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np.testing.assert_allclose(mu, mu2)
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np.testing.assert_allclose(var, var2)
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def test_mean_function(self):
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from GPy.core.parameterization.param import Param
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from GPy.core.mapping import Mapping
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self.setup()
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class Parabola(Mapping):
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def __init__(self, variance, degree=2, name="parabola"):
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super(Parabola, self).__init__(1, 1, name)
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self.variance = Param("variance", np.ones(degree + 1) * variance)
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self.degree = degree
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self.link_parameter(self.variance)
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def f(self, X):
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p = self.variance[0] * np.ones(X.shape)
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for i in range(1, self.degree + 1):
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p += self.variance[i] * X ** (i)
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return p
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def gradients_X(self, dL_dF, X):
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grad = np.zeros(X.shape)
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for i in range(1, self.degree + 1):
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grad += (i) * self.variance[i] * X ** (i - 1)
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return grad
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def update_gradients(self, dL_dF, X):
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for i in range(self.degree + 1):
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self.variance.gradient[i] = (dL_dF * X ** (i)).sum(0)
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X = np.linspace(-2, 2, 100)[:, None]
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k = GPy.kern.RBF(1)
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k.randomize()
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p = Parabola(0.3)
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p.randomize()
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Y = (
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p.f(X)
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+ np.random.multivariate_normal(
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np.zeros(X.shape[0]), k.K(X) + np.eye(X.shape[0]) * 1e-8
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)[:, None]
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+ np.random.normal(0, 0.1, (X.shape[0], 1))
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)
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m = GPy.models.GPRegression(X, Y, mean_function=p)
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m.randomize()
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assert m.checkgrad()
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_ = m.predict(m.X)
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