[coverage] tests for coverage increase

GPy/testing/gp_tests.py

@@ -0,0 +1,99 @@
+'''
+Created on 4 Sep 2015
+
+@author: maxz
+'''
+import unittest
+import numpy as np, GPy
+from GPy.core.parameterization.variational import NormalPosterior
+
+class Test(unittest.TestCase):
+
+
+    def setUp(self):
+        np.random.seed(12345)
+        self.N = 20
+        self.N_new = 50
+        self.D = 1
+        self.X = np.random.uniform(-3., 3., (self.N, 1))
+        self.Y = np.sin(self.X) + np.random.randn(self.N, self.D) * 0.05
+        self.X_new = np.random.uniform(-3., 3., (self.N_new, 1))
+
+
+    def test_setxy_bgplvm(self):
+        k = GPy.kern.RBF(1)
+        m = GPy.models.BayesianGPLVM(self.Y, 2, kernel=k)
+        mu, var = m.predict(m.X)
+        X = m.X.copy()
+        Xnew = NormalPosterior(m.X.mean[:10].copy(), m.X.variance[:10].copy())
+        m.set_XY(Xnew, m.Y[:10])
+        assert(m.checkgrad())
+        m.set_XY(X, self.Y)
+        mu2, var2 = m.predict(m.X)
+        np.testing.assert_allclose(mu, mu2)
+        np.testing.assert_allclose(var, var2)
+
+    def test_setxy_gplvm(self):
+        k = GPy.kern.RBF(1)
+        m = GPy.models.GPLVM(self.Y, 2, kernel=k)
+        mu, var = m.predict(m.X)
+        X = m.X.copy()
+        Xnew = X[:10].copy()
+        m.set_XY(Xnew, m.Y[:10])
+        assert(m.checkgrad())
+        m.set_XY(X, self.Y)
+        mu2, var2 = m.predict(m.X)
+        np.testing.assert_allclose(mu, mu2)
+        np.testing.assert_allclose(var, var2)
+
+    def test_setxy_gp(self):
+        k = GPy.kern.RBF(1)
+        m = GPy.models.GPRegression(self.X, self.Y, kernel=k)
+        mu, var = m.predict(m.X)
+        X = m.X.copy()
+        m.set_XY(m.X[:10], m.Y[:10])
+        assert(m.checkgrad())
+        m.set_XY(X, self.Y)
+        mu2, var2 = m.predict(m.X)
+        np.testing.assert_allclose(mu, mu2)
+        np.testing.assert_allclose(var, var2)
+
+    def test_mean_function(self):
+        from GPy.core.parameterization.param import Param
+        from GPy.core.mapping import Mapping
+        class Parabola(Mapping):
+            def __init__(self, variance, degree=2, name='parabola'):
+                super(Parabola, self).__init__(1, 1, name)
+                self.variance = Param('variance', np.ones(degree+1) * variance)
+                self.degree = degree
+                self.link_parameter(self.variance)
+
+            def f(self, X):
+                p = self.variance[0] * np.ones(X.shape)
+                for i in range(1, self.degree+1):
+                    p += self.variance[i] * X**(i)
+                return p
+
+            def gradients_X(self, dL_dF, X):
+                grad = np.zeros(X.shape)
+                for i in range(1, self.degree+1):
+                    grad += (i) * self.variance[i] * X**(i-1)
+                return grad
+
+            def update_gradients(self, dL_dF, X):
+                for i in range(self.degree+1):
+                    self.variance.gradient[i] = (dL_dF * X**(i)).sum(0)
+        X = np.linspace(-2, 2, 100)[:, None]
+        k = GPy.kern.RBF(1)
+        k.randomize()
+        p = Parabola(.3)
+        p.randomize()
+        Y = p.f(X) + np.random.multivariate_normal(np.zeros(X.shape[0]), k.K(X))[:,None] + np.random.normal(0, .1, (X.shape[0], 1))
+        m = GPy.models.GPRegression(X, Y, mean_function=p)
+        m.randomize()
+        assert(m.checkgrad())
+        _ = m.predict(m.X)
+
+if __name__ == "__main__":
+    #import sys;sys.argv = ['', 'Test.testName']
+    unittest.main()