From 2ecd13b08d93448167fc66e5cd3cfff58916083d Mon Sep 17 00:00:00 2001
From: beckdaniel <daniel.e.beck@gmail.com>
Date: Mon, 7 Mar 2016 10:46:42 +0000
Subject: [PATCH] moved cubic sine from tests to examples

---
 GPy/examples/regression.py | 26 ++++++++++++++++++++++++++
 GPy/testing/model_tests.py | 29 +----------------------------
 2 files changed, 27 insertions(+), 28 deletions(-)

diff --git a/GPy/examples/regression.py b/GPy/examples/regression.py
index 11734564..b6bc07a0 100644
--- a/GPy/examples/regression.py
+++ b/GPy/examples/regression.py
@@ -550,3 +550,29 @@ def parametric_mean_function(max_iters=100, optimize=True, plot=True):
     return m
 
 
+def warped_gp_cubic_sine(max_iters=100):
+    """
+    A test replicating the sine regression problem from
+    Snelson's paper.
+    """
+    X = (2 * np.pi) * np.random.random(151) - np.pi
+    Y = np.sin(X) + np.random.normal(0,0.2,151)
+    Y = np.array([np.power(abs(y),float(1)/3) * (1,-1)[y<0] for y in Y])
+        
+    warp_k = GPy.kern.RBF(1)
+    warp_f = GPy.util.warping_functions.TanhFunction(n_terms=2)
+    warp_m = GPy.models.WarpedGP(X[:, None], Y[:, None], kernel=warp_k, warping_function=warp_f)
+
+    m = GPy.models.GPRegression(X[:, None], Y[:, None])
+    m.optimize_restarts(parallel=False, robust=True, num_restarts=5, max_iters=max_iters)
+    warp_m.optimize_restarts(parallel=False, robust=True, num_restarts=5, max_iters=max_iters)
+    print(warp_m)
+    print(warp_m['.*warp.*'])
+    warp_m.predict_in_warped_space = False
+    warp_m.plot(title="Warped GP - Latent space")
+    warp_m.predict_in_warped_space = True
+    warp_m.plot(title="Warped GP - Warped space")
+    m.plot(title="Standard GP")
+    #warp_f.plot(X.min()-10, X.max()+10)
+    warp_m.plot_warping()
+    pb.show()
diff --git a/GPy/testing/model_tests.py b/GPy/testing/model_tests.py
index 351aa210..5ec64e15 100644
--- a/GPy/testing/model_tests.py
+++ b/GPy/testing/model_tests.py
@@ -311,6 +311,7 @@ class MiscTests(unittest.TestCase):
         """
         A WarpedGP with the log warping function should be
         equal to a standard GP with log labels.
+        Note that we predict the median here.
         """
         k = GPy.kern.RBF(1)
         Y = np.abs(self.Y)
@@ -327,34 +328,6 @@ class MiscTests(unittest.TestCase):
  
         np.testing.assert_almost_equal(np.exp(preds), warp_preds, decimal=4)
 
-    @unittest.skip('Comment this to plot the modified sine function')
-    def test_warped_gp_sine(self):
-        """
-        A test replicating the sine regression problem from
-        Snelson's paper.
-        """
-        X = (2 * np.pi) * np.random.random(151) - np.pi
-        Y = np.sin(X) + np.random.normal(0,0.2,151)
-        Y = np.array([np.power(abs(y),float(1)/3) * (1,-1)[y<0] for y in Y])
-        
-        import matplotlib.pyplot as plt
-        warp_k = GPy.kern.RBF(1)
-        warp_f = GPy.util.warping_functions.TanhFunction(n_terms=2)
-        warp_m = GPy.models.WarpedGP(X[:, None], Y[:, None], kernel=warp_k, warping_function=warp_f)
-
-        m = GPy.models.GPRegression(X[:, None], Y[:, None])
-        m.optimize_restarts(parallel=False, robust=True, num_restarts=5)
-        warp_m.optimize_restarts(parallel=False, robust=True, num_restarts=5)
-        print(warp_m)
-        print(warp_m['.*warp.*'])
-        warp_m.predict_in_warped_space = False
-        warp_m.plot()
-        warp_m.predict_in_warped_space = True
-        warp_m.plot()
-        m.plot()
-        warp_f.plot(X.min()-10, X.max()+10)
-        plt.show()
-        
 
 class GradientTests(np.testing.TestCase):
     def setUp(self):