Heaviside transformation fixed

GPy/examples/classification.py

@@ -166,3 +166,35 @@ def FITC_crescent_data(num_inducing=10, seed=default_seed):
     print(m)
     m.plot()
     return m
+
+
+def toy_heavyside(seed=default_seed):
+    """
+    Simple 1D classification example using a heavy side gp transformation
+    :param seed : seed value for data generation (default is 4).
+    :type seed: int
+    """
+
+    data = GPy.util.datasets.toy_linear_1d_classification(seed=seed)
+    Y = data['Y'][:, 0:1]
+    Y[Y.flatten() == -1] = 0
+
+    # Model definition
+    noise_model = GPy.likelihoods.binomial(GPy.likelihoods.noise_models.gp_transformations.Heaviside())
+    likelihood = GPy.likelihoods.EP(Y,noise_model)
+    m = GPy.models.GPClassification(data['X'], likelihood=likelihood)
+
+    # Optimize
+    m.update_likelihood_approximation()
+    # Parameters optimization:
+    m.optimize()
+    #m.pseudo_EM()
+
+    # Plot
+    fig, axes = pb.subplots(2,1)
+    m.plot_f(ax=axes[0])
+    m.plot(ax=axes[1])
+    print(m)
+
+    return m
+

GPy/likelihoods/noise_models/binomial_noise.py

@@ -74,8 +74,6 @@ class Binomial(NoiseDistribution):
         else:
             raise NotImplementedError
 
-
-
     def _mass(self,gp,obs):
         #NOTE obs must be in {0,1}
         p = self.gp_link.transf(gp)

GPy/likelihoods/noise_models/gp_transformations.py

@@ -116,10 +116,10 @@ class Heaviside(GPTransformation):
     """
     def transf(self,f):
         #transformation goes here
-        return np.where(f>0, 1, -1)
+        return np.where(f>0, 1, 0)
 
     def dtransf_df(self,f):
-        raise NotImplementedError, "this function is not differentiable!"
+        raise NotImplementedError, "This function is not differentiable!"
 
     def d2transf_df2(self,f):
-        raise NotImplementedError, "this function is not differentiable!"
+        raise NotImplementedError, "This function is not differentiable!"

GPy/models/gp_classification.py

@@ -14,7 +14,7 @@ class GPClassification(GP):
     This is a thin wrapper around the models.GP class, with a set of sensible defaults
 
     :param X: input observations
-    :param Y: observed values
+    :param Y: observed values, can be None if likelihood is not None
     :param likelihood: a GPy likelihood, defaults to Binomial with probit link_function
     :param kernel: a GPy kernel, defaults to rbf
     :param normalize_X:  whether to normalize the input data before computing (predictions will be in original scales)