examples

GPy/examples/classification.py

@@ -0,0 +1,93 @@
+"""
+Simple Gaussian Processes classification
+"""
+import pylab as pb
+import numpy as np
+import GPy
+pb.ion()
+pb.close('all')
+
+default_seed=10000
+######################################
+## 2 dimensional example
+def crescent_data(model_type='Full', inducing=10, seed=default_seed):
+    """Run a Gaussian process classification on the crescent data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood.
+
+    :param model_type: type of model to fit ['Full', 'FITC', 'DTC'].
+    :param seed : seed value for data generation.
+    :type seed: int
+    :param inducing : number of inducing variables (only used for 'FITC' or 'DTC').
+    :type inducing: int
+    """
+    data = GPy.util.datasets.crescent_data(seed=seed)
+    likelihood = GPy.inference.likelihoods.probit(data['Y'])
+
+    if model_type=='Full':
+        m = GPy.models.simple_GP_EP(data['X'],likelihood)
+    else:
+        # create sparse GP EP model
+        m = GPy.models.sparse_GP_EP(data['X'],likelihood=likelihood,inducing=inducing,ep_proxy=model_type)
+
+    m.approximate_likelihood()
+    print(m)
+
+    # optimize
+    m.em()
+    print(m)
+
+    # plot
+    m.plot()
+    return m
+
+def oil():
+    """Run a Gaussian process classification on the oil data. The demonstration calls the basic GP classification model and uses EP to approximate the likelihood."""
+    data = GPy.util.datasets.oil()
+    likelihood = GPy.inference.likelihoods.probit(data['Y'][:, 0:1])
+
+    # create simple GP model
+    m = GPy.models.simple_GP_EP(data['X'],likelihood)
+
+    # contrain all parameters to be positive
+    m.constrain_positive('')
+    m.tie_param('lengthscale')
+    m.approximate_likelihood()
+
+    # optimize
+    m.optimize()
+
+    # plot
+    #m.plot()
+    print(m)
+    return m
+
+def toy_linear_1d_classification(model_type='Full', inducing=4, seed=default_seed):
+    """Simple 1D classification example.
+    :param model_type: type of model to fit ['Full', 'FITC', 'DTC'].
+    :param seed : seed value for data generation (default is 4).
+    :type seed: int
+    :param inducing : number of inducing variables (only used for 'FITC' or 'DTC').
+    :type inducing: int
+    """
+    data = GPy.util.datasets.toy_linear_1d_classification(seed=seed)
+    likelihood = GPy.inference.likelihoods.probit(data['Y'][:, 0:1])
+    assert model_type in ('Full','DTC','FITC')
+
+    # create simple GP model
+    if model_type=='Full':
+        m = GPy.models.simple_GP_EP(data['X'],likelihood)
+    else:
+        # create sparse GP EP model
+        m = GPy.models.sparse_GP_EP(data['X'],likelihood=likelihood,inducing=inducing,ep_proxy=model_type)
+            
+
+    m.constrain_positive('var')
+    m.constrain_positive('len')
+    m.tie_param('lengthscale')
+    m.approximate_likelihood()
+
+    # Optimize and plot
+    m.em(plot_all=False) # EM algorithm
+    m.plot()
+
+    print(m)
+    return m