From 5d772a8c1100c1504a6808aebcbfbeeb8e55f47c Mon Sep 17 00:00:00 2001
From: Max Zwiessele <ibinbei@gmail.com>
Date: Thu, 3 Dec 2015 08:48:28 +0000
Subject: [PATCH] [examples] added bgplvm stochastic example and parameter for
 dimensionality

---
 GPy/examples/dimensionality_reduction.py | 34 ++++++++++++++++++++++--
 1 file changed, 32 insertions(+), 2 deletions(-)

diff --git a/GPy/examples/dimensionality_reduction.py b/GPy/examples/dimensionality_reduction.py
index 0d72949a..024b12ee 100644
--- a/GPy/examples/dimensionality_reduction.py
+++ b/GPy/examples/dimensionality_reduction.py
@@ -393,12 +393,12 @@ def ssgplvm_simulation(optimize=True, verbose=1,
 
 def bgplvm_simulation_missing_data(optimize=True, verbose=1,
                       plot=True, plot_sim=False,
-                      max_iters=2e4, percent_missing=.1,
+                      max_iters=2e4, percent_missing=.1, d=13,
                       ):
     from GPy import kern
     from GPy.models.bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch
 
-    D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 400, 3, 4
+    D1, D2, D3, N, num_inducing, Q = d, 5, 8, 400, 3, 4
     _, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
     Y = Ylist[0]
     k = kern.Linear(Q, ARD=True)  # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
@@ -421,6 +421,36 @@ def bgplvm_simulation_missing_data(optimize=True, verbose=1,
         m.kern.plot_ARD('BGPLVM Simulation ARD Parameters')
     return m
 
+def bgplvm_simulation_missing_data_stochastics(optimize=True, verbose=1,
+                      plot=True, plot_sim=False,
+                      max_iters=2e4, percent_missing=.1, d=13, batchsize=2,
+                      ):
+    from GPy import kern
+    from GPy.models.bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch
+
+    D1, D2, D3, N, num_inducing, Q = d, 5, 8, 400, 3, 4
+    _, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
+    Y = Ylist[0]
+    k = kern.Linear(Q, ARD=True)  # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
+
+    inan = _np.random.binomial(1, percent_missing, size=Y.shape).astype(bool)  # 80% missing data
+    Ymissing = Y.copy()
+    Ymissing[inan] = _np.nan
+
+    m = BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing,
+                      kernel=k, missing_data=True, stochastic=True, batchsize=batchsize)
+
+    m.Yreal = Y
+
+    if optimize:
+        print("Optimizing model:")
+        m.optimize('bfgs', messages=verbose, max_iters=max_iters,
+                   gtol=.05)
+    if plot:
+        m.X.plot("BGPLVM Latent Space 1D")
+        m.kern.plot_ARD('BGPLVM Simulation ARD Parameters')
+    return m
+
 
 def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
     from GPy import kern