no thing much, profiling, see #3

examples/gplvm_aep_examples.py

@@ -2,15 +2,23 @@
 import numpy as np
 import pdb
 import matplotlib.pylab as plt
-from .context import aep
 from scipy import special
 
+# from .context import aep
+
+import sys
+import os
+sys.path.insert(0, os.path.abspath(
+    os.path.join(os.path.dirname(__file__), '..')))
+import geepee.aep_models as aep
+
+np.random.seed(42)
 
 def run_cluster():
 	import GPy
 	# create dataset
 	print "creating dataset..."
-	N = 50
+	N = 100
 	k1 = GPy.kern.RBF(5, variance=1, lengthscale=1. /
 	                  np.random.dirichlet(np.r_[10, 10, 10, 0.1, 0.1]), ARD=True)
 	k2 = GPy.kern.RBF(5, variance=1, lengthscale=1. /
@@ -28,7 +36,7 @@ def run_cluster():
 
 	# inference
 	print "inference ..."
-	M = 20
+	M = 30
 	D = 5
 	lvm = aep.SGPLVM(Y, D, M, lik='Gaussian')
 
@@ -121,11 +129,7 @@ def run_mnist():
 
 
 def run_oil():
-
-	# data_path = '/Users/thangbui/Desktop/gplvm/tmp/data/'
-	# data_path = '../tmp/data/'
-	data_path = './tmp/data/'
-
+	data_path = '/scratch/tdb40/datasets/lvm/three_phase_oil_flow/'
 
 	def oil(data_set='oil'):
 	    """The three phase oil data from Bishop and James (1993)."""
@@ -389,6 +393,7 @@ def run_frey():
 
 if __name__ == '__main__':
 	run_cluster()
-	run_semicircle()
-	run_pinwheel()
-	run_xor()
+	# run_semicircle()
+	# run_pinwheel()
+	# run_xor()
+	# run_oil()

geepee/aep_models.py

@@ -141,7 +141,7 @@ def _forward_prop_random_thru_post_mm(self, mx, vx):
         vout = psi0 + Bpsi2 - mout**2
         return mout, vout
 
-    # @profile
+    @profile
     def backprop_grads_lvm(self, m, v, dm, dv, psi1, psi2, mx, vx, alpha=1.0):
         N = self.N
         M = self.M
@@ -713,6 +713,7 @@ def __init__(self, y_train, hidden_size, no_pseudo,
         self.x_post_1 = np.zeros((N, Din))
         self.x_post_2 = np.zeros((N, Din))
 
+    @profile
     def objective_function(self, params, idxs, alpha=1.0):
         N = self.N
         yb = self.y_train[idxs, :]

tests/test_grads_aep.py

@@ -1284,20 +1284,20 @@ def kink(T, process_noise, obs_noise, xprev=None):
 
 
 if __name__ == '__main__':
-    # test_gplvm_aep_gaussian()
-    # test_gplvm_aep_probit()
-    # test_gplvm_aep_gaussian_scipy()
-    # test_gplvm_aep_probit_scipy()
-
-    test_gpr_aep_gaussian()
-    test_gpr_aep_probit()
-    test_gpr_aep_gaussian_scipy()
-    test_gpr_aep_probit_scipy()
-
-    test_dgpr_aep_gaussian()
-    test_dgpr_aep_probit()
-    test_dgpr_aep_gaussian_scipy()
-    test_dgpr_aep_probit_scipy()
+    test_gplvm_aep_gaussian()
+    test_gplvm_aep_probit()
+    test_gplvm_aep_gaussian_scipy()
+    test_gplvm_aep_probit_scipy()
+
+    # test_gpr_aep_gaussian()
+    # test_gpr_aep_probit()
+    # test_gpr_aep_gaussian_scipy()
+    # test_gpr_aep_probit_scipy()
+
+    # test_dgpr_aep_gaussian()
+    # test_dgpr_aep_probit()
+    # test_dgpr_aep_gaussian_scipy()
+    # test_dgpr_aep_probit_scipy()
 
     # test_gpssm_aep_gaussian()
     # np.random.seed(42)