Merge branch 'devel' of github.com:SheffieldML/GPy into devel

GPy/core/model.py

@@ -66,7 +66,7 @@ class model(parameterised):
 
 
         # check constraints are okay
-        if isinstance(what, (priors.gamma, priors.log_Gaussian)):
+        if isinstance(what, (priors.gamma, priors.inverse_gamma, priors.log_Gaussian)):
             constrained_positive_indices = [i for i, t in zip(self.constrained_indices, self.constraints) if t.domain == 'positive']
             if len(constrained_positive_indices):
                 constrained_positive_indices = np.hstack(constrained_positive_indices)

GPy/core/priors.py

@@ -26,7 +26,6 @@ class Gaussian(prior):
     :param mu: mean
     :param sigma: standard deviation
 
-
     .. Note:: Bishop 2006 notation is used throughout the code
 
     """
@@ -144,7 +143,6 @@ def gamma_from_EV(E,V):
     b = E/V
     return gamma(a,b)
 
-
 class gamma(prior):
     """
     Implementation of the Gamma probability function, coupled with random variables.
@@ -155,7 +153,6 @@ class gamma(prior):
     .. Note:: Bishop 2006 notation is used throughout the code
 
     """
-    
     def __init__(self,a,b):
         self.a = float(a)
         self.b = float(b)
@@ -183,3 +180,30 @@ class gamma(prior):
 
     def rvs(self,n):
         return np.random.gamma(scale=1./self.b,shape=self.a,size=n)
+
+class inverse_gamma(prior):
+    """
+    Implementation of the inverse-Gamma probability function, coupled with random variables.
+
+    :param a: shape parameter
+    :param b: rate parameter (warning: it's the *inverse* of the scale)
+
+    .. Note:: Bishop 2006 notation is used throughout the code
+
+    """
+    def __init__(self,a,b):
+        self.a = float(a)
+        self.b = float(b)
+        self.constant = -gammaln(self.a) + a*np.log(b)
+
+    def __str__(self):
+        return "iGa("+str(np.round(self.a))+', '+str(np.round(self.b))+')'
+
+    def lnpdf(self,x):
+        return self.constant - (self.a+1)*np.log(x) - self.b/x
+
+    def lnpdf_grad(self,x):
+        return -(self.a+1.)/x + self.b/x**2
+
+    def rvs(self,n):
+        return 1./np.random.gamma(scale=1./self.b,shape=self.a,size=n)

GPy/examples/dimensionality_reduction.py

@@ -313,9 +313,7 @@ def brendan_faces():
     m = GPy.models.GPLVM(Yn, Q)#, M=Y.shape[0]/4)
 
     # optimize
-    # m.constrain_fixed('white', 1e-2)
-    # m.constrain_bounded('noise', 1e-6, 10)
-    m.constrain('rbf', GPy.core.transformations.logexp_clipped())
+    m.constrain('rbf|noise|white', GPy.core.transformations.logexp_clipped())
 
     m.ensure_default_constraints()
     m.optimize('scg', messages=1, max_f_eval=10000)

GPy/inference/SCG.py

@@ -39,8 +39,10 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
     function_eval number of fn evaluations
     status: string describing convergence status
     """
-    print "  SCG"
-    print ' {0:{mi}s}   {1:11s}    {2:11s}    {3:11s}'.format("I", "F", "Scale", "|g|", mi=len(str(maxiters)))
+    
+    if display:
+        print "  SCG"
+        print ' {0:{mi}s}   {1:11s}    {2:11s}    {3:11s}'.format("I", "F", "Scale", "|g|", mi=len(str(maxiters)))
 
     if xtol is None:
         xtol = 1e-6

GPy/util/datasets.py

@@ -5,6 +5,8 @@ import GPy
 import scipy.sparse
 import scipy.io
 import cPickle as pickle
+import urllib2 as url
+
 data_path = os.path.join(os.path.dirname(__file__), 'datasets')
 default_seed = 10000
 
@@ -15,6 +17,25 @@ def sample_class(f):
     c = np.where(c, 1, -1)
     return c
 
+def fetch_dataset(resource, save_name = None, save_file = True, messages = True):
+    if messages:
+        print "Downloading resource: " , resource, " ... "
+    response = url.urlopen(resource)
+    # TODO: Some error checking...
+    # ... 
+    html = response.read()
+    response.close()
+    if save_file:
+        # TODO: Check if already exists...
+        # ...
+        with open(save_name, "w") as text_file:
+            text_file.write("%s"%html)
+            if messages:
+                print "Done!"
+    return html
+        
+    
+
 def della_gatta_TRP63_gene_expression(gene_number=None):
     mat_data = scipy.io.loadmat(os.path.join(data_path, 'DellaGattadata.mat'))
     X = np.double(mat_data['timepoints'])

GPy/util/mocap_fetch.py

@@ -0,0 +1,13 @@
+import GPy
+import urllib2
+
+# TODO...
+class mocap_fetch(base_url = 'http://mocap.cs.cmu.edu:8080/subjects/', skel_store_dir = './', motion_store_dir = './'):
+    def __init__(self):
+        self.base_url = base_url
+        self.store_dir = store_dir
+        self.motion_dict = []
+    
+    def fetch_motions(self, motion_dict = None):
+        response = urllib2.urlopen(...)
+        html = response.read()