Part working version of sympy covariance with new params version.

GPy/kern/__init__.py

@@ -4,7 +4,9 @@ from _src.kern import Kern
 from _src.linear import Linear
 from _src.brownian import Brownian
 from _src.stationary import Exponential, Matern32, Matern52, ExpQuad
-#from _src.bias import Bias
+from _src.sympykern import Sympykern
+#from _src.kern import kern_test
+from _src.bias import Bias
 #import coregionalize
 #import exponential
 #import eq_ode1

GPy/kern/_src/bias.py

@@ -3,6 +3,7 @@
 
 
 from kern import Kern
+import numpy as np
 from ...core.parameterization import Param
 from ...core.parameterization.transformations import Logexp
 

GPy/kern/_src/kern.py

@@ -36,7 +36,7 @@ class Kern(Parameterized):
         raise NotImplementedError
     def gradients_X_diag(self, dL_dK, X):
         raise NotImplementedError
-    def update_gradients_full(self, dL_dK, X):
+    def update_gradients_full(self, dL_dK, X, X2):
         """Set the gradients of all parameters when doing full (N) inference."""
         raise NotImplementedError
     def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
@@ -125,7 +125,7 @@ class Kern(Parameterized):
 from GPy.core.model import Model
 
 class Kern_check_model(Model):
-    """This is a dummy model class used as a base class for checking that the gradients of a given kernel are implemented correctly. It enables checkgradient() to be called independently on a kernel."""
+    """This is a dummy model class used as a base class for checking that the gradients of a given kernel are implemented correctly. It enables checkgrad() to be called independently on a kernel."""
     def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
         Model.__init__(self, 'kernel_test_model')
         num_samples = 20
@@ -165,9 +165,10 @@ class Kern_check_dK_dtheta(Kern_check_model):
         Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=X2)
 
     def _log_likelihood_gradients(self):
-        return self.kernel._param_grad_helper(self.dL_dK, self.X, self.X2)
+        
-
+        target = np.zeros_like(self._get_params())
-
+        self.kernel._param_grad_helper(self.dL_dK, self.X, self.X2, target)
+        return target
 
 
 

GPy/kern/_src/stationary.py

@@ -18,7 +18,7 @@ class Stationary(Kern):
                 lengthscale = np.ones(1)
             else:
                 lengthscale = np.asarray(lengthscale)
-                assert lengthscale.size == 1 "Only  lengthscale needed for non-ARD kernel"
+                assert lengthscale.size == 1, "Only  lengthscale needed for non-ARD kernel"
         else:
             if lengthscale is not None:
                 lengthscale = np.asarray(lengthscale)

GPy/kern/_src/sympykern.py

@@ -2,35 +2,17 @@
 try: 
     import sympy as sp
     sympy_available=True
+    from sympy.utilities.lambdify import lambdify
 except ImportError:
     sympy_available=False
     exit()
 
-from sympy.core.cache import clear_cache
-from sympy.utilities.codegen import codegen
-
-try:
-    from scipy import weave
-    weave_available = True
-except ImportError:
-    weave_available = False
-
-import os
-current_dir = os.path.dirname(os.path.abspath(os.path.dirname(__file__)))
-import sys
 import numpy as np
-import re
+from kern import Kern
-import tempfile
-import pdb
-import ast
-
-from kernpart import Kernpart
 from ...core.parameterization import Param
 from ...core.parameterization.transformations import Logexp
-# TODO have this set up in a set up file!
-user_code_storage = tempfile.gettempdir()
 
-class spkern(Kernpart):
+class Sympykern(Kern):
     """
     A kernel object, where all the hard work in done by sympy.
 
@@ -51,10 +33,10 @@ class spkern(Kernpart):
             name='sympykern'
         if k is None:
             raise ValueError, "You must provide an argument for the covariance function."
-        super(spkern, self).__init__(input_dim, name)
+        super(Sympykern, self).__init__(input_dim, name)
 
         self._sp_k = k
-
+        
         # pull the variable names out of the symbolic covariance function.
         sp_vars = [e for e in k.atoms() if e.is_Symbol]
         self._sp_x= sorted([e for e in sp_vars if e.name[0:2]=='x_'],key=lambda x:int(x.name[2:]))
@@ -66,6 +48,10 @@ class spkern(Kernpart):
         assert len(self._sp_x)==len(self._sp_z)
         x_dim=len(self._sp_x)
 
+        self._sp_kdiag = k
+        for x, z in zip(self._sp_x, self._sp_z):
+            self._sp_kdiag = self._sp_kdiag.subs(z, x)
+            
         # If it is a multi-output covariance, add an input for indexing the outputs.
         self._real_input_dim = x_dim
         # Check input dim is number of xs + 1 if output_dim is >1
@@ -97,6 +83,8 @@ class spkern(Kernpart):
                 #setattr(self, theta, np.ones(self.output_dim))
             
             self.num_shared_params = len(self._sp_theta)
+            for theta_i, theta_j in zip(self._sp_theta_i, self._sp_theta_j):
+                self._sp_kdiag = self._sp_kdiag.subs(theta_j, theta_i)
             #self.num_params = self.num_shared_params+self.num_split_params*self.output_dim
             
         else:
@@ -112,43 +100,33 @@ class spkern(Kernpart):
             if param is not None:
                 if param.has_key(theta):
                     val = param[theta]
-            #setattr(self, theta.name, val)
             setattr(self, theta.name, Param(theta.name, val, None))
             self.add_parameters(getattr(self, theta.name))
         #deal with param            
         #self._set_params(self._get_params())
 
         # Differentiate with respect to parameters.
-        self._sp_dk_dtheta = [sp.diff(k,theta).simplify() for theta in self._sp_theta]
+        derivative_arguments = self._sp_x + self._sp_theta
         if self.output_dim > 1:
-            self._sp_dk_dtheta_i = [sp.diff(k,theta).simplify() for theta in self._sp_theta_i]
+            derivative_arguments += self._sp_theta_i
-
+        
-        # differentiate with respect to input variables.
+        self.derivatives = {theta.name : sp.diff(self._sp_k,theta).simplify() for theta in derivative_arguments}
-        self._sp_dk_dx = [sp.diff(k,xi).simplify() for xi in self._sp_x]
+        self.diag_derivatives = {theta.name : sp.diff(self._sp_kdiag,theta).simplify() for theta in derivative_arguments}
-
+        
+        
+        # This gives the parameters for the arg list.
+        self.arg_list = self._sp_x + self._sp_z + self._sp_theta
+        self.diag_arg_list = self._sp_x + self._sp_theta
+        if self.output_dim > 1:
+            self.arg_list += self._sp_theta_i + self._sp_theta_j
+            self.diag_arg_list += self._sp_theta_i
         # psi_stats aren't yet implemented.
         if False:
             self.compute_psi_stats()
 
-        self._code = {}
-
         # generate the code for the covariance functions
         self._gen_code()
 
-        if weave_available:
-            if False:
-                extra_compile_args = ['-ftree-vectorize', '-mssse3', '-ftree-vectorizer-verbose=5']
-            else:
-                extra_compile_args = []
-            
-                self.weave_kwargs = {
-                    'support_code': None, #self._function_code,
-                    'include_dirs':[user_code_storage, os.path.join(current_dir,'parts/')],
-                    'headers':['"sympy_helpers.h"', '"'+self.name+'.h"'],
-                    'sources':[os.path.join(current_dir,"parts/sympy_helpers.cpp"), os.path.join(user_code_storage, self.name+'.cpp')],
-                    'extra_compile_args':extra_compile_args,
-                    'extra_link_args':['-lgomp'],
-                    'verbose':True}
         self.parameters_changed() # initializes caches
 
 
@@ -157,407 +135,128 @@ class spkern(Kernpart):
 
     def _gen_code(self):
 
-        argument_sequence = self._sp_x+self._sp_z+self._sp_theta
+        self._K_function = lambdify(self.arg_list, self._sp_k, 'numpy')
-        code_list = [('k',self._sp_k)]
+        for key in self.derivatives.keys():
-        # gradients with respect to covariance input
+            setattr(self, '_K_diff_' + key, lambdify(self.arg_list, self.derivatives[key], 'numpy'))
-        code_list += [('dk_d%s'%x.name,dx) for x,dx in zip(self._sp_x,self._sp_dk_dx)]
+
-        # gradient with respect to parameters
+        self._Kdiag_function = lambdify(self.diag_arg_list, self._sp_kdiag, 'numpy')
-        code_list += [('dk_d%s'%theta.name,dtheta) for theta,dtheta in zip(self._sp_theta,self._sp_dk_dtheta)]
+        for key in self.derivatives.keys():
-        # gradient with respect to multiple output parameters
+            setattr(self, '_Kdiag_diff_' + key, lambdify(self.diag_arg_list, self.diag_derivatives[key], 'numpy'))
-        if self.output_dim > 1:
+
-            argument_sequence += self._sp_theta_i + self._sp_theta_j
+    def K(self,X,X2=None):        
-            code_list += [('dk_d%s'%theta.name,dtheta) for theta,dtheta in zip(self._sp_theta_i,self._sp_dk_dtheta_i)]
+        self._K_computations(X, X2)
-        # generate c functions from sympy objects
+        return self._K_function(**self._arguments)
-        if weave_available:
-            code_type = "C"
-        else:
-            code_type = "PYTHON"
-        # Need to add the sympy_helpers header in here.
-        (foo_c,self._function_code), (foo_h,self._function_header) = \
-                                     codegen(code_list,
-                                             code_type,
-                                             self.name,
-                                             argument_sequence=argument_sequence)
 
 
-        # Use weave to compute the underlying functions.
+    def Kdiag(self,X):
-        if weave_available:
+        self._K_computations(X)
-            # put the header file where we can find it
+        return self._Kdiag_function(**self._diag_arguments)
-            f = file(os.path.join(user_code_storage, self.name + '.h'),'w')
-            f.write(self._function_header)
-            f.close()
     
-
-        if weave_available:
-            # Substitute any known derivatives which sympy doesn't compute
-            self._function_code = re.sub('DiracDelta\(.+?,.+?\)','0.0',self._function_code)
-            # put the cpp file in user code storage (defaults to temp file location)
-            f = file(os.path.join(user_code_storage, self.name + '.cpp'),'w')
-        else:
-            # put the python file in user code storage
-            f = file(os.path.join(user_code_storage, self.name + '.py'),'w')
-        f.write(self._function_code)
-        f.close()
-
-        if weave_available:
-            # arg_list will store the arguments required for the C code.
-            input_arg_list = (["X2(i, %s)"%x.name[2:] for x in self._sp_x]
-                        + ["Z2(j, %s)"%z.name[2:] for z in self._sp_z])
-
-            # for multiple outputs reverse argument list is also required
-            if self.output_dim>1:
-                reverse_input_arg_list = list(input_arg_list)
-                reverse_input_arg_list.reverse()
-
-            # This gives the parameters for the arg list.
-            param_arg_list = [shared_params.name for shared_params in self._sp_theta]
-            arg_list = input_arg_list + param_arg_list
-
-            precompute_list=[]
-            if self.output_dim > 1:
-                reverse_arg_list= reverse_input_arg_list + list(param_arg_list)
-                # For multiple outputs, also need the split parameters.
-                split_param_arg_list = ["%s1(%s)"%(theta.name[:-2].upper(),index) for index in ['ii', 'jj'] for theta in self._sp_theta_i]
-                split_param_reverse_arg_list = ["%s1(%s)"%(theta.name[:-2].upper(),index) for index in ['jj', 'ii'] for theta in self._sp_theta_i]
-                arg_list += split_param_arg_list
-                reverse_arg_list += split_param_reverse_arg_list
-                # Extract the right output indices from the inputs.
-                c_define_output_indices = [' '*16 + "int %s=(int)%s(%s, %i);"%(index, var, index2, self.input_dim-1) for index, var, index2 in zip(['ii', 'jj'], ['X2', 'Z2'], ['i', 'j'])]
-                precompute_list += c_define_output_indices
-                reverse_arg_string = ", ".join(reverse_arg_list)
-            arg_string = ", ".join(arg_list)
-            precompute_string = "\n".join(precompute_list)
-
-            # Now we use the arguments in code that computes the separate parts.
-
-            # Any precomputations will be done here eventually.
-            self._precompute = \
-                """
-                // Precompute code would go here. It will be called when parameters are updated. 
-                """
-
-            # Here's the code to do the looping for K
-            self._code['K'] =\
-            """
-            // _K_code
-            // Code for computing the covariance function.
-            int i;
-            int j;
-            int n = target_array->dimensions[0];
-            int num_inducing = target_array->dimensions[1];
-            int input_dim = X_array->dimensions[1];
-            //#pragma omp parallel for private(j)
-            for (i=0;i<n;i++){
-                for (j=0;j<num_inducing;j++){
-                    %s
-                    //target[i*num_inducing+j] = 
-                    TARGET2(i, j) += k(%s);
-                }
-            }
-            %s
-            """%(precompute_string,arg_string,"/*"+str(self._sp_k)+"*/")
-           # adding a string representation of the function in the
-           # comment forces recompile when needed
-            self._code['K_X'] = self._code['K'].replace('Z2(', 'X2(')
-
-
-            # Code to compute diagonal of covariance.
-            diag_arg_string = re.sub('Z','X',arg_string)
-            diag_arg_string = re.sub('int jj','//int jj',diag_arg_string)
-            diag_arg_string = re.sub('j','i',diag_arg_string)
-            diag_precompute_string = re.sub('int jj','//int jj',precompute_string)
-            diag_precompute_string = re.sub('Z','X',diag_precompute_string)
-            diag_precompute_string = re.sub('j','i',diag_precompute_string)
-            # Code to do the looping for Kdiag
-            self._code['Kdiag'] =\
-            """
-            // _code['Kdiag']
-            // Code for computing diagonal of covariance function.
-            int i;
-            int n = target_array->dimensions[0];
-            int input_dim = X_array->dimensions[1];
-            //#pragma omp parallel for
-            for (i=0;i<n;i++){
-                    %s
-                    //target[i] =
-                    TARGET1(i)=k(%s);
-            }
-            %s
-            """%(diag_precompute_string,diag_arg_string,"/*"+str(self._sp_k)+"*/") #adding a string representation forces recompile when needed
-
-            # Code to compute gradients
-            if self.output_dim>1:
-                for i, theta in enumerate(self._sp_theta_i):
-                    grad_func_list = [' '*26 + 'TARGET1(ii) += PARTIAL2(i, j)*dk_d%s(%s);'%(theta.name, arg_string)]
-                    grad_func_list += [' '*26 + 'TARGET1(jj) += PARTIAL2(i, j)*dk_d%s(%s);'%(theta.name, reverse_arg_string)]
-                    grad_func_list = c_define_output_indices+grad_func_list
-
-                    grad_func_string = '\n'.join(grad_func_list) 
-                    self._code['dK_d' + theta.name] =\
-                      """
-                      int i;
-                      int j;
-                      int n = partial_array->dimensions[0];
-                      int num_inducing = partial_array->dimensions[1];
-                      int input_dim = X_array->dimensions[1];
-                      //#pragma omp parallel for private(j)
-                      for (i=0;i<n;i++){
-                        for (j=0;j<num_inducing;j++){
-%s
-                        }
-                      }
-                      %s
-                      """%(grad_func_string,"/*"+str(self._sp_k)+"*/") # adding a string representation forces recompile when needed
-                    self._code['dK_d' +theta.name + '_X'] = self._code['dK_d' + theta.name].replace('Z2(', 'X2(')
-                    # Code to compute gradients for Kdiag TODO: needs clean up
-                    diag_grad_func_string = re.sub('Z','X',grad_func_string,count=0)
-                    diag_grad_func_string = re.sub('int jj','//int jj',diag_grad_func_string)
-                    diag_grad_func_string = re.sub('j','i',diag_grad_func_string)
-                    diag_grad_func_string = re.sub('PARTIAL2\(i, i\)','PARTIAL(i)',diag_grad_func_string)
-                    self._code['dKdiag_d' + theta.name] =\
-                      """
-                      // _dKdiag_dtheta_code
-                      // Code for computing gradient of diagonal with respect to parameters.
-                      int i;
-                      int n = partial_array->dimensions[0];
-                      int input_dim = X_array->dimensions[1];
-                      for (i=0;i<n;i++){
-                        %s
-                      }
-                      %s
-                      """%(diag_grad_func_string,"/*"+str(self._sp_k)+"*/") #adding a string representation forces recompile when needed
-
-            for i, theta in enumerate(self._sp_theta):
-                grad_func_list = [' '*26 + 'TARGET1(%i) += PARTIAL2(i, j)*dk_d%s(%s);'%(i,theta.name,arg_string)]
-                grad_func_string = '\n'.join(grad_func_list) 
-
-                self._code['dK_d' + theta.name] =\
-                  """
-                  // _dK_dtheta_code
-                  // Code for computing gradient of covariance with respect to parameters.
-                  int i;
-                  int j;
-                  int n = partial_array->dimensions[0];
-                  int num_inducing = partial_array->dimensions[1];
-                  int input_dim = X_array->dimensions[1];
-                  //#pragma omp parallel for private(j)
-                  for (i=0;i<n;i++){
-                    for (j=0;j<num_inducing;j++){
-                      %s
-                    }
-                  }
-                  %s
-                  """%(grad_func_string,"/*"+str(self._sp_k)+"*/") # adding a string representation forces recompile when needed
-                self._code['dK_d' + theta.name +'_X'] = self._code['dK_d' + theta.name].replace('Z2(', 'X2(')
-                # Code to compute gradients for Kdiag TODO: needs clean up
-                diag_grad_func_string = re.sub('Z','X',grad_func_string,count=0)
-                diag_grad_func_string = re.sub('int jj','//int jj',diag_grad_func_string)
-                diag_grad_func_string = re.sub('j','i',diag_grad_func_string)
-                diag_grad_func_string = re.sub('PARTIAL2\(i, i\)','PARTIAL(i)',diag_grad_func_string)
-                self._code['dKdiag_d' + theta.name] =\
-                   """
-                   // _dKdiag_dtheta_code
-                   // Code for computing gradient of diagonal with respect to parameters.
-                   int i;
-                   int n = partial_array->dimensions[0];
-                   int input_dim = X_array->dimensions[1];
-                   for (i=0;i<n;i++){
-                     %s
-                   }
-                   %s
-                   """%(diag_grad_func_string,"/*"+str(self._sp_k)+"*/") #adding a string representation forces recompile when needed
-
-
-
-            # Code for gradients wrt X, TODO: may need to deal with special case where one input is actually an output.
-            gradX_func_list = []
-            if self.output_dim>1:
-                gradX_func_list += c_define_output_indices
-            gradX_func_list += ["TARGET2(i, %i) += partial[i*num_inducing+j]*dk_dx_%i(%s);"%(q,q,arg_string) for q in range(self._real_input_dim)]
-            gradX_func_string = "\n".join(gradX_func_list)
-
-            self._code['dK_dX'] = \
-            """
-            // _dK_dX_code
-            // Code for computing gradient of covariance with respect to inputs.
-            int i;
-            int j;
-            int n = partial_array->dimensions[0];
-            int num_inducing = partial_array->dimensions[1];
-            int input_dim = X_array->dimensions[1];
-            //#pragma omp parallel for private(j)
-            for (i=0;i<n; i++){
-              for (j=0; j<num_inducing; j++){
-                %s
-              }
-            }
-            %s
-            """%(gradX_func_string,"/*"+str(self._sp_k)+"*/") #adding a string representation forces recompile when needed
-            self._code['dK_dX_X'] = self._code['dK_dX'].replace('Z2(', 'X2(')
-
-
-            diag_gradX_func_string = re.sub('Z','X',gradX_func_string,count=0)
-            diag_gradX_func_string = re.sub('int jj','//int jj',diag_gradX_func_string)
-            diag_gradX_func_string = re.sub('j','i',diag_gradX_func_string)
-            diag_gradX_func_string = re.sub('PARTIAL2\(i\, i\)','2*PARTIAL(i)',diag_gradX_func_string)
-
-            # Code for gradients of Kdiag wrt X
-            self._code['dKdiag_dX'] = \
-            """
-            // _dKdiag_dX_code
-            // Code for computing gradient of diagonal with respect to inputs.
-            int n = partial_array->dimensions[0];
-            int input_dim = X_array->dimensions[1];
-            for (int i=0;i<n; i++){
-                %s
-            }
-            %s
-            """%(diag_gradX_func_string,"/*"+str(self._sp_k)+"*/") #adding a
-            # string representation forces recompile when needed Get rid
-            # of Zs in argument for diagonal. TODO: Why wasn't
-            # diag_func_string called here? Need to check that.
-
-            
-
-
-            #TODO: insert multiple functions here via string manipulation
-            #TODO: similar functions for psi_stats
-            #TODO: similar functions when cython available.
-            #TODO: similar functions when only python available.
-            
-    def _get_arg_names(self, target=None, Z=None, partial=None):
-        arg_names = ['X']
-        if target is not None:
-            arg_names += ['target']
-        for shared_params in self._sp_theta:
-            arg_names += [shared_params.name]
-        if Z is not None:
-            arg_names += ['Z']
-        if partial is not None:
-            arg_names += ['partial']
-        if self.output_dim>1:
-            arg_names += self._split_theta_names
-            arg_names += ['output_dim']
-        return arg_names
-
-    def _generate_inline(self, code, X, target=None, Z=None, partial=None):
-        output_dim = self.output_dim
-        # Need to extract parameters to local variables first
-        for shared_params in self._sp_theta:
-            locals()[shared_params.name] = getattr(self, shared_params.name)
-            
-        for split_params in self._split_theta_names:
-            locals()[split_params] = np.asarray(getattr(self, split_params))
-        arg_names = self._get_arg_names(target, Z, partial)        
-
-        if weave_available:
-            return weave.inline(code=code, arg_names=arg_names,**self.weave_kwargs)
-        else:
-            raise RuntimeError('Weave not available and other variants of sympy covariance not yet implemented')
-
-    def K(self,X,Z,target):        
-        if Z is None:
-            self._generate_inline(self._code['K_X'], X, target)
-        else:
-            self._generate_inline(self._code['K'], X, target, Z)
-
-
-    def Kdiag(self,X,target):
-        self._generate_inline(self._code['Kdiag'], X, target)
-
     def _param_grad_helper(self,partial,X,Z,target):
-        if Z is None:
+        pass
-            self._generate_inline(self._code['dK_dtheta_X'], X, target, Z, partial)
-        else:
-            self._generate_inline(self._code['dK_dtheta'], X, target, Z, partial)
 
-    def dKdiag_dtheta(self,partial,X,target):
-        self._generate_inline(self._code['dKdiag_dtheta'], X, target, Z=None, partial=partial).namelocals()[shared_params.name] = getattr(self, shared_params.name)
                
-    def gradients_X(self,partial,X,Z,target):
+    def gradients_X(self, dL_dK, X, X2=None):
-        if Z is None:
+        #if self._X is None or X.base is not self._X.base or X2 is not None:
-            self._generate_inline(self._code['dK_dX_X'], X, target, Z, partial)
+        self._K_computations(X, X2)
-        else:
+        gradients_X = np.zeros((X.shape[0], X.shape[1]))
-            self._generate_inline(self._code['dK_dX'], X, target, Z, partial)
+        for i, x in enumerate(self._sp_x):
+            gf = getattr(self, '_K_diff_' + x.name)
+            gradients_X[:, i] = (gf(**self._arguments)*dL_dK).sum(1)
+        if X2 is None:
+            gradients_X *= 2
+        return gradients_X
 
-    def dKdiag_dX(self,partial,X,target):
+    def gradients_X_diag(self, dL_dK, X):
-        self._generate_inline(self._code['dKdiag_dX'], X, target, Z, partial)
+        self._K_computations(X)
-
+        dX = np.zeros_like(X)
-    def compute_psi_stats(self):
+        for i, x in enumerate(self._sp_x):
-        #define some normal distributions
+            gf = getattr(self, '_Kdiag_diff_' + x.name)
-        mus = [sp.var('mu_%i'%i,real=True) for i in range(self.input_dim)]
+            dX[:, i] = gf(**self._diag_arguments)*dL_dK
-        Ss = [sp.var('S_%i'%i,positive=True) for i in range(self.input_dim)]
+        return dX
-        normals = [(2*sp.pi*Si)**(-0.5)*sp.exp(-0.5*(xi-mui)**2/Si) for xi, mui, Si in zip(self._sp_x, mus, Ss)]
-
-        #do some integration!
-        #self._sp_psi0 = ??
-        self._sp_psi1 = self._sp_k
-        for i in range(self.input_dim):
-            print 'perfoming integrals %i of %i'%(i+1,2*self.input_dim)
-            sys.stdout.flush()
-            self._sp_psi1 *= normals[i]
-            self._sp_psi1 = sp.integrate(self._sp_psi1,(self._sp_x[i],-sp.oo,sp.oo))
-            clear_cache()
-        self._sp_psi1 = self._sp_psi1.simplify()
-
-        #and here's psi2 (eek!)
-        zprime = [sp.Symbol('zp%i'%i) for i in range(self.input_dim)]
-        self._sp_psi2 = self._sp_k.copy()*self._sp_k.copy().subs(zip(self._sp_z,zprime))
-        for i in range(self.input_dim):
-            print 'perfoming integrals %i of %i'%(self.input_dim+i+1,2*self.input_dim)
-            sys.stdout.flush()
-            self._sp_psi2 *= normals[i]
-            self._sp_psi2 = sp.integrate(self._sp_psi2,(self._sp_x[i],-sp.oo,sp.oo))
-            clear_cache()
-        self._sp_psi2 = self._sp_psi2.simplify()
-
-    def parameters_changed(self):
-        # Reset the caches
-        self._cache, self._cache2 = np.empty(shape=(2, 1))
-        self._cache3, self._cache4, self._cache5 = np.empty(shape=(3, 1)) 
-
-    def update_gradients_full(self, dL_dK, X):
-        # Need to extract parameters to local variables first
-        self._K_computations(X, None)
-        for shared_params in self._sp_theta:
-            parameter = getattr(self, shared_params.name)
-            code = self._code['dK_d' + shared_params.name]
-            setattr(parameter, 'gradient', self._generate_inline(code, X, target=None, Z=None, partial=dL_dK))
-            
-        for split_params in self._split_theta_names:
-            parameter = getattr(self, split_params.name)
-            code = self._code['dK_d' + split_params.name]
-            setattr(parameter, 'gradient', self._generate_inline(code, X, target=None, Z=None, partial=dL_dK))
     
+    def update_gradients_full(self, dL_dK, X, X2=None):
+        # Need to extract parameters to local variables first
+        self._K_computations(X, X2)
+        for theta in self._sp_theta:
+            parameter = getattr(self, theta.name)
+            gf = getattr(self, '_K_diff_' + theta.name)
+            gradient = (gf(**self._arguments)*dL_dK).sum()
+            if X2 is not None:
+                gradient += (gf(**self._reverse_arguments)*dL_dK).sum()
+            setattr(parameter, 'gradient', gradient)
+        if self.output_dim>1:
+            for theta in self._sp_theta_i:
+                parameter = getattr(self, theta.name[:-2])
+                gf = getattr(self, '_K_diff_' + theta.name)
+                A = gf(**self._arguments)*dL_dK
+                gradient = np.asarray([A[np.where(self._output_ind==i)].T.sum()
+                                       for i in np.arange(self.output_dim)])
+                if X2 is None:
+                    gradient *= 2
+                else:
+                    A = gf(**self._reverse_arguments)*dL_dK.T
+                    gradient += np.asarray([A[np.where(self._output_ind2==i)].T.sum()
+                                 for i in np.arange(self.output_dim)])
+                setattr(parameter, 'gradient', gradient)
+                
 
-    # def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
+    def update_gradients_diag(self, dL_dKdiag, X):
-    #     #contributions from Kdiag
+        self._K_computations(X)
-    #     self.variance.gradient = np.sum(dL_dKdiag)
+        for theta in self._sp_theta:
-        
+            parameter = getattr(self, theta.name)
-    #     #from Knm
+            gf = getattr(self, '_Kdiag_diff_' + theta.name)
-    #     self._K_computations(X, Z)
+            setattr(parameter, 'gradient', (gf(**self._diag_arguments)*dL_dKdiag).sum())
-    #     self.variance.gradient += np.sum(dL_dKnm * self._K_dvar)
+        if self.output_dim>1:
-    #     if self.ARD:
+            for theta in self._sp_theta_i:
-    #         self.lengthscale.gradient = self._dL_dlengthscales_via_K(dL_dKnm, X, Z)
+                parameter = getattr(self, theta.name[:-2])
-            
+                gf = getattr(self, '_Kdiag_diff_' + theta.name)
-    #     else:
+                a = gf(**self._diag_arguments)*dL_dKdiag
-    #         self.lengthscale.gradient = (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKnm)
+                setattr(parameter, 'gradient',
-            
+                        np.asarray([a[np.where(self._output_ind==i)].sum()
-    #     #from Kmm
+                         for i in np.arange(self.output_dim)]))
-    #     self._K_computations(Z, None)
+    
-    #     self.variance.gradient += np.sum(dL_dKmm * self._K_dvar)
+    def _K_computations(self, X, X2=None):
-    #     if self.ARD:
+        """Set up argument lists for the derivatives."""
-    #         self.lengthscale.gradient += self._dL_dlengthscales_via_K(dL_dKmm, Z, None)
+        # Could check if this needs doing or not, there could
-    #     else:
+        # definitely be some computational savings by checking for
-    #         self.lengthscale.gradient += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKmm)
+        # parameter updates here.
-            
+        self._arguments = {}
-            
+        self._diag_arguments = {}
-    #---------------------------------------#
+        for i, x in enumerate(self._sp_x):
-    #            Precomputations            #
+            self._arguments[x.name] =  X[:, i][:, None]
-    #---------------------------------------#
+            self._diag_arguments[x.name] =  X[:, i][:, None]
+        if self.output_dim > 1:
+            self._output_ind = np.asarray(X[:, -1], dtype='int')
+            for i, theta in enumerate(self._sp_theta_i):
+                self._arguments[theta.name] = np.asarray(getattr(self, theta.name[:-2])[self._output_ind])[:, None]
+                self._diag_arguments[theta.name] = self._arguments[theta.name]
+        for theta in self._sp_theta:
+            self._arguments[theta.name] = np.asarray(getattr(self, theta.name))
+            self._diag_arguments[theta.name] = self._arguments[theta.name]
 
-    def _K_computations(self, X, Z):
+        if X2 is not None:
-        if Z is None:
+            for i, z in enumerate(self._sp_z):
-            self._generate_inline(self._precompute, X)
+                self._arguments[z.name] =  X2[:, i][None, :]
+            if self.output_dim > 1:
+                self._output_ind2 = np.asarray(X2[:, -1], dtype='int')
+                for i, theta in enumerate(self._sp_theta_j):
+                    self._arguments[theta.name] = np.asarray(getattr(self, theta.name[:-2])[self._output_ind2])[None, :]
         else:
-            self._generate_inline(self._precompute, X, Z=Z)
+            for z in self._sp_z:
+                self._arguments[z.name] =  self._arguments['x_'+z.name[2:]].T
+            if self.output_dim > 1:
+                self._output_ind2 = self._output_ind
+                for theta in self._sp_theta_j:
+                    self._arguments[theta.name] = self._arguments[theta.name[:-2] + '_i'].T
+        if X2 is not None:
+            # These arguments are needed in gradients when X2 is not equal to X.
+            self._reverse_arguments = self._arguments
+            for x, z in zip(self._sp_x, self._sp_z):
+                self._reverse_arguments[x.name] = self._arguments[z.name].T
+                self._reverse_arguments[z.name] = self._arguments[x.name].T
+            if self.output_dim > 1:
+                for theta_i, theta_j in zip(self._sp_theta_i, self._sp_theta_j):
+                    self._reverse_arguments[theta_i.name] = self._arguments[theta_j.name].T
+                    self._reverse_arguments[theta_j.name] = self._arguments[theta_i.name].T
+

GPy/util/linalg.py

@@ -62,41 +62,41 @@ def force_F_ordered(A):
     print "why are your arrays not F order?"
     return np.asfortranarray(A)
 
+# def jitchol(A, maxtries=5):
+#     A = force_F_ordered_symmetric(A)
+#     L, info = lapack.dpotrf(A, lower=1)
+#     if info == 0:
+#         return L
+#     else:
+#         if maxtries==0:
+#             raise linalg.LinAlgError, "not positive definite, even with jitter."
+#         diagA = np.diag(A)
+#         if np.any(diagA <= 0.):
+#             raise linalg.LinAlgError, "not pd: non-positive diagonal elements"
+#         jitter = diagA.mean() * 1e-6
+
+#         return jitchol(A+np.eye(A.shape[0])*jitter, maxtries-1)
+
 def jitchol(A, maxtries=5):
-    A = force_F_ordered_symmetric(A)
+   A = np.ascontiguousarray(A)
-    L, info = lapack.dpotrf(A, lower=1)
+   L, info = lapack.dpotrf(A, lower=1)
-    if info == 0:
+   if info == 0:
-        return L
+       return L
-    else:
+   else:
-        if maxtries==0:
+       diagA = np.diag(A)
-            raise linalg.LinAlgError, "not positive definite, even with jitter."
+       if np.any(diagA <= 0.):
-        diagA = np.diag(A)
+           raise linalg.LinAlgError, "not pd: non-positive diagonal elements"
-        if np.any(diagA <= 0.):
+       jitter = diagA.mean() * 1e-6
-            raise linalg.LinAlgError, "not pd: non-positive diagonal elements"
+       while maxtries > 0 and np.isfinite(jitter):
-        jitter = diagA.mean() * 1e-6
+           print 'Warning: adding jitter of {:.10e}'.format(jitter)
+           try:
+               return linalg.cholesky(A + np.eye(A.shape[0]).T * jitter, lower=True)
+           except:
+               jitter *= 10
+           finally:
+               maxtries -= 1
+       raise linalg.LinAlgError, "not positive definite, even with jitter."
 
-        return jitchol(A+np.eye(A.shape[0])*jitter, maxtries-1)
-
-#def jitchol(A, maxtries=5):
-#    A = np.ascontiguousarray(A)
-#    L, info = lapack.dpotrf(A, lower=1)
-#    if info == 0:
-#        return L
-#    else:
-#        diagA = np.diag(A)
-#        if np.any(diagA <= 0.):
-#            raise linalg.LinAlgError, "not pd: non-positive diagonal elements"
-#        jitter = diagA.mean() * 1e-6
-#        while maxtries > 0 and np.isfinite(jitter):
-#            print 'Warning: adding jitter of {:.10e}'.format(jitter)
-#            try:
-#                return linalg.cholesky(A + np.eye(A.shape[0]).T * jitter, lower=True)
-#            except:
-#                jitter *= 10
-#            finally:
-#                maxtries -= 1
-#        raise linalg.LinAlgError, "not positive definite, even with jitter."
-#