mirror of
https://github.com/SheffieldML/GPy.git
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Merge branch 'params' of github.com:SheffieldML/GPy into params
This commit is contained in:
James Hensman
commit
712be15f6d
1 changed files with 386 additions and 246 deletions
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@ -1,16 +1,34 @@
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import numpy as np
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import sympy as sp
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from sympy.utilities.codegen import codegen
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# Check Matthew Rocklin's blog post.
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try:
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import sympy as sp
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sympy_available=True
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except ImportError:
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sympy_available=False
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exit()
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from sympy.core.cache import clear_cache
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from scipy import weave
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import re
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from sympy.utilities.codegen import codegen
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try:
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from scipy import weave
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weave_available = True
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except ImportError:
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weave_available = False
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import os
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import sys
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current_dir = os.path.dirname(os.path.abspath(os.path.dirname(__file__)))
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import sys
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import numpy as np
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import re
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import tempfile
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import pdb
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import ast
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from kernpart import Kernpart
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from ...core.parameterization import Param
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from ...core.parameterization.transformations import Logexp
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# TODO have this set up in a set up file!
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user_code_storage = tempfile.gettempdir()
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class spkern(Kernpart):
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"""
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@ -28,96 +46,117 @@ class spkern(Kernpart):
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- to handle multpile correlated outputs, you'll need to add parameters with an index, such as lengthscale_i and lengthscale_j.
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"""
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def __init__(self, input_dim, k=None, output_dim=1, name=None, param=None):
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if name is None:
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self.name='sympykern'
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else:
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self.name = name
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name='sympykern'
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if k is None:
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raise ValueError, "You must provide an argument for the covariance function."
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super(spkern, self).__init__(input_dim, name)
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self._sp_k = k
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# pull the variable names out of the symbolic covariance function.
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sp_vars = [e for e in k.atoms() if e.is_Symbol]
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self._sp_x= sorted([e for e in sp_vars if e.name[0:2]=='x_'],key=lambda x:int(x.name[2:]))
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self._sp_z= sorted([e for e in sp_vars if e.name[0:2]=='z_'],key=lambda z:int(z.name[2:]))
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# Check that variable names make sense.
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assert all([x.name=='x_%i'%i for i,x in enumerate(self._sp_x)])
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assert all([z.name=='z_%i'%i for i,z in enumerate(self._sp_z)])
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assert len(self._sp_x)==len(self._sp_z)
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self.input_dim = len(self._sp_x)
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self._real_input_dim = self.input_dim
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if output_dim > 1:
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self.input_dim += 1
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assert self.input_dim == input_dim
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x_dim=len(self._sp_x)
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# If it is a multi-output covariance, add an input for indexing the outputs.
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self._real_input_dim = x_dim
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# Check input dim is number of xs + 1 if output_dim is >1
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assert self.input_dim == x_dim + int(output_dim > 1)
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self.output_dim = output_dim
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# extract parameter names
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# extract parameter names from the covariance
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thetas = sorted([e for e in sp_vars if not (e.name[0:2]=='x_' or e.name[0:2]=='z_')],key=lambda e:e.name)
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# Look for parameters with index.
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# Look for parameters with index (subscripts), they are associated with different outputs.
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if self.output_dim>1:
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self._sp_theta_i = sorted([e for e in thetas if (e.name[-2:]=='_i')], key=lambda e:e.name)
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self._sp_theta_j = sorted([e for e in thetas if (e.name[-2:]=='_j')], key=lambda e:e.name)
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# Make sure parameter appears with both indices!
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assert len(self._sp_theta_i)==len(self._sp_theta_j)
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assert all([theta_i.name[:-2]==theta_j.name[:-2] for theta_i, theta_j in zip(self._sp_theta_i, self._sp_theta_j)])
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# Extract names of shared parameters
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# Extract names of shared parameters (those without a subscript)
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self._sp_theta = [theta for theta in thetas if theta not in self._sp_theta_i and theta not in self._sp_theta_j]
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self.num_split_params = len(self._sp_theta_i)
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self._split_theta_names = ["%s"%theta.name[:-2] for theta in self._sp_theta_i]
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for theta in self._split_theta_names:
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setattr(self, theta, np.ones(self.output_dim))
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setattr(self, theta, Param(theta, np.ones(self.output_dim), None))
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self.add_parameters(getattr(self, theta))
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#setattr(self, theta, np.ones(self.output_dim))
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self.num_shared_params = len(self._sp_theta)
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self.num_params = self.num_shared_params+self.num_split_params*self.output_dim
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#self.num_params = self.num_shared_params+self.num_split_params*self.output_dim
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else:
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self.num_split_params = 0
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self._split_theta_names = []
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self._sp_theta = thetas
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self.num_shared_params = len(self._sp_theta)
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self.num_params = self.num_shared_params
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#self.num_params = self.num_shared_params
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# Add parameters to the model.
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for theta in self._sp_theta:
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val = 1.0
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if param is not None:
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if param.has_key(theta):
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val = param[theta]
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setattr(self, theta.name, val)
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#setattr(self, theta.name, val)
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setattr(self, theta.name, Param(theta.name, val, None))
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self.add_parameters(getattr(self, theta.name))
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#deal with param
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self._set_params(self._get_params())
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#self._set_params(self._get_params())
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#Differentiate!
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# Differentiate with respect to parameters.
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self._sp_dk_dtheta = [sp.diff(k,theta).simplify() for theta in self._sp_theta]
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if self.output_dim > 1:
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self._sp_dk_dtheta_i = [sp.diff(k,theta).simplify() for theta in self._sp_theta_i]
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# differentiate with respect to input variables.
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self._sp_dk_dx = [sp.diff(k,xi).simplify() for xi in self._sp_x]
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# psi_stats aren't yet implemented.
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if False:
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self.compute_psi_stats()
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self._code = {}
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# generate the code for the covariance functions
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self._gen_code()
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if False:
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extra_compile_args = ['-ftree-vectorize', '-mssse3', '-ftree-vectorizer-verbose=5']
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else:
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extra_compile_args = []
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if weave_available:
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if False:
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extra_compile_args = ['-ftree-vectorize', '-mssse3', '-ftree-vectorizer-verbose=5']
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else:
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extra_compile_args = []
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self.weave_kwargs = {
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'support_code':self._function_code,
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'include_dirs':[tempfile.gettempdir(), os.path.join(current_dir,'parts/')],
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'headers':['"sympy_helpers.h"'],
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'sources':[os.path.join(current_dir,"parts/sympy_helpers.cpp")],
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'extra_compile_args':extra_compile_args,
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'extra_link_args':['-lgomp'],
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'verbose':True}
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self.weave_kwargs = {
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'support_code': None, #self._function_code,
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'include_dirs':[user_code_storage, os.path.join(current_dir,'parts/')],
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'headers':['"sympy_helpers.h"', '"'+self.name+'.h"'],
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'sources':[os.path.join(current_dir,"parts/sympy_helpers.cpp"), os.path.join(user_code_storage, self.name+'.cpp')],
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'extra_compile_args':extra_compile_args,
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'extra_link_args':['-lgomp'],
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'verbose':True}
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self.parameters_changed() # initializes caches
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def __add__(self,other):
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return spkern(self._sp_k+other._sp_k)
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def _gen_code(self):
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#generate c functions from sympy objects
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argument_sequence = self._sp_x+self._sp_z+self._sp_theta
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code_list = [('k',self._sp_k)]
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# gradients with respect to covariance input
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@ -128,194 +167,268 @@ class spkern(Kernpart):
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if self.output_dim > 1:
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argument_sequence += self._sp_theta_i + self._sp_theta_j
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code_list += [('dk_d%s'%theta.name,dtheta) for theta,dtheta in zip(self._sp_theta_i,self._sp_dk_dtheta_i)]
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# generate c functions from sympy objects
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if weave_available:
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code_type = "C"
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else:
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code_type = "PYTHON"
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# Need to add the sympy_helpers header in here.
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(foo_c,self._function_code), (foo_h,self._function_header) = \
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codegen(code_list, "C",'foobar',argument_sequence=argument_sequence)
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#put the header file where we can find it
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f = file(os.path.join(tempfile.gettempdir(),'foobar.h'),'w')
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f.write(self._function_header)
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codegen(code_list,
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code_type,
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self.name,
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argument_sequence=argument_sequence)
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# Use weave to compute the underlying functions.
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if weave_available:
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# put the header file where we can find it
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f = file(os.path.join(user_code_storage, self.name + '.h'),'w')
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f.write(self._function_header)
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f.close()
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if weave_available:
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# Substitute any known derivatives which sympy doesn't compute
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self._function_code = re.sub('DiracDelta\(.+?,.+?\)','0.0',self._function_code)
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# put the cpp file in user code storage (defaults to temp file location)
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f = file(os.path.join(user_code_storage, self.name + '.cpp'),'w')
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else:
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# put the python file in user code storage
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f = file(os.path.join(user_code_storage, self.name + '.py'),'w')
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f.write(self._function_code)
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f.close()
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# Substitute any known derivatives which sympy doesn't compute
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self._function_code = re.sub('DiracDelta\(.+?,.+?\)','0.0',self._function_code)
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if weave_available:
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# arg_list will store the arguments required for the C code.
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input_arg_list = (["X2(i, %s)"%x.name[2:] for x in self._sp_x]
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+ ["Z2(j, %s)"%z.name[2:] for z in self._sp_z])
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# This is the basic argument construction for the C code.
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#arg_list = (["X[i*input_dim+%s]"%x.name[2:] for x in self._sp_x]
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# + ["Z[j*input_dim+%s]"%z.name[2:] for z in self._sp_z])
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arg_list = (["X2(i, %s)"%x.name[2:] for x in self._sp_x]
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+ ["Z2(j, %s)"%z.name[2:] for z in self._sp_z])
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if self.output_dim>1:
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reverse_arg_list = list(arg_list)
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reverse_arg_list.reverse()
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# for multiple outputs reverse argument list is also required
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if self.output_dim>1:
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reverse_input_arg_list = list(input_arg_list)
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reverse_input_arg_list.reverse()
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param_arg_list = [shared_params.name for shared_params in self._sp_theta]
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arg_list += param_arg_list
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# This gives the parameters for the arg list.
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param_arg_list = [shared_params.name for shared_params in self._sp_theta]
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arg_list = input_arg_list + param_arg_list
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precompute_list=[]
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if self.output_dim > 1:
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reverse_arg_list+=list(param_arg_list)
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split_param_arg_list = ["%s1(%s)"%(theta.name[:-2].upper(),index) for index in ['ii', 'jj'] for theta in self._sp_theta_i]
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split_param_reverse_arg_list = ["%s1(%s)"%(theta.name[:-2].upper(),index) for index in ['jj', 'ii'] for theta in self._sp_theta_i]
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arg_list += split_param_arg_list
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reverse_arg_list += split_param_reverse_arg_list
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# Extract the right output indices from the inputs.
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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'])]
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precompute_list += c_define_output_indices
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reverse_arg_string = ", ".join(reverse_arg_list)
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arg_string = ", ".join(arg_list)
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precompute_string = "\n".join(precompute_list)
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# Here's the code to do the looping for K
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self._K_code =\
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"""
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// _K_code
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// Code for computing the covariance function.
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int i;
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int j;
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int N = target_array->dimensions[0];
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int num_inducing = target_array->dimensions[1];
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int input_dim = X_array->dimensions[1];
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//#pragma omp parallel for private(j)
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for (i=0;i<N;i++){
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for (j=0;j<num_inducing;j++){
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%s
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//target[i*num_inducing+j] =
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TARGET2(i, j) += k(%s);
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precompute_list=[]
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if self.output_dim > 1:
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reverse_arg_list= reverse_input_arg_list + list(param_arg_list)
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# For multiple outputs, also need the split parameters.
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split_param_arg_list = ["%s1(%s)"%(theta.name[:-2].upper(),index) for index in ['ii', 'jj'] for theta in self._sp_theta_i]
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split_param_reverse_arg_list = ["%s1(%s)"%(theta.name[:-2].upper(),index) for index in ['jj', 'ii'] for theta in self._sp_theta_i]
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arg_list += split_param_arg_list
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reverse_arg_list += split_param_reverse_arg_list
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# Extract the right output indices from the inputs.
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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'])]
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precompute_list += c_define_output_indices
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reverse_arg_string = ", ".join(reverse_arg_list)
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arg_string = ", ".join(arg_list)
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precompute_string = "\n".join(precompute_list)
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# Now we use the arguments in code that computes the separate parts.
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# Any precomputations will be done here eventually.
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self._precompute = \
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"""
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// Precompute code would go here. It will be called when parameters are updated.
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"""
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# Here's the code to do the looping for K
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self._code['K'] =\
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"""
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// _K_code
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// Code for computing the covariance function.
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int i;
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int j;
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int n = target_array->dimensions[0];
|
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int num_inducing = target_array->dimensions[1];
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int input_dim = X_array->dimensions[1];
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//#pragma omp parallel for private(j)
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for (i=0;i<n;i++){
|
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for (j=0;j<num_inducing;j++){
|
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%s
|
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//target[i*num_inducing+j] =
|
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TARGET2(i, j) += k(%s);
|
||||
}
|
||||
}
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}
|
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%s
|
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"""%(precompute_string,arg_string,"/*"+str(self._sp_k)+"*/") #adding a string representation forces recompile when needed
|
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%s
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"""%(precompute_string,arg_string,"/*"+str(self._sp_k)+"*/")
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||||
# adding a string representation of the function in the
|
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# comment forces recompile when needed
|
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self._code['K_X'] = self._code['K'].replace('Z2(', 'X2(')
|
||||
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# Code to compute diagonal of covariance.
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diag_arg_string = re.sub('Z','X',arg_string)
|
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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)
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||||
diag_precompute_string = re.sub('j','i',diag_precompute_string)
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||||
# Code to do the looping for Kdiag
|
||||
self._Kdiag_code =\
|
||||
"""
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||||
// _Kdiag_code
|
||||
// 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
|
||||
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||||
# Code to compute gradients
|
||||
grad_func_list = []
|
||||
if self.output_dim>1:
|
||||
grad_func_list += c_define_output_indices
|
||||
grad_func_list += [' '*16 + 'TARGET1(%i+ii) += partial[i*num_inducing+j]*dk_d%s(%s);'%(self.num_shared_params+i*self.output_dim, theta.name, arg_string) for i, theta in enumerate(self._sp_theta_i)]
|
||||
grad_func_list += [' '*16 + 'TARGET1(%i+jj) += partial[i*num_inducing+j]*dk_d%s(%s);'%(self.num_shared_params+i*self.output_dim, theta.name, reverse_arg_string) for i, theta in enumerate(self._sp_theta_i)]
|
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grad_func_list += ([' '*16 + 'TARGET1(%i) += partial[i*num_inducing+j]*dk_d%s(%s);'%(i,theta.name,arg_string) for i,theta in enumerate(self._sp_theta)])
|
||||
grad_func_string = '\n'.join(grad_func_list)
|
||||
|
||||
self._dK_dtheta_code =\
|
||||
"""
|
||||
// _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
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||||
# 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
|
||||
"""%(grad_func_string,"/*"+str(self._sp_k)+"*/") # adding a string representation forces recompile when needed
|
||||
%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 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('partial\[i\*num_inducing\+i\]','partial[i]',diag_grad_func_string)
|
||||
self._dKdiag_dtheta_code =\
|
||||
"""
|
||||
// _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++){
|
||||
|
||||
# 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
|
||||
"""%(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._dK_dX_code = \
|
||||
"""
|
||||
// _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
|
||||
|
||||
"""%(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('partial\[i\*num_inducing\+i\]','2*partial[i]',diag_gradX_func_string)
|
||||
|
||||
# Code for gradients of Kdiag wrt X
|
||||
self._dKdiag_dX_code= \
|
||||
"""
|
||||
// _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++){
|
||||
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
|
||||
}
|
||||
%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.
|
||||
#self._dKdiag_dX_code = self._dKdiag_dX_code.replace('Z[j', 'X[i')
|
||||
"""%(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.
|
||||
|
||||
# Code to use when only X is provided.
|
||||
self._K_code_X = self._K_code.replace('Z[', 'X[')
|
||||
self._dK_dtheta_code_X = self._dK_dtheta_code.replace('Z[', 'X[')
|
||||
self._dK_dX_code_X = self._dK_dX_code.replace('Z[', 'X[').replace('+= partial[', '+= 2*partial[')
|
||||
self._K_code_X = self._K_code.replace('Z2(', 'X2(')
|
||||
self._dK_dtheta_code_X = self._dK_dtheta_code.replace('Z2(', 'X2(')
|
||||
self._dK_dX_code_X = self._dK_dX_code.replace('Z2(', 'X2(')
|
||||
|
||||
|
||||
|
||||
#TODO: insert multiple functions here via string manipulation
|
||||
#TODO: similar functions for psi_stats
|
||||
def _get_arg_names(self, Z=None, partial=None):
|
||||
arg_names = ['target','X']
|
||||
#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:
|
||||
|
|
@ -326,45 +439,49 @@ class spkern(Kernpart):
|
|||
arg_names += self._split_theta_names
|
||||
arg_names += ['output_dim']
|
||||
return arg_names
|
||||
|
||||
def _weave_inline(self, code, X, target, Z=None, partial=None):
|
||||
|
||||
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)
|
||||
|
||||
# Need to extract parameters first
|
||||
|
||||
for split_params in self._split_theta_names:
|
||||
locals()[split_params] = getattr(self, split_params)
|
||||
arg_names = self._get_arg_names(Z, partial)
|
||||
weave.inline(code=code, arg_names=arg_names,**self.weave_kwargs)
|
||||
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._weave_inline(self._K_code_X, X, target)
|
||||
self._generate_inline(self._code['K_X'], X, target)
|
||||
else:
|
||||
self._weave_inline(self._K_code, X, target, Z)
|
||||
self._generate_inline(self._code['K'], X, target, Z)
|
||||
|
||||
|
||||
def Kdiag(self,X,target):
|
||||
self._weave_inline(self._Kdiag_code, X, target)
|
||||
self._generate_inline(self._code['Kdiag'], X, target)
|
||||
|
||||
def _param_grad_helper(self,partial,X,Z,target):
|
||||
if Z is None:
|
||||
self._weave_inline(self._dK_dtheta_code_X, X, target, Z, partial)
|
||||
self._generate_inline(self._code['dK_dtheta_X'], X, target, Z, partial)
|
||||
else:
|
||||
self._weave_inline(self._dK_dtheta_code, X, target, Z, partial)
|
||||
|
||||
self._generate_inline(self._code['dK_dtheta'], X, target, Z, partial)
|
||||
|
||||
def dKdiag_dtheta(self,partial,X,target):
|
||||
self._weave_inline(self._dKdiag_dtheta_code, X, target, Z=None, partial=partial)
|
||||
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):
|
||||
if Z is None:
|
||||
self._weave_inline(self._dK_dX_code_X, X, target, Z, partial)
|
||||
self._generate_inline(self._code['dK_dX_X'], X, target, Z, partial)
|
||||
else:
|
||||
self._weave_inline(self._dK_dX_code, X, target, Z, partial)
|
||||
self._generate_inline(self._code['dK_dX'], X, target, Z, partial)
|
||||
|
||||
def dKdiag_dX(self,partial,X,target):
|
||||
self._weave.inline(self._dKdiag_dX_code, X, target, Z, partial)
|
||||
self._generate_inline(self._code['dKdiag_dX'], X, target, Z, partial)
|
||||
|
||||
def compute_psi_stats(self):
|
||||
#define some normal distributions
|
||||
|
|
@ -394,30 +511,53 @@ class spkern(Kernpart):
|
|||
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 _set_params(self,param):
|
||||
assert param.size == (self.num_params)
|
||||
for i, shared_params in enumerate(self._sp_theta):
|
||||
setattr(self, shared_params.name, param[i])
|
||||
|
||||
if self.output_dim>1:
|
||||
for i, split_params in enumerate(self._split_theta_names):
|
||||
start = self.num_shared_params + i*self.output_dim
|
||||
end = self.num_shared_params + (i+1)*self.output_dim
|
||||
setattr(self, split_params, param[start:end])
|
||||
|
||||
|
||||
def _get_params(self):
|
||||
params = np.zeros(0)
|
||||
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:
|
||||
params = np.hstack((params, getattr(self, shared_params.name)))
|
||||
if self.output_dim>1:
|
||||
for split_params in self._split_theta_names:
|
||||
params = np.hstack((params, getattr(self, split_params).flatten()))
|
||||
return params
|
||||
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 _get_param_names(self):
|
||||
if self.output_dim>1:
|
||||
return [x.name for x in self._sp_theta] + [x.name[:-2] + str(i) for x in self._sp_theta_i for i in range(self.output_dim)]
|
||||
# def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
|
||||
# #contributions from Kdiag
|
||||
# self.variance.gradient = np.sum(dL_dKdiag)
|
||||
|
||||
# #from Knm
|
||||
# self._K_computations(X, Z)
|
||||
# self.variance.gradient += np.sum(dL_dKnm * self._K_dvar)
|
||||
# if self.ARD:
|
||||
# self.lengthscale.gradient = self._dL_dlengthscales_via_K(dL_dKnm, X, Z)
|
||||
|
||||
# else:
|
||||
# self.lengthscale.gradient = (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKnm)
|
||||
|
||||
# #from Kmm
|
||||
# self._K_computations(Z, None)
|
||||
# self.variance.gradient += np.sum(dL_dKmm * self._K_dvar)
|
||||
# if self.ARD:
|
||||
# self.lengthscale.gradient += self._dL_dlengthscales_via_K(dL_dKmm, Z, None)
|
||||
# else:
|
||||
# self.lengthscale.gradient += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKmm)
|
||||
|
||||
|
||||
#---------------------------------------#
|
||||
# Precomputations #
|
||||
#---------------------------------------#
|
||||
|
||||
def _K_computations(self, X, Z):
|
||||
if Z is None:
|
||||
self._generate_inline(self._precompute, X)
|
||||
else:
|
||||
return [x.name for x in self._sp_theta]
|
||||
self._generate_inline(self._precompute, X, Z=Z)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue