mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-06-02 14:45:15 +02:00
Fixed stick datasets bug ... but sympykern is currently in a rewrite so will be broken
This commit is contained in:
Neil Lawrence
parent
87200c6e12
commit
1a46026015
6 changed files with 120 additions and 65 deletions
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@ -302,8 +302,8 @@ if sympy_available:
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Z = sp.symbols('z_:' + str(input_dim))
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variance = sp.var('variance',positive=True)
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if ARD:
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lengthscales = [sp.var('lengthscale_%i' % i, positive=True) for i in range(input_dim)]
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dist_string = ' + '.join(['(x_%i-z_%i)**2/lengthscale_%i**2' % (i, i, i) for i in range(input_dim)])
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lengthscales = sp.symbols('lengthscale_:' + str(input_dim))
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dist_string = ' + '.join(['(x_%i-z_%i)**2/lengthscale%i**2' % (i, i, i) for i in range(input_dim)])
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dist = parse_expr(dist_string)
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f = variance*sp.exp(-dist/2.)
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else:
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@ -313,6 +313,25 @@ if sympy_available:
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f = variance*sp.exp(-dist/(2*lengthscale**2))
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return kern(input_dim, [spkern(input_dim, f, name='rbf_sympy')])
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def eq_sympy(input_dim, output_dim, ARD=False, variance=1., lengthscale=1.):
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"""
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Exponentiated quadratic with multiple outputs.
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"""
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X = sp.symbols('x_:' + str(input_dim))
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Z = sp.symbols('z_:' + str(input_dim))
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variance = sp.var('variance',positive=True)
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if ARD:
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lengthscales = [sp.var('lengthscale%i_i lengthscale%i_j' % i, positive=True) for i in range(input_dim)]
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dist_string = ' + '.join(['(x_%i-z_%i)**2/(lengthscale%i_i*lengthscale%i_j)' % (i, i, i) for i in range(input_dim)])
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dist = parse_expr(dist_string)
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f = variance*sp.exp(-dist/2.)
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else:
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lengthscale = sp.var('lengthscale_i lengthscale_j',positive=True)
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dist_string = ' + '.join(['(x_%i-z_%i)**2' % (i, i) for i in range(input_dim)])
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dist = parse_expr(dist_string)
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f = variance*sp.exp(-dist/(2*lengthscale**2))
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return kern(input_dim, [spkern(input_dim, f, name='eq_sympy')])
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def sinc(input_dim, ARD=False, variance=1., lengthscale=1.):
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"""
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TODO: Not clear why this isn't working, suggests argument of sinc is not a number.
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@ -672,8 +672,13 @@ def kern_test(kern, X=None, X2=None, verbose=False):
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pass_checks = True
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if X==None:
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X = np.random.randn(10, kern.input_dim)
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for ind in kern.output_indicator:
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X[:, ind] = np.random.randint(kern.output_dim, X.shape[0])
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if X2==None:
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X2 = np.random.randn(20, kern.input_dim)
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for ind in kern.output_indicator:
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X2[:, ind] = np.random.randint(kern.output_dim, X2.shape[0])
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if verbose:
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print("Checking covariance function is positive definite.")
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result = Kern_check_model(kern, X=X).is_positive_definite()
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@ -5,15 +5,20 @@
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class Kernpart(object):
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def __init__(self,input_dim):
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"""
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The base class for a kernpart: a positive definite function which forms part of a kernel
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The base class for a kernpart: a positive definite function which forms part of a covariance function (kernel).
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:param input_dim: the number of input dimensions to the function
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:type input_dim: int
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Do not instantiate.
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"""
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# stores indices of any inputs that are for indicating outputs
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self.output_indicator = []
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# the input dimensionality for the covariance
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self.input_dim = input_dim
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# the number of optimisable parameters
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self.num_params = 1
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# the name of the covariance function.
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self.name = 'unnamed'
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def _get_params(self):
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@ -27,7 +27,7 @@ class spkern(Kernpart):
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- to handle multiple inputs, call them x_1, z_1, etc
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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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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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@ -44,7 +44,9 @@ class spkern(Kernpart):
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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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if output_dim > 1:
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self.output_indicator=[self.input_dim]
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self.input_dim += 1
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assert self.input_dim == input_dim
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self.output_dim = output_dim
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# extract parameter names
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@ -63,26 +65,28 @@ class spkern(Kernpart):
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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_param_names = ["%s"%theta.name[:-2] for theta in self._sp_theta_i]
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for params in self._split_param_names:
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setattr(self, params, np.ones(self.output_dim))
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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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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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else:
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self.num_split_params = 0
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self._split_param_names = []
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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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#deal with param
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if param is None:
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param = np.ones(self.num_params)
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assert param.size==self.num_params
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self._set_params(param)
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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, val)
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#deal with param
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self._set_params(self._get_params())
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#Differentiate!
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self._sp_dk_dtheta = [sp.diff(k,theta).simplify() for theta in self._sp_theta]
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@ -90,53 +94,29 @@ class spkern(Kernpart):
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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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self._sp_dk_dx = [sp.diff(k,xi).simplify() for xi in self._sp_x]
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#self._sp_dk_dz = [sp.diff(k,zi) for zi in self._sp_z]
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#self.compute_psi_stats()
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if False:
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self.compute_psi_stats()
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self._gen_code()
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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':['-ftree-vectorize', '-mssse3', '-ftree-vectorizer-verbose=5'],\
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'extra_compile_args':[],\
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'extra_link_args':['-lgomp'],\
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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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def __add__(self,other):
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return spkern(self._sp_k+other._sp_k)
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def compute_psi_stats(self):
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#define some normal distributions
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mus = [sp.var('mu_%i'%i,real=True) for i in range(self.input_dim)]
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Ss = [sp.var('S_%i'%i,positive=True) for i in range(self.input_dim)]
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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)]
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#do some integration!
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#self._sp_psi0 = ??
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self._sp_psi1 = self._sp_k
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for i in range(self.input_dim):
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print 'perfoming integrals %i of %i'%(i+1,2*self.input_dim)
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sys.stdout.flush()
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self._sp_psi1 *= normals[i]
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self._sp_psi1 = sp.integrate(self._sp_psi1,(self._sp_x[i],-sp.oo,sp.oo))
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clear_cache()
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self._sp_psi1 = self._sp_psi1.simplify()
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#and here's psi2 (eek!)
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zprime = [sp.Symbol('zp%i'%i) for i in range(self.input_dim)]
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self._sp_psi2 = self._sp_k.copy()*self._sp_k.copy().subs(zip(self._sp_z,zprime))
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for i in range(self.input_dim):
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print 'perfoming integrals %i of %i'%(self.input_dim+i+1,2*self.input_dim)
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sys.stdout.flush()
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self._sp_psi2 *= normals[i]
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self._sp_psi2 = sp.integrate(self._sp_psi2,(self._sp_x[i],-sp.oo,sp.oo))
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clear_cache()
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self._sp_psi2 = self._sp_psi2.simplify()
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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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@ -201,8 +181,10 @@ class spkern(Kernpart):
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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)
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diag_arg_string = re.sub('j','i',diag_arg_string)
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diag_precompute_string = re.sub('Z','X',precompute_string)
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diag_precompute_string = re.sub('int jj','//int jj',precompute_string)
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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
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self._Kdiag_code =\
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@ -245,6 +227,7 @@ class spkern(Kernpart):
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# Code to compute gradients for Kdiag TODO: needs clean up
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diag_func_string = re.sub('Z','X',func_string,count=0)
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diag_func_string = re.sub('int jj','//int jj',diag_func_string)
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diag_func_string = re.sub('j','i',diag_func_string)
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diag_func_string = re.sub('partial\[i\*num_inducing\+i\]','partial[i]',diag_func_string)
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self._dKdiag_dtheta_code =\
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@ -279,6 +262,7 @@ class spkern(Kernpart):
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diag_gradient_funcs = re.sub('Z','X',gradient_funcs,count=0)
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diag_gradient_funcs = re.sub('int jj','//int jj',diag_gradient_funcs)
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diag_gradient_funcs = re.sub('j','i',diag_gradient_funcs)
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diag_gradient_funcs = re.sub('partial\[i\*num_inducing\+i\]','2*partial[i]',diag_gradient_funcs)
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@ -312,7 +296,7 @@ class spkern(Kernpart):
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if partial is not None:
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arg_names += ['partial']
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if self.output_dim>1:
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arg_names += self._split_param_names
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arg_names += self._split_theta_names
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arg_names += ['output_dim']
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return arg_names
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@ -320,7 +304,7 @@ class spkern(Kernpart):
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param, output_dim = self._shared_params, self.output_dim
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# Need to extract parameters first
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for split_params in self._split_param_names:
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for split_params in self._split_theta_names:
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locals()[split_params] = getattr(self, split_params)
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arg_names = self._get_arg_names(Z, partial)
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weave.inline(code=code, arg_names=arg_names,**self.weave_kwargs)
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@ -353,21 +337,55 @@ class spkern(Kernpart):
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def dKdiag_dX(self,partial,X,target):
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self._weave.inline(self._dKdiag_dX_code, X, target, Z, partial)
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def _set_params(self,param):
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#print param.flags['C_CONTIGUOUS']
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def compute_psi_stats(self):
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#define some normal distributions
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mus = [sp.var('mu_%i'%i,real=True) for i in range(self.input_dim)]
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Ss = [sp.var('S_%i'%i,positive=True) for i in range(self.input_dim)]
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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)]
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#do some integration!
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#self._sp_psi0 = ??
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self._sp_psi1 = self._sp_k
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for i in range(self.input_dim):
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print 'perfoming integrals %i of %i'%(i+1,2*self.input_dim)
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sys.stdout.flush()
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self._sp_psi1 *= normals[i]
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self._sp_psi1 = sp.integrate(self._sp_psi1,(self._sp_x[i],-sp.oo,sp.oo))
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clear_cache()
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self._sp_psi1 = self._sp_psi1.simplify()
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#and here's psi2 (eek!)
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zprime = [sp.Symbol('zp%i'%i) for i in range(self.input_dim)]
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self._sp_psi2 = self._sp_k.copy()*self._sp_k.copy().subs(zip(self._sp_z,zprime))
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for i in range(self.input_dim):
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print 'perfoming integrals %i of %i'%(self.input_dim+i+1,2*self.input_dim)
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sys.stdout.flush()
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self._sp_psi2 *= normals[i]
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self._sp_psi2 = sp.integrate(self._sp_psi2,(self._sp_x[i],-sp.oo,sp.oo))
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clear_cache()
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self._sp_psi2 = self._sp_psi2.simplify()
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def _set_params(self,param):
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assert param.size == (self.num_params)
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self._shared_params = param[0:self.num_shared_params]
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for i, shared_params in enumerate(self._sp_theta):
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start = i
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end = i+1
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setattr(self, shared_params, param[start:end])
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if self.output_dim>1:
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for i, split_params in enumerate(self._split_param_names):
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for i, split_params in enumerate(self._split_theta_names):
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start = self.num_shared_params + i*self.output_dim
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end = self.num_shared_params + (i+1)*self.output_dim
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setattr(self, split_params, param[start:end])
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def _get_params(self):
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params = self._shared_params
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params = np.zeros(0)
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for shared_params in self._sp_theta:
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params = np.hstack((params, getattr(self, shared_params)))
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if self.output_dim>1:
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for split_params in self._split_param_names:
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for split_params in self._split_theta_names:
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params = np.hstack((params, getattr(self, split_params).flatten()))
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return params
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@ -25,6 +25,14 @@ class KernelTests(unittest.TestCase):
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kern = GPy.kern.rbf_sympy(5)
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self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
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def test_eq_sympykernel(self):
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kern = GPy.kern.eq_sympy(5, 3)
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self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
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def test_sinckernel(self):
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kern = GPy.kern.sinc(5)
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self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
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def test_rbf_invkernel(self):
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kern = GPy.kern.rbf_inv(5)
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self.assertTrue(GPy.kern.kern_test(kern, verbose=verbose))
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@ -491,11 +491,11 @@ def ripley_synth(data_set='ripley_prnn_data'):
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def osu_run1(data_set='osu_run1', sample_every=4):
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if not data_available(data_set):
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download_data(data_set)
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zip = zipfile.ZipFile(os.path.join(data_path, data_set, 'sprintTXT.ZIP'), 'r')
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zip = zipfile.ZipFile(os.path.join(data_path, data_set, 'run1TXT.ZIP'), 'r')
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path = os.path.join(data_path, data_set)
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for name in zip.namelist():
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zip.extract(name, path)
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Y, connect = GPy.util.mocap.load_text_data('Aug210107', path)
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Y, connect = GPy.util.mocap.load_text_data('Aug210106', path)
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Y = Y[0:-1:sample_every, :]
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return data_details_return({'Y': Y, 'connect' : connect}, data_set)
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