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

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Max Zwiessele 2013-11-27 13:16:19 +00:00
commit 546a9bd205
2 changed files with 42 additions and 30 deletions
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45
GPy/kern/kern.py
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@ -487,12 +487,11 @@ class kern(Parameterized):
p1.psi1(Z, mu, S, psi11) p1.psi1(Z, mu, S, psi11)
Mu, Sigma = p1._crossterm_mu_S(Z, mu, S) Mu, Sigma = p1._crossterm_mu_S(Z, mu, S)
Mu, Sigma = Mu.reshape(NM,self.input_dim), Sigma.reshape(NM,self.input_dim) Mu, Sigma = Mu.reshape(NM,self.input_dim), Sigma.reshape(NM,self.input_dim)
p2.psi1(Z, Mu, Sigma, psi12) p2.psi1(Z, Mu, Sigma, psi12)
eK2 = psi12.reshape(N, M, M) eK2 = psi12.reshape(N, M, M)
crossterms = eK2 * (psi11[:, :, None] + psi11[:, None, :]) crossterms = eK2 * (psi11[:, :, None] + psi11[:, None, :])
target += crossterms target += crossterms
#import ipdb;ipdb.set_trace()
else: else:
raise NotImplementedError, "psi2 cannot be computed for this kernel" raise NotImplementedError, "psi2 cannot be computed for this kernel"
return target return target
@ -540,15 +539,15 @@ class kern(Parameterized):
# turn around to have rbf in front # turn around to have rbf in front
p1, p2 = self.parts[i2], self.parts[i1] p1, p2 = self.parts[i2], self.parts[i1]
ps1, ps2 = self.param_slices[i2], self.param_slices[i1] ps1, ps2 = self.param_slices[i2], self.param_slices[i1]
N, M = mu.shape[0], Z.shape[0]; NM=N*M N, M = mu.shape[0], Z.shape[0]; NM=N*M
psi11 = np.zeros((N, M)) psi11 = np.zeros((N, M))
p1.psi1(Z, mu, S, psi11) p1.psi1(Z, mu, S, psi11)
Mu, Sigma = p1._crossterm_mu_S(Z, mu, S) Mu, Sigma = p1._crossterm_mu_S(Z, mu, S)
Mu, Sigma = Mu.reshape(NM,self.input_dim), Sigma.reshape(NM,self.input_dim) Mu, Sigma = Mu.reshape(NM,self.input_dim), Sigma.reshape(NM,self.input_dim)
tmp1 = np.zeros_like(target[ps1]) tmp1 = np.zeros_like(target[ps1])
tmp2 = np.zeros_like(target[ps2]) tmp2 = np.zeros_like(target[ps2])
# for n in range(N): # for n in range(N):
@ -559,7 +558,7 @@ class kern(Parameterized):
# Mu, Sigma= Mu.reshape(N,M,self.input_dim), Sigma.reshape(N,M,self.input_dim) # Mu, Sigma= Mu.reshape(N,M,self.input_dim), Sigma.reshape(N,M,self.input_dim)
# p2.dpsi1_dtheta((dL_dpsi2[n:n+1,m:m+1,m_prime:m_prime+1]*(psi11[n:n+1,m_prime:m_prime+1]))[0], Z[m:m+1], Mu[n:n+1,m], Sigma[n:n+1,m], target[ps2]) # p2.dpsi1_dtheta((dL_dpsi2[n:n+1,m:m+1,m_prime:m_prime+1]*(psi11[n:n+1,m_prime:m_prime+1]))[0], Z[m:m+1], Mu[n:n+1,m], Sigma[n:n+1,m], target[ps2])
# p2.dpsi1_dtheta((dL_dpsi2[n:n+1,m:m+1,m_prime:m_prime+1]*(psi11[n:n+1,m:m+1]))[0], Z[m_prime:m_prime+1], Mu[n:n+1, m_prime], Sigma[n:n+1, m_prime], target[ps2])#Z[m_prime:m_prime+1], Mu[n+m:(n+m)+1], Sigma[n+m:(n+m)+1], target[ps2]) # p2.dpsi1_dtheta((dL_dpsi2[n:n+1,m:m+1,m_prime:m_prime+1]*(psi11[n:n+1,m:m+1]))[0], Z[m_prime:m_prime+1], Mu[n:n+1, m_prime], Sigma[n:n+1, m_prime], target[ps2])#Z[m_prime:m_prime+1], Mu[n+m:(n+m)+1], Sigma[n+m:(n+m)+1], target[ps2])
if isinstance(p1, RBF) and isinstance(p2, RBF): if isinstance(p1, RBF) and isinstance(p2, RBF):
psi12 = np.zeros((N, M)) psi12 = np.zeros((N, M))
p2.psi1(Z, mu, S, psi12) p2.psi1(Z, mu, S, psi12)
@ -571,11 +570,11 @@ class kern(Parameterized):
if isinstance(p1, RBF) and isinstance(p2, Linear): if isinstance(p1, RBF) and isinstance(p2, Linear):
#import ipdb;ipdb.set_trace() #import ipdb;ipdb.set_trace()
pass pass
p2.dpsi1_dtheta((dL_dpsi2*(psi11[:,:,None] + psi11[:,None,:])).reshape(NM,M), Z, Mu, Sigma, tmp2) p2.dpsi1_dtheta((dL_dpsi2*(psi11[:,:,None] + psi11[:,None,:])).reshape(NM,M), Z, Mu, Sigma, tmp2)
target[ps1] += tmp1 target[ps1] += tmp1
target[ps2] += tmp2 target[ps2] += tmp2
else: else:
raise NotImplementedError, "psi2 cannot be computed for this kernel" raise NotImplementedError, "psi2 cannot be computed for this kernel"
@ -615,17 +614,17 @@ class kern(Parameterized):
psi11 = np.zeros((N, M)) psi11 = np.zeros((N, M))
psi12 = np.zeros((NM, M)) psi12 = np.zeros((NM, M))
#psi12_t = np.zeros((N,M)) #psi12_t = np.zeros((N,M))
p1.psi1(Z, mu, S, psi11) p1.psi1(Z, mu, S, psi11)
Mu, Sigma = p1._crossterm_mu_S(Z, mu, S) Mu, Sigma = p1._crossterm_mu_S(Z, mu, S)
Mu, Sigma = Mu.reshape(NM,self.input_dim), Sigma.reshape(NM,self.input_dim) Mu, Sigma = Mu.reshape(NM,self.input_dim), Sigma.reshape(NM,self.input_dim)
p2.psi1(Z, Mu, Sigma, psi12) p2.psi1(Z, Mu, Sigma, psi12)
tmp1 = np.zeros_like(target) tmp1 = np.zeros_like(target)
p1.dpsi1_dZ((dL_dpsi2*psi12.reshape(N,M,M)).sum(1), Z, mu, S, tmp1) p1.dpsi1_dZ((dL_dpsi2*psi12.reshape(N,M,M)).sum(1), Z, mu, S, tmp1)
p1.dpsi1_dZ((dL_dpsi2*psi12.reshape(N,M,M)).sum(2), Z, mu, S, tmp1) p1.dpsi1_dZ((dL_dpsi2*psi12.reshape(N,M,M)).sum(2), Z, mu, S, tmp1)
target += tmp1 target += tmp1
#p2.dpsi1_dtheta((dL_dpsi2*(psi11[:,:,None] + psi11[:,None,:])).reshape(NM,M), Z, Mu, Sigma, target) #p2.dpsi1_dtheta((dL_dpsi2*(psi11[:,:,None] + psi11[:,None,:])).reshape(NM,M), Z, Mu, Sigma, target)
p2.dpsi1_dZ((dL_dpsi2*(psi11[:,:,None] + psi11[:,None,:])).reshape(NM,M), Z, Mu, Sigma, target) p2.dpsi1_dZ((dL_dpsi2*(psi11[:,:,None] + psi11[:,None,:])).reshape(NM,M), Z, Mu, Sigma, target)
else: else:
@ -666,21 +665,21 @@ class kern(Parameterized):
psi11 = np.zeros((N, M)) psi11 = np.zeros((N, M))
psi12 = np.zeros((NM, M)) psi12 = np.zeros((NM, M))
#psi12_t = np.zeros((N,M)) #psi12_t = np.zeros((N,M))
p1.psi1(Z, mu, S, psi11) p1.psi1(Z, mu, S, psi11)
Mu, Sigma = p1._crossterm_mu_S(Z, mu, S) Mu, Sigma = p1._crossterm_mu_S(Z, mu, S)
Mu, Sigma = Mu.reshape(NM,self.input_dim), Sigma.reshape(NM,self.input_dim) Mu, Sigma = Mu.reshape(NM,self.input_dim), Sigma.reshape(NM,self.input_dim)
p2.psi1(Z, Mu, Sigma, psi12) p2.psi1(Z, Mu, Sigma, psi12)
p1.dpsi1_dmuS((dL_dpsi2*psi12.reshape(N,M,M)).sum(1), Z, mu, S, target_mu, target_S) p1.dpsi1_dmuS((dL_dpsi2*psi12.reshape(N,M,M)).sum(1), Z, mu, S, target_mu, target_S)
p1.dpsi1_dmuS((dL_dpsi2*psi12.reshape(N,M,M)).sum(2), Z, mu, S, target_mu, target_S) p1.dpsi1_dmuS((dL_dpsi2*psi12.reshape(N,M,M)).sum(2), Z, mu, S, target_mu, target_S)
#p2.dpsi1_dtheta((dL_dpsi2*(psi11[:,:,None] + psi11[:,None,:])).reshape(NM,M), Z, Mu, Sigma, target) #p2.dpsi1_dtheta((dL_dpsi2*(psi11[:,:,None] + psi11[:,None,:])).reshape(NM,M), Z, Mu, Sigma, target)
p2.dpsi1_dmuS((dL_dpsi2*(psi11[:,:,None])).sum(1)*2, Z, Mu.reshape(N,M,self.input_dim).sum(1), Sigma.reshape(N,M,self.input_dim).sum(1), target_mu, target_S) p2.dpsi1_dmuS((dL_dpsi2*(psi11[:,:,None])).sum(1)*2, Z, Mu.reshape(N,M,self.input_dim).sum(1), Sigma.reshape(N,M,self.input_dim).sum(1), target_mu, target_S)
else: else:
raise NotImplementedError, "psi2 cannot be computed for this kernel" raise NotImplementedError, "psi2 cannot be computed for this kernel"
return target_mu, target_S return target_mu, target_S
def plot(self, x=None, plot_limits=None, which_parts='all', resolution=None, *args, **kwargs): def plot(self, x=None, plot_limits=None, which_parts='all', resolution=None, *args, **kwargs):
if which_parts == 'all': if which_parts == 'all':
which_parts = [True] * self.num_parts which_parts = [True] * self.num_parts
@ -754,7 +753,7 @@ class Kern_check_model(Model):
dL_dK = np.ones((X.shape[0], X.shape[0])) dL_dK = np.ones((X.shape[0], X.shape[0]))
else: else:
dL_dK = np.ones((X.shape[0], X2.shape[0])) dL_dK = np.ones((X.shape[0], X2.shape[0]))
self.kernel=kernel self.kernel=kernel
self.X = X self.X = X
self.X2 = X2 self.X2 = X2
@ -769,7 +768,7 @@ class Kern_check_model(Model):
return False return False
else: else:
return True return True
def _get_params(self): def _get_params(self):
return self.kernel._get_params() return self.kernel._get_params()
@ -784,7 +783,7 @@ class Kern_check_model(Model):
def _log_likelihood_gradients(self): def _log_likelihood_gradients(self):
raise NotImplementedError, "This needs to be implemented to use the kern_check_model class." raise NotImplementedError, "This needs to be implemented to use the kern_check_model class."
class Kern_check_dK_dtheta(Kern_check_model): class Kern_check_dK_dtheta(Kern_check_model):
"""This class allows gradient checks for the gradient of a kernel with respect to parameters. """ """This class allows gradient checks for the gradient of a kernel with respect to parameters. """
def __init__(self, kernel=None, dL_dK=None, X=None, X2=None): def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
@ -799,7 +798,7 @@ class Kern_check_dKdiag_dtheta(Kern_check_model):
Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=None) Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=None)
if dL_dK==None: if dL_dK==None:
self.dL_dK = np.ones((self.X.shape[0])) self.dL_dK = np.ones((self.X.shape[0]))
def log_likelihood(self): def log_likelihood(self):
return (self.dL_dK*self.kernel.Kdiag(self.X)).sum() return (self.dL_dK*self.kernel.Kdiag(self.X)).sum()
@ -816,7 +815,7 @@ class Kern_check_dK_dX(Kern_check_model):
def _get_param_names(self): def _get_param_names(self):
return ['X_' +str(i) + ','+str(j) for j in range(self.X.shape[1]) for i in range(self.X.shape[0])] return ['X_' +str(i) + ','+str(j) for j in range(self.X.shape[1]) for i in range(self.X.shape[0])]
def _get_params(self): def _get_params(self):
return self.X.flatten() return self.X.flatten()
@ -838,7 +837,7 @@ class Kern_check_dKdiag_dX(Kern_check_model):
def _get_param_names(self): def _get_param_names(self):
return ['X_' +str(i) + ','+str(j) for j in range(self.X.shape[1]) for i in range(self.X.shape[0])] return ['X_' +str(i) + ','+str(j) for j in range(self.X.shape[1]) for i in range(self.X.shape[0])]
def _get_params(self): def _get_params(self):
return self.X.flatten() return self.X.flatten()
@ -964,4 +963,4 @@ def kern_test(kern, X=None, X2=None, output_ind=None, verbose=False, X_positive=
return False return False
return pass_checks return pass_checks
del Model del Model

27
README.md
View file

@ -13,26 +13,39 @@ Getting started
=============== ===============
Installing with pip Installing with pip
------------------- -------------------
The simplest way to install GPy is using pip. The simplest way to install GPy is using pip. ubuntu users can do:
pip install gpy
sudo apt-get install python-pip
pip install gpy
If you'd like to install from source, or want to contribute to the project (e.g. by sending pull requests via github), read on.
Ubuntu Ubuntu
------ ------
For the most part, the developers are using ubuntu. To install the required packages: For the most part, the developers are using ubuntu. To install the required packages:
sudo apt-get install python-numpy python-scipy python-matplotlib
sudo apt-get install python-numpy python-scipy python-matplotlib
clone this git repository and add it to your path: clone this git repository and add it to your path:
git clone git@github.com:SheffieldML/GPy.git \<destination\>
echo "PYTHONPATH=$PYTHONPATH:\<detination\> > ~/.bashrc git clone git@github.com:SheffieldML/GPy.git ~/SheffieldML
echo 'PYTHONPATH=$PYTHONPATH:~/SheffieldML' >> ~/.bashrc
Windows Windows
------- -------
On windows, we recommend the ![anaconda python distribution](http://continuum.io/downloads). We've also had luck with ![enthought](http://www.enthought.com). git clone or unzip the source to a suitable directory, and add a PYTHONPATH environement variable. On windows, we recommend the ![anaconda python distribution](http://continuum.io/downloads). We've also had luck with ![enthought](http://www.enthought.com). git clone or unzip the source to a suitable directory, and add an approptiate PYTHONPATH environment variable.
On windows 7 (and possibly earlier versions) there's a bug in scipy version 0.13 which tries to write very long filenames. Reverting to scipy 0.12 seems to do the trick:
conda install scipy=0.12
OSX OSX
--- ---
everything appears to work out-of-the box using ![enthought](http://www.enthought.com) on osx Mavericks. Everything appears to work out-of-the box using ![enthought](http://www.enthought.com) on osx Mavericks. Download/clone GPy, and then add GPy to your PYTHONPATH
git clone git@github.com:SheffieldML/GPy.git ~/SheffieldML
echo 'PYTHONPATH=$PYTHONPATH:~/SheffieldML' >> ~/.profile
Compiling documentation: Compiling documentation: