mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-09 12:02:38 +02:00
REFACTORING: model names, lowercase, classes uppercase
This commit is contained in:
Max Zwiessele
parent
2a39440619
commit
2e5e8ac026
50 changed files with 436 additions and 3307 deletions
|
|
@ -21,7 +21,7 @@ from periodic_Matern32 import periodic_Matern32 as periodic_Matern32part
|
|||
from periodic_Matern52 import periodic_Matern52 as periodic_Matern52part
|
||||
from prod import prod as prodpart
|
||||
from symmetric import symmetric as symmetric_part
|
||||
from coregionalise import coregionalise as coregionalise_part
|
||||
from coregionalise import Coregionalise as coregionalise_part
|
||||
from rational_quadratic import rational_quadratic as rational_quadraticpart
|
||||
from rbfcos import rbfcos as rbfcospart
|
||||
from independent_outputs import independent_outputs as independent_output_part
|
||||
|
|
@ -33,8 +33,8 @@ def rbf(D,variance=1., lengthscale=None,ARD=False):
|
|||
"""
|
||||
Construct an RBF kernel
|
||||
|
||||
:param D: dimensionality of the kernel, obligatory
|
||||
:type D: int
|
||||
:param input_dim: dimensionality of the kernel, obligatory
|
||||
:type input_dim: int
|
||||
:param variance: the variance of the kernel
|
||||
:type variance: float
|
||||
:param lengthscale: the lengthscale of the kernel
|
||||
|
|
@ -51,7 +51,7 @@ def linear(D,variances=None,ARD=False):
|
|||
|
||||
Arguments
|
||||
---------
|
||||
D (int), obligatory
|
||||
input_dimD (int), obligatory
|
||||
variances (np.ndarray)
|
||||
ARD (boolean)
|
||||
"""
|
||||
|
|
@ -64,7 +64,7 @@ def white(D,variance=1.):
|
|||
|
||||
Arguments
|
||||
---------
|
||||
D (int), obligatory
|
||||
input_dimD (int), obligatory
|
||||
variance (float)
|
||||
"""
|
||||
part = whitepart(D,variance)
|
||||
|
|
@ -74,8 +74,8 @@ def exponential(D,variance=1., lengthscale=None, ARD=False):
|
|||
"""
|
||||
Construct an exponential kernel
|
||||
|
||||
:param D: dimensionality of the kernel, obligatory
|
||||
:type D: int
|
||||
:param input_dim: dimensionality of the kernel, obligatory
|
||||
:type input_dim: int
|
||||
:param variance: the variance of the kernel
|
||||
:type variance: float
|
||||
:param lengthscale: the lengthscale of the kernel
|
||||
|
|
@ -90,8 +90,8 @@ def Matern32(D,variance=1., lengthscale=None, ARD=False):
|
|||
"""
|
||||
Construct a Matern 3/2 kernel.
|
||||
|
||||
:param D: dimensionality of the kernel, obligatory
|
||||
:type D: int
|
||||
:param input_dim: dimensionality of the kernel, obligatory
|
||||
:type input_dim: int
|
||||
:param variance: the variance of the kernel
|
||||
:type variance: float
|
||||
:param lengthscale: the lengthscale of the kernel
|
||||
|
|
@ -106,8 +106,8 @@ def Matern52(D,variance=1., lengthscale=None, ARD=False):
|
|||
"""
|
||||
Construct a Matern 5/2 kernel.
|
||||
|
||||
:param D: dimensionality of the kernel, obligatory
|
||||
:type D: int
|
||||
:param input_dim: dimensionality of the kernel, obligatory
|
||||
:type input_dim: int
|
||||
:param variance: the variance of the kernel
|
||||
:type variance: float
|
||||
:param lengthscale: the lengthscale of the kernel
|
||||
|
|
@ -124,7 +124,7 @@ def bias(D,variance=1.):
|
|||
|
||||
Arguments
|
||||
---------
|
||||
D (int), obligatory
|
||||
input_dim (int), obligatory
|
||||
variance (float)
|
||||
"""
|
||||
part = biaspart(D,variance)
|
||||
|
|
@ -133,7 +133,7 @@ def bias(D,variance=1.):
|
|||
def finite_dimensional(D,F,G,variances=1.,weights=None):
|
||||
"""
|
||||
Construct a finite dimensional kernel.
|
||||
D: int - the number of input dimensions
|
||||
input_dim: int - the number of input dimensions
|
||||
F: np.array of functions with shape (n,) - the n basis functions
|
||||
G: np.array with shape (n,n) - the Gram matrix associated to F
|
||||
variances : np.ndarray with shape (n,)
|
||||
|
|
@ -145,8 +145,8 @@ def spline(D,variance=1.):
|
|||
"""
|
||||
Construct a spline kernel.
|
||||
|
||||
:param D: Dimensionality of the kernel
|
||||
:type D: int
|
||||
:param input_dim: Dimensionality of the kernel
|
||||
:type input_dim: int
|
||||
:param variance: the variance of the kernel
|
||||
:type variance: float
|
||||
"""
|
||||
|
|
@ -157,8 +157,8 @@ def Brownian(D,variance=1.):
|
|||
"""
|
||||
Construct a Brownian motion kernel.
|
||||
|
||||
:param D: Dimensionality of the kernel
|
||||
:type D: int
|
||||
:param input_dim: Dimensionality of the kernel
|
||||
:type input_dim: int
|
||||
:param variance: the variance of the kernel
|
||||
:type variance: float
|
||||
"""
|
||||
|
|
@ -204,8 +204,8 @@ def periodic_exponential(D=1,variance=1., lengthscale=None, period=2*np.pi,n_fre
|
|||
"""
|
||||
Construct an periodic exponential kernel
|
||||
|
||||
:param D: dimensionality, only defined for D=1
|
||||
:type D: int
|
||||
:param input_dim: dimensionality, only defined for input_dim=1
|
||||
:type input_dim: int
|
||||
:param variance: the variance of the kernel
|
||||
:type variance: float
|
||||
:param lengthscale: the lengthscale of the kernel
|
||||
|
|
@ -222,8 +222,8 @@ def periodic_Matern32(D,variance=1., lengthscale=None, period=2*np.pi,n_freq=10,
|
|||
"""
|
||||
Construct a periodic Matern 3/2 kernel.
|
||||
|
||||
:param D: dimensionality, only defined for D=1
|
||||
:type D: int
|
||||
:param input_dim: dimensionality, only defined for input_dim=1
|
||||
:type input_dim: int
|
||||
:param variance: the variance of the kernel
|
||||
:type variance: float
|
||||
:param lengthscale: the lengthscale of the kernel
|
||||
|
|
@ -240,8 +240,8 @@ def periodic_Matern52(D,variance=1., lengthscale=None, period=2*np.pi,n_freq=10,
|
|||
"""
|
||||
Construct a periodic Matern 5/2 kernel.
|
||||
|
||||
:param D: dimensionality, only defined for D=1
|
||||
:type D: int
|
||||
:param input_dim: dimensionality, only defined for input_dim=1
|
||||
:type input_dim: int
|
||||
:param variance: the variance of the kernel
|
||||
:type variance: float
|
||||
:param lengthscale: the lengthscale of the kernel
|
||||
|
|
@ -256,14 +256,14 @@ def periodic_Matern52(D,variance=1., lengthscale=None, period=2*np.pi,n_freq=10,
|
|||
|
||||
def prod(k1,k2,tensor=False):
|
||||
"""
|
||||
Construct a product kernel over D from two kernels over D
|
||||
Construct a product kernel over input_dim from two kernels over input_dim
|
||||
|
||||
:param k1, k2: the kernels to multiply
|
||||
:type k1, k2: kernpart
|
||||
:rtype: kernel object
|
||||
"""
|
||||
part = prodpart(k1,k2,tensor)
|
||||
return kern(part.D, [part])
|
||||
return kern(part.input_dim, [part])
|
||||
|
||||
def symmetric(k):
|
||||
"""
|
||||
|
|
@ -273,7 +273,7 @@ def symmetric(k):
|
|||
k_.parts = [symmetric_part(p) for p in k.parts]
|
||||
return k_
|
||||
|
||||
def coregionalise(Nout,R=1, W=None, kappa=None):
|
||||
def Coregionalise(Nout,R=1, W=None, kappa=None):
|
||||
p = coregionalise_part(Nout,R,W,kappa)
|
||||
return kern(1,[p])
|
||||
|
||||
|
|
@ -282,8 +282,8 @@ def rational_quadratic(D,variance=1., lengthscale=1., power=1.):
|
|||
"""
|
||||
Construct rational quadratic kernel.
|
||||
|
||||
:param D: the number of input dimensions
|
||||
:type D: int (D=1 is the only value currently supported)
|
||||
:param input_dim: the number of input dimensions
|
||||
:type input_dim: int (input_dim=1 is the only value currently supported)
|
||||
:param variance: the variance :math:`\sigma^2`
|
||||
:type variance: float
|
||||
:param lengthscale: the lengthscale :math:`\ell`
|
||||
|
|
@ -300,7 +300,7 @@ def fixed(D, K, variance=1.):
|
|||
|
||||
Arguments
|
||||
---------
|
||||
D (int), obligatory
|
||||
input_dim (int), obligatory
|
||||
K (np.array), obligatory
|
||||
variance (float)
|
||||
"""
|
||||
|
|
@ -321,6 +321,6 @@ def independent_outputs(k):
|
|||
for sl in k.input_slices:
|
||||
assert (sl.start is None) and (sl.stop is None), "cannot adjust input slices! (TODO)"
|
||||
parts = [independent_output_part(p) for p in k.parts]
|
||||
return kern(k.D+1,parts)
|
||||
return kern(k.input_dim+1,parts)
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue