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kernels are now consistent with pep8 and common reason
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Nicolo Fusi
parent
bbca026a21
commit
6ee8732cf4
29 changed files with 47 additions and 75 deletions
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@ -1,33 +1,9 @@
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# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import numpy as np
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from kern import kern
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from rbf import rbf as rbfpart
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from white import white as whitepart
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from linear import linear as linearpart
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from exponential import exponential as exponentialpart
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from Matern32 import Matern32 as Matern32part
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from Matern52 import Matern52 as Matern52part
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from bias import bias as biaspart
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from fixed import Fixed as fixedpart
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from finite_dimensional import finite_dimensional as finite_dimensionalpart
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from spline import spline as splinepart
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from Brownian import Brownian as Brownianpart
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from periodic_exponential import periodic_exponential as periodic_exponentialpart
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from periodic_Matern32 import periodic_Matern32 as periodic_Matern32part
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from periodic_Matern52 import periodic_Matern52 as periodic_Matern52part
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from prod import prod as prodpart
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from symmetric import symmetric as symmetric_part
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from coregionalise import Coregionalise as coregionalise_part
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from rational_quadratic import rational_quadratic as rational_quadraticpart
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from rbfcos import rbfcos as rbfcospart
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from independent_outputs import IndependentOutputs as independent_output_part
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#TODO these s=constructors are not as clean as we'd like. Tidy the code up
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#using meta-classes to make the objects construct properly wthout them.
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import parts
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def rbf(input_dim,variance=1., lengthscale=None,ARD=False):
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"""
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@ -42,7 +18,7 @@ def rbf(input_dim,variance=1., lengthscale=None,ARD=False):
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:param ARD: Auto Relevance Determination (one lengthscale per dimension)
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:type ARD: Boolean
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"""
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part = rbfpart(input_dim,variance,lengthscale,ARD)
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part = parts.rbf.RBF(input_dim,variance,lengthscale,ARD)
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return kern(input_dim, [part])
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def linear(input_dim,variances=None,ARD=False):
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@ -55,7 +31,7 @@ def linear(input_dim,variances=None,ARD=False):
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variances (np.ndarray)
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ARD (boolean)
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"""
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part = linearpart(input_dim,variances,ARD)
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part = parts.linear.Linear(input_dim,variances,ARD)
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return kern(input_dim, [part])
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def white(input_dim,variance=1.):
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@ -67,7 +43,7 @@ def white(input_dim,variance=1.):
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input_dimD (int), obligatory
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variance (float)
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"""
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part = whitepart(input_dim,variance)
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part = parts.white.White(input_dim,variance)
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return kern(input_dim, [part])
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def exponential(input_dim,variance=1., lengthscale=None, ARD=False):
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@ -83,7 +59,7 @@ def exponential(input_dim,variance=1., lengthscale=None, ARD=False):
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:param ARD: Auto Relevance Determination (one lengthscale per dimension)
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:type ARD: Boolean
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"""
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part = exponentialpart(input_dim,variance, lengthscale, ARD)
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part = parts.exponential.Exponential(input_dim,variance, lengthscale, ARD)
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return kern(input_dim, [part])
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def Matern32(input_dim,variance=1., lengthscale=None, ARD=False):
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@ -99,7 +75,7 @@ def Matern32(input_dim,variance=1., lengthscale=None, ARD=False):
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:param ARD: Auto Relevance Determination (one lengthscale per dimension)
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:type ARD: Boolean
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"""
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part = Matern32part(input_dim,variance, lengthscale, ARD)
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part = parts.Matern32.Matern32(input_dim,variance, lengthscale, ARD)
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return kern(input_dim, [part])
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def Matern52(input_dim, variance=1., lengthscale=None, ARD=False):
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@ -115,7 +91,7 @@ def Matern52(input_dim, variance=1., lengthscale=None, ARD=False):
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:param ARD: Auto Relevance Determination (one lengthscale per dimension)
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:type ARD: Boolean
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"""
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part = Matern52part(input_dim, variance, lengthscale, ARD)
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part = parts.Matern52.Matern52(input_dim, variance, lengthscale, ARD)
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return kern(input_dim, [part])
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def bias(input_dim, variance=1.):
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@ -127,7 +103,7 @@ def bias(input_dim, variance=1.):
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input_dim (int), obligatory
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variance (float)
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"""
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part = biaspart(input_dim, variance)
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part = parts.bias.Bias(input_dim, variance)
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return kern(input_dim, [part])
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def finite_dimensional(input_dim, F, G, variances=1., weights=None):
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@ -138,7 +114,7 @@ def finite_dimensional(input_dim, F, G, variances=1., weights=None):
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G: np.array with shape (n,n) - the Gram matrix associated to F
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variances : np.ndarray with shape (n,)
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"""
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part = finite_dimensionalpart(input_dim, F, G, variances, weights)
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part = parts.finite_dimensional.FiniteDimensional(input_dim, F, G, variances, weights)
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return kern(input_dim, [part])
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def spline(input_dim, variance=1.):
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@ -150,7 +126,7 @@ def spline(input_dim, variance=1.):
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:param variance: the variance of the kernel
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:type variance: float
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"""
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part = splinepart(input_dim, variance)
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part = parts.spline.Spline(input_dim, variance)
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return kern(input_dim, [part])
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def Brownian(input_dim, variance=1.):
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@ -162,7 +138,7 @@ def Brownian(input_dim, variance=1.):
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:param variance: the variance of the kernel
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:type variance: float
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"""
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part = Brownianpart(input_dim, variance)
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part = parts.Brownian.Brownian(input_dim, variance)
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return kern(input_dim, [part])
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try:
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@ -215,7 +191,7 @@ def periodic_exponential(input_dim=1, variance=1., lengthscale=None, period=2 *
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:param n_freq: the number of frequencies considered for the periodic subspace
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:type n_freq: int
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"""
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part = periodic_exponentialpart(input_dim, variance, lengthscale, period, n_freq, lower, upper)
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part = parts.periodic_exponential.PeriodicExponential(input_dim, variance, lengthscale, period, n_freq, lower, upper)
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return kern(input_dim, [part])
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def periodic_Matern32(input_dim, variance=1., lengthscale=None, period=2 * np.pi, n_freq=10, lower=0., upper=4 * np.pi):
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@ -233,7 +209,7 @@ def periodic_Matern32(input_dim, variance=1., lengthscale=None, period=2 * np.pi
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:param n_freq: the number of frequencies considered for the periodic subspace
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:type n_freq: int
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"""
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part = periodic_Matern32part(input_dim, variance, lengthscale, period, n_freq, lower, upper)
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part = parts.periodic_Matern32.PeriodicMatern32(input_dim, variance, lengthscale, period, n_freq, lower, upper)
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return kern(input_dim, [part])
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def periodic_Matern52(input_dim, variance=1., lengthscale=None, period=2 * np.pi, n_freq=10, lower=0., upper=4 * np.pi):
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@ -251,7 +227,7 @@ def periodic_Matern52(input_dim, variance=1., lengthscale=None, period=2 * np.pi
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:param n_freq: the number of frequencies considered for the periodic subspace
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:type n_freq: int
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"""
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part = periodic_Matern52part(input_dim, variance, lengthscale, period, n_freq, lower, upper)
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part = parts.periodic_Matern52part(input_dim, variance, lengthscale, period, n_freq, lower, upper)
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return kern(input_dim, [part])
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def prod(k1,k2,tensor=False):
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@ -262,7 +238,7 @@ def prod(k1,k2,tensor=False):
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:type k1, k2: kernpart
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:rtype: kernel object
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"""
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part = prodpart(k1,k2,tensor)
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part = parts.prodpart(k1,k2,tensor)
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return kern(part.input_dim, [part])
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def symmetric(k):
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@ -270,11 +246,11 @@ def symmetric(k):
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Construct a symmetrical kernel from an existing kernel
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"""
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k_ = k.copy()
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k_.parts = [symmetric_part(p) for p in k.parts]
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k_.parts = [symmetric.Symmetric(p) for p in k.parts]
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return k_
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def Coregionalise(Nout,R=1, W=None, kappa=None):
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p = coregionalise_part(Nout,R,W,kappa)
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def coregionalise(Nout,R=1, W=None, kappa=None):
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p = parts.coregionalise.Coregionalise(Nout,R,W,kappa)
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return kern(1,[p])
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@ -291,10 +267,10 @@ def rational_quadratic(input_dim, variance=1., lengthscale=1., power=1.):
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:rtype: kern object
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"""
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part = rational_quadraticpart(input_dim, variance, lengthscale, power)
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part = parts.rational_quadratic.RationalQuadratic(input_dim, variance, lengthscale, power)
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return kern(input_dim, [part])
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def Fixed(input_dim, K, variance=1.):
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def fixed(input_dim, K, variance=1.):
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"""
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Construct a Fixed effect kernel.
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@ -304,23 +280,21 @@ def Fixed(input_dim, K, variance=1.):
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K (np.array), obligatory
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variance (float)
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"""
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part = fixedpart(input_dim, K, variance)
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part = parts.fixed.Fixed(input_dim, K, variance)
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return kern(input_dim, [part])
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def rbfcos(input_dim, variance=1., frequencies=None, bandwidths=None, ARD=False):
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"""
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construct a rbfcos kernel
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"""
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part = rbfcospart(input_dim, variance, frequencies, bandwidths, ARD)
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part = parts.rbfcos.RBFCos(input_dim, variance, frequencies, bandwidths, ARD)
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return kern(input_dim, [part])
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def IndependentOutputs(k):
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def independent_outputs(k):
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"""
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Construct a kernel with independent outputs from an existing kernel
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"""
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for sl in k.input_slices:
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assert (sl.start is None) and (sl.stop is None), "cannot adjust input slices! (TODO)"
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parts = [independent_output_part(p) for p in k.parts]
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parts = [independent_outputs.IndependentOutputs(p) for p in k.parts]
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return kern(k.input_dim+1,parts)
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