kerns

GPy/kern/constructors.py

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+import numpy as np
+from kern import kern
+
+from rbf import rbf as rbfpart
+from rbf_ARD import rbf_ARD as rbf_ARD_part
+from white import white as whitepart
+from linear import linear as linearpart
+from linear_ARD import linear_ARD as linear_ARD_part
+from exponential import exponential as exponentialpart
+from Matern32 import Matern32 as Matern32part
+from Matern52 import Matern52 as Matern52part
+from bias import bias as biaspart
+from finite_dimensional import finite_dimensional as finite_dimensionalpart
+from spline import spline as splinepart
+from Brownian import Brownian as Brownianpart
+
+#TODO these s=constructors are not as clean as we'd like. Tidy the code up
+#using meta-classes to make the objects construct properly wthout them.
+
+def rbf(D,variance=1., lengthscale=1.):
+    """
+    Construct an RBF kernel
+
+    :param D: dimensionality of the kernel, obligatory
+    :type D: int
+    :param variance: the variance of the kernel
+    :type variance: float
+    :param lengthscale: the lengthscale of the kernel
+    :type lengthscale: float
+    """
+    part = rbfpart(D,variance,lengthscale)
+    return kern(D, [part])
+
+def rbf_ARD(D,variance=1., lengthscales=None):
+    """
+    Construct an RBF kernel with Automatic Relevance Determination (ARD)
+
+    :param D: dimensionality of the kernel, obligatory
+    :type D: int
+    :param variance: the variance of the kernel
+    :type variance: float
+    :param lengthscales: the lengthscales of the kernel
+    :type lengthscales: None|np.ndarray
+    """
+    part = rbf_ARD_part(D,variance,lengthscales)
+    return kern(D, [part])
+
+def linear(D,lengthscales=None):
+    """
+     Construct a linear kernel.
+
+     Arguments
+     ---------
+     D (int), obligatory
+     lengthscales (np.ndarray)
+    """
+    part = linearpart(D,lengthscales)
+    return kern(D, [part])
+
+def linear_ARD(D,lengthscales=None):
+    """
+     Construct a linear ARD kernel.
+
+     Arguments
+     ---------
+     D (int), obligatory
+     lengthscales (np.ndarray)
+    """
+    part = linear_ARD_part(D,lengthscales)
+    return kern(D, [part])
+
+def white(D,variance=1.):
+    """
+     Construct a white kernel.
+
+     Arguments
+     ---------
+     D (int), obligatory
+     variance (float)
+    """
+    part = whitepart(D,variance)
+    return kern(D, [part])
+
+def exponential(D,variance=1., lengthscales=None):
+    """
+     Construct a exponential kernel.
+
+     Arguments
+     ---------
+     D (int), obligatory
+     variance (float)
+     lengthscales (np.ndarray)
+    """
+    part = exponentialpart(D,variance, lengthscales)
+    return kern(D, [part])
+
+def Matern32(D,variance=1., lengthscales=None):
+    """
+     Construct a Matern 3/2 kernel.
+
+     Arguments
+     ---------
+     D (int), obligatory
+     variance (float)
+     lengthscales (np.ndarray)
+    """
+    part = Matern32part(D,variance, lengthscales)
+    return kern(D, [part])
+
+def Matern52(D,variance=1., lengthscales=None):
+    """
+     Construct a Matern 5/2 kernel.
+
+     Arguments
+     ---------
+     D (int), obligatory
+     variance (float)
+     lengthscales (np.ndarray)
+    """
+    part = Matern52part(D,variance, lengthscales)
+    return kern(D, [part])
+
+def bias(D,variance=1.):
+    """
+     Construct a bias kernel.
+
+     Arguments
+     ---------
+     D (int), obligatory
+     variance (float)
+    """
+    part = biaspart(D,variance)
+    return kern(D, [part])
+
+def finite_dimensional(D,F,G,variances=1.,weights=None):
+    """
+    Construct a finite dimensional kernel.
+    D: 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,)
+    """
+    part = finite_dimensionalpart(D,F,G,variances,weights)
+    return kern(D, [part])
+
+def spline(D,variance=1.):
+    """
+    Construct a spline kernel.
+
+    :param D: Dimensionality of the kernel
+    :type D: int
+    :param variance: the variance of the kernel
+    :type variance: float
+    """
+    part = splinepart(D,variance)
+    return kern(D, [part])
+
+def Brownian(D,variance=1.):
+    """
+    Construct a Brownian motion kernel.
+
+    :param D: Dimensionality of the kernel
+    :type D: int
+    :param variance: the variance of the kernel
+    :type variance: float
+    """
+    part = Brownianpart(D,variance)
+    return kern(D, [part])