Source code for glimix_core.mean._kron

from numpy import asarray, kron, zeros
from optimix import Function, Vector

from glimix_core._util import unvec, vec

[docs]class KronMean(Function): """ Kronecker mean function, (A⊗X)vec(B). Let - n be the number of samples; - p the number of traits; and - c the number of covariates. The mathematical representation is 𝐦 = (A⊗X)vec(B) where A is a p×p trait design matrix of fixed effects and X is a n×c sample design matrix of fixed effects. B is a c×p matrix of fixed-effect sizes. """ def __init__(self, A, X): """ Constructor. Parameters ---------- A : array_like p×p array. X : array_like n×c array. """ self._A = asarray(A, float) self._X = asarray(X, float) vecB = zeros((X.shape[1], A.shape[0])).ravel() self._vecB = Vector(vecB) self._nparams = vecB.size Function.__init__(self, "KronMean", vecB=self._vecB) @property def nparams(self): """ Number of parameters. """ return self._nparams @property def A(self): """ Matrix A. """ return self._A @property def X(self): """ Matrix X. """ return self._X @property def AX(self): """ A ⊗ X. """ return kron(self.A, self.X)
[docs] def value(self): """ Kronecker mean function. Returns ------- 𝐦 : ndarray (A⊗X)vec(B). """ return self.AX @ self._vecB.value
[docs] def gradient(self): """ Gradient of the linear mean function. Returns ------- vecB : ndarray Derivative of M over vec(B). """ return {"vecB": self.AX}
@property def B(self): """ Effect-sizes parameter, B. """ return unvec(self._vecB.value, (self.X.shape[1], self.A.shape[0])) @B.setter def B(self, v): self._vecB.value = vec(asarray(v, float)) def __str__(self): tname = type(self).__name__ msg = "{}(A=..., X=...)".format(tname) if is not None: msg += ": {}".format( msg += "\n" mat = format(self.B) msg += " B: " + "\n ".join(mat.split("\n")) return msg