def __init__(self, Y, F=None, A=None, Fstar=None):
"""
Args:
Y: phenotype matrix [N, P]
F: list of sample fixed effect designs.
Each term must have first dimension N
A: list of trait fixed effect design.
Each term must have second dimension P
Fstar: list out-of-sample fixed effect design.
Each term must have first dimension No
"""
Cached.__init__(self)
Y = assert_make_float_array(Y, 'Y')
if F is not None:
try:
assert_type_or_list_type(F, np.ndarray, 'F')
except TypeError as e:
raise TypeError(e.message + ' Parameter F might also be set'
' to None.')
if A is not None:
try:
assert_type_or_list_type(A, np.ndarray, 'A')
except TypeError as e:
raise TypeError(e.message + ' Parameter A might also be set'
' to None.')
assert Fstar is None, 'This constructor still does not support Fstar.'
self.Y = Y
self.setDesigns(F, A)
self.Fstar = Fstar
self.setFIinv(None)
self._set_relay(None)
def __init__(self, Y, identity_trick=False):
""" init data term """
Y = assert_make_float_array(Y, 'Y')
assert_finite_array(Y)
self.Y = Y
self.identity_trick = identity_trick
self.clearFixedEffect()
def __init__(self, Y, identity_trick=False):
""" init data term """
Y = assert_make_float_array(Y, 'Y')
assert_finite_array(Y)
self.Y = Y
self.identity_trick=identity_trick
self.clearFixedEffect()
def __init__(self, K0, Kcross0=None):
"""
Args:
K0: semi-definite positive matrix that defines the fixed-form covariance
Kcross0: cross covariance between training and test samples
(used only for out-of-sample predictions)
"""
Covariance.__init__(self)
self._scale_act = True
self.K0 = assert_make_float_array(K0, "K0")
assert_finite_array(self.K0)
if Kcross0 is not None:
Kcross0 = assert_make_float_array(Kcross0, "Kcross0")
assert_finite_array(Kcross0)
self.Kcross0 = Kcross0
self.params = np.zeros(1)
def __init__(self, X, Xstar=None):
"""
X: [dim, 1] input matrix
Xstar: [dim_star, 1] out-of-sample input matrix
"""
Covariance.__init__(self)
self._scale_act = True
self._length_act = True
X = assert_make_float_array(X, "X")
assert_finite_array(X)
self.X = X
if Xstar is not None:
Xstar = assert_make_float_array(Xstar, "Xstar")
assert_finite_array(Xstar)
self.Xstar = Xstar
self.params = np.zeros(2)
def __init__(self, X, Xstar=None):
"""
X: [dim, 1] input matrix
Xstar: [dim_star, 1] out-of-sample input matrix
"""
Covariance.__init__(self)
self._scale_act = True
self._length_act = True
X = assert_make_float_array(X, "X")
assert_finite_array(X)
self.X = X
if Xstar is not None:
Xstar = assert_make_float_array(Xstar, "Xstar")
assert_finite_array(Xstar)
self.Xstar = Xstar
self.params = np.zeros(2)
