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 y(self,value):
assert_finite_array(value)
assert value.shape[1] == 1, 'MeanBase: phenotype has to be a one column vector'
self._N = value.shape[0]
self._P = 1
self._y = value
# notify
self._notify()
self.clear_cache('Yres')
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, N] input matrix
Xstar: [dim_star, N] 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
self.penalty_function = None
if Xstar is not None:
Xstar = assert_make_float_array(Xstar, "Xstar")
assert_finite_array(Xstar)
self.Xstar = Xstar
self.params = np.zeros(2)