def test_types_scalar_fix():
a = Scalar(1.0)
assert_(not a.isfixed)
a.fix()
assert_(a.isfixed)
class Foo1(Function):
def __init__(self):
self._a = Vector([0, 0])
self._b = Vector([0, 0])
self._c = Scalar(1)
super(Foo1, self).__init__("Foo1", a=self._a, b=self._b, c=self._c)
@property
def a(self):
return self._a.value
@property
def b(self):
return self._b.value
@property
def c(self):
return self._c.value
def fix_c(self):
self._c.fix()
def unfix_c(self):
self._c.unfix()
@c.setter
def c(self, v):
self._c.value = v
def value(self):
a = self.a
b = self.b
c = self.c
return (a @ b - 3 + a @ [1, 1] - b @ [1, 2] + 1 / c)**2
def gradient(self):
a = self.a
b = self.b
c = self.c
v = a @ b - 3 + a @ [1, 1] - b @ [1, 2] + 1 / c
da = 2 * v * array([b[0] + 1, b[1] + 1])
db = 2 * v * array([a[0] - 1, a[1] - 2])
dc = 2 * v * -1 / (c**2)
return {"a": da, "b": db, "c": dc}
def check_grad(self):
return self._check_grad()
def __init__(self, y, X, QS=None, restricted=False):
"""
Constructor.
Parameters
----------
y : array_like
Outcome.
X : array_like
Covariates as a two-dimensional array.
QS : tuple
Economic eigendecompositon in form of ``((Q0, ), S0)`` of a
covariance matrix ``K``.
restricted : bool
``True`` for restricted maximum likelihood optimization; ``False``
otherwise. Defaults to ``False``.
"""
from numpy_sugar import is_all_finite
logistic = Scalar(0.0)
logistic.listen(self._delta_update)
logistic.bounds = (-numbers.logmax, +numbers.logmax)
Function.__init__(self, "LMM", logistic=logistic)
self._logistic = logistic
y = asarray(y, float).ravel()
if not is_all_finite(y):
raise ValueError("There are non-finite values in the outcome.")
if len(y) == 0:
raise ValueError("The outcome array is empty.")
X = atleast_2d(asarray(X, float).T).T
if not is_all_finite(X):
raise ValueError("There are non-finite values in the covariates matrix.")
self._optimal = {"beta": False, "scale": False}
if QS is None:
QS = economic_qs_zeros(len(y))
self._B = B(QS[0][0], QS[1], 0.0, 1.0)
self.delta = 1.0
logistic.fix()
else:
self._B = B(QS[0][0], QS[1], 0.5, 0.5)
self.delta = 0.5
if QS[0][0].shape[0] != len(y):
msg = "Sample size differs between outcome and covariance decomposition."
raise ValueError(msg)
if y.shape[0] != X.shape[0]:
msg = "Sample size differs between outcome and covariates."
raise ValueError(msg)
self._y = y
self._Q0 = QS[0][0]
self._S0 = QS[1]
self._Xsvd = SVD(X)
self._tbeta = zeros(self._Xsvd.rank)
self._scale = 1.0
self._fix = {"beta": False, "scale": False}
self._restricted = restricted