def setUp(self):
np.random.seed(1)
# define phenotype
N = 200
P = 2
Y = sp.randn(N,P)
# define row caoriance
f = 10
G = 1.*(sp.rand(N, f)<0.2)
X = 1.*(sp.rand(N, f)<0.2)
R = covar_rescale(sp.dot(X,X.T))
R+= 1e-4 * sp.eye(N)
# define col covariances
Cg = FreeFormCov(P)
self._Cg = Cg
Cn = FreeFormCov(P)
Cg.setCovariance(0.5 * sp.cov(Y.T))
Cn.setCovariance(0.5 * sp.cov(Y.T))
# define gp
self.gp = GP3KronSumLR(Y = Y, Cg = Cg, Cn = Cn, R = R, G = G, rank = 1)
def setUp(self):
np.random.seed(1)
# define phenotype
N = 200
P = 2
Y = sp.randn(N, P)
# define row caoriance
f = 10
G = 1. * (sp.rand(N, f) < 0.2)
X = 1. * (sp.rand(N, f) < 0.2)
R = covar_rescale(sp.dot(X, X.T))
R += 1e-4 * sp.eye(N)
# define col covariances
Cg = FreeFormCov(P)
self._Cg = Cg
Cn = FreeFormCov(P)
Cg.setCovariance(0.5 * sp.cov(Y.T))
Cn.setCovariance(0.5 * sp.cov(Y.T))
# define gp
self.gp = GP3KronSumLR(Y=Y, Cg=Cg, Cn=Cn, R=R, G=G, rank=1)
def setUp(self):
np.random.seed(1)
# define phenotype
N = 200
P = 2
Y = sp.randn(N,P)
# define fixed effects
F = []; A = []
F.append(1.*(sp.rand(N,2)<0.5))
A.append(sp.eye(P))
# define row caoriance
f = 10
G = 1.*(sp.rand(N, f)<0.2)
# define col covariances
Cr = FreeFormCov(P)
self._Cr = Cr
Cn = FreeFormCov(P)
Cr.setCovariance(0.5 * sp.cov(Y.T))
Cn.setCovariance(0.5 * sp.cov(Y.T))
# define gp
self.gp = GP2KronSumLR(Y = Y, F = F, A = A, Cn = Cn, G = G)
def setUp(self):
np.random.seed(1)
# define phenotype
N = 200
P = 2
Y = sp.randn(N, P)
# define fixed effects
F = []
A = []
F.append(1. * (sp.rand(N, 2) < 0.5))
A.append(sp.eye(P))
# define row caoriance
f = 10
G = 1. * (sp.rand(N, f) < 0.2)
# define col covariances
Cr = FreeFormCov(P)
self._Cr = Cr
Cn = FreeFormCov(P)
Cr.setCovariance(0.5 * sp.cov(Y.T))
Cn.setCovariance(0.5 * sp.cov(Y.T))
# define gp
self.gp = GP2KronSumLR(Y=Y, F=F, A=A, Cn=Cn, G=G)
class TestGPBase(unittest.TestCase):
def setUp(self):
np.random.seed(1)
# define phenotype
N = 200
P = 2
self.Y = sp.randn(N, P)
# define fixed effects
self.F = []; self.A = []
self.F.append(1.*(sp.rand(N,2)<0.5))
self.A.append(sp.eye(P))
# define row caoriance
f = 10
X = 1.*(sp.rand(N, f)<0.2)
self.R = covar_rescale(sp.dot(X,X.T))
self.R += 1e-4 * sp.eye(N)
# define col covariances
self.Cg = FreeFormCov(P)
self.Cn = FreeFormCov(P)
self.Cg.setCovariance(0.5 * sp.cov(self.Y.T))
self.Cn.setCovariance(0.5 * sp.cov(self.Y.T))
# define gp
self.gp = GP2KronSum(Y=self.Y, F=self.F, A=self.A, Cg=self.Cg,
Cn=self.Cn, R=self.R)
@unittest.skip("someone has to fix it")
def test_grad(self):
gp = self.gp
def func(x, i):
params = gp.getParams()
params['covar'] = x
gp.setParams(params)
return gp.LML()
def grad(x, i):
params = gp.getParams()
params['covar'] = x
gp.setParams(params)
grad = gp.LML_grad()
return grad['covar'][i]
x0 = gp.getParams()['covar']
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0., decimal=4)
def test_grad_activation(self):
gp = self.gp
self.Cg._K_act = False
def func(x, i):
params = gp.getParams()
params['covar'] = x
gp.setParams(params)
return gp.LML()
def grad(x, i):
params = gp.getParams()
params['covar'] = x
gp.setParams(params)
grad = gp.LML_grad()
return grad['covar'][i]
x0 = gp.getParams()['covar']
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0., decimal=4)
self.Cg._K_act = True
self.Cn._K_act = False
def func(x, i):
params = gp.getParams()
params['covar'] = x
gp.setParams(params)
return gp.LML()
def grad(x, i):
params = gp.getParams()
params['covar'] = x
gp.setParams(params)
grad = gp.LML_grad()
return grad['covar'][i]
x0 = gp.getParams()['covar']
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0., decimal=4)
def test_correct_inputs(self):
np.asarray(None, dtype=float)
class TestGPBase(unittest.TestCase):
def setUp(self):
np.random.seed(1)
# define phenotype
N = 200
P = 2
self.Y = sp.randn(N, P)
# define fixed effects
self.F = []
self.A = []
self.F.append(1. * (sp.rand(N, 2) < 0.5))
self.A.append(sp.eye(P))
# define row caoriance
f = 10
X = 1. * (sp.rand(N, f) < 0.2)
self.R = covar_rescale(sp.dot(X, X.T))
self.R += 1e-4 * sp.eye(N)
# define col covariances
self.Cg = FreeFormCov(P)
self.Cn = FreeFormCov(P)
self.Cg.setCovariance(0.5 * sp.cov(self.Y.T))
self.Cn.setCovariance(0.5 * sp.cov(self.Y.T))
# define gp
self.gp = GP2KronSum(Y=self.Y,
F=self.F,
A=self.A,
Cg=self.Cg,
Cn=self.Cn,
R=self.R)
def test_grad(self):
gp = self.gp
def func(x, i):
params = gp.getParams()
params['covar'] = x
gp.setParams(params)
return gp.LML()
def grad(x, i):
params = gp.getParams()
params['covar'] = x
gp.setParams(params)
grad = gp.LML_grad()
return grad['covar'][i]
x0 = gp.getParams()['covar']
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0., decimal=4)
def test_grad_activation(self):
gp = self.gp
self.Cg._K_act = False
def func(x, i):
params = gp.getParams()
params['covar'] = x
gp.setParams(params)
return gp.LML()
def grad(x, i):
params = gp.getParams()
params['covar'] = x
gp.setParams(params)
grad = gp.LML_grad()
return grad['covar'][i]
x0 = gp.getParams()['covar']
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0., decimal=4)
self.Cg._K_act = True
self.Cn._K_act = False
def func(x, i):
params = gp.getParams()
params['covar'] = x
gp.setParams(params)
return gp.LML()
def grad(x, i):
params = gp.getParams()
params['covar'] = x
gp.setParams(params)
grad = gp.LML_grad()
return grad['covar'][i]
x0 = gp.getParams()['covar']
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0., decimal=4)
def test_correct_inputs(self):
np.asarray(None, dtype=float)