def test_Kgrad_activation(self):
self._cov.act_length = False
def func(x, i):
self._cov.scale = x[i]
return self._cov.K()
def grad(x, i):
self._cov.scale = x[i]
return self._cov.K_grad_i(0)
x0 = np.array([self._cov.scale])
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0.0)
self._cov.act_scale = False
self._cov.act_length = True
def func(x, i):
self._cov.length = x[i]
return self._cov.K()
def grad(x, i):
self._cov.length = x[i]
return self._cov.K_grad_i(0)
x0 = np.array([self._cov.length])
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0.0)
def test_Kgrad(self):
def func(x, i):
self._cov.scale = x[i]
return self._cov.K()
def grad(x, i):
self._cov.scale = x[i]
return self._cov.K_grad_i(0)
x0 = np.array([self._cov.scale])
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0, decimal=5)
def func(x, i):
self._cov.length = x[i]
return self._cov.K()
def grad(x, i):
self._cov.scale = x[i]
return self._cov.K_grad_i(1)
x0 = np.array([self._cov.scale])
err = mcheck_grad(func, grad, x0)
def test_penalty(self):
self._cov.setPenalty(10.,2.)
def func(x, i):
self._cov.scale = x[i]
return self._cov.K()
def grad(x, i):
self._cov.scale = x[i]
return self._cov.K_grad_i(0)
x0 = np.array([self._cov.scale])
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0, decimal=5)
def func(x, i):
self._cov.length = x[i]
return self._cov.K()
def grad(x, i):
self._cov.scale = x[i]
return self._cov.K_grad_i(1)
x0 = np.array([self._cov.scale])
err = mcheck_grad(func, grad, x0)
def test_Kgrad_activation(self):
self._cov.act_length = False
def func(x, i):
self._cov.scale = x[i]
return self._cov.K()
def grad(x, i):
self._cov.scale = x[i]
return self._cov.K_grad_i(0)
x0 = np.array([self._cov.scale])
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0.)
self._cov.act_scale = False
self._cov.act_length = True
def func(x, i):
self._cov.length = x[i]
return self._cov.K()
def grad(x, i):
self._cov.length = x[i]
return self._cov.K_grad_i(0)
x0 = np.array([self._cov.length])
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0.)
def test_Kgrad(self):
def func(x, i):
self._cov.scale = x[i]
return self._cov.K()
def grad(x, i):
self._cov.scale = x[i]
return self._cov.K_grad_i(0)
x0 = np.array([self._cov.scale])
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0.)
def func(x, i):
self._cov.length = x[i]
return self._cov.K()
def grad(x, i):
self._cov.scale = x[i]
return self._cov.K_grad_i(1)
x0 = np.array([self._cov.scale])
err = mcheck_grad(func, grad, x0)
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_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_logdet_grad(self):
def func(x, i):
self.C.setParams(x)
return self.C.logdet()
def grad(x, i):
self.C.setParams(x)
return self.C.logdet_grad_i(i)
x0 = self.C.getParams()
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0., decimal=5)
def test_logdet_grad(self):
def func(x, i):
self.C.setParams(x)
return self.C.logdet()
def grad(x, i):
self.C.setParams(x)
return self.C.logdet_grad_i(i)
x0 = self.C.getParams()
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0., decimal = 3)
def test_Khess(self):
def func(x, i):
self._cov.scale = x[i]
return self._cov.K_grad_i(0)
def grad(x, i):
self._cov.scale = x[i]
return self._cov.K_hess_i_j(0, 0)
x0 = np.array([self._cov.scale])
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0., decimal=5)
def func(x, i):
self._cov.length = x[i]
return self._cov.K_grad_i(0)
def grad(x, i):
self._cov.length = x[i]
return self._cov.K_hess_i_j(0, 1)
x0 = np.array([self._cov.scale])
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0., decimal=5)
def func(x, i):
self._cov.length = x[i]
return self._cov.K_grad_i(1)
def grad(x, i):
self._cov.length = x[i]
return self._cov.K_hess_i_j(1, 1)
x0 = np.array([self._cov.scale])
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0., decimal=5)
def test_Kgrad(self):
cov = self._cov
def func(x, i):
cov.setParams(x)
return cov.K()
def grad(x, i):
cov.setParams(x)
return cov.K_grad_i(i)
x0 = cov.getParams()
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0.)
def test_softKronKgrad(self):
cov = self._cov
cov.Iok = self._Iok
def func(x, i):
cov.setParams(x)
return cov.K()
def grad(x, i):
cov.setParams(x)
return cov.K_grad_i(i)
x0 = cov.getParams()
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0., decimal=5)
def test_Khess(self):
cov = self._cov
for j in range(cov.getNumberParams()):
def func(x, i):
cov.setParams(x)
return cov.K_grad_i(j)
def grad(x, i):
cov.setParams(x)
return cov.K_hess_i_j(j, i)
x0 = cov.getParams()
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0.)
def test_Khess(self):
cov = self.C
for j in range(cov.getNumberParams()):
def func(x, i):
cov.setParams(x)
return cov.K_grad_i(j)
def grad(x, i):
cov.setParams(x)
return cov.K_hess_i_j(j, i)
x0 = cov.getParams()
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0.)
def test_grad(self):
from limix.utils.check_grad import mcheck_grad
def func(x, i):
params = self.getParams()
params['covar'] = x
self.setParams(params)
return self.LML()
def grad(x, i):
params = self.getParams()
params['covar'] = x
self.setParams(params)
grad = self.LML_grad()
return grad['covar'][i]
x0 = self.getParams()['covar']
err = mcheck_grad(func, grad, x0)
print err
def test_grad(self):
from limix.utils.check_grad import mcheck_grad
def func(x, i):
params = self.getParams()
params['covar'] = x
self.setParams(params)
return self.LML()
def grad(x, i):
params = self.getParams()
params['covar'] = x
self.setParams(params)
grad = self.LML_grad()
return grad['covar'][i]
x0 = self.getParams()['covar']
err = mcheck_grad(func, grad, x0)
print err
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, 0.0, decimal=5)
def test_grad_gpbase_missdata(self):
gp = self._gpm
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=5)