def test_grad_fun1_fd(self):
for test_params in self.params:
#gtrue = self.x.sum(0)
gtrue = self.gradtrue(test_params)
fun = self.fun()
epsilon = 1e-6
gfd = numdiff.approx_fprime1(test_params, fun, epsilon=epsilon,
args=self.args)
gfd += numdiff.approx_fprime1(test_params, fun, epsilon=-epsilon,
args=self.args)
gfd /= 2.
assert_almost_equal(gtrue, gfd, decimal=DEC6)
def test_grad_fun1_fd(self):
for test_params in self.params:
#gtrue = self.x.sum(0)
gtrue = self.gradtrue(test_params)
fun = self.fun()
epsilon = 1e-6
gfd = numdiff.approx_fprime1(test_params, fun, epsilon=epsilon,
args=self.args)
gfd += numdiff.approx_fprime1(test_params, fun, epsilon=-epsilon,
args=self.args)
gfd /= 2.
assert_almost_equal(gtrue, gfd, decimal=DEC6)
def test_grad_fun1_fdc(self):
for test_params in self.params:
#gtrue = self.x.sum(0)
gtrue = self.gradtrue(test_params)
fun = self.fun()
epsilon = 1e-6 #default epsilon 1e-6 is not precise enough
gfd = numdiff.approx_fprime1(test_params, fun, epsilon=1e-8,
args=self.args, centered=True)
assert_almost_equal(gtrue, gfd, decimal=DEC5)
def test_grad_fun1_fdc(self):
for test_params in self.params:
#gtrue = self.x.sum(0)
gtrue = self.gradtrue(test_params)
fun = self.fun()
epsilon = 1e-6 #default epsilon 1e-6 is not precise enough
gfd = numdiff.approx_fprime1(test_params, fun, epsilon=1e-8,
args=self.args, centered=True)
assert_almost_equal(gtrue, gfd, decimal=DEC5)
def test_score(self):
pass
#assert_almost_equal(self.res1.params, self.res2.params, DECIMAL_4)
for test_params in self.params:
sc = self.mod.score(test_params)
scfd = numdiff.approx_fprime1(test_params.ravel(), self.mod.loglike)
assert_almost_equal(sc, scfd, decimal=1)
sccs = numdiff.approx_fprime_cs(test_params.ravel(), self.mod.loglike)
assert_almost_equal(sc, sccs, decimal=13)
def test_score(self):
pass
#assert_almost_equal(self.res1.params, self.res2.params, DECIMAL_4)
for test_params in self.params:
sc = self.mod.score(test_params)
scfd = numdiff.approx_fprime1(test_params.ravel(), self.mod.loglike)
assert_almost_equal(sc, scfd, decimal=1)
sccs = numdiff.approx_fprime_cs(test_params.ravel(), self.mod.loglike)
assert_almost_equal(sc, sccs, decimal=13)
print approx_fprime((1, 2, 3), fun, epsilon, x)
gradtrue = x.sum(0)
print x.sum(0)
gradcs = approx_fprime_cs((1, 2, 3), fun, (x, ), h=1.0e-20)
print gradcs, maxabs(gradcs, gradtrue)
print approx_hess_cs((1, 2, 3), fun, (x, ),
h=1.0e-20) #this is correctly zero
print approx_hess_cs((1, 2, 3), fun2,
(y, x), h=1.0e-20) - 2 * np.dot(x.T, x)
print numdiff.approx_hess(xk, fun2, 1e-3, (y, x))[0] - 2 * np.dot(x.T, x)
gt = (-x * 2 * (y - np.dot(x, [1, 2, 3]))[:, None])
g = approx_fprime_cs((1, 2, 3), fun1, (y, x),
h=1.0e-20) #.T #this shouldn't be transposed
gd = numdiff.approx_fprime1((1, 2, 3), fun1, epsilon, (y, x))
print maxabs(g, gt)
print maxabs(gd, gt)
import statsmodels.api as sm
data = sm.datasets.spector.load()
data.exog = sm.add_constant(data.exog)
#mod = sm.Probit(data.endog, data.exog)
mod = sm.Logit(data.endog, data.exog)
#res = mod.fit(method="newton")
test_params = [1, 0.25, 1.4, -7]
loglike = mod.loglike
score = mod.score
hess = mod.hessian
y = np.dot(x, beta) + 0.1*np.random.randn(nobs)
xkols = np.dot(np.linalg.pinv(x),y)
print approx_fprime((1,2,3),fun,epsilon,x)
gradtrue = x.sum(0)
print x.sum(0)
gradcs = approx_fprime_cs((1,2,3), fun, (x,), h=1.0e-20)
print gradcs, maxabs(gradcs, gradtrue)
print approx_hess_cs((1,2,3), fun, (x,), h=1.0e-20) #this is correctly zero
print approx_hess_cs((1,2,3), fun2, (y,x), h=1.0e-20)-2*np.dot(x.T, x)
print numdiff.approx_hess(xk,fun2,1e-3, (y,x))[0] - 2*np.dot(x.T, x)
gt = (-x*2*(y-np.dot(x, [1,2,3]))[:,None])
g = approx_fprime_cs((1,2,3), fun1, (y,x), h=1.0e-20)#.T #this shouldn't be transposed
gd = numdiff.approx_fprime1((1,2,3),fun1,epsilon,(y,x))
print maxabs(g, gt)
print maxabs(gd, gt)
import statsmodels.api as sm
data = sm.datasets.spector.load()
data.exog = sm.add_constant(data.exog)
#mod = sm.Probit(data.endog, data.exog)
mod = sm.Logit(data.endog, data.exog)
#res = mod.fit(method="newton")
test_params = [1,0.25,1.4,-7]
loglike = mod.loglike
score = mod.score
hess = mod.hessian
