def test_hess_fun1_fd(self):
for test_params in self.params:
#hetrue = 0
hetrue = self.hesstrue(test_params)
if not hetrue is None: #Hessian doesn't work for 2d return of fun
fun = self.fun()
#default works, epsilon 1e-6 or 1e-8 is not precise enough
hefd = numdiff.approx_hess(test_params, fun, #epsilon=1e-8,
args=self.args)[0] #TODO:should be kwds
assert_almost_equal(hetrue, hefd, decimal=DEC3)
def test_hess_fun1_fd(self):
for test_params in self.params:
#hetrue = 0
hetrue = self.hesstrue(test_params)
if not hetrue is None: #Hessian doesn't work for 2d return of fun
fun = self.fun()
#default works, epsilon 1e-6 or 1e-8 is not precise enough
hefd = numdiff.approx_hess(test_params, fun, #epsilon=1e-8,
args=self.args)[0] #TODO:should be kwds
assert_almost_equal(hetrue, hefd, decimal=DEC3)
#xk = np.zeros(3)
beta = xk
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")
xk = np.array([1,2,3])
xk = np.array([1.,1.,1.])
#xk = np.zeros(3)
beta = xk
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")
