def __init__(self):
super(self.__class__, self).__init__() #initialize DGP
nobs = self.nobs
y_true, x, exog = self.y_true, self.x, self.exog
np.random.seed(8765993)
sigma_noise = 0.1
y = y_true + sigma_noise * np.random.randn(nobs)
m = AdditiveModel(x)
m.fit(y)
res_gam = m.results #TODO: currently attached to class
res_ols = OLS(y, exog).fit()
#Note: there still are some naming inconsistencies
self.res1 = res1 = Dummy() #for gam model
#res2 = Dummy() #for benchmark
self.res2 = res2 = res_ols #reuse existing ols results, will add additional
res1.y_pred = res_gam.predict(x)
res2.y_pred = res_ols.model.predict(res_ols.params, exog)
res1.y_predshort = res_gam.predict(x[:10])
slopes = [i for ss in m.smoothers for i in ss.params[1:]]
const = res_gam.alpha + sum([ss.params[1] for ss in m.smoothers])
#print const, slopes
res1.params = np.array([const] + slopes)
x1.sort()
x2 = R.standard_normal(500)
x2.sort()
y = R.standard_normal((500, ))
f1 = lambda x1: (x1 + x1**2 - 3 - 1.5 * x1**3 + np.exp(-x1))
f2 = lambda x2: (x2 + x2**2 - np.exp(x2))
z = standardize(f1(x1)) + standardize(f2(x2))
z = standardize(z) * 0.1
y += z
d = np.array([x1, x2]).T
if example == 1:
print "normal"
m = AdditiveModel(d)
m.fit(y)
x = np.linspace(-2, 2, 50)
print m
import scipy.stats, time
if example == 2:
print "binomial"
f = family.Binomial()
b = np.asarray([scipy.stats.bernoulli.rvs(p) for p in f.link.inverse(y)])
b.shape = y.shape
m = GAM(b, d, family=f)
toc = time.time()
m.fit(b)
