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)
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 = np.linspace(lb, ub, nobs)
x2 = np.sin(2*x1)
x = np.column_stack((x1/x1.max()*2, x2))
exog = (x[:,:,None]**np.arange(order+1)[None, None, :]).reshape(nobs, -1)
idx = range((order+1)*2)
del idx[order+1]
exog_reduced = exog[:,idx] #remove duplicate constant
y_true = exog.sum(1) / 2.
z = y_true #alias check
d = x
y = y_true + sigma_noise * np.random.randn(nobs)
example = 1
if example == 1:
m = AdditiveModel(d)
m.fit(y)
y_pred = m.results.predict(d)
for ss in m.smoothers:
print ss.params
res_ols = OLS(y, exog_reduced).fit()
print res_ols.params
#assert_almost_equal(y_pred, res_ols.fittedvalues, 3)
if example > 0:
import matplotlib.pyplot as plt
x1 = np.linspace(lb, ub, nobs)
x2 = np.sin(2 * x1)
x = np.column_stack((x1 / x1.max() * 2, x2))
exog = (x[:, :, None]**np.arange(order + 1)[None, None, :]).reshape(nobs, -1)
idx = range((order + 1) * 2)
del idx[order + 1]
exog_reduced = exog[:, idx] #remove duplicate constant
y_true = exog.sum(1) / 2.
z = y_true #alias check
d = x
y = y_true + sigma_noise * np.random.randn(nobs)
example = 1
if example == 1:
m = AdditiveModel(d)
m.fit(y)
y_pred = m.results.predict(d)
for ss in m.smoothers:
print ss.params
res_ols = OLS(y, exog_reduced).fit()
print res_ols.params
#assert_almost_equal(y_pred, res_ols.fittedvalues, 3)
if example > 0:
import matplotlib.pyplot as plt
