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.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)
x2 = R.standard_normal(nobs)
x2.sort()
y = R.standard_normal((nobs,))
f1 = lambda x1: (x1 + x1**2 - 3 - 1 * x1**3 + 0.1 * np.exp(-x1/4.))
f2 = lambda x2: (x2 + x2**2 - 0.1 * np.exp(x2/4.))
z = standardize(f1(x1)) + standardize(f2(x2))
z = standardize(z) * 2 # 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
y_pred = m.results.predict(d)
plt.figure()
plt.plot(y, '.')
plt.plot(z, 'b-', label='true')
plt.plot(y_pred, 'r-', label='AdditiveModel')
plt.legend()
plt.title('gam.AdditiveModel')
import scipy.stats, time
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