def test_norm_constant_calculation(self):
custom_gauss = kernels.CustomKernel(lambda x: np.exp(-x**2 / 2.0))
gauss_true_const = 0.3989422804014327
npt.assert_almost_equal(gauss_true_const, custom_gauss.norm_const)
if __name__ == "__main__":
#from statsmodels.sandbox.nonparametric import smoothers as s
from statsmodels.sandbox.nonparametric import smoothers, kernels
import matplotlib.pyplot as plt
#from numpy import sin, array, random
import time
np.random.seed(500)
x = np.random.normal(size=250)
y = np.array(
[np.sin(i * 5) / i + 2 * i + (3 + i) * np.random.normal() for i in x])
K = kernels.Biweight(0.25)
K2 = kernels.CustomKernel(lambda x: (1 - x * x)**2,
0.25,
domain=[-1.0, 1.0])
KS = smoothers.KernelSmoother(x, y, K)
KS2 = smoothers.KernelSmoother(x, y, K2)
KSx = np.arange(-3, 3, 0.1)
start = time.time()
KSy = KS.conf(KSx)
KVar = KS.std(KSx)
print time.time() - start # This should be significantly quicker...
start = time.time() #
KS2y = KS2.conf(KSx) #
K2Var = KS2.std(KSx) #
print time.time() - start # ...than this.
