def test_kernels_vectorized(self, case):
name, bw = case
rvs = self.rvs
x_plot = self.x_plot
kde = []
kce = []
for xi in x_plot:
kde.append(kern.pdf_kernel_asym(xi, rvs, bw, name))
kce.append(kern.cdf_kernel_asym(xi, rvs, bw, name))
kde = np.asarray(kde)
kce = np.asarray(kce)
kde1 = kern.pdf_kernel_asym(x_plot, rvs, bw, name)
kce1 = kern.cdf_kernel_asym(x_plot, rvs, bw, name)
assert_allclose(kde1, kde, rtol=1e-12)
assert_allclose(kce1, kce, rtol=1e-12)
def test_kernels_weights(self, case):
name, bw = case
rvs = self.rvs
x = self.x_plot
kde2 = kern.pdf_kernel_asym(x, rvs, bw, name)
kce2 = kern.cdf_kernel_asym(x, rvs, bw, name)
n = len(rvs)
w = np.ones(n) / n
kde1 = kern.pdf_kernel_asym(x, rvs, bw, name, weights=w)
kce1 = kern.cdf_kernel_asym(x, rvs, bw, name, weights=w)
assert_allclose(kde1, kde2, rtol=1e-12)
assert_allclose(kce1, kce2, rtol=1e-12)
# weights that do not add to 1 are valid, but do not produce pdf, cdf
n = len(rvs)
w = np.ones(n) / n * 2
kde1 = kern.pdf_kernel_asym(x, rvs, bw, name, weights=w)
kce1 = kern.cdf_kernel_asym(x, rvs, bw, name, weights=w)
assert_allclose(kde1, kde2 * 2, rtol=1e-12)
assert_allclose(kce1, kce2 * 2, rtol=1e-12)
def test_kernels(self, case):
name, bw = case
rvs = self.rvs
x_plot = self.x_plot
kde = []
kce = []
for xi in x_plot:
kde.append(kern.pdf_kernel_asym(xi, rvs, bw, name))
kce.append(kern.cdf_kernel_asym(xi, rvs, bw, name))
kde = np.asarray(kde)
kce = np.asarray(kce)
# average mean squared error
amse = ((kde - self.pdf_dgp)**2).mean()
assert_array_less(amse, self.amse_pdf)
amse = ((kce - self.cdf_dgp)**2).mean()
assert_array_less(amse, self.amse_cdf)