def test1DCDFandPPF(self):
# prepare data
U = Normal(0.5, 0.1, 0, 1)
train_samples = U.rvs(1000).reshape(1000, 1)
dist = SGDEdist.byLearnerSGDEConfig(train_samples,
config={
"grid_level": 5,
"grid_type": "poly",
"refinement_numSteps": 0,
"refinement_numPoints": 10,
"regularization_type":
"Laplace",
"crossValidation_lambda":
0.000562341,
"crossValidation_enable":
False,
"crossValidation_kfold": 5,
"crossValidation_silent": True
},
bounds=U.getBounds())
fig = plt.figure()
plt.hist(train_samples, bins=10, normed=True)
plotDensity1d(U)
plotDensity1d(dist)
plt.title("original space")
fig.show()
transformed_samples = dist.cdf(train_samples)
fig = plt.figure()
plt.hist(transformed_samples, bins=10, normed=True)
plt.title("uniform space")
fig.show()
transformed_samples = dist.ppf(transformed_samples)
fig = plt.figure()
plt.hist(transformed_samples, bins=10, normed=True)
plotDensity1d(U)
plotDensity1d(dist)
plt.title("original space")
fig.show()
plt.show()
def test1DCDFandPPF(self):
# prepare data
U = Normal(0.5, 0.1, 0, 1)
train_samples = U.rvs(1000).reshape(1000, 1)
dist = KDEDist(train_samples, kernelType=KernelType_EPANECHNIKOV)
rc('font', **{'size': 18})
fig = plt.figure()
x = np.linspace(0, 1, 1000)
plt.plot(x, dist.cdf(x), label="estimated")
plt.plot(x, [U.cdf(xi) for xi in x], label="analytic")
plt.legend(loc="lower right")
fig.show()
fig = plt.figure()
plt.hist(train_samples, normed=True)
plotDensity1d(U, label="analytic")
plotDensity1d(dist, label="estimated")
plt.title("original space")
plt.legend()
fig.show()
transformed_samples = dist.cdf(train_samples)
fig = plt.figure()
plt.hist(transformed_samples, normed=True)
plt.title("uniform space")
fig.show()
transformed_samples = dist.ppf(transformed_samples)
fig = plt.figure()
plt.hist(transformed_samples, normed=True)
plotDensity1d(U, label="analytic")
plotDensity1d(dist, label="estimated")
plt.title("original space")
plt.legend()
fig.show()
plt.show()