def test1DNormalDist(self):
# prepare data
U = dists.Normal(1.85, .3, 0, 3)
trainSamples = np.array([U.rvs(500)]).T
testSamples = np.array([U.rvs(1000)]).T
# build parameter set
dist = KDEDist(trainSamples,
kernelType=KernelType_GAUSSIAN,
bandwidthOptimizationType=
BandwidthOptimizationType_MAXIMUMLIKELIHOOD,
bounds=U.getBounds())
# fig = plt.figure()
# plotDensity1d(U)
# plotDensity1d(dist)
print("quad = %s" % (quad(lambda x: dist.pdf([x]), 0, 3), ))
print("mean = %g ~ %g" % (U.mean(), dist.mean()))
print("var = %g ~ %g" % (U.var(), dist.var()))
print("KL = %g" % U.klDivergence(dist, testSamples, testSamples))
print("CE = %g" % dist.crossEntropy(testSamples))
print("MSE = %g" % dist.l2error(U, testSamples, testSamples))
plt.show()
def test2DNormalMoments(self):
mean = 0
var = 0.5
U = dists.J([dists.Normal(mean, var, -2, 2),
dists.Normal(mean, var, -2, 2)])
trainSamples = U.rvs(10000)
dist = KDEDist(trainSamples)
# -----------------------------------------------
self.assertTrue(np.abs(U.mean() - dist.mean()) < 1e-2, "KDE mean wrong")
self.assertTrue(np.abs(U.var() - dist.var()) < 1e-2, "KDE variance wrong")