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 estimateKDEDensity(functionName,
trainSamples,
testSamples=None,
iteration=0,
plot=False,
out=True,
label="kde_gaussian",
bandwidthOptimizationTypeStr="rot"):
print("train: %i x %i (mean=%g, var=%g)" %
(trainSamples.shape[0], trainSamples.shape[1], np.mean(trainSamples),
np.var(trainSamples)))
if testSamples is not None:
print("test : %i x %i (mean=%g, var=%g)" %
(testSamples.shape[0], testSamples.shape[1],
np.mean(testSamples), np.var(testSamples)))
if "gaussian" in label:
kernelType = KernelType_GAUSSIAN
elif "epanechnikov" in label:
kernelType = KernelType_EPANECHNIKOV
else:
raise AttributeError("label is unknown")
bandwidthOptimizationType = strTobandwidthOptimizationType(
bandwidthOptimizationTypeStr)
kdeDist = KDEDist(trainSamples,
kernelType=kernelType,
bandwidthOptimizationType=bandwidthOptimizationType)
# -----------------------------------------------------------
cvKDE = kdeDist.crossEntropy(testSamples)
if plot and kdeDist.getDim() == 2:
fig = plt.figure()
plotDensity2d(kdeDist)
plt.title("log=%g" % cvKDE)
if out:
plt.tight_layout()
plt.savefig(
os.path.join(pathResults, "kde_dist.%s.i%i.jpg" %
(functionName, iteration)))
plt.savefig(
os.path.join(pathResults, "kde_dist.%s.i%i.pdf" %
(functionName, iteration)))
if out:
plt.close(fig)
else:
plt.show()
print("CV test = %g" % cvKDE)
# -----------------------------------------------------------
if out:
pathResults = os.path.join("data", label)
# serialize cross entropies
out_crossEntropies = os.path.join(
pathResults,
"kde_cross_entropies.%s.i%i.csv" % (functionName, iteration))
fd = open(out_crossEntropies, 'wb')
file_writer = csv.writer(fd)
file_writer.writerow(["crossEntropy"])
file_writer.writerow([cvKDE])
fd.close()
# serialize samples
np.savetxt(
os.path.join(
pathResults,
"kde_train_samples.%s.i%i.csv" % (functionName, iteration)),
trainSamples)
np.savetxt(
os.path.join(
pathResults,
"kde_test_samples.%s.i%i.csv" % (functionName, iteration)),
testSamples)
if plot:
# plot density
fig = plt.figure()
plotDensity2d(kdeDist)
plt.title("%s -> CV = %g" % (kdeDist.getBandwidths(), cvKDE))
plt.savefig(
os.path.join(pathResults,
"kde_pdf.%s.i%i.jpg" % (functionName, iteration)))
plt.close(fig)
# serialize best configuration to json
out_bestDist = os.path.join(
pathResults,
"kde_best_config.%s.i%i.json" % (functionName, iteration))
text = kdeDist.toJson()
fd = open(out_bestDist, "w")
fd.write(text)
fd.close()
# stats
stats = {
'config': {
'functionName': functionName,
'numDims': 2,
'label': label,
'bandwidth_optimization':
BandwidthOptimizationType_MAXIMUMLIKELIHOOD,
'kernelType': kernelType,
'iteration': iteration
},
'trainSamples': trainSamples,
'testSamples': testSamples,
'crossEntropyTrainKDE': kdeDist.crossEntropy(trainSamples),
'crossEntropyTestKDE': cvKDE,
'KDEDist_json': kdeDist.toJson()
}
return kdeDist, stats