def test(self):
N = 3 # how many points per function
tree = bt.BinaryBranchingTree(0, 10, fDebug=False) # set to true to print debug messages
tree.add(None, 1, 0.5) # single branching point
(fm, fmb) = tree.GetFunctionBranchTensor()
# print fmb
tree.printTree()
print('fm', fm)
# print fmb
t = np.linspace(0.01, 1, 10)
(XForKernel, indicesBranch, Xtrue) = tree.GetFunctionIndexList(t, fReturnXtrue=True)
# GP flow kernel
Bvalues = np.expand_dims(np.asarray(tree.GetBranchValues()), 1)
KbranchParam = bk.BranchKernelParam(gpflow.kernels.RBF(1), fm, b=Bvalues)
KbranchParam.kern.lengthscales = 2
KbranchParam.kern.variance = 1
K = KbranchParam.compute_K(Xtrue, Xtrue)
assert KbranchParam.Bv.value == 0.5
samples, L, K = bk.SampleKernel(KbranchParam, XForKernel, D=1, tol=1e-6, retChol=True)
samples2 = bk.SampleKernel(KbranchParam, XForKernel, D=1, tol=1e-6, retChol=False)
# Also try the independent kernel
indKernel = bk.IndKern(gpflow.kernels.RBF(1))
samples3, L, K = bk.SampleKernel(indKernel, XForKernel, D=1, tol=1e-6, retChol=True)
samples4 = KbranchParam.SampleKernel(XForKernel, b=Bvalues)
XAssignments = bk.GetFunctionIndexSample(t) # assign to either branch randomly
XAssignments[XAssignments[:, 0] <= tree.GetBranchValues(), 1] = 1
samples5 = KbranchParam.SampleKernelFromTree(XAssignments, b=tree.GetBranchValues())
def test(self):
branchingPoint = 0.5
tree = bt.BinaryBranchingTree(0, 10, fDebug=False) # set to true to print debug messages
tree.add(None, 1, branchingPoint) # single branching point
(fm, fmb) = tree.GetFunctionBranchTensor()
# Specify where to evaluate the kernel
t = np.linspace(0.01, 1, 60)
(XForKernel, indicesBranch, Xtrue) = tree.GetFunctionIndexList(t, fReturnXtrue=True)
# Specify the kernel and its hyperparameters
# These determine how smooth and variable the branching functions are
Bvalues = np.expand_dims(np.asarray(tree.GetBranchValues()), 1)
KbranchParam = bk.BranchKernelParam(gpflow.kernels.RBF(1), fm, b=Bvalues)
KbranchParam.kern.lengthscales = 2
KbranchParam.kern.variance = 1
# Sample the kernel
samples = bk.SampleKernel(KbranchParam, XForKernel)
# Plot the sample
bk.PlotSample(XForKernel, samples, B=Bvalues)
# Fit model
BgridSearch = [0.1, branchingPoint, 1.1]
globalBranchingLabels = XForKernel[:, 1] # use correct labels for tests
# could add a mistake
print('Sparse model')
d = FitBranchingModel.FitModel(BgridSearch, XForKernel[:, 0], samples, globalBranchingLabels,
maxiter=20, priorConfidence=0.80, M=10)
bmode = BgridSearch[np.argmax(d['loglik'])]
assert bmode == branchingPoint, bmode
# Plot model
pred = d['prediction'] # prediction object from GP
_=bplot.plotBranchModel(bmode, XForKernel[:, 0], samples, pred['xtest'], pred['mu'], pred['var'],
d['Phi'], fPlotPhi=True, fColorBar=True, fPlotVar = True)
_=bplot.PlotBGPFit(samples, XForKernel[:, 0], BgridSearch, d)
print('Try dense model')
d = FitBranchingModel.FitModel(BgridSearch, XForKernel[:, 0], samples, globalBranchingLabels,
maxiter=20, priorConfidence=0.80, M=0)
bmode = BgridSearch[np.argmax(d['loglik'])]
assert bmode == branchingPoint, bmode
print('Try sparse model with fixed inducing points')
d = FitBranchingModel.FitModel(BgridSearch, XForKernel[:, 0], samples, globalBranchingLabels,
maxiter=20, priorConfidence=0.80, M=20, fixInducingPoints=True)
bmode = BgridSearch[np.argmax(d['loglik'])]
assert bmode == branchingPoint, bmode
print('Try sparse model with fixed hyperparameters')
d = FitBranchingModel.FitModel(BgridSearch, XForKernel[:, 0], samples, globalBranchingLabels,
maxiter=20, priorConfidence=0.80, M=15,
likvar=1e-3, kerlen=2., kervar=1., fixHyperparameters=True)
# You can rerun the same code as many times as you want and get different sample paths
# We can also sample independent functions. This is the assumption in the overlapping mixtures of GPs model (OMGP) discussed in the paper.
indKernel = bk.IndKern(gpflow.kernels.RBF(1))
samplesInd = bk.SampleKernel(indKernel, XForKernel)
def test(self):
tree = bt.BinaryBranchingTree(
0, 10, fDebug=False) # set to true to print debug messages
tree.add(None, 1, 0.5) # single branching point
(fm, fmb) = tree.GetFunctionBranchTensor()
# print fmb
tree.printTree()
print("fm", fm)
# print fmb
t = np.linspace(0.01, 1, 10)
(XForKernel, indicesBranch,
Xtrue) = tree.GetFunctionIndexList(t, fReturnXtrue=True)
# GP flow kernel
Bvalues = np.expand_dims(np.asarray(tree.GetBranchValues()), 1)
KbranchParam = bk.BranchKernelParam(
gpflow.kernels.SquaredExponential(), fm, b=Bvalues)
KbranchParam.kern.lengthscales.assign(2)
KbranchParam.kern.variance.assign(1)
_ = KbranchParam.K(Xtrue, Xtrue)
assert KbranchParam.Bv == 0.5
_ = bk.SampleKernel(KbranchParam,
XForKernel,
D=1,
tol=1e-6,
retChol=True)
_ = bk.SampleKernel(KbranchParam,
XForKernel,
D=1,
tol=1e-6,
retChol=False)
# Also try the independent kernel
indKernel = bk.IndKern(gpflow.kernels.SquaredExponential())
_ = bk.SampleKernel(indKernel, XForKernel, D=1, tol=1e-6, retChol=True)
_ = KbranchParam.SampleKernel(XForKernel, b=Bvalues)
XAssignments = bk.GetFunctionIndexSample(
t) # assign to either branch randomly
XAssignments[XAssignments[:, 0] <= tree.GetBranchValues(), 1] = 1
_ = KbranchParam.SampleKernelFromTree(XAssignments,
b=tree.GetBranchValues())
def test(self):
branchingPoint = 0.5
tree = bt.BinaryBranchingTree(
0, 10, fDebug=False) # set to true to print debug messages
tree.add(None, 1, branchingPoint) # single branching point
(fm, fmb) = tree.GetFunctionBranchTensor()
# Specify where to evaluate the kernel
t = np.linspace(0.01, 1, 60)
(XForKernel, indicesBranch,
Xtrue) = tree.GetFunctionIndexList(t, fReturnXtrue=True)
# Specify the kernel and its hyperparameters
# These determine how smooth and variable the branching functions are
Bvalues = np.expand_dims(np.asarray(tree.GetBranchValues()), 1)
KbranchParam = bk.BranchKernelParam(
gpflow.kernels.SquaredExponential(), fm, b=Bvalues)
KbranchParam.kern.lengthscales.assign(2.0)
KbranchParam.kern.variance.assign(1.0)
# Sample the kernel
samples = bk.SampleKernel(KbranchParam, XForKernel)
# Plot the sample
bk.PlotSample(XForKernel, samples, B=Bvalues)
# Fit model
BgridSearch = [0.0001, branchingPoint, 1.1]
globalBranchingLabels = XForKernel[:,
1] # use correct labels for tests
# could add a mistake
print("Sparse model")
d = FitBranchingModel.FitModel(
BgridSearch,
XForKernel[:, 0],
samples,
globalBranchingLabels,
maxiter=40,
priorConfidence=0.80,
M=10,
)
bmode = BgridSearch[np.argmax(d["loglik"])]
print("tensorflow version", tf.__version__, "GPflow version",
gpflow.__version__)
print(
"TestSamplingAndPlotting:: Sparse Log likelihood",
d["loglik"],
"BgridSearch",
BgridSearch,
)
assert bmode == branchingPoint, bmode
# Plot model
pred = d["prediction"] # prediction object from GP
_ = bplot.plotBranchModel(
bmode,
XForKernel[:, 0],
samples,
pred["xtest"],
pred["mu"],
pred["var"],
d["Phi"],
fPlotPhi=True,
fColorBar=True,
fPlotVar=True,
)
_ = bplot.PlotBGPFit(samples, XForKernel[:, 0], BgridSearch, d)
d = FitBranchingModel.FitModel(
BgridSearch,
XForKernel[:, 0],
samples,
globalBranchingLabels,
maxiter=40,
priorConfidence=0.80,
M=0,
)
bmode = BgridSearch[np.argmax(d["loglik"])]
print(
"TestSamplingAndPlotting:: Dense Log likelihood",
d["loglik"],
"BgridSearch",
BgridSearch,
)
assert bmode == branchingPoint, bmode
print("Try sparse model with fixed hyperparameters")
d = FitBranchingModel.FitModel(
BgridSearch,
XForKernel[:, 0],
samples,
globalBranchingLabels,
maxiter=20,
priorConfidence=0.80,
M=15,
likvar=1e-3,
kerlen=2.0,
kervar=1.0,
fixHyperparameters=True,
)
# You can rerun the same code as many times as you want and get different sample paths
# We can also sample independent functions.
# This is the assumption in the overlapping mixtures of GPs model (OMGP) discussed in the paper.
indKernel = bk.IndKern(gpflow.kernels.SquaredExponential())
_ = bk.SampleKernel(indKernel, XForKernel)
# %% [markdown] # Specify the kernel and its hyperparameters # These determine how smooth and variable the branching functions are # %% Bvalues = np.expand_dims(np.asarray(tree.GetBranchValues()), 1) KbranchParam = bk.BranchKernelParam(gpflow.kernels.RBF(1), fm, b=Bvalues) KbranchParam.kern.lengthscales = 2 KbranchParam.kern.variance = 1 # %% [markdown] # Sample the kernel # %% samples = bk.SampleKernel(KbranchParam, XForKernel) # %% [markdown] # Plot the sample # %% bk.PlotSample(XForKernel, samples) # %% [markdown] # You can rerun the same code as many times as you want and get different sample paths # %% [markdown] # We can also sample independent functions. This is the assumption in the overlapping mixtures of GPs model (OMGP) discussed in the paper. # %% indKernel = bk.IndKern(gpflow.kernels.RBF(1))