test_HybridFactorGraph.TestHybridGaussianFactorGraph Class Reference

Inheritance diagram for test_HybridFactorGraph.TestHybridGaussianFactorGraph:

Public Member Functions
def	estimate_marginals (cls, target, HybridBayesNet proposal_density, N=10000)
def	test_create (self)
def	test_evaluate (self)
def	test_optimize (self)
def	test_ratio (self)
def	test_tiny (self)
Public Member Functions inherited from gtsam.utils.test_case.GtsamTestCase
None	assertEqualityOnPickleRoundtrip (self, object obj, tol=1e-9)
def	gtsamAssertEquals (self, actual, expected, tol=1e-9)

Static Public Member Functions
def	calculate_ratio (HybridBayesNet bayesNet, HybridGaussianFactorGraph fg, HybridValues sample)
gtsam.VectorValues	measurements (HybridValues sample, indices)
HybridBayesNet	tiny (int num_measurements=1, float prior_mean=5.0, float prior_sigma=0.5)

Detailed Description

Unit tests for HybridGaussianFactorGraph.

Definition at line 28 of file test_HybridFactorGraph.py.

Member Function Documentation

calculate_ratio()

def test_HybridFactorGraph.TestHybridGaussianFactorGraph.calculate_ratio ( HybridBayesNet  bayesNet, HybridGaussianFactorGraph  fg, HybridValues  sample  )

static

Calculate ratio between Bayes net and factor graph.

Definition at line 248 of file test_HybridFactorGraph.py.

estimate_marginals()

def test_HybridFactorGraph.TestHybridGaussianFactorGraph.estimate_marginals	(		cls,
			target,
		HybridBayesNet	proposal_density,
			N = 10000
	)

Do importance sampling to estimate discrete marginal P(mode).

Definition at line 158 of file test_HybridFactorGraph.py.

measurements()

gtsam.VectorValues test_HybridFactorGraph.TestHybridGaussianFactorGraph.measurements ( HybridValues  sample,   indices  )

static

Create measurements from a sample, grabbing Z(i) for indices.

Definition at line 150 of file test_HybridFactorGraph.py.

test_create()

def test_HybridFactorGraph.TestHybridGaussianFactorGraph.test_create

(

self

)

Test construction of hybrid factor graph.

Definition at line 30 of file test_HybridFactorGraph.py.

test_evaluate()

def test_HybridFactorGraph.TestHybridGaussianFactorGraph.test_evaluate

(

self

)

Test evaluate with two different prior noise models.

Definition at line 122 of file test_HybridFactorGraph.py.

test_optimize()

def test_HybridFactorGraph.TestHybridGaussianFactorGraph.test_optimize

(

self

)

Test construction of hybrid factor graph.

Definition at line 57 of file test_HybridFactorGraph.py.

test_ratio()

def test_HybridFactorGraph.TestHybridGaussianFactorGraph.test_ratio

(

self

)

Given a tiny two variable hybrid model, with 2 measurements, test the
ratio of the bayes net model representing P(z,x,n)=P(z|x, n)P(x)P(n)
and the factor graph P(x, n | z)=P(x | n, z)P(n|z),
both of which represent the same posterior.

Definition at line 254 of file test_HybridFactorGraph.py.

test_tiny()

def test_HybridFactorGraph.TestHybridGaussianFactorGraph.test_tiny

(

self

)

Test a tiny two variable hybrid model.

Definition at line 177 of file test_HybridFactorGraph.py.

tiny()

HybridBayesNet test_HybridFactorGraph.TestHybridGaussianFactorGraph.tiny ( int   num_measurements = 1, float   prior_mean = 5.0, float   prior_sigma = 0.5  )

static

Create a tiny two variable hybrid model which represents
the generative probability P(Z, x0, mode) = P(Z|x0, mode)P(x0)P(mode).
num_measurements: number of measurements in Z = {z0, z1...}

Definition at line 82 of file test_HybridFactorGraph.py.

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The documentation for this class was generated from the following file:

• test_HybridFactorGraph.py