59 static double f(
double z,
double u,
double p) {
60 return logSqrt2PI - 0.5 * log(p) + 0.5 * (z - u) * (z - u) * p;
75 double e = u - z, e2 = e * e;
77 Vector g1 = (Vector(1) << -e * p).finished();
78 Vector g2 = (Vector(1) << 0.5 / p - 0.5 * e2).finished();
79 Matrix G11 = (Matrix(1, 1) << p).finished();
80 Matrix G12 = (Matrix(1, 1) << e).finished();
81 Matrix G22 = (Matrix(1, 1) << 0.5 / (p * p)).finished();
95 Base(), z_(z), meanKey_(meanKey), precisionKey_(precisionKey) {
111 void print(
const std::string& p =
"WhiteNoiseFactor",
112 const KeyFormatter& keyFormatter = DefaultKeyFormatter)
const override {
114 std::cout << p +
".z: " << z_ << std::endl;
122 size_t dim()
const override {
128 return f(z_, x.
at<
double>(meanKey_), x.
at<
double>(precisionKey_));
139 return (Vector(1) << std::sqrt(2 *
error(x))).finished();
157 double u = x.
at<
double>(meanKey_);
158 double p = x.
at<
double>(precisionKey_);
160 Key j2 = precisionKey_;
Contains the HessianFactor class, a general quadratic factor.
Non-linear factor base classes.
Global functions in a separate testing namespace.
Definition: chartTesting.h:28
const double logSqrt2PI
constant needed below
Definition: WhiteNoiseFactor.h:26
std::uint64_t Key
Integer nonlinear key type.
Definition: types.h:69
std::function< std::string(Key)> KeyFormatter
Typedef for a function to format a key, i.e. to convert it to a string.
Definition: Key.h:35
This is the base class for all factor types.
Definition: Factor.h:56
A Gaussian factor using the canonical parameters (information form)
Definition: HessianFactor.h:101
boost::shared_ptr< This > shared_ptr
A shared_ptr to this class.
Definition: HessianFactor.h:110
Nonlinear factor base class.
Definition: NonlinearFactor.h:43
void print(const std::string &s="", const KeyFormatter &keyFormatter=DefaultKeyFormatter) const override
print
Definition: NonlinearFactor.cpp:26
A non-templated config holding any types of Manifold-group elements.
Definition: Values.h:63
const ValueType at(Key j) const
Retrieve a variable by key j.
Definition: Values-inl.h:346
Binary factor to estimate parameters of zero-mean Gaussian white noise.
Definition: WhiteNoiseFactor.h:39
boost::shared_ptr< GaussianFactor > linearize(const Values &x) const override
linearize returns a Hessianfactor that is an approximation of error(p)
Definition: WhiteNoiseFactor.h:156
~WhiteNoiseFactor() override
Destructor.
Definition: WhiteNoiseFactor.h:103
static HessianFactor::shared_ptr linearize(double z, double u, double p, Key j1, Key j2)
linearize returns a Hessianfactor that approximates error Hessian is
Definition: WhiteNoiseFactor.h:73
void print(const std::string &p="WhiteNoiseFactor", const KeyFormatter &keyFormatter=DefaultKeyFormatter) const override
Print.
Definition: WhiteNoiseFactor.h:111
WhiteNoiseFactor(double z, Key meanKey, Key precisionKey)
Construct from measurement.
Definition: WhiteNoiseFactor.h:94
virtual Vector unwhitenedError(const Values &x) const
Vector of errors "unwhitened" does not make sense for this factor What is meant typically is only "e"...
Definition: WhiteNoiseFactor.h:138
static double f(double z, double u, double p)
negative log likelihood as a function of mean and precision
Definition: WhiteNoiseFactor.h:59
double error(const Values &x) const override
Calculate the error of the factor, typically equal to log-likelihood.
Definition: WhiteNoiseFactor.h:127
size_t dim() const override
get the dimension of the factor (number of rows on linearization)
Definition: WhiteNoiseFactor.h:122