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| drwnGaussian (int n=1) |
| | construct an n dimensional zero-mean identity-covariance gaussian
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| drwnGaussian (const VectorXd &mu, double sigma2) |
| | construct a gaussian with given mean and isotropic covariance
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| drwnGaussian (const VectorXd &mu, const MatrixXd &sigma2) |
| | construct a gaussian with given mean and covariance
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| drwnGaussian (const vector< double > &mu, double sigma2) |
| | construct a gaussian with given mean and isotropic covariance
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| drwnGaussian (const drwnSuffStats &stats) |
| | construct a gaussian from given second-order sufficient statistics
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| drwnGaussian (const drwnGaussian &model) |
| | copy constructor
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void | initialize (int n) |
| | initialize the gaussian to be n dimensional zero-mean and identity-covariance
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void | initialize (const VectorXd &mu, double sigma2) |
| | initialize the gaussian to have mean mu and isotropic variance sigma2
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void | initialize (const VectorXd &mu, const MatrixXd &sigma2) |
| | initialize the gaussian to the given mean and covariance
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drwnGaussian * | marginalize (const vector< int > &indx) const |
| | Generate a gaussian with the variables not in indx marginalized out. The calling function is responsible for deleting the returned object.
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drwnGaussian | reduce (const vector< double > &x, const vector< int > &indx) const |
| | Returns a gaussian conditioned on x. Repeated calls to reduce on the same set of variables should use the conditionOn function.
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drwnGaussian | reduce (const map< int, double > &x) const |
| | Returns a gaussian conditioned on x.
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drwnConditionalGaussian | conditionOn (const vector< int > &indx) const |
| | Returns a temporary objects for conditioning on a set of variables.
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const char * | type () const |
| | returns object type as a string (e.g., Foo::type() { return "Foo"; })
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drwnGaussian * | clone () const |
| | returns a copy of the class usually implemented as virtual Foo* clone() { return new Foo(*this); }
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bool | save (drwnXMLNode &xml) const |
| | write object to XML node (see also write)
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bool | load (drwnXMLNode &xml) |
| | read object from XML node (see also read)
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void | evaluate (const MatrixXd &x, VectorXd &p) const |
| | compute the log-likelihood of each row of X and put the results in p
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void | evaluate (const vector< vector< double > > &x, vector< double > &p) const |
| | compute the log-likelihood of each vector in x and put the results in p
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double | evaluateSingle (const VectorXd &x) const |
| | compute the log-likelihood of a given vector
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double | evaluateSingle (const vector< double > &x) const |
| | compute the log-likelihood of a given vector
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double | evaluateSingle (double x) const |
| | compute the log-likelihood of a scalar (for one dimensional gaussians only)
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void | sample (VectorXd &x) const |
| | generate a random sample from the gaussian
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void | sample (vector< double > &x) const |
| | generate a random sample from the gaussian
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void | train (const MatrixXd &x, double lambda=0.0) |
| | Estimate the mean and covariance of the gaussian from a matrix of training examples. Data should be arranged row-wise. The parameter lambda can be used to regularize the covariance matrix with an additive isotropic component.
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void | train (const vector< vector< double > > &x, double lambda=0.0) |
| | See above.
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void | train (const vector< double > &x, double lambda=0.0) |
| | See above, but for one dimensional gaussians.
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void | train (const drwnSuffStats &stats, double lambda=0.0) |
| | See above, but using given second-order sufficient statistics.
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unsigned | dimension () const |
| | returns the dimensionality of the gaussian
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const VectorXd & | mean () const |
| | returns the mean of the gaussian
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const MatrixXd & | covariance () const |
| | returns the covariance matrix for the gaussian
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double | logPartitionFunction () const |
| | computes the log partition function of the gaussian
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double | klDivergence (const drwnGaussian &model) const |
| | computes the KL divergence between the gaussian and the given model
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double | klDivergence (const drwnSuffStats &stats) const |
| | computes the KL divergence between the gaussian and distribution induced by the given sufficient statistics
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drwnGaussian & | operator= (const drwnGaussian &model) |
| | assignment operator
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bool | write (const char *filename) const |
| | write object to file (calls save)
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bool | read (const char *filename) |
| | read object from file (calls load)
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void | dump () const |
| | print object's current state to standard output (for debugging)
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Implements a multi-variate gaussian distribution.
![\[ \log p(x; \mu, \Sigma) = -\frac{n}{2} \log(2 \pi) -\frac{1}{2} \log |\Sigma| - \frac{1}{2}\left(x - \mu\right)^T \Sigma^{-1} \left(x - \mu\right) \]](form_31.png)
- See Also
- drwnML Tutorial
Public Member Functions inherited from drwnWriteable
1.8.6