Learn the distance metric base class with full set of constraints (i.e., loss function over all targets and imposters).
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| drwnNNGraphLearner (const drwnNNGraph &graph, double lambda) |
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virtual void | setTransform (const MatrixXd &Lt)=0 |
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virtual MatrixXd | getTransform () const =0 |
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virtual double | learn (unsigned maxCycles) |
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const drwnNNGraph & | getSrcGraph () const |
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const drwnNNGraph & | getPosGraph () const |
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const drwnNNGraph & | getNegGraph () const |
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void | clearLabelWeights () |
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void | setLabelWeights (const vector< double > &w) |
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const vector< double > & | getLabelWeights () const |
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static double | ALPHA_ZERO = 5.0e-6 |
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static unsigned | METRIC_ITERATIONS = 500 |
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static unsigned | SEARCH_ITERATIONS = 20 |
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virtual double | computeObjective () const |
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virtual double | computeLossFunction () const |
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virtual MatrixXd | computeSubGradient ()=0 |
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virtual void | subGradientStep (const MatrixXd &G, double alpha)=0 |
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virtual void | startMetricCycle () |
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virtual void | endMetricCycle () |
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void | updateGraphFeatures () |
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void | nearestNeighbourUpdate (unsigned nCycle, unsigned maxIterations) |
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MatrixXd | initializeTransform () const |
| | initialize transform as feature whitener (diagonal Mahalanobis)
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const drwnNNGraph & | _graph |
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double | _lambda |
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vector< double > | _labelWeights |
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drwnNNGraph | _posGraph |
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drwnNNGraph | _negGraph |
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unsigned | _dim |
| | feature vector dimensions
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MatrixXd | _X |
| | cached (x_u - x_v) (x_u - x_v)^T
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Learn the distance metric base class with full set of constraints (i.e., loss function over all targets and imposters).
The documentation for this class was generated from the following files:
1.8.6