Learn the distance metric base class with sparse set of constraints (i.e., loss function over further target and nearest imposter only).
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| drwnNNGraphSparseLearner (const drwnNNGraph &graph, double lambda) |
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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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virtual double | computeLossFunction () const |
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virtual double | computeObjective () 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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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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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 sparse set of constraints (i.e., loss function over further target and nearest imposter only).
The documentation for this class was generated from the following files: