@Override public Vector getMean() { return this.parameters.scale(1.0 / this.parameters.norm1()); }
@Override public Vector getMean() { return this.parameters.scale(1.0 / this.parameters.norm1()); }
@Override public Vector getMean() { return this.parameters.scale( this.numTrials/this.parameters.norm1() ); }
@Override public Vector getMean() { return this.parameters.scale( this.numTrials / this.parameters.norm1() ); }
@Override public Vector getMean() { return this.parameters.scale( this.parameters.norm1() ); }
public Vector evaluate( Vector input) { return input.scale( this.getScaleFactor() ); }
@Override public Vector getMean() { return this.parameters.scale( this.numTrials/this.parameters.norm1() ); }
public Vector evaluate( Vector input) { return input.scale( this.getScaleFactor() ); }
@Override public Vector getMean() { return this.parameters.scale( this.numTrials/this.parameters.norm1() ); }
@Override public Vector getMean() { return this.parameters.scale( this.numTrials / this.parameters.norm1() ); }
@Override public Vector getMean() { return this.parameters.scale( this.parameters.norm1() ); }
@Override public Vector getMean() { return this.parameters.scale( this.numTrials / this.parameters.norm1() ); }
@Override public Vector getMean() { return this.parameters.scale( this.parameters.norm1() ); }
public Vector evaluate( Vector input) { return input.scale( this.getScaleFactor() ); }
@Override public Vector getMean() { return this.parameters.scale(1.0 / this.parameters.norm1()); }
@Override public Vector getMean() { RingAccumulator<Vector> mean = new RingAccumulator<Vector>(); final int K = this.getDistributionCount(); for( int k = 0; k < K; k++ ) { mean.accumulate( this.getDistributions().get(k).getMean().scale( this.getPriorWeights()[k] ) ); } return mean.getSum().scale( 1.0 / this.getPriorWeightSum() ); }
@Override protected boolean initializeAlgorithm() { this.result = new DefaultInputOutputPair<Vector, Double>( this.initialGuess, this.data.evaluate( this.initialGuess ) ); this.gradient = this.data.differentiate( this.initialGuess ); this.lineFunction = new DirectionalVectorToDifferentiableScalarFunction( this.data, this.initialGuess, this.gradient.scale(-1.0) ); return true; }
@Override protected boolean initializeAlgorithm() { this.result = new DefaultInputOutputPair<Vector, Double>( this.initialGuess, this.data.evaluate( this.initialGuess ) ); this.gradient = this.data.differentiate( this.initialGuess ); this.lineFunction = new DirectionalVectorToDifferentiableScalarFunction( this.data, this.initialGuess, this.gradient.scale(-1.0) ); return true; }
@Override protected boolean initializeAlgorithm() { this.result = new DefaultInputOutputPair<Vector, Double>( this.initialGuess, this.data.evaluate( this.initialGuess ) ); this.gradient = this.data.differentiate( this.initialGuess ); this.lineFunction = new DirectionalVectorToDifferentiableScalarFunction( this.data, this.initialGuess, this.gradient.scale(-1.0) ); return true; }
@Override public Matrix init(int rows, int cols) { Matrix currentValues = getCurrentValues(); Vector mean = currentValues.sumOfRows().scale(1f/currentValues.getNumRows()); Matrix m = DenseMatrixFactoryMTJ.INSTANCE.createMatrix(rows, cols); for (int r = 0; r < m.getNumRows(); r++) { m.setRow(r, mean); } return m; }