/** * Creates a uniform initial-probability Vector * @param numStates * Number of states to create the Vector for * @return * Uniform probability Vector. */ protected static Vector createUniformInitialProbability( int numStates ) { return VectorFactory.getDefault().createVector(numStates, 1.0/numStates); }
@Override public Vector convertToVector() { return VectorFactory.getDefault().createVector(0); }
/** * Creates a new instance of MultivariateDiscriminantWithBias. * * @param discriminant internal matrix to premultiply input vectors by. */ public MultivariateDiscriminantWithBias( final Matrix discriminant ) { this( discriminant, VectorFactory.getDefault().createVector(discriminant.getNumRows()) ); }
/** * Converts the index to select into a vector (of length 1). * * @return * A 1-dimensional vector containing the index. */ @Override public Vector convertToVector() { return VectorFactory.getDefault().createVector(1, this.getIndex()); }
@Override protected void initializeVectors( final int dimensionality) { super.initializeVectors(dimensionality); this.termEntropiesSum = this.getVectorFactory().createVector( dimensionality); }
@Override protected void initializeVectors( final int dimensionality) { super.initializeVectors(dimensionality); this.termEntropiesSum = this.getVectorFactory().createVector( dimensionality); }
public Vector createDefaultState() { return VectorFactory.getDefault().createVector( this.getStateDimensionality()); }
/** * {@inheritDoc} * * @return {@inheritDoc} */ public Vector createEmptyInputVector() { return VectorFactory.getDefault().createVector(this.getInputDimensionality()); }
/** * Converts the index to select into a vector (of length 1). * * @return * A 1-dimensional vector containing the index. */ @Override public Vector convertToVector() { return VectorFactory.getDefault().createVector(1, this.getIndex()); }
/** * Converts the index to select into a vector (of length 1). * * @return * A 1-dimensional vector containing the index. */ @Override public Vector convertToVector() { return VectorFactory.getDefault().createVector(1, this.getIndex()); }
/** * Creates a new instance of Function * @param dimensionality * Dimensionality of the inputs */ public Function( int dimensionality ) { super( new LinearDiscriminantWithBias( VectorFactory.getDefault().createVector( dimensionality ), 0.0 ), new LogisticDistribution.CDF() ); }
/** * Creates a new instance of MultivariateGaussian. * * @param dimensionality Dimensionality of the Gaussian to create. */ public MultivariateGaussian( int dimensionality) { this(VectorFactory.getDefault().createVector(dimensionality), MatrixFactory.getDefault().createIdentity(dimensionality, dimensionality)); }
/** * Creates a distribution with the given dimensionality. * @param dimensionality * Dimensionality of the distribution. */ public MultivariateStudentTDistribution( int dimensionality ) { this( DEFAULT_DEGREES_OF_FREEDOM, VectorFactory.getDefault().createVector(dimensionality), MatrixFactory.getDefault().createIdentity(dimensionality,dimensionality) ); }
/** * Converts this function into its parameters, which consists of the * threshold value * @return one-element Vector consisting of the threshold value */ public Vector convertToVector() { Vector parameters = VectorFactory.getDefault().createVector(1); parameters.setElement(0, this.getThreshold()); return parameters; }
/** * Creates a new instance of MultivariateGaussian. * * @param dimensionality Dimensionality of the Gaussian to create. */ public MultivariateGaussian( int dimensionality) { this(VectorFactory.getDefault().createVector(dimensionality), MatrixFactory.getDefault().createIdentity(dimensionality, dimensionality)); }
/** * Creates a distribution with the given dimensionality. * @param dimensionality * Dimensionality of the distribution. */ public MultivariateStudentTDistribution( int dimensionality ) { this( DEFAULT_DEGREES_OF_FREEDOM, VectorFactory.getDefault().createVector(dimensionality), MatrixFactory.getDefault().createIdentity(dimensionality,dimensionality) ); }
@Override protected void growVectors( final int newDimensionality) { super.growVectors(newDimensionality); this.termEntropiesSum = this.termEntropiesSum.stack( this.getVectorFactory().createVector( newDimensionality - this.termEntropiesSum.getDimensionality())); }
@Override public Vector convertToVector() { final int dim = this.getInputDimensionality() + 1; Vector p = VectorFactory.getDefault().createVector(dim); for( int i = 0; i < dim-1; i++ ) { p.setElement(i, this.weightVector.getElement(i) ); } p.setElement(dim-1, this.bias); return p; }
/** * Creates a new instance of MultivariateGaussianMeanBayesianEstimator * @param knownCovarianceInverse * Known covariance matrix of the estimated mean. */ public MultivariateGaussianMeanBayesianEstimator( Matrix knownCovarianceInverse ) { this( knownCovarianceInverse, new MultivariateGaussian( VectorFactory.getDefault().createVector(knownCovarianceInverse.getNumRows()), MatrixFactory.getDefault().createIdentity(knownCovarianceInverse.getNumRows(), knownCovarianceInverse.getNumColumns()) ) ); }
/** * Creates a new instance of MultivariateGaussianMeanBayesianEstimator * @param knownCovarianceInverse * Known covariance matrix of the estimated mean. */ public MultivariateGaussianMeanBayesianEstimator( Matrix knownCovarianceInverse ) { this( knownCovarianceInverse, new MultivariateGaussian( VectorFactory.getDefault().createVector(knownCovarianceInverse.getNumRows()), MatrixFactory.getDefault().createIdentity(knownCovarianceInverse.getNumRows(), knownCovarianceInverse.getNumColumns()) ) ); }