/** * @param _init les valeurs initiales */ public CtuluArrayDoubleImmutable(final TDoubleArrayList _init) { this(_init.toNativeArray()); }
/** * Copies a slice of the list into a native array. * * @param offset the offset at which to start copying * @param len the number of values to copy. * @return an <code>double[]</code> value */ public double[] toNativeArray(int offset, int len) { double[] rv = new double[len]; toNativeArray(rv, offset, len); return rv; }
/** * @param _init les valeurs initiales */ public CtuluListDouble(final TDoubleArrayList _init) { this(_init.toNativeArray()); }
/** * @return tableau des x */ public double[] getArrayX() { return t_.toNativeArray(); }
/** * @param _init les valeurs initiales */ public CtuluArrayDouble(final TDoubleArrayList _init) { this(_init.toNativeArray()); }
/** * Copies the contents of the list into a native array. * * @return an <code>double[]</code> value */ public double[] toNativeArray() { return toNativeArray(0, _pos); }
public GISCoordinateSequence(final TDoubleArrayList _l) { super(_l.toNativeArray(), 3); if (_l.size() % 3 != 0) { throw new IllegalArgumentException("size must be a multiple of 3"); } }
private final static double[] decodeArrayDouble(final String _tab) { // System.err.println("appel de decodeArrayDouble"); final StringTokenizer stk = new StringTokenizer(_tab," "); TDoubleArrayList liste = new TDoubleArrayList(_tab.length()/5); while(stk.hasMoreElements()) { liste.add(Double.parseDouble(stk.nextToken())); } return liste.toNativeArray(); }
private final static double[] decodeArrayDouble(final String _tab) { // System.err.println("appel de decodeArrayDouble"); final StringTokenizer stk = new StringTokenizer(_tab," "); TDoubleArrayList liste = new TDoubleArrayList(_tab.length()/5); while(stk.hasMoreElements()) { liste.add(Double.parseDouble(stk.nextToken())); } return liste.toNativeArray(); }
/** * Sheds any excess capacity above and beyond the current size of * the list. */ public void trimToSize() { if (_data.length > size()) { double[] tmp = new double[size()]; toNativeArray(tmp, 0, tmp.length); _data = tmp; } }
protected void initWith(final EvolutionReguliereAbstract _evol) { if (_evol == null) { return; } used_ = _evol.used_; nom_ = _evol.nom_; unite_ = _evol.unite_; xVal_ = _evol.xVal_; yVal_ = _evol.yVal_; listener_ = _evol.listener_; val_.clear(); val_.add(_evol.val_.toNativeArray()); }
public static double[] parseStringDouble(final String _s, final String _sepChar) { if ((_s == null) || (_sepChar == null)) { return null; } final List l = (parseStringList(_s, _sepChar)); final TDoubleArrayList res = new TDoubleArrayList(); for (int i = 0; i < l.size(); i++) { try { res.add(CtuluDoubleParser.parseValue((String) l.get(i))); } catch (final NumberFormatException _evt) { FuLog.error(_evt); } } return res.toNativeArray(); }
void checkMeanStd (TDoubleArrayList ell, double mu, double sigma) { double[] vals = ell.toNativeArray (); double mean1 = MatrixOps.mean (vals); double std1 = MatrixOps.stddev (vals); assertEquals (mu, mean1, 0.025); assertEquals (sigma, std1, 0.01); }
void checkMeanStd (TDoubleArrayList ell, double mu, double sigma) { double[] vals = ell.toNativeArray (); double mean1 = MatrixOps.mean (vals); double std1 = MatrixOps.stddev (vals); assertEquals (mu, mean1, 0.025); assertEquals (sigma, std1, 0.01); }
public void testSample () { Variable var = new Variable (Variable.CONTINUOUS); Randoms r = new Randoms (2343); Factor f = new UniNormalFactor (var, -1.0, 2.0); TDoubleArrayList lst = new TDoubleArrayList (); for (int i = 0; i < 10000; i++) { Assignment assn = f.sample (r); lst.add (assn.getDouble (var)); } double[] vals = lst.toNativeArray (); double mean = MatrixOps.mean (vals); double std = MatrixOps.stddev (vals); assertEquals (-1.0, mean, 0.025); assertEquals (Math.sqrt(2.0), std, 0.01); }
public void testSample2 () { Variable var = new Variable (Variable.CONTINUOUS); Randoms r = new Randoms (2343); Factor f = new BetaFactor (var, 0.7, 0.5, 3.0, 8.0); TDoubleArrayList lst = new TDoubleArrayList (); for (int i = 0; i < 100000; i++) { Assignment assn = f.sample (r); lst.add (assn.getDouble (var)); } double[] vals = lst.toNativeArray (); double mean = MatrixOps.mean (vals); assertEquals (5.92, mean, 0.01); }
public void testSample () { Variable var = new Variable (Variable.CONTINUOUS); Randoms r = new Randoms (2343); Factor f = new UniformFactor (var, -1.0, 1.5); TDoubleArrayList lst = new TDoubleArrayList (); for (int i = 0; i < 10000; i++) { Assignment assn = f.sample (r); lst.add (assn.getDouble (var)); } double[] vals = lst.toNativeArray (); double mean = MatrixOps.mean (vals); assertEquals (0.25, mean, 0.01); }
public void testSample () { Variable var = new Variable (Variable.CONTINUOUS); Randoms r = new Randoms (2343); Factor f = new BetaFactor (var, 0.7, 0.5); TDoubleArrayList lst = new TDoubleArrayList (); for (int i = 0; i < 100000; i++) { Assignment assn = f.sample (r); lst.add (assn.getDouble (var)); } double[] vals = lst.toNativeArray (); double mean = MatrixOps.mean (vals); assertEquals (0.7 / (0.5 + 0.7), mean, 0.01); }
public void testSample () { Variable var = new Variable (Variable.CONTINUOUS); Randoms r = new Randoms (2343); Factor f = new BetaFactor (var, 0.7, 0.5); TDoubleArrayList lst = new TDoubleArrayList (); for (int i = 0; i < 100000; i++) { Assignment assn = f.sample (r); lst.add (assn.getDouble (var)); } double[] vals = lst.toNativeArray (); double mean = MatrixOps.mean (vals); assertEquals (0.7 / (0.5 + 0.7), mean, 0.01); }
public void testSample2 () { Variable var = new Variable (Variable.CONTINUOUS); Randoms r = new Randoms (2343); Factor f = new BetaFactor (var, 0.7, 0.5, 3.0, 8.0); TDoubleArrayList lst = new TDoubleArrayList (); for (int i = 0; i < 100000; i++) { Assignment assn = f.sample (r); lst.add (assn.getDouble (var)); } double[] vals = lst.toNativeArray (); double mean = MatrixOps.mean (vals); assertEquals (5.92, mean, 0.01); }