Package weka.core.matrix

Examples of weka.core.matrix.DoubleVector

309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
    X.lsqrSelection( Y, pvt, 1 );
    X.positiveDiagonal( Y, pvt );
   
    PaceMatrix sol = (PaceMatrix) Y.clone();
    X.rsolve( sol, pvt, pvt.size() );
    DoubleVector r = Y.getColumn( pvt.size(), n-1, 0);
    double sde = Math.sqrt(r.sum2() / r.size());
   
    DoubleVector aHat = Y.getColumn( 0, pvt.size()-1, 0).times( 1./sde );

    DoubleVector aTilde = null;
    switch( paceEstimator) {
    case ebEstimator:
    case nestedEstimator:
    case subsetEstimator:
      NormalMixture d = new NormalMixture();
      d.fit( aHat, MixtureDistribution.NNMMethod );
      if( paceEstimator == ebEstimator )
  aTilde = d.empiricalBayesEstimate( aHat );
      else if( paceEstimator == ebEstimator )
  aTilde = d.subsetEstimate( aHat );
      else aTilde = d.nestedEstimate( aHat );
      break;
    case pace2Estimator:
    case pace4Estimator:
    case pace6Estimator:
      DoubleVector AHat = aHat.square();
      ChisqMixture dc = new ChisqMixture();
      dc.fit( AHat, MixtureDistribution.NNMMethod );
      DoubleVector ATilde;
      if( paceEstimator == pace6Estimator )
  ATilde = dc.pace6( AHat );
      else if( paceEstimator == pace2Estimator )
  ATilde = dc.pace2( AHat );
      else ATilde = dc.pace4( AHat );
      aTilde = ATilde.sqrt().times( aHat.sign() );
      break;
    case olsEstimator:
      aTilde = aHat.copy();
      break;
    case aicEstimator:
    case bicEstimator:
    case ricEstimator:
    case olscEstimator:
      if(paceEstimator == aicEstimator) olscThreshold = 2;
      else if(paceEstimator == bicEstimator) olscThreshold = Math.log( n );
      else if(paceEstimator == ricEstimator) olscThreshold = 2*Math.log( kr );
      aTilde = aHat.copy();
      for( int i = 0; i < aTilde.size(); i++ )
  if( Math.abs(aTilde.get(i)) < Math.sqrt(olscThreshold) )
    aTilde.set(i, 0);
    }
    PaceMatrix YTilde = new PaceMatrix((new PaceMatrix(aTilde)).times( sde ));
    X.rsolve( YTilde, pvt, pvt.size() );
    DoubleVector betaTilde = YTilde.getColumn(0).unpivoting( pvt, kr );
   
    return betaTilde.getArrayCopy();
  }
View Full Code Here
386
387
388
389
390
391
392
393
394
395
    //make a copy of the array
    double []disCopy = new double[distance.length];
    for (int i=0;i<distance.length; i++)
      disCopy[i]= distance[i];

    DoubleVector sortVector = new DoubleVector(disCopy);
    sortVector.sort();
    sorted = sortVector.getArrayCopy();
    return sorted;
  }
View Full Code Here
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
    X.lsqrSelection( Y, pvt, 1 );
    X.positiveDiagonal( Y, pvt );
   
    PaceMatrix sol = (PaceMatrix) Y.clone();
    X.rsolve( sol, pvt, pvt.size() );
    DoubleVector r = Y.getColumn( pvt.size(), n-1, 0);
    double sde = Math.sqrt(r.sum2() / r.size());
   
    DoubleVector aHat = Y.getColumn( 0, pvt.size()-1, 0).times( 1./sde );

    DoubleVector aTilde = null;
    switch( paceEstimator) {
    case ebEstimator:
    case nestedEstimator:
    case subsetEstimator:
      NormalMixture d = new NormalMixture();
      d.fit( aHat, MixtureDistribution.NNMMethod );
      if( paceEstimator == ebEstimator )
  aTilde = d.empiricalBayesEstimate( aHat );
      else if( paceEstimator == ebEstimator )
  aTilde = d.subsetEstimate( aHat );
      else aTilde = d.nestedEstimate( aHat );
      break;
    case pace2Estimator:
    case pace4Estimator:
    case pace6Estimator:
      DoubleVector AHat = aHat.square();
      ChisqMixture dc = new ChisqMixture();
      dc.fit( AHat, MixtureDistribution.NNMMethod );
      DoubleVector ATilde;
      if( paceEstimator == pace6Estimator )
  ATilde = dc.pace6( AHat );
      else if( paceEstimator == pace2Estimator )
  ATilde = dc.pace2( AHat );
      else ATilde = dc.pace4( AHat );
      aTilde = ATilde.sqrt().times( aHat.sign() );
      break;
    case olsEstimator:
      aTilde = aHat.copy();
      break;
    case aicEstimator:
    case bicEstimator:
    case ricEstimator:
    case olscEstimator:
      if(paceEstimator == aicEstimator) olscThreshold = 2;
      else if(paceEstimator == bicEstimator) olscThreshold = Math.log( n );
      else if(paceEstimator == ricEstimator) olscThreshold = 2*Math.log( kr );
      aTilde = aHat.copy();
      for( int i = 0; i < aTilde.size(); i++ )
  if( Math.abs(aTilde.get(i)) < Math.sqrt(olscThreshold) )
    aTilde.set(i, 0);
    }
    PaceMatrix YTilde = new PaceMatrix((new PaceMatrix(aTilde)).times( sde ));
    X.rsolve( YTilde, pvt, pvt.size() );
    DoubleVector betaTilde = YTilde.getColumn(0).unpivoting( pvt, kr );
   
    return betaTilde.getArrayCopy();
  }
View Full Code Here
289
290
291
292
293
294
295
296
297
298
299
300
  public static void main( String args[] )
  {
 
    double points[] = {2,1,2,3,3};
    double values[] = {3,2,4,1,3};
    DiscreteFunction d = new DiscreteFunction( new DoubleVector( points ),
                 new DoubleVector( values ));
    System.out.println( d );
    d.normalize();
    System.out.println( "d (after normalize) = \n" + d );
    points[1] = 10;
    System.out.println( "d (after setting [1]) = \n" + d);
View Full Code Here
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
   * 
   *  @param data the data supposedly generated from the mixture
   *  @return the set of fitting intervals
   */
  public PaceMatrix  fittingIntervals( DoubleVector data ) {
    DoubleVector left = data.cat( data.minus( fittingIntervalLength ) );
    DoubleVector right = data.plus( fittingIntervalLength ).cat( data );
 
    PaceMatrix a = new PaceMatrix(left.size(), 2);
 
    a.setMatrix(0, left.size()-1, 0, left);
    a.setMatrix(0, right.size()-1, 1, right);
 
    return a;
  }
View Full Code Here
202
203
204
205
206
207
208
209
210
211
212
213
214
   * @param x the value
   * @return the empirical Bayes estimate
   */
  public double  empiricalBayesEstimate ( double x ) {
    if( Math.abs(x) > 10 ) return x; // pratical consideration; modify later
    DoubleVector d =
    Maths.dnormLog( x, mixingDistribution.getPointValues(), 1 );
   
    d.minusEquals( d.max() );
    d = d.map("java.lang.Math", "exp");
    d.timesEquals( mixingDistribution.getFunctionValues() );
    return mixingDistribution.getPointValues().innerProduct( d ) / d.sum();
  }
View Full Code Here
218
219
220
221
222
223
224
225
226
227
228
   *
   * @param x the vector
   * @return the empirical Bayes estimate
   */
  public DoubleVector empiricalBayesEstimate( DoubleVector x ) {
    DoubleVector pred = new DoubleVector( x.size() );
    for(int i = 0; i < x.size(); i++ )
      pred.set(i, empiricalBayesEstimate(x.get(i)) );
    trim( pred );
    return pred;
  }
View Full Code Here
233
234
235
236
237
238
239
240
241
242
243
244
245
246
   * @param x the vector
   * @return the optimal nested model estimate
   */
  public DoubleVector  nestedEstimate( DoubleVector x ) {
   
    DoubleVector chf = new DoubleVector( x.size() );
    for(int i = 0; i < x.size(); i++ ) chf.set( i, hf( x.get(i) ) );
    chf.cumulateInPlace();
    int index = chf.indexOfMax();
    DoubleVector copy = x.copy();
    if( index < x.size()-1 ) copy.set( index + 1, x.size()-1, 0 );
    trim( copy );
    return copy;
  }
View Full Code Here
251
252
253
254
255
256
257
258
259
260
261
262
   * @param x the vector
   * @return the estimate of optimal subset selection
   */
  public DoubleVector  subsetEstimate( DoubleVector x ) {

    DoubleVector h = h( x );
    DoubleVector copy = x.copy();
    for( int i = 0; i < x.size(); i++ )
      if( h.get(i) <= 0 ) copy.set(i, 0);
    trim( copy );
    return copy;
  }
View Full Code Here
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
   * 
   *  @param x the value
   *  @return the value of h(x) / f(x)
   */
  public double hf( double x ) {
    DoubleVector points = mixingDistribution.getPointValues();
    DoubleVector values = mixingDistribution.getFunctionValues();

    DoubleVector d = Maths.dnormLog( x, points, 1 );
    d.minusEquals( d.max() );

    d = (DoubleVector) d.map("java.lang.Math", "exp");
    d.timesEquals( values ); 

    return ((DoubleVector) points.times(2*x).minusEquals(x*x))
    .innerProduct( d ) / d.sum();
  }
View Full Code Here
TOP

Related Classes of weka.core.matrix.DoubleVector

  • weka.classifiers.functions.pace.ChisqMixture
  • weka.classifiers.functions.pace.DiscreteFunction
  • weka.classifiers.functions.pace.MixtureDistribution
  • weka.classifiers.functions.pace.NormalMixture
  • weka.classifiers.functions.pace.PaceMatrix
  • weka.classifiers.functions.PaceRegression
  • weka.classifiers.mi.MIOptimalBall
  • java.lang.reflect.Method
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.