Package org.apache.mahout.math.hadoop.decomposer

Examples of org.apache.mahout.math.hadoop.decomposer.DistributedLanczosSolver.run()

270
271
272
273
274
275
276
277
278
279
280
    DistributedLanczosSolver solver = new DistributedLanczosSolver();
    Configuration conf = new Configuration();
    solver.setConf(conf);
    Path testData = getTestTempDirPath("testdata");
    int sampleDimension = sampleData.get(0).get().size();
    solver.run(testData, output, tmp, sampleData.size(), sampleDimension, false, desiredRank, 0.5, 0.0, true);
    Path cleanEigenvectors = new Path(output, EigenVerificationJob.CLEAN_EIGENVECTORS);

    // build in-memory data matrix A
    Matrix a = new DenseMatrix(sampleData.size(), sampleDimension);
    int i = 0;
View Full Code Here
336
337
338
339
340
341
342
343
344
345
346
    Configuration config = new Configuration();
    solver.setConf(config);
    Path testData = getTestTempDirPath("testdata");
    int sampleDimension = sampleData.get(0).get().size();
    // Run EigenVerificationJob from within DistributedLanczosSolver.run(...)
    solver.run(testData, output, tmp, sampleData.size(), sampleDimension, false, desiredRank, 0.5, 0.0, false);
    Path cleanEigenvectors = new Path(output, EigenVerificationJob.CLEAN_EIGENVECTORS);

    // now multiply the testdata matrix and the eigenvector matrix
    DistributedRowMatrix svdT = new DistributedRowMatrix(cleanEigenvectors, tmp, desiredRank - 1, sampleDimension);
    JobConf conf = new JobConf(config);
View Full Code Here
369
370
371
372
373
374
375
376
377
378
379
    Configuration config = new Configuration();
    solver.setConf(config);
    Path testData = getTestTempDirPath("testdata");
    int sampleDimension = sampleData.get(0).get().size();
    // call EigenVerificationJob separately
    solver.run(testData, output, tmp, sampleData.size(), sampleDimension, false, desiredRank);
    Path rawEigenvectors = new Path(output, DistributedLanczosSolver.RAW_EIGENVECTORS);
    JobConf conf = new JobConf(config);
    new EigenVerificationJob().run(testData, rawEigenvectors, output, tmp, 0.5, 0.0, true, conf);
    Path cleanEigenvectors = new Path(output, EigenVerificationJob.CLEAN_EIGENVECTORS);
View Full Code Here
290
291
292
293
294
295
296
297
298
299
300
    Configuration conf = new Configuration();
    solver.setConf(conf);
    Path testData = getTestTempDirPath("testdata");
    int sampleDimension = sampleData.get(0).get().size();
    int desiredRank = 15;
    solver.run(testData, output, tmp, null, sampleData.size(), sampleDimension,
        false, desiredRank, 0.5, 0.0, true);
    Path cleanEigenvectors = new Path(output,
        EigenVerificationJob.CLEAN_EIGENVECTORS);
   
    // build in-memory data matrix A
View Full Code Here
354
355
356
357
358
359
360
361
362
363
364
    solver.setConf(config);
    Path testData = getTestTempDirPath("testdata");
    int sampleDimension = sampleData.get(0).get().size();
    // Run EigenVerificationJob from within DistributedLanczosSolver.run(...)
    int desiredRank = 13;
    solver.run(testData, output, tmp, null, sampleData.size(), sampleDimension,
        false, desiredRank, 0.5, 0.0, false);
   
    Path cleanEigenvectors = new Path(output,
        EigenVerificationJob.CLEAN_EIGENVECTORS);
   
View Full Code Here
395
396
397
398
399
400
401
402
403
404
405
    solver.setConf(config);
    Path testData = getTestTempDirPath("testdata");
    int sampleDimension = sampleData.get(0).get().size();
    // call EigenVerificationJob separately
    int desiredRank = 13;
    solver.run(testData, output, tmp, null, sampleData.size(), sampleDimension,
        false, desiredRank);
    Path rawEigenvectors = new Path(output,
        DistributedLanczosSolver.RAW_EIGENVECTORS);
    Configuration conf = new Configuration(config);
    new EigenVerificationJob().run(testData, rawEigenvectors, output, tmp, 0.5,
View Full Code Here
278
279
280
281
282
283
284
285
286
287
288
    Configuration conf = new Configuration();
    solver.setConf(conf);
    Path testData = getTestTempDirPath("testdata");
    int sampleDimension = sampleData.get(0).get().size();
    int desiredRank = 15;
    solver.run(testData, output, tmp, null, sampleData.size(), sampleDimension, false, desiredRank,
        0.5, 0.0, true);
    Path cleanEigenvectors = new Path(output, EigenVerificationJob.CLEAN_EIGENVECTORS);

    // build in-memory data matrix A
    Matrix a = new DenseMatrix(sampleData.size(), sampleDimension);
View Full Code Here
338
339
340
341
342
343
344
345
346
347
348
    solver.setConf(config);
    Path testData = getTestTempDirPath("testdata");
    int sampleDimension = sampleData.get(0).get().size();
    // Run EigenVerificationJob from within DistributedLanczosSolver.run(...)
    int desiredRank = 13;
    solver.run(testData, output, tmp, null, sampleData.size(), sampleDimension,
        false, desiredRank, 0.5, 0.0, false);

    Path cleanEigenvectors = new Path(output, EigenVerificationJob.CLEAN_EIGENVECTORS);

    // now multiply the testdata matrix and the eigenvector matrix
View Full Code Here
375
376
377
378
379
380
381
382
383
384
385
    solver.setConf(config);
    Path testData = getTestTempDirPath("testdata");
    int sampleDimension = sampleData.get(0).get().size();
    // call EigenVerificationJob separately
    int desiredRank = 13;
    solver.run(testData, output, tmp, null, sampleData.size(), sampleDimension, false, desiredRank);
    Path rawEigenvectors = new Path(output, DistributedLanczosSolver.RAW_EIGENVECTORS);
    Configuration conf = new Configuration(config);
    new EigenVerificationJob().run(testData, rawEigenvectors, output, tmp, 0.5, 0.0, true, conf);
    Path cleanEigenvectors = new Path(output, EigenVerificationJob.CLEAN_EIGENVECTORS);
View Full Code Here
292
293
294
295
296
297
298
299
300
301
302
    Configuration conf = new Configuration();
    solver.setConf(conf);
    Path testData = getTestTempDirPath("testdata");
    int sampleDimension = sampleData.get(0).get().size();
    int desiredRank = 15;
    solver.run(testData, output, tmp, null, sampleData.size(), sampleDimension,
        false, desiredRank, 0.5, 0.0, true);
    Path cleanEigenvectors = new Path(output,
        EigenVerificationJob.CLEAN_EIGENVECTORS);
   
    // build in-memory data matrix A
View Full Code Here
TOP
Copyright © 2018 www.massapi.com. 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.