Package org.apache.mahout.math

Examples of org.apache.mahout.math.SparseRowMatrix

212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
    File outputDir = getTestTempDir("output");
    outputDir.delete();
    File tmpDir = getTestTempDir("tmp");

    Double na = Double.NaN;
    Matrix preferences = new SparseRowMatrix(new int[] { 4, 4 }, new Vector[] {
        new DenseVector(new double[] {5.0, 5.0, 2.0,  na }),
        new DenseVector(new double[] {2.0,  na, 3.0, 5.0 }),
        new DenseVector(new double[] { na, 5.0,  na, 3.0 }),
        new DenseVector(new double[] {3.0,  na,  na, 5.0 }) });

    StringBuilder prefsAsText = new StringBuilder();
    String separator = "";
    Iterator<MatrixSlice> sliceIterator = preferences.iterateAll();
    while (sliceIterator.hasNext()) {
      MatrixSlice slice = sliceIterator.next();
      Iterator<Vector.Element> elementIterator = slice.vector().iterateNonZero();
      while (elementIterator.hasNext()) {
        Vector.Element e = elementIterator.next();
        if (!Double.isNaN(e.get())) {
          prefsAsText.append(separator).append(slice.index()).append(',').append(e.index()).append(',').append(e.get());
          separator = "\n";
        }
      }
    }
    logger.info("Input matrix:\n" + prefsAsText);
    writeLines(inputFile, prefsAsText.toString());

    ParallelALSFactorizationJob alsFactorization = new ParallelALSFactorizationJob();

    Configuration conf = new Configuration();
    conf.set("mapred.input.dir", inputFile.getAbsolutePath());
    conf.set("mapred.output.dir", outputDir.getAbsolutePath());
    conf.setBoolean("mapred.output.compress", false);

    alsFactorization.setConf(conf);
    int numFeatures = 3;
    int numIterations = 5;
    double lambda = 0.065;
    alsFactorization.run(new String[] { "--tempDir", tmpDir.getAbsolutePath(), "--lambda", String.valueOf(lambda),
        "--numFeatures", String.valueOf(numFeatures), "--numIterations", String.valueOf(numIterations) });

    Matrix u = MathHelper.readEntries(conf, new Path(outputDir.getAbsolutePath(), "U/part-r-00000"),
        preferences.numRows(), numFeatures);
    Matrix m = MathHelper.readEntries(conf, new Path(outputDir.getAbsolutePath(), "M/part-r-00000"),
      preferences.numCols(), numFeatures);

    RunningAverage avg = new FullRunningAverage();
    sliceIterator = preferences.iterateAll();
    while (sliceIterator.hasNext()) {
      MatrixSlice slice = sliceIterator.next();
      Iterator<Vector.Element> elementIterator = slice.vector().iterateNonZero();
      while (elementIterator.hasNext()) {
        Vector.Element e = elementIterator.next();
View Full Code Here
257
258
259
260
261
262
263
264
265
266
267
    if (inMemory) {
      List<Vector> eigenVectors = new ArrayList<Vector>();
      for (MatrixSlice slice : eigens) {
        eigenVectors.add(slice.vector());
      }
      eigensToVerify = new SparseRowMatrix(new int[] {eigenVectors.size(), eigenVectors.get(0).size()},
                                           eigenVectors.toArray(new Vector[eigenVectors.size()]),
                                           true,
                                           true);

    } else {
View Full Code Here
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
  public static Matrix randomSequentialAccessSparseMatrix(int numRows,
                                                          int nonNullRows,
                                                          int numCols,
                                                          int entriesPerRow,
                                                          double entryMean) {
    SparseRowMatrix m = new SparseRowMatrix(new int[]{numRows, numCols});
    //double n = 0;
    //Random r = RandomUtils.getRandom();
    Random r = new Random(1234L);
    for (int i = 0; i < nonNullRows; i++) {
      SequentialAccessSparseVector v = new SequentialAccessSparseVector(numCols);
      for (int j = 0; j < entriesPerRow; j++) {
        int col = r.nextInt(numCols);
        double val = r.nextGaussian();
        v.set(col, val * entryMean);
      }
      int c = r.nextInt(numRows);
      if (r.nextBoolean() || numRows == nonNullRows) {
        m.assignRow(numRows == nonNullRows ? i : c, v);
      } else {
        Vector other = m.getRow(r.nextInt(numRows));
        if (other != null && other.getLengthSquared() > 0) {
          m.assignRow(c, other.clone());
        }
      }
      //n += m.getRow(c).getLengthSquared();
    }
    return m;
View Full Code Here
282
283
284
285
286
287
288
289
290
291
292
    if (inMemory) {
      List<Vector> eigenVectors = Lists.newArrayList();
      for (MatrixSlice slice : eigens) {
        eigenVectors.add(slice.vector());
      }
      eigensToVerify = new SparseRowMatrix(eigenVectors.size(), eigenVectors.get(0).size(),
          eigenVectors.toArray(new Vector[eigenVectors.size()]), true, true);

    } else {
      eigensToVerify = eigens;
    }
View Full Code Here
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
  }

  @Test
  public void toyExampleImplicit() throws Exception {

    Matrix observations = new SparseRowMatrix(4, 4, new Vector[] {
        new DenseVector(new double[] { 5.0, 5.0, 2.0, 0 }),
        new DenseVector(new double[] { 2.0, 0,   3.0, 5.0 }),
        new DenseVector(new double[] { 0,   5.0, 0,   3.0 }),
        new DenseVector(new double[] { 3.0, 0,   0,   5.0 }) });

    Matrix preferences = new SparseRowMatrix(4, 4, new Vector[] {
        new DenseVector(new double[] { 1.0, 1.0, 1.0, 0 }),
        new DenseVector(new double[] { 1.0, 0,   1.0, 1.0 }),
        new DenseVector(new double[] { 0,   1.0, 0,   1.0 }),
        new DenseVector(new double[] { 1.0, 0,   0,   1.0 }) });

    double alpha = 20;

    ALSWRFactorizer factorizer = new ALSWRFactorizer(dataModel, 3, 0.065, 5, true, alpha);

    SVDRecommender svdRecommender = new SVDRecommender(dataModel, factorizer);

    RunningAverage avg = new FullRunningAverage();
    Iterator<MatrixSlice> sliceIterator = preferences.iterateAll();
    while (sliceIterator.hasNext()) {
      MatrixSlice slice = sliceIterator.next();
      for (Vector.Element e : slice.vector().all()) {

        long userID = slice.index() + 1;
View Full Code Here
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
   * </pre>
   */
  private void explicitExample(int numThreads) throws Exception {

    Double na = Double.NaN;
    Matrix preferences = new SparseRowMatrix(4, 4, new Vector[] {
        new DenseVector(new double[] { 5.0, 5.0, 2.0, na }),
        new DenseVector(new double[] { 2.0, na,  3.0, 5.0 }),
        new DenseVector(new double[] { na,  5.0, na,  3.0 }),
        new DenseVector(new double[] { 3.0, na,  na,  5.0 }) });

    writeLines(inputFile, preferencesAsText(preferences));

    ParallelALSFactorizationJob alsFactorization = new ParallelALSFactorizationJob();
    alsFactorization.setConf(conf);

    int numFeatures = 3;
    int numIterations = 5;
    double lambda = 0.065;

    alsFactorization.run(new String[] { "--input", inputFile.getAbsolutePath(), "--output", outputDir.getAbsolutePath(),
        "--tempDir", tmpDir.getAbsolutePath(), "--lambda", String.valueOf(lambda),
        "--numFeatures", String.valueOf(numFeatures), "--numIterations", String.valueOf(numIterations),
        "--numThreadsPerSolver", String.valueOf(numThreads) });

    Matrix u = MathHelper.readMatrix(conf, new Path(outputDir.getAbsolutePath(), "U/part-m-00000"),
        preferences.numRows(), numFeatures);
    Matrix m = MathHelper.readMatrix(conf, new Path(outputDir.getAbsolutePath(), "M/part-m-00000"),
        preferences.numCols(), numFeatures);

    StringBuilder info = new StringBuilder();
    info.append("\nA - users x items\n\n");
    info.append(MathHelper.nice(preferences));
    info.append("\nU - users x features\n\n");
View Full Code Here
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
  public void completeJobImplicitToyExampleMultithreaded() throws Exception {
    implicitExample(2);
  }

  public void implicitExample(int numThreads) throws Exception {
    Matrix observations = new SparseRowMatrix(4, 4, new Vector[] {
        new DenseVector(new double[] { 5.0, 5.0, 2.0, 0 }),
        new DenseVector(new double[] { 2.0, 0,   3.0, 5.0 }),
        new DenseVector(new double[] { 0,   5.0, 0,   3.0 }),
        new DenseVector(new double[] { 3.0, 0,   0,   5.0 }) });

    Matrix preferences = new SparseRowMatrix(4, 4, new Vector[] {
        new DenseVector(new double[] { 1.0, 1.0, 1.0, 0 }),
        new DenseVector(new double[] { 1.0, 0,   1.0, 1.0 }),
        new DenseVector(new double[] { 0,   1.0, 0,   1.0 }),
        new DenseVector(new double[] { 1.0, 0,   0,   1.0 }) });
View Full Code Here
263
264
265
266
267
268
269
270
271
272
273
    if (inMemory) {
      List<Vector> eigenVectors = Lists.newArrayList();
      for (MatrixSlice slice : eigens) {
        eigenVectors.add(slice.vector());
      }
      eigensToVerify = new SparseRowMatrix(eigenVectors.size(), eigenVectors.get(0).size(),
                                           eigenVectors.toArray(new Vector[eigenVectors.size()]),
                                           true,
                                           true);

    } else {
View Full Code Here
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
    }
  }

  public static Matrix sampledCorpus(Matrix matrix, Random random,
      int numDocs, int numSamples, int numTopicsPerDoc) {
    Matrix corpus = new SparseRowMatrix(numDocs, matrix.numCols());
    LDASampler modelSampler = new LDASampler(matrix, random);
    Vector topicVector = new DenseVector(matrix.numRows());
    for (int i = 0; i < numTopicsPerDoc; i++) {
      int topic = random.nextInt(topicVector.size());
      topicVector.set(topic, topicVector.get(topic) + 1);
    }
    for (int docId = 0; docId < numDocs; docId++) {
      for (int sample : modelSampler.sample(topicVector, numSamples)) {
        corpus.set(docId, sample, corpus.get(docId, sample) + 1);
      }
    }
    return corpus;
  }
View Full Code Here
185
186
187
188
189
190
191
192
193
194
195
    while (true) {
      try {
        List<TrainerRunnable> runnables = Lists.newArrayList();
        for (Map.Entry<Vector, Vector> entry : batch.entrySet()) {
          runnables.add(new TrainerRunnable(readModel, null, entry.getKey(),
              entry.getValue(), new SparseRowMatrix(numTopics, numTerms, true),
              numDocTopicsIters));
        }
        threadPool.invokeAll(runnables);
        if (update) {
          for (TrainerRunnable runnable : runnables) {
View Full Code Here
TOP

Related Classes of org.apache.mahout.math.SparseRowMatrix

  • org.apache.mahout.cf.taste.hadoop.als.ParallelALSFactorizationJobTest
  • org.apache.mahout.cf.taste.impl.recommender.svd.ALSWRFactorizerTest
  • org.apache.mahout.classifier.naivebayes.NaiveBayesModel
  • org.apache.mahout.clustering.ClusteringTestUtils
  • org.apache.mahout.clustering.lda.cvb.CVB0DocInferenceMapper
  • org.apache.mahout.clustering.lda.cvb.InMemoryCollapsedVariationalBayes0
  • org.apache.mahout.clustering.lda.cvb.ModelTrainer
  • org.apache.mahout.math.decomposer.lanczos.LanczosSolver
  • org.apache.mahout.math.decomposer.SolverTest
  • org.apache.mahout.math.hadoop.decomposer.EigenVerificationJob
    • 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.