Package org.apache.mahout.math

Examples of org.apache.mahout.math.DenseMatrix

274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
    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;
    for (VectorWritable vw : sampleData) {
      a.assignRow(i++, vw.get());
    }
    // extract the eigenvectors into P
    Matrix p = new DenseMatrix(39, desiredRank - 1);
    FileSystem fs = FileSystem.get(cleanEigenvectors.toUri(), conf);
    SequenceFile.Reader reader = new SequenceFile.Reader(fs, cleanEigenvectors, conf);
    try {
      Writable key = reader.getKeyClass().asSubclass(Writable.class).newInstance();
      Writable value = reader.getValueClass().asSubclass(Writable.class).newInstance();
      i = 0;
      while (reader.next(key, value)) {
        Vector v = ((VectorWritable) value).get();
        p.assignColumn(i, v);
        System.out.println("k=" + key.toString() + " V=" + AbstractCluster.formatVector(v, termDictionary));
        value = reader.getValueClass().asSubclass(Writable.class).newInstance();
        i++;
      }
    } finally {
View Full Code Here
40
41
42
43
44
45
46
47
48
    this.corpus = corpus;
    this.desiredRank = desiredRank;
    intitializeBasisAndSingularVectors();
    setBasisVector(0, initialVector);
    scaleFactor = 0;
    diagonalMatrix = new DenseMatrix(desiredRank, desiredRank);
    singularValues = Maps.newHashMap();
    iterationNumber = 1;
  }
View Full Code Here
153
154
155
156
157
158
159
160
161
162
    return digammaGamma;
  }
 
  private void createPhiMatrix(int docLength) {
    if (phi == null || phi.rowSize() != docLength) {
      phi = new DenseMatrix(state.getNumTopics(), docLength);
    } else {
      phi.assign(0);
    }
  }
View Full Code Here
99
100
101
102
103
104
105
106
107
108
109
110
111
112
    }

  }

  static Matrix generateDenseOrthonormalRandom(int m, int n, Random rnd) {
    Matrix result = new DenseMatrix(m, n);
    for (int j = 0; j < n; j++) {
      for (int i = 0; i < m; i++) {
        result.setQuick(i, j, rnd.nextDouble() - 0.5);
      }
    }
    GramSchmidt.orthonormalizeColumns(result);
    SSVDPrototypeTest.assertOrthonormality(result, false, 1.0e-10);
    return result;
View Full Code Here
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
    double[][] a = SSVDSolver.loadDistributedRowMatrix(fs, aPath, conf);

    // SingularValueDecompositionImpl svd=new SingularValueDecompositionImpl(new
    // Array2DRowRealMatrix(a));
    SingularValueDecomposition svd2 =
      new SingularValueDecomposition(new DenseMatrix(a));

    double[] svalues2 = svd2.getSingularValues();
    dumpSv(svalues2);

    for (int i = 0; i < k + p; i++) {
      assertTrue(Math.abs(svalues2[i] - stochasticSValues[i]) <= s_epsilon);
    }

    double[][] mQ =
      SSVDSolver.loadDistributedRowMatrix(fs, new Path(svdOutPath, "Bt-job/"
          + BtJob.OUTPUT_Q + "-*"), conf);

    SSVDPrototypeTest
      .assertOrthonormality(new DenseMatrix(mQ), false, s_epsilon);

    /*
     * removing tests on U and V to keep this test leaner. I will keep U,V
     * computation and assertions in the dense tests though.
     */
 
View Full Code Here
54
55
56
57
58
59
60
61
62
63
64
        "56,34",
        "56,56",
        "56,78",
        "78,34");

    Matrix expectedAdjacencyMatrix = new DenseMatrix(new double[][] {
        { 0, 1, 1, 0 },
        { 0, 1, 0, 1 },
        { 1, 1, 1, 1 },
        { 0, 1, 0, 0 } });
View Full Code Here
100
101
102
103
104
105
106
107
108
109
110
        "34,78",
        "56,34",
        "56,56",
        "56,78");

    Matrix expectedAdjacencyMatrix = new DenseMatrix(new double[][] {
        { 0, 1, 1, 0 },
        { 1, 1, 1, 1 },
        { 1, 1, 1, 1 },
        { 0, 1, 1, 0 } });
View Full Code Here
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
    randomWalkWithRestart.run(new String[]{"--vertices", verticesFile.getAbsolutePath(),
        "--edges", edgesFile.getAbsolutePath(), "--sourceVertexIndex", String.valueOf(2),
        "--output", outputDir.getAbsolutePath(), "--numIterations", String.valueOf(2),
        "--stayingProbability", String.valueOf(0.75), "--tempDir", tempDir.getAbsolutePath()});

    Matrix expectedAdjacencyMatrix = new DenseMatrix(new double[][] {
        { 0, 1, 1, 0 },
        { 0, 1, 0, 1 },
        { 1, 1, 1, 1 },
        { 0, 1, 0, 0 } });

    int numVertices = HadoopUtil.readInt(new Path(tempDir.getAbsolutePath(), AdjacencyMatrixJob.NUM_VERTICES), conf);
    assertEquals(4, numVertices);
    Matrix actualAdjacencyMatrix = MathHelper.readMatrix(conf, new Path(tempDir.getAbsolutePath(),
        AdjacencyMatrixJob.ADJACENCY_MATRIX + "/part-r-00000"), numVertices, numVertices);

    StringBuilder info = new StringBuilder();
    info.append("\nexpected adjacency matrix\n\n");
    info.append(MathHelper.nice(expectedAdjacencyMatrix));
    info.append("\nactual adjacency matrix \n\n");
    info.append(MathHelper.nice(actualAdjacencyMatrix));
    info.append('\n');
    log.info(info.toString());

    Matrix expectedTransitionMatrix = new DenseMatrix(new double[][] {
        { 0,     0,     0.1875, 0    },
        { 0.375, 0.375, 0.1875, 0.75 },
        { 0.375, 0,     0.1875, 0    },
        { 0,     0.375, 0.1875, 0    } });
View Full Code Here
123
124
125
126
127
128
129
130
131
132
133
      FileSystem fs = FileSystem.get(context.getConfiguration());
      Path uHatPath = new Path(context.getConfiguration().get(PROP_UHAT_PATH));

      Path sigmaPath = new Path(context.getConfiguration().get(PROP_SIGMA_PATH));

      uHat = new DenseMatrix(SSVDSolver.loadDistributedRowMatrix(fs,
          uHatPath, context.getConfiguration()));
      // since uHat is (k+p) x (k+p)
      kp = uHat.columnSize();
      k = context.getConfiguration().getInt(PROP_K, kp);
      vRow = new DenseVector(k);
View Full Code Here
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
    double[][] mQ =
      SSVDSolver.loadDistributedRowMatrix(fs, new Path(svdOutPath, "Bt-job/"
          + BtJob.OUTPUT_Q + "-*"), conf);

    SSVDPrototypeTest.assertOrthonormality(new DenseMatrix(mQ),
                                           false,
                                           s_epsilon);

    double[][] u =
      SSVDSolver.loadDistributedRowMatrix(fs,
                                          new Path(svdOutPath, "U/[^_]*"),
                                          conf);

    SSVDPrototypeTest.assertOrthonormality(new DenseMatrix(u), false, s_epsilon);
    double[][] v =
      SSVDSolver.loadDistributedRowMatrix(fs,
                                          new Path(svdOutPath, "V/[^_]*"),
                                          conf);

    SSVDPrototypeTest.assertOrthonormality(new DenseMatrix(v), false, s_epsilon);
  }
View Full Code Here
TOP

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

  • com.mapr.stats.bandit.ContextualBayesBandit
  • com.mapr.stats.bandit.ContextualBayesBanditTest
  • org.apache.mahout.cf.taste.impl.recommender.svd.ImplicitLinearRegressionFactorizer
  • org.apache.mahout.cf.taste.impl.recommender.svd.ImplicitLinearRegressionFactorizer$FeatureVectorCallable
  • org.apache.mahout.cf.taste.impl.recommender.svd.ParallelSGDFactorizerTest
  • org.apache.mahout.classifier.AbstractVectorClassifier
  • org.apache.mahout.classifier.ConfusionMatrix
  • org.apache.mahout.classifier.discriminative.PerceptronTrainerTest
  • org.apache.mahout.classifier.discriminative.WinnowTrainerTest
  • org.apache.mahout.classifier.evaluation.Auc
    • 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.