Package gnu.trove

Examples of gnu.trove.TDoubleArrayList

38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
//    Matrix var = new DenseMatrix (new double[][] {{ 0.5, 2.0 }, { 2.0, 0.75 }});

    VarSet vars = new HashVarSet (new Variable[] { v1, v2 });
    Factor f = new NormalFactor (vars, mu, var);

    TDoubleArrayList v1lst = new TDoubleArrayList ();
    TDoubleArrayList v2lst = new TDoubleArrayList ();
    for (int i = 0; i < 100000; i++) {
      Assignment assn = f.sample (r);
      v1lst.add (assn.getDouble (v1));
      v2lst.add (assn.getDouble (v2));
    }

    checkMeanStd (v1lst, 1.0, Math.sqrt (1/0.5));
    checkMeanStd (v2lst, 2.0, Math.sqrt (1/0.75));
  }
View Full Code Here
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
  private SparseMatrixn make3dMatrix ()
  {
    int[] sizes = new int[]{2, 3, 4};
    TIntArrayList idxs = new TIntArrayList ();
    TDoubleArrayList vals = new TDoubleArrayList ();

    for (int i = 0; i < 24; i++) {
      if (i % 3 != 0) {
        idxs.add (i);
        vals.add (2.0 * i);
      }
    }

    SparseMatrixn a = new SparseMatrixn (sizes, idxs.toNativeArray (), vals.toNativeArray ());
    return a;
  }
View Full Code Here
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
  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);
  }
View Full Code Here
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
  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);
  }
View Full Code Here
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
126
127
      vals[i] = ptl.logValue (i);
    }

    RankedFeatureVector rfv = new RankedFeatureVector (new Alphabet(), idxs, vals);
    TIntArrayList idxList = new TIntArrayList ();
    TDoubleArrayList valList = new TDoubleArrayList ();

    double mass = Double.NEGATIVE_INFINITY;
    double logAlpha = Math.log (alpha);
    for (int rank = 0; rank < rfv.numLocations (); rank++) {
      int idx = rfv.getIndexAtRank (rank);
      double val = rfv.value (idx);
      mass = Maths.sumLogProb (mass, val);
      idxList.add (idx);
      valList.add (val);
      if (mass > logAlpha) {
        break;
      }
    }

    int[] szs = computeSizes (ptl);
    SparseMatrixn m = new SparseMatrixn (szs, idxList.toNativeArray (), valList.toNativeArray ());

    TableFactor result = new TableFactor (computeVars (ptl));
    result.setValues (m);

    return result;
View Full Code Here
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
  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);
  }
View Full Code Here
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
  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);
  }
View Full Code Here
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
    Dirichlet dirichlet = new Dirichlet (NUM_OUTCOMES, 1.0);
    double[] pA = dirichlet.randomVector (random);
    double[] pB = dirichlet.randomVector (random);

    TDoubleArrayList pC = new TDoubleArrayList (NUM_OUTCOMES * NUM_OUTCOMES * NUM_OUTCOMES);
    for (int i = 0; i < (NUM_OUTCOMES * NUM_OUTCOMES); i++) {
      pC.add (dirichlet.randomVector (random));
    }

    Variable[] vars = new Variable[] { new Variable (NUM_OUTCOMES), new Variable (NUM_OUTCOMES),
            new Variable (NUM_OUTCOMES) };
    DirectedModel mdl = new DirectedModel ();
    mdl.addFactor (new CPT (new TableFactor (vars[0], pA), vars[0]));
    mdl.addFactor (new CPT (new TableFactor (vars[1], pB), vars[1]));
    mdl.addFactor (new CPT (new TableFactor (vars, pC.toNativeArray ()), vars[2]));

    return mdl;
  }
View Full Code Here
51
52
53
54
55
56
57
    this.numLabels = numLabels;
    this.useValues = useValues;
    this.normalize = normalize;
    this.constraints = new TIntObjectHashMap<MaxEntL2IndGEConstraint>();
    this.indexCache = new TIntArrayList();
    this.valueCache = new TDoubleArrayList();
  }
View Full Code Here
41
42
43
44
45
46
47
    this.numFeatures = numFeatures;
    this.numLabels = numLabels;
    this.useValues = useValues;
    this.constraints = new TIntObjectHashMap<MaxEntFLGEConstraint>();
    this.indexCache = new TIntArrayList();
    this.valueCache = new TDoubleArrayList();
  }
View Full Code Here
TOP

Related Classes of gnu.trove.TDoubleArrayList

  • cc.mallet.classify.constraints.ge.MaxEntFLGEConstraints
  • cc.mallet.classify.constraints.ge.MaxEntRangeL2FLGEConstraints
  • cc.mallet.classify.constraints.pr.MaxEntFLPRConstraints
  • cc.mallet.grmm.test.TestBetaFactor
  • cc.mallet.grmm.test.TestInference
  • cc.mallet.grmm.test.TestNormalFactor
  • cc.mallet.grmm.test.TestUniformFactor
  • cc.mallet.grmm.test.TestUniNormalFactor
  • cc.mallet.grmm.types.Factors
  • cc.mallet.types.tests.TestSparseMatrixn
    • 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.