Package org.apache.mahout.math

Examples of org.apache.mahout.math.Vector.times()

55
56
57
58
59
60
61
62
63
64
65
    sampleMean = sum.divide(sampleN);

    Vector sampleVar = new DenseVector(2);
    for (VectorWritable v : sampleData) {
      Vector delta = v.get().minus(sampleMean);
      sampleVar.assign(delta.times(delta), Functions.PLUS);
    }
    sampleVar = sampleVar.divide(sampleN - 1);
    sampleStd = sampleVar.clone();
    sampleStd.assign(new SquareRootFunction());
    log.info("Observing {} samples m=[{}, {}] sd=[{}, {}]",
View Full Code Here
150
151
152
153
154
155
156
157
158
159
160
        int itemIDIndex = usedItemsIterator.next().index();
        numberOfSimilarItemsUsed.setQuick(itemIDIndex, numberOfSimilarItemsUsed.getQuick(itemIDIndex) + 1);
      }

      numerators = numerators == null
          ? prefValue == BOOLEAN_PREF_VALUE ? simColumn.clone() : simColumn.times(prefValue)
          : numerators.plus(prefValue == BOOLEAN_PREF_VALUE ? simColumn : simColumn.times(prefValue));

      simColumn.assign(ABSOLUTE_VALUES);
      denominators = denominators == null ? simColumn : denominators.plus(simColumn);
    }
View Full Code Here
151
152
153
154
155
156
157
158
159
160
161
        numberOfSimilarItemsUsed.setQuick(itemIDIndex, numberOfSimilarItemsUsed.getQuick(itemIDIndex) + 1);
      }

      numerators = numerators == null
          ? prefValue == BOOLEAN_PREF_VALUE ? simColumn.clone() : simColumn.times(prefValue)
          : numerators.plus(prefValue == BOOLEAN_PREF_VALUE ? simColumn : simColumn.times(prefValue));

      simColumn.assign(ABSOLUTE_VALUES);
      denominators = denominators == null ? simColumn : denominators.plus(simColumn);
    }
View Full Code Here
54
55
56
57
58
59
60
61
62
63
64
    sampleMean = sum.divide(sampleN);

    Vector sampleVar = new DenseVector(2);
    for (VectorWritable v : sampleData) {
      Vector delta = v.get().minus(sampleMean);
      delta.times(delta).addTo(sampleVar);
    }
    sampleVar = sampleVar.divide(sampleN - 1);
    sampleStd = sampleVar.clone();
    sampleStd.assign(new SquareRootFunction());
    log.info("Observing {} samples m=[{}, {}] sd=[{}, {}]",
View Full Code Here
137
138
139
140
141
142
143
144
145
146
147
      blckCnt++;
      cnt = 0;
    }

    for (int i = 0; i < n; i++) {
      btEmitter.emitRow(i, qRowV.times(aRow.getQuick(i)));
    }

  }

View Full Code Here
230
231
232
233
234
235
236
237
238
239
240
    for (Map.Entry<Integer, Vector> btRowEntry : btRows.entrySet()) {
      Vector btRow = btRowEntry.getValue();
      assert btRow.size() == kp;
      for (int i = 0; i < kp; i++) {
        bbtEmitter.emitRow(i, btRow.times(btRow.getQuick(i)));
      }
    }

    double[][] bbtValues = new double[kp][];
    for (int i = 0; i < kp; i++) {
View Full Code Here
341
342
343
344
345
346
347
348
349
350
351
    for (Map.Entry<Integer, Vector> btRowEntry : btRows.entrySet()) {
      Vector btRow = btRowEntry.getValue();
      assert btRow.size() == kp;
      for (int i = 0; i < kp; i++) {
        bbtEmitter.emitRow(i, btRow.times(btRow.getQuick(i)));
      }
    }

    double[][] bbtValues = new double[kp][];
    for (int i = 0; i < kp; i++) {
View Full Code Here
54
55
56
57
58
59
60
61
62
63
64
    sampleMean = sum.divide(sampleN);

    Vector sampleVar = new DenseVector(2);
    for (VectorWritable v : sampleData) {
      Vector delta = v.get().minus(sampleMean);
      sampleVar.assign(delta.times(delta), Functions.PLUS);
    }
    sampleVar = sampleVar.divide(sampleN - 1);
    sampleStd = sampleVar.clone();
    sampleStd.assign(new SquareRootFunction());
    log.info("Observing {} samples m=[{}, {}] sd=[{}, {}]",
View Full Code Here
36
37
38
39
40
41
42
43
44
45
46
    for (int c = 0; c < n; c++) {
      Vector col = mx.viewColumn(c);
      for (int c1 = 0; c1 < c; c1++) {
        Vector viewC1 = mx.viewColumn(c1);
        col.assign(col.minus(viewC1.times(viewC1.dot(col))));

      }
      final double norm2 = col.norm(2);
      col.assign(new DoubleFunction() {
        @Override
View Full Code Here
55
56
57
58
59
60
61
62
63
64
65
    sampleMean = sum.divide(sampleN);

    Vector sampleVar = new DenseVector(2);
    for (VectorWritable v : sampleData) {
      Vector delta = v.get().minus(sampleMean);
      sampleVar.assign(delta.times(delta), Functions.PLUS);
    }
    sampleVar = sampleVar.divide(sampleN - 1);
    sampleStd = sampleVar.clone();
    sampleStd.assign(new SquareRootFunction());
    log.info("Observing {} samples m=[{}, {}] sd=[{}, {}]",
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.