本文整理了Java中org.apache.mahout.math.Vector.divide()方法的一些代码示例，展示了Vector.divide()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Vector.divide()方法的具体详情如下：
包路径：org.apache.mahout.math.Vector
类名称：Vector
方法名：divide

Vector.divide介绍

[英]Return a new vector containing the values of the recipient divided by the argument
[中]返回一个新向量，该向量包含收件人的值除以参数

代码示例

代码示例来源：origin: apache/mahout

@Override
public Vector divide(double x) {
 return delegate.divide(x);
}

代码示例来源：origin: apache/mahout

@Override
public Vector divide(double x) {
 return delegate.divide(x);
}

代码示例来源：origin: apache/mahout

u = u.divide(beta);

代码示例来源：origin: apache/mahout

@Test
public void testDivideDouble() throws Exception {
 Vector val = test.divide(3);
 assertEquals("size", 3, val.size());
 for (int i = 0; i < test.size(); i++) {
  assertEquals("get [" + i + ']', values[OFFSET + i] / 3, val.get(i), EPSILON);
 }
}

代码示例来源：origin: apache/mahout

@Test
public void testDivideDouble() {
 Vector val = test.divide(3);
 assertEquals("size", test.size(), val.size());
 for (int i = 0; i < test.size(); i++) {
  if (i % 2 == 0) {
   assertEquals("get [" + i + ']', 0.0, val.get(i), EPSILON);
  } else {
   assertEquals("get [" + i + ']', values[i/2] / 3.0, val.get(i), EPSILON);
  }
 }
}

代码示例来源：origin: apache/mahout

@Test
public void testUpdate() {
 MultiNormal f = new MultiNormal(20);
 Vector a = f.sample();
 Vector b = f.sample();
 Vector c = f.sample();
 DenseVector x = new DenseVector(a);
 Centroid x1 = new Centroid(1, x);
 x1.update(new Centroid(2, new DenseVector(b)));
 Centroid x2 = new Centroid(x1);
 x1.update(c);
 // check for correct value
 Vector mean = a.plus(b).plus(c).assign(Functions.div(3));
 assertEquals(0, x1.getVector().minus(mean).norm(1), 1.0e-8);
 assertEquals(3, x1.getWeight(), 0);
 assertEquals(0, x2.minus(a.plus(b).divide(2)).norm(1), 1.0e-8);
 assertEquals(2, x2.getWeight(), 0);
 assertEquals(0, new Centroid(x1.getIndex(), x1, x1.getWeight()).minus(x1).norm(1), 1.0e-8);
 // and verify shared storage
 assertEquals(0, x.minus(x1).norm(1), 0);
 assertEquals(3, x1.getWeight(), 1.0e-8);
 assertEquals(1, x1.getIndex());
}

代码示例来源：origin: org.apache.mahout/mahout-math

@Override
public Vector divide(double x) {
 return delegate.divide(x);
}

代码示例来源：origin: org.apache.mahout/mahout-math

@Override
public Vector divide(double x) {
 return delegate.divide(x);
}

代码示例来源：origin: apache/mahout

expected = vec1.divide(cube);

代码示例来源：origin: apache/mahout

assertEquals(0, dv1.divide(z).getDistanceSquared(v1.divide(z)), 1.0e-12);
assertEquals(0, dv1.times(z).getDistanceSquared(v1.times(z)), 1.0e-12);
assertEquals(0, dv1.plus(z).getDistanceSquared(v1.plus(z)), 1.0e-12);

代码示例来源：origin: org.apache.mahout/mahout-core

@Override
public void compute() {
 if (s0 != 0.0) {
  mean = s1.divide(s0);
  std = s2.times(s0).minus(s1.times(s1)).assign(new SquareRootFunction()).divide(s0);
 }
}

代码示例来源：origin: org.apache.mahout/mahout-mr

@Override
public void compute() {
 if (s0 != 0.0) {
  mean = s1.divide(s0);
  std = s2.times(s0).minus(s1.times(s1)).assign(new SquareRootFunction()).divide(s0);
 }
}

代码示例来源：origin: org.apache.mahout/mahout-mrlegacy

@Override
public void compute() {
 if (s0 != 0.0) {
  mean = s1.divide(s0);
  std = s2.times(s0).minus(s1.times(s1)).assign(new SquareRootFunction()).divide(s0);
 }
}

代码示例来源：origin: cheng-li/pyramid

private void updateGradient(){
  Vector weights = this.mlLogisticRegression.getWeights().getAllWeights();
  this.gradient = this.predictedCounts.minus(empiricalCounts).plus(weights.divide(gaussianPriorVariance));
}

代码示例来源：origin: org.apache.mahout/mahout-mr

/**
 * Compute the centroid by averaging the pointTotals
 * 
 * @return the new centroid
 */
public Vector computeCentroid() {
 return getS0() == 0 ? getCenter() : getS1().divide(getS0());
}

代码示例来源：origin: org.apache.mahout/mahout-core

/**
 * Compute the centroid by averaging the pointTotals
 * 
 * @return the new centroid
 */
public Vector computeCentroid() {
 return getS0() == 0 ? getCenter() : getS1().divide(getS0());
}

代码示例来源：origin: org.apache.mahout/mahout-core

@Override
public Vector classify(Vector instance) {
 Vector result = classifyNoLink(instance);
 // Convert to probabilities by exponentiation.
 double max = result.maxValue();
 result.assign(Functions.minus(max)).assign(Functions.EXP);
 result = result.divide(result.norm(1));
 return result.viewPart(1, result.size() - 1);
}

代码示例来源：origin: cheng-li/pyramid

private Vector penaltyGradient(){
  Vector weightsVector = this.logisticRegression.getWeights().getAllWeights();
  Vector penalty = new DenseVector(weightsVector.size());
  penalty = penalty.plus(weightsVector.divide(priorGaussianVariance));
  for (int j:logisticRegression.getWeights().getAllBiasPositions()){
    penalty.set(j,0);
  }
  return penalty;
}

代码示例来源：origin: org.apache.mahout/mahout-mrlegacy

@Override
public Vector classify(Vector instance) {
 Vector result = classifyNoLink(instance);
 // Convert to probabilities by exponentiation.
 double max = result.maxValue();
 result.assign(Functions.minus(max)).assign(Functions.EXP);
 result = result.divide(result.norm(1));
 return result.viewPart(1, result.size() - 1);
}

代码示例来源：origin: cheng-li/pyramid

public static Weights getMean(CBM bmm, int label){
  int numClusters = bmm.getNumComponents();
  int length = ((LogisticRegression)bmm.getBinaryClassifiers()[0][0]).getWeights().getAllWeights().size();
  int numFeatures = ((LogisticRegression)bmm.getBinaryClassifiers()[0][0]).getNumFeatures();
  Vector mean = new DenseVector(length);
  for (int k=0;k<numClusters;k++){
    mean = mean.plus(((LogisticRegression)bmm.getBinaryClassifiers()[k][label]).getWeights().getAllWeights());
  }
  mean = mean.divide(numClusters);
  return new Weights(2,numFeatures,mean);
}