org.apache.mahout.math.WeightedVector.getWeight()方法的使用及代码示例
本文整理了Java中org.apache.mahout.math.WeightedVector.getWeight()方法的一些代码示例，展示了WeightedVector.getWeight()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。WeightedVector.getWeight()方法的具体详情如下：
包路径：org.apache.mahout.math.WeightedVector
类名称：WeightedVector
方法名：getWeight
WeightedVector.getWeight介绍
暂无
代码示例
代码示例来源：origin: apache/mahout
@Override
public int compare(WeightedVector a, WeightedVector b) {
if (a == b) {
return 0;
}
double aWeight = a.getWeight();
double bWeight = b.getWeight();
int r = Double.compare(aWeight, bWeight);
if (r != 0 && Math.abs(aWeight - bWeight) >= DOUBLE_EQUALITY_ERROR) {
return r;
}
double diff = a.minus(b).norm(1);
if (diff < 1.0e-12) {
return 0;
}
for (Vector.Element element : a.all()) {
r = Double.compare(element.get(), b.get(element.index()));
if (r != 0) {
return r;
}
}
return 0;
}代码示例来源：origin: apache/mahout
public Centroid(WeightedVector original) {
super(original.getVector().like().assign(original), original.getWeight(), original.getIndex());
}代码示例来源：origin: apache/mahout
public static Centroid create(int key, Vector initialValue) {
if (initialValue instanceof WeightedVector) {
return new Centroid(key, new DenseVector(initialValue), ((WeightedVector) initialValue).getWeight());
} else {
return new Centroid(key, new DenseVector(initialValue), 1);
}
}代码示例来源：origin: apache/mahout
@Test
public void testProjection() {
Vector v1 = new DenseVector(10).assign(Functions.random());
WeightedVector v2 = new WeightedVector(v1, v1, 31);
assertEquals(v1.dot(v1), v2.getWeight(), 1.0e-13);
assertEquals(31, v2.getIndex());
Matrix y = new DenseMatrix(10, 4).assign(Functions.random());
Matrix q = new QRDecomposition(y.viewPart(0, 10, 0, 3)).getQ();
Vector nullSpace = y.viewColumn(3).minus(q.times(q.transpose().times(y.viewColumn(3))));
WeightedVector v3 = new WeightedVector(q.viewColumn(0).plus(q.viewColumn(1)), nullSpace, 1);
assertEquals(0, v3.getWeight(), 1.0e-13);
Vector qx = q.viewColumn(0).plus(q.viewColumn(1)).normalize();
WeightedVector v4 = new WeightedVector(qx, q.viewColumn(0), 2);
assertEquals(Math.sqrt(0.5), v4.getWeight(), 1.0e-13);
WeightedVector v5 = WeightedVector.project(q.viewColumn(0), qx);
assertEquals(Math.sqrt(0.5), v5.getWeight(), 1.0e-13);
}代码示例来源：origin: org.apache.mahout/mahout-mr
/**
* Computes the total weight of the points in the given Vector iterable.
* @param data iterable of points
* @return total weight
*/
public static double totalWeight(Iterable<? extends Vector> data) {
double sum = 0;
for (Vector row : data) {
Preconditions.checkNotNull(row);
if (row instanceof WeightedVector) {
sum += ((WeightedVector)row).getWeight();
} else {
sum++;
}
}
return sum;
}
}代码示例来源：origin: org.apache.mahout/mahout-core
/**
* Computes the total weight of the points in the given Vector iterable.
* @param data iterable of points
* @return total weight
*/
public static double totalWeight(Iterable<? extends Vector> data) {
double sum = 0;
for (Vector row : data) {
Preconditions.checkNotNull(row);
if (row instanceof WeightedVector) {
sum += ((WeightedVector)row).getWeight();
} else {
sum++;
}
}
return sum;
}
}代码示例来源：origin: org.apache.mahout/mahout-mrlegacy
/**
* Computes the total weight of the points in the given Vector iterable.
* @param data iterable of points
* @return total weight
*/
public static double totalWeight(Iterable<? extends Vector> data) {
double sum = 0;
for (Vector row : data) {
Preconditions.checkNotNull(row);
if (row instanceof WeightedVector) {
sum += ((WeightedVector)row).getWeight();
} else {
sum++;
}
}
return sum;
}
}代码示例来源：origin: org.apache.mahout/mahout-math
@Override
public int compare(WeightedVector a, WeightedVector b) {
if (a == b) {
return 0;
}
double aWeight = a.getWeight();
double bWeight = b.getWeight();
int r = Double.compare(aWeight, bWeight);
if (r != 0 && Math.abs(aWeight - bWeight) >= DOUBLE_EQUALITY_ERROR) {
return r;
}
double diff = a.minus(b).norm(1);
if (diff < 1.0e-12) {
return 0;
}
for (Vector.Element element : a.all()) {
r = Double.compare(element.get(), b.get(element.index()));
if (r != 0) {
return r;
}
}
return 0;
}代码示例来源：origin: org.apache.mahout/mahout-core
public HashedVector(WeightedVector weightedVector, Matrix projection, long mask) {
super(weightedVector.getVector(), weightedVector.getWeight(), weightedVector.getIndex());
this.hash = mask & computeHash64(weightedVector, projection);
}代码示例来源：origin: org.apache.mahout/mahout-mr
public HashedVector(WeightedVector weightedVector, Matrix projection, long mask) {
super(weightedVector.getVector(), weightedVector.getWeight(), weightedVector.getIndex());
this.hash = mask & computeHash64(weightedVector, projection);
}代码示例来源：origin: org.apache.mahout/mahout-mrlegacy
public HashedVector(WeightedVector weightedVector, Matrix projection, long mask) {
super(weightedVector.getVector(), weightedVector.getWeight(), weightedVector.getIndex());
this.hash = mask & computeHash64(weightedVector, projection);
}代码示例来源：origin: org.apache.mahout/mahout-mrlegacy
/**
* Selects some of the original points randomly with probability proportional to their weights. This is much
* less sophisticated than the kmeans++ approach, however it is faster and coupled with
*
* The side effect of this method is to fill the centroids structure itself.
*
* @param datapoints The datapoints to select from. These datapoints should be WeightedVectors of some kind.
*/
private void initializeSeedsRandomly(List<? extends WeightedVector> datapoints) {
int numDatapoints = datapoints.size();
double totalWeight = 0;
for (WeightedVector datapoint : datapoints) {
totalWeight += datapoint.getWeight();
}
Multinomial<Integer> seedSelector = new Multinomial<Integer>();
for (int i = 0; i < numDatapoints; ++i) {
seedSelector.add(i, datapoints.get(i).getWeight() / totalWeight);
}
for (int i = 0; i < numClusters; ++i) {
int sample = seedSelector.sample();
seedSelector.delete(sample);
Centroid centroid = new Centroid(datapoints.get(sample));
centroid.setIndex(i);
centroids.add(centroid);
}
}代码示例来源：origin: org.apache.mahout/mahout-core
/**
* Selects some of the original points randomly with probability proportional to their weights. This is much
* less sophisticated than the kmeans++ approach, however it is faster and coupled with
*
* The side effect of this method is to fill the centroids structure itself.
*
* @param datapoints The datapoints to select from. These datapoints should be WeightedVectors of some kind.
*/
private void initializeSeedsRandomly(List<? extends WeightedVector> datapoints) {
int numDatapoints = datapoints.size();
double totalWeight = 0;
for (WeightedVector datapoint : datapoints) {
totalWeight += datapoint.getWeight();
}
Multinomial<Integer> seedSelector = new Multinomial<Integer>();
for (int i = 0; i < numDatapoints; ++i) {
seedSelector.add(i, datapoints.get(i).getWeight() / totalWeight);
}
for (int i = 0; i < numClusters; ++i) {
int sample = seedSelector.sample();
seedSelector.delete(sample);
Centroid centroid = new Centroid(datapoints.get(sample));
centroid.setIndex(i);
centroids.add(centroid);
}
}代码示例来源：origin: org.apache.mahout/mahout-mr
/**
* Selects some of the original points randomly with probability proportional to their weights. This is much
* less sophisticated than the kmeans++ approach, however it is faster and coupled with
*
* The side effect of this method is to fill the centroids structure itself.
*
* @param datapoints The datapoints to select from. These datapoints should be WeightedVectors of some kind.
*/
private void initializeSeedsRandomly(List<? extends WeightedVector> datapoints) {
int numDatapoints = datapoints.size();
double totalWeight = 0;
for (WeightedVector datapoint : datapoints) {
totalWeight += datapoint.getWeight();
}
Multinomial<Integer> seedSelector = new Multinomial<>();
for (int i = 0; i < numDatapoints; ++i) {
seedSelector.add(i, datapoints.get(i).getWeight() / totalWeight);
}
for (int i = 0; i < numClusters; ++i) {
int sample = seedSelector.sample();
seedSelector.delete(sample);
Centroid centroid = new Centroid(datapoints.get(sample));
centroid.setIndex(i);
centroids.add(centroid);
}
}代码示例来源：origin: org.apache.mahout/mahout-math
public Centroid(WeightedVector original) {
super(original.getVector().like().assign(original), original.getWeight(), original.getIndex());
}代码示例来源：origin: org.apache.mahout/mahout-math
public static Centroid create(int key, Vector initialValue) {
if (initialValue instanceof WeightedVector) {
return new Centroid(key, new DenseVector(initialValue), ((WeightedVector) initialValue).getWeight());
} else {
return new Centroid(key, new DenseVector(initialValue), 1);
}
}代码示例来源：origin: org.apache.mahout/mahout-mr
double vectorWeight;
if (vector instanceof WeightedVector) {
vectorWeight = ((WeightedVector) vector).getWeight();
} else {
vectorWeight = 1;代码示例来源：origin: org.apache.mahout/mahout-core
double vectorWeight;
if (vector instanceof WeightedVector) {
vectorWeight = ((WeightedVector) vector).getWeight();
} else {
vectorWeight = 1;代码示例来源：origin: org.apache.mahout/mahout-mrlegacy
double vectorWeight;
if (vector instanceof WeightedVector) {
vectorWeight = ((WeightedVector) vector).getWeight();
} else {
vectorWeight = 1;代码示例来源：origin: org.apache.mahout/mahout-core
for (WeightedVector testDatapoint : trainTestSplit.getSecond()) {
WeightedVector closest = (WeightedVector) centroids.searchFirst(testDatapoint, false).getValue();
closest.setWeight(closest.getWeight() + testDatapoint.getWeight());内容来源于网络，如有侵权，请联系作者删除！
