dense_vector存储稠密向量,sparse_vector存储稀疏向量;它们的value都是单一的float数值,可以是0、负数或正数;dense_vector数组的最大长度不能超过1024,每个文档的数组长度可以不同;sparse_vector存储的是个非嵌套类型的json对象,对象key是向量的位置,即integer类型的字符串,范围[0,65535]。 dense_vector与sparse_vector...
SparseVector : an index and a value array def: Vectors.sparse(size: Int, indices: Array[Int], values: Array[Double]) (存储元素的个数、以及非零元素的编号index和值value) import org.apache.spark.mllib.linalg.{Vector, Vectors} // Create a dense vector (1.0, 0.0, 3.0). val dv: Vect...
SparseVector :稀疏向量 其创建方式有两种: 方法一:Vector.sparse(向量长度,索引数组,与索引数组所对应的数值数组) 方法二:Vector.sparse(向量长度,(索引,数值),(索引,数值),(索引,数值),...(索引,数值)) 示例: 比如向量(1,0,3,4)的创建有三种方法: 稠密向量:直接Vectors.dense(1,0,3,4) 稀疏向量: ...
SparseVector :稀疏向量 其创建方式有两种: 方法一:Vector.sparse(向量长度,索引数组,与索引数组所对应的数值数组) 方法二:Vector.sparse(向量长度,(索引,数值),(索引,数值),(索引,数值),...(索引,数值)) 示例: 比如向量(1,0,3,4)的创建有三种方法: 稠密向量:直接Vectors.dense(1,0,3,4) 稀疏向量: ...
...missing的值 missing: Float = Float.NaN, hasGroup: Boolean = false): (Booster, Map[String, Array...SparseVector和DenseVector都用于表示一个向量,两者之间仅仅是存储结构的不同。 其中,DenseVector就是普通的Vector存储,按序存储Vector中的每一个值。...而事实上XGBoost on Spark也的确将Sparse Vector...
First elements of a dense vector to be multiplied with first elements of a first row of a sparse array may be determined. The determined first elements of the dense vector may be written into a memory. A dot product for the first elements of the sparse array and the first elements of ...
Scales the sparse vector x by alpha and adds the result to the dense vector y, with both vectors containing single-precision values.
func sparse_matrix_vector_product_dense_double( _ transa: CBLAS_TRANSPOSE, _ alpha: Double, _ A: sparse_matrix_double!, _ x: UnsafePointer<Double>!, _ incx: sparse_stride, _ y: UnsafeMutablePointer<Double>!, _ incy: sparse_stride ) -> sparse_status ...
Finally, you can combine these two types of embeddings into a hybrid retrieval system, either using a multi-stage pipeline or through vector concatenation. Example of Using Dense and Sparse Embeddings Together: If you decide to use dense embeddings from Ope...
Infinity is a cutting-edge AI-native database that provides a wide range of search capabilities for rich data types such as dense vector, sparse vector, tensor, full-text, and structured data. It provides robust support for various LLM applications, including search, recommenders, question-answeri...