vectorB[800] =7; Assert.AreEqual(50.0, vectorA.DotProduct(vectorB)); } 开发者ID:XiBeichuan,项目名称:hydronumerics,代码行数:16,代码来源:SparseVectorTest.cs voidDotProductTestThrowsExceptionWhenDimensionInvalid(){ SparseVector vec2 =newSparseVector(TestVector.Dimension -1); vec2.DotProduct(TestVe...
SparseVector(nums)Initializes the object with the vectornums dotProduct(vec)Compute the dot product between the instance ofSparseVectorandvec A sparse vector is a vector that has mostly zero values, you should store the sparse vector efficiently and compute the dot product between twoSparseVector....
SparseVector(nums)Initializes the object with the vectornums dotProduct(vec)Compute the dot product between the instance ofSparseVectorandvec A sparse vector is a vector that has mostly zero values, you should store the sparse vector efficiently and compute the dot product between twoSparseVector....
However, sparse vector-vector dot product, a key primitive in sparse CNNs, would be inefficient using the representation adopted by SCNN. The dot product requires finding and accessing non-zero elements in matching positions in the two sparse vectors -- an inner join using the position as the...
示例1: test_dot ▲点赞 7▼ # 需要导入模块: from pyspark.mllib.linalg import SparseVector [as 别名]# 或者: from pyspark.mllib.linalg.SparseVector importdot[as 别名]deftest_dot(self):sv = SparseVector(4, {1:1,3:2}) dv = DenseVector(array([1.0,2.0,3.0,4.0])) ...
_dotmvroutine computes a sparse matrix-vector product and dot product: y:=alpha*op(A)*x+beta*yd:=∑ixi*yi(real case)d:=∑iconj(xi)*yi(complex case) where alphaandbetaare scalars. xandyare vectors. Ais anm-by-kmatrix. conjrepresents complex conjugation....
Efficient sparse matrix-vector multiplication on cache-based gpus. In: 2012 Innovative Parallel Computing (InPar), pp. 1–12. IEEE, (2012) Graillat, S., Jézéquel, F., Mary, T., Molina, R.: Adaptive precision sparse matrix-vector product and its application to krylov solvers. SIAM J....
An alternative to the scalar method, which we call the vector kernel, assigns one warp to each matrix row. An implementation of this described in Figure 22. The vector kernel can be viewed as an application of the vector strip mining pattern to the sparse dot product computed for each ...
kml_sparse_?dotci_sub Compute the conjugate dot product of vectors (Hilbert space). x · y = xˆH * y. That is, multiply the conjugate complex number of an element in vector x by the corresponding element in vector y and then add the products. The conjugate of the complex number ...
sparse vector / sparse vector addition, subtraction, dot product sparse/dense matrix operations Algorithms Outer iterator on compressed sparse matrices sparse vector iteration sparse vectors joint non zero iterations simple sparse Cholesky decomposition (requires opting into an LGPL license) ...