本文整理汇总了Python中pyspark.mllib.linalg.SparseVector.dot方法的典型用法代码示例。如果您正苦于以下问题：Python SparseVector.dot方法的具体用法？Python SparseVector.dot怎么用？Python SparseVector.dot使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pyspark.mllib.linalg.SparseVector的用法示例。
在下文中一共展示了SparseVector.dot方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_dot
# 需要导入模块: from pyspark.mllib.linalg import SparseVector [as 别名]
# 或者: from pyspark.mllib.linalg.SparseVector import dot [as 别名]
def test_dot(self):
sv = SparseVector(4, {1: 1, 3: 2})
dv = DenseVector(array([1.0, 2.0, 3.0, 4.0]))
lst = DenseVector([1, 2, 3, 4])
mat = array([[1.0, 2.0, 3.0, 4.0], [1.0, 2.0, 3.0, 4.0], [1.0, 2.0, 3.0, 4.0], [1.0, 2.0, 3.0, 4.0]])
self.assertEquals(10.0, sv.dot(dv))
self.assertTrue(array_equal(array([3.0, 6.0, 9.0, 12.0]), sv.dot(mat)))
self.assertEquals(30.0, dv.dot(dv))
self.assertTrue(array_equal(array([10.0, 20.0, 30.0, 40.0]), dv.dot(mat)))
self.assertEquals(30.0, lst.dot(dv))
self.assertTrue(array_equal(array([10.0, 20.0, 30.0, 40.0]), lst.dot(mat)))示例2: test_dot
# 需要导入模块: from pyspark.mllib.linalg import SparseVector [as 别名]
# 或者: from pyspark.mllib.linalg.SparseVector import dot [as 别名]
def test_dot(self):
sv = SparseVector(4, {1: 1, 3: 2})
dv = DenseVector(array([1., 2., 3., 4.]))
lst = DenseVector([1, 2, 3, 4])
mat = array([[1., 2., 3., 4.],
[1., 2., 3., 4.],
[1., 2., 3., 4.],
[1., 2., 3., 4.]])
arr = pyarray.array('d', [0, 1, 2, 3])
self.assertEqual(10.0, sv.dot(dv))
self.assertTrue(array_equal(array([3., 6., 9., 12.]), sv.dot(mat)))
self.assertEqual(30.0, dv.dot(dv))
self.assertTrue(array_equal(array([10., 20., 30., 40.]), dv.dot(mat)))
self.assertEqual(30.0, lst.dot(dv))
self.assertTrue(array_equal(array([10., 20., 30., 40.]), lst.dot(mat)))
self.assertEqual(7.0, sv.dot(arr))示例3: SparseVector
# 需要导入模块: from pyspark.mllib.linalg import SparseVector [as 别名]
# 或者: from pyspark.mllib.linalg.SparseVector import dot [as 别名]
'incorrect number of keys in sampleOHEDictManual')
# ** Sparse vectors **
import numpy as np
from pyspark.mllib.linalg import SparseVector
aDense = np.array([0., 3., 0., 4.])
aSparse = SparseVector(4, [[0,0.], [1,3.], [2,0.], [3,4.]])
bDense = np.array([0., 0., 0., 1.])
bSparse = SparseVector(4, [[0,0.], [1,0.], [2,0.], [3,1.]])
w = np.array([0.4, 3.1, -1.4, -.5])
print aDense.dot(w)
print aSparse.dot(w)
print bDense.dot(w)
print bSparse.dot(w)
# TEST Sparse Vectors
Test.assertTrue(isinstance(aSparse, SparseVector), 'aSparse needs to be an instance of SparseVector')
Test.assertTrue(isinstance(bSparse, SparseVector), 'aSparse needs to be an instance of SparseVector')
Test.assertTrue(aDense.dot(w) == aSparse.dot(w),
'dot product of aDense and w should equal dot product of aSparse and w')
Test.assertTrue(bDense.dot(w) == bSparse.dot(w),
'dot product of bDense and w should equal dot product of bSparse and w')
# ** OHE features as sparse vectors **
sampleOneOHEFeatManual = SparseVector(7,[2,3],[1.0,1.0])