def test_squared_distance(self):
from scipy.sparse import lil_matrix
lil = lil_matrix((4, 1))
lil[1, 0] = 3
lil[3, 0] = 2
dv = DenseVector(array([1., 2., 3., 4.]))
sv = SparseVector(4, {0: 1, 1: 2, 2: 3, 3: 4})
self.assertEquals(15.0, dv.squared_distance(lil))
self.assertEquals(15.0, sv.squared_distance(lil))
def test_squared_distance(self):
from scipy.sparse import lil_matrix
lil = lil_matrix((4, 1))
lil[1, 0] = 3
lil[3, 0] = 2
dv = DenseVector(array([1., 2., 3., 4.]))
sv = SparseVector(4, {0: 1, 1: 2, 2: 3, 3: 4})
self.assertEqual(15.0, dv.squared_distance(lil))
self.assertEqual(15.0, sv.squared_distance(lil))
# Created by Raju Kumar Mishra # Book PySpark Recipes # Chapter 9 # Recipe 9-2. Create a Sparse Vector. # Run following PySpark code lines, line by line in PySpark shell from pyspark.mllib.linalg import SparseVector sparseDataList = [1.0, 3.2] sparseDataVector = SparseVector(8, [0, 7], sparseDataList) sparseDataVector sparseDataVector[1] sparseDataVector[7] sparseDataVector.numNonzeros() sparseDataList1 = [3.0, 1.4, 2.5, 1.2] sparseDataVector1 = SparseVector(8, [0, 3, 4, 6], sparseDataList1) squaredDistance = sparseDataVector.squared_distance(sparseDataVector1) squaredDistance
