def test_norms(self):
a = DenseVector([0, 2, 3, -1])
self.assertAlmostEqual(a.norm(2), 3.742, 3)
self.assertTrue(a.norm(1), 6)
self.assertTrue(a.norm(inf), 3)
a = SparseVector(4, [0, 2], [3, -4])
self.assertAlmostEqual(a.norm(2), 5)
self.assertTrue(a.norm(1), 7)
self.assertTrue(a.norm(inf), 4)
tmp = SparseVector(4, [0, 2], [3, 0])
self.assertEqual(tmp.numNonzeros(), 1)
# 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
