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jian wang commented on DATAFU-16: --------------------------------- Experiment to test the algorithm's item sample probability estimation correctness using the same methodology described in the original issue: https://github.com/linkedin/datafu/issues/80. Using (weight / sum(weight)) as the ground truth of each item's sampling probability, calculate the average squared error of the algo's per item sampling probability. Using exponential jump in weighted reservoir sampling in accumulate mode seems OK, but it is not sure if it is OK for algebraic mode since it has higher error than other algos. [is verifying the test code to see if it is something wrong with test code] The testAccumulateExpJ() to simulate Accumulate() for data stream The testAlgebraicExpJ() to simulate the Initial/Interm/Final using 100 combiners and each initial processes only one sample, which is the majority of real world cases. Experiment result err_ws: 1.174525314652248E-5 err_acc: 1.1883407123610779E-5 err_alg: 1.2130630748818072E-5 err_skip_acc: 1.2081897301243E-5 err_skip_alg: 1.3854125917604345E-4 err_ws is for weighted sampling UDF err_acc is for weighted reservoir sampling accumulate err_alg is for weighted reservoir sampling algebraic err_skip_acc is for weighted reservoir sampling exponential jump accumulate err_skip_alg is for weighted reservoir sampling exponential jump algebraic Pls see test code as attached > weighted reservoir sampling with exponential jumps UDF > ------------------------------------------------------ > > Key: DATAFU-16 > URL: https://issues.apache.org/jira/browse/DATAFU-16 > Project: DataFu > Issue Type: New Feature > Environment: Mac, Linux > pig-0.11 > Reporter: jian wang > Priority: Minor > > Create a weightedReservoirSampleWithExpJump UDF to implement the weighted > reservoir sampling algorithm with exponential jumps. Investigation is tracked > in https://github.com/linkedin/datafu/issues/80. This task is part of > experiment of different weighted sampling algorithms. -- This message was sent by Atlassian JIRA (v6.1.5#6160)