1 files changed, 161 insertions, 0 deletions
diff --git a/src/tools/rustfmt/tests/source/issue-2896.rs b/src/tools/rustfmt/tests/source/issue-2896.rs
new file mode 100644
index 00000000000..f648e64b1e3
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+++ b/src/tools/rustfmt/tests/source/issue-2896.rs
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+extern crate rand;
+extern crate timely;
+extern crate differential_dataflow;
+
+use rand::{Rng, SeedableRng, StdRng};
+
+use timely::dataflow::operators::*;
+
+use differential_dataflow::AsCollection;
+use differential_dataflow::operators::*;
+use differential_dataflow::input::InputSession;
+
+// mod loglikelihoodratio;
+
+fn main() {
+
+  // define a new timely dataflow computation. 
+  timely::execute_from_args(std::env::args().skip(6), move |worker| {
+
+    // capture parameters of the experiment.
+    let users: usize = std::env::args().nth(1).unwrap().parse().unwrap();
+    let items: usize = std::env::args().nth(2).unwrap().parse().unwrap();
+    let scale: usize = std::env::args().nth(3).unwrap().parse().unwrap();
+    let batch: usize = std::env::args().nth(4).unwrap().parse().unwrap();
+    let noisy: bool = std::env::args().nth(5).unwrap() == "noisy";
+
+    let index = worker.index();
+    let peers = worker.peers();
+
+    let (input, probe) = worker.dataflow(|scope| {
+
+      // input of (user, item) collection.
+      let (input, occurrences) = scope.new_input();
+      let occurrences = occurrences.as_collection();
+
+      //TODO adjust code to only work with upper triangular half of cooccurrence matrix
+
+      /* Compute the cooccurrence matrix C = A'A from the binary interaction matrix A. */
+      let cooccurrences = 
+      occurrences
+        .join_map(&occurrences, |_user, &item_a, &item_b| (item_a, item_b))
+        .filter(|&(item_a, item_b)| item_a != item_b)
+        .count();
+
+      /* compute the rowsums of C indicating how often we encounter individual items. */
+      let row_sums = 
+      occurrences
+        .map(|(_user, item)| item)
+        .count();
+
+      // row_sums.inspect(|record| println!("[row_sums] {:?}", record));
+
+      /* Join the cooccurrence pairs with the corresponding row sums. */
+      let mut cooccurrences_with_row_sums = cooccurrences
+        .map(|((item_a, item_b), num_cooccurrences)| (item_a, (item_b, num_cooccurrences)))
+        .join_map(&row_sums, |&item_a, &(item_b, num_cooccurrences), &row_sum_a| {
+          assert!(row_sum_a > 0);
+          (item_b, (item_a, num_cooccurrences, row_sum_a))
+        })
+        .join_map(&row_sums, |&item_b, &(item_a, num_cooccurrences, row_sum_a), &row_sum_b| {
+          assert!(row_sum_a > 0);
+          assert!(row_sum_b > 0);
+          (item_a, (item_b, num_cooccurrences, row_sum_a, row_sum_b))
+        });
+
+      // cooccurrences_with_row_sums
+      //     .inspect(|record| println!("[cooccurrences_with_row_sums] {:?}", record));
+
+      // //TODO compute top-k "similar items" per item
+      // /* Compute LLR scores for each item pair. */
+      // let llr_scores = cooccurrences_with_row_sums.map(
+      //   |(item_a, (item_b, num_cooccurrences, row_sum_a, row_sum_b))| {
+
+      //     println!(
+      //       "[llr_scores] item_a={} item_b={}, num_cooccurrences={} row_sum_a={} row_sum_b={}",
+      //       item_a, item_b, num_cooccurrences, row_sum_a, row_sum_b);
+
+      //     let k11: isize = num_cooccurrences;
+      //     let k12: isize = row_sum_a as isize - k11;
+      //     let k21: isize = row_sum_b as isize - k11;
+      //     let k22: isize = 10000 - k12 - k21 + k11;
+
+      //     let llr_score = loglikelihoodratio::log_likelihood_ratio(k11, k12, k21, k22);
+
+      //     ((item_a, item_b), llr_score)
+      //   });
+
+      if noisy {
+        cooccurrences_with_row_sums = 
+        cooccurrences_with_row_sums
+          .inspect(|x| println!("change: {:?}", x));
+      }
+
+      let probe = 
+      cooccurrences_with_row_sums
+          .probe();
+/*
+      // produce the (item, item) collection
+      let cooccurrences = occurrences
+        .join_map(&occurrences, |_user, &item_a, &item_b| (item_a, item_b));
+      // count the occurrences of each item.
+      let counts = cooccurrences
+        .map(|(item_a,_)| item_a)
+        .count();
+      // produce ((item1, item2), count1, count2, count12) tuples
+      let cooccurrences_with_counts = cooccurrences
+        .join_map(&counts, |&item_a, &item_b, &count_item_a| (item_b, (item_a, count_item_a)))
+        .join_map(&counts, |&item_b, &(item_a, count_item_a), &count_item_b| {
+          ((item_a, item_b), count_item_a, count_item_b)
+        });
+      let probe = cooccurrences_with_counts
+        .inspect(|x| println!("change: {:?}", x))
+        .probe();
+*/
+      (input, probe)
+    });
+
+    let seed: &[_] = &[1, 2, 3, index];
+    let mut rng1: StdRng = SeedableRng::from_seed(seed);  // rng for edge additions
+    let mut rng2: StdRng = SeedableRng::from_seed(seed);  // rng for edge deletions
+
+    let mut input = InputSession::from(input);
+
+    for count in 0 .. scale {
+      if count % peers == index {
+        let user = rng1.gen_range(0, users);
+        let item = rng1.gen_range(0, items);
+        // println!("[INITIAL INPUT] ({}, {})", user, item);
+        input.insert((user, item));
+      }
+    }
+
+    // load the initial data up!
+    while probe.less_than(input.time()) { worker.step(); }
+
+    for round in 1 .. {
+
+      for element in (round * batch) .. ((round + 1) * batch) {
+        if element % peers == index {
+          // advance the input timestamp.
+          input.advance_to(round * batch);
+          // insert a new item.
+          let user = rng1.gen_range(0, users);
+          let item = rng1.gen_range(0, items);
+          if noisy { println!("[INPUT: insert] ({}, {})", user, item); }
+          input.insert((user, item));
+          // remove an old item.
+          let user = rng2.gen_range(0, users);
+          let item = rng2.gen_range(0, items);
+          if noisy { println!("[INPUT: remove] ({}, {})", user, item); }
+          input.remove((user, item));
+        }
+      }
+
+      input.advance_to(round * batch);
+      input.flush();
+
+      while probe.less_than(input.time()) { worker.step(); }
+    }
+  }).unwrap();
+}