forked from M-Labs/nalgebra
Merge pull request #1039 from Andlon/remove-proptest-patch
Remove redundant proptest patch in `nalgebra-sparse`
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commit
56a4835b71
@ -5,11 +5,6 @@
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//! The strategies provided here are generally expected to be able to generate the entire range
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//! of possible outputs given the constraints on dimensions and values. However, there are no
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//! particular guarantees on the distribution of possible values.
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// Contains some patched code from proptest that we can remove in the (hopefully near) future.
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// See docs in file for more details.
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mod proptest_patched;
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use crate::coo::CooMatrix;
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use crate::csc::CscMatrix;
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use crate::csr::CsrMatrix;
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@ -31,16 +26,10 @@ fn dense_row_major_coord_strategy(
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let mut booleans = vec![true; nnz];
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booleans.append(&mut vec![false; (nrows * ncols) - nnz]);
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// Make sure that exactly `nnz` of the booleans are true
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// TODO: We cannot use the below code because of a bug in proptest, see
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// https://github.com/AltSysrq/proptest/pull/217
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// so for now we're using a patched version of the Shuffle adapter
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// (see also docs in `proptest_patched`
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// Just(booleans)
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// // Need to shuffle to make sure they are randomly distributed
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// .prop_shuffle()
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proptest_patched::Shuffle(Just(booleans)).prop_map(move |booleans| {
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Just(booleans)
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// Need to shuffle to make sure they are randomly distributed
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.prop_shuffle()
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.prop_map(move |booleans| {
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booleans
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.into_iter()
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.enumerate()
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@ -1,146 +0,0 @@
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//! Contains a modified implementation of `proptest::strategy::Shuffle`.
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//!
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//! The current implementation in `proptest` does not generate all permutations, which is
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//! problematic for our proptest generators. The issue has been fixed in
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//! https://github.com/AltSysrq/proptest/pull/217
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//! but it has yet to be merged and released. As soon as this fix makes it into a new release,
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//! the modified code here can be removed.
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//!
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/*!
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This code has been copied and adapted from
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https://github.com/AltSysrq/proptest/blob/master/proptest/src/strategy/shuffle.rs
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The original licensing text is:
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//-
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// Copyright 2017 Jason Lingle
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//
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// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
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// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license
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// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your
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// option. This file may not be copied, modified, or distributed
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// except according to those terms.
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*/
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use proptest::num;
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use proptest::prelude::Rng;
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use proptest::strategy::{NewTree, Shuffleable, Strategy, ValueTree};
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use proptest::test_runner::{TestRng, TestRunner};
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use std::cell::Cell;
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#[derive(Clone, Debug)]
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#[must_use = "strategies do nothing unless used"]
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pub struct Shuffle<S>(pub(super) S);
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impl<S: Strategy> Strategy for Shuffle<S>
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where
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S::Value: Shuffleable,
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{
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type Tree = ShuffleValueTree<S::Tree>;
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type Value = S::Value;
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fn new_tree(&self, runner: &mut TestRunner) -> NewTree<Self> {
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let rng = runner.new_rng();
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self.0.new_tree(runner).map(|inner| ShuffleValueTree {
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inner,
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rng,
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dist: Cell::new(None),
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simplifying_inner: false,
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})
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}
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}
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#[derive(Clone, Debug)]
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pub struct ShuffleValueTree<V> {
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inner: V,
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rng: TestRng,
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dist: Cell<Option<num::usize::BinarySearch>>,
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simplifying_inner: bool,
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}
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impl<V: ValueTree> ShuffleValueTree<V>
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where
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V::Value: Shuffleable,
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{
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fn init_dist(&self, dflt: usize) -> usize {
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if self.dist.get().is_none() {
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self.dist.set(Some(num::usize::BinarySearch::new(dflt)));
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}
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self.dist.get().unwrap().current()
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}
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fn force_init_dist(&self) {
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if self.dist.get().is_none() {
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let _ = self.init_dist(self.current().shuffle_len());
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}
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}
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}
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impl<V: ValueTree> ValueTree for ShuffleValueTree<V>
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where
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V::Value: Shuffleable,
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{
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type Value = V::Value;
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fn current(&self) -> V::Value {
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let mut value = self.inner.current();
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let len = value.shuffle_len();
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// The maximum distance to swap elements. This could be larger than
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// `value` if `value` has reduced size during shrinking; that's OK,
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// since we only use this to filter swaps.
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let max_swap = self.init_dist(len);
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// If empty collection or all swaps will be filtered out, there's
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// nothing to shuffle.
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if 0 == len || 0 == max_swap {
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return value;
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}
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let mut rng = self.rng.clone();
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for start_index in 0..len - 1 {
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// Determine the other index to be swapped, then skip the swap if
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// it is too far. This ordering is critical, as it ensures that we
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// generate the same sequence of random numbers every time.
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// NOTE: The below line is the whole reason for the existence of this adapted code
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// We need to be able to swap with the same element, so that some elements remain in
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// place rather being swapped
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// let end_index = rng.gen_range(start_index + 1..len);
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let end_index = rng.gen_range(start_index..len);
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if end_index - start_index <= max_swap {
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value.shuffle_swap(start_index, end_index);
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}
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}
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value
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}
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fn simplify(&mut self) -> bool {
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if self.simplifying_inner {
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self.inner.simplify()
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} else {
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// Ensure that we've initialised `dist` to *something* to give
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// consistent non-panicking behaviour even if called in an
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// unexpected sequence.
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self.force_init_dist();
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if self.dist.get_mut().as_mut().unwrap().simplify() {
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true
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} else {
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self.simplifying_inner = true;
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self.inner.simplify()
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}
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}
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}
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fn complicate(&mut self) -> bool {
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if self.simplifying_inner {
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self.inner.complicate()
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} else {
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self.force_init_dist();
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self.dist.get_mut().as_mut().unwrap().complicate()
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}
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}
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}
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