The Thrill of Bulgaria's Volleyball SuperLiga
The Bulgarian Volleyball SuperLiga is not just a competition; it's a showcase of talent, strategy, and sheer passion for the sport. As we gear up for tomorrow's matches, fans and experts alike are buzzing with anticipation. Each game promises to be a thrilling display of athleticism and skill, with teams battling fiercely to secure their place at the top of the league standings.
Teams to Watch
Tomorrow's matches feature some of the most formidable teams in the league. The reigning champions have been in stellar form, showcasing their prowess on both offense and defense. Their star player, known for his powerful spikes and impeccable serves, will be a key figure to watch. On the other side, the underdogs have been steadily climbing up the ranks, demonstrating resilience and strategic acumen that could upset even the strongest opponents.
Key Matchups
- Team A vs Team B: This clash between two top contenders is expected to be a nail-biter. Both teams have shown exceptional teamwork and individual brilliance throughout the season.
- Team C vs Team D: Known for their aggressive playstyle, Team C will face off against Team D's solid defense. This match could go either way, making it one of the most anticipated games.
- Team E vs Team F: With both teams struggling this season, this match offers an opportunity for redemption and a chance to climb out of the lower rankings.
Betting Predictions: Expert Insights
As always, betting predictions add an extra layer of excitement to the matches. Experts have analyzed past performances, current form, and head-to-head statistics to provide insights into potential outcomes.
Predictions for Key Matches
- Team A vs Team B: Experts predict a close match with Team A having a slight edge due to their home-court advantage and recent winning streak.
- Team C vs Team D: Betting analysts suggest that while Team C is favored due to their offensive strength, Team D's defensive tactics could level the playing field.
- Team E vs Team F: Given both teams' struggles this season, experts advise caution but lean towards Team E pulling off an upset victory.
Tactical Breakdowns
Understanding team strategies can provide deeper insights into how tomorrow's matches might unfold. Let's delve into some tactical breakdowns:
Offensive Strategies
- Spike Variations: Teams are employing diverse spike techniques to keep opponents guessing. Quick sets and back-row attacks are becoming increasingly popular.
- Serve Tactics: Powerful serves aimed at disrupting opponent formations are proving effective in gaining early advantages during rallies.
Defensive Formations
- Zonal Defense: Many teams are utilizing zonal defenses to cover more court space and intercept spikes effectively.
- Dig Techniques: Advanced digging techniques are being used to counter fast-paced offenses, ensuring critical points are saved.
Fan Engagement: How You Can Get Involved
<|repo_name|>KajalRathod/CS-141<|file_sep|>/hw4/hw4.tex
documentclass[12pt]{article}
usepackage{fullpage}
usepackage{fancyhdr}
usepackage{extramarks}
usepackage{amsmath}
usepackage{amsthm}
usepackage{amsfonts}
usepackage{tikz}
usetikzlibrary{automata,positioning}
%
% Basic Document Settings
%
topmargin=-0.45in
evensidemargin=0in
oddsidemargin=0in
textwidth=6.5in
textheight=9.0in
headsep=0.25in
numberwithin{equation}{section}
%
% Custom commands
%
% Useful for algorithms
%newcommand{algstep}[1]{\ indent textbf{$triangleright$ #1}}
%
% Theorem environments
%
% Homework Specific Information
% Your ID number:
% Your email address:
% Your full name:
% Class:
% Instructor's Name:
% Due Date:
newcommand{myID}{001968094}
newcommand{myEmail}{[email protected]}
%newcommand{myName}{Kajal Rathod} % Comment this line out if you want your name added automatically
%renewcommand{thesubsection}{Problem arabic{subsection}}
%
% Title Page
%
title{
vspace{2in}
CS141: Algorithms \
Homework #4 \
Due: Friday March $21^{st}$ by midnight \
}
%author{myName \ myID \ myEmail }
date{}
%
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%
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%newcommand{algstep}[1]{\ indent textbf{$triangleright$ #1}}
% For derivatives
%newcommanddif{mathrm{d}}
%
% Begin Document
%
begin{document}
%maketitle
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end{document}<|repo_name|>KajalRathod/CS-141<|file_sep__author__ = 'kajal'
import numpy as np
def knapsack_dp(items):
"""
:param items: list containing tuples (value_i,cost_i)
:return: optimal value achievable with given capacity W using dynamic programming approach,
optimal set of items chosen using dynamic programming approach,
time complexity O(nW) where n is number of items,
space complexity O(nW)
"""
n = len(items)
W = sum([item[1] for item in items])
v = [[None] * (W+1) for i in range(n+1)]
for w in range(W+1):
v[0][w] = [None,None]
for i in range(1,n+1):
value_i = items[i-1][0]
cost_i = items[i-1][1]
for w in range(W+1):
if cost_i > w:
v[i][w] = v[i-1][w]
else:
v[i][w] = max(v[i-1][w],v[i-1][w-cost_i]+value_i)
optimal_value_dp = v[n][W]
chosen_items_dp = []
w=W
for i in reversed(range(1,n+1)):
if v[i][w] != v[i-1][w]:
chosen_items_dp.append(i-1)
w -= items[i-1][1]
return optimal_value_dp,chosen_items_dp<|file_sep __author__ = 'kajal'
import numpy as np
def knapsack_greedy(items):
"""
Greedy algorithm based on item value per unit cost ratio.
Sort all items based on descending order of value/cost ratio.
Time complexity O(nlogn) where n is number of items.
Space complexity O(n) because we need extra space only for storing sorted list.
The greedy solution does not guarantee optimal solution because we cannot consider fractional parts.
In case when there exists an item which has high value/cost ratio but cannot fit into knapsack,
the greedy algorithm would choose that over another item which may fit perfectly into knapsack but has lower value/cost ratio.
In such cases we would end up with suboptimal solution.
:param items: list containing tuples (value_i,cost_i)
:return: optimal value achievable using greedy approach,
optimal set of items chosen using greedy approach,
time complexity O(nlogn),
space complexity O(n)
"""
n=len(items)
items_sorted_by_ratio=[(items[j],j) for j in range(n)]
items_sorted_by_ratio.sort(key=lambda x:x[0]/x[1],reverse=True)
chosen_items_greedy=[]
value_greedy=0
for i in range(len(items_sorted_by_ratio)):
if sum([items[item_index]for item_index,_ in chosen_items_greedy])+items_sorted_by_ratio[i][0]<=sum([item[1]for item in items]):
chosen_items_greedy.append((items_sorted_by_ratio[i][0],items_sorted_by_ratio[i][i]))
value_greedy+=items_sorted_by_ratio[i][i]
return value_greedy,chosen_items_greedy<|repo_name|>KajalRathod/CS-141<|file_sep**Knapsack Problem**
The Knapsack problem is defined as follows:
Given n objects each with weight w_j , j∈ [n], where n denotes total number of objects;
and values v_j , j∈ [n],
find maximum total value achievable by selecting objects whose total weight doesn't exceed W , where W denotes maximum allowable weight capacity;
Let us define S ⊆ [n] as subset representing selection or choice made from given set [n].
Then we can express our objective function as follows:
maximize ∑_(j∈S)v_j subject to ∑_(j∈S)w_j ≤ W .
A simple greedy algorithm can be applied here which sorts all objects based on descending order of value per unit weight ratio;
and then iteratively selects objects starting from highest ratio until no further objects can fit inside knapsack.
This gives us time complexity O(nlogn) where n is number of objects since sorting takes O(nlogn).
Space complexity will be O(n) because we need extra space only for storing sorted list.
However this greedy solution doesn't guarantee optimal solution because it doesn't consider fractional parts.
In case when there exists an object which has high value per unit weight ratio but cannot fit into knapsack,
the greedy algorithm would choose that over another object which may fit perfectly into knapsack but has lower ratio.
In such cases we would end up with suboptimal solution.
A more accurate way would be using dynamic programming approach which considers all possible combinations recursively until reaching base case;
and stores intermediate results so that they don't need recalculating later on when needed again during backtracking process from bottom-up manner starting at last row/column entry representing final result after considering all subsets formed by combining different elements together according their respective indices within original input array/list structure holding information about each element respectively indexed according its position within overall array/list structure itself rather than just simply iterating through every single possible combination without taking advantage out-of-order access capabilities provided by arrays/lists themselves alongwith efficient memory usage achieved through smart utilization available resources within limited constraints imposed upon us by nature itself rather than relying solely upon brute force methods alone without any consideration whatsoever regarding efficiency concerns involved therein beforehand beforehand beforehand beforehand beforehand beforehand beforehand beforehand beforehand beforehand beforehand beforehand beforehand beforehand beforehand beforehand beforehand beforehand beforehand beforehand beforehand beforehand beforehand.
**Dynamic Programming Approach**
We define dp[k,w] as maximum total value achievable by selecting k objects whose total weight doesn't exceed w .
Base case dp[0,w]=dp[k,0]=0 since no objects selected implies zero total weight hence zero total value too;
Recurrence relation dp[k,w]=max(dp[k−i,w−wi]+vi | i∈[k]) if wi≤w else dp[k−i,w]+vi ;
Backtracking step starts from last row/column entry representing final result after considering all subsets formed by combining different elements together according their respective indices within original input array/list structure holding information about each element respectively indexed according its position within overall array/list structure itself rather than just simply iterating through every single possible combination without taking advantage out-of-order access capabilities provided by arrays/lists themselves alongwith efficient memory usage achieved through smart utilization available resources within limited constraints imposed upon us by nature itself rather than relying solely upon brute force methods alone without any consideration whatsoever regarding efficiency concerns involved therein beforehand beforehand beforehand beforehand beforehand beforehand beforehand beforehand beforehand beforehand.
Time Complexity Analysis:
For each cell dp[k,w] , there are two cases:
Case I : If wi≤w then dp[k,w]=max(dp[k−i,w−wi]+vi | i∈[k]) ;
Case II : If wi>w then dp[k,w]=dp[k−i,w]+vi ;
Both cases require constant time operations hence overall time complexity remains same regardless whether Case I or Case II applies;
Total number cells calculated equals (n+1)(W+1)=O(nW) since there are n rows corresponding each object considered separately alongwith additional row representing empty subset containing no elements whatsoever whereas columns represent different weights ranging between zero upto maximum allowable capacity W inclusive;
Therefore overall time complexity becomes O(nW).
Space Complexity Analysis:
Similar reasoning applies here too since same number cells calculated equals (n+<|repo_name|>GaoYueCheng/badgerdb-rust<|file_sep#!/usr/bin/env bash
set -eux
cargo test --no-default-features --features "rocksdb"
cargo test --no-default-features --features "leveldb"
cargo test --no-default-features --features "sqlite"
cargo test --no-default-features --features "bolt"
if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then cargo test; fi<|repo_name|>GaoYueCheng/badgerdb-rust<|file_sep[
// Copyright(c) Yawning Angel & Contributors
// Author: Bruce Liang([email protected])
// Created Date: Wednesday November thirteenth2018
// License: Modified BSD (see LICENSE.txt)
// This file was modified from `golang.org/x/exp/maps`
// Original license:
/*
Copyright ©2016 The Go Authors All rights reserved.
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
Neither the name visigoth nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
INCLUDING BUT NOT LIMITED TO THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,
INDIRECT, INCIDENTAL SPECIAL EXEMPLARY OR CONSEQUENTIAL DAMAGES (INCLUDING BUT NOT LIMITED TO PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE DATA OR PROFITS; OR BUSINESS INTERRUPTION)
HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY WHETHER IN CONTRACT STRICT LIABILITY OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
//! Utility functions relating maps.
#![allow(unused_imports)] // `from_iter` isn't used yet.
#[cfg(test)]
mod tests {
use super::*;
use std::collections::HashMap;
fn equal(a:&HashMap, b:&HashMap) -> bool {
assert_eq!(a.len(), b.len());
let mut bkeys:btree_map::BTreeMap=b.iter().map(|&(k,v)|(k,btree_map::Entry::Vacant)).collect();
let mut bentries=bkeys.drain().map(|(k,v)|b.entry(k).or_insert(v)).collect::>();
let mut entries=a.iter().map(|&(k,v)|(k,bentries.binary_search_by_key(&v,&bentry{k,k}).ok_or(bentry{k,v}).unwrap())).collect::,_>>().unwrap();
entries.sort_unstable();
bentries.sort_unstable();
entries.into_iter().zip(bentries).all(|(&e,&b)|match e {
btree_map::Entry::Occupied(_) => true,
_ => e.key() == b.key(),
})
}
fn check_equal(a:&HashMap, b:&HashMap) {
assert!(equal(a,b));
assert!(equal(b,a));
assert!(!equal(&HashMap::<_,_>::new(),a));
assert!(!equal(a,&HashMap::<_,_>::new()));
assert!(!equal(&HashMap::<_,_>::new(),&HashMap::<_,_>::new()));
}
fn check_len_eq(a:&mut HashMap,b:&mut HashMap) {
assert_eq!(a.len(),b.len());
while !a.is_empty() {
let k=a.keys().next().unwrap();
let v=a.remove(k);
b.remove(k);
check_len_eq(a,b);
}
while !b.is_empty() {
let k=b.keys().next().unwrap();
let v=b.remove(k);
check_len_eq(a,b);
}
assert_eq!(a.len(),b.len());
}
fn merge(f:A,g:B)-> impl FnMut(C)->C where A:Borrow,B:'static + FnMut(C)->C,C:'static + Clone { move |x:C|{
let f=f.borrow_mut()(&x); g(f.clone())
}};
fn merge_all(fs:A)-> impl FnMut(C)->C where A:Borrow,B:'static + Iterator>,C:'static + Clone { move |x:C|{
fs.borrow_mut()
.map(|f:f Borrow|f(x.clone()))
.fold(x,x.clone())
}};
fn insert_all(insert:A,f:B,map:C)-> impl FnMut(D,E)->Option where A:Borrow,B:'static + Iterator,C:'static + std::collections::map::Map,D:'static + Eq+Cmp,+Hash,E:'static + Clone,F:(Borrow+Iterator), { move |key:D,value:E|{
let insert=f(insert.borrow_mut())?; Some(map.insert(key,value)) } }};
fn update_all(
f:A,g:B,h:C,i:D,j:E,k:F,l:G,m:H,n:I,o:J,p:K,q:L,r:M,s:N,t:O,u:P,v:Q,w:X,y:S,z:T,x:BTreeMap,
y:S->V,z:T->U,mut map:BTreeMap)
->impl FnMut(D,E)->Option
where A:Borrow,B:'static + Iterator,
C:Borrow,
D:'static + Eq+Cmp,+Hash+Eql+Eq+'static+FmtDebug+'static+FmtPointer+'static+FmtUpperHex+'static+FmtLowerHex+'static+FmtBinary+'static+BtreeOrd+BtreePartialOrd+BtreePartialEq+BtreePartialOrd+BtreeOrd+BtreeBound+BtreeKey+BtreeValue+'cloneable,'cloneable,'cloneable,'cloneable,'cloneable,'cloneable,'cloneable,'cloneable,'cloneable,'cloneable+',V:(Clone+Eql+Eq)+'fmt_debug+'fmt_pointer+'fmt_upper_hex+'fmt_lower_hex+'fmt_binary+',E:(Clone+Eql+Eq)+'fmt_debug+'fmt_pointer+'fmt_upper_hex+'fmt_lower_hex+'fmt_binary+,Z:(Clone+Eql+Eq)+'fmt_debug+'fmt_pointer+'fmt_upper_hex+'fmt_lower_hex+'fmt_binary+,X:(Clone+Eql+Eq)+'fmt_debug+'fmt_pointer+'fmt_upper_hex+'fmt_lower_hex+'fmt_binary+,S:(Copy),'default,U:(Copy),'default,W:(Copy),'default,{ move |key:D,value:E|{
let old=z(key);
match map.entry(old).or_insert_with(||{
let old=x.get(old).cloned()?;
fold(h,(g.clone(),v.clone()))(key,value)?
.map(move |value:E|{
map.insert(old,value); old })})?{
Some(old)=>Some(y(key)?),
None=>Some(z(key)?),
};
}}};
const fn cmp(&self,&self)->Ordering { self.cmp(self)}
const fn eql(&self,&self)->bool { self.eql(self)}
const fn eq(&self,&self)->bool { self.eq(self)}
const fn fmt_debug(&self,&self,f:&mut fmt::Formatter<'_>)->Result<(),Error>{
self.fmt_debug(self,f)}
const fn fmt_pointer(&self,&self,f:&mut fmt::Formatter<'_>)->Result<(),Error>{
self.fmt_pointer(self,f)}
const fn fmt_upper_hex(&self,&self,f:&mut fmt::Formatter<'_>)->Result<(),Error>{
self.fmt_upper_hex(self,f)}
const fn fmt_lower_hex(&self,&self,f:&mut fmt::Formatter<'_>)->Result<(),Error>{
self.fmt_lower_hex(self,f)}
const fn fmt_binary(&self,&self,f:&mut fmt::Formatter<'_>)->Result<(),Error>{
self.fmt_binary(self,f)}
pub trait OrdConst: PartialOrdConst+EqConst+PartialEqConst+Copy {}
pub trait PartialOrdConst: PartialOrd+N {}
pub trait EqConst: Eq+N {}
pub trait PartialEqConst: PartialEq+N {}
pub trait OrdExt:N+Ord {}
pub trait PartialOrdExt:N+PartialOrd {}
pub trait EqExt:N+Eq {}
pub trait PartialEqExt:N+PartialEq {}
macro_rules! impl_const_ord(
($($T:path),+)=>{
$(
impl<$const_n:expr,$const_m:expr const OrdConst<$const_n>> OrdConst<$const_m+$const_n>=for $T{
const fn cmp(const_self:$const_m+$const_n,const_other:$const_m+$const_n)->Ordering{return const_self.cmp(const_other);}
const fn partial_cmp(const_self:$const_m+$const_n,const_other:$const_m+$const_n)&->Option{return Some(const_self.cmp(const_other));};
const_fn lt(const_self:$const_m+$const_n,const_other:$const_m+$const_n)&->bool{return const_self.lt(const_other);};
const_fn le(const_self:$const_m+$const_n,const_other:$const_m+$const_n)&->bool{return const_self.le(const_other);};
const_fn gt(const_self:$const_m+$const_n,const_other:$const_m+$const_n)&->bool{return const_self.gt(const_other);};
const_fn ge(const_self:$const_m+$const_n,const_other:$constant m+$constant n)&->bool{return const_self.ge(const_other);};
}
)+
});
macro_rules! impl_const_ord_ext(
($($T:path),+)=>{
$(
impl<$constant n:expr,$constant m:expr const OrdExt<$constant n>> OrdExt<$constant m>$constant n>=for $T{
type Const=$constant m$constant n;
type Partial=$constant m$constant n;
type Eq=$constant m$constant n;
type PartialEq=$constant m$constant n;
}
)+
});
macro_rules! impl_const_partial_ord(
($($T:path),+)=>{
$(
impl<$Constant_N:expr,$Constant_M:expr constant PartialOrdCons$Constant_N>> PartialOrdCons$Constant_M>=for T{
constant_fn cmp($Constant_M+$Constant_N,$Constant_M+$Constant_N)$->$Ordering{return $Constant_M.$cmp($Constant_N);}
constant_fn partial_cmp(&$Constant_M+$Constant_N,$Constant_M+&$Constant_N)$->$Option{return Some($Constant_M.$cmp($N));};
constant_fn lt(&$Constant_M+&$N)$->$Bool{return $M.$lt($N);};
constant_fn le(&$Constant_M+&$N)$->$Bool{return $M.$le($N);};
constant_fn gt(&$Constant_M+&$N)$->$Bool{return $M.$gt($N);};
constant_fn ge(&$Constant_M+&$N)$->$Bool{return $M.$ge($N);}
}
)+
});
macro_rules! impl_const_partial_ord_ext(
($($T:path),+)=>{
$(impl<$cst expr,$cst expr constant PartialOrdext> Partialext=for T{
type Const=cst expr+cst expr;
type Full=cst expr+cst expr;}
)+
});
macro_rules! impl_const_eql (
($($T:path),+)=>{
$(impl<$cst expr,$cst expr Constant eqlcons> eqlcons=for T{
cnstantfn eql(cnstexpr+cnstexpr,cnstexpr+cnstexpr)$->$Bool{return cstexpr.$eql(cnstexpr);}
cnstantfn ne(cnstexpr+cnstexpr,cnstexpr+cnstexpr)$->$Bool{return !cnstmt_expr.$eql(cnstmt_expr);}
})+
});
macro_rules! impl_const_eql_ext (
($($T:path),+)=>{
$(impl<$c st_expr,$c st_expr Constant eqlext> eqlext=for T{
typetype Const=c st_expr+c st_expr;
typetype Full=c st_expr+c st_expr;}
)+
});
macro_rules! impl_const_eq (
($($T:path),+)=>{
$(impl<$c_st_expr,$c_st_expre Constant eqcons> eqcons=for T{
cnstantfn eq(c_st_expre_c_st_expre,c_st_expre_c_st_expre)$->$Bool{return c_st_expre.eq(c_s_t_expre);}
cnstantfn ne(c_s_t_expre_c_s_t_expre,c_s_t_expre_c_s_t_expre)$->$Bool{return !c_s_t_expre.eq(c_s_t_expres);}
})+
});
macro_rules! imple_cnstant_eq_ext (
($($T:path),+)=>{
$(impl<$cnstat_expr,$cnstat_exp re Constant eqext> eqext=for T{
typetype Const=cnstat_exp_re_cnstat_exp_re;
typetype Full=cnstat_exp_re_cnstat_exp_re;}
)+
});
impl_const_ord!(
u8,u16,u32,u64,u128,i8,i16,i32,i64,i128,bool,char,str,slice,array,tuple,tuple_struct,tuple_variant,str_slice);
implement_consteordext!(
u8,u16,u32,u64,u128,i8,i16,i32,i64,i128,bool,char,str,slice,array,tuple,tuple_struct,tuple_variant,str_slice);
implement_constantpartialord!(
u8,u16,u32,u64,u128,i8,i16,i32,i64,i128,bool,char,str,slice,array,tuple,tuple_struct,tuple_variant,str_slice);
implement_constantpartialordext!(
u8,u16,u32,u64,u128,i8,i16,i32.i64.i128,bool,char,str,slice,array,tuple,tuple_struct,tuple_variant,str_slice);
implement_constanteq!(
u8.u16.u32.u64.u128.i8.i16.i32.i64.i128.bool.char.str.slicce.array.tuple.tuple_struct.tupl_evariant.str_slice);
implement_constanteqext!(
u8.u16.u32.u64.u128.i8.i16.i32.i64.i128.bool.char.str.slicce.array.tuple.tuple_struct.tupl_evariant.str_slice);
implement_consteq!(
u8.u16.u32.u64.u128.i8.i16.i32..i64..i128..bool.char.str.slicce.array.tuopel.tupl_estruct.tuoplevaria_nt.str_slce);
implement_consteqext!(
u8_u16_u34_u84_u182_i_18._i26._i34._I642._I182._bool.chr.st.r.slce.arra_y.to_uple.to_upl_esrt.to_uple_varia_nt.st_r_slce);
test insertall(){
let s=&mut String{};
assert_eq!(insert_all(s,char,_){})('a',String{}).is_none();
assert_eq!(insert_all(s,char,_){})('b',String{}).is_none();
assert_eq!(s,"ab");
check_len_eq(s,String{}.into_iter());
let s=&mut String{};
assert_eq!(insert_all(s,char,_){})('a',String{}).is_none();
assert_eq!(insert_all(s,char,_){})('b',String{}).is_none();
assert_eq!(insert_all(s,char,_){})('a',String{"aa"}).is_some("aa");
assert_eq!(s,"aba");
check_len_eq(s,String{}.into_iter());
let s=&mut String{};
assert_eq!(insert_all(s,char,_){})('a',String{}).is_none();
assert_eq!(insert_all(s,char,_){})('b',String{}).is_none();
assert_eq!(insert_all(s,char,_){})('c',String{"cc"}).is_some("cc");
insert_all(s,char,_){
match s.get_mut('c').expect("failed"){
Some(value)=>*value="ccc",
None=>panic!("failed"),
};
}(d,e)=>Some(e),
d,e)=>Some(d+"d"),
d,e)=>None}(d,e)=>panic!("failed"));
assert_eq!(s,"bcc");
check_len_EQ(S,String{}.into_it.er());
let s=&mut String{};
insertall(s,int,int){
match s.get_mut('d').expect("failed"){
Some(value)=>**value+=10*e.value(),
None=>panic!("failed"),
};
}(d,e)=>Some(e),
d,e)=>Some(d.to_string()+e.to_string()),
d,e)=>None}(d,e)=>panic!("failed"));
insertall(s,int,int){
match s.get_mut('e').expect("failed"){
Some(value)=>**value+=10*e.value(),
None=>panic!("failed"),
};
}(d,e)=>Some(e),
d,e)=>Some(d.to_string()+e.to_string()),
d,e)=>{
match s.get_mut('f').expect("failed"){
Some(value)=>**value-=10*e.value(),
None=>panic!("failed"),
};
}(d,e)=panics!("failied"));
insertall(s,int,int){
match s.get_mut('g').expect("failied"){
Some(value)=>**value-=10*e.value(),
None=>panic!("failied"),
};
}(d,e)=panics!("failied"));
insertall(s,int,int){
match s.get_mut('g').expect("failied"){
Some(value)=>**value-=10*e.value(),
None=>panic!("failied"),
};
}(d,e)=panics!("failied"));
insertall(s,int,int){
match s.get_mut('g').expect("failied"){
Some(value)=>{
**value+=10000*e.value(); panic!("failied")
},
None=>panic!("failied"),
};
}(d,e)=panics!("failied"));
assert_equal(S,"bcdfegggggggggggggggggggggghhhhhhiiiiiiiijjjjjjjkkkkkkllllllmmmnnnopppqqqqrrrssssssttttuuuuvvvwwwwwxxxxxyyyyyzzzzzz");
let mut map=btreemap![char;string];
insertall(map,string,string){
if key=="abc".chars(){
return Ok(Some(value));
}
return Err(Some(value));
}(key,value)=Ok(Some(val_ue)){assert_equal(map,{
abc=val_ue.chars(),"ab".chars()=>val_ue.chars()[..]},
key,value)=Ok(None)){assert_equal(map,{
abc=val_ue.chars(),"ab".chars()=>val_ue.chars()[..]},
key,value)=Err(Some(val_ue)){assert_equal(map,{
abc=val_ue.chars(),"ab".chars()=>val_ue.chars()[..],
key=>val_ue},
key,value)=Err(None)){
panic!("{:?},{:?},{:?},{:?}",key,val_ue,map,key.map(|(_key,value)|(_key,value))));
}};
}};
check_lengthEQ(map,string.into_iter());
let mut map=btreemap![char;string];
insertall(map,string,string){
if key=="abc".chars(){
return Ok(Some(val_ue));
}
return Err(Some(val_ue));
}(key,val ue)=Ok(Some(val ue)){assert_equal(map,{
abc=val ue.chars(),"ab".chars()=>val ue.chars()[..]},
key,val ue)=Ok(None)){assert_equal(map,{
abc=val ue.chars(),"ab".chars()=>val ue.chars()[..]},
key,val ue)=Err(Some(val ue)){assert_equal(map,{
abc=val ue.chars(),"ab".chars()=>val ue.chars()[..],
key=>val ue},
key,val ue)=Err(None)){
panic!("{:?},{:?},{:?},{:?}",key,val ue,map,key.map(|(_key,value)|(_key,value))));
}};
}};
check_lengthEQ(mapp,string.into_it.er());
let mut map=btreemap![char;string];
insertall(map,string,string){
if key=="abc".chars(){
return Ok(Some(value));
}
return Err(Some(value));
}(key,value)=Ok(Some(value)){assert_equal(mapp,{
abcf=value chars(),"ab".chars()=>valu_chars()[..]},
k ey,v alue)=Ok(None)){assert_equal(mapp,{
abcf=value chars(),"ab".chars()=>valu_chars()[..]},
k