This repo contains the code to build and test the dotnet-patcher tool.
Motivations
I like modding games, and many games are now using .Net as a scripting language.
It can a home-made solution or just because the game is developped using the UnityEngine. I’m also a Linux user. I don’t have any
Windows machine. So it means that I play on Linux, using Steam’s Proton, or Wine
when the game isn’t natively supported on Linux.
Some games have official mod support like Cities: Skylines. Unfortunately you
may be quite limited to what you can mod because no extension points are
provided for everything.
Some games have unofficial, closed-source, mod loaders like, for example Raft, which is using RaftModLoader. Unfortunately, it doesn’t play
well with Linux/Proton. I failed to make it work. Some documentation do mentions
some step to make it work involving installing it on a Windows, to then copy on
the Linux. I don’t find this an acceptable workaround. Documentation also
mentions that it may work better if using the compiler from a Mono installation
but that would require too much work then it won’t be done. As it’s
closed-source I can’t contribute to solve the situation.
Some other games rely on Harmony
to provide mod support. This is by far the least problematic as it’s a framework
to patch assemblies. The only issues I have with this it that its a bit complex
to use. Sometimes I juste want to make a minor change so I don’t want to create
a full new project, dealing with dependencies, installing Harmony in the game
and all that stuff just to change two or three opcodes.
And finally, some other games or applications just don’t have any mod support.
Technically, I could make it work using Harmony, as it’s supposed to be working
on any .Net or Mono application. But I still finds it overkill for small
patching.
Also, I am disappointed by the Reflexil‘s UI/UX on
Windows and even more disappointed that there is no Linux’s version of it.
The AvaloniaILSpy fork is
crashing very often, it’s UI is full of bugs. It’s a huge beast, relying on
technologies I dislike (WPF for example), so that I don’t want to get involved
into it to solve my issues.
For all these reasons, I created this tool. I aim at making it as lightweight,
easy to use, portable as possible.
As this is written using .Net Core, it should run everywhere this framework is
available. Including Windows, Linux and Mac OS. Thus being said, I develop and
test only on Linux. I don’t have any other OS to test, so I may not be able to
reproduce your issue.
Build from source
git clone git@github.com:ctxnop/dotnet-patcher.git
cd dotnet-patcher
dotnet build
Patching an assembly
Create a class that implement the IPatch interface. It may be located anywhere
in the project’s folder, but you may want to keep things tidy so put it in the Patches folder.
Rebuild the tool using dotnet build, then you can patch an assembly using the
following command:
Currently, patching an assembly means using Mono.Cecil and requires
understanding the IL code which is a kind-of assembler. This can be tricky, so
you probably want to get some help from tools like ILSpy or Reflexil, to check
what was the original code, what is the patched code, how does it translate from
C# to IL and vice-versa…
publicboolApply(AssemblyDefinitionasm){asm.Patch((td)=>{// This is a selection predicate: return 'true' to select this TypeDefinition// Called for all TypeDefinition found in the AssemblyDefinitionreturnstring.CompareOrdinal(td.FullName,"TypeToPatch")==0;},(md)=>{// This is a selection predicate: return 'true' to select this MethodDefinition// Called for all TypeDefinition selected by the above predicatereturnstring.CompareOrdinal(md.Name,"MethodToPatch")==0;},(ilp)=>{ilp.Clear();// Remove all previous instructionsilp.Emit(OpCodes.Ret);// This method will now just return and do nothing.});}
A very early and incomplete support for C# compilation is added, so that you can
provide a method’s body code. It will generate a source code corresponding to
the original but with the code you provided as the method’s body. It will then
compiles everything into an in-memory assembly using Roslyn. And finally it use
Mono.Cecil back to read the resulting assembly and replace the original
intructions with the one founds in the generated assembly.
When invoking the patch command, a backup of the original assembly is created
in the same directory, with a .dporg extension. The backup is made only if not
already existed. Patches are applies on the backup file, so that calling patch
a second time on the same assembly won’t result in a different patched assembly.
This means that you can revert any change by restoring the backup.
Keep in mind that this client’s purpose is for trading in the bittrex exchange; so, be thoughtful and take the time to understand how to use this application before you start trading.
In order to start trading, make sure that you create an api key in bittrex with the following permissions:
Notes to run the client
Make sure that the node server is running to serve the files to the browser. All supporting files (bootstrap,jquery, etc) are served to the client from the node_modules folder
When the page is loaded for the first time, click on ‘Update Settings’ and enter the api key and secret key to start trading – this information is only maintained in the browser local storage. Make sure to keep this information safe, in case the data is loss and needs to be re-entered.
After updating the settings for the first time, refresh the page to reconnect to the server with the api information – this only needs to be done when the api info is entered, from thereon, it’ll be loaded from the local storage
Detecting Target of Sarcasm Using Ensemble Methods
Code related to the ALTA’s 2019 Shared Task – “Detecting Target of Sarcasm using Ensemble Methods”. Our work was presented in ALTA 2019
The 17th Annual Workshop of the Australasian Language Technology Association
Basically , it is to give you an overview on what is this project about. Keep in mind that I’ve built this very quick and dirty way. It is not in the best shape but I am trying to make sure that at least people can download it and make it easier for them to download and use it
Requirements
python 3.7
numpy
sckikit-learn
pandas
ALTA 2019 Shared Data Challenge Dataset (Please kindly obtain for them)
Will update with pip requirements.txt so that you can have the exact one. I will make it easier for you all :). At the moment , I apologize – you just need to go through the files for the imports and install the pip To make your life a bit easier , the major imports or the stuff that I use can be found in the following files below 🙂
What are the files for
DoItAll.py – It runs the rule-based system. Just load the CSV file with the title on the target. It will pick it up and get back to you
LinearRegressionClassifer.py – The main linear regresson. You can take a look at the parameters that were used and also the way how we load the embeddings (.npy which was used from Google Sentence Encoder)
lstm.py – This to generate the word embeddings – “Universal Sentence Encoder” . I should rename it , but this shows how you can quickly transform it
rule_weighting.py – Our genetic algorithim in optimizing the weightages for it
rules_implement.py – The rules from Joshi et al (2018) with our modifications. The original methods are still kept so that you may able to reproduce it
Problems Running ?
Raise an Issue or Reach out to me – I can help. Again it’s my first time of publishing some public project. I try to follow best practices , so please pardon me if there are mistakes
TO-DO
Improve the Read Me
Refactor the Code – some of the file names do not even make sense or even the method. It can be optimized further
Add PIP Requirements
Publish Jupyter-Notebook (to help people step-by-step and explain it to them)
QUICK and DIRTY INFO
generate migration files (not dumps!) with indexes, and foreign keys, for one table, comma separated list of tables, by part of table name, for all tables by
generate migrations based on table data – in two ways – as batchInsert Query or as insert via model
generate migrations based on PHPDOC and model properties
to the require-dev section of your composer.json file.
Just install, go to gii and use (By default composer bootstrap hook)
ANNOTATION SYNTAX
In general the syntax of column definitions is based on style of yii-migration, only separated by “|” and provide a little more opportunities for reducing code
as you see in examples – empty brackets not necessary
also shortcut expr() will be replaced to defaultExpression() and default() to defaultValue
You can add annotations in your model(not necessary AR or yii\base\Model or Object or stdClass)
@db (db2) – specify connection id required for migration ‘db’ – by default”
@table ({{%my_table}})– specify table for migration”
Supported column annotations:
As separate annotation above class or above current variable
create new class, inherited from \insolita\migrik\resolver*ColumnResolver
override required methods, or create methods for exclusive columns based on database types – see insolita\migrik\resolver\BaseColumnResolver resolveColumn() phpdoc and realization
Use own resolver for definition of indexes or relations
create new class, inherited from \insolita\migrik\resolver\TableResolver
DSA-Bootcamps is a initiative by Code Dazzlers to help students in problem solving. Here you will going to solve 200++ problems in the span of 6 week from all DSA topics.
All session will be live on Google Meet.
If you miss the live session, you can checkout the recorded session at our youtube channel.
DSA-Bootcamps is a initiative by Code Dazzlers to help students in problem solving. Here you will going to solve 200++ problems in the span of 6 week from all DSA topics.
All session will be live on Google Meet.
If you miss the live session, you can checkout the recorded session at our youtube channel.
새로 기록하는 dp에 이전경로 전체를 포함하는 경로가 아닌… 바로 이전 위치만을 표기하는 것이 보통 더 좋다. 출력할때 는 역추적하여 출력하면 됨
학습내용:
2020년
Dynamic Programming; DP
특정 범위까지의 값을 구하기 위해서 그것과 다른 범위까지의 값을 이용하여 효율적으로 값을 구하는 알고리즘 설계 기법이다.
조금 장난스럽게 말해서 답을 재활용하는 거다. 앞에서 구했던 답을 뒤에서도 이용하고, 옆에서도 이용하고…엄밀히 말해 동적 계획법은 구체적인 알고리즘이라기보다는 문제해결 패러다임에 가깝다. 동적 계획법은 “어떤 문제를 풀기 위해 그 문제를 더 작은 문제의 연장선으로 생각하고, 과거에 구한 해를 활용하는” 방식의 알고리즘을 총칭한다.
DP알고리즘- LIS알고리즘(+LCS)알고리즘
#include<vector>//LIS알고리즘intmain() {
vector<int> v;
int N; //N수열 갯수
cin >> N;
int k;
for (int i = 0; i < N; i++) {
cin >> k; //수열을 하나씩auto it = lower_bound(v.begin(), v.end(), k);
// 알맞은 자리 찾기
*it = k;
}
cout << v.size() << '\n';
}
//LCS 알고리즘for (int i = 1; i <= len1; i++) {
for (int j = 1; j <= len2; j++) {
if (str1[i - 1] != str2[j - 1]) {
DP[i][j] = max(DP[i - 1][j], DP[i][j - 1]);
}
else {
DP[i][j] = DP[i - 1][j - 1] + 1;
}
}
}
cout << DP[len1][len2] << '\n';
이차원 배열 , 배열을 이용해서 이전값,과 계속 비교해서 최적의 값을 찾는 DP
약수 구할때
vector<int> v;
for(int i=1; i<=n ;i++){
if(n % i ==0)
v.push_back(i);
} //이런 식으로 구하는 것 보다for (int i = 2; i * i <= g; i++)
if (!(g % i)) {
ans.push_back(i);
if (i != g / i) ans.push_back(g / i);
}//이렇게 하고 필요하면 sort로 정렬하는것이 한참 빠르다
유클리드 호제법 – 두 수의 최대 공약수 구하기
intgcd(int a, int b)
{
return b ? gcd(b, a%b) : a;
}
intgcd(int a, int b)
{
int c;
while(b)
{
c = a % b;
a = b;
b = c;
}
return a;
}
이항정리
페르마 소정리
피보나치수 피사노주기(피보나치수의 어떠한 수에 나머지는 일정주기가 있음 생각해 보면 당연한 것 )
p(n)= abc,p(n+1)=abc+1 일때
p(n*자연수+ i )%abc= p(i)% abc= p(i) 일것.
벨만 포드 알고리즘, 다익트라알고리즘
다익트라 알고리즘
매번 방문하지 않은 노드 중에서 최단 거리가 가장 짧은 노드를 선택합니다.
음수 간선이 없다면 최적의 해를 찾을 수 있습니다.
벨만 포드 알고리즘
매번 모든 간선을 전부 확인합니다.
따라서 다익스트라 알고리즘에서의 최적의 해를 항상 포함합니다. 시간복잡도는 더 높음
다익스트라 알고리즘에 비해서 시간이 오래 걸리지만 음수 간선 순환을 탐지 할 수 있습니다.
importsysinput=sys.stdin.readlineINF=int(1e9) #무한을 의미하는 값으로 10억을 설정defbf(start):
# 시작 노드에 대해서 초기화dist[start]=0# 전체 n번의 라운드(round)를 반복foriinrange(n):
# 매 반복마다 "모든 간선"을 확인하며forjinrange(m):
cur=edges[j][0]
next_node=edges[j][1]
cost=edges[j][2]
# 현재 간선을 거쳐서 다른 노드로 이동하는 거리가 더 짧은 경우ifdist[cur] !=INFanddist[next_node] >dist[cur] +cost:
dist[next_node]=dist[cur]+cost# n 번째 라운드에서도 값이 갱신 된다면 음수 순환이 존재ifi==n-1:
returnTruereturnFalse# 노드의 개수, 간선의 개수 입력받기n,m=map(int, input().split())
# 모든 간선에 대한 정보를 담는 리스트 만들기edges= []
# 최단 거리 테이블을 모두 무한으로 초기화dist=[INF] * (n+1)
# 모든 간선 정보를 입력 받기 for_inrange(m):
a, b, c=map(int, input().split())
# a번 노드에서 b번 노드로 가는 비용이 c라는 의미edges.append((a,b,c))
# 벨만 포드 알고리즘을 수행negative_cycle=bf(1) # 1번 노드가 시작노드ifnegative_cycle:
print("-1")
else:
# 1번 노드를 제외한 다른 모든 노드로 가기 위한 최단 거리 출력foriinrange(2,n+1):
# 도달할 수 없는 경우, -1을 출력ifdist[i] ==INF:
print("-1")
# 도달할 수 있는 경우 거리를 출력else:
print(dist[i])
플로이드 와샬 알고리즘
모든 정점에서 다른 모든 정점으로의 최단경로를 찾는 알고리즘
이차원 배열로 각 정점에서 각정점으로가는 최단거리 초기화 후, 각 정점을 지난다고 가정하여 반복문
# baek11779 최소비용 구하기2 importsysinput=sys.stdin.readlinen=int(input())
m=int(input())
INF=int(1e9)
# 입력 ,변수 초기화edges=[[INFforiinrange(n+1)]for_inrange(n+1)]
for_inrange(m):
a,b,c=map(int,input().split())
edges[a][b]=min(c,edges[a][b]) # 경로가 같은데 비용이 다른 것 있다는 언급이 없었지만 생각했어야..start_city,end_city=map(int,input().split())
costs=[INFforiinrange(n+1)] # 다익스트라 start_city에서의 거리 배열parents=[0foriinrange(n+1)] # 경로 추적용 이전위치visitied=[Falseforiinrange(n+1)]
deffind_lowest_cost_node(costs): # 다익스트라 최단거리 찾는 함수lowest_cost=INFlowest_cost_node=Nonefornodeinrange(len(costs)):
cost=costs[node]
ifcost<lowest_costandvisitied[node]==False:
lowest_cost=costlowest_cost_node=nodereturnlowest_cost_node# 다익스트라node=start_citycosts[start_city]=0visitied[start_city]=TruewhilenodeisnotNone:
cost=costs[node]
neighbors=edges[node]
foriinrange(len(neighbors)):
new_cost=cost+neighbors[i]
ifcosts[i]>new_cost: # 현재 가지고 있는 cost보다 최단 거리 라면costs[i]=new_costparents[i]=nodevisitied[node]=Truenode=find_lowest_cost_node(costs)
print(costs[end_city])
trace=[]
current=end_citywhilecurrent!=start_city:
trace.append(current)
current=parents[current]
trace.append(start_city)
trace.reverse()
print(len(trace))
foriintrace:
print(i,end=' ')
트리의 지름 (임의의 두 노드 중 가장 먼 노드 길이)
# baek1167 트리의 지름# 트리의 지름을 구하는 방법은 정해져있다. # 먼저 임의의 정점부터 모든 정점까지의 거리를 구하여 # 가장 먼 거리를 가진 정점을 구한다. # 그 후에 그 정점으로부터 모든 정점까지의 거리를 다시 구해서 # 그 거리들 중 가장 먼 값이 트리의 지름이 된다.importsysinput=sys.stdin.readline# inputv=int(input())
# variable initedge=[[] for_inrange(v+1)]
visited=[Falsefor_inrange(v+1)]
maxIndex=0# 최대 값 indexmaxDistance=0# 최대 값defdfs(node ,cost): #dfs함수globalmaxDistanceglobalmaxIndexifvisited[node] !=False: #첫 방문 시에만 진행returnifmaxDistance<cost: # 최대값 설정maxDistance=costmaxIndex=nodevisited[node] =True# 방문 설정fornext,disinedge[node]: # 연결된 노드 dfsdfs(next,cost+dis)
# inputforiinrange(1,v+1):
temp=input().split()
forjinrange(1,len(temp)-1,+2):
edge[i].append([int(temp[j]),int(temp[j+1])])
# 임의의 노드(1)에서 dfs(bfs)수행 후 dfs(1,0)
# 가장 먼 노드에서 다시 dfs(bfs)수행 하면for_inrange(v+1): visited[_]=Falsedfs(maxIndex,0)
# 이때 나온 최장 거리가 트리의 지름print(maxDistance)
파이썬의 재귀함수최대는 1000
재귀함수를 사용할 때 ,맞는 거 같은데 이상하게 런타임 에러가 발생할 때는 sys.setrecursionlimit(10**9)
을 이용해 재귀함수 제한을 늘려서 시도
Union_find (합집합 찾기)
재귀를 이용하여 – 이것도 재귀 깊이 제한을 늘려야한다
# baek1717 집합의 표현importsysinput=sys.stdin.readlinesys.setrecursionlimit(10**9)
n,m=map(int,input().split())
arr=[-1for_inrange(n+1)]
parent=[_for_inrange(n+1)]
defgetParent(parent,x): # 부모 노드를 찾는 함수ifparent[x]==x : returnxparent[x]=getParent(parent,parent[x])
returnparent[x]
defunionParent(parent,a,b):# 두 부모 노드를 합치는 함수a=getParent(parent,a)
b=getParent(parent,b)
ifa<b: parent[b]=aelse : parent[a]=bdefcheckUnion(parent,a,b): # 두 노드가 같은 부모노드를 가지는지 확인하는 함수a=getParent(parent, a)
b=getParent(parent, b)
ifa==b: returnTruereturnFalsefor_inrange(m):
check,a,b=map(int,input().split())
ifcheck==0:
unionParent(parent,a,b)
elifcheck==1:
ifcheckUnion(parent,a,b):
print("YES")
else:
print("NO")
크루스칼 알고리즘
가장 가중치가 낮은 간선부터 정렬하여 union find를 사용하여 연결되어 있지 않은 집합끼리 간선 연결, 모든 정점이 연결되면 종료
# baek1197 최소 스패닝 트리fromcollectionsimportdequeimportsysinput=sys.stdin.readlinesys.setrecursionlimit(10**9)
# union-find 함수들defgetParent(parent,x): # 부모 노드를 찾는 함수ifparent[x]==x : returnxparent[x]=getParent(parent,parent[x])
returnparent[x]
defunionParent(parent,a,b):# 두 부모 노드를 합치는 함수a=getParent(parent,a)
b=getParent(parent,b)
ifa<b: parent[b]=aelse : parent[a]=bdefcheckUnion(parent,a,b): # 두 노드가 같은 부모노드를 가지는지 확인하는 함수a=getParent(parent, a)
b=getParent(parent, b)
ifa==b: returnTruereturnFalse# union-find 함수들# inputv,e=map(int,input().split())
parent=[ _for_inrange(v)]
edges=[]
for_inrange(e):
a,b,c=map(int, input().split())
edges.append([c,a-1,b-1])
edges=sorted(edges) # 정렬 후cnt=0# 현재 연결된 정점 갯수 확인을 위한total_weight=0# 최소 가중치 합 forweight,a,binedges: # 정렬된 간선으로 반복문ifnotcheckUnion(parent,a,b): # 현재 간선의 두 정점이 연결되어 있지 않으면total_weight+=weight# 최소 가중치 합에 포함cnt+=1unionParent(parent,a,b) # 연결되었음을 union-find 기법으로ifcnt>=v-1: # 모두 연결되엇으면-종료breakprint(total_weight) # 결과 출력
어떤 작업(예제에서는, 앞으로 한 칸 가기)을 여러 번 반복해야 할 때, 이를 빠르게 처리할 수 있게 해 줍니다.
하나의 값을 채우기 위해서, table을 두 번 연속으로 참조하는 과정이 들어갑니다.
정점이나 원소의 개수(N)뿐만 아니라, 반복할 횟수(K)가 시간·공간복잡도에 들어갑니다.
만약 이 K가 20, 30 하는 정도로 굉장히 작다면 sparse table의 크기도 굉장히 작아지고, 사실 쓸 필요가 없을 수도 있습니다.
반대로 K가 굉장히 커져서 2^64까지도 간다면 sparse table의 크기도 커집니다. *“어차피 log를 붙이면 64배밖에 안 되는 거 아냐?”* 하고 생각하실 지도 모르지만, N=500,000 (50만)인 경우에 (64·N)짜리 int 배열의 크기는 122 MiB나 됩니다. 메모리 제한이 빡빡하다면 어려울 지도 몰라요.
기록 대상을 미리 알고 있어야 하며, 중간에 바뀌면 안 됩니다. 예를 들어 한 화살표가 바뀌면, 그로 인해 배열 전체를 갈아엎고 새로 계산해야 할 지도 모릅니다. 그러면 전처리(preprocessing)를 해놓은 의미가 없죠.
SMBIOS bumped to MacBookPro12,1 since Monterey dropped support for older Macs.
Installer will not boot on without proper power management since macOS 10.15.
Booting from USB will either require installing with a port limit patch + USB2 or my USBMap.kext.
My USBMap will only work for MacBookPro12,1 SMBIOS.
Inbuilt Intel WiFi may work with itlwm, but can’t test since I sold and replaced. I use Broadcom BCM94352HMB (only use Brcm kexts if you also have this setup).
Either way make sure using BlueToolFixup.kext instead of BrcmBluetoothInjector.kext or IntelBluetoothInjector.kext on macOS 12.
What works (as of macOS 12 beta 2):
Ethernet
USB / Card Reader
Battery Status (patched)
Multi-touch Trackpad Gestures
Hotkeys for Audio and Brightness (patched)
Audio
Out: Speakers, Jack and HDMI
In: Webcam (Motherboard does not support combojack input)
Webcam + Microphone
WIFI/Bluetooth – Broadcom BCM94352HMB (see BIOS whitelist removal)
Also big thanks to RehabMan for all his amazing resources.
Flashing your BIOS / Whitelist removal
Only do this with guidance of an expert
You won’t be able to flash a new BIOS from a USB stick since this is write protected (even with sleep bug). This laptop sadly has no Libreboot / Coreboot support, so you will need to get someone to unlock your image for you.
Get in touch with an expert bios-mods (or contact the guy who helped me at pythonic2016@gmail.com). I don’t get paid for linking this, I am just happy with the result and their work. Consider giving them a good tip!
Once you have a person to help you. Open up your laptop and unplug your batteries (CMOS and the main battery that you need to remove in order to open up the laptop).
Locate your BIOS Chip (W25Q64BV ID:0xEF4017 Size: 8192KB). In terms of connecting the clip cable make sure PIN 1 of SPI and the Chip; there is a little mark; are connected (HQ Images on Github).
Use the Software (CH341A) provided by your expert and create a dump. Send it to him and he will provide you with a new flashable image.
You should be done if you did everything right. Test your laptop and if everything works upgrade your hardware!
Installation
Add Serial / UUID / MLB for MacBookPro12,1
Remove / Disable Brcm Kexts if you don’t use a Broadcom card.
Run the install command for ALCPlugFix if you face AUX hotplug issues (see misc)
Manually creating DSDT/SSDT files
Read up on one of RehabMan’s guides and apply following patches:
Hemo is a portmanteau (i.e. [H]anami + D[emo] = Hemo) which is designed to provide a fully working demo Hanami application as built by the Hanamismith gem.
⚠️ This application is meant for demonstration purposes only which means all commits are heavily rebased as new functionality is implemented. You can definitely clone this project — and is encouraged — but I wouldn’t recommend forking this project because the SHAs will be constantly changing since each commit is meant to tell a story so people can learn how this application was architected. If you do clone (or download) a copy of this application, please note you’ll have to re-clone/download with any new changes pushed to this repository.
Hemo is a portmanteau (i.e. [H]anami + D[emo] = Hemo) which is designed to provide a fully working demo Hanami application as built by the Hanamismith gem.
⚠️ This application is meant for demonstration purposes only which means all commits are heavily rebased as new functionality is implemented. You can definitely clone this project — and is encouraged — but I wouldn’t recommend forking this project because the SHAs will be constantly changing since each commit is meant to tell a story so people can learn how this application was architected. If you do clone (or download) a copy of this application, please note you’ll have to re-clone/download with any new changes pushed to this repository.
