日本欧洲视频一区_国模极品一区二区三区_国产熟女一区二区三区五月婷_亚洲AV成人精品日韩一区18p

代做Lab 2: Time Series Prediction with GP

時間:2024-03-21  來源:  作者: 我要糾錯



Evolutionary Computation 2023/2024
Lab 2: Time Series Prediction with GP
Released: February 26, 2024
Deadline: March 18, 2024
Weight: 25 %
You need to implement one program that solves Exercises 1-3 using any programming language.
In Exercise 5, you will run a set of experiments and describe the result using plots and a short
discussion.
(In the following, replace abc123 with your username.) You need to submit one zip file
with the name ec2024-lab2-abc123.zip. The zip file should contain one directory named
ec2024-lab2-abc123 containing the following files:
• the source code for your program
• a Dockerfile (see the appendix for instructions)
• a PDF file for Exercises 4 and 5
In this lab, we will do a simple form of time series prediction. We assume that we are given some
historical data, (e.g. bitcoin prices for each day over a year), and need to predict the next value in
the time series (e.g., tomorrow’s bitcoin value).
1
We formulate the problem as a regression problem. The training data consists of a set of m
input vectors X = (x
(0), . . . , x(m−1)) representing historical data, and a set of m output values
Y = (x
(0), . . . , x(m−1)), where for each 0 ≤ j ≤ m − 1, x
(j) ∈ R
n and y
(j) ∈ R. We will use genetic
programming to evolve a prediction model f : R
n → R, such that f(x
(j)
) ≈ y
(j)
.
Candidate solutions, i.e. programs, will be represented as expressions, where each expression evaluates to a value, which is considered the output of the program. When evaluating an expression,
we assume that we are given a current input vector x = (x0, . . . , xn−1) ∈ R
n. Expressions and evaluations are defined recursively. Any floating number is an expression which evaluates to the value
of the number. If e1, e2, e3, and e4 are expressions which evaluate to v1, v2, v3 and v4 respectively,
then the following are also expressions
• (add e1 e2) is addition which evaluates to v1 + v2, e.g. (add 1 2)≡ 3
• (sub e1 e2) is subtraction which evaluates to v1 − v2, e.g. (sub 2 1)≡ 1
• (mul e1 e2) is multiplication which evaluates to v1v2, e.g. (mul 2 1)≡ 2
• (div e1 e2) is division which evaluates to v1/v2 if v2 ̸= 0 and 0 otherwise, e.g., (div 4 2)≡ 2,
and (div 4 0)≡ 0,
• (pow e1 e2) is power which evaluates to v
v2
1
, e.g., (pow 2 3)≡ 8
• (sqrt e1) is the square root which evaluates to √
v1, e.g.(sqrt 4)≡ 2
• (log e1) is the logarithm base 2 which evaluates to log(v1), e.g. (log 8)≡ 3
• (exp e1) is the exponential function which evaluates to e
v1
, e.g. (exp 2)≡ e
2 ≈ 7.39
• (max e1 e2) is the maximum which evaluates to max(v1, v2), e.g., (max 1 2)≡ 2
• (ifleq e1 e2 e3 e4) is a branching statement which evaluates to v3 if v1 ≤ v2, otherwise the
expression evaluates to v4 e.g. (ifleq 1 2 3 4)≡ 3 and (ifleq 2 1 3 4)≡ 4
• (data e1) is the j-th element xj of the input, where j ≡ |⌊v1⌋| mod n.
• (diff e1 e2) is the difference xk − xℓ where k ≡ |⌊v1⌋| mod n and ℓ ≡ |⌊v2⌋| mod n
• (avg e1 e2) is the average 1
|k−ℓ|
Pmax(k,ℓ)−1
t=min(k,ℓ)
xt where k ≡ |⌊v1⌋| mod n and ℓ ≡ |⌊v2⌋|
mod n
In all cases where the mathematical value of an expression is undefined or not a real number (e.g.,

−1, 1/0 or (avg 1 1)), the expression should evaluate to 0.
We can build large expressions from the recursive definitions. For example, the expression
(add (mul 2 3) (log 4))
evaluates to
2 · 3 + log(4) = 6 + 2 = 8.
2
To evaluate the fitness of an expression e on a training data (X , Y) of size m, we use the mean
square error
f(e) = 1
m
mX−1
j=0

y
(j) − e(x
(j)
)
2
,
where e(x
(j)
) is the value of the expression e when evaluated on the input vector x
(j)
.
3
Exercise 1. (30 % of the marks)
Implement a routine to parse and evaluate expressions. You can assume that the input describes a
syntactically correct expression. Hint: Make use of a library for parsing s-expressions1
, and ensure
that you evaluate expressions exactly as specified on page 2.
Input arguments:
• -expr an expression
• -n the dimension of the input vector n
• -x the input vector
• -question the question number (always 1 in this case)
Output:
• the value of the expression
Example: In this example, we assume that your program has been compiled to an executable with
the name my lab solution.
[pkl@phi ocamlec]$ my_lab_solution -question 1 -n 1 -x "1.0"
-expr "(mul (add 1 2) (log 8))"
9.0
[pkl@phi ocamlec]$ my_lab_solution -question 1 -n 2 -x "1.0 2.0"
-expr "(max (data 0) (data 1))"
2.0
Exercise 2. (10 % of the marks) Implement a routine which computes the fitness of an expression
given a training data set.
Input arguments:
• -expr an expression
• -n the dimension of the input vector
• -m the size of the training data (X , Y)
• -data the name of a file containing the training data in the form of m lines, where each line
contains n + 1 values separated by tab characters. The first n elements in a line represents
an input vector x, and the last element in a line represents the output value y.
• -question the question number (always 2 in this case)
1See e.g. implementations here http://rosettacode.org/wiki/S-Expressions
4
Output:
• The fitness of the expression, given the data.
Exercise 3. (30 % of the marks)
Design a genetic programming algorithm to do time series forecasting. You can use any genetic
operators and selection mechanism you find suitable.
Input arguments:
• -lambda population size
• -n the dimension of the input vector
• -m the size of the training data (X , Y)
• -data the name of a file containing training data in the form of m lines, where each line
contains n + 1 values separated by tab characters. The first n elements in a line represents
an input vector x, and the last element in a line represents the output value y.
• -time budget the number of seconds to run the algorithm
• -question the question number (always 3 in this case)
Output:
• The fittest expression found within the time budget.
Exercise 4. (10 % of the marks) Here, you should do one of the following exercises.
If you follow LH Evolutionary Computation, do the following exercise: Describe your
algorithm from Exercise 3 in the form of pseudo-code. The pseudo-code should be sufficiently detailed
to allow an exact re-implementation.
If you follow LM Evolutionary Computation (extended), do the following exercise:
Describe in 150 words or less the result in one recent research paper on the topic “symbolic regression
using genetic programming”. The paper needs to be published in 2020 or later in the proceedings of
one of the following conferences: GECCO, PPSN, CEC, or FOGA.
5
Exercise 5. (20 % of the marks)
In this final task, you should try to determine parameter settings for your algorithm which lead to
as fit expressions as possible.
Your algorithm is likely to have several parameters, such as the population size, mutation rates,
selection mechanism, and other mechanisms components, such as diversity mechanisms.
Choose parameters which you think are essential for the behaviour of your algorithm. Run a set of
experiments to determine the impact of these parameters on the solution quality. For each parameter
setting, run 100 repetitions, and plot box plots of the fittest solution found within the time budget.
6
A. Docker Howto
Follow these steps exactly to build, test, save, and submit your Docker image. Please replace abc123
in the text below with your username.
1. Install Docker CE on your machine from the following website:
https://www.docker.com/community-edition
2. Copy the PDF file from Exercises 4 and 5 all required source files, and/or bytecode to an
empty directory named ec2024-lab2-abc123 (where you replace abc123 with your username).
mkdir ec2024 - lab2 - abc123
cd ec2024 - lab2 - abc123 /
cp ../ exercise . pdf .
cp ../ abc123 . py .
3. Create a text file Dockerfile file in the same directory, following the instructions below.
# Do not change the following line . It specifies the base image which
# will be downloaded when you build your image .
FROM pklehre / ec2024 - lab2
# Add all the files you need for your submission into the Docker image ,
# e . g . source code , Java bytecode , etc . In this example , we assume your
# program is the Python code in the file abc123 . py . For simplicity , we
# copy the file to the / bin directory in the Docker image . You can add
# multiple files if needed .
ADD abc123 . py / bin
# Install all the software required to run your code . The Docker image
# is derived from the Debian Linux distribution . You therefore need to
# use the apt - get package manager to install software . You can install
# e . g . java , python , ghc or whatever you need . You can also
# compile your code if needed .
# Note that Java and Python are already installed in the base image .
# RUN apt - get update
# RUN apt - get -y install python - numpy
# The final line specifies your username and how to start your program .
# Replace abc123 with your real username and python / bin / abc123 . py
# with what is required to start your program .
CMD [" - username " , " abc123 " , " - submission " , " python / bin / abc123 . py "]
7
4. Build the Docker image as shown below. The base image pklehre/ec2024-lab2 will be
downloaded from Docker Hub
docker build . -t ec2024 - lab2 - abc123
5. Run the docker image to test that your program starts. A battery of test cases will be executed
to check your solution.
docker run ec2024 - lab2 - abc123
6. Once you are happy with your solution, compress the directory containing the Dockerfile as
a zip-file. The directory should contain the source code, the Dockerfile, and the PDF file
for Exercise 4 and 5. The name of the zip-file should be ec2024-lab2-abc123.zip (again,
replace the abc123 with your username).
Following the example above, the directory structure contained in the zip file should be as
follows:
ec2024-lab2-abc123/exercise.pdf
ec2024-lab2-abc123/abc123.py
ec2024-lab2-abc123/Dockerfile
Submissions which do not adhere to this directory structure will be rejected!
7. Submit the zip file ec2024-lab2-abc123.zip on Canvas.
請加QQ:99515681  郵箱:99515681@qq.com   WX:codehelp 

標簽:

掃一掃在手機打開當前頁
  • 上一篇:代寫CSIE3310、代做c++/Python編程
  • 下一篇:AIST1110代做、Python編程設計代寫
  • 無相關信息
    昆明生活資訊

    昆明圖文信息
    蝴蝶泉(4A)-大理旅游
    蝴蝶泉(4A)-大理旅游
    油炸竹蟲
    油炸竹蟲
    酸筍煮魚(雞)
    酸筍煮魚(雞)
    竹筒飯
    竹筒飯
    香茅草烤魚
    香茅草烤魚
    檸檬烤魚
    檸檬烤魚
    昆明西山國家級風景名勝區
    昆明西山國家級風景名勝區
    昆明旅游索道攻略
    昆明旅游索道攻略
  • NBA直播 短信驗證碼平臺 幣安官網下載 歐冠直播 WPS下載

    關于我們 | 打賞支持 | 廣告服務 | 聯系我們 | 網站地圖 | 免責聲明 | 幫助中心 | 友情鏈接 |

    Copyright © 2025 kmw.cc Inc. All Rights Reserved. 昆明網 版權所有
    ICP備06013414號-3 公安備 42010502001045

    日本欧洲视频一区_国模极品一区二区三区_国产熟女一区二区三区五月婷_亚洲AV成人精品日韩一区18p

              亚洲国产日韩精品| 欧美激情综合亚洲一二区 | 国产一区二区久久| 一区二区三区欧美在线| 永久555www成人免费| 欧美视频中文字幕在线| 久久夜精品va视频免费观看| 亚洲一区一卡| 在线亚洲+欧美+日本专区| 精品999在线播放| 国产视频一区二区在线观看| 国产精品久久久久国产a级| 欧美成人国产va精品日本一级| 亚洲欧美一区二区精品久久久| 亚洲精品日韩欧美| 永久免费精品影视网站| 欧美午夜精品一区| 欧美顶级艳妇交换群宴| 久久只有精品| 久久99在线观看| 亚洲欧美日韩在线| 亚洲线精品一区二区三区八戒| 永久久久久久| 国产日韩综合| 国产亚洲精品高潮| 狠狠干成人综合网| 精品69视频一区二区三区| 国产日韩亚洲| 国产综合久久| 韩国在线视频一区| 亚洲大胆美女视频| 精品福利免费观看| 亚洲国产天堂久久综合| 最近看过的日韩成人| 亚洲人成网在线播放| 亚洲国产欧美在线人成| 亚洲国产导航| 亚洲精品视频在线播放| 日韩视频在线观看一区二区| 亚洲日本中文| 亚洲精品影视在线观看| 亚洲精品影院| 亚洲精品免费一区二区三区| 99视频超级精品| 亚洲影视在线| 亚洲少妇自拍| 久久激情五月激情| 欧美成人一区二区三区| 国产精品视频第一区| 国产视频欧美| 亚洲韩国精品一区| 午夜精品久久久久久久久| 欧美一区二区三区免费在线看| 久久久久综合网| 欧美刺激午夜性久久久久久久| 欧美大片18| 国产精品a久久久久| 国产午夜精品一区二区三区欧美| 国产在线拍揄自揄视频不卡99| 国产亚洲精久久久久久| 在线观看亚洲a| 一区二区三区精密机械公司 | 激情欧美日韩一区| 亚洲精品乱码久久久久久日本蜜臀 | 欧美日本在线看| 国产亚洲永久域名| 亚洲欧洲一区二区三区| 99视频一区二区| 欧美专区在线观看一区| 欧美日本国产精品| 在线观看av一区| 香蕉国产精品偷在线观看不卡| 欧美成人自拍视频| 韩日精品在线| 午夜久久久久久| 国产精品免费小视频| 亚洲精品国久久99热| 久久久久久91香蕉国产| 国产欧美日韩亚洲精品| 一区二区三区欧美亚洲| 欧美精品一区二区精品网 | 久久一区二区三区四区| 国产精品欧美一区二区三区奶水| 亚洲人成毛片在线播放| 男女激情久久| 亚洲成色精品| 久久色中文字幕| 韩国亚洲精品| 久久精品久久综合| 国产亚洲亚洲| 欧美中文字幕第一页| 国产精品日韩欧美一区| 亚洲欧美日韩精品久久久久| 国产精品久久久久9999吃药| 一区二区三区**美女毛片 | 亚洲天堂网在线观看| 欧美国产91| 亚洲精品你懂的| 欧美日韩国产精品自在自线| 亚洲精一区二区三区| 欧美激情影院| 在线亚洲美日韩| 国产精品国产a级| 午夜伦理片一区| 国产主播精品| 久久人人97超碰国产公开结果| 国产在线精品一区二区中文| 久久亚洲一区二区| 亚洲人屁股眼子交8| 欧美日韩另类一区| 亚洲主播在线| 国语自产精品视频在线看8查询8| 久久se精品一区二区| 亚洲国产成人久久综合一区| 欧美bbbxxxxx| 亚洲网在线观看| 国内一区二区三区在线视频| 久久综合国产精品| 一本大道久久a久久精二百| 国产精品免费区二区三区观看| 久久成人综合视频| 91久久精品日日躁夜夜躁欧美| 欧美日韩一区二区三区在线看 | 国产欧美日韩一级| 麻豆精品一区二区综合av| 亚洲最新中文字幕| 国内视频精品| 国产精品裸体一区二区三区| 久久久久国产精品人| 亚洲精品乱码久久久久久蜜桃91| 国产精品久久久久久久午夜片| 久久久久www| 一区二区三区精品久久久| 国产一区二区三区电影在线观看| 欧美激情一区在线| 久久这里只有| 欧美一区二区视频在线观看2020| 亚洲精品1区| 国内成人精品视频| 国产精品videosex极品| 麻豆av福利av久久av| 亚洲日本一区二区三区| 国产一区二区三区不卡在线观看| 欧美日韩在线免费观看| 麻豆精品一区二区av白丝在线| 亚洲一区二区三区四区五区黄 | 欧美日韩精品一区二区在线播放| 久久er99精品| 香蕉久久夜色| 亚洲综合视频网| av成人手机在线| 亚洲人成在线观看| 在线播放豆国产99亚洲| 国语自产在线不卡| 国产欧美一区二区三区久久 | 一区在线播放| 国产一区欧美日韩| 国产目拍亚洲精品99久久精品| 欧美三区美女| 欧美午夜大胆人体| 欧美香蕉视频| 欧美午夜精品电影| 欧美日韩一区不卡| 欧美日韩在线精品一区二区三区| 欧美激情欧美狂野欧美精品| 免费在线欧美黄色| 免费黄网站欧美| 免费看的黄色欧美网站| 欧美成人免费视频| 欧美jizz19hd性欧美| 欧美风情在线观看| 欧美日韩精品二区| 国产精品久久国产愉拍| 国产精品久久久久aaaa| 国产精品日韩久久久久| 国产热re99久久6国产精品| 国产亚洲欧洲| 亚洲国产精品一区二区www在线 | 欧美久久婷婷综合色| 欧美精品在线网站| 国产精品欧美久久| 国产日韩欧美在线看| 亚洲一区免费网站| 伊人久久综合97精品| 国产视频久久久久| 亚洲网站在线观看| 精品999日本| 亚洲国产99精品国自产| 亚洲精选在线观看| 亚洲一区二区三区乱码aⅴ蜜桃女 亚洲一区二区三区乱码aⅴ | 欧美伊久线香蕉线新在线| 久久精品国产第一区二区三区| 久久男人资源视频| 欧美日韩色婷婷| 黑人巨大精品欧美黑白配亚洲| 亚洲成人资源| 午夜精彩国产免费不卡不顿大片| 久久久国产精品亚洲一区| 欧美精品国产一区| 国产欧美日韩亚洲精品|