合肥生活安徽新聞合肥交通合肥房產生活服務合肥教育合肥招聘合肥旅游文化藝術合肥美食合肥地圖合肥社保合肥醫院企業服務合肥法律

        MATH2033代做、代寫Java,Python編程
        MATH2033代做、代寫Java,Python編程

        時間:2024-12-19  來源:合肥網hfw.cc  作者:hfw.cc 我要糾錯



        MATH2033 Introduction to Scientific Computation
        — Coursework 2 —
        Submission deadline: 15:00 Friday 20 December 2024
        This coursework contributes 10% towards your mark for this module.
        Rules
        It is not permitted to use generative artificial intelligence (AI) software for this coursework. Ensure that
        you have read and have understood the Policy on academic misconduct. One of the things stated in
        this policy is that “The submission of work that is generated and/or improved by software that is not
        permitted for that assessment, for the purpose of gaining marks will be regarded as false authorship
        and seen as an attempt to gain an unpermitted academic advantage”.
        This coursework should be your own individual work, with the exceptions that:
        1. You may ask for and receive help from the lecturer Richard Rankin although not all questions will be
        answered and those that are will be answered to all students that attend the class.
        2. You may copy from material provided on the Moodle pages:
        • Introduction to Scientific Computation (MATH2033 UNNC) (FCH1 24-25)
        • Analytical and Computational Foundations (MATH1028 UNNC) (FCH1 2**4)
        • Calculus (MATH1027 UNNC) (FCH1 2**4)
        • Linear Mathematics (MATH1030 UNNC) (FCH1 2**4)
        Coding Environment
        You should write and submit a py file. You are strongly encouraged to use the Spyder IDE (integrated
        development environment). You should not write or submit an ipynb file and so you should not use
        Jupyter Notebook.
        It will be assumed that numpy is imported as np, and that matplotlib.pyplot is imported as plt.
        Submission Procedure:
        To submit, upload your linear systems.py file through the Coursework 2 assignment activity in the
        Coursework 2 section of the Moodle page Introduction to Scientific Computation (MATH2033 UNNC)
        (FCH1 24-25).
        Marking
        Your linear systems.py file will be mainly marked by running your functions with certain inputs and comparing
         the output with the correct output.
        Department of Mathematical Sciences Page 1 of 51. The linear systems.py file contains an unfinished function with the following first line:
        def smax (w ,s , i ) :
        Assume that:
        • The type of the input w is numpy.ndarray.
        • The type of the input s is numpy.ndarray.
        • The type of the input i is int.
        • There exists an int n such that the shape of w is (n,) and the shape of s is (n,).
        • The input i is a nonnegative integer that is less than n.
        Complete the function smax so that it returns an int p which is the smallest integer for which
        i ≤ p < n
        and
        |w[p]|
        s[p]
         = max
        j∈{i,i+1,...,n−1}
        |w[j]|
        s[j]
        .
        A test that you can perform on your function smax is to run the Question 1 cell of the tests.py file
        and check that what is printed is:
        1
        [20 marks]
        Coursework 2 Page 2 of 52. Suppose that A ∈ R
        n×n, that det(A) 6= 0 and that b ∈ R
        n.
        The linear systems.py file contains an unfinished function with the following first line:
        def spp (A ,b , c ) :
        Assume that:
        • The type of the input A is numpy.ndarray.
        • The type of the input b is numpy.ndarray.
        • The type of the input c is int.
        • There exists an int n such that n > 1, the shape of A is (n,n) and the shape of b is (n,1).
        • The input A represents A.
        • The input b represents b.
        • The input c is a positive integer that is less than n.
        Complete the function spp so that it returns a tuple (U, v) where:
        • U is a numpy.ndarray with shape (n,n) that represents the matrix comprised of the first n
        columns of the matrix arrived at by performing forward elimination with scaled partial pivoting
        on the matrix 
        A b 
        until all of the entries below the main diagonal in the first c columns are
        0.
        • v is a numpy.ndarray with shape (n,1) that represents the last column of the matrix arrived at
        by performing forward elimination with scaled partial pivoting on the matrix 
        A b 
        until all of
        the entries below the main diagonal in the first c columns are 0.
        A test that you can perform on your function spp is to run the Question 2 cell of the tests.py file
        and check that what is printed is:
        [[ 10. 0. 20.]
        [ 0. -5. -1.]
        [ 0. 10. -11.]]
        [[ 70.]
        [ -13.]
        [ -13.]]
        [30 marks]
        Coursework 2 Page 3 of 53. Suppose that A ∈ R
        n×n, that det(A) 6= 0, that all of the entries on the main diagonal of A are
        nonzero and that b ∈ R
        n. Let x ∈ R
        n be the solution to Ax = b. Let x
        (k) be the approximation
        to x obtained after performing k iterations of the Gauss–Seidel method starting with the initial
        approximation x
        (0)
        .
        The linear systems.py file contains an unfinished function with the following first line:
        def GS (A ,b ,g ,t , N ) :
        Assume that:
        • The type of the input A is numpy.ndarray.
        • The type of the input b is numpy.ndarray.
        • The type of the input g is numpy.ndarray.
        • The type of the input t is numpy.float64, float or int.
        • The type of the input N is int.
        • There exists an int n such that the shape of A is (n,n), the shape of b is (n,1) and the shape
        of g is (n,1).
        • The input A represents A.
        • The input b represents b.
        • The input g represents x
        (0)
        .
        • The input t is a real number.
        • The input N is a nonnegative integer.
        Complete the function GS so that it returns a tuple (y, r) where:
        • y is a numpy.ndarray with shape (n, M + 1) which is such that, for j = 0, 1, . . . , n − 1,
        y[j, k] =x
        (k)
        j+1 for k = 0, 1, . . . , M where M is the smallest nonnegative integer less than N for
        which
        is less than t if such an integer M exists and M = N otherwise.
        • r is a bool which is such that r = True if
        is less than t and r = False otherwise.
        A test that you can perform on your function GS is to run the Question 3 cell of the tests.py file and
        check that what is printed is:
        [[ 0. 12. 12.75 ]
        [ 0. 3. 3.9375 ]
        [ 0. 6.75 6.984375]]
        False
        [25 marks]
        Coursework 2 Page 4 of 54. Suppose that A ∈ R
        n×n, that det(A) 6= 0, that all of the entries on the main diagonal of A are
        nonzero and that b ∈ R
        n. Let x ∈ R
        n be the solution to Ax = b. Let x
        (k) be the approximation
        to x obtained after performing k iterations of the Gauss–Seidel method starting with the initial
        approximation x
        (0)
        .
        The linear systems.py file contains an unfinished function with the following first line:
        def GS_plot (A ,b ,g ,x , N ) :
        Assume that:
        • The type of the input A is numpy.ndarray.
        • The type of the input b is numpy.ndarray.
        • The type of the input g is numpy.ndarray.
        • The type of the input x is numpy.ndarray.
        • The type of the input N is int.
        • There exists an int n such that the shape of A is (n,n), the shape of b is (n,1), the shape of g
        is (n,1) and the shape of x is (n,1).
        • The input A represents A.
        • The input b represents b.
        • The input g represents x
        (0)
        .
        • The input x represents x.
        • The input N is a nonnegative integer.
        Complete the function GS plot so that it returns a matplotlib.figure.Figure, with an appropriate
        legend and a single pair of appropriately labelled axes, on which there is a semilogy plot
        of:
        A test that you can perform on your function GS plot is to run the Question 4 cell of the tests.py
        file.
        [25 marks]
        Coursework 2 Page 5 of 5

        請加QQ:99515681  郵箱:99515681@qq.com   WX:codinghelp


         

        掃一掃在手機打開當前頁
      1. 上一篇:代做COMP2012J、java編程語言代寫
      2. 下一篇:DSCI 510代寫、代做Python編程語言
      3. ·代做DI11004、Java,Python編程代寫
      4. ·03CIT4057代做、代寫c++,Python編程
      5. ·代寫CHEE 4703、代做Java/Python編程設計
      6. ·代做INT2067、Python編程設計代寫
      7. ·CS 7280代做、代寫Python編程語言
      8. ·CSCI 201代做、代寫c/c++,Python編程
      9. ·代寫G6077程序、代做Python編程設計
      10. ·代做COMP SCI 7412、代寫Java,python編程
      11. ·代做COMP642、代寫Python編程設計
      12. ·代寫CSSE7030、代做Python編程設計
      13. 合肥生活資訊

        合肥圖文信息
        急尋熱仿真分析?代做熱仿真服務+熱設計優化
        急尋熱仿真分析?代做熱仿真服務+熱設計優化
        出評 開團工具
        出評 開團工具
        挖掘機濾芯提升發動機性能
        挖掘機濾芯提升發動機性能
        海信羅馬假日洗衣機亮相AWE  復古美學與現代科技完美結合
        海信羅馬假日洗衣機亮相AWE 復古美學與現代
        合肥機場巴士4號線
        合肥機場巴士4號線
        合肥機場巴士3號線
        合肥機場巴士3號線
        合肥機場巴士2號線
        合肥機場巴士2號線
        合肥機場巴士1號線
        合肥機場巴士1號線
      14. 短信驗證碼 酒店vi設計 NBA直播 幣安下載

        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 hfw.cc Inc. All Rights Reserved. 合肥網 版權所有
        ICP備06013414號-3 公安備 42010502001045

        主站蜘蛛池模板: 国产精品99精品一区二区三区| 精品无码中出一区二区| 亚洲乱码av中文一区二区| 国产AV一区二区精品凹凸 | 国产香蕉一区二区三区在线视频| 久久精品国产一区二区| 亚洲日韩一区二区三区| 亚洲一区免费观看| 国产AV一区二区三区无码野战 | 亚洲av无码一区二区三区四区 | 国产一区二区影院| 无码日本电影一区二区网站| 国产成人无码一区二区在线播放 | 精彩视频一区二区三区| 国产精品一区二区毛卡片| 国产乱人伦精品一区二区| 精品一区二区三区无码免费视频| 日本一区二区三区在线观看 | 精品一区二区三区视频| 糖心vlog精品一区二区三区| 精品国产一区二区三区四区| 日韩人妻一区二区三区免费| 少妇无码一区二区三区免费| 久久91精品国产一区二区| 亚洲一区二区成人| 精品无码人妻一区二区免费蜜桃| 色婷婷一区二区三区四区成人网 | 亚洲无线码一区二区三区| 综合人妻久久一区二区精品| 手机看片福利一区二区三区 | 久久精品无码一区二区三区日韩| 精品国产一区二区三区AV性色| 精品国产日韩亚洲一区| 中文国产成人精品久久一区| 国产av一区二区三区日韩| 99精品国产一区二区三区不卡| 无码乱码av天堂一区二区| 国产成人无码aa精品一区| 亚洲一区视频在线播放| 亚洲成AV人片一区二区| 国模私拍福利一区二区|