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CMSC 420: Coding Project
Creation date：2024-07-05 16:37:20
Belonging category：计算机computer science
Coding Project
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CMSC 420: Coding Project 3
B-Trees
1 Due Date and Time
Due to Gradescope by Sunday 29 October at 11:59pm. You can submit as many times as you wish
before that.
2 Get Your Hands Dirty!
This document is intentionally brief and much of what is written here will be more clear once you
start looking at the provided files and submitting.
3 Assignment
We have provided the template btree.py which you will need to complete. More specifically you
will fill in the code details to manage insertion, deletion, and search for a very slight modification
of a B-tree. This modification is clarified in the Order of Operations section below.
As a slight change from the previous two projects we have implemented a Btree class as well as
a Node class. The Btree class stores only the m value as well as a pointer to the root Node. The
insert, delete, and search functions are then implemented as methods of the btree class.
As is noted in the btree.py template you are welcome to make minor augmentations to the Node
class. For example we have included a parent pointer but you can delete this if you don’t use it.
Please look at this file as soon as possible.
4 Details
The functions should do the following:
• def insert(self, key: int, value: str):
Insert the key-value pair into the tree and rebalance as per instructions below. The key is
guaranteed not to be in the tree.
• def delete(self, key: int):
Delete the key-value pair associated to the key from the tree and rebalance as per instructions
below. The key is guaranteed to be in the tree.
1
• def search(self, key: int):
Calculate the list of child indices followed on the path from the root to the node which includes
the key with the associated value appended at the end. If the key is in the root, return a list
containing just the associated value. The key is guaranteed to be in the tree.
5 Order of Operations
For insertion, follow these steps when correcting an overfull node. Note that the first two are a bit
different from a standard B-tree:
1. First see if a left sibling exists and has space. If so then let T be the total number of keys in
the overfull node plus the left sibling and left rotate until the overfull node is down to dT /2e
keys.
2. Second see if a right sibling exists and has space. If so then let T be the total number of keys
in the overfull node plus the right sibling and right rotate until the overfull node is down to
dT /2e keys.
3. Split.
For deletion, follow these steps when correcting an underfull node: Note that the first two are a bit
different from a standard B-tree:
1. First see if a left sibling exists and has space. If so then let T be the total number of keys in
the underfull node plus the left sibling and right rotate until the underfull node is up to bT /2c
keys.
2. Second see if a right sibling exists and has space. If so then let T be the total number of keys
in the underfull node plus the right sibling and left rotate until the underfull node is up to
bT /2c keys.
3. Merge with left sibling if possible.
4. Merge with right sibling if possible.
6 Additional Functions
You will probably want some helper functions as well as Btree class methods to handle the rotations,
merging, and splitting.
7 What to Submit
You should only submit your completed btree.py code to Gradescope for grading. We suggest that
you begin by uploading it as-is (it will run!), before you make any changes, just to see how the
autograder works and what the tests look like. Please submit this file as soon as possible.
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EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 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CMP7000A CMP-7038B Math 263 LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 ​COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 ​BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 ​LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ​ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 ​FINC5090 ​MATH1052 ​SciComp Elec4622 ​FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 ​MAT 237 BISM2201 EEE8099 Physics 11 ECE2238B Biostat 234 ​ECON30009 INF10025 CHEM252 CCMA4008 CIVE 3007 STAT0031 ELEC ENG 3108 MGMT90140 CHEM 252 STAT3016 MMM095 ​MMGT6016 Legal STAT 311 ELEC9764 COMP 370 TCS 3053 EECS 1021 FIT1051 C/C++ EE3C COMP2003J COM398 COMP51915 Simulator FIN42330 8PRO102 PGD ELEC5160 XJCO1921 COMP5123M INFS 2042 CHE1148H ECA5313 IN3329 Microsoft Modeling B15 2TT COMP3687 COMP2432 DMS2030 KAN4TSF CFKG CSE 5322 CSE 445 STATS 3DA3 CHC5223 COMP1048 EL3105 EECS 3221 CS231 ME 4434 ITP4510 ITP4501 CSCI 5708 Controller EECE 5699 CPEN 231L CPN programs CSC8016 MATU9D2 ENGN4213 2. SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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