{"id":22352773,"url":"https://github.com/alinpahontu2912/numerical-methods","last_synced_at":"2025-03-26T12:14:11.073Z","repository":{"id":207572125,"uuid":"387447790","full_name":"alinpahontu2912/Numerical-Methods","owner":"alinpahontu2912","description":"First Homework for the Numerical Methods course","archived":false,"fork":false,"pushed_at":"2021-07-19T11:57:39.000Z","size":6,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-31T13:24:21.873Z","etag":null,"topics":["algorithms","data-science","matlab","numerical-methods","octave"],"latest_commit_sha":null,"homepage":"","language":"MATLAB","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/alinpahontu2912.png","metadata":{"files":{"readme":"README.txt","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2021-07-19T11:57:15.000Z","updated_at":"2021-07-28T19:42:33.000Z","dependencies_parsed_at":"2023-11-16T14:45:26.849Z","dependency_job_id":null,"html_url":"https://github.com/alinpahontu2912/Numerical-Methods","commit_stats":null,"previous_names":["alinpahontu2912/numerical-methods"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alinpahontu2912%2FNumerical-Methods","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alinpahontu2912%2FNumerical-Methods/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alinpahontu2912%2FNumerical-Methods/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alinpahontu2912%2FNumerical-Methods/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/alinpahontu2912","download_url":"https://codeload.github.com/alinpahontu2912/Numerical-Methods/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245650484,"owners_count":20650105,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["algorithms","data-science","matlab","numerical-methods","octave"],"created_at":"2024-12-04T12:27:07.853Z","updated_at":"2025-03-26T12:14:11.049Z","avatar_url":"https://github.com/alinpahontu2912.png","language":"MATLAB","funding_links":[],"categories":[],"sub_categories":[],"readme":"Part-1\nPentru prima functie, imi imaginez ca labirintul este o matrice inferior \ndiagonala si o bordez cu 0. Pentru a afla probabilitatile numar cate elemente \nnenule, care respecta directiile labirintului,  are fiecare numar. De exemplu,\nnu pot sa merg la pozitia aferenta liniei mele -q, coloana +1.\n\nPentru partea a doua am parcurs matricea si am scos valurile nenule in vectorul\nvalues. Am pus in colind indicii fiecarei valori nenule gasite, iar in rowptr\nal catelea element nenul se gaseste primul pe fiecare coloana.\n\nPentru partea a treia am implementat factorizarea Jacobi, urmarind formulele \ndeja consacrate.\n\nPentru partea a patra, am initializat x si x vechi(la mine y) si am oprit \nalgoritmul Jacobi_sparse atunci cand norma celor doi vectori era mai mica\ndecat toleranta data.\n\nFeedback: Probabl cel mai simplu de inteles si implementat task, insa\na durat ceva sa imi dau seama dupa exemplul vostru de la matrix_to_csr \nca rowptr nu trebuie sa contina de fapt, primul element nenul de pe fiecare\nlinie, ci numarul acestuia.\n\nPart-2\nPentru prima functie, am calculat initial media aritmetica a punctelor in \nfunctie de clusterul aferent lor. Apoi am inceput sa recalculez centroizii\nin functie de punctul cel mai apropiat de ei si sa repet asta pana cand \npozitia lor nu se mai schimba.\n\nPentru a doua functie am facut suma distantelor minime de la puncte la centroizi\n\nFeedback: Putin greu de inteles la inceput, ar fi fost mai bine sa avem un \nexemplu care sa ne arata efectiv ce se intampla la fiecare pas.\n\nPart-3\nPentru prima functie am calculat intervalele in care se incadreaza fiecare\npixel. Am aflat cati pixeli sunt in fiecare interval facand diferenta dintre\nlungimile a doua intervale vecine.\n\nPentru a doua functie am folosit aceeasi gandire ca pentru prima, insa am\nimplementat si functi rgb2hsv.\n\nPentru functiile Householder si SST am implementat algoritmi pentru aflarea \nmatricilor Q si R si rezolvarea unui sistem superior triunghiular.\n\nPentru functia learn, am folosit functiile Householder si SST pentru a \ndetermina w.\n\nFunctiile preprocess si evaluate sunt asemanatoare, intrucat a trebuit sa \nparcurg folderele cu toate pozele cu si fara pisici si  sa contruiesc X si y.\nLa evaluate am calculat si procentajul pozelor corect identificate.\n\nFeedback: Cu siguranta cel mai greu de implementat task din aceasta tema. \nProblema cu care m-am confruntat a fost timpul de rezolvare, insa am  invatat sa \nvectorizez pentru a rezolva aceasta problema.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falinpahontu2912%2Fnumerical-methods","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falinpahontu2912%2Fnumerical-methods","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falinpahontu2912%2Fnumerical-methods/lists"}