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387 lines
30 KiB
Plaintext
387 lines
30 KiB
Plaintext
1 year ago
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# SOM (Self Organizing Map, 자기조직화지도)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 개요\n",
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"* 비지도학습 중 하나로, 대뇌피질의 시각피질의 학습 과정을 모델화한 인공신경망으로써 자율 학습에 의한 클러스터링을 수행하는 알고리즘"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 역사\n",
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"* 1980년대 후반에 핀란드의 컴퓨터 과학자 Teuvo Kohonen에 의해 개발\n",
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"* SOM은 분류, 군집화, 데이터 시각화 등 다양한 분야에서 사용되며, 인공신경망 분야에서 가장 성공적인 알고리즘 중 하나로 평가됨\n",
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"* 초기에는 이미지 인식과 음성 인식 분야에서 주로 사용되었으나 현재는 데이터 분석 등의 다양한 분야에서도 사용됨"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 특징\n",
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"* 인간의 뇌에서 발견된 \"자기 조직화\" 원리를 모델링하여 입력 데이터의 유사성을 기반으로 뉴런들을 2D 혹은 3D 지도상에 배치함\n",
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"* Topological Ordering (위상적 순서)에 따라 지도상에서 인접한 뉴런들이 입력 데이터의 유사성을 반영하여 군집화(Clusturing)함\n",
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"입력 데이터에 대한 어떤 사전 정보나 라벨이 없이도, 데이터가 가지고 있는 패턴과 특성을 스스로 학습하고 이를 지도상으로 표현함\n",
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"* 주로 데이터의 시각화 및 차원 축소에 사용되며, 입력 데이터의 분포를 파악하여 그룹화하여 시각적으로 표현하므로, 복잡한 데이터를 시각적으로 이해하고 분석하는 데 도움을 줌"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 주요 용어\n",
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"* 클러스터링(clustering): 데이터의 유사성에 기초하여 데이터를 몇몇의 그룹으로 분류하는 기법\n",
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"* 입력층(input layer): 입력 벡터를 입력받는 층\n",
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"* 경쟁층(competitive layer): 입력 벡터의 특성에 따라 입력 벡터가 한 점으로 클러스터링 되는 층\n",
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"* 가중치(weight): 인공신경망에서 가중치는 각 입력 값에 대한 입력 값의 중요도를 값을 말함\n",
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"* 노드(node): 경쟁층에서 입력 벡터들이 서로의 유사성에 의해 모이는 하나의 영역"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 알고리즘\n",
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"1-1) 가중치 행렬 각 원소의 값을 임의의 0보다 크고 1보다 작은 값으로 초기화\n",
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"\n",
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"2-1) 입력 벡터와 경쟁층에 존재하는 j\n",
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"개의 노드에 대해 입력 벡터와 노드 간의 거리 Dij\n",
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"를 계산\n",
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"\n",
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"2-2) 현재 입력 벡터와 Dij\n",
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"값이 가장 작은 경쟁층의 노드를 선택\n",
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"\n",
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"2-3) 해당 노드의 가중치와 이웃 노드의 가중치를 수정\n",
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"\n",
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"5-1) 현재 입력 벡터가 마지막 입력 벡터라면 다음 과정으로 이동하고, 그렇지 않다면 과정 2로 돌아간다\n",
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"\n",
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"6-1) 현재 반복 횟수가 최대 반복 횟수라면 알고리즘을 종료\n",
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"\n",
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"6-2) 현재 반복 횟수가 최대 반복 횟수가 아니면 현재 입력 벡터를 처음 입력 벡터로 설정하고 과정 2로 돌아간다"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 참고\n",
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"https://untitledtblog.tistory.com/5"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### SOM 예제 코드"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 69,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 70,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[0.76959259, 0.03105338],\n",
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" [0.91933906, 0.8980492 ],\n",
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" [0.74919767, 0.46775453],\n",
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" [0.66010482, 0.70532455],\n",
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" [0.07347433, 0.31437638],\n",
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" [0.52162755, 0.75475037],\n",
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" [0.81627506, 0.7724125 ],\n",
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" [0.7708446 , 0.98667885],\n",
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" [0.57884267, 0.92658698],\n",
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" [0.48815338, 0.04666865],\n",
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" [0.51407113, 0.3925697 ],\n",
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" [0.97608349, 0.58044996],\n",
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" [0.67676596, 0.51393561],\n",
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" [0.37059459, 0.99168193],\n",
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" [0.17015263, 0.17250751],\n",
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" [0.6980668 , 0.47156525],\n",
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" [0.9674704 , 0.29847506],\n",
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" [0.85297608, 0.15389296],\n",
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" [0.35572734, 0.77261987],\n",
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" [0.96259553, 0.10738437],\n",
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" [0.0178066 , 0.68856146],\n",
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" [0.78219693, 0.6523648 ],\n",
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" [0.18744908, 0.73438952],\n",
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" [0.19713904, 0.56662014],\n",
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" [0.7668028 , 0.7619133 ],\n",
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" [0.55338936, 0.78892134],\n",
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" [0.91421121, 0.60350262],\n",
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" [0.32014712, 0.61909491],\n",
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" [0.03320721, 0.69900356],\n",
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" [0.0806021 , 0.68826953],\n",
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" [0.52828811, 0.78636528],\n",
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" [0.49048742, 0.60616199],\n",
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" [0.07021355, 0.64982408],\n",
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" [0.35444256, 0.84292181],\n",
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" [0.90941177, 0.30758414],\n",
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" [0.87616741, 0.18060184],\n",
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" [0.22973575, 0.50353087],\n",
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" [0.33569227, 0.82710827],\n",
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" [0.03575231, 0.86915894],\n",
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" [0.59710596, 0.91274408],\n",
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" [0.61854338, 0.06007 ],\n",
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" [0.96359265, 0.66780466],\n",
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" [0.6180583 , 0.36145526],\n",
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" [0.22507441, 0.24977 ],\n",
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" [0.82067222, 0.65858647],\n",
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" [0.26533719, 0.50457457],\n",
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" [0.20615695, 0.52028333],\n",
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" [0.78530409, 0.23287443],\n",
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" [0.66607148, 0.0717416 ],\n",
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" [0.14660118, 0.14759826],\n",
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" [0.89091339, 0.12148139],\n",
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" [0.48159329, 0.76667587],\n",
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" [0.73766975, 0.76237422],\n",
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" [0.32449576, 0.11193535],\n",
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" [0.05351455, 0.37051478],\n",
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" [0.15487227, 0.0884276 ],\n",
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" [0.40902546, 0.73216362],\n",
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" [0.6227993 , 0.54298886],\n",
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" [0.70489165, 0.60729615],\n",
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" [0.43506953, 0.26510733],\n",
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" [0.17818975, 0.41551925],\n",
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" [0.07820619, 0.31987945],\n",
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" [0.27646196, 0.00825832],\n",
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" [0.30999502, 0.67284126],\n",
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" [0.6372436 , 0.48780002],\n",
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" [0.66962877, 0.44225145],\n",
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" [0.48185839, 0.17663244]])"
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]
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},
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"execution_count": 70,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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|
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"# 입력 데이터 생성\n",
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"data = np.random.rand(100, 2)\n",
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"# data = np.array([[0.8, 0.0064],\n",
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"# [0.12, 0.2848],\n",
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"# [0.24, 0.7472],\n",
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"# [0.68, 0.3488]])\n",
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"data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 71,
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"metadata": {},
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"outputs": [],
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"source": [
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|
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"# SOM 파라미터 설정\n",
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"map_width = 10\n",
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"map_height = 10\n",
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"lr = 0.1\n",
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"num_iterations = 1000\n",
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"# map_width = 2\n",
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"# map_height = 2\n",
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"# lr = 0.1\n",
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"# num_iterations = 1000"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 78,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(10, 10, 2)\n"
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]
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},
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{
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"data": {
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|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# SOM 초기화\n",
|
||
|
|
"som_map = np.random.rand(map_width, map_height, 2)\n",
|
||
|
|
"print(som_map.shape)\n",
|
||
|
|
"fig, (ax1, ax2) = plt.subplots(1, 2)\n",
|
||
|
|
"ax1.imshow(som_map[:,:,0])\n",
|
||
|
|
"ax2.imshow(som_map[:,:,1])\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 73,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# SOM 학습\n",
|
||
|
|
"for i in range(num_iterations):\n",
|
||
|
|
" # 랜덤한 입력 데이터 선택\n",
|
||
|
|
" input_data = data[np.random.choice(data.shape[0])]\n",
|
||
|
|
" \n",
|
||
|
|
" # 가장 유사한 뉴런 찾기\n",
|
||
|
|
" # 3D som_map과 input_data의 차이^2를 구하여 z축으로 모두 합한 거리\n",
|
||
|
|
" distances = np.sum((som_map - input_data) ** 2, axis=2) \n",
|
||
|
|
" # 위에서 구한 z축의 거리들 중 가장 작은 뉴런\n",
|
||
|
|
" winner = np.argmin(distances)\n",
|
||
|
|
" # 3D z축에서 선택된 가장 작은 뉴런을 2D map으로 맵핑한 좌표\n",
|
||
|
|
" x, y = np.unravel_index(winner, (map_width, map_height))\n",
|
||
|
|
" \n",
|
||
|
|
" # 학습률 계산\n",
|
||
|
|
" learning_rate = lr * (1 - i/num_iterations)\n",
|
||
|
|
" \n",
|
||
|
|
" # 뉴런 가중치 업데이트\n",
|
||
|
|
" for j in range(map_width):\n",
|
||
|
|
" for k in range(map_height):\n",
|
||
|
|
" dist = np.sqrt((x-j)**2 + (y-k)**2)\n",
|
||
|
|
" if dist < 3:\n",
|
||
|
|
" som_map[j, k] += learning_rate * (input_data - som_map[j, k])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 74,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"(10, 10, 2)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# SOM 시각화\n",
|
||
|
|
"print(som_map.shape)\n",
|
||
|
|
"fig, (ax1, ax2) = plt.subplots(1, 2)\n",
|
||
|
|
"ax1.imshow(som_map[:,:,0])\n",
|
||
|
|
"ax2.imshow(som_map[:,:,1])\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### 참고\n",
|
||
|
|
"ChatGPT"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.10.6 (main, Mar 10 2023, 10:55:28) [GCC 11.3.0]"
|
||
|
|
},
|
||
|
|
"orig_nbformat": 4,
|
||
|
|
"vscode": {
|
||
|
|
"interpreter": {
|
||
|
|
"hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|