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[Python] 22. Kmeans from sklearn.cluster import KMeans import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import mglearn from sklearn.datasets import load_iris from sklearn.model_selection import GridSearchCV import warnings warnings.simplefilter('ignore') Kmeans mglearn.plots.plot_kmeans_algorithm() # 랜덤하게 점 찍은 후 가까운 점들 분류하고 센터로 이동 분류 이동 ... 데이터 불러오기 (pd.read_csv) python 파..
[Python] 21. SVM(서포트벡터머신) from sklearn.datasets import make_classification from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.model_selection import cross_val_score, cross_validate import multiprocessing from sklearn.svm import SVC, SVR from sklearn.manifold import TSNE from sklearn.linear_model import SGDRegressor, SGDClassifier from sklear..
[Python] 20. 나이브베이즈 from sklearn.datasets import make_classification from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.model_selection import cross_val_score, cross_validate import multiprocessing from sklearn.svm import SVC, SVR from sklearn.manifold import TSNE from sklearn.linear_model import SGDRegressor, SGDClassifier from sklear..
[Python] 19. MLP : pima-indians 예제 from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.model_selection import validation_curve from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from sklearn.metrics import confusion_matrix import sklearn.metrics as metrics from sklearn.ensemble import BaggingClassifier, RandomForestClassifier, AdaBoostClassifier from sklearn.ens..
[Python] 18. Ensemble(앙상블) : breast_cancer,wine 예제 import pandas as pd import matplotlib.pyplot as plt from sklearn.ensemble import VotingClassifier from sklearn.linear_model import LogisticRegression from sklearn.neighbors import KNeighborsClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.preprocessing import StandardScaler from sklearn.ensemble import BaggingClassifier, RandomForestClassifier from sklearn.model_selection ..
[Python] 17. Decision Tree(의사결정나무) : iris, breast_cancer from sklearn.datasets import make_classification from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.model_selection import cross_val_score, cross_validate import multiprocessing import os from sklearn.datasets import load_iris from sklearn.pipeline import make_pipeline, Pipeline from sklearn.preprocessing import Sta..
[Python] 16. 강아지&고양이 이미지 분류 실습 import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import mglearn from sklearn.model_selection import train_test_split from sklearn.neural_network import MLPClassifier from sklearn.linear_model import LogisticRegression import sklearn.metrics as m # from tensorflow.keras.preprocessing.image import img_to_array from tensorflow.keras.preprocessing import i..
[Python] 15. 이미지분류 : mnist, MLPClassifier import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import mglearn from sklearn.model_selection import train_test_split from sklearn.neural_network import MLPClassifier import sklearn.metrics as m from sklearn.datasets import fetch_openml import warnings warnings.simplefilter('ignore') from sklearn.datasets import fetch_openml mnist = fetch_openml('mnist_..
[Python] 14. NN : XOR문제, MLPClassifier import numpy as np import pandas as pd import seaborn as sb import matplotlib.pyplot as plt import mglearn from sklearn.model_selection import train_test_split from sklearn.neural_network import MLPClassifier from sklearn.linear_model import LogisticRegression import warnings warnings.simplefilter('ignore') x_data = np.array( [[0,0],[0,1],[1,0],[1,1]]) y_data = np.array( [[0],[1],[1],[0]]) Logis..
[Python] 13. KNN분류 from sklearn.datasets import make_classification, load_iris from sklearn.cluster import KMeans import numpy as np import pandas as pd import seaborn as sb import matplotlib.pyplot as plt import mglearn from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import GridSearchCV import warnings warnings.simplefilter('igno..
[Python] 12. 다중분류 from sklearn.datasets import make_classification, load_breast_cancer,load_iris from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from sklearn.model_selection import GridSearchCV import pandas as pd i..
[Python] 11. softmax import numpy as np def fn(x): print(x/x.sum()) a = np.array([2.0,1.0,0.1]) fn(a) [OUT]: [0.64516129 0.32258065 0.03225806] -> 전체 합에서 차지하는 비율 Softmax def softmax(x): e = np.exp(x) print(e) print( e/np.sum(e)) a = np.array([2.0,1.0,0.1]) softmax(a) [OUT]: [7.3890561 2.71828183 1.10517092] [0.65900114 0.24243297 0.09856589] -> e^x를 하여 확률이 높은곳에 가중치를 더 주는 형식 review - 다중분류 시 사용되는 softmax
[Python] 10. confusion matrix : precision,recall,f1,ROC from sklearn.datasets import make_classification, load_breast_cancer from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split import pandas as pd import matplotlib.pyplot as plt import numpy as np import seaborn as sns from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from sklearn.preprocessing import OneHo..
부산 해운대 맛집 [땡초김밥] 떡볶이 & 땡초속김밥 & 냉쫄면 프랜차이즈 땡초김밥 전문점 땡초김밥 매운 땡초김밥이 먹고 싶을 때면 생각나는 곳 내가 가장 좋아하는 메뉴이자 땡초김밥의 대표 메뉴 땡초속김밥 가격은 2줄에 6,500원 일반 김밥과 다른 점은 바로 "RICE" 일반 흰쌀밥이 아닌 다진 당근과 땡초가 버무려진 밥 고추가 다른 속재료들과 조화를 이뤄 중독성있게 알싸하니 맛있다 뒤이어 나온 떡볶이 떡볶이의 가격은 6,000원 사실 떡볶이를 먹고 싶었던 게 아니라 떡볶이 국물에 김밥을 찍어먹고 싶어서 시켰는데 여기 떡볶이도 잘하네 그런데 김밥보다 맵다 진짜 진짜 매워서 솔직히 반 정도 남겼다 머리로는 또 먹고 싶은데 고통스러워서 혀가 거부하는 맛 음 불닭 맵기 정도 지금 생각하니 또 먹고 싶다 맛있어 그리고 마지막으로 나온 냉쫄면 냉쫄면의 가격은 7,000원 냉..
[Python] 9. Logistic Regression (로지스틱 회귀) from sklearn.datasets import make_classification from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score import sklearn.metrics as metrics from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler import pandas as pd import numpy as np import matplotlib.pyplot as..
[Python] 8. Sigmoid & Logistic import numpy as np import pandas as pd import matplotlib.pyplot as plt import math x = np.array( [1., 2., 3., 4., 5., 6.] ) y = np.array( [ 5., 7., 9., 11., 13., 15.] ) w = 0 b = 0 n = len(x) epochs = 5000 learning_rate = 0.01 for i in range(epochs): hy = w*x + b # w=2, b=3 cost = np.sum((hy-y)**2)/n gradientW = np.sum(2*x*(w*x+b-y))/n gradientB = np.sum(2*1*(w*x+b-y))/n w=w-learning_rate*gradie..
[Python] 7. Sigmoid 함수 import numpy as np import pandas as pd import matplotlib.pyplot as plt import math print(2**2) print(2**3) print(2**(-2)) print(2**(-3)) [OUT]: 4 8 0.25 0.125 math.e [OUT]: 2.718281828459045 Sigmoid (시그모이드 함수) x=0: 0.5이상의 값을 뱉음 def sigmoid(z): return 1/(1+math.e**(-z)) print(sigmoid(-100)) print(sigmoid(-10)) print(sigmoid(-1)) print(sigmoid(0)) print(sigmoid(1)) print(sigmoid(10)) print(sigmoid..
[Python] 6. L1 norm, L2 norm import pandas as pd import numpy as np from numpy import linalg import matplotlib.pyplot as plt import warnings import math warnings.simplefilter('ignore') p = 1 L1 Norm p = 2 L2 Norm 출처 : en.wikipedia.org/wiki/Norm_(mathematics) Norm (mathematics) - Wikipedia Length in a vector space In mathematics, a norm is a function from a real or complex vector space to the nonnegative real numbers that be..
[Python] 5. 문자열encoding : LabelEncoder, OneHotEncoder, get_dummies(), make_column_transformer 예제 import pandas as pd import numpy as np import seaborn as sns from sklearn.datasets import load_boston, load_iris from sklearn.linear_model import Ridge,Lasso,ElasticNet,LinearRegression from sklearn.preprocessing import PolynomialFeatures from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from sklearn.neural_network import MLPRegressor from sklearn.model_..
[Python] 4. 다중선형회귀 : 릿지L2규제, 라쏘L1규제, 엘라스틱넷 import pandas as pd import numpy as np import seaborn as sns from sklearn.datasets import load_boston, load_iris from sklearn.linear_model import Ridge,Lasso,ElasticNet,LinearRegression from sklearn.preprocessing import PolynomialFeatures from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from sklearn.neural_network import MLPRegressor from sklearn.model_..
[Python] 3. 다중선형회귀 import pandas as pd import numpy as np import seaborn as sns from sklearn.datasets import load_boston, load_iris from sklearn.datasets import fetch_california_housing from sklearn.linear_model import LinearRegression,Ridge, SGDRegressor from sklearn.neural_network import MLPRegressor from sklearn.model_selection import train_test_split from sklearn.model_selection import GridSearchCV from sklear..
[Python] 2. 정규화 : 상관관계, 다중공선성 import pandas as pd import numpy as np import seaborn as sns from sklearn.datasets import load_boston, load_iris from sklearn.linear_model import LinearRegression,Ridge, SGDRegressor from sklearn.neural_network import MLPRegressor from sklearn.model_selection import train_test_split from sklearn.model_selection import GridSearchCV from sklearn.model_selection import cross_val_score from sklearn...
[Python] 1. 선형회귀분석 import pandas as pd import numpy as np import seaborn as sns from sklearn.datasets import load_boston, load_iris from sklearn.linear_model import LinearRegression,Ridge, SGDRegressor # SGDRegressor은 학습 나머지는 공식 from sklearn.neural_network import MLPRegressor # MLPRegressor은 딥러닝 학습 from sklearn.metrics import r2_score # 선형 모델(Linear Models) from sklearn.model_selection import train_test_split impo..
[Python] 0. sklearn 구조 scikit-learn 특징다양한 머신러닝 알고리즘을 구현한 파이썬 라이브러리심플하고 일관성 있는 API, 유용한 온라인 문서, 풍부한 예제머신러닝을 위한 쉽고 효율적인 개발 라이브러리 제공다양한 머신러닝 관련 알고리즘과 개발을 위한 프레임워크와 API 제공많은 사람들이 사용하며 다양한 환경에서 검증된 라이브러리scikit-learn 주요 모듈 estimator API일관성: 모든 객체는 일관된 문서를 갖춘 제한된 메서드 집합에서 비롯된 공통 인터페이스 공유검사(inspection): 모든 지정된 파라미터 값은 공개 속성으로 노출구성: 많은 머신러닝 작업은 기본 알고리즘의 시퀀스로 나타낼 수 있으며, Scikit-Learn은 가능한 곳이라면 어디서든 이 방식을 사용합리적인 기본값: 모델이 사용자 지정 파..
부산 기장 일광 맛집 [온정식당] 해물탕면과 치즈새우카레 늦은 후기 온정식당 해물탕면의 비주얼을 보자마자 여기다 1일 15그릇 한정이므로 미리 전화를 해서 30분 내로 갈 예정인데 해물탕면 주문 가능할까요? 여쭤보고 갔었다 해물탕면 가격은 10,900원 오징어가 통으로 들어있고 새우, 홍합, 전복 등 여러 해물이 가득 해물짬뽕 같은 비주얼이지만 짬뽕보다는 해물탕에 가까운 맛 생각보다 안 비리고 얼큰하니 깔끔 치즈새우 카레의 가격은 9,900원 베이스로는 카레가 깔려있고 토네이도 오므라이스 모양의 밥+치즈+계란 그리고 구운 새우 4마리 치즈가 가득 들은 데다 카레는 적당히 밀도 있고 풍미가 깊어 통통한 새우와 먹으니 맛있어 맛있어 기장 온정식당 맛도, 분위기도 만족
[Python] 8. 이산확률분포 import scipy.stats as stats from scipy.special import comb,perm import numpy as np import matplotlib.pyplot as plt import matplotlib matplotlib.rcParams['font.family']='Malgun Gothic' matplotlib.rcParams['axes.unicode_minus'] = False 지난 시간 복습용 연습문제 1. 숫자 1부터 10까지 적혀있는 카드 10장이 있다. 이 중에서 하나의 카드를 뽑았을 때, 8이 적힌 카드가 나올 확률을 구하시오. 2. 주사위를 하나 던져 3이 나올 확률을 구하시오 3. 주사위를 2개 던져 눈금의 합이 6이 나올 확률을 구하시오 4. 1000 ..
[Python] 7. t검정,카이제곱 검정,F검정 import scipy.stats as stats import numpy as np import matplotlib.pyplot as plt import math import matplotlib matplotlib.rcParams['font.family']='Malgun Gothic' matplotlib.rcParams['axes.unicode_minus'] = False 지난 시간 복습용 연습문제 표본 : 20명의 벤처기업 경영자 혈압 평균 135, 표준편차 25 일반인의 혈압 평균 115인 경우 벤처기업 경영자의 혈압이 일반인보다 높은지 아닌지 검정하시오 (양측 검정) 95% Solution 표본 : 20명의 벤처기업 경영자 혈압 평균 135, 표준편차 25 일반인의 혈압 평균 115인 경우 벤처기업..
[Python] 6. 표준정규분포(z검정,t검정) import numpy as np import pandas as pd import matplotlib.pyplot as plt import math 정규분포 확률분포함수 pmf 확률질량함수(probability mass function) pdf 확률밀도함수(probability density function) cdf 누적분포함수(cumulative distribution function) ppf 누적분포함수의 역함수(inverse cumulative distribution function) sf 생존함수(survival function) = 1 - 누적분포함수 isf 생존함수의 역함수(inverse survival function) rvs 랜덤 표본 생성(random variable sampling) x..
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