bro's coding

https://broscoding.tistory.com/114 머신러닝.iris data 불러오기 import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_iris iris=load_iris() iris dir(iris) ['DESCR', 'data', 'feature_names', 'target', 'target_names'] iris.data.shape.. broscoding.tistory.com https://broscoding.tistory.com/115 머신러닝.테스트데이터 뽑기 from sklearn.model_selection import train_test_split X_train,X_test,y..

https://broscoding.tistory.com/114 머신러닝.iris data 불러오기 import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_iris iris=load_iris() iris dir(iris) ['DESCR', 'data', 'feature_names', 'target', 'target_names'] iris.data.shape.. broscoding.tistory.com https://broscoding.tistory.com/115 머신러닝.테스트데이터 뽑기 from sklearn.model_selection import train_test_split X_train,X_test,y..

https://broscoding.tistory.com/132 머신러닝.linear_model.LogisticRegression(Class=3) https://broscoding.tistory.com/128 머신러닝.datasets .make_blobs 사용하기 from sklearn.datasets import make_blobs X,y=make_blobs(400,2,[[0,0],[5,5]],[2,3]) # 400 : 행의 갯수 # 2 : 속성의 갯수 2개(축.. broscoding.tistory.com C가 커질 수록 과적합 된다 C가 커질수록 세밀하게 나눠준다 1/C=a c:(cost,lose,penalty) for j in range(-5,5): from sklearn.linear_model im..

https://broscoding.tistory.com/128 머신러닝.datasets .make_blobs 사용하기 from sklearn.datasets import make_blobs X,y=make_blobs(400,2,[[0,0],[5,5]],[2,3]) # 400 : 행의 갯수 # 2 : 속성의 갯수 2개(축)(전부 X값임) # 중심점의 위치 # 각 중심점에 대한 편차 2, 3 plt.scatter(X[:.. broscoding.tistory.com # data 준비 from sklearn.datasets import make_blobs X,y=make_blobs(300,2,[[0,0],[-10,10],[10,10]],[2,3,5]) # model 훈련 from sklearn.linear_mo..

https://broscoding.tistory.com/129 머신러닝.linear_model.LogisticRegression(로지스틱 회귀) import numpy as np import matplotlib.pyplot as plt # data 준비 from sklearn.datasets import make_blobs X,y=make_blobs(400,2,[[0,0],[5,5]],[2,3]) https://broscoding.tistory.com/128 머신러닝.datasets.. broscoding.tistory.com # predict_proba() # proba = probablity(확률) # 0일 확률 1일 확률 표시 np.round(model.predict_proba(X)[:10],2) ..

def sigmoid(x): return 1/(1+np.exp(-x)) xxx=np.arange(-15,15,0.01) yyy=sigmoid(xxx) plt.plot(xxx,yyy) yyy=sigmoid(xxx*0.5) plt.plot(xxx,yyy) yyy=sigmoid(xxx*10) plt.plot(xxx,yyy) lim(x->inf) =1 lim(x->-inf)=0 lim(x->+-0)=0.5 확률 값을 계산 할 때 사용 신경망에서 가장 중요한 함수 함수의 성격이 매우 중요 p86

import numpy as np import matplotlib.pyplot as plt # data 준비 from sklearn.datasets import make_blobs X,y=make_blobs(400,2,[[0,0],[5,5]],[2,3]) https://broscoding.tistory.com/128 머신러닝.datasets .make_blobs 사용하기 from sklearn.datasets import make_blobs X,y=make_blobs(400,2,[[0,0],[5,5]],[2,3]) # 400 : 행의 갯수 # 2 : 속성의 갯수 2개(축)(전부 X값임) # 중심점의 위치 # 각 중심점에 대한 편차 2, 3 plt.scatter(X[:.. broscoding.tistory..

from sklearn.datasets import make_blobs X,y=make_blobs(400,2,[[0,0],[5,5]],[2,3]) # 400 : 행의 갯수 # 2 : 속성의 갯수 2개(축)(전부 X값임) # 중심점의 위치 # 각 중심점에 대한 편차 2, 3 plt.scatter(X[:,0],X[:,1],c=y,s=60,alpha=0.3) plt.colorbar()

# 와인의 속성을 사용해서 점수 예측 import numpy as np import matplotlib.pyplot as plt wine=np.loadtxt('winequality-red.csv',skiprows=1,delimiter=';') # x=전체 속성값 X=wine[:,:-1] # y=와인 등급 y=wine[:,-1] # lienar regression from sklearn.linear_model import LinearRegression model=LinearRegression() model.fit(X,y) #result w=model.coef_ b=model.intercept_ print('w=',w) print('b=',b) ''' w= [ 2.49905527e-02 -1.08359026..

import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_iris # data 수집 iris=load_iris() # column 컨트롤 col1=2 col2=3 # 전체 data에 대한 scatterplot graph plt.scatter(iris.data[:,col1],iris.data[:,col2],c=iris.target,alpha=0.7) X=iris.data[:,[col1]] # 주의 : col1->[col1] y=iris.data[:,col2] # LinearRegression model = LinearRegression() # train model.fit(X,y) #predict pred_y=mo..