분류 전체보기96 (C/C++) Filter (Denoising) 1. Noise Generation Y += rand() % Err - (Err >> 1); 2. Salt-and-Pepper Noise if ((rand() % prob) == 0) Y = 255; else if ((rand() % prob) == 1) Y = 0; else Y = Y; 3. Image Restoration Noise를 줄이기 위한 이미지 전처리(Denoising) 손상된 이미지에서 고품질 이미지를 얻는 작업 4. Median Filter 사전정의된 window 내 모든 pixel의 중앙값 제공 Salt-and-pepper noise에 효과적 5. Mean Filter 사전정의된 window 내 모든 pixel의 평균값 제공 노이즈 감소 및 평활화 // Filter int f_size .. 2024. 4. 1. Discriminant analysis(판별 분석) [Discriminant Analysis 이용 당뇨병 환자 예측] (4) 1. DatasetTrain Testx_train = train.drop(['Diabetes'], axis=1)y_train = train['Diabetes']x_test = test 2. LDA# LDAfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysisfrom sklearn.metrics import accuracy_scorecld = LinearDiscriminantAnalysis()cld.fit(x_train, y_train)y_train_pred = cld.predict(x_train)y_test_cld_pred = cld.predict(x_test)print(accuracy_score(y_train, y_train_pred).. 2024. 3. 31. Discriminant analysis(판별 분석) [Discriminant Analysis 이용 원자력발전소 상태 예측] (3) LDA# LDAfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysisfrom sklearn.metrics import accuracy_score, confusion_matrixcld = LinearDiscriminantAnalysis()cld.fit(x_train, y_train)y_train_pred = cld.predict(x_train)y_test_cld_pred = cld.predict(x_test)print(accuracy_score(y_train,y_train_pred))QDA# QDAfrom sklearn.discriminant_analysis import QuadraticDiscriminantAnalysisfrom skle.. 2024. 3. 31. Discriminant analysis(판별 분석) [Iris] (2) 1. Load Dataset # Iris data 불러오기 import seaborn as sns iris = sns.load_dataset('iris') x = iris.drop('species', axis=1) y = iris['species'] 2. Preprocessing # Label Encoder from sklearn.preprocessing import LabelEncoder classle = LabelEncoder() y = classle.fit_transform(iris['species'].values) # Split Dataset from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test =.. 2024. 3. 31. 이전 1 ··· 14 15 16 17 18 19 20 ··· 24 다음
