iris=load_iris()data=iris.data # Normalize thedatascaler=MinMaxScaler()data=scaler.fit_transform(data)# Initializeandtrain SOM som=MiniSom(x=5,y=5,input_len=data.shape[1],sigma=0.5,learning_rate=0.5)som.random_weights_init(data)som.train_random(data,100)# Visualize the SOM plt.figure(fig...
:sx=MinMaxScaler((0,1))sx.fit([[0], [1]])x[:,[i]]=sx.transform(x[:,[i]])meta_info[key]['scaler']=sxtrain_x,test_x,train_y,test_y=train_test_split(x,y,test_size=0.2,random_state=random_state)returntrain_x,test_x,train_y,test_y,task_type,meta_info,metric_wrapper(...
): data_path='./datasets/' save_path='D:/GitHub/selfADVAE-AD/checkpoints' device='cuda' batch_size=32 n_jobs_dataloader=0 #load_dataset train_data,train_label,test_data,test_label,sample_dim,rep_dim=load_tab_data(data_path=data_path, dataset_name=dataset_name) scaler=MinMaxScaler()...
dataset = dataframe.values#将整型变为floatdataset = dataset.astype('float32')#数据归一化scaler = preprocessing.MinMaxScaler(feature_range=(0,1)) dataset = scaler.fit_transform(dataset)#数据分割train_size =int(len(dataset) *0.65) trainlist = dataset[:train_size] testlist = dataset[train_size:...
sklearn.preprocessing.MinMaxScaler sklearn.preprocessing.StandardScaler sklearn.preprocessing.MaxAbsScaler sklearn.preprocessing.RobustScaler sklearn.preprocessing.PowerTransformer sklearn.preprocessing.QuantileTransformer sklearn.preprocessing.OneHotEncoder sklearn.preprocessing.LabelEncoder category_encoders seaborn sk...