X = (X_scaled - self.feature_range[0]) / scale# 反向缩放X = X * (self.data_max_ - self.data_min_) + self.data_min_# 反向转换到原始数据returnXdeffit_transform(self, X, y=None):returnself.fit(X).transform(X)
0.,0.],[0.,1.,-1.]])min_max_scaler=preprocessing.MinMaxScaler()#默认为范围0~1,拷贝操作#min_max_scaler = preprocessing.MinMaxScaler(feature_range = (1,3),copy = False)#范围改为1~3,对原数组操作x_minmax=min_max_scaler.fit_transform(x)print('x_minmax = ',x_minmax)print('x = '...
import numpy as np import torch from sklearn.preprocessing import MinMaxScaler scaler=MinMaxScaler(feature_range=(0,1)) #这里的feature_range表示设置归一化范围为(0,1) x=numpy.random.randint(1,10,(4,1)) #生成(4,1)的1-10之间的整数 print(x) [[2] [9] [3] [4]] print(x.min(axis=0...
self.n_samples_seen_ = X.shape[0] else: data_min = np.minimum(self.data_min_, data_min) data_max = np.maximum(self.data_max_, data_max) self.n_samples_seen_ += X.shape[0] # Next steps data_range = data_max - data_min self.scale_ = (feature_range[1] - feature_range[0...
MinMaxScaler() 和 StandardScaler() 之间有什么区别。 mms = MinMaxScaler(feature_range = (0, 1)) (用于机器学习模型) sc = StandardScaler() (在另一个机器学习模型中,他们使用标准缩放器而不是最小最大...
X_scaler = MinMaxScaler(feature_range=(0, 1)) Y_scaler = MinMaxScaler(feature_range=(0, 1)) # Scale both training inputs and outputs X_scaled_training = X_scaler.fit_transform(X_training) Y_scaled_training = Y_scaler.fit_transform(Y_training) ...
通过调整feature_range参数,用户可以自定义归一化后的数据范围。 错误信息表明'feature_range'需要是一个'tuple'实例: 当你遇到错误信息“the 'feature_range' parameter of MinMaxScaler must be an instance of 'tuple'”时,这意味着你在设置feature_range参数时提供的数据类型不是元组(tuple)。错误信息明确指出...
def __init__(self, feature_range=(0, 1), copy=True): self.feature_range = feature_range self.copy = copy def _reset(self): """Reset internal data-dependent state of the scaler, if necessary. __init__ parameters are not touched. ...
1、基础案例 MinMaxScaler简介 MinMaxScaler函数解释 MinMaxScaler底层代码 class MinMaxScaler Found at: sklearn.preprocessing.dataclass MinMaxScaler(BaseEstimator, TransformerMixin):def __init__(self, feature_range=(0, 1), copy=True):self.feature_range = feature_rangeself.copy = copydef _reset(self):"...
1、基础案例 MinMaxScaler简介 MinMaxScaler函数解释 MinMaxScaler底层代码 classMinMaxScalerFound at:sklearn.preprocessing.dataclassMinMaxScaler(BaseEstimator,TransformerMixin):def__init__(self,feature_range=(0,1),copy=True):self.feature_range=feature_range ...