标签：min max self feature range 归一化 data
class MinMaxScaler:
def __init__(self, feature_range=(0, 1),max_val =None,min_val = None):
self.feature_range = feature_range
self.data_min_ = min_val
self.data_max_ = max_val
def fit(self, X):
X = np.asarray(X)
if self.data_min_ is None:
self.data_min_ = X.min(axis=0)
if self.data_max_ is None:
self.data_max_ = X.max(axis=0)
return self
def transform(self, X):
X = np.asarray(X)
X_scaled = (X - self.data_min_) / (self.data_max_ - self.data_min_)
scale = self.feature_range[1] - self.feature_range[0]
X_scaled = X_scaled * scale + self.feature_range[0]
return X_scaled
def inverse_transform(self, X_scaled):
X_scaled = np.asarray(X_scaled)
scale = self.feature_range[1] - self.feature_range[0]
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
return X
def fit_transform(self, X, y=None):
return self.fit(X).transform(X)
标签：min,
max,
self,
feature,
range,
归一化,
data
From： https://www.cnblogs.com/conpi/p/18417634