x_min = np.min(X, axis=0) x_max = np.max(X, axis=0) # 缩放特征 X_scaled = (X - x_min) / (x_max - x_min) return X_scaled # 示例 X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) X_scaled = min_max_scaling(X) print(X_scaled) # 输出: # [[0. 0...
from sklearnimportpreprocessingimportnumpyasnpX=np.array([[1.,-1.,2.],[2.,0.,0.],[0.,1.,-1.]])min_max_scaler=preprocessing.MinMaxScaler()X_minMax=min_max_scaler.fit_transform(X) 最后输出: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 array([[0.5,0.,1.],[1.,0.5,0.3333333...
MaxMinScaler方法 代码语言:javascript 代码运行次数:0 运行 AI代码解释 import numpy as np from sklearn import preprocessing X_train = np.array([[ 1., -1., 2.], [ 2., 0., 0.], [ 0., 1., -1.]]) min_max_sacler = preprocessing.MinMaxScaler() min_max_sacler.fit(X_train) print...
array([ 0.5 , 0.5 , 0.33...]) >>> min_max_scaler.min_ array([ 0. , 0.5 , 0.33...]) 当然,在构造类对象的时候也可以直接指定最大最小值的范围:feature_range=(min, max),此时应用的公式变为: X_std=(X-X.min(axis=0))/(X.max(axis=0)-X.min(axis=0)) X_scaled=X_std/(max-...
(X_train)# 缩放训练集X_train_scaled=scaler.transform(X_train)# 缩放检验集X_test=np.array([[185,0.25],[150,0.55]])X_test_scaled=scaler.transform(X_test)# 查看结果print(X_train)print(X_train_scaled)print(X_test_scaled)[[1.00e+022.00e-01][8.00e+012.50e-01][7.00e+011.50e-01][...
>>> from sklearn.preprocessing import MinMaxScaler >>> data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]] >>> scaler = MinMaxScaler() >>> print(scaler.fit(data)) MinMaxScaler() >>> print(scaler.data_max_) [ 1. 18.] >>> print(scaler.transform(data)) [[0. 0. ] [0.25...
#coding:utf-8fromsklearnimportpreprocessingimportnumpy as np x= np.array([[5000.],[58000.],[16000.]]) min_max_scaler=preprocessing.MinMaxScaler() minmax_x=min_max_scaler.fit_transform(x)print(minmax_x) 结果: [[0. ] [1. ]
standardized=scaler.fit_transform(feature) standardized # 转化方式见下面, 表示距离 平均值多少个 标准差 array([[-0.76058269], [-0.54177196], [-0.35009716], [-0.32271504], [1.97516685]]) Discussion Acommonalternativetomin-maxscalingisrescalingoffeaturestobeapproximatelystandardnormallydistributed.Toachievethis...
MaxAbsScaler类以及maxabs_scale函数的功能十分简单,将每个元素除以该列特征中的最大值,因此它们的可选参数只有一个,不同的是,函数将原数据对象作为参数,而在转换器中,元数据是转换器调用链的其中一个环节的参数。 copy:boolean类型,默认为True,其含义即是否对原数据对象进行修改。
X_test = pd.DataFrame(scaler.transform(dataset_test), columns=dataset_test.columns, index=dataset_test.index) tf.random.set_seed(10) act_func ='relu' # Input layer: model=Sequential() # First hidden layer, connected to input vecto...