利用Min-Max规范化的方法将属性的值映射到0至1的范围内,那么属性income的16000元将被转化为多少? #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(minm...
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...
print('仅仅拟合为进行标准化转换的scaler标准差:scaler_std=',np.sqrt(scaler.var_)) #var_算的是训练集的方差 print('\n标准化 X 转换后:scaler_transform_X\n',scaler.transform(X) ) print('\n标准化 X 转换后均值:mean=',scaler.transform(X).mean(axis= 0 )) print('标准化 X 转换后标准差...
le=sklearn.preprocessing.LabelEncoder() le.fit([1,2,2,6]) le.transform([1,1,2,6])#array([0, 0, 1, 2]) #非数值型转化为数值型 le.fit(["paris","paris","tokyo","amsterdam"]) le.transform(["tokyo","tokyo","paris"])#array([2, 2, 1]) 8.特征中含异常值时 1 sklearn.prep...
scaled_data = scaler.fit_transform(data) print(scaled_data) 3)范数缩放(Normalization) 范数缩放通常是将每个样本缩放到单位范数(每个样本的向量长度为1)。可以使用 Normalizer 类来实现: from sklearn.preprocessing import Normalizer # 示例数据 data = np.array([[1, 2, 3], [4, 5, 6], [7, 8,...
min_max_scaler = preprocessing.MinMaxScaler() min_max = min_max_scaler.fit_transform(raw_data) #零-均值规范化 z_score = preprocessing.scale(raw_data) # 小数定标规范化 # 主要是通过NumPy库来计算小数点的位数 k = np.ceil(np.log10(np.max(abs(raw_data))) decimal...
scaler.fit_transform(df.iloc[i].values.reshape(-1,1)) # use minMax scaler X_test, y_test = split_intervals(input, n_timestamps) predictions = neuralnet.predict(X_test) X_test = X_test[:,:,0] predictions = predictions[:,:,0] predicted_stock_price = scaler.inverse_transform(...
# 归一化数据scaler = StandardScaler()X_train_scaled = scaler.fit_transform(X_train)X_test_scaled = scaler.transform(X_test) # 获取归一化后的混淆矩阵cm_with_norm = get_confusion_matrix(X_train_scaled, X_test_scaled, ...
from sklearn import preprocessingimport pandas as pdimport numpy as npprint("去除空值并且归一化处理")y=data1.dropna(axis=0).iloc[:,1:]#去除空值min_max_scaler=preprocessing.MinMaxScaler()x_minmax=min_max_scaler.fit_transform(y)print(x_minmax) ...
preprocessing import MinMaxScaler min_max_scaler = MinMaxScaler() min_max_scaler.fit_transform(df) z标准化 代码语言:javascript 代码运行次数:0 运行 AI代码解释 # 自定义函数 df.apply(lambda x : (x-x.mean())/ x.std(ddof=0)) sales rand 0 -1.38873 -1.336306 1 0.46291 0.267261 2 0.92582...