专注机器学习之路

协方差矩阵的几何解释 sklearn.GaussianMixture covariance_type : {‘full’, ‘tied’, ‘diag’, ‘spherical’}, ‘full’ (each component has its own general covariance matrix), ‘tied’ (all components share the sam

打乱数据顺序 # shufle from sklearn.utils import shuffle b_train,b_label = shuffle(b_train,b_label) 按比例将数据分割成训练集+测试集 # split the original training data into two parts: training data and validation data # this

http://scikit-learn.org/stable/modules/preprocessing.html 代码： from sklearn.preprocessing import StandardScaler data = np.arange(9).reshape((3,3)) print('#'*60) print('原始数据:\n',data) test = [[10,11...

将[0 1 2 3 4 5 6 7 8 9]标签转换成OneHot编码 from sklearn.preprocessing import OneHotEncoder ohe = OneHotEncoder() ohe.fit(np.arange(10).reshape(-1,1)) labels = ohe.transform(labels).astype('uint8').toarray(...

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on 2019-01-20 @author: liuguokai """ import numpy as np from sklearn.preprocessing import MinMaxScaler de