读取wine.csv数据，对数据进行解析，分出类属性和变量属性，数据预处理，标准化，生成测试机和训练集，用k折交叉研究分类结果。python代码

以下是对wine数据集进行数据预处理、标准化、生成测试集和训练集、使用k折交叉验证进行分类的Python代码：

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import KFold

# 读取wine.csv数据
df = pd.io.parsers.read_csv('https://raw.githubusercontent.com/rasbt/pattern_classification/master/data/wine_data.csv', header=None, usecols=[0,1,2])

# 分出类属性和变量属性
X = df.iloc[:, 1:].values
y = df.iloc[:, 0].values

# 数据预处理
sc = StandardScaler()
X_std = sc.fit_transform(X)

# 生成测试集和训练集
X_train, X_test, y_train, y_test = train_test_split(X_std, y, test_size=0.3, random_state=0)

# 使用k折交叉验证进行分类
kf = KFold(n_splits=10, shuffle=True, random_state=0)
for train_index, test_index in kf.split(X_std):
    X_train, X_test = X_std[train_index], X_std[test_index]
    y_train, y_test = y[train_index], y[test_index]
    mlp = MLPClassifier(hidden_layer_sizes=(10,), max_iter=1000)
    mlp.fit(X_train, y_train)
    print("训练集得分: {:.2f}".format(mlp.score(X_train, y_train)))
    print("测试集得分: {:.2f}".format(mlp.score(X_test, y_test)))