想体验一把No.1的快乐吗？话不多说直接上代码。

代码：

import os

import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
import numpy as np


def get_dataset(path):
    dataset, labels = [], []
    filenames = os.listdir(path)

    for filename in filenames:
        labels.append(filename[0])
        filepath = os.path.join(path, filename)
        dataset.append(np.fromfile(filepath, dtype=np.uint8))


    return dataset, labels


if __name__ == '__main__':
    X_train, y_train = get_dataset("train")
    X_test, y_test = get_dataset("test")

    # 数据标准化
    scaler = StandardScaler()
    X_train = scaler.fit_transform(X_train)
    X_test = scaler.transform(X_test)  # 使用同一个scaler的transform，避免误差
    y_train = list(y_train)

    model = SVC()
    model.fit(X_train, y_train)

    y_pred_test = model.predict(X_test)

    # 保存预测结果到result.csv
    results = pd.DataFrame({'label': y_test, 'num': y_pred_test})
    results.to_csv('result.csv', index=False)

Tips： 

相信大家也发现了，是Wrong Answer。诧异这种情况也能排名，所以发出来供大家娱乐一下，稍微修改一下就是能过的代码。仅供娱乐，仅供娱乐，仅供娱乐。