sklearn实战之构建SVM多分类器

最新推荐文章于 2024-08-18 11:53:15 发布

Cassiel.Lee

最新推荐文章于 2024-08-18 11:53:15 发布

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文章标签： SVM classifier image classification

本文链接：https://blog.youkuaiyun.com/qq_31207499/article/details/88635833

本文介绍如何借助sklearn库轻松构建支持向量机（SVM）分类器，实现图像分类任务。

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利用sklearn库构建SVM分类器十分简单，因为这个库已经封装好了，只用调用相应的函数即可。

# -*- coding: utf-8 -*-
"""
Created on Fri Nov 23 18:44:37 2018

@author: 13260
"""

import os
import numpy as np
import matplotlib.pyplot as plt
from itertools import cycle
from sklearn import svm, metrics, preprocessing
from sklearn.metrics import roc_curve, auc, classification_report
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.multiclass import OneVsRestClassifier
from sklearn.externals import joblib
from scipy import interp

# 加载图像特征及标签
'''
def read_features(filedir):
    file_list = os.listdir(filedir)
    X = []
    tmp_y = os.listdir("F:/shiyan/TensorFlow/retrain/data/train")
    # print(len(y))
    y = []
    for file in file_list:
        
        tmp_file = filedir + "/" + file
        tmp = np.loadtxt(tmp_file,dtype=str)
        # np格式转换
        feature = tmp.astype(np.float)
        X.append(feature)
        old_filename = file[:-3].split("_")