This code is an example of a Support Vector Machine (SVM) classifier in machine learning

import time
import random
import xlsxwriter
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA

def file2matrix(filename):
    fr = open(filename)
    lines = fr.readlines()
    numberOfLines = len(lines)
    returnMat = []
    returnLabel = []

    for line in lines:
        line = line.strip()
        listFromLine = line.split('\t')
        if len(listFromLine) == 51:  # 假设每行包含50个特征值和1个标签值
            returnMat.append(listFromLine[0:50])  # 将前50个元素添加到returnMat中
            returnLabel.append(listFromLine[-1])  # 将最后一个元素作为标签添加到returnLabel中

    returnMat = np.array(returnMat, dtype=float)  # 将returnMat转换为浮点类型的NumPy数组
    returnLabel = np.array(returnLabel, dtype=float)  # 将returnLabel转换为浮点类型的NumPy数组
    return returnMat, returnLabel

def autoNorm(dataSet):
    minVals = dataSet.min(0)
    maxVals = dataSet.max(0)
    normDataSet = (dataSet - minVals) / (maxVals - minVals)
    return normDataSet

# 从文件中加载数据
datingDataMat, datingLabels = file2matrix(r'D:\Project\50数据.txt')

# 对数据进行归一化
dataSet = autoNorm(datingDataMat)

# 使用PCA进行降维
pca = PCA(n_components=3)
dataSet = pca.fit_transform(dataSet)

start = time.perf_counter()

testsize = 0.25
# 将数据集拆分为训练集和测试集
xtrain, xtest, ytrain, ytest = train_test_split(dataSet, datingLabels, test_size=testsize)

# 创建并训练SVM模型
model = svm.SVC()
model.fit(xtrain, ytrain)

error = 0
for i in range(len(xtest)):
    # 预测每个测试实例的标签
    result = model.predict(xtest[i, :].reshape(1, -1))
    if result != ytest[i]:
        error += 1

accuracy = (1 - error / len(xtest)) * 100
print('准确率：', accuracy, '%')

end = time.perf_counter()
duration = end - start
print('耗时', duration, '秒')

Oak

This code is an example of a Support Vector Machine (SVM) classifier in machine learning.

The main functions are as follows:

1. Read the dataset from the given file (filepath filename).
2. Normalize the dataset to scale the eigenvalues ​​to the same range.
3. The dimensionality reduction of the data set was performed using principal component analysis (PCA), and the data was reduced from the original 50 dimensions to 3 dimensions.
4. Split the dataset into training and testing sets.
5. Create an SVM classifier model and train the model using the training set.
6. A prediction is made for each instance in the test set and the accuracy is calculated.
7. Output accuracy and execution time of the code.

Overall, the code implements an SVM-based classifier for classifying a dataset and evaluates the accuracy of the classifier.