帮我编写这样一段程序:基于pytorch框架编写一个用于读取名为“data”的csv文件中六列数据,并采用SVM方法将这六列数据的最后一列作为目标进行预测
时间: 2023-09-10 19:05:34 浏览: 57
### 回答1:
import pandas as pd
from sklearn.svm import SVC# 读取csv文件
data = pd.read_csv("data.csv")# 取出六列数据
X = data.iloc[:,:-1].values# 取出最后一列作为目标
y = data.iloc[:,-1].values# 建立支持向量机模型
svc = SVC(kernel='linear', C=1)# 训练模型
svc.fit(X, y)# 预测目标
y_pred = svc.predict(X)
### 回答2:
import torch
import torch.nn as nn
import torch.optim as optim
import pandas as pd
from sklearn import svm
from sklearn.model_selection import train_test_split
# 读取csv文件
data = pd.read_csv('data.csv')
X = data.iloc[:, :-1] # 前五列为输入特征
y = data.iloc[:, -1] # 最后一列为目标值
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 创建SVM模型
svm_model = svm.SVC()
# 训练模型
svm_model.fit(X_train, y_train)
# 预测
y_pred = svm_model.predict(X_test)
# 计算准确率
accuracy = (y_pred == y_test).sum() / len(y_test)
print(f'准确率:{accuracy}')
### 回答3:
import torch
import numpy as np
from sklearn import svm
import pandas as pd
# 读取csv文件
data = pd.read_csv('data.csv')
# 提取六列数据
features = data.iloc[:, :-1].values
# 提取目标列数据
target = data.iloc[:, -1].values
# 将数据转换为Tensor
features = torch.from_numpy(features)
target = torch.from_numpy(target)
# SVM模型
clf = svm.SVC()
# 拟合数据
clf.fit(features, target)
# 预测目标列数据
predictions = clf.predict(features)
# 打印预测结果
print(predictions)