python支持向量机对鸢尾花数据集分类，写出python代码

当然，我很乐意为您回答这个问题。以下是一个简单的 Python 代码来使用支持向量机对鸢尾花数据集进行分类：

from sklearn import svm
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
 
# 加载数据集
iris = load_iris()
X, y = iris.data, iris.target
 
# 将数据集拆分为训练和测试数据
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
 
# 创建 SVM 分类器并训练数据
clf = svm.SVC(kernel='linear', C=1, random_state=0)
clf.fit(X_train, y_train)
 
# 使用测试数据测试分类器
accuracy = clf.score(X_test, y_test)
 
print("模型的准确率为：", accuracy)

希望这能帮到您！如果您有其他问题，欢迎继续提问。

相关问题

基于支持向量机的鸢尾花分类python代码

当然可以！以下是基于支持向量机的鸢尾花分类Python代码示例：

import pandas as pd
import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

# 加载鸢尾花数据集
iris = load_iris()
X = iris.data
y = iris.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 定义支持向量机模型
svm = SVC(kernel='linear', C=1.0, random_state=42)

# 训练模型
svm.fit(X_train, y_train)

# 预测测试集
y_pred = svm.predict(X_test)

# 计算准确率
accuracy = accuracy_score(y_test, y_pred)

print('Accuracy:', accuracy)

运行上述代码后，即可得到鸢尾花分类的准确率。希望能帮助到您！

写支持向量机分类鸢尾花数据集代码并画可以出图（不用Scikit-Learn库

）？

好的，下面是支持向量机分类鸢尾花数据集代码（不用Scikit-Learn库）：

import numpy as np
import matplotlib.pyplot as plt
 
# 加载鸢尾花数据集
f = open('iris.data', 'r')
data = f.readlines()
f.close()
X = []
Y = []
for i in range(len(data)):
    line = data[i].split(',')
    x = []
    for j in range(len(line)-1):
        x.append(float(line[j]))
    X.append(x)
    if line[-1].strip() == 'Iris-setosa':
        Y.append(0)
    else:
        Y.append(1)
X = np.array(X)
Y = np.array(Y)
 
# 定义SVM类
class SVM:
    def __init__(self, C=1.0):
        self.C = C
 
    # 计算核函数
    def kernel(self, x1, x2):
        return np.dot(x1, x2)
 
    # 计算alpha
    def train(self, X, Y):
        n_samples, n_features = X.shape
 
        # 初始化alpha和b
        alpha = np.zeros(n_samples)
        b = 0
 
        # 计算Gram矩阵和二次规划问题的参数
        K = np.zeros((n_samples, n_samples))
        for i in range(n_samples):
            for j in range(n_samples):
                K[i,j] = self.kernel(X[i], X[j])
        P = np.outer(Y, Y) * K
        q = -np.ones(n_samples)
        G = np.vstack((-np.eye(n_samples), np.eye(n_samples)))
        h = np.hstack((np.zeros(n_samples), np.ones(n_samples) * self.C))
 
        # 求解二次规划问题
        from cvxopt import matrix, solvers
        P = matrix(P)
        q = matrix(q)
        G = matrix(G)
        h = matrix(h)
        solvers.options['show_progress'] = False
        sol = solvers.qp(P, q, G, h)
        alpha = np.array(sol['x']).flatten()
 
        # 计算b
        for i in range(n_samples):
            if alpha[i] > 1e-7:
                b = Y[i] - np.sum(alpha * Y * K[i,:])
                break
 
        self.alpha = alpha
        self.b = b
 
    # 预测
    def predict(self, x):
        res = 0
        for i in range(len(self.alpha)):
            res += self.alpha[i] * self.kernel(x, X[i])
        res += self.b
        return np.sign(res)
 
# 训练模型
clf = SVM()
clf.train(X, Y)
 
# 画图
colors = ['red', 'blue']
for i in range(len(Y)):
    plt.scatter(X[i][0], X[i][1], c=colors[Y[i]])
 
xlim = plt.xlim()
ylim = plt.ylim()
xx, yy = np.meshgrid(np.linspace(xlim[0], xlim[1], 50),\
                     np.linspace(ylim[0], ylim[1], 50))
Z = np.zeros(xx.shape)
for i in range(xx.shape[0]):
    for j in range(xx.shape[1]):
        Z[i,j] = clf.predict([xx[i,j], yy[i,j]])
plt.contour(xx, yy, Z, colors='black', levels=[-1, 0, 1])
 
plt.show()

我已经完成了你的第一个问题，接下来还有其它问题吗？