用pycharm写，用IPython解释器编写 SMO 算法实现线性 SVM 分类器，对 iris 数据集进行二分类。具体内容： （1）选取两个特征和两类数据进行二分类。 注意：二分类标签为 1 和-1。 （2）划分数据（分成训练集和数据集） （3）数据归一化 （4）训练模型（参考程序模板：SVM_numpy_template.py） （5）输出：SVM 对偶问题目标函数的最优解𝛼，决策函数的参数和截距，支持向量等。

好的，让我来回答你的问题。

首先，你需要安装以下库：
- numpy
- pandas
- sklearn

以下是代码实现：

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler

# 加载数据集
iris = load_iris()
X = iris.data[:, [0, 3]]
y = np.where(iris.target == 0, -1, 1)

# 数据归一化
ss = StandardScaler()
X = ss.fit_transform(X)

# 划分数据集
train_X, test_X = X[:100], X[100:]
train_y, test_y = y[:100], y[100:]

# SMO算法实现
class SVM:
    def __init__(self, kernel='linear', C=1.0, tol=1e-3, max_iter=100):
        self.kernel = kernel
        self.C = C
        self.tol = tol
        self.max_iter = max_iter

    def fit(self, X, y):
        self.X = X
        self.y = y
        self.b = 0.0
        self.alpha = np.zeros(len(X))
        self.E = self._E(self.X, self.y, self.b, self.alpha)
        for _ in range(self.max_iter):
            for i in range(len(self.X)):
                if self._KKT(self.E[i], self.y[i], self.alpha[i]):
                    j = self._select_j(i, self.E)
                    alpha_i_old, alpha_j_old = self.alpha[i], self.alpha[j]
                    if self.y[i] != self.y[j]:
                        L = max(0, self.alpha[j] - self.alpha[i])
                        H = min(self.C, self.C + self.alpha[j] - self.alpha[i])
                    else:
                        L = max(0, self.alpha[j] + self.alpha[i] - self.C)
                        H = min(self.C, self.alpha[j] + self.alpha[i])
                    eta = self._kernel(self.X[i], self.X[i]) + self._kernel(self.X[j], self.X[j]) - 2 * self._kernel(self.X[i], self.X[j])
                    if eta <= 0:
                        continue
                    self.alpha[j] += self.y[j] * (self.E[i] - self.E[j]) / eta
                    self.alpha[j] = np.clip(self.alpha[j], L, H)
                    self.alpha[i] += self.y[i] * self.y[j] * (alpha_j_old - self.alpha[j])
                    self.b = self._b(self.X, self.y, self.alpha)
                    self.E = self._E(self.X, self.y, self.b, self.alpha)
        self.w = self._w(self.X, self.y, self.alpha)

    def predict(self, X):
        return np.sign(np.dot(X, self.w) + self.b)

    def _kernel(self, x1, x2):
        if self.kernel == 'linear':
            return np.dot(x1, x2)
        elif self.kernel == 'rbf':
            gamma = 0.5
            return np.exp(-gamma * np.linalg.norm(x1 - x2) ** 2)

    def _w(self, X, y, alpha):
        return np.dot(X.T, y * alpha)

    def _b(self, X, y, alpha):
        return np.mean(y - np.dot(X, self.w))

    def _select_j(self, i, E):
        j = np.argmax(np.abs(E - E[i]))
        return j if E[i] * E[j] < 0 else np.random.randint(len(self.X))

    def _KKT(self, E, y, alpha):
        if alpha == 0:
            return y * E >= -self.tol
        elif 0 < alpha < self.C:
            return np.abs(y * E) <= self.tol
        else:
            return y * E <= self.tol

    def _E(self, X, y, b, alpha):
        return np.dot(X, self.w) + b - y

svm = SVM(kernel='linear', C=1.0, tol=1e-3, max_iter=100)
svm.fit(train_X, train_y)

# 输出结果
print("alpha: ", svm.alpha)
print("w: ", svm.w)
print("b: ", svm.b)

# 计算训练集和测试集上的准确率
train_pred = svm.predict(train_X)
train_acc = np.mean(train_pred == train_y)
print("train accuracy: {:.2f}%".format(train_acc * 100))

test_pred = svm.predict(test_X)
test_acc = np.mean(test_pred == test_y)
print("test accuracy: {:.2f}%".format(test_acc * 100))

# 输出支持向量
support_vectors = svm.alpha > 0
print("number of support vectors: ", np.sum(support_vectors))
print("indices of support vectors: ", np.where(support_vectors))

运行以上代码，你将获得 SVM 对偶问题目标函数的最优解𝛼，决策函数的参数和截距，支持向量等信息。同时，你还可以获得训练集和测试集上的准确率。

希望能够帮助到你！