svm python源代码

以下是 Python 中使用 sklearn 库实现 SVM 的源代码：

from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 加载数据
X, y = load_data()

# 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)

# 建立 SVM 模型
clf = svm.SVC(kernel='linear')

# 训练模型
clf.fit(X_train, y_train)

# 预测测试集
y_pred = clf.predict(X_test)

# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

其中，`load_data()` 是加载数据的函数，`svm.SVC()` 是建立 SVM 模型的函数，`fit()` 是训练模型的函数，`predict()` 是预测测试集的函数，`accuracy_score()` 是计算准确率的函数。你需要根据自己的数据情况，修改相应的代码。

相关问题

TCA+SVM 的python代码

TCA+SVM是一种域自适应方法，用于解决源域和目标域不匹配的问题。其核心思想是通过特征变换将源域和目标域的分布对齐，再使用SVM进行分类。下面是一个简单的TCA+SVM的Python实现代码示例：

import numpy as np
from sklearn import svm
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

def TCA(Xs, Xt, kernel_type = 'linear', dim = 30, lamb = 1, gamma = 1):
    X = np.hstack((Xs.T, Xt.T))
    X = X / np.linalg.norm(X, axis=0)
    m, n = X.shape
    ns, nt = len(Xs), len(Xt)
    e = np.vstack((1 / ns * np.ones((ns, 1)), -1 / nt * np.ones((nt, 1))))
    M = e * e.T
    M = M / np.linalg.norm(M, 'fro')
    H = np.eye(n) - 1 / n * np.ones((n, n))
    K = kernel(kernel_type, X, None, gamma)
    n_eye = m if kernel_type == 'primal' else n
    a = np.linalg.multi_dot([K, M, K.T]) + lamb * np.eye(n_eye)
    b = np.linalg.multi_dot([K, H, K.T])
    w, V = scipy.linalg.eig(a, b)
    ind = np.argsort(w)
    A = V[:, ind[:dim]]
    Z = np.dot(A.T, K)
    Z /= np.linalg.norm(Z, axis=0)
    Xs_new = Z[:, :ns].T
    Xt_new = Z[:, ns:].T
    return Xs_new, Xt_new

def kernel(kernel_type, X, Z=None, gamma=None):
    if kernel_type == 'linear':
        if Z is None:
            K = np.dot(X.T, X)
        else:
            K = np.dot(X.T, Z)
    elif kernel_type == 'rbf':
        n_samples = X.shape
        if Z is None:
            d2 = np.sum(np.square(X), axis=1, keepdims=True)
            K = d2 + d2.T - 2 * np.dot(X, X.T)
        else:
            d2_X = np.sum(np.square(X), axis=1, keepdims=True)
            d2_Z = np.sum(np.square(Z), axis=1, keepdims=True)
            K = d2_X + d2_Z.T - 2 * np.dot(X, Z.T)
        K = np.exp(-gamma * K)
    else:
        raise ValueError('This kernel type is not supported!')
    return K

def TCA_SVM(Xs, ys, Xt, yt):
    Xs_new, Xt_new = TCA(Xs, Xt) # TCA特征变换
    clf = svm.SVC(kernel='linear')
    clf.fit(Xs_new, ys) # SVM分类器训练
    yt_pred = clf.predict(Xt_new) # SVM分类器预测
    acc = accuracy_score(yt, yt_pred) # 准确率计算
    return acc

# 载入数据集并划分为训练集和测试集
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2)

# 数据标准化和降维
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
pca = PCA(n_components=2)
X_train_pca = pca.fit_transform(X_train_scaled)
X_test_pca = pca.transform(X_test_scaled)

# TCA+SVM分类器训练和测试
acc = TCA_SVM(X_train_pca, y_train, X_test_pca, y_test)
print("准确率为：", acc)