多核支持向量机实践

最新推荐文章于 2023-08-06 20:56:44 发布

solumin

最新推荐文章于 2023-08-06 20:56:44 发布

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CC 4.0 BY-SA版权

分类专栏：机器学习实验机器学习

本文链接：https://blog.youkuaiyun.com/solumin/article/details/103841065

本文介绍了如何在sklearn中实现多核支持向量机，通过自定义内核，详细阐述了数据加载、多核构造以及模型训练的过程。

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sklearn中具有自定义内核的功能
参考sklearn文档

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets

# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2]  # we only take the first two features. We could
                      # avoid this ugly slicing by using a two-dim dataset
Y = iris.target
def my_kernel(X, Y):
    """
    We create a custom kernel:

                 (2  0)
    k(X, Y) = X  (    ) Y.T
                 (0  1)
    """
    M = np.array([[2, 0], [0, 1.0]])
    return np.dot(np.dot(X, M), Y.T)

h = .02  # step size in the mesh
# we create an instance of SVM and fit out data.
clf = svm.SVC(kernel=my_kernel)
clf.fit(X, Y)
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max