DL之Perceptron&AdalineGD：基于iris莺尾花数据集利用Perceptron感知机和AdalineGD算法实现二分类

 

 

目录

基于iris莺尾花数据集利用Perceptron感知机算法实现二分类

设计思路

输出结果

核心代码

---

 

 

相关文章
DL之Perceptron&AdalineGD：基于iris莺尾花数据集利用Perceptron感知机和AdalineGD算法实现二分类
DL之Perceptron&AdalineGD：基于iris莺尾花数据集利用Perceptron感知机和AdalineGD算法实现二分类实现

基于iris莺尾花数据集利用Perceptron感知机算法实现二分类

设计思路

 

 

 

输出结果

<bound method DataFrame.info of SepalLength_cm SepalWidth_cm ... PetalWidth_cm label 0 5.1 3.5 ... 0.2 Iris-setosa 1 4.9 3.0 ... 0.2 Iris-setosa 2 4.7 3.2 ... 0.2 Iris-setosa 3 4.6 3.1 ... 0.2 Iris-setosa 4 5.0 3.6 ... 0.2 Iris-setosa .. ... ... ... ... ... 145 6.7 3.0 ... 2.3 Iris-virginica 146 6.3 2.5 ... 1.9 Iris-virginica 147 6.5 3.0 ... 2.0 Iris-virginica 148 6.2 3.4 ... 2.3 Iris-virginica 149 5.9 3.0 ... 1.8 Iris-virginica[150 rows x 5 columns]>SepalLength_cm SepalWidth_cm ... PetalWidth_cm label 0 5.0 3.5 ... 0.3 Iris-setosa 1 6.3 2.5 ... 1.9 Iris-virginica 2 4.4 3.0 ... 0.2 Iris-setosa 3 5.7 2.8 ... 1.3 Iris-versicolor 4 6.8 3.2 ... 2.3 Iris-virginica[5 rows x 5 columns] Iris-setosa 50 Iris-versicolor 50 Iris-virginica 50 Name: label, dtype: int64 (150, 2) [5, 0, 0, 0, 0, 0, 0, 0, 0, 0] CE_res (37, 0, 0, 0) recall precision accurency 1.0 1.0 1.0

 

核心代码

class Perceptron(object):def __init__(self, rate = 0.01, niter = 10):self.rate = rateself.niter = niterdef fit(self, X, y):self.weight = np.zeros(1 + X.shape[1])self.errors = [] # Number of misclassificationsfor i in range(self.niter):err = 0for xi, target in zip(X, y):delta_w= self.rate * (target-self.predict(xi))self.weight[1:] += delta_w * xiself.weight[0] += delta_werr += int(delta_w != 0.0)self.errors.append(err)return selfdef net_input(self, X):return np.dot(X, self.weight[1:]) + self.weight[0]def predict(self, X):return np.where(self.net_input(X) >= 0.0, 1, -1)

 

 

 

 

 

 

 

 

总结

以上是生活随笔为你收集整理的DL之PerceptronAdalineGD：基于iris莺尾花数据集利用Perceptron感知机和AdalineGD算法实现二分类的全部内容，希望文章能够帮你解决所遇到的问题。

如果觉得生活随笔网站内容还不错，欢迎将生活随笔推荐给好友。