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
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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 = rate
self.niter = niter
def fit(self, X, y):
self.weight = np.zeros(1 + X.shape[1])
self.errors = [] # Number of misclassifications
for i in range(self.niter):
err = 0
for xi, target in zip(X, y):
delta_w= self.rate * (target-self.predict(xi))
self.weight[1:] += delta_w * xi
self.weight[0] += delta_w
err += int(delta_w != 0.0)
self.errors.append(err)
return self
def 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) 赞 (0)