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

落日映苍穹つ 2022-12-27 04:44 127阅读 0赞

**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_Perceptron_AdalineGD_iris_Perceptron_AdalineGD]  
[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][DL_Perceptron_AdalineGD_iris_Perceptron_AdalineGD 1]

# 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 ##

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70][]

## 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

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 1][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 2][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 3][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 4][]

## 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)

[DL_Perceptron_AdalineGD_iris_Perceptron_AdalineGD]: https://yunyaniu.blog.csdn.net/article/details/110847605
[DL_Perceptron_AdalineGD_iris_Perceptron_AdalineGD 1]: https://yunyaniu.blog.csdn.net/article/details/110942261
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