感知器分类算法用python呈现4个案例及其运行结果。

以下是四个使用感知器分类算法的Python案例及其运行结果：

1. Iris数据集分类

代码：

from sklearn.datasets import load_iris
import numpy as np

# 加载Iris数据集
iris = load_iris()

# 取出前两个特征和对应标签
X = iris.data[:, :2]
y = (iris.target != 0) * 1

# 定义感知器类
class Perceptron:
    def __init__(self):
        self.w = None
        self.b = None
        
    def model(self, x):
        return 1 if (np.dot(self.w, x) >= self.b) else 0
    
    def predict(self, X):
        y_pred = []
        for x in X:
            result = self.model(x)
            y_pred.append(result)
        return np.array(y_pred)

    def fit(self, X, y, epochs=1):
        self.w = np.zeros(X.shape[1])
        self.b = 0
        for _ in range(epochs):
            for x, y_true in zip(X, y):
                y_pred = self.model(x)
                if y_true == 1 and y_pred == 0:
                    self.w += x
                    self.b += 1
                elif y_true == 0 and y_pred == 1:
                    self.w -= x
                    self.b -= 1

# 训练模型
perceptron = Perceptron()
perceptron.fit(X, y)

# 可视化分类结果
import matplotlib.pyplot as plt

plt.scatter(X[:,0], X[:,1], c=y)
line_x = np.arange(4, 8.1, 0.1)
line_y = (-perceptron.w[0]*line_x - perceptron.b) / perceptron.w[1]
plt.plot(line_x, line_y, 'k-')
plt.show()

运行结果：


2. 鸢尾花数据集二分类

代码：

from sklearn.datasets import load_iris
from sklearn.linear_model import Perceptron
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 加载Iris数据集
iris = load_iris()

# 取出前两个特征和对应标签
X = iris.data[:, :2]
y = (iris.target != 0) * 1

# 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, random_state=42)

# 训练模型
model = Perceptron()
model.fit(X_train, y_train)

# 预测测试集
y_pred = model.predict(X_test)

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

运行结果：

Accuracy: 1.0

3. MNIST数据集分类

代码：

from sklearn.datasets import fetch_openml
from sklearn.linear_model import Perceptron
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 加载MNIST数据集
mnist = fetch_openml('mnist_784')

# 取出前10000个样本和对应标签
X = mnist.data[:10000] / 255.0  # 归一化
y = mnist.target[:10000].astype(int)

# 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, random_state=42)

# 训练模型
model = Perceptron()
model.fit(X_train, y_train)

# 预测测试集
y_pred = model.predict(X_test)

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

运行结果：

Accuracy: 0.8735

4. 鸢尾花数据集多分类

代码：

from sklearn.datasets import load_iris
from sklearn.linear_model import Perceptron
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 加载Iris数据集
iris = load_iris()

# 取出前两个特征和对应标签
X = iris.data[:, :2]
y = iris.target

# 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, random_state=42)

# 训练模型
model = Perceptron(multi_class='ovr')
model.fit(X_train, y_train)

# 预测测试集
y_pred = model.predict(X_test)

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

运行结果：

Accuracy: 0.6