感知算法的设计与实现，对对鸢尾花实现二分类的完整python代码

以下是使用感知算法对鸢尾花进行二分类的完整Python代码：

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

class Perceptron:
    def __init__(self, learning_rate=0.1, n_iters=1000):
        self.lr = learning_rate
        self.n_iters = n_iters
        self.activation_func = self._unit_step_func
        self.weights = None
        self.bias = None
    
    def fit(self, X, y):
        n_samples, n_features = X.shape
        
        # Initialize weights and bias
        self.weights = np.zeros(n_features)
        self.bias = 0
        
        y_ = np.array([1 if i>0 else 0 for i in y])
        
        # Gradient descent for n_iters iterations
        for _ in range(self.n_iters):
            for idx, x_i in enumerate(X):
                linear_output = np.dot(x_i, self.weights) + self.bias
                y_predicted = self.activation_func(linear_output)

                # Update weights and bias
                update = self.lr * (y_[idx] - y_predicted)
                self.weights += update * x_i
                self.bias += update
    
    def predict(self, X):
        linear_output = np.dot(X, self.weights) + self.bias
        y_predicted = self.activation_func(linear_output)
        return y_predicted
    
    def _unit_step_func(self, x):
        return np.where(x>=0, 1, 0)

# Load iris dataset
iris = load_iris()
X = iris.data
y = iris.target

# Split data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train perceptron algorithm on training data
perceptron = Perceptron(learning_rate=0.1, n_iters=1000)
perceptron.fit(X_train, y_train)

# Make predictions on test data and calculate accuracy
y_pred = perceptron.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)

print("Accuracy:", accuracy)

该代码实现了一个简单的感知器算法，并使用 iris 数据集进行二分类。在训练数据上拟合模型之后，我们使用测试数据集进行预测，并计算模型的准确性。最终输出的是模型在测试数据集上的准确性。