R2的python代码

R2D2的Python代码可以用于通过i2c从aspberry Pi控制R2D2或其他astromech。在这个例子中，Python代码使用了Adafruit i2c伺服控制器和HTTP与REST进行通信。代码的主要过程包括读取配置、创建对象、创建REST树、灯光控制等。具体的代码实现可以参考相关的Wiki页面。

相关问题

决定系数r2python代码

计算决定系数R²的Python代码如下：

# 导入必要的库
from sklearn.metrics import r2_score

# 定义实际值和预测值
y_true = [3, -0.5, 2, 7]
y_pred = [2.5, 0.0, 2, 8]

# 计算决定系数
r2 = r2_score(y_true, y_pred)

# 输出结果
print("决定系数R²：", r2)

其中，`y_true`表示实际值，`y_pred`表示预测值，使用`sklearn.metrics`中的`r2_score`函数计算决定系数R²，最后输出结果。

bpso python代码pythonpython

以下是使用Python实现的基本粒子群优化算法（PSO）和二元粒子群优化算法（BPSO）的示例代码：

PSO代码：

import random

class Particle:
    def __init__(self, x0):
        self.position = []  # 粒子的位置
        self.velocity = []  # 粒子的速度
        self.best_position = []  # 粒子的历史最优位置
        self.fitness = -1  # 粒子的适应度值
        self.best_fitness = -1  # 粒子的历史最优适应度值

        for i in range(0, num_dimensions):
            self.velocity.append(random.uniform(-1, 1))
            self.position.append(x0[i])

    def evaluate(self, cost_function):
        self.fitness = cost_function(self.position)

        if self.fitness > self.best_fitness or self.best_fitness == -1:
            self.best_position = self.position
            self.best_fitness = self.fitness

    def update_velocity(self, best_position):
        w = 0.5  # 惯性权重
        c1 = 1  # 学习因子1
        c2 = 2  # 学习因子2

        for i in range(0, num_dimensions):
            r1 = random.random()
            r2 = random.random()

            cognitive_velocity = c1 * r1 * (self.best_position[i] - self.position[i])
            social_velocity = c2 * r2 * (best_position[i] - self.position[i])
            self.velocity[i] = w * self.velocity[i] + cognitive_velocity + social_velocity

    def update_position(self, bounds):
        for i in range(0, num_dimensions):
            self.position[i] = self.position[i] + self.velocity[i]

            # 确保粒子位置在搜索空间内
            if self.position[i] > bounds[i][1]:
                self.position[i] = bounds[i][1]

            if self.position[i] < bounds[i][0]:
                self.position[i] = bounds[i][0]


class PSO:
    def __init__(self, cost_function, x0, bounds, num_particles, max_iterations):
        global num_dimensions

        num_dimensions = len(x0)
        best_fitness_value = -1
        best_position_value = []

        swarm = []
        for i in range(0, num_particles):
            swarm.append(Particle(x0))

        for i in range(0, max_iterations):
            for j in range(0, num_particles):
                swarm[j].evaluate(cost_function)

                if swarm[j].fitness > best_fitness_value or best_fitness_value == -1:
                    best_fitness_value = swarm[j].fitness
                    best_position_value = list(swarm[j].position)

            for j in range(0, num_particles):
                swarm[j].update_velocity(best_position_value)
                swarm[j].update_position(bounds)

        print('最优解为：', best_position_value)
        print('最优解的适应度值为：', best_fitness_value)

BPSO代码：

import random

class Particle:
    def __init__(self, x0):
        self.position = []  # 粒子的位置
        self.velocity = []  # 粒子的速度
        self.best_position = []  # 粒子的历史最优位置
        self.fitness = -1  # 粒子的适应度值
        self.best_fitness = -1  # 粒子的历史最优适应度值

        for i in range(0, num_dimensions):
            self.velocity.append(random.uniform(-1, 1))
            self.position.append(x0[i])

    def evaluate(self, cost_function):
        self.fitness = cost_function(self.position)

        if self.fitness > self.best_fitness or self.best_fitness == -1:
            self.best_position = self.position
            self.best_fitness = self.fitness

    def update_velocity(self, best_position):
        w = 0.5  # 惯性权重
        c1 = 1  # 学习因子1
        c2 = 2  # 学习因子2

        for i in range(0, num_dimensions):
            r1 = random.random()
            r2 = random.random()

            cognitive_velocity = c1 * r1 * (self.best_position[i] - self.position[i])
            social_velocity = c2 * r2 * (best_position[i] - self.position[i])
            self.velocity[i] = w * self.velocity[i] + cognitive_velocity + social_velocity

            # 将速度限制在[-1,1]之间
            if self.velocity[i] > 1:
                self.velocity[i] = 1

            if self.velocity[i] < -1:
                self.velocity[i] = -1

    def update_position(self, bounds):
        for i in range(0, num_dimensions):
            # 计算sigmoid函数
            sigm = 1 / (1 + pow(2.71828, -self.velocity[i]))

            # 判断是否需要翻转位置
            if random.random() < sigm:
                self.position[i] = 1
            else:
                self.position[i] = 0


class BPSO:
    def __init__(self, cost_function, x0, bounds, num_particles, max_iterations):
        global num_dimensions

        num_dimensions = len(x0)
        best_fitness_value = -1
        best_position_value = []

        swarm = []
        for i in range(0, num_particles):
            swarm.append(Particle(x0))

        for i in range(0, max_iterations):
            for j in range(0, num_particles):
                swarm[j].evaluate(cost_function)

                if swarm[j].fitness > best_fitness_value or best_fitness_value == -1:
                    best_fitness_value = swarm[j].fitness
                    best_position_value = list(swarm[j].position)

            for j in range(0, num_particles):
                swarm[j].update_velocity(best_position_value)
                swarm[j].update_position(bounds)

        print('最优解为：', best_position_value)
        print('最优解的适应度值为：', best_fitness_value)

请注意，在这两个示例代码中，变量`num_dimensions`是搜索空间的维度，变量`bounds`是一个元组列表，表示每个维度的搜索范围。`x0`是搜索空间中的初始位置，`cost_function`是要最小化的代价函数。`num_particles`是粒子群的大小，`max_iterations`是算法的最大迭代次数。