RANSAC算法 python下载

RANSAC算法是一种用于处理包含噪音的数据点的拟合算法。它的主要思想是通过随机采样子集的方式，在数据集中寻找能够最好拟合数据的模型。在Python中，你可以使用以下代码实现RANSAC算法：

import numpy as np
import random

def ransac(X, Y, iters, epsilon, threshold):
    best_a, best_b = 0, 0
    pre_total = 0

    for i in range(iters):
        sample_index = random.sample(range(len(X)), 2)
        x_1 = X[sample_index[0]]
        x_2 = X[sample_index[1]]
        y_1 = Y[sample_index[0]]
        y_2 = Y[sample_index[1]]

        a = (y_2 - y_1) / (x_2 - x_1)
        b = y_1 - a * x_1
        total_in = 0

        for index in range(len(X)):
            y_estimate = a * X[index] + b
            if abs(y_estimate - Y[index]) < epsilon:
                total_in += 1

        if total_in > pre_total:
            pre_total = total_in
            best_a = a
            best_b = b

        if total_in > len(X) * threshold:
            break

    return best_a, best_b

# 示例用法
X = np.linspace(0, 10, 50)
Y = [3 * i + 10 + 2 * random.random() for i in X[:-20]] + [random.randint(0, int(i)) for i in X[-20:]]
X_data = np.array(X)
Y_data = np.array(Y)

iters = 1000
epsilon = 5
threshold = 0.8

best_a, best_b = ransac(X_data, Y_data, iters, epsilon, threshold)
print("最佳拟合直线方程为: y = {}x + {}".format(best_a, best_b))