File Content: plot_ransac.py """ =========================================== Robust linear model estimation using RANSAC =========================================== In this example we see how to robustly fit a linear model to faulty data using the RANSAC algorithm. """ import numpy as np from matplotlib import pyplot as plt from sklearn import linear_model, datasets n_samples = 1000 n_outliers = 50 X, y, coef = datasets.make_regression(n_samples=n_samples, n_features=1, n_informative=1, noise=10, coef=True, random_state=0) # Add outlier data np.random.seed(0) X[:n_outliers] = 3 + 0.5 * np.random.normal(size=(n_outliers, 1)) y[:n_outliers] = -3 + 10 * np.random.normal(size=n_outliers) # Fit line using all data model = linear_model.LinearRegression() model.fit(X, y) # Robustly fit linear model with RANSAC algorithm model_ransac = linear_model.RANSACRegressor(linear_model.LinearRegression()) model_ransac.fit(X, y) inlier_mask = model_ransac.inlier_mask_ outlier_mask = np.logical_not(inlier_mask) # Predict data of estimated models line_X = np.arange(-5, 5) line_y = model.predict(line_X[:, np.newaxis]) line_y_ransac = model_ransac.predict(line_X[:, np.newaxis]) # Compare estimated coefficients print("Estimated coefficients (true, normal, RANSAC):") print(coef, model.coef_, model_ransac.estimator_.coef_) lw = 2 plt.scatter(X[inlier_mask], y[inlier_mask], color='yellowgreen', marker='.', label='Inliers') plt.scatter(X[outlier_mask], y[outlier_mask], color='gold', marker='.', label='Outliers') plt.plot(line_X, line_y, color='navy', linestyle='-', linewidth=lw, label='Linear regressor') plt.plot(line_X, line_y_ransac, color='cornflowerblue', linestyle='-', linewidth=lw, label='RANSAC regressor') plt.legend(loc='lower right') plt.show() Submit