ransac改进的groupsac代码
时间: 2024-06-09 17:10:56 浏览: 10
以下是一个基于RANSAC改进的GroupSAC算法的示例代码,用于估计平面模型:
```
import numpy as np
from sklearn.cluster import DBSCAN
from sklearn.decomposition import PCA
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.utils import check_random_state
class GroupSAC:
def __init__(self, estimator, min_samples, threshold, max_trials, epsilon, metric='euclidean', random_state=None):
self.estimator = estimator
self.min_samples = min_samples
self.threshold = threshold
self.max_trials = max_trials
self.epsilon = epsilon
self.metric = metric
self.random_state = check_random_state(random_state)
def fit(self, X, y=None):
n_samples = X.shape[0]
best_score = -np.inf
for i in range(self.max_trials):
# 1. 随机选择 min_samples 个点
indices = self.random_state.permutation(n_samples)[:self.min_samples]
# 2. 估计模型
model = self.estimator.fit(X[indices])
# 3. 计算当前模型的得分
score = self.score(X, model)
if score > best_score:
best_score = score
best_model = model
# 4. 根据得分阈值和 epsilon 聚类
distance_matrix = pairwise_distances(X, model.predict(X).reshape(-1, 1), metric=self.metric).ravel()
clusterer = DBSCAN(eps=self.epsilon, min_samples=self.min_samples)
clusters = clusterer.fit_predict(distance_matrix.reshape(-1, 1))
# 5. 计算每个簇的得分
pca = PCA(n_components=1)
for label in np.unique(clusters):
if label == -1:
continue
cluster_indices = np.where(clusters == label)[0]
cluster_X = X[cluster_indices]
cluster_distance = distance_matrix[cluster_indices]
cluster_X_pca = pca.fit_transform(cluster_X)
cluster_inliers = cluster_X[cluster_distance < self.threshold]
score = len(cluster_inliers)
if score > best_score:
best_score = score
best_model = self.estimator.fit(cluster_inliers)
self.best_model_ = best_model
return self
def score(self, X, model):
distance_matrix = pairwise_distances(X, model.predict(X).reshape(-1, 1), metric=self.metric).ravel()
return np.sum(distance_matrix < self.threshold)
```
该算法的思路是先使用RANSAC随机选择一些点来估计模型,然后根据得分阈值和epsilon将数据点分为若干簇,对每个簇进行拟合,最终选择具有最高得分的模型作为最终模型。其中,得分的计算方式为模型预测出的点到对应样本点的距离小于阈值的数量。
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