optics聚类算法 python

Optics 聚类算法是一种基于密度的聚类算法，它可以处理任意形状的簇，并且不需要预设簇的数量。在 Python 中，可以使用 Scikit-learn 库来实现 Optics 聚类算法。具体实现步骤包括数据预处理、构建 Optics 对象、拟合数据和可视化聚类结果等。以下是一个简单的示例代码：

from sklearn.cluster import OPTICS

# 准备数据
X = [[2, 4], [2, 5], [4, 6], [4, 7], [4, 8], [6, 9], [6, 10], [7, 8]]
 
# 构建 Optics 对象
optics_model = OPTICS(min_samples=2, xi=0.05)
 
# 拟合数据
optics_model.fit(X)
 
# 可视化聚类结果
import matplotlib.pyplot as plt

labels = optics_model.labels_
reachability = optics_model.reachability_
plt.figure(figsize=(10, 7))
plt.title('Optics Clustering')
plt.scatter(X[:,0], X[:,1], c=labels, cmap='viridis')
plt.show()

该示例代码使用了一个简单的二维数据集，构建 Optics 对象时设置了最小样本数为 2，$\xi$ 参数为 0.05。最后，可视化聚类结果并展示给用户。

相关问题

optics聚类算法python代码

好的，以下是一个简单的Python实现的optics聚类算法代码：

import numpy as np
from sklearn.neighbors import NearestNeighbors

class OPTICS:
    def __init__(self, eps, min_samples):
        self.eps = eps
        self.min_samples = min_samples

    def fit(self, X):
        self.X = X
        self.n_samples = X.shape[0]
        self.visited_ = np.zeros(self.n_samples, dtype=bool)
        self.ordering_ = []
        self.core_distances_ = np.zeros(self.n_samples)
        self.reachability_distances_ = np.ones(self.n_samples) * np.inf
        self.process()

    def process(self):
        self.tree_ = NearestNeighbors(radius=self.eps, algorithm='ball_tree').fit(self.X)
        for point_idx in range(self.n_samples):
            if not self.visited_[point_idx]:
                self.visited_[point_idx] = True
                self.ordering_.append(point_idx)
                neighbors_idx = self.tree_.radius_neighbors([self.X[point_idx]], return_distance=False)[0]
                n_neighbors = len(neighbors_idx)
                if n_neighbors >= self.min_samples:
                    self.core_distances_[point_idx] = np.max(self.tree_.kneighbors([self.X[point_idx]], n_neighbors=self.min_samples)[0])
                    self.update(neighbors_idx, point_idx)

        self.ordering_ = np.array(self.ordering_)
        self.cluster_ordering_ = np.argsort(self.reachability_distances_)
        self.reachability_distances_[self.reachability_distances_ == np.inf] = -1

    def update(self, neighbors_idx, point_idx):
        core_dist = self.core_distances_[point_idx]
        for neighbor in neighbors_idx:
            if not self.visited_[neighbor]:
                new_reach_dist = max(core_dist, np.linalg.norm(self.X[point_idx] - self.X[neighbor]))
                if np.isinf(self.reachability_distances_[neighbor]):
                    self.reachability_distances_[neighbor] = new_reach_dist
                else:
                    self.reachability_distances_[neighbor] = min(new_reach_dist, self.reachability_distances_[neighbor])

    def extract_clusters(self):
        clusters = []
        idx = 0
        while idx < self.n_samples:
            if self.reachability_distances_[self.cluster_ordering_[idx]] == -1:
                idx += 1
                continue

            cluster = [self.cluster_ordering_[idx]]
            while True:
                idx += 1
                if idx == self.n_samples:
                    break
                if self.reachability_distances_[self.cluster_ordering_[idx]] == -1:
                    continue
                if self.reachability_distances_[self.cluster_ordering_[idx]] > self.eps:
                    break
                cluster.append(self.cluster_ordering_[idx])
            clusters.append(cluster)

        return clusters

OPTICS聚类算法python代码

OPTICS聚类算法的Python代码如下所示：

from numpy import unique
from numpy import where
from sklearn.datasets import make_classification
from sklearn.cluster import OPTICS
from matplotlib import pyplot

# 定义数据集
X, _ = make_classification(n_samples=1000, n_features=2, n_informative=2, n_redundant=0, n_clusters_per_class=1, random_state=4)

# 定义模型
model = OPTICS(eps=0.8, min_samples=10)

# 模型拟合与聚类预测
yhat = model.fit_predict(X)

# 检索唯一群集
clusters = unique(yhat)

# 为每个群集的样本创建散点图
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#### 引用[.reference_title]
-  *1* *2* *3* [十种常用聚类算法（python完整代码演示）](https://blog.csdn.net/lk_mm_love/article/details/127040710)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}}] [.reference_item style="max-width: 100%"]
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