import pandas as pd import matplotlib.pyplot as plt from sklearn.cluster import DBSCAN from sklearn import metrics # 读取CSV文件 df = pd.read_csv('D:/TJU/交通数据/obike_1.csv',encoding='gb18030') # 绘制样本点分布图 plt.scatter(df['olgt'], df['olat'], s=5) plt.xlabel('Longitude') plt.ylabel('Latitude') plt.show() # 进行聚类 eps_values = [0.01, 0.02, 0.03, 0.04, 0.05] # eps值列表 min_samples_values = [5, 10, 15, 20, 25] # min_samples值列表 silhouette_scores = [] # 轮廓系数列表 for eps in eps_values: for min_samples in min_samples_values: dbscan = DBSCAN(eps=eps, min_samples=min_samples) labels = dbscan.fit_predict(df[['olgt', 'olat']]) silhouette_scores.append(metrics.silhouette_score(df[['olgt', 'olat']], labels)) # 绘制关于eps的折线图 plt.plot(eps_values, silhouette_scores) plt.xlabel('Eps') plt.ylabel('Silhouette Score') plt.show()，报错ValueError: Number of labels is 1. Valid values are 2 to n_samples - 1 (inclusive)，给出修改后的代码解决该问题

import pandas as pd from sklearn.cluster import DBSCAN from sklearn.metrics import confusion_matrix import matplotlib.pyplot as plt # 读取Excel数据 data = pd.read_excel('C:/Users/86178/Desktop/test/test/TF-IDF/SSG hole span版.xlsx') # 提取特征列 feature_columns = ["Bridge length (m)","Pier type","Foundation type","Hole","Span (m)", "Bearing type","Plane linear"] X = data[feature_columns] # 使用DBSCAN进行聚类 dbscan = DBSCAN(eps=0.5, min_samples=5) dbscan.fit(X) # 获取聚类结果 labels = dbscan.labels_

import matplotlib.pyplot as plt # 读取Excel数据 data = pd.read_excel('C:/Users/86178/Desktop/test/test/TF-IDF/SSG hole span版.xlsx') # 提取特征列 feature_columns = ["Bridge length (m)","Pier type",...

import pandas as pd from sklearn.cluster import DBSCAN from sklearn.metrics import confusion_matrix import seaborn as sns import matplotlib.pyplot as plt # 读取Excel数据 data = pd.read_excel('C:/Users/86178/Desktop/test/test/TF-IDF/SSG hole span版.xlsx') # 提取特征列 feature_columns = ["Bridge length (m)","Pier type","Foundation type","Hole","Span (m)", "Bearing type","Plane linear"] X = data[feature_columns] # 使用DBSCAN进行聚类 dbscan = DBSCAN(eps=0.5, min_samples=5) dbscan.fit(X) # 获取聚类结果 labels = dbscan.labels_ # 获取实际类别（如果有的话） actual_labels = data['Vulnerability grade'] # 计算混淆矩阵 confusion_mat = confusion_matrix(actual_labels, labels) # 绘制热力图 sns.heatmap(confusion_mat, annot=True, cmap="YlGnBu") plt.xlabel('Predicted Labels') plt.ylabel('Actual Labels') plt.title('Confusion Matrix') plt.show()

这段代码使用了 pandas、sklearn 和 seaborn 库来进行数据聚类和绘制混淆矩阵的热力图。在代码中，你需要确保以下几点： 1. 你已经安装了 pandas、sklearn 和 seaborn 库，并正确导入了所需的模块。 2. Excel 数据...

import pandas as pd import numpy as np from sklearn.preprocessing import StandardScaler from sklearn.cluster import DBSCAN from sklearn.metrics import silhouette_score import matplotlib.pyplot as plt # 加载数据 df = pd.read_excel(r'C:\Users\SINmingsheng\Desktop\data.xlsx') # 替换为你的数据文件路径 # 数据预处理 selected_columns = df.columns.tolist() selected_columns.remove('序号') # 如果序号列存在且不需要聚类 selected_columns.remove('S5') # 假设S5是开放题，不参与聚类 from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X_scaled = scaler.fit_transform(df) db = DBSCAN(eps=0.5, min_samples=5).fit(X_scaled) import numpy as np labels = db.labels_ valid_labels = [label for label in labels if label != -1] unique_labels = np.unique(valid_labels) if len(unique_labels) < 2: print("有效簇不足，跳过轮廓系数计算") else: silhouette_score(df, labels) from sklearn.cluster import DBSCAN from sklearn.metrics import silhouette_score from sklearn.preprocessing import StandardScaler # 数据标准化 scaler = StandardScaler() X_scaled = scaler.fit_transform(df) # 参数设置 db = DBSCAN(eps=0.5, min_samples=5).fit(X_scaled) labels = db.labels_ # 检查有效簇数量 valid_labels = [label for label in labels if label != -1] if len(np.unique(valid_labels)) >= 2: score = silhouette_score(X_scaled, labels) print(f"轮廓系数: {score}") else: print("有效簇不足，无法计算轮廓系数") # 标准化 scaler = StandardScaler() scaled_data = scaler.fit_transform(df[selected_columns]) # DBSCAN参数优化 param_grid = {'eps': np.linspace(0.1, 1.0, 10), 'min_samples': range(2, 11)} eps_values = param_grid['eps'] min_samples_values = param_grid['min_samples'] dbscan_scores = [] # 遍历参数组合 for eps in eps_values: for min_samples in min_samples_values: clusterer = DBSCAN(eps=eps, min_samples=min_samples) clusters = clusterer.fit_predict(scaled_data) # 获取有效样本掩码（非噪声点） valid_mask = clusters != -1 n_valid = np.sum(valid_mask) # 检查有效样本和簇的数量 if n_valid < 2: dbscan_scores.append(0)

from sklearn.cluster import DBSCAN from sklearn.metrics import silhouette_score best_score = -1 best_params = {'eps': 0, 'min_samples': 0} for eps in np.arange(0.3, 0.8, 0.05): for min_samples...

# DBSCAN Clustering # Importing the libraries import numpy as np import pandas as pd # Importing the dataset dataset = pd.read_csv('D:\电池诊断\Mall_Customers.csv')#读取数据集合（csv文件） X = dataset.iloc[:, [3, 4]].values#自变量 # Using the elbow method to find the optimal number of clusters from sklearn.cluster import DBSCAN dbscan=DBSCAN(eps=3,min_samples=4) # Fitting the model model=dbscan.fit(X) labels=model.labels_ from sklearn import metrics #identifying the points which makes up our core points sample_cores=np.zeros_like(labels,dtype=bool) sample_cores[dbscan.core_sample_indices_]=True #Calculating the number of clusters n_clusters=len(set(labels))- (1 if -1 in labels else 0) print(metrics.silhouette_score(X,labels))

import matplotlib.pyplot as plt # Plotting the clusters plt.scatter(X[labels==0,0],X[labels==0,1],s=50,color='red',label='Cluster 1') plt.scatter(X[labels==1,0],X[labels==1,1],s=50,color='blue',label...

import numpy as np import pandas as pd from matplotlib.font_manager import FontProperties from sklearn.cluster import DBSCAN from sklearn import metrics from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt import seaborn as sns # 全局字体设置 plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 plt.rcParams['font.family'] = 'Times New Roman' plt.rcParams['axes.titlesize'] = 35 plt.rcParams['axes.labelsize'] = 35 plt.rcParams['axes.linewidth'] = 3 # 坐标轴线加粗 # 使用Pandas读取CSV以便更容易地处理数据 df = pd.read_csv('6月能耗.csv', encoding='utf-8') # 选择特征进行聚类 X=df[['D0:C_PRC403.PV~建模', 'Z4ZH00116017S1220210000974~建模', 'A0:FI2003.PV~建模', 'A0:FI2801.PV~建模']] # X = df[['A0:FI0301A.PV','A0:FI0301B.PV']] # X = df.iloc[:, :-1] # X = df.iloc[:, 1:] # X= df[['target','A0:FIC0402.PV']] X_scaled = StandardScaler().fit_transform(X) db = DBSCAN(eps=0.6, min_samples=30).fit(X_scaled) # print(X_scaled[:5]) # 应用DBSCAN聚类 # db = DBSCAN(eps=0.2, min_samples=8).fit(X_scaled) #总拔 0.5 20 # print(X_scaled.shape) # 重叠度 2.3 60 沥青产量 0.2 8 # db = DBSCAN(eps=2.5,min_samples=6 # ).fit(X_scaled) core_samples_mask = np.zeros_like(db.labels_, dtype=bool) # 设置一个样本个数长度的全false向量 core_samples_mask[db.core_sample_indices_] = True # 将核心样本部分设置为true # 将聚类标签添加到原始DataFrame中 df['cluster_db'] = db.labels_ # 获取聚类个数（排除噪声点） n_clusters_ = len(set(db.labels_)) - (1 if -1 in db.labels_ else 0) # 模型评估 print('估计的聚类个数为: %d' % n_clusters_) print("轮廓系数: %0.3f " % metrics.silhouette_score(X_scaled, db.labels_)) # 数据保存 df.to_csv('能耗聚类.csv', encoding='utf_8_sig', index=None) # 调参日志 with open('能耗工况聚类调参log.txt', mode='a') as txt: print("eps: %0.1f, min_samples: %d, 轮廓系数: %0.3f, 聚类个数: %d" % ( db.eps, db.min_samples, metrics.silhouette_score(X_scaled, db.labels_), n_clusters_), file=txt)这段关于DBSCAN的代码，你可以加上自适应的技术吗

from sklearn.neighbors import NearestNeighbors def auto_params(X, k=None): """自动选择eps和min_samples参数""" # 计算min_samples默认值 d = X.shape[1] # 数据维度 min_samples = k if k else 2 * d # ...

# DBSCAN from sklearn.cluster import DBSCAN import pandas as pd from sklearn.preprocessing import StandardScaler from sklearn.metrics import silhouette_score import matplotlib.pyplot as plt train_data = pd.read_csv('train.csv') test_data = pd.read_csv('test.csv') features = ['Annual Income', 'Years in current job', 'Tax Liens', 'Number of Open Accounts', 'Maximum Open Credit', 'Number of Credit Problems', 'Bankruptcies', 'Current Loan Amount', 'Current Credit Balance', 'Monthly Debt', 'Credit Score'] train_data[features] = train_data[features].fillna(train_data[features].mean()) test_data[features] = test_data[features].fillna(test_data[features].mean()) # 标准化 scaler = StandardScaler() train_scaled = scaler.fit_transform(train_data[features]) test_scaled = scaler.transform(test_data[features]) # DBSCAN聚类 dbscan = DBSCAN(eps=0.5, min_samples=5) dbscan.fit(train_scaled) # 将DBSCAN的聚类结果添加到DataFrame中 train_data['DBSCAN_Cluster'] = dbscan.labels_ # Agglomerative Clustering agg = AgglomerativeClustering(n_clusters=3) agg.fit(train_scaled) # 将Agglomerative Clustering的聚类结果添加到DataFrame中 train_data['Agglomerative_Cluster'] = agg.labels_ # Gaussian Mixture Model gmm = GaussianMixture(n_components=3) gmm.fit(train_scaled) # 将Gaussian Mixture Model的聚类结果添加到DataFrame中 train_data['GMM_Cluster'] = gmm.predict(train_scaled) # 可视化 plt.figure(figsize=(18, 6)) # DBSCAN聚类结果 plt.subplot(131) plt.scatter(train_data['Credit Score'], train_data['Annual Inc'], c=train_data['DBSCAN_Cluster'], cmap='viridis') plt.xlabel('Credit Score') plt.ylabel('Annual Income') plt.title('DBSCAN Clustering Results') # Agglomerative Clustering结果 plt.subplot(132) plt.scatter(train_data['Credit Score'], train_data['Annual Inc'], c=train_data['Agglomerative_Cluster'], cmap='viridis') plt.xlabel('Credit Score') plt.ylabel('Annual Income') plt.title('Agglomerative Clustering Results') # Gaussian Mixture Model结果 plt.subplot(133) plt.scatter(train_data['Credit Score'], train_data['Annual Inc'], c=train_data['GMM_Cluster'], cmap='viridis') plt.xlabel('Credit Score') plt.ylabel('Annual Income') plt.title('Gaussian Mixture Model Results') plt.tight_layout() plt.show()ValueError Traceback (most recent call last) ~\AppData\Local\Temp\ipykernel_47176\2994685919.py in ?() ---> 17 # DBSCAN 18 from sklearn.cluster import DBSCAN 19 import pandas as pd 20 from sklearn.preprocessing import StandardScaler e:\Anaconda-python\envs\is6423new\lib\site-packages\sklearn\utils\_set_output.py in ?(self, X, *args, **kwargs) 138 @wraps(f) 139 def wrapped(self, X, *args, **kwargs): --> 140 data_to_wrap = f(self, X, *args, kwargs) 141 if isinstance(data_to_wrap, tuple): 142 # only wrap the first output for cross decomposition 143 return_tuple = ( e:\Anaconda-python\envs\is6423new\lib\site-packages\sklearn\base.py in ?(self, X, y, fit_params) 911 # non-optimized default implementation; override when a better 912 # method is possible for a given clustering algorithm 913 if y is None: 914 # fit method of arity 1 (unsupervised transformation) --> 915 return self.fit(X, fit_params).transform(X) 916 else: 917 # fit method of arity 2 (supervised transformation) 918 return self.fit(X, y, fit_params).transform(X) ... e:\Anaconda-python\envs\is6423new\lib\site-packages\pandas\core\generic.py in ?(self, dtype) 2069 def array(self, dtype: npt.DTypeLike | None = None) -> np.ndarray: -> 2070 return np.asarray(self._values, dtype=dtype) ValueError: could not convert string to float: '10+ years' 怎么改

from sklearn.neighbors import NearestNeighbors nn = NearestNeighbors(n_neighbors=5) nn.fit(train_scaled) distances, _ = nn.kneighbors(train_scaled) distances = np.sort(distances[:, -1]) plt.plot...

做出来的k值不理想，请你帮我优化优化代码 import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.preprocessing import StandardScaler from sklearn.cluster import KMeans # 读取数据（需替换为实际路径） file_path = r"C:\Users\86137\Desktop\test.py\445vsc分析.xlsx" # 使用原始字符串避免转义问题 df = pd.read_excel(file_path, sheet_name="Sheet1", usecols="B:G") # 提取B到G列（前五列） # 数据预处理 df = df.dropna() # 删除缺失值 print(f"有效样本量：{len(df)}") # 标准化数据 scaler = StandardScaler() scaled_data = scaler.fit_transform(df) # 肘部图分析 inertia = [] k_range = range(2, 11) # 测试k=2到k=10 for k in k_range: kmeans = KMeans(n_clusters=k, random_state=42, n_init=10) # 显式设置n_init避免警告 kmeans.fit(scaled_data) inertia.append(kmeans.inertia_) # 绘制肘部图 plt.figure(figsize=(10, 5)) plt.plot(k_range, inertia, marker="o", linestyle="--") plt.xlabel("Number of Clusters (K)") plt.ylabel("Inertia") plt.title("Elbow Method for Optimal K") plt.xticks(k_range) plt.grid(True) plt.show() # 根据肘部图选择最佳K值（假设选择k=4） best_k = 4 kmeans = KMeans(n_clusters=best_k, random_state=42, n_init=10) df["Cluster"] = kmeans.fit_predict(scaled_data) # 分析聚类结果（原始尺度） cluster_centers = scaler.inverse_transform(kmeans.cluster_centers_) cluster_profile = pd.DataFrame( cluster_centers, columns=["了解度", "频率", "使用意愿", "学历", "年龄"] ).round(2) print("\n聚类中心特征（原始尺度）：") print(cluster_profile) # 补充分析：各簇样本分布 print("\n各簇样本分布：") print(df["Cluster"].value_counts().sort_index())

from sklearn.metrics import silhouette_score, calinski_harabasz_score from sklearn.cluster import KMeans scores = [] for k in range(2, 15): model = KMeans(n_clusters=k) labels = model.fit_...

import numpy as np import pandas as pd from sklearn.decomposition import PCA import matplotlib.pyplot as plt # 加载数据集 iris = pd.read_csv('iris_pca.csv') X = iris.iloc[:, :-1] y = iris.iloc[:, -1] # PCA降维 pca = PCA(n_components=2) X_pca = pca.fit_transform(X) # DBSCAN聚类 def dbscan(X, eps=0.5, min_samples=5): m, n = X.shape visited = np.zeros(m, dtype=bool) labels = np.zeros(m, dtype=int) cluster_id = 1 for i in range(m): if not visited[i]: visited[i] = True neighbors = get_neighbors(X, i, eps) if len(neighbors) < min_samples: labels[i] = -1 else: expand_cluster(X, i, neighbors, visited, labels, cluster_id, eps, min_samples) cluster_id += 1 return labels def get_neighbors(X, i, eps): dists = np.sum((X - X[i]) 2, axis=1) neighbors = np.where(dists < eps 2)[0] return neighbors def expand_cluster(X, i, neighbors, visited, labels, cluster_id, eps, min_samples): labels[i] = cluster_id for j in neighbors: if not visited[j]: visited[j] = True new_neighbors = get_neighbors(X, j, eps) if len(new_neighbors) >= min_samples: neighbors = np.union1d(neighbors, new_neighbors) if labels[j] == 0: labels[j] = cluster_id labels = dbscan(X_pca, eps=0.5, min_samples=5) # 簇的总数 n_clusters = len(set(labels)) - (1 if -1 in labels else 0) print("簇的总数：", n_clusters) # 各样本所归属簇的编号 print("各样本所归属簇的编号：", labels) # 外部指标 from sklearn.metrics import adjusted_rand_score, fowlkes_mallows_score ri = adjusted_rand_score(y, labels) fmi = fowlkes_mallows_score(y, labels) print("RI：", ri) print("FMI：", fmi) # 内部指标 from sklearn.metrics import davies_bouldin_score dbi = davies_bouldin_score(X_pca, labels) print("DBI：", dbi) # 可视化输出 plt.scatter(X_pca[:, 0], X_pca[:, 1], c=labels) plt.show(),请为我分析这段代码的运行结果

然后使用自己实现的DBSCAN聚类算法对降维后的数据进行聚类，其中eps和min_samples是DBSCAN算法的超参数。聚类完成后，输出簇的总数和各样本所归属簇的编号。接着计算聚类结果的外部指标RI和FMI，以及内部指标DBI，并...

本关的实验任务为：统计聚类的各个类别的数目；统计聚类结果的轮廓系数。相关任务（1）首先加载之前创建好的K-means、AgglomerativeClustering、DBSCAN三个模型,这里从models中导入； import models kmeans_model = models.model1 agglo_model = models.model2 dbscan_model = models.model3 data_zs = models.data_zs (2) 统计聚类的各个类别的数目； #以r1为例，计算r2,r3的值 r1 = pd.Series(kmeans_model.labels_).value_counts() #k-means # Begin # r2 = r3 = # End # print("AgglomerativeClustering count:"+r2) print("DBSCAN count:"+r3) （3）聚类结果的轮廓系数； #以score1为例，计算score2,score3的值 score1=silhouette_score(data_zs,kmeans_model.labels_,metric='euclidean')) # Begin # score2= score3= # End # print("AgglomerativeClustering score:"+score2) print("dbscan score:"+score3) 测试说明平台会对你编写的代码进行测试，通过正确的输出处理之后的数据进行验证，所以请勿修改函数返回内容。import models from sklearn.metrics import silhouette_score import pandas as pd kmeans_model = models.model1 agglo_model = models.model2 dbscan_model = models.model3 data_zs = models.data_zs #统计聚类的各个类别的数目

import matplotlib.pyplot as plt plt.figure(figsize=(12, 5)) # 层次聚类结果 plt.subplot(1, 2, 1) plt.scatter(X[:, 0], X[:, 1], c=agg_labels, cmap='tab20') plt.title(f'AgglomerativeClustering ...

data['Cluster'] = clusters # 统计各个集群的数量分布 cluster_counts = data.groupby('Cluster').size() print(cluster_counts) # 可视化某些关键维度的结果 import seaborn as sns sns.scatterplot(x='应付金额', y='实际金额', hue='Cluster', data=data) plt.title('Clustering Results by Amounts') plt.show()

from sklearn.metrics import silhouette_score score = silhouette_score(scaled_data, labels=clusters) print("Silhouette Score:", score) ##### **3. 结果解释的意义延伸思考** 除了简单呈现各类别的比例...

Traceback (most recent call last): File "D:\facerec\audiocluster.py", line 243, in <module> main() File "D:\facerec\audiocluster.py", line 219, in main kmeans, result_df, scaled_features = perform_clustering(merged_data, n_clusters=n_clusters) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\facerec\audiocluster.py", line 78, in perform_clustering cluster_labels = kmeans.fit_predict(scaled_features) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py", line 1068, in fit_predict return self.fit(X, sample_weight=sample_weight).labels_ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\anaconda\Lib\site-packages\sklearn\base.py", line 1151, in wrapper return fit_method(estimator, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py", line 1509, in fit self._check_mkl_vcomp(X, X.shape[0]) File "D:\anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py", line 927, in _check_mkl_vcomp modules = threadpool_info() ^^^^^^^^^^^^^^^^^ File "D:\anaconda\Lib\site-packages\sklearn\utils\fixes.py", line 83, in threadpool_info return threadpoolctl.threadpool_info() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\anaconda\Lib\site-packages\threadpoolctl.py", line 124, in threadpool_info return _ThreadpoolInfo(user_api=_ALL_USER_APIS).todicts() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\anaconda\Lib\site-packages\threadpoolctl.py", line 340, in init self._load_modules() File "D:\anaconda\Lib\site-packages\threadpoolctl.py", line 373, in _load_modules self._find_modules_with_enum_process_module_ex() File "D:\anaconda\Lib\site-packages\threadpoolctl.py", line 485, in _find_modules_with_enum_process_module_ex self._make_module_from_path(filepath) File "D:\anaconda\Lib\site-packages\threadpoolctl.py", line 515, in _make_module_from_path module = module_class(filepath, prefix, user_api, internal_api) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "D:\anaconda\Lib\site-packages\threadpoolctl.py", line 606, in init self.version = self.get_version() ^^^^^^^^^^^^^^^^^^ File "D:\anaconda\Lib\site-packages\threadpoolctl.py", line 646, in get_version config = get_config().split() ^^^^^^^^^^^^^^^^^^ AttributeError: 'NoneType' object has no attribute 'split'

import matplotlib.pyplot as plt import seaborn as sns # 修复后的CSV合并函数 def merge_csv_with_skip_and_source(folder_path, output_file): """ 合并文件夹内所有CSV文件，跳过第二行，并添加来源列 ...

请编写代码实现鸢尾花数据DBSCAN聚类，鸢尾花数据保存在iris_pca_self.csv文件中（设 eps=0.5, min_samples=5）（注意：不得直接调用 sklearn 或其他库中的 Kmeans 或类似函数），并输出聚类结果（簇总数，各样本的所属簇编号）。调用 matplotlib 的 scatter 函数将聚类后各样本可视化输出（不同簇内的样本用不同的颜色表示）。调用 sklearn 库中函数，计算得到外部指标（RI、FMI）和内部指标（DBI）。

import matplotlib.pyplot as plt # 绘制散点图 colors = ['r', 'g', 'b', 'c', 'm', 'y', 'k'] for i in range(len(data)): plt.scatter(data.iloc[i, 0], data.iloc[i, 1], c=colors[clusters[i]]) plt.show() ...

用python实现：用pandas库读取csv文件，其中olgt列为起点经度，olat列为起点纬度，绘制样本点分布图，使用DBSCAN对其进行聚类，并根据不同eps和min_samples值选择，通过轮廓系数评价其聚类效果。

import matplotlib.pyplot as plt from sklearn.cluster import DBSCAN from sklearn.metrics import silhouette_score 2. 读取csv文件 python df = pd.read_csv('data.csv') 3. 绘制样本点分布图 ...

Review and compare different clustering algorithms and choose one clustering algorithm for your course project dataset. Reference link: [Clustering Algorithms](https://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py) 根据这个和上面的代码，帮我完成任务

import matplotlib.pyplot as plt from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering from sklearn.mixture import GaussianMixture # 定义算法 algorithms = { "K-Means": KMeans(n_...

用python实现用pandas库读取csv文件，其中olgt列为起点经度，olat列为起点纬度，绘制样本点分布图，使用DBSCAN对其进行聚类，并根据不同eps和min_samples值选择，通过轮廓系数评价其聚类效果，分别绘制关于eps和min_samples的折线图的步骤。

import matplotlib.pyplot as plt plt.scatter(X['olgt'], X['olat']) plt.xlabel('olgt') plt.ylabel('olat') plt.show() 然后，您可以使用sklearn库中的DBSCAN算法对数据进行聚类： python from sklearn...

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