优化这段代码# import modules 导入模块 from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.model_selection import train_test_split from sklearn import datasets from matplotlib.colors import ListedColormap from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict from sklearn.model_selection import KFold from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn import svm from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_auc_score import math import datetime import multiprocessing as mp from sklearn.ensemble import StackingClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC import random

运行下面代码需要安装哪些包from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.model_selection import train_test_split from sklearn import datasets from matplotlib.colors import ListedColormap from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict from sklearn.model_selection import KFold from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn import svm from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_auc_score import math import datetime import multiprocessing as mp from sklearn.ensemble import StackingClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC import random

运行上述代码需要安装以下Python包： - scikit-learn：提供了机器学习算法和工具，包括模型选择、特征提取、预处理等。可以使用pip install scikit-learn进行安装。 - pandas：提供了高性能、易于使用的数据结构...

在Python中想运行from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.model_selection import train_test_split from sklearn import datasets from matplotlib.colors import ListedColormap from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict from sklearn.model_selection import KFold from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn import svm from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_auc_score import math import datetime import multiprocessing as mp from sklearn.ensemble import StackingClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC import random怎么操作

from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt ...

from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA from sklearn.model_selection import train_test_split from sklearn.model_selection import ShuffleSplit from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report

它导入了StandardScaler用于数据标准化，PCA用于数据降维，train_test_split用于数据集划分，ShuffleSplit用于交叉验证，confusion_matrix用于混淆矩阵计算，classification_report用于分类报告生成。...

import pandas as pd import numpy as np import scipy.stats as stats import seaborn as sns from sklearn.metrics import RocCurveDisplay from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix,accuracy_score from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn import tree from sklearn.decomposition import PCA import matplotlib.pyplot as plt from sklearn.tree import DecisionTreeClassifier请在此基础上续写代码块，要求是（1）读入数据后，选取自变量"sysBP", "diaBP","age","totChol","BMI", "heartRate", "glucose"记为X，因变量"TenYearCHD"记为y，组成新的数据集。¶

from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix,accuracy_score from sklearn.preprocessing import StandardScaler from sklearn.linear_model import ...

import pandas as pd from sklearn.datasets import load_wine from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.feature_selection import SelectKBest, f_classif from sklearn.decomposition import PCA from sklearn.metrics import accuracy_score, classification_report from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score from sklearn.ensemble import RandomForestClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.naive_bayes import GaussianNB from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC data = load_wine() # 导入数据集 X = pd.DataFrame(data.data, columns=data.feature_names) y = pd.Series(data.target) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) # 构建分类模型 model = LogisticRegression() model.fit(X_train, y_train) # 预测测试集结果 y_pred = model.predict(X_test) #评估模型性能 accuracy = accuracy_score(y_test, y_pred) report = classification_report(y_test, y_pred) print('准确率:', accuracy) # 特征选择 selector = SelectKBest(f_classif, k=6) X_new = selector.fit_transform(X, y) print('所选特征:', selector.get_support()) # 模型降维 pca = PCA(n_components=2) X_new = pca.fit_transform(X_new) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X_new, y, test_size=0.2, random_state=0) def Sf(model,X_train, X_test, y_train, y_test,modelname): mode = model() mode.fit(X_train, y_train) y_pred = mode.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print(modelname, accuracy) importance = mode.feature_importances_ print(importance) def Sf1(model,X_train, X_test, y_train, y_test,modelname): mode = model() mode.fit(X_train, y_train) y_pred = mode.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print(modelname, accuracy) modelname='支持向量机' Sf1(SVC,X_train, X_test, y_train, y_test,modelname) modelname='逻辑回归' Sf1(LogisticRegression,X_train, X_test, y_train, y_test,modelname) modelname='高斯朴素贝叶斯算法训练分类器' Sf1(GaussianNB,X_train, X_test, y_train, y_test,modelname) modelname='K近邻分类' Sf1(KNeighborsClassifier,X_train, X_test, y_train, y_test,modelname) modelname='决策树分类' Sf(DecisionTreeClassifier,X_train, X_test, y_train, y_test,modelname) modelname='随机森林分类' Sf(RandomForestClassifier,X_train, X_test, y_train, y_test,modelname)加一个画图展示

抱歉，由于我是一个纯文本AI，无法进行画图展示。但是，你可以使用Python中的matplotlib库进行数据可视化。...这段代码可以在Sf函数中的print(importance)下方添加，并在运行时展示特征重要性条形图。

dyy2 = np.array(data) print(pd.DataFrame(dyy2[:,0:8])) print(pd.DataFrame(dyy2[:,8:9])) features = dyy2[:,1:8] target = dyy2[:,8:9] # 单变量特征显示，例如绘制直方图、箱线图等 plt.hist(dyy2[:,1:3]) plt.show() #特征选择 from sklearn.feature_selection import SelectKBest from sklearn.feature_selection import chi2 # 选择与目标变量相关性最好的前k个特征 k = 7 selector = SelectKBest(chi2, k) X_selected = selector.fit_transform(features, target) #特征变换 from sklearn.decomposition import PCA # 使用PCA进行特征变换 pca = PCA(n_components=2) X_transformed = pca.fit_transform(X_selected) #样本集分割 from sklearn.model_selection import train_test_split # 将数据集分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X_transformed, target, test_size=0.2, random_state=42)

这段代码是进行特征选择、特征变换和样本集分割的操作。首先，它将数据集转换为NumPy数组，并根据需要选择特征和目标变量。然后，它使用SelectKBest和chi2方法选择与目标变量相关性最好的前k个特征。接下来，它使用...

from sklearn.datasets import load_iris from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score iris = load_iris() X = iris.data y = iris.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) # 使用 'multinomial' 多分类方法 model2 = LogisticRegression(multi_class='multinomial', solver='lbfgs') model2.fit(X_train, y_train) y_pred2 = model2.predict(X_test) acc2 = accuracy_score(y_test, y_pred2) print('Accuracy score using "multinomial" method:', acc2)修改该程序使上述程序数据可视化

from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from sklearn.decomposition import PCA # 加载数据并进行主成分分析 iris = load_iris() pca = PCA(n_...

import pandas as pd from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA from sklearn.model_selection import train_test_split from keras.models import Sequential from keras.layers import Dense, Conv1D, MaxPooling1D, Flatten from sklearn.metrics import accuracy_score from sklearn.metrics import confusion_matrix, classification_report from sklearn.metrics import roc_auc_score from sklearn.utils.class_weight import compute_class_weight # 读取数据 data = pd.read_csv('database.csv') # 数据预处理 X = data.iloc[:, :-1].values y = data.iloc[:, -1].values scaler = StandardScaler() X = scaler.fit_transform(X) # 特征选择 pca = PCA(n_components=10) X = pca.fit_transform(X) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) class_weights = compute_class_weight(class_weight='balanced', classes=np.unique(y_train), y=y_train) # 构建CNN模型 model = Sequential() model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(10, 1))) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(10, activation='relu')) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # 训练模型 X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1)) X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1)) model.fit(X_train, y_train,class_weight=class_weights,epochs=100, batch_size=64, validation_data=(X_test, y_test)) # 预测结果 y_pred = model.predict(X_test) #检验值 accuracy = accuracy_score(y_test, y_pred) auc = roc_auc_score(y_test, y_pred) print(auc) print("Accuracy:", accuracy) print('Confusion Matrix:\n', confusion_matrix(y_test, y_pred)) print('Classification Report:\n', classification_report(y_test, y_pred))

这是一个使用卷积神经网络(CNN)进行二分类的模型，数据经过了标准化和PCA特征选择的预处理。模型使用了一个卷积层(Conv1D)和一个池化层(MaxPooling1D)，然后通过一个全连接层(Dense)输出最终结果。训练过程中使用了...

from sklearn.metrics import RocCurveDisplay from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix,accuracy_score from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn import tree from sklearn.decomposition import PCA import matplotlib.pyplot as plt from sklearn.tree import DecisionTreeClassifier data = pd.read_csv("C:\\Users\\sa'y\\Desktop\\framinghamData(1).csv") X = data[["sysBP", "diaBP", "age", "totChol", "BMI", "heartRate", "glucose"]] y = data[['TenYearCHD']] new_data = pd.concat([X, y], axis=1) print(new_data.head()) scaler = StandardScaler() X_scaled = scaler.fit_transform(X) print(X_scaled) corr = new_data.corr(method='pearson') sns.heatmap(corr, annot=True, cmap='coolwarm') plt.show() pca = PCA(n_components=7) newX = pca.fit_transform(X) x_data = ['PC1','PC2','PC3','PC4','PC5','PC6','PC7'] y_data = np.around(pca.explained_variance_ratio_, 2) plt.bar(x=x_data, height=y_data,color='steelblue', alpha=0.8) plt.show() 在上述代码基础上，请给出下一步的代码，要求是：计算所有主成分之间的皮尔逊相关系数，并用热图Heatmap的形式展示出来

from matplotlib import pyplot as plt import seaborn as sns # 计算所有主成分之间的皮尔逊相关系数 corr_pca = np.corrcoef(newX.T) # Heatmap展示相关系数 sns.set(font_scale=1) sns.heatmap(corr_pca, cbar...

给这些代码引入pca研究特征选择过程对不同算法分类器的影响：import numpy as np import matplotlib.pyplot as plt from sklearn.naive_bayes import GaussianNB from sklearn.datasets import load_digits from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from PIL import Image # 加载手写数字数据集 digits = load_digits() # 将数据集分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.3, random_state=42) # 使用高斯朴素贝叶斯分类器进行训练 gnb = GaussianNB() gnb.fit(X_train, y_train) # 对测试集进行预测 y_pred = gnb.predict(X_test) # 计算准确率 accuracy = accuracy_score(y_test, y_pred) print("Accuracy:", accuracy) # 预处理手写数字图片并进行预测 image = Image.open("digit.jpg").convert("L") image = image.resize((8, 8)) image = np.array(image) image = image.reshape((64,)) prediction = gnb.predict([image]) print("Prediction:", prediction[0]) # 显示手写数字图片 plt.imshow(image.reshape((8,8)), cmap="gray") plt.show()

from sklearn.decomposition import PCA # 对训练集进行PCA降维处理 pca = PCA(n_components=0.95) X_train = pca.fit_transform(X_train) X_test = pca.transform(X_test) # 使用高斯朴素贝叶斯分类器进行训练 ...

# This is a sample Python script. # Press Shift+F10 to execute it or replace it with your code. # Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings. def print_hi(name): # Use a breakpoint in the code line below to debug your script. print(f'Hi, {name}') # Press Ctrl+F8 to toggle the breakpoint. # Press the green button in the gutter to run the script. if name == 'main': print_hi('PyCharm') # See PyCharm help at https://www.jetbrains.com/help/pycharm/ from sklearn.datasets import load_iris from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score iris = load_iris() X = iris.data y = iris.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) # 使用 'ovr' 多分类方法 model1 = LogisticRegression(multi_class='ovr', solver='liblinear') model1.fit(X_train, y_train) y_pred1 = model1.predict(X_test) acc1 = accuracy_score(y_test, y_pred1) print('Accuracy score using "ovr" method:', acc1)修改该程序使上述程序结果数据可视化

from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from sklearn.decomposition import PCA # 加载数据 iris = load_iris() X = iris.data y = iris.target # ...

import numpy as np import pandas as pd import tensorflow as tf from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Dropout, Activation from sklearn.metrics import auc, accuracy_score, f1_score, recall_score # 读入数据 data = pd.read_csv('company_data.csv') X = data.iloc[:, :-1].values y = data.iloc[:, -1].values # 利用LabelEncoder将标签进行编码 encoder = LabelEncoder() y = encoder.fit_transform(y) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 对特征进行PCA降维 pca = PCA(n_components=17) X_train = pca.fit_transform(X_train) X_test = pca.transform(X_test) # 对数据reshape为符合卷积层输入的格式 X_train = X_train.reshape(-1, 17, 1) X_test = X_test.reshape(-1, 17, 1) # 构建卷积神经网络模型 model = Sequential() model.add(Conv1D(filters=128, kernel_size=3, activation='relu', input_shape=(17, 1))) model.add(Conv1D(filters=128, kernel_size=4, activation='relu')) model.add(Conv1D(filters=128, kernel_size=5, activation='relu')) model.add(MaxPooling1D(pool_size=2)) model.add(Flatten()) model.add(Dense(units=64, activation='relu')) model.add(Dense(units=1, activation='sigmoid')) # 编译模型 model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # 训练模型 model.fit(X_train, y_train, batch_size=64, epochs=10, validation_data=(X_test, y_test), verbose=1) # 在测试集上评估模型 y_pred = model.predict(X_test) y_pred = np.round(y_pred).flatten() # 计算各项指标 auc_score = auc(y_test, y_pred) accuracy = accuracy_score(y_test, y_pred) f1score = f1_score(y_test, y_pred) recall = recall_score(y_test, y_pred) # 打印输出各项指标 print("AUC score:", auc_score) print("Accuracy:", accuracy) print("F1 score:", f1score) print("Recall:", recall) 这个代码有什么错误

[这段代码没有明显的语法错误，但是有一些潜在的问题： 1. 没有对数据进行标0, 1, 2, 3, 4, 5, 8, 9, 6, 7] 准化处理，可能会影响模型的性能。 2. 没有使用交叉验证来评估模型的性[0, 1, 2, 3, 4, 5, 8, 9, 7, 6能...

from sklearn.decomposition import PCA from sklearn.cluster import KMeans from sklearn.metrics import accuracy_score import numpy as np import matplotlib.pyplot as plt from tensorflow.examples.tutorials.mnist import input_data import datetime # 导入数据集 start = datetime.datetime.now() #计算程序运行时间 mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) X_train = mnist.train.images y_train = mnist.train.labels X_test = mnist.test.images y_test = mnist.test.labels #PCA降维 pca = PCA(n_components=10) X_train_pca = pca.fit_transform(X_train) X_test_pca = pca.fit_transform(X_test) # 可视化 plt.scatter(X_train_pca[:, 0], X_train_pca[:, 1], c=np.argmax(y_train, axis=1)) plt.show() # K-means聚类 kmeans_centers = [] # 用于存储初始类中心 for i in range(10): idx = np.where(np.argmax(y_train, axis=1) == i)[0] # 获取第i类数字的索引列表 sample_idx = np.random.choice(idx) # 随机指定一个样本作为初始类中心 kmeans_centers.append(X_train_pca[sample_idx]) # 将初始类中心添加到列表中 kmeans = KMeans(n_clusters=10,init=kmeans_centers,n_init=1) kmeans.fit(X_train_pca) # 计算分类错误率 y_pred = kmeans.predict(X_test_pca) acc = accuracy_score(np.argmax(y_test, axis=1), y_pred) print("分类错误率：{:.2%}".format(1-acc)) # 计算程序运行时间 end = datetime.datetime.now() print("程序运行时间为:"+str((end-start).seconds)+"秒")优化这段代码，输出其中pca降维的因子负荷量

from sklearn.decomposition import PCA from sklearn.cluster import KMeans from sklearn.metrics import accuracy_score import numpy as np import matplotlib.pyplot as plt from tensorflow.examples....

解释一下这段代码：import pandas as pd from sklearn.decomposition import PCA # 读取数据集 data = pd.read_csv('D:/Desktop/diabetes.csv') # 获取特征列 features = data.columns[:-1] # 创建PCA对象，设置降维后的维度为8 pca = PCA(n_components=8) # 对数据进行降维操作 reduced_data = pca.fit_transform(data[features]) # 计算每个特征的方差贡献率 variance_ratio = pca.explained_variance_ratio_ # 输出每个特征的方差贡献率 for i, feature in enumerate(features): print('{}: {:.2f}%'.format(feature, variance_ratio[i]*100))

这段代码主要是使用 PCA（Principal Component Analysis，主成分分析）算法对数据进行降维操作，并计算每个特征的方差贡献率。具体解释如下： - 首先通过 pandas 库的 read_csv 方法读取 diabetes.csv 文件中的数据...

from sklearn.decomposition import LatentDirichletAllocation from sklearn.feature_extraction.text import CountVectorizer # LDA 分析 vectorizer = CountVectorizer() X = vectorizer.fit_transform(data['seg_word']) feature_names = vectorizer.get_feature_names() lda_model = LatentDirichletAllocation(n_components=2, random_state=0) lda_model.fit(vectorizer.fit_transform(data['seg_word']))

这段代码是使用sklearn库中的LatentDirichletAllocation模型进行LDA主题模型分析，其中使用了CountVectorizer将文本转化为词频矩阵。在这个例子中，我们将文本数据data['seg_word']作为输入，经过CountVectorizer...

import numpy as np import pandas as pd from sklearn.cluster import KMeans from sklearn.preprocessing import StandardScaler from scipy.spatial.distance import cdist import matplotlib.pyplot as plt from pandas import DataFrame from sklearn.decomposition import PCA 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 pd.set_option('display.max_rows', None)#显示全部行 pd.set_option('display.max_columns', None)#显示全部列 np.set_printoptions(threshold=np.inf) pd.set_option('display.max_columns', 9000) pd.set_option('display.width', 9000) pd.set_option('display.max_colwidth', 9000) df = pd.read_csv(r'附件1.csv',encoding='gbk') X = np.array(df.iloc[:, 1:]) X=X[0:,1:] k=93 kmeans_model = KMeans(n_clusters=k, random_state=123) fit_kmeans = kmeans_model.fit(X) # 模型训练 #查看聚类结果 kmeans_cc = kmeans_model.cluster_centers_ # 聚类中心 print('各类聚类中心为：\n', kmeans_cc) kmeans_labels = kmeans_model.labels_ # 样本的类别标签 print('各样本的类别标签为：\n', kmeans_labels) r1 = pd.Series(kmeans_model.labels_).value_counts() # 统计不同类别样本的数目 print('最终每个类别的数目为：\n', r1) # 输出聚类分群的结果 # cluster_center = pd.DataFrame(kmeans_model.cluster_centers_, # columns=[ str(x) for x in range(1,94)]) # 将聚类中心放在数据框中 # cluster_center.index = pd.DataFrame(kmeans_model.labels_). \ # drop_duplicates().iloc[:, 0] # 将样本类别作为数据框索引 # print(cluster_center)代码解释

from sklearn.decomposition import PCA 2. 读取数据集 python df = pd.read_csv(r'附件1.csv',encoding='gbk') X = np.array(df.iloc[:, 1:]) X=X[0:,1:] 3. 使用KMeans算法进行聚类分析 python ...

import numpy as np import matplotlib.pyplot as plt from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA from sklearn.cluster import KMeans from scipy.spatial import Voronoi, voronoi_plot_2d # 生成示例数据 data = df.iloc[:,1:15] # 标准化处理 scaler = StandardScaler() data_scaled = scaler.fit_transform(data) # 主成分分析 pca = PCA(n_components=5) data_pca = pca.fit_transform(data_scaled) # 聚类分析 kmeans = KMeans(n_clusters=3) kmeans.fit(data_pca) labels = kmeans.labels_ centers = kmeans.cluster_centers_ # 绘制Voronoi图 vor = Voronoi(centers) voronoi_plot_2d(vor) # 绘制样本点 plt.scatter(data_pca[:, 0], data_pca[:, 1], c=labels) # 设置坐标轴标签和标题 plt.xlabel('PC1') plt.ylabel('PC2') plt.title('Voronoi Diagram') # 显示图形 plt.show()

from sklearn.decomposition import PCA from sklearn.cluster import KMeans from scipy.spatial import Voronoi, voronoi_plot_2d # 生成示例数据 data = df.iloc[:, 1:15] # 标准化处理 scaler = ...

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