plt.plot(range(1, len(pca.explained_variance_ratio_)+1), pca.explained_variance_ratio_, marker='o')

import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn import datasets from mpl_toolkits.mplot3d import Axes3D from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler data=pd.read_csv('H:/analysis_results/mean_HN.csv') data.head() x=data.iloc[:,1:7] y=data.iloc[:,6] scaler=StandardScaler() scaler.fit(x) x_scaler=scaler.transform(x) print(x_scaler.shape) pca=PCA(n_components=3) x_pca=pca.fit_transform(x_scaler) print(x_pca.shape) #查看各个主成分对应的方差大小和占全部方差的比例 #可以看到前2个主成分已经解释了样本分布的90%的差异了 print('explained_variance_:',pca.explained_variance_) print('explained_variance_ratio_:',pca.explained_variance_ratio_) print('total explained variance ratio of first 6 principal components:',sum(pca.explained_variance_ratio_)) #可视化各个主成分贡献的方差 #fig1=plt.figure(figsize=(10,10)) #plt.rcParams['figure.dpi'] = 300#设置像素参数值 plt.rcParams['path.simplify'] = False#禁用抗锯齿效果 plt.figure() plt.plot(np.arange(1,4),pca.explained_variance_,color='blue', linestyle='-',linewidth=2) plt.xticks(np.arange(1, 4, 1))#修改X轴间隔为1 plt.title('PCA_plot_HN') plt.xlabel('components_n',fontsize=16) plt.ylabel('explained_variance_',fontsize=16) plt.show() plt.pause(0.5) plt.savefig('H:/analysis_results/Cluster analysis/pca_explained_variance_HN.png')保存的图像中，一片空白，如何修改

plt.plot(np.arange(1,4),pca.explained_variance_,color='blue', linestyle='-',linewidth=2) plt.xticks(np.arange(1, 4, 1))#修改X轴间隔为1 plt.title('PCA_plot_HN') plt.xlabel('components_n',fontsize=16...

pca=PCA() pca.fit(X) eigenvalues = pca.explained_variance_ cumulative_variances = np.cumsum(eigenvalues) / np.sum(eigenvalues) plt.plot(range(1, len(eigenvalues) + 1), cumulative_variances, marker='o') plt.xlabel('Number of components') plt.ylabel('Cumulative variance ratio') plt.title('Scree plot') plt.show()

这段代码使用PCA算法对数据X进行降维，并绘制Scree plot（屏幕图）。Scree plot是一种可视化方法，用于帮助我们确定应该保留多少主成分（即降维后的特征数量），以保留原始数据中的信息量。在Scree plot中，横坐标...

import pandas as pd import numpy as np from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt import seaborn as sns df = pd.read_csv("RESSET_DRESSTK_2016_2020_1.csv") df = df[['Oppr','Hipr','Lopr','Clpr','Trdvol']] df = df.dropna() #标准化 scaler = StandardScaler() df_scaled = scaler.fit_transform(df) df_scaled = pd.DataFrame(df_scaled, columns=df.columns) pca=PCA() pca.fit(df_scaled) explained_variance = pca.explained_variance_ratio_ cumulative_variance = np.cumsum(explained_variance) n_components = np.argmax(cumulative_variance >= 0.95) + 1 print(f'Number of components to explain 95% of variance: {n_components}') # 使用确定的主成分数量重新拟合PCA pca = PCA(n_components=n_components) pca.fit(df_scaled) df_pca = pca.transform(df_scaled) df_pca = pd.DataFrame(df_pca, columns=[f'PC{i+1}' for i in range(n_components)]) # 5. 解释主成分的经济意义 loadings = pca.components_ loadings_df = pd.DataFrame(loadings, columns=df.columns, index=[f'PC{i+1}' for i in range(n_components)]) print('Loadings:') print(loadings_df) #累积方差图 plt.figure(figsize=(10, 6)) plt.plot(range(1, len(cumulative_variance) + 1), cumulative_variance, marker='o') plt.xlabel('Number of Components') plt.ylabel('Cumulative Explained Variance') plt.title('Cumulative Explained Variance by Principal Components') plt.grid(True) plt.show() # 主成分散点图 plt.figure(figsize=(10, 6)) sns.scatterplot(x=df_pca['PC1'], y=df_pca['PC2']) plt.xlabel('Principal Component 1') plt.ylabel('Principal Component 2') plt.title('Scatter Plot of Principal Components') plt.grid(True) plt.show() 逐句解释以上代码

plt.plot(range(1, len(cumulative_variance)+1), cumulative_variance, marker='o') plt.xlabel('Number of Components') plt.ylabel('Cumulative Explained Variance') plt.title('Cumulative Explained Variance ...

plt.plot(range(1, num_components + 1), explained_variance_ratio, marker='o')上面这行代码有错

在自己建立模型的情况下，我们没有explained_variance_ratio这个变量，因为它是PCA库提供的一个属性。在自己建立模型时，我们可以使用特征值来表示方差解释比例。特征值表示每个主成分所解释的方差量。以下是...

from sklearn.decomposition import PCA pca = PCA(n_components=17) pca.fit(X) print(pca.explained_variance_ratio_) [0.17513053,0.12941834,0.11453698,0.07323991,0.05889187,0.05690304, 0.04869476,0.0393374,0.03703477,0.03240863,0.03062932,0.02574137, 0.01887462,0.0180381,0.01606983,0.01453912,0.01318003] sum(pca.explained_variance_ratio_) X_NEW = pca.transform(X) X_NEW X_NEW.shape X_train,X_test,y_train,y_test = train_test_split(X_NEW,y,test_size=0.20,random_state=123) rf = RandomForestClassifier(max_depth=5) rf.fit(X_train, y_train) y_prob = rf.predict_proba(X_test)[:, 1] y_pred = np.where(y_prob > 0.5, 1, 0) rf.score(X_test, y_pred) confusion_matrix(y_test, y_pred) metrics.roc_auc_score(y_test, y_pred) from sklearn.metrics import roc_curve, auc false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_prob) roc_auc = auc(false_positive_rate, true_positive_rate) import matplotlib.pyplot as plt plt.figure(figsize=(10, 10)) plt.title('ROC') plt.plot(false_positive_rate, true_positive_rate, color='red', label='AUC = %0.2f' % roc_auc) plt.legend(loc='lower right') plt.plot([0, 1], [0, 1], linestyle='--') plt.axis('tight') plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.show() 这段代码的意思

首先，导入了PCA类，通过指定n_components参数为17进行初始化，并且使用fit方法对数据集X进行拟合，然后通过打印explained_variance_ratio_属性展示了每个主成分占总方差的比例。接着，使用transform方法将数据集X...

import pandas as pd import numpy as np from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt # 创建模拟数据 np.random.seed(42) # 设置随机种子以确保结果可复现 num_rows = 18 # 行数 num_cols = 1000 # 列数 data = np.random.rand(num_rows, num_cols) # 随机生成数据 # 创建DataFrame df = pd.DataFrame(data, columns=[f'Feature_{i+1}' for i in range(num_cols)]) # 提取前250列 df = df.iloc[:, :250] # 检查数据完整性 print("Data shape before cleaning:", df.shape) df = df.dropna(axis=1, how='all') # 删除全为空值的列 df = df.dropna(axis=0, how='any') # 删除包含空值的行 print("Data shape after cleaning:", df.shape) # 数据标准化 scaler = StandardScaler() df_scaled = scaler.fit_transform(df) # 执行PCA pca = PCA(n_components=min(df_scaled.shape)) # 计算所有主成分 pca.fit(df_scaled) # 获取特征值、奇异值、主成分得分和累计方差贡献率 eigenvalues = pca.explained_variance_ singular_values = pca.singular_values_ pca_scores = pca.transform(df_scaled) cumulative_variance = np.cumsum(pca.explained_variance_ratio_) # 获取贡献度较高的10列 top_10_indices = np.argsort(eigenvalues)[-10:][::-1] # 获取前10个最大特征值的索引 # 创建一个新DataFrame保存结果 results = pd.DataFrame({ 'Principal Component': top_10_indices + 1, # 主成分编号从1开始 'Eigenvalue': eigenvalues[top_10_indices], 'Singular_Value': singular_values[top_10_indices], 'PCA_Score': pca_scores[:, top_10_indices].tolist(), 'Cumulative_Variance': cumulative_variance[top_10_indices] }) # 保存到Excel文件 results.to_excel('PCA_Results.xlsx', index=False) # 绘制陡坡图（Scree Plot） plt.figure(figsize=(10, 6)) plt.plot(range(1, len(eigenvalues) + 1), eigenvalues, marker='o') plt.title('Scree Plot') plt.xlabel('Principal Component') plt.ylabel('Eigenvalue') plt.grid(True) plt.savefig('Scree_Plot.jpg', dpi=300) # 保存为JPG格式，高清晰度 plt.show()然后报错ValueError: All arrays must be of the same length

if not all(len(col) == len(data.iloc[:, 0]) for col in data.columns): raise ValueError("Columns have different lengths.") 上述代码可以用来检测是否有列的长度与其他列不符的情况[^1]。 --- #### ...

import pandas as pd import numpy as np from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt # 读取数据 data = pd.read_csv('D:/pythonProject/venv/BostonHousing2.csv') # 提取前13个指标的数据 X = data.iloc[:, 5:18].values # 数据标准化 scaler = StandardScaler() X_scaled = scaler.fit_transform(X) # 主成分分析 pca = PCA() X_pca = pca.fit_transform(X_scaled) # 特征值和特征向量 eigenvalues = pca.explained_variance_ eigenvectors = pca.components_.T # 碎石图 variance_explained = np.cumsum(eigenvalues / np.sum(eigenvalues)) plt.plot(range(6, 19), variance_explained, marker='o') plt.xlabel('Number of Components') plt.ylabel('Cumulative Proportion of Variance Explained') plt.title('Scree Plot') plt.show() # 选择主成分个数 n_components = np.sum(variance_explained <= 0.95) + 1 # 前2个主成分的载荷图 loadings = pd.DataFrame(eigenvectors[:, 0:2], columns=['PC1', 'PC2'], index=data.columns[0:13]) plt.figure(figsize=(10, 6)) plt.scatter(loadings['PC1'], loadings['PC2'], alpha=0.7) for i, feature in enumerate(loadings.index): plt.text(loadings['PC1'][i], loadings['PC2'][i], feature) plt.xlabel('PC1') plt.ylabel('PC2') plt.title('Loading Plot') plt.grid() plt.show() # 主成分得分图 scores = pd.DataFrame(X_pca[:, 0:n_components], columns=['PC{}'.format(i+1) for i in range(n_components)]) plt.figure(figsize=(10, 6)) plt.scatter(scores['PC1'], scores['PC2'], alpha=0.7) for i, label in enumerate(data['MEDV']): plt.text(scores['PC1'][i], scores['PC2'][i], label) plt.xlabel('PC1') plt.ylabel('PC2') plt.title('Scores Plot') plt.grid() plt.show() # 综合评估和排序 data['PC1_score'] = X_pca[:, 0] sorted_data = data.sort_values(by='PC1_score') # 主成分回归模型 from sklearn.linear_model import LinearRegression Y = data['MEDV'].values.reshape(-1, 1) X_pca_regression = X_pca[:, 0].reshape(-1, 1) regression_model = LinearRegression() regression_model.fit(X_pca_regression, Y) # 回归方程 intercept = regression_model.intercept_[0] slope = regression_model.coef_[0][0] equation = "MEDV = {:.2f} + {:.2f} * PC1".format(intercept, slope) print("Regression Equation:", equation) # 最小二乘估计结果 from statsmodels.api import OLS X_const = np.concatenate((np.ones((506, 1)), X_pca_regression), axis=1) ols_model = OLS(Y, X_const).fit() print("OLS Regression Summary:") print(ols_model.summary())

这段代码是用 Python 对波士顿房价数据进行主成分分析（PCA）。该代码读取了一个名为 "BostonHousing2.csv" 的数据文件，并将前 13 个指标的数据提取出来，进行了数据标准化和主成分分析。其中，碎石图展示了不同...

import pandas as pd import numpy as np from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt # 读取数据 data = pd.read_csv('D:\\pythonProject\\venv\\BostonHousing2.csv') # 提取前13个指标的数据 X = data.iloc[:, 5:18].values # 数据标准化 scaler = StandardScaler() X_scaled = scaler.fit_transform(X) # 主成分分析 pca = PCA() X_pca = pca.fit_transform(X_scaled) # 特征值和特征向量 eigenvalues = pca.explained_variance_ eigenvectors = pca.components_.T # 碎石图 # variance_explained我给你放到下一个cell里面了，这里用eigenvalues代替variance_explained plt.plot(range(1, 14), eigenvalues, marker='o') plt.xlabel('Number of Components') plt.ylabel('Cumulative Proportion of Variance Explained') plt.title('Scree Plot') plt.show() # 选择主成分个数 variance_explained = np.cumsum(eigenvalues / np.sum(eigenvalues)) n_components = np.sum(variance_explained <= 0.95) + 1 # 前2个主成分的载荷图 loadings = pd.DataFrame(eigenvectors[:, 0:2], columns=['PC1', 'PC2'], index=data.columns[0:13]) plt.figure(figsize=(10, 6)) plt.scatter(loadings['PC1'], loadings['PC2'], alpha=0.7) for i, feature in enumerate(loadings.index): plt.text(loadings['PC1'][i], loadings['PC2'][i], feature) plt.xlabel('PC1') plt.ylabel('PC2') plt.title('Loading Plot') plt.grid() plt.show() # 主成分得分图 scores = pd.DataFrame(X_pca[:, 0:n_components], columns=['PC{}'.format(i+1) for i in range(n_components)]) plt.figure(figsize=(10, 6)) plt.scatter(scores['PC1'], scores['PC2'], alpha=0.7) for i, label in enumerate(data['medv']): plt.text(scores['PC1'][i], scores['PC2'][i], label) plt.xlabel('PC1') plt.ylabel('PC2') plt.title('Scores Plot') plt.grid() plt.show() # 综合评估和排序 data['PC1_score'] = X_pca[:, 0] sorted_data = data.sort_values(by='PC1_score') # 主成分回归模型 from sklearn.linear_model import LinearRegression Y = data['medv'].values.reshape(-1, 1) X_pca_regression = X_pca[:, 0].reshape(-1, 1) regression_model = LinearRegression() regression_model.fit(X_pca_regression, Y) # 回归方程 intercept = regression_model.intercept_[0] slope = regression_model.coef_[0][0] equation = "medv = {:.2f} + {:.2f} * PC1".format(intercept, slope) print("Regression Equation:", equation) # 最小二乘估计结果 from statsmodels.api import OLS X_const = np.concatenate((np.ones((506, 1)), X_pca_regression), axis=1) ols_model = OLS(Y, X_const).fit() print("OLS Regression Summary:") print(ols_model.summary())

这段代码是做主成分分析（PCA）的，它的目的是将原始数据转换为更少的几个维度，以便于分析。具体来说，代码将Boston房价数据集中的前13个指标进行了标准化处理，然后使用PCA进行降维。在降维的过程中，代码画出了...

import matplotlib matplotlib.use('TkAgg') import numpy as np from sklearn.decomposition import PCA from sklearn.datasets import fetch_olivetti_faces import matplotlib.pyplot as plt from sklearn.metrics.pairwise import euclidean_distances plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False plt.rcParams['figure.figsize'] = (12, 8) def main(): # 加载人脸数据集 faces = fetch_olivetti_faces() X_train = faces.data target = faces.target # 获取每个人的标签 # 执行PCA降维 pca = PCA(n_components=50, random_state=42) X_pca = pca.fit_transform(X_train) # 计算解释方差比 explained_variance = pca.explained_variance_ratio_ cumulative_variance = np.cumsum(explained_variance) # 显示数据集信息 print("\n提示: 请输入0-399之间的整数作为图片索引\n") # 获取用户输入的测试图片索引 while True: try: test_idx = int(input("请输入测试图片的索引 (0-399): ")) if 0 <= test_idx < 400: break else: print("错误：索引必须在0到399之间！") except ValueError: print("错误：请输入有效的整数！") # 处理测试图片 T = X_train[test_idx].reshape(1, -1) T_pca = pca.transform(T) # 重建测试图片 reconstructed_img = pca.inverse_transform(T_pca).reshape(64, 64) # 计算欧氏距离并找到匹配图片 distances = euclidean_distances(X_pca, T_pca).flatten() matched_idx = np.argmin(distances) # 重建匹配图片 matched_reconstructed = pca.inverse_transform(X_pca[matched_idx].reshape(1, -1)).reshape(64, 64) # 创建可视化结果 fig = plt.figure(figsize=(14, 10)) # 创建网格布局 gs = fig.add_gridspec(3, 3) # 原始测试图片 ax1 = fig.add_subplot(gs[0, 0]) ax1.imshow(T.reshape(64, 64), cmap='gray') ax1.set_title(f'原始图片\n(索引: {test_idx}, 人物: {target[test_idx]})') ax1.axis('off') # PCA重建后的测试图片 ax2 = fig.add_subplot(gs[0, 1]) ax2.imshow(reconstructed_img, cmap='gray') ax2.set_title(f'PCA重建图片\n(索引: {test_idx})') ax2.axis('off') plt.tight_layout(rect=[0, 0.05, 1, 0.95]) plt.subplots_adjust(top=0.92, hspace=0.3, wspace=0.2) plt.show() if name == "main": main() 优化该代码

我们参考上述引用来优化PCA降维和图像重建的代码。主要优化方向包括：1.... plt.plot(np.cumsum(pca.explained_variance_ratio_)) # 方差累积曲线量化评估降维效果，科学选择主成分数量[^3]

import scipy.io as sio import matplotlib.pyplot as plt import numpy as np # 构建文件路径 file_path = 'Cuprite3.mat' # 读取.mat文件 mat_data = sio.loadmat(file_path) # 获取数据 W = mat_data['W'] # 创建从0开始的x轴数据列表 x_axis = np.arange(W.shape[0]) # 设置中文字体和负号显示 plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False # 绘制折线图 plt.plot(x_axis, W[:, 0], label='岩石') plt.plot(x_axis, W[:, 1], label='树木') plt.plot(x_axis, W[:, 2], label='水') # 添加图例、标题和轴标签 plt.legend() plt.title('反射波谱曲线') plt.xlabel('数轴') plt.ylabel('反射率') # 设置 x 轴范围，让 0 位于原点 plt.xlim(left=0) # 设置 y 轴范围，让 0 位于原点 plt.ylim(bottom=0) # 显示图表 plt.show() 主成分分析python实现

plt.ylabel(f'PC2 ({pca.explained_variance_ratio_[1]*100:.1f}%)') plt.legend() plt.title('PCA of Iris Dataset') plt.show() ![PCA可视化结果示例]...

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() PCA_data = pd.DataFrame(newX, columns=['PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6', 'PC7']) correlation_matrix = PCA_data.corr() sns.heatmap(correlation_matrix, cmap='coolwarm', annot=True) plt.show() 上述代码是对数据进行主成分分析，展示每个主成分的贡献率，并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，请在上述代码基础上，请给出下一步的代码，要求是：划分训练集测试集，使用前六个主成分作为自变量训练逻辑回归模型。显示模型预测的准确率，显示模型的混淆矩阵，试着画出ROC曲线。

# 划分训练集测试集 from sklearn.model_selection ...plt.plot([0, 1], [0, 1], linestyle='--', color='k') plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('ROC Curve') plt.show()

E:\Anaconda\python.exe C:\Users\cheny\Desktop\PythonProject2\图7聚类中心为4的聚类效果图.py E:\Anaconda\Lib\site-packages\sklearn\cluster\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2. warnings.warn( 进程已结束，退出代码为 0 报错内容如上下面是原代码 import pandas as pd import numpy as np from sklearn.cluster import KMeans from sklearn.decomposition import PCA import matplotlib.pyplot as plt def plot_kmeans_clustering(file_path): # 读取 Excel 文件 excel_file = pd.ExcelFile(file_path) # 获取所有表名 sheet_names = excel_file.sheet_names # 获取指定工作表中的数据 df = excel_file.parse('Sheet1') # 去除可能的非数值列（第一列是No编号列，从第二列开始为特征数据） X = df.iloc[:, 1:].values # 进行 K-Means 聚类，聚类中心为 4 kmeans = KMeans(n_clusters=4, random_state=42) kmeans.fit(X) labels = kmeans.labels_ # 使用 PCA 将数据降到二维以便绘图 pca = PCA(n_components=2) X_pca = pca.fit_transform(X) # 对聚类中心进行PCA转换（因为原始中心是高维的） centers_pca = pca.transform(kmeans.cluster_centers_) # 设置图片清晰度 plt.rcParams['figure.dpi'] = 300 # 设置 matplotlib 支持中文 plt.rcParams['font.sans-serif'] = ['SimHei', 'WenQuanYi Zen Hei', 'Heiti TC'] plt.rcParams['axes.unicode_minus'] = False # 绘制聚类效果图 plt.figure(figsize=(10, 6)) colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728'] # 四种不同颜色 for i in range(4): cluster_points = X_pca[labels == i] plt.scatter(cluster_points[:, 0], cluster_points[:, 1], c=colors[i], label=f'聚类 {i + 1}', alpha=0.7, s=50) # 绘制聚类中心（使用转换后的二维中心） plt.scatter(centers_pca[:, 0], centers_pca[:, 1], s=200, c='black', marker='*', label='聚类中心') plt.title('聚类中心为4的K-Means聚类效果图') plt.xlabel('主成分1') plt.ylabel('主成分2') plt.legend() plt.tight_layout() # 自动调整布局 plt.show() if name == 'main': # 修改为你的文件实际存储路径 file_path = '附件 1_预处理后.xlsx' plot_kmeans_clustering(file_path)

plt.xlabel(f'主成分1 (解释方差: {pca.explained_variance_ratio_[0]*100:.1f}%)') plt.ylabel(f'主成分2 (解释方差: {pca.explained_variance_ratio_[1]*100:.1f}%)') # 添加图例 plt.legend(title='聚类...

import pandas as pd import matplotlib.pyplot as plt import numpy as np from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler # 设置文件路径（请替换为你的实际文件路径） file_path = "附件 1_预处理后.xlsx" # 请确保路径正确 # 读取数据 df = pd.read_excel(file_path, index_col='No') # 数据标准化（PCA前通常需要标准化处理） scaler = StandardScaler() scaled_data = scaler.fit_transform(df) # 执行主成分分析 pca = PCA() pca.fit(scaled_data) # 获取各主成分的贡献率 explained_variance_ratio = pca.explained_variance_ratio_ cumulative_variance_ratio = np.cumsum(explained_variance_ratio) # 设置中文字体 plt.rcParams["font.family"] = ["SimHei", "WenQuanYi Micro Hei", "Heiti TC"] plt.rcParams['axes.unicode_minus'] = False # 创建图形 plt.figure(figsize=(12, 6)) # 绘制单个主成分的贡献率（柱状图） plt.bar(range(1, len(explained_variance_ratio) + 1), explained_variance_ratio, alpha=0.7, label='单个贡献率') # 绘制累积贡献率（折线图） plt.plot(range(1, len(cumulative_variance_ratio) + 1), cumulative_variance_ratio, 'r-', marker='o', label='累积贡献率') # 添加参考线（通常选择累积贡献率达到95%的点） threshold = 0.95 n_components = np.argmax(cumulative_variance_ratio >= threshold) + 1 plt.axhline(y=threshold, color='g', linestyle='--', label=f'95%阈值') plt.axvline(x=n_components, color='g', linestyle='--') # 添加标签和标题 plt.title('图4 原始贡献率', fontsize=15) plt.xlabel('主成分数量', fontsize=12) plt.ylabel('贡献率', fontsize=12) plt.xlim(0, min(20, len(explained_variance_ratio))) # 限制显示前20个主成分，避免过于拥挤 plt.ylim(0, 1.05) plt.legend() plt.grid(axis='y', linestyle='--', alpha=0.5) # 调整布局 plt.tight_layout() # 显示图形 plt.show() 代码内容如上| 报错内容如下 E:\Anaconda\python.exe C:\Users\cheny\Desktop\PythonProject2\图4原始贡献率.py findfont: Font family 'WenQuanYi Micro Hei' not found. findfont: Font family 'Heiti TC' not found. findfont: Font family 'WenQuanYi Micro Hei' not found. findfont: Font family 'Heiti TC' not found. findfont: Font family 'WenQuanYi Micro Hei' not found. findfont: Font family 'Heiti TC' not found. findfont: Font family 'WenQuanYi Micro Hei' not found. findfont: Font family 'Heiti TC' not found. findfont: Font family 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found.

line, = plt.plot(range(1, len(cumulative_variance_ratio) + 1), cumulative_variance_ratio, 'r-', marker='o', label='累积贡献率') # 添加参考线（通常选择累积贡献率达到95%的点） threshold = 0.95 n_...

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1、安装vscode 2、安装C/C++插件在左边栏的扩展(Extensions)中查找安装 3、打开一个C++工程文件夹，点击左边栏中的Run and Debug，点击create a launch.json file，选择C++(GDB/LLDB)，选择g++ build and debug active file， ...

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