import numpy as np import matploplib.pyplot as plt import pandas as pd import openpyxl from sklearn import decomposition def loadDataSet(filename): ex = openpyxl.load_workbook(filename) sheet = ex.get_sheet_by_name("高钾") data1 = [] data2 = [] for i in range(2,20): res = [] for j in range (2,16): res.append(sheet.cell(row=i,column = j).value) data1.append(res) sheet = ex.get_sheet_by_name("铅钡") for i in range(2,51): res = [] for j in range (2,16): res.append(sheet.cell(row=i,column = j).value) data2.append(res) return data1 , data2 def process(data,n,n): a=[] for i in range(0,n): down = 1 num = 0 b = [] for j in range(0,m): if data[i][j] == 0:continue else: down = down*float(data[i][j]) num += 1 down = pow(down,1/num) for j in range(0,m): if data[i][j]: b.append(0) else: b.append(np.log(float(data[i][j])/down)) a.append(b) return a filename = '归一化后用于log变换.xlsx' data1 , data2 = loadDataSet(filename) data1 = process(data1,len(data1),len(data1[0])) data2 = process(data2,len(data1),len(data2[0])) wk = openpyxl.Workbook sheet1 = wk.create_sheet("高钾") sheet1 = wk.create_sheet("铅钡") for i in range(1,len(data1) + 1): for j in range(1,len(data1[0]) + 1): sheet1.cell(row = i,column = j).value = data1[i-1][j-1] for i in range(1,len(data2) + 1): for j in range(1,len(data2[0]) + 1): sheet2.cell(row = i,column = j).value = data2[i-1][j-1] wk.save('../第二小问/Log-ratio变换.xlsx')分析代码工作流程

机器学习-PCA算法的Python实现.zip

from sklearn.decomposition import PCA import numpy as np import pandas as pd 假设我们已经有了一个二维数据集data，存储在numpy数组或pandas DataFrame中。我们可以先进行预处理，例如标准化数据，确保...

import torchimport pandas as pdimport numpy as npimport matplotlib.pyplot as pltfrom torch.utils.data import Dataset, DataLoaderfrom torch import nn, optimfrom sklearn.decomposition import PCA运算过程

- pandas：是一个用于数据处理和分析的Python库，支持导入和导出各种数据格式。 - numpy：是一个用于科学计算的Python库，支持大量的数值运算。 - matplotlib：是一个用于绘制图形的Python库，支持各种类型的图形...

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

import pandas as pd import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from matplotlib.colors import ListedColormap from sklearn....

import pandas as pd import numpy as np import matplotlib.pyplot as plt %matplotlib inline from matplotlib.font_manager import FontProperties plt.rcParams['font.sans-serif'] = ['simhei'] import pandas as pd data1=pd.read_csv('G://data2.csv',index_col=0) data1.head() X = (data1 - data1.mean()) / data1.std() from sklearn.decomposition import PCA n = 5 pca = PCA(n_components = n) pca.fit(X)可以帮我生成一段代码吗

import numpy as np import matplotlib.pyplot as plt %matplotlib inline from matplotlib.font_manager import FontProperties plt.rcParams['font.sans-serif'] = ['simhei'] data1 = pd.read_csv('G://data2....

from sklearn.decomposition import PCA import numpy as np import matplotlib.pyplot as plt df = pd.get_dummies(df) X = np.array(df) # print(X) pca=PCA(n_components=15)#n_components 选择降维数量 pca.fit(X) # print(pca.transform(X)) X = pca.transform(X) x = np.array(X)[:, 0] y = np.array(X)[:, 1] plt.scatter(x, y, c = y) plt.show()

代码中，首先使用pandas库中的get_dummies函数将数据进行独热编码，然后将其转化为numpy数组。接着，使用sklearn库中的PCA函数进行降维，设置n_components参数为15，表示最终降至15维。最后，将降维后的数据绘制成...

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from pandas_profiling import ProfileReport 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') columns=['folder', 'volume', 'convex_volume', 'surface_area','length','max_width', 'max_depth'] data.head() values=data.iloc[:,1:7] correlation=values.corr() fig,ax=plt.subplots(figsize=(12,10)) sns.heatmap(correlation,annot=True,annot_kws={'size':16},cmap='Reds',square=True,ax=ax) sns.pairplot(data,hue='folder') plt.show()如何保存这两张图

你可以在代码的最后添加以下语句来保存这两张图： python fig.savefig('heatmap.png') sns_plot = sns.pairplot(data,hue='folder') sns_plot.savefig('pairplot.png') 这将会把热力图保存为 heatmap.png...

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。¶

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 ...

import random import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score from sklearn.decomposition import PCA plt.rcParams['font.sans-serif'] = ['SimHei'] dataset=pd.read_excel('C:\\Users\\86180\\Desktop\\第一次数学建模\\湘菜口感好物质.xlsx') dataset = dataset.drop(dataset.index[:1], inplace=False) dataset = dataset.drop(dataset.columns[:1], axis=1, inplace=False) #matrix=dataset.values matrix=np.array(dataset) matrix=matrix.T matrix_xiang=matrix[:27] # 将NaN值替换为0 matrix_xiang = np.nan_to_num(matrix_xiang) # 检测矩阵中是否还有NaN值这个代码报错TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''应如何修改

import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score from sklearn.decomposition import PCA plt....

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

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....

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

import matplotlib.pyplot as plt from pandas import DataFrame 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...

import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.datasets import load_iris from sklearn.decomposition import PCA iris = load_iris() X = iris.data y = iris.target pca = PCA(n_components=2) X_pca = pca.fit_transform(X) def dbscan(X, eps, min_samples): labels = np.zeros(len(X)) C = 0 visited = set() for i in range(len(X)): if i not in visited: visited.add(i) neighbors = find_neighbors(X, i, eps) if len(neighbors) < min_samples: labels[i] = -1 else: C += 1 labels[i] = C expand_cluster(X, labels, i, neighbors, C, eps, min_samples, visited) return labels def find_neighbors(X, i, eps): neighbors = [] for j in range(len(X)): if np.linalg.norm(X[i] - X[j]) < eps: neighbors.append(j) return neighbors def expand_cluster(X, labels, i, neighbors, C, eps, min_samples, visited): for j in neighbors: if j not in visited: visited.add(j) neighbors_j = find_neighbors(X, j, eps) if len(neighbors_j) >= min_samples: neighbors.extend(neighbors_j) if labels[j] == 0: labels[j] = C eps = 0.5 min_samples = 5 labels = dbscan(X_pca, eps, min_samples) plt.scatter(X_pca[:, 0], X_pca[:, 1], c=labels) plt.xlabel('PCA Component 1') plt.ylabel('PCA Component 2') plt.title('DBSCAN Clustering with PCA') plt.show()将上述代码解读并加以注释

import pandas as pd import matplotlib.pyplot as plt from sklearn.datasets import load_iris from sklearn.decomposition import PCA iris = load_iris() X = iris.data y = iris.target pca = PCA(n_...

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...

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