import matplotlib.pyplot as plt from sklearn.decomposition import PCA # 进行PCA降维 pca = PCA(n_components=2) x_pca = pca.fit_transform(x) # 绘制散点图 fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5)) ax1.scatter(x_pca[:, 0], x_pca[:, 1], c=y) ax1.set_title('Original dataset') # 进行ADASYN过采样 adasyn = ADASYN(random_state=42) x_resampled, y_resampled = adasyn.fit_resample(x, y) # 进行PCA降维 x_resampled_pca = pca.transform(x_resampled) # 绘制散点图 ax2.scatter(x_resampled_pca[:, 0], x_resampled_pca[:, 1], c=y_resampled) ax2.set_title('Resampled dataset') plt.show()把点弄小一点

Matplotlib scatter绘制散点图的方法实现

主要介绍了Matplotlib scatter绘制散点图的方法实现，文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值，需要的朋友们下面随着小编来一起学习学习吧

PCA.zip_PCA散点图_Python PCA_python pca 画图_python 图_降维

import matplotlib.pyplot as plt import seaborn as sns from sklearn.datasets import load_iris from sklearn.decomposition import PCA 接下来，加载鸢尾花数据集： python iris = load_iris() X = ...

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 matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from matplotlib.colors import ListedColormap from sklearn.model_selection import train_test_split, ...

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 matplotlib.pyplot as plt from sklearn.tree import DecisionTreeClassifier # 读入数据 data = pd.read_csv("heart.csv") # 选取自变量 X = data[["sysBP", "diaBP", "age", "totChol", "BMI", "heart...

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()

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_...

import matplotlib.pyplot as plt from sklearn.decomposition import PCA import pandas as pd df = pd.read_csv("iris.csv") pca = PCA(n_components=2) X = df.iloc[:, :-1].values Y = df.iloc[:, -1].values X_reduced = pca.fit_transform(X) plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=Y) plt.show() df_reduced = pd.DataFrame(data=X_reduced, columns=["PC1", "PC2"]) df_reduced["label"] = Y df_reduced.to_csv("iris_reduced.csv", index=False)这段代码运行报错，请为我修正

from sklearn.decomposition import PCA df = pd.read_csv("iris.csv") pca = PCA(n_components=2) X = df.iloc[:, :-1].values Y = df.iloc[:, -1].values X_reduced = pca.fit_transform(X) plt.scatter(X_...

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()

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散点图，其中x、y分别表示第一、第二维的数据，c表示颜色，使用y值来表示。

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

以上代码段中没有明显的运算过程，...- PCA：是一个用于数据降维的算法，可以通过sklearn库来实现。在实际的机器学习代码中，这些库和模块通常会结合起来使用，进行数据处理、特征提取、模型构建和训练等各种操作。

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

from sklearn.decomposition import PCA n = 5 pca = PCA(n_components=n) pca.fit(X) 这段代码读取名为 'G://data2.csv' 的文件并将其存储在 DataFrame 对象 data1 中。然后，它对数据进行标准化处理，即将每...

import matplotlib.pyplot as plt from sklearn.decomposition import PCA from sklearn.datasets import load_iris data = load_iris() y = data.target x = data.data pca = PCA(n_components = 2) #加载PCA算法，设置降维后主成分数目为2 reduced_x = pca.fit_transform(x) #对样本进行降维 #在平面中画出降维后的样本点的分布 red_x,red_y = [],[] blue_x,blue_y = [],[] green_x,green_y = [],[] for i in range(len(reduced_x)): if y[i] == 0: red_x.append(reduced_x[i][0]) red_y.append(reduced_x[i][1]) elif y[i] == 1: blue_x.append(reduced_[i][0]) blue_y.append(reduced_[i][1]) else: green_x.append(reduced_x[i][0]) green_y.append(reduced_x[i][1]) plt.scatter(red_x,red_y,c='r',marker='x') plt.scatter(blue_x,blue_y,c='b',marker='D') plt.scatter(green_x,green_y,c='g',marker='.') plt.show()找出代码的错误

import matplotlib.pyplot as plt from sklearn.decomposition import PCA 2. 在第十四行代码中，变量名错误，将 reduced_x 写成了 reduced_。正确的第十四行代码应该是： blue_x.append(reduced_x[i]...

from sklearn.datasets import load_iris from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA import matplotlib.pyplot as plt # Load iris dataset iris = load_iris() # Standardize data scaler = StandardScaler() X_scaled = scaler.fit_transform(iris.data) # Perform PCA pca = PCA(n_components=2) y = pca.fit_transform(X_scaled) print(y) # Plot results plt.scatter(y[:, 0], y[:, 1], c=iris.target) plt.xlabel('First principal component') plt.ylabel('Second principal component') plt.show()对上述代码处理后的鸢尾花数据，采用（自选）1种分类方法进行分类实验；再使用PCA方法降维后的由前2个主成分构成的新数据集，采用同种分类方法进行分类实验；最后对2种分类方法的结果进行比较。

pca = PCA(n_components=2) X_pca = pca.fit_transform(X_scaled) X_train_pca, X_test_pca, y_train, y_test = train_test_split(X_pca, iris.target, test_size=0.2, random_state=42) svm_clf_pca = SVC(kernel=...

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

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_...

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 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'] ...

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 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 =...

import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_iris from sklearn.cluster import KMeans # 加载鸢尾花数据集 iris = load_iris() X = iris.data[:, :2] # 取前两个特征作为萼片长和宽 # 进行Kmeans聚类划分 kmeans = KMeans(n_clusters=3, random_state=0).fit(X) labels = kmeans.labels_ # 绘制散点图 plt.scatter(X[labels == 0, 0], X[labels == 0, 1], c='red', label='Cluster 1') plt.scatter(X[labels == 1, 0], X[labels == 1, 1], c='blue', label='Cluster 2') plt.scatter(X[labels == 2, 0], X[labels == 2, 1], c='green', label='Cluster 3') plt.xlabel('Sepal Length') plt.ylabel('Sepal Width') plt.title('Kmeans Clustering of Iris Dataset') plt.legend() plt.show()改进此代码

import matplotlib.pyplot as plt from sklearn.datasets import load_iris from sklearn.cluster import KMeans from sklearn.decomposition import PCA # 加载鸢尾花数据集 iris = load_iris() X = iris....

基于springboot个人公务员考试管理系统源码数据库文档.zip

bimdata_api_client-4.2.1-py3-none-any.whl

numpy-1.20.2-cp39-cp39-linux_armv7l.whl

matplotlib-3.3.2-cp39-cp39-linux_armv7l.whl

相关推荐

Matplotlib scatter绘制散点图的方法实现

PCA.zip_PCA散点图_Python PCA_python pca 画图_python 图_降维

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

基于springboot个人公务员考试管理系统源码数据库文档.zip

bimdata_api_client-4.2.1-py3-none-any.whl

numpy-1.20.2-cp39-cp39-linux_armv7l.whl

matplotlib-3.3.2-cp39-cp39-linux_armv7l.whl

最新推荐

基于springboot个人公务员考试管理系统源码数据库文档.zip

bimdata_api_client-4.2.1-py3-none-any.whl

numpy-1.20.2-cp39-cp39-linux_armv7l.whl

matplotlib-3.3.2-cp39-cp39-linux_armv7l.whl

bimdata_api_client-4.0.0-py3-none-any.whl

基于Python和Opencv的车牌识别系统实现

管理建模和仿真的文件

网络隔离与防火墙策略：防御网络威胁的终极指南

在密码学中，对称加密和非对称加密有哪些关键区别，它们各自适用于哪些场景？

我的代码小部件库：统计、MySQL操作与树结构功能