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a/盘荣博/图/例10.py b/盘荣博/图/例10.py index c97f2a0..ed45820 100644 --- a/盘荣博/图/例10.py +++ b/盘荣博/图/例10.py @@ -25,4 +25,4 @@ nx.draw_networkx_edge_labels(G,pos,edge_labels=w)#绘制标签 path_edges=list(zip(p,p[1:])) print(type(path_edges),"\npath_edges=",path_edges) nx.draw_networkx_edges(G,pos,edgelist=path_edges,edge_color="r",width=1) -plt.savefig("figure10_9.png");plt.show() \ No newline at end of file +plt.savefig("figure10_9.png",dpi=1000);plt.show() \ No newline at end of file diff --git a/盘荣博/数据可视化/shuju.jpg b/盘荣博/数据可视化/shuju.jpg new file mode 100644 index 0000000000000000000000000000000000000000..2c8e7dab56e1c8d57b4536d2bdf30cf702120b5d GIT binary patch literal 2813674 zcmeF)2{@E%|2XhR$`sXDn$z^+o%rGHdF{PmC6=McE(Ui2@@(&Au78$WtXjx zMA-@12HBahj#-}h-_E<8(|g|cci#8^+y4LSc3pGTjAtIt@|pX2?(g^h-oyLK>q1r> z(bm;Q`1trRHJBO%;k6-$ki`N57=cBLF&NB}C5r`@iY!|yB(!v`@G7i`q&QAWQd~k} zy^Or<`it=klp_A2k&uX5-oEp45{x<^i(HZU|gV{Bq^@zP~WD{GtUPR=f_ zZtfm8Z~5K!zjHSrH0;5{N8yhnB4guTynGd(keKu)JtH$KJ0~~qeMxEAhw_gVpX%xx 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z2EYIq00UqE41fVJ00zJS7ytuc01SWuFaQR?02lxRU;qq&0WbgtzyKHk17H9QfB`T7 z2EYIq00UqE41fVJ00zJS7ytuc01SWuFaQR?02lxRU;qq&0WbgtzyKHk17H9QfB`T7 q2EYIq00UqE41fVJ00zJS7ytuc01SWuFaQR?02ugxV?g8W^S=R8Z)~&x literal 0 HcmV?d00001 diff --git a/盘荣博/数据可视化/week1.py b/盘荣博/数据可视化/week1.py index 12f7640..74a1462 100644 --- a/盘荣博/数据可视化/week1.py +++ b/盘荣博/数据可视化/week1.py @@ -1,9 +1,42 @@ import pandas as pd +from scipy.stats import zscore import matplotlib.pyplot as plt +from matplotlib.pyplot import ylabel df = pd.read_excel("棉花产量论文作业的数据.xlsx") -plt.plot(df["年份"],df["单产"]) +# plt.plot(df["年份"],df["单产"]) plt.rcParams['font.sans-serif']="SimHei" -plt.ylabel('单产') -plt.xlabel('年份') -plt.show() -print(df) \ No newline at end of file +# plt.rcParams['size'] =10 +# plt.ylabel('单产') +# plt.xlabel('年份') + +# print(df) +d = df.to_numpy()[:,1:] +print(d) +plt.subplot(4,1,1) +plt.scatter(d[:,:1],d[:,1:2],c='r') +ylabel('原始数据'),plt.title("单产和种子费用的关系") +#公式调用标准化,遵守标准正态分布 +data = zscore(d) +print(data) +plt.subplot(4,1,2) +plt.scatter(data[:,:1],data[:,1:2],c='b',) +ylabel('zscore') + +print(d.max(axis=0)) +print(d.std(axis=0)) +print(d.mean(axis=0)) +#手写标准正态分布 +data1=(d-d.mean(axis=0))/d.std(axis=0) +print(data1) +plt.subplot(4,1,3) +plt.scatter(data1[:,:1],data1[:,1:2],c='y') +ylabel('手写标准正态分布') + +data2=(d-d.min(axis=0))/(d.max(axis=0)-d.min(axis=0)) +plt.subplot(4,1,4) +plt.scatter(data2[:,:1],data2[:,1:2],c='g') +plt.xlabel('压缩到0~1') +print(data==data1) + +plt.savefig("shuju.jpg",dpi=2000) +plt.show() \ No newline at end of file -- 2.34.1 From 7c694554d44b302ec0bac96b954049d0409addda Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=9B=98=E8=8D=A3=E5=8D=9A?= <2858829498@qq.com> Date: Sat, 20 Jul 2024 14:02:27 +0800 Subject: [PATCH 2/2] =?UTF-8?q?=E4=BF=AE=E6=94=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 盘荣博/数据可视化/week1.py | 23 +++++++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/盘荣博/数据可视化/week1.py b/盘荣博/数据可视化/week1.py index 74a1462..6e9c134 100644 --- a/盘荣博/数据可视化/week1.py +++ b/盘荣博/数据可视化/week1.py @@ -1,5 +1,7 @@ import pandas as pd +import numpy as np from scipy.stats import zscore +from sklearn.decomposition import PCA import matplotlib.pyplot as plt from matplotlib.pyplot import ylabel df = pd.read_excel("棉花产量论文作业的数据.xlsx") @@ -38,5 +40,22 @@ plt.scatter(data2[:,:1],data2[:,1:2],c='g') plt.xlabel('压缩到0~1') print(data==data1) -plt.savefig("shuju.jpg",dpi=2000) -plt.show() \ No newline at end of file +# plt.savefig("shuju.jpg",dpi=2000) +# plt.show() +md= PCA().fit(data) +cf = np.cov(data.T)#求协方差矩阵 +print(cf) +c, d= np.linalg.eig(cf) +print("特征值:\n",c) +print(md.explained_variance_) +e=c/c.sum() +# for _ in range(len(e)): +# if(_!=0): +# e[_]+=e[_-1] +print('贡献率:') +print(e) +print(md.explained_variance_ratio_) +print('特征向量:') +print(d.T) +print(md.components_) +print(md.components_-d.T<=0.1) \ No newline at end of file -- 2.34.1