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@ -1,5 +1,11 @@
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import pandas as pd
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<<<<<<< HEAD
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import numpy as np
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from scipy.stats import zscore
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from sklearn.decomposition import PCA
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=======
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from scipy.stats import zscore
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>>>>>>> remotes/origin/盘荣博
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import matplotlib.pyplot as plt
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from matplotlib.pyplot import ylabel
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df = pd.read_excel("棉花产量论文作业的数据.xlsx")
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@ -38,5 +44,27 @@ plt.scatter(data2[:,:1],data2[:,1:2],c='g')
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plt.xlabel('压缩到0~1')
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print(data==data1)
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<<<<<<< HEAD
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# plt.savefig("shuju.jpg",dpi=2000)
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# plt.show()
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md= PCA().fit(data)
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cf = np.cov(data.T)#求协方差矩阵
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print(cf)
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c, d= np.linalg.eig(cf)
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print("特征值：\n",c)
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print(md.explained_variance_)
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e=c/c.sum()
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# for _ in range(len(e)):
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# if(_!=0):
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# e[_]+=e[_-1]
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print('贡献率：')
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print(e)
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print(md.explained_variance_ratio_)
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print('特征向量：')
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print(d.T)
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print(md.components_)
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print(md.components_-d.T<=0.1)
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=======
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plt.savefig("shuju.jpg",dpi=2000)
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plt.show()
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>>>>>>> remotes/origin/盘荣博
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