机械学习将鸢尾花的特征值和特征向量进行组合

import pandas as pd
#第一步数据读取
data = pd.read_csv('IrisData.csv')
data.columns = ['sepal_len','sepal_wid','petal_len','petal_wid','classes']
#第二步提取特征
X = data[['sepal_len','sepal_wid','petal_len','petal_wid']].values
y = data['classes'].values

feature_names = ['sepal_len','sepal_wid','petal_len','petal_wid']
label_names = data['classes'].unique()

#第三步对每一个特征的样 品类别做直方图
for feature in range(len(feature_names)):
    plt.subplot(2,2,feature+1)
    for label in label_names:
        plt.hist(X[y==label,feature],bins=10,alpha=0.5,label=label)
    plt.legend(loc='best')
plt.show()
#第四步对特征进行标准化操作
from sklearn.preprocessing import StandardScaler

std_feature = StandardScaler().fit_transform(X)
 # 第五步 对特征去除均值，并构造协方差矩阵，也可以使用np. conv进行构造
mean_fea = std_feature.mean(axis=0)
cov_matrix = (std_feature - mean_fea).T.dot(std_feature-mean_fea)
#第六步使用np.1inalg.eig求出协方差矩阵的特征值和特征向量
eig_val,eig_vector = np.linalg.eig(cov_matrix)
#第七步:我们将特征值和特征向量进行组合
eig_paries = [(eig_val[j],eig_vector[:,j]) for j in range(len(eig_val))]

eig_vector_two = np.vstack([eig_paries[0][1],eig_paries[1][1]])
print(eig_vector_two)
trans_std_X = std_feature.dot(eig_vector_two.T)

鸢尾花数据

sepal length,sepal width,petal length,petal width,Species
5.1,3.5,1.4,0.2,Setosa
4.9,3,1.4,0.2,Setosa
4.7,3.2,1.3,0.2,Setosa
4.6,3.1,1.5,0.2,Setosa
5,3.6,1.4,0.2,Setosa
5.4,3.9,1.7,0.4,Setosa
4.6,3.4,1.4,0.3,Setosa
5,3.4,1.5,0.2,Setosa
4.4,2.9,1.4,0.2,Setosa
4.9,3.1,1.5,0.1,Setosa
5.4,3.7,1.5,0.2,Setosa
4.8,3.4,1.6,0.2,Setosa
4.8,3,1.4,0.1,Setosa
4.3,3,1.1,0.1,Setosa
5.8,4,1.2,0.2,Setosa
5.7,4.4,1.5,0.4,Setosa
5.4,3.9,1.3,0.4,Setosa
5.1,3.5,1.4,0.3,Setosa
5.7,3.8,1.7,0.3,Setosa
5.1,3.8,1.5,0.3,Setosa
5.4,3.4,1.7,0.2,Setosa
5.1,3.7,1.5,0.4,Setosa
4.6,3.6,1,0.2,Setosa
5.1,3.3,1.7,0.5,Setosa
4.8,3.4,1.9,0.2,Setosa
5,3,1.6,0.2,Setosa
5,3.4,1.6,0.4,Setosa
5.2,3.5,1.5,0.2,Setosa
5.2,3.4,1.4,0.2,Setosa
4.7,3.2,1.6,0.2,Setosa
4.8,3.1,1.6,0.2,Setosa
5.4,3.4,1.5,0.4,Setosa
5.2,4.1,1.5,0.1,Setosa
5.5,4.2,1.4,0.2,Setosa
4.9,3.1,1.5,0.2,Setosa
5,3.2,1.2,0.2,Setosa
5.5,3.5,1.3,0.2,Setosa
4.9,3.6,1.4,0.1,Setosa
4.4,3,1.3,0.2,Setosa
5.1,3.4,1.5,0.2,Setosa
5,3.5,1.3,0.3,Setosa
4.5,2.3,1.3,0.3,Setosa
4.4,3.2,1.3,0.2,Setosa
5,3.5,1.6,0.6,Setosa
5.1,3.8,1.9,0.4,Setosa
4.8,3,1.4,0.3,Setosa
5.1,3.8,1.6,0.2,Setosa
4.6,3.2,1.4,0.2,Setosa
5.3,3.7,1.5,0.2,Setosa
5,3.3,1.4,0.2,Setosa
7,3.2,4.7,1.4,Versicolour
6.4,3.2,4.5,1.5,Versicolour
6.9,3.1,4.9,1.5,Versicolour
5.5,2.3,4,1.3,Versicolour
6.5,2.8,4.6,1.5,Versicolour
5.7,2.8,4.5,1.3,Versicolour
6.3,3.3,4.7,1.6,Versicolour
4.9,2.4,3.3,1,Versicolour
6.6,2.9,4.6,1.3,Versicolour
5.2,2.7,3.9,1.4,Versicolour
5,2,3.5,1,Versicolour
5.9,3,4.2,1.5,Versicolour
6,2.2,4,1,Versicolour
6.1,2.9,4.7,1.4,Versicolour
5.6,2.9,3.6,1.3,Versicolour
6.7,3.1,4.4,1.4,Versicolour
5.6,3,4.5,1.5,Versicolour
5.8,2.7,4.1,1,Versicolour
6.2,2.2,4.5,1.5,Versicolour
5.6,2.5,3.9,1.1,Versicolour
5.9,3.2,4.8,1.8,Versicolour
6.1,2.8,4,1.3,Versicolour
6.3,2.5,4.9,1.5,Versicolour
6.1,2.8,4.7,1.2,Versicolour
6.4,2.9,4.3,1.3,Versicolour
6.6,3,4.4,1.4,Versicolour
6.8,2.8,4.8,1.4,Versicolour
6.7,3,5,1.7,Versicolour
6,2.9,4.5,1.5,Versicolour
5.7,2.6,3.5,1,Versicolour
5.5,2.4,3.8,1.1,Versicolour
5.5,2.4,3.7,1,Versicolour
5.8,2.7,3.9,1.2,Versicolour
6,2.7,5.1,1.6,Versicolour
5.4,3,4.5,1.5,Versicolour
6,3.4,4.5,1.6,Versicolour
6.7,3.1,4.7,1.5,Versicolour
6.3,2.3,4.4,1.3,Versicolour
5.6,3,4.1,1.3,Versicolour
5.5,2.5,4,1.3,Versicolour
5.5,2.6,4.4,1.2,Versicolour
6.1,3,4.6,1.4,Versicolour
5.8,2.6,4,1.2,Versicolour
5,2.3,3.3,1,Versicolour
5.6,2.7,4.2,1.3,Versicolour
5.7,3,4.2,1.2,Versicolour
5.7,2.9,4.2,1.3,Versicolour
6.2,2.9,4.3,1.3,Versicolour
5.1,2.5,3,1.1,Versicolour
5.7,2.8,4.1,1.3,Versicolour
6.3,3.3,6,2.5,Virginica
5.8,2.7,5.1,1.9,Virginica
7.1,3,5.9,2.1,Virginica
6.3,2.9,5.6,1.8,Virginica
6.5,3,5.8,2.2,Virginica
7.6,3,6.6,2.1,Virginica
4.9,2.5,4.5,1.7,Virginica
7.3,2.9,6.3,1.8,Virginica
6.7,2.5,5.8,1.8,Virginica
7.2,3.6,6.1,2.5,Virginica
6.5,3.2,5.1,2,Virginica
6.4,2.7,5.3,1.9,Virginica
6.8,3,5.5,2.1,Virginica
5.7,2.5,5,2,Virginica
5.8,2.8,5.1,2.4,Virginica
6.4,3.2,5.3,2.3,Virginica
6.5,3,5.5,1.8,Virginica
7.7,3.8,6.7,2.2,Virginica
7.7,2.6,6.9,2.3,Virginica
6,2.2,5,1.5,Virginica
6.9,3.2,5.7,2.3,Virginica
5.6,2.8,4.9,2,Virginica
7.7,2.8,6.7,2,Virginica
6.3,2.7,4.9,1.8,Virginica
6.7,3.3,5.7,2.1,Virginica
7.2,3.2,6,1.8,Virginica
6.2,2.8,4.8,1.8,Virginica
6.1,3,4.9,1.8,Virginica
6.4,2.8,5.6,2.1,Virginica
7.2,3,5.8,1.6,Virginica
7.4,2.8,6.1,1.9,Virginica
7.9,3.8,6.4,2,Virginica
6.4,2.8,5.6,2.2,Virginica
6.3,2.8,5.1,1.5,Virginica
6.1,2.6,5.6,1.4,Virginica
7.7,3,6.1,2.3,Virginica
6.3,3.4,5.6,2.4,Virginica
6.4,3.1,5.5,1.8,Virginica
6,3,4.8,1.8,Virginica
6.9,3.1,5.4,2.1,Virginica
6.7,3.1,5.6,2.4,Virginica
6.9,3.1,5.1,2.3,Virginica
5.8,2.7,5.1,1.9,Virginica
6.8,3.2,5.9,2.3,Virginica
6.7,3.3,5.7,2.5,Virginica
6.7,3,5.2,2.3,Virginica
6.3,2.5,5,1.9,Virginica
6.5,3,5.2,2,Virginica
6.2,3.4,5.4,2.3,Virginica
5.9,3,5.1,1.8,Virginica