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Jupyter notebook zadacha1.ipynb

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Kernel: Python 3 (Ubuntu Linux)
import pandas as pd

import  numpy as np

import matplotlib.pyplot as plt

attr_names="""class
Alcohol
Malic acid
Ash
Alcalinity of ash  
Magnesium
Total phenols
Flavanoids
Nonflavanoid phenols
Proanthocyanins
Color intensity
Hue
OD280/OD315 of diluted wines
"""
attr_names = attr_names.split("\n")
print(attr_names)
['class', 'Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash ', 'Magnesium', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', 'Hue', 'OD280/OD315 of diluted wines', ''] 
iris_df = pd.read_csv('data/wine.data',sep =',',names=attr_names )

print(type(iris_df))
iris_df
<class 'pandas.core.frame.DataFrame'>
WARNING: Some output was deleted. 
iris_df.index
RangeIndex(start=0, stop=178, step=1)
X1=iris_df[attr_names[0]].values
X2=iris_df[attr_names[1]].values
X3=iris_df[attr_names[2]].values
X4=iris_df[attr_names[4]].values
X5=iris_df[attr_names[5]].values
X6=iris_df[attr_names[6]].values
X7=iris_df[attr_names[7]].values
X8=iris_df[attr_names[8]].values
X9=iris_df[attr_names[9]].values
X10=iris_df[attr_names[10]].values
X11=iris_df[attr_names[11]].values
X12=iris_df[attr_names[12]].values

X=np.c_[X1,X2,X3,X4,X5,X6, X7, X8,X9,X10,X11,X12]
print(X)
[[ 1. 14.23 1.71 ..., 5.64 1.04 3.92] [ 1. 13.2 1.78 ..., 4.38 1.05 3.4 ] [ 1. 13.16 2.36 ..., 5.68 1.03 3.17] ..., [ 3. 13.27 4.28 ..., 10.2 0.59 1.56] [ 3. 13.17 2.59 ..., 9.3 0.6 1.62] [ 3. 14.13 4.1 ..., 9.2 0.61 1.6 ]] 
Y = iris_df["class"].values
print(Y)
[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3] 
plt.figure(figsize=(5,5))
idx1 = (Y==1)
idx2 = (Y==2)
plt.scatter(X[:,1][idx1], X[:,2][idx1], s=36 ,c='r',label = 'класс 1')
plt.scatter(X[:,1][idx2], X[:,2][idx2], s=36 ,c='b',label = 'класс 2')
plt.xlabel(attr_names[1])  
plt.ylabel(attr_names[1])  
plt.grid(1)
plt.show()
Image in a Jupyter notebook


 List=[[10,1],[2,3],[4,5],[6,7],[8,9],[10,11],[11,10],[1,2],[3,4], [5,6],[7,8],[9,10]]
plt.figure(figsize = (18.0,12.0))
for k in range(0,len(List)):    
        i, j = List[k]
        idx1 = (Y==1)
        idx2 = (Y==2)  
        idx3 = (Y==3)   
        plt.subplot( 4, 3, k+1)
        plt.ylabel(attr_names[i])
        plt.xlabel(attr_names[j])
         
       
        
        
        plt.scatter(X[:,j][idx1], X[:, i][idx1], s=64,  c='r',label = 'класс 1')
        plt.scatter(X[:,j][idx2], X[:, i][idx2], s=64, c='b',label = 'класс 2')
        plt.scatter(X[:,j][idx3], X[:, i][idx3], s=64, c='g',label = 'класс 3')
plt.grid(1)
plt.tight_layout()
plt.show()
Image in a Jupyter notebook