Write a Python 3 program called pa1.py that implements a three-class linear classifier using...

Question

Write a Python 3 program called pa1.py that implements a three-class linear classifier

using the following method:

Training (using the training data set):

Compute the centroid of each class (e.g. A, B, and C).
Construct a discriminant function between each pair of classes (e.g. A/B, B/C, and

A/C), halfway between the two centroids and orthogonal to the line connecting the two centroids. This is the “basic linear classifier” that we have discussed.

pa1-mkzkvqjh.pdf pa1-lkfvrpb2.py evaluate-dqcfo2ki.py testing1-4oqfyatz.txt training1-tepilkog.txt

Pushpendra · Accepted Answer

#!/usr/bin/env python
# coding: utf-8
# In[89]:
pwd
# In[4]:
import pandas as pd
import numpy as np
# In[6]:
df1=pd.read_excel("training_LDA.xlsx")
df1.head()
# In[8]:
df2=pd.read_excel("testing_LDA.xlsx")
df2.head()
# # Centroid
# In[9]:
from sklearn.cluster import KMeans
from sklearn import metrics
import numpy as np
print("*****Centroid for A******")
x1=df1.iloc[:,0:1].values
K =1
kmeans_model = KMeans(n_clusters=K).fit(x1)
print(kmeans_model.cluster_centers_)
centers = np.array(kmeans_model.cluster_centers_)
# In[10]:
print("*****Centroid for B******")
x2=df1.iloc[:,1:2].values
K =1
kmeans_model = KMeans(n_clusters=K).fit(x2)
print(kmeans_model.cluster_centers_)
centers = np.array(kmeans_model.cluster_centers_)
# In[11]:
print("*****Centroid for C******")
x3=df1.iloc[:,2:3].values
K =1
kmeans_model = KMeans(n_clusters=K).fit(x3)
print(kmeans_model.cluster_centers_)
centers = np.array(kmeans_model.cluster_centers_)
# # Analysis
# In[5]:
X_train=df1.drop(('Y'),axis=1)
X_train
# In[6]:
Y_train=df1.pop('Y')
Y_train
# In[9]:
X_test=df2.drop(('Y'),axis=1)
X_test
# In[10]:
Y_test=df2.pop('Y')
Y_test
# In[11]:
#Import svm model
from sklearn import svm
#Create a svm Classifier
clf = svm.SVC(kernel='linear') # Linear Kernel
#Train the model using the training sets
clf.fit(X_train,

PA1-5extra.pdf: Accuracy =( TP +TN)/total. TPR= TP/(TP +FN). PA1-5data.html: Shown below EXTRA INFORMATION: You can use numpy. You can use numpy to conduct dot operation. numpy.dot(a,b) will be dot...

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