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Indian_pines_knnc.py
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Indian_pines_knnc.py
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import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn import decomposition
from sklearn import datasets
# load dataset into Pandas DataFrame
df = pd.read_csv("D:\Python_programs\ML\Complete_Data_.csv")
from sklearn.preprocessing import StandardScaler
n=[]
ind=[]
for i in range(200):
n.append(i+1)
for i in range(200):
ind.append('px'+str(n[i]))
features = ind
X = df.loc[:, features].values
# Separating out the target
Y = df.loc[:,['target']].values
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
X, Y, test_size = 0.3, random_state = 100)
y_train=y_train.ravel()
y_test=y_test.ravel()
#classifier.fit(X_train, y_train.squeeze())
from sklearn.neighbors import KNeighborsClassifier # FOR K=13 ,IT HAS ACCURACY AROUND 72.7488902980
from sklearn import metrics
import time
#model = KNeighborsClassifier()
model=KNeighborsClassifier(n_neighbors =13, weights='uniform', algorithm='auto')
model.fit(X_train, y_train)
start = time.time()
Yhat = model.predict(X_test)
end = time.time()
print('Time Taken For Classification is :',(end - start))
print("Accuracy :",metrics.accuracy_score(Yhat, y_test)*100)
print('\n','*'*11,'Accuracy of INDIAN-PINES Dataset Before PCA','*'*11)
print('*'*11,' Classifier : K-NEAREST NEIGHBOUR ','*'*11)
for K in range(25):
K_value = K+1
neigh = KNeighborsClassifier(n_neighbors = K_value, weights='uniform', algorithm='auto')
neigh.fit(X_train, y_train)
y_pred = neigh.predict(X_test)
print ("Accuracy is :%1.10f"%(metrics.accuracy_score(y_test,y_pred)*100),"% ","for K-Value: %4d"%(K_value))