ML_algorithms

TP1/KNN.py

@@ -1,11 +1,13 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 """
-Created on Sat Dec 11 15:40:46 2021
+Created on Wed Dec 15 19:07:59 2021
 
 @author: chouiya
 """
 
+
+
 from time import time
 
 import numpy as np
@@ -105,52 +107,4 @@ for s in change_percent:
     
     
     
-    
+    
-    
-    
-    
-    
-    
-#*******************Variation de la taille de l'echantillon training *****************
-
-tab_sample=[5000,6000,8000,10000,20000,50000,70000]
-for x in range(len(tab_sample)):
-    index= np.random.randint(70000, size=tab_sample[x])
-    data = mnist.data[index]
-    target = mnist.target[index]
-    clf = neighbors.KNeighborsClassifier(10)
-    xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=0.8)
-    clf.fit(xtrain,ytrain)
-    prediction = clf.predict(xtest)
-    score = clf.score(xtest, ytest)
-    
-    print("sample size= {} , accuracy = {} ".format(tab_sample[x], score))
-
-#*****************Variation du type de la distance p *******
-xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=0.8, test_size=0.2)
-
-for i in range(0,3):
-    
-    tab_dist=["manhattan","euclidean", "minkowski"]
-    
-
-    clf = neighbors.KNeighborsClassifier(10, p=(i+1))
-    clf.fit(xtrain,ytrain)
-    prediction = clf.predict(xtrain)
-    score = clf.score(xtrain, ytrain)
-    print("type de distance : {}, Score: {}".format(tab_dist[i], score))
-    
-
-# ************** fixer n_jobs à 1 puis à -1 **********
-
-
-for i in [-1,1]:
-    clf = neighbors.KNeighborsClassifier(5,n_jobs=i)
-    clf.fit(xtrain, ytrain)
-    time_start = time()
-    prediction = clf.predict(xtest)
-    time_stop = time()
-    score = clf.score(xtest, ytest)
-    print("n_jobs : {}, Temps total : {}".format(i,time_stop-time_start))
-
-