Abhishek2019: Basic Machine Learing

Kth Nearest Neighbour (ML Algorithm)/KNN.py

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+from numpy import *
+import operator
+
+
+def classify(x,dataset,labels,k):
+ datasetsize=dataset.shape[0]
+ diffmat=tile(x,(datasetsize,1))-dataset
+ sqdiffmat=diffmat**2
+ sqdistances=sqdiffmat.sum(axis=1)
+ distances=sqdistances**0.5
+ sorteddistindices=distances.argsort()
+ classcount={}
+ for i in range(k):
+
+ voteIlabel=labels[sorteddistindices[i]]
+ classcount[voteIlabel]=classcount.get(voteIlabel,0) +1
+
+ sortedclasscount=sorted(classcount.items(),key=operator.itemgetter(1),reverse=True)
+ return sortedclasscount[0][0]
+
+def autonorm(dataset):
+ minval=dataset.min(0)
+ maxval=dataset.max(0)
+ ranges=maxval-minval
+ normdataset=zeros(shape(dataset))
+ m=dataset.shape[0]
+ normdataset=dataset-tile(minval,(m,1))
+ normdataset=normdataset/tile(ranges,(m,1))
+ return normdataset,ranges,minval
+
+
+def file2matrix(filename):
+ fr=open(filename)
+ numberoflines=len(fr.readlines())
+ returnmat=zeros((numberoflines,7))
+ classLabelvec=[]
+ fr=open(filename)
+ index=0
+ for line in fr.readlines():
+ line=line.strip()
+ listfromline=line.split(",")
+ returnmat[index,:]=listfromline[1:8]
+ classLabelvec.append((listfromline[0]))
+ index+=1
+ return returnmat,classLabelvec
+
+def classperson():
+ print("Abalone is a common name for any of a group of small to very large sea snails, marine gastropod molluscs in the family Haliotidae.")
+ print("\n")
+ print("From certain input about Abalone this algorithm will predict whether Abalone is \nM-> Male,\nF->Female or\nI->Infant ")
+ print("\n")
+ print("Accuracy of this algorithm is 65 to 70%")
+ input1 =float(input("enter length of Abalone"))
+ input2 = float(input("enter diameter"))
+ input3 = float(input("enter height"))
+ input4 = float(input("enter whole weight"))
+ input5 = float(input("enter shucked weight"))
+ input6 = float(input("enter viscera weight"))
+ input7 = float(input("enter shell weight"))
+
+
+ datamat, lab = file2matrix("KNN_support/abalone.data")
+ normmat, ran, minval = autonorm(datamat)
+
+ inarr=array([input1,input2,input3,input4,input5,input6,input7])
+ classresult=classify((inarr-minval)/ran,normmat,lab,3)
+ print("\n")
+ print(classresult)
+
+
+classperson()