4.2 Nearest Neighbor Rule classification (k-nearest Neighbor) KNN algorithm application

1 Data Set Description: Iris

150 instances sepals length, sepals width, petal length, petal width(sepal length, sepal width, petal length and petal width) Category:Iris Setosa, Iris versicolor, Iris virginica.  

  2. Use Python's machine learning library sklearn: sklearnexample.py from sklearn import neighborsfrom sklearn import datasets  KNN = neighbors. Kneighborsclassifier ()   iris = Datasets.load_iris ()   print Iris knn.fit (Iris.data, Iris.target)  predictedlabel = Knn.predict ([[0.1, 0.2, 0.3, 0.4]])  print predictedlabel     3. KNN implementation implementation:  # Example of KNN implemented from Scratch in Python import csvimport randomimport ma Thimport operator def loaddataset (filename, split, trainingset=[], testset=[]):    with open (filename, ' RB ') as csvfile:        lines = Csv.reader (csvfile)         DataSet = list (lines) &N Bsp       for x in range (len (DataSet)-1):            for y in range (4):  &NB Sp             dataset[x][y] = float (dataset[x][y])             IF Random. Random () < split:                trainingset.append (Dataset[x])     & nbsp       else:                testset.append (dataset[x])   d EF euclideandistance (Instance1, Instance2, length):    distance = 0    for x in range (length):  & nbsp     Distance + = POW ((instance1[x]-instance2[x]), 2)     return math.sqrt (distance)  def Getneigh Bors (Trainingset, Testinstance, K):    distances = []    length = Len (testinstance) -1    for X in range (len (trainingset)):        dist = euclideandistance (testinstance, trainingset[x], length) & nbsp       Distances.append ((trainingset[x], Dist)     Distances.sort (Key=operator.itemgetter (1) )     Neighbors = []    for x in range (k):        neighbors.append (distances[x][0])     REturn neighbors def getResponse (neighbors):    classvotes = {}    for x in range (len (neighbors)) :        response = neighbors[x][-1]        If response in classvotes:  &nbsp ;         Classvotes[response] + + 1        else:          & nbsp Classvotes[response] = 1    Sortedvotes = sorted (Classvotes.iteritems (), Key=operator.itemgetter (1), reverse =true)     return sortedvotes[0][0] def getaccuracy (testset, predictions):    correct = 0    for x in range (len (testset)):        if testset[x][-1] = = predictions[x]:      & nbsp     Correct + = 1    return (Correct/float (Len (testset))) * 100.0    def Main ():  & nbsp # Prepare data    trainingset=[]    testset=[]    split = 0.67    Loaddataset (R ' D: \maiziedu\dEeplearningbasics_machinelearning\datasets\iris.data.txt ', Split, Trainingset, Testset)     print ' Train set : ' + repr (len (trainingset))     print ' Test set: ' + repr (len (testset))     # Generate PREDICTIONS&NBSP ;   predictions=[]    k = 3    for x in range (len (testset)):        Neighbors = Getneighbors (Trainingset, testset[x], K)         result = GetResponse (neighbors)       & nbsp Predictions.append (Result)         print (' > predicted= ' + repr (Result) + ', actual= ' + repr (testset[ X][-1])     accuracy = getaccuracy (Testset, predictions)     print (' accuracy: ' + repr (accuracy) + '% ')     main ()

4.2 Nearest Neighbor Rule classification (k-nearest Neighbor) KNN algorithm application