Question
Hello, I am struggling to call kNearestNeighbors function on my training and testing matrices. I keep getting this error: What can I do about it
Hello, I am struggling to call kNearestNeighbors function on my training and testing matrices. I keep getting this error: What can I do about it ?
IndexError: index 57 is out of bounds for axis 0 with size 57
import numpy as np import pandas as pd from math import sqrt from csv import reader
#euclideanDistance: d(x, y)=sqrRootofsum(xi-yi)^2 def euclideanDistance(x, y): #calculate the distance between two rows in a dataset sum=0.0 #Initialize the sum to 0 for j in range(len(x)-1): #Iterate through all of the components in the arrays sum += (x[j]-y[j])**2 #get the difference between squareRoot = sqrt(sum) return squareRoot # def kNearestNeighbors(trainingData, testingDataRow, K): euclideanDistances=list() for i in trainingData: #for each row in the training data distance=euclideanDistance(testingDataRow, i) print(distance)#get the euclidean distance between the testing data row and the training data rows euclideanDistances.append((i, distance)) #add the traiing data and distances between the training data and the testing data row to the list euclideanDistances.sort(key=lambda x: x[1]) #sort the lists KNN=list() #create list of k nearest neighbors for j in range(K): KNN.append(euclideanDistances[j][0]) #add the smallest k euclidean distances to the list of k nearest neighbors return KNN
#classification def classification(trainingData, testingDataRow, K): KNN=[row[-1] for row in kNearestNeighbors(trainingData, testingDataRow, K)] #get the last column for all of the rows and put it in a vector classPrediction=max(set(KNN), key=KNN.count) #get the highest count of a classication from the set of classes return classPrediction
spam_test=pd.read_csv('spam_test.csv') #put the spam_test.csv file in variable spam_test spam_train=pd.read_csv('spam_train.csv') #put the spam_test.csv file in variable spam_train print(spam_test)
k_values=[1, 5, 11, 21, 41, 61, 81, 101, 201, 401] #put all of the different K values in a list index= [ 'f'+str(i) for i in range(1,58) ] #turn the feature labels into strings
testMatrix = spam_test[index].values trainMatrix = spam_train[index].values print(testMatrix) kNearestNeighbors(trainMatrix, testMatrix, 3)
Here is an example of my testing data csv file by the way if that helps in anyway
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Distributed Relational Database Architecture Connectivity Guide
Authors: Teresa Hopper
4th Edition
0133983064, 978-0133983067
