Awesome
ios-KRKNN
Machine Learning (マシンラーニング) in this project, it implemented KNN(k-Nearest Neighbor) that classification method. It can be used on products recommendation (おすすめの商品), user behavior analysis (ユーザーの行動分析), data mining (データマイニング) and data analysis (データ分析).
Podfile
platform :ios, '7.0'
pod "KRKNN", "~> 1.1.4"
How to use
To import the KRKNN.h file
#import "KRKNN.h"
Normal Sample
Current implemented distance of kernel methods are :
KRKNNKernelEuclidean
KRKNNKernelCosineSimilarity
KRKNNKernelRBF
KRKNN *_knn = [KRKNN sharedInstance];
// To use Cosine Simlarity or Euclidean that will have different results, suggests to use Cosine Similarity
_knn.kernel = KRKNNKernelEuclidean;
// Features are the wordings appeared times on a paper as below :
// Apple, OS, Mobile, Taiwan, Japan, Developer
[_knn addFeatures:@[@20, @9, @1, @3, @6, @2]
group:@"Apple Fans"
identifier:@"Smith"];
[_knn addFeatures:@[@52, @32, @18, @7, @0, @1]
group:@"Apple Fans"
identifier:@"John"];
[_knn addFeatures:@[@2, @20, @15, @5, @9, @16]
group:@"Linux Fans"
identifier:@"James"];
[_knn addFeatures:@[@7, @11, @2, @12, @1, @0]
group:@"Linux Fans"
identifier:@"Terry"];
[_knn addFeatures:@[@20, @8, @3, @21, @8, @25]
group:@"Android Fans"
identifier:@"Sam"];
[_knn addFeatures:@[@2, @30, @8, @6, @33, @29]
group:@"Android Fans"
identifier:@"Amy"];
// If you have batch-patterns (ex : 10 patterns) wanna classify that you could use for-loop to run the classify function,
// In this demo that classifies a pattern by once time.
[_knn classifyFeatures:@[@20, @1, @10, @2, @12, @3]
identifier:@"Bob"
kNeighbor:[_knn chooseK]
completion:^(BOOL success, NSString *ownGroup, NSInteger neighborCount, NSArray *allPatterns) {
if( success )
{
NSLog(@"ownGroup : %@", ownGroup);
NSLog(@"neighborCount : %li", neighborCount);
NSLog(@"allPatterns : %@", allPatterns);
// Looping that all classified patterns.
for( KRKNNPattern *pattern in allPatterns )
{
NSLog(@"pattern id is 「%@」 and group name is 「%@」", pattern.identifier, pattern.groupName);
}
}
}];
Version
V1.1.4
License
MIT.