Updated V1.3

Added methods to save trained centers.

Author: Kalvar

FCM/KRFuzzyCMeans.h

@@ -47,6 +47,8 @@ typedef void(^KRFuzzyCMeansPerIteration)(NSInteger times, NSArray *clusters, NSA
 @property (nonatomic, assign) NSInteger maxIteration;
 //FCM 公式 m 參數
 @property (nonatomic, assign) NSInteger m;
+//訓練完是否自動儲存
+@property (nonatomic, assign) BOOL doneThenSave;
 
 @property (nonatomic, assign) KRFuzzyCMeansDistanceFormula distanceFormula;
 
@@ -55,12 +57,16 @@ typedef void(^KRFuzzyCMeansPerIteration)(NSInteger times, NSArray *clusters, NSA
 
 +(instancetype)sharedFCM;
 -(instancetype)init;
+
 -(void)clusterWithCompletion:(KRFuzzyCMeansClusteringCompletion)_completion perIteration:(KRFuzzyCMeansPerIteration)_generation;
 -(void)clusterWithCompletion:(KRFuzzyCMeansClusteringCompletion)_completion;
 -(void)cluster;
 -(void)directClusterPatterns:(NSArray *)_directPatterns;
--(void)addCentralX:(float)_x y:(float)_y;
+
+-(void)addCenters:(NSArray *)_theCenters;
 -(void)addPatterns:(NSArray *)_theSets;
+
+-(void)recallCenters;
 -(void)printResults;
 
 #pragma --mark Blocks

FCM/KRFuzzyCMeans.m

@@ -7,13 +7,16 @@
 //
 
 #import "KRFuzzyCMeans.h"
+#import "KRFuzzyCMeansSaves.h"
 
 @interface KRFuzzyCMeans()
 
 //上一次運算的群聚中心點集合
 @property (nonatomic, strong) NSMutableArray *lastCenters;
 //當前的迭代數
 @property (nonatomic, assign) NSInteger currentIteration;
+//儲存訓練好的群心和其它數據
+@property (nonatomic, strong) KRFuzzyCMeansSaves *trainedSaves;
 
 @end
 
@@ -75,21 +78,40 @@ -(float)_distanceX1:(NSArray *)_x1 x2:(NSArray *)_x2
 
 @end
 
+@implementation KRFuzzyCMeans(fixSaves)
+
+-(void)_saveCenters
+{
+    [self.trainedSaves saveCenters:self.centrals];
+}
+
+-(NSArray *)_fetchSavedCenters
+{
+    return [self.trainedSaves fetchCenters];
+}
+
+@end
+
 @implementation KRFuzzyCMeans(fixClusters)
 
 -(void)_doneClustering
 {
+    if( self.doneThenSave )
+    {
+        [self _saveCenters];
+    }
+    
     if( self.clusterCompletion )
     {
         self.clusterCompletion(YES, self.results, self.centrals, self.currentIteration);
     }
 }
 
-//更新群聚形心
+// 更新群聚形心
 -(NSArray *)_updateCenters:(NSArray *)_centers memberships:(NSArray *)_xMembersips patterns:(NSArray *)_sources
 {
     /*
-     * @ Design thinks
+     * @ Design thinks before
      *   - 如果在上面的 for{} 區塊進行歸屬度累加計算，就必須同時考量存取 N 個 Array ( 裡面儲存對每一個形心做 SUM 的數值 ) 的效能，
      *     同時重複存取 Array 的次數越多，其 performance 就越差，會遠比使用單一 for{} 來重複做事要更沒效能，
      *     因此，這裡才改用單一 for{} 來做多重存取，以避免重複操作太多 arraies 造成效能大降的問題，
@@ -145,8 +167,8 @@ -(NSArray *)_updateCenters:(NSArray *)_centers memberships:(NSArray *)_xMembersi
     return _newCentrals;
 }
 
-//進行 Patterns 分類和歸屬度運算
-//return [0] = 分類好的群聚, [1] = 每一個 Pattern 對每一群心的歸屬度
+// 進行 Patterns 分類和歸屬度運算
+// return [0] = 分類好的群聚, [1] = 每一個 Pattern 對每一群心的歸屬度
 -(NSArray *)_clusteringPatterns:(NSArray *)_sources centers:(NSArray *)_centers
 {
     float _m                  = self.m;
@@ -221,7 +243,7 @@ -(NSArray *)_clusteringPatterns:(NSArray *)_sources centers:(NSArray *)_centers
     return @[_clusters, _xMembersips];
 }
 
-//取出跟上一次舊的群聚形心相比的最大誤差值
+// 取出跟上一次舊的群聚形心相比的最大誤差值
 -(float)_calculateErrorDistanceFromNewCenters:(NSArray *)_newCenters oldCenters:(NSArray *)_oldCenters
 {
     float _maxDistance     = 0.0f;
@@ -243,9 +265,7 @@ -(float)_calculateErrorDistanceFromNewCenters:(NSArray *)_newCenters oldCenters:
     return _maxDistance;
 }
 
-/*
- * @ 依照群聚中心點(形心) _centers 進行 _sources 群聚分類
- */
+// 依照群聚中心點(形心) _centers 進行 _sources 群聚分類
 -(void)_clusterSources:(NSArray *)_sources compareCenters:(NSArray *)_centers
 {
     if( [_centers count] > 0 )
@@ -254,7 +274,6 @@ -(void)_clusterSources:(NSArray *)_sources compareCenters:(NSArray *)_centers
         {
             self.m = 2;
         }
-        
         NSArray *_results     = [self _clusteringPatterns:_sources centers:_centers];
         NSArray *_clusters    = [_results objectAtIndex:0];
         NSArray *_membersips  = [_results objectAtIndex:1];
@@ -294,25 +313,11 @@ -(void)_clusterSources:(NSArray *)_sources compareCenters:(NSArray *)_centers
 
 @implementation KRFuzzyCMeans
 
-@synthesize centrals           = _centrals;
-@synthesize patterns            = _patterns;
-@synthesize results            = _results;
-@synthesize convergenceError   = _convergenceError;
-@synthesize maxIteration   = _maxIteration;
-@synthesize m                  = _m;
-
-@synthesize clusterCompletion  = _clusterCompletion;
-@synthesize perIteration     = _perIteration;
-
-@synthesize lastCenters        = _lastCenters;
-@synthesize currentIteration = _currentIteration;
-
 +(instancetype)sharedFCM
 {
     static dispatch_once_t pred;
     static KRFuzzyCMeans *_object = nil;
-    dispatch_once(&pred, ^
-    {
+    dispatch_once(&pred, ^{
         _object = [[KRFuzzyCMeans alloc] init];
     });
     return _object;
@@ -329,6 +334,7 @@ -(instancetype)init
         _convergenceError   = 0.001f;
         _maxIteration       = 5000;
         _m                  = 2;
+        _doneThenSave       = YES;
 
         _clusterCompletion  = nil;
         _perIteration       = nil;
@@ -337,6 +343,7 @@ -(instancetype)init
         _currentIteration   = 0;
 
         _distanceFormula    = KRFuzzyCMeansDistanceFormulaByEuclidean;
+        _trainedSaves       = [KRFuzzyCMeansSaves sharedInstance];
     }
     return self;
 }
@@ -406,7 +413,7 @@ -(void)clusterWithCompletion:(KRFuzzyCMeansClusteringCompletion)_completion perI
 {
     _clusterCompletion  = _completion;
     _perIteration       = _generation;
-    _currentIteration = 0;
+    _currentIteration   = 0;
     [self _clusterSources:_patterns compareCenters:_centrals];
 }
 
@@ -420,16 +427,27 @@ -(void)cluster
     [self clusterWithCompletion:nil];
 }
 
--(void)addCentralX:(float)_x y:(float)_y
+-(void)addCenters:(NSArray *)_theCenters
 {
-    [_centrals addObject:@[[NSNumber numberWithFloat:_x], [NSNumber numberWithFloat:_y]]];
+    [_centrals addObject:_theCenters];
 }
 
 -(void)addPatterns:(NSArray *)_theSets
 {
     [_patterns addObjectsFromArray:_theSets];
 }
 
+// Recalling trained centers which saved in KRFuzzyCMeansSaves
+-(void)recallCenters
+{
+    NSArray *_savedCenters = [self _fetchSavedCenters];
+    if( nil != _savedCenters )
+    {
+        [_centrals removeAllObjects];
+        [_centrals addObjectsFromArray:_savedCenters];
+    }
+}
+
 -(void)printResults
 {
     NSLog(@"=================== printResults (Start) =================\n\n\n");
@@ -452,7 +470,7 @@ -(void)setClusterCompletion:(KRFuzzyCMeansClusteringCompletion)_theBlock
 
 -(void)setPerIteration:(KRFuzzyCMeansPerIteration)_theBlock
 {
-    _perIteration    = _theBlock;
+    _perIteration = _theBlock;
 }
 
 @end

FCM/KRFuzzyCMeansSaves.h

@@ -0,0 +1,20 @@
+//
+//  KRFuzzySaves.h
+//  KRFuzzyCMeans
+//
+//  Created by Kalvar Lin on 2015/11/15.
+//  Copyright © 2015年 Kalvar. All rights reserved.
+//
+
+#import <Foundation/Foundation.h>
+
+@interface KRFuzzyCMeansSaves : NSObject
+
++(instancetype)sharedInstance;
+-(instancetype)init;
+
+-(void)saveCenters:(NSArray *)_centers;
+-(NSArray *)fetchCenters;
+-(void)deleteCenters;
+
+@end

FCM/KRFuzzyCMeansSaves.m

@@ -0,0 +1,81 @@
+//
+//  KRFuzzySaves.h
+//  KRFuzzyCMeans
+//
+//  Created by Kalvar Lin on 2015/11/15.
+//  Copyright © 2015年 Kalvar. All rights reserved.
+//
+
+#import "KRFuzzyCMeansSaves.h"
+
+static NSString *kKRFuzzyCMeansSavesCentersKey = @"kKRFuzzyCMeansSavesCentersKey";
+
+@implementation KRFuzzyCMeansSaves (fixSaves)
+
+-(NSUserDefaults *)_userDefaults
+{
+    return [NSUserDefaults standardUserDefaults];
+}
+
+-(void)_synchronize
+{
+    [[self _userDefaults] synchronize];
+}
+
+-(instancetype)_defaultValueForKey:(NSString *)_key
+{
+    return [[self _userDefaults] objectForKey:_key];
+}
+
+-(void)_saveDefaultValue:(NSArray *)_value forKey:(NSString *)_forKey
+{
+    [[self _userDefaults] setObject:_value forKey:_forKey];
+    [self _synchronize];
+}
+
+-(void)_removeValueForKey:(NSString *)_key
+{
+    [[self _userDefaults] removeObjectForKey:_key];
+    [self _synchronize];
+}
+
+@end
+
+@implementation KRFuzzyCMeansSaves
+
++(instancetype)sharedInstance
+{
+    static dispatch_once_t pred;
+    static KRFuzzyCMeansSaves *_object = nil;
+    dispatch_once(&pred, ^{
+        _object = [[KRFuzzyCMeansSaves alloc] init];
+    });
+    return _object;
+}
+
+-(instancetype)init
+{
+    self = [super init];
+    if( self )
+    {
+        
+    }
+    return self;
+}
+
+-(void)saveCenters:(NSArray *)_centers
+{
+    [self _saveDefaultValue:[_centers copy] forKey:kKRFuzzyCMeansSavesCentersKey];
+}
+
+-(NSArray *)fetchCenters
+{
+    return (NSArray *)[self _defaultValueForKey:kKRFuzzyCMeansSavesCentersKey];
+}
+
+-(void)deleteCenters
+{
+    [self _removeValueForKey:kKRFuzzyCMeansSavesCentersKey];
+}
+
+@end

KRFuzzyCMeans.podspec

@@ -1,6 +1,6 @@
 Pod::Spec.new do |s|
   s.name         = "KRFuzzyCMeans"
-  s.version      = "1.2"
+  s.version      = "1.3"
   s.summary      = "Fuzzy C-Means is clustering algorithm combined fuzzy theory on Machine Learning."
   s.description  = <<-DESC
                    KRFuzzyCMeans has implemented Fuzzy C-Means (FCM) the fuzzy (ファジー理論) clustering / classification algorithm (クラスタリング分類) in Machine Learning (マシンラーニング). It could be used in data mining (データマイニング) and image compression (画像圧縮).

KRFuzzyCMeans.xcodeproj/project.pbxproj

@@ -7,6 +7,7 @@
 	objects = {
 
 /* Begin PBXBuildFile section */
+		8E3F30DA1BF83278004FEC88 /* KRFuzzyCMeansSaves.m in Sources */ = {isa = PBXBuildFile; fileRef = 8E3F30D91BF83278004FEC88 /* KRFuzzyCMeansSaves.m */; settings = {ASSET_TAGS = (); }; };
 		8E44AE621AD1A26D00A3F154 /* main.m in Sources */ = {isa = PBXBuildFile; fileRef = 8E44AE611AD1A26D00A3F154 /* main.m */; };
 		8E44AE651AD1A26D00A3F154 /* AppDelegate.m in Sources */ = {isa = PBXBuildFile; fileRef = 8E44AE641AD1A26D00A3F154 /* AppDelegate.m */; };
 		8E44AE681AD1A26D00A3F154 /* ViewController.m in Sources */ = {isa = PBXBuildFile; fileRef = 8E44AE671AD1A26D00A3F154 /* ViewController.m */; };
@@ -28,6 +29,8 @@
 /* End PBXContainerItemProxy section */
 
 /* Begin PBXFileReference section */
+		8E3F30D81BF83278004FEC88 /* KRFuzzyCMeansSaves.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; path = KRFuzzyCMeansSaves.h; sourceTree = "<group>"; };
+		8E3F30D91BF83278004FEC88 /* KRFuzzyCMeansSaves.m */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.objc; path = KRFuzzyCMeansSaves.m; sourceTree = "<group>"; };
 		8E44AE5C1AD1A26D00A3F154 /* KRFuzzyCMeans.app */ = {isa = PBXFileReference; explicitFileType = wrapper.application; includeInIndex = 0; path = KRFuzzyCMeans.app; sourceTree = BUILT_PRODUCTS_DIR; };
 		8E44AE601AD1A26D00A3F154 /* Info.plist */ = {isa = PBXFileReference; lastKnownFileType = text.plist.xml; path = Info.plist; sourceTree = "<group>"; };
 		8E44AE611AD1A26D00A3F154 /* main.m */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.objc; path = main.m; sourceTree = "<group>"; };
@@ -126,6 +129,8 @@
 		8E6E14E21AFD29FC000F743C /* FCM */ = {
 			isa = PBXGroup;
 			children = (
+				8E3F30D81BF83278004FEC88 /* KRFuzzyCMeansSaves.h */,
+				8E3F30D91BF83278004FEC88 /* KRFuzzyCMeansSaves.m */,
 				8E6E14E31AFD29FC000F743C /* KRFuzzyCMeans.h */,
 				8E6E14E41AFD29FC000F743C /* KRFuzzyCMeans.m */,
 			);
@@ -235,6 +240,7 @@
 				8E44AE681AD1A26D00A3F154 /* ViewController.m in Sources */,
 				8E44AE651AD1A26D00A3F154 /* AppDelegate.m in Sources */,
 				8E6E14E51AFD29FC000F743C /* KRFuzzyCMeans.m in Sources */,
+				8E3F30DA1BF83278004FEC88 /* KRFuzzyCMeansSaves.m in Sources */,
 				8E44AE621AD1A26D00A3F154 /* main.m in Sources */,
 			);
 			runOnlyForDeploymentPostprocessing = 0;