add new files

build/lib.linux-x86_64-3.11/fABBA/__init__.py

@@ -10,7 +10,7 @@
     from .separate.aggregation import aggregate
 
 
-__version__ = '1.2.7'
+__version__ = '1.2.8'
 from .load_datasets import load_images, loadData
 from .fabba import (image_compress, image_decompress, ABBAbase, ABBA,
                                       get_patches, patched_reconstruction, fABBA,

build/lib.linux-x86_64-3.11/fABBA/fabba.py

@@ -275,6 +275,7 @@ def _inverse_compress(pieces, start):
 
 
 
+
 def symbolsAssign(clusters, alphabet_set=0):
     """
     Automatically assign symbols to different groups, start with '!'
@@ -295,6 +296,8 @@ def symbolsAssign(clusters, alphabet_set=0):
     labels to symbols, repectively.
 
     """
+    clusters = pd.Series(clusters)
+    N = len(clusters.unique())
 
     if alphabet_set == 0:
         alphabets = ['A','a','B','b','C','c','D','d','E','e',
@@ -311,7 +314,7 @@ def symbolsAssign(clusters, alphabet_set=0):
                     'w', 'x', 'y', 'z']
 
     elif isinstance(alphabet_set, list):
-        if len(clusters) <= len(alphabet_set):
+        if N <= len(alphabet_set):
             alphabets = alphabet_set
         else:
             raise ValueError("Please ensure the length of ``alphabet_set`` is greatere than ``clusters``.")
@@ -323,9 +326,6 @@ def symbolsAssign(clusters, alphabet_set=0):
                     'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V',
                     'W', 'X', 'Y', 'Z']
 
-    clusters = pd.Series(clusters)
-    N = len(clusters.unique())
-
     cluster_sort = [0] * N 
     counter = collections.Counter(clusters)
     for ind, el in enumerate(counter.most_common()):
@@ -340,6 +340,7 @@ def symbolsAssign(clusters, alphabet_set=0):
     string = alphabets[clusters]
     return string, alphabets
 
+
 
 
 

build/lib.linux-x86_64-3.11/fABBA/jabba/jabba.py

@@ -52,6 +52,8 @@ def symbolsAssign(clusters, alphabet_set=0):
     labels to symbols, repectively.
 
     """
+    clusters = pd.Series(clusters)
+    N = len(clusters.unique())
 
     if alphabet_set == 0:
         alphabets = ['A','a','B','b','C','c','D','d','E','e',
@@ -68,7 +70,7 @@ def symbolsAssign(clusters, alphabet_set=0):
                     'w', 'x', 'y', 'z']
 
     elif isinstance(alphabet_set, list):
-        if len(clusters) <= len(alphabet_set):
+        if N <= len(alphabet_set):
             alphabets = alphabet_set
         else:
             raise ValueError("Please ensure the length of ``alphabet_set`` is greatere than ``clusters``.")
@@ -80,9 +82,6 @@ def symbolsAssign(clusters, alphabet_set=0):
                     'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V',
                     'W', 'X', 'Y', 'Z']
 
-    clusters = pd.Series(clusters)
-    N = len(clusters.unique())
-
     cluster_sort = [0] * N 
     counter = collections.Counter(clusters)
     for ind, el in enumerate(counter.most_common()):

fABBA.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: fABBA
-Version: 1.2.7
+Version: 1.2.8
 Summary: An efficient aggregation method for the symbolic representation of temporal data
 Home-page: https://github.com/nla-group/fABBA
 Author: Xinye Chen, Stefan Güttel

fABBA/.ipynb_checkpoints/__init__-checkpoint.py

@@ -0,0 +1,26 @@
+try:
+    try:
+        from .separate.aggregation_cm import aggregate
+    except:
+        from .separate.aggregation_c import aggregate
+
+    from .extmod.inverse_tc import *
+
+except (ModuleNotFoundError, ValueError):
+    from .separate.aggregation import aggregate
+
+
+__version__ = '1.2.8'
+from .load_datasets import load_images, loadData
+from .fabba import (image_compress, image_decompress, ABBAbase, ABBA,
+                                      get_patches, patched_reconstruction, fABBA,
+                                      symbolsAssign, fillna)
+
+from .fabba import _compress as compress
+from .fabba import _inverse_compress as inverse_compress
+from .digitization import digitize, inverse_digitize, quantize, wcss, calculate_group_centers
+
+from .jabba.jabba import fastABBA, JABBA
+from .jabba import jabba
+from .jabba import preprocessing
+from .jabba.fkmns import sampledKMeansInter as fkmeans