ispn

README.md

@@ -82,7 +82,7 @@ print ( res_dfs )
 
 # Usage example 3 : Novel NLP sequence alignment
 
-Finding a word in a text is a simple and trivial problem in computer science. However matching a sequence of characters to a larger text segment is not. In this example you will be shown how to employ the impetuous text fitting procedure. The strength of the fit is conveyed via the returned score, higher being a stronger match between the two texts. This becomes costly for large texts and we thus break the text into segments and words. If there is a strong word to word match then the entire segment score is calculated. The off and main diagonal power terms refer to how to evaluate an string shift. Fortinbras and Faortinbraaks are probably the same word eventhough the latter has two character shifts in it. In this example both "requests" and "BeautifulSoup" are employed to parse internet text.
+Finding a word in a text is a simple and trivial problem in computer science. However matching a sequence of characters to a larger text segment is not. In this example you will be shown how to employ the impetuous text fitting procedure. The strength of the fit is conveyed via the returned score, higher being a stronger match between the two texts. This becomes costly for large texts and we thus break the text into segments and words. If there is a strong word to word match then the entire segment score is calculated. The off and main diagonal power terms refer to how to evaluate a string shift. Fortinbras and Faortinbraaks are probably the same word eventhough the latter has two character shifts in it. In this example both "requests" and "BeautifulSoup" are employed to parse internet text.
 
 ```
 import numpy as np

setup.py

@@ -5,7 +5,7 @@ with open("README.md", "r") as fh:
 
 setuptools.setup(
     name = "impetuous-gfa",
-    version = "0.44.1",
+    version = "0.44.5",
     author = "Richard Tjörnhammar",
     author_email = "richard.tjornhammar@gmail.com",
     description = "Impetuous Quantification, a Statistical Learning library for Humans : Alignments, Clustering, Enrichments and Group Analysis",

src/impetuous/quantification.py

@@ -95,7 +95,7 @@ def quantify_density_probability ( rpoints , cutoff = None ) :
     if not cutoff is None :
         resolution = 10. ; nbins = 100.
         #
-        # ONLY FOR ASSESING 
+        # ONLY FOR ASSESING
         h1,h2      = np.histogram(rpoints,bins=int(np.ceil(len(rpoints)/resolution)))
         bin_radius = 0.5 * ( h2[1:] + h2[:-1] )
         radial_density = np.cumsum( h1 )/np.sum( h1 ) # lt
@@ -117,7 +117,7 @@ def interpret_problem ( analyte_df , journal_df , formula , bVerbose=False ) :
     # THE JOURNAL_DF IS THE COARSE GRAINED DATA (THE MODEL)
     # THE ANALYTE_DF IS THE   FINE GRAINED DATA  (THE DATA)
     # THE FORMULA IS THE SEMANTIC DESCRIPTION OF THE PROBLEM
-    # 
+    #
     interaction_pairs = find_category_interactions ( formula.split('~')[1] )
     add_pairs = []
     if len( interaction_pairs )>0 :
@@ -143,7 +143,7 @@ def interpret_problem ( analyte_df , journal_df , formula , bVerbose=False ) :
         if encoding_df is None :
             encoding_df = add_df.T
         else :
-            encoding_df = pd.concat([ encoding_df.T , 
+            encoding_df = pd.concat([ encoding_df.T ,
                             journal_df.loc[ [ c.replace(' ','') for c in formula.split('~')[1].split('+') if not 'C(' in c] , : ] ]).T
     return ( encoding_df )
 
@@ -167,12 +167,12 @@ def calculate_alignment_properties ( encoding_df , quantx, quanty, scorex,
         exit(1)
     #
     # THESE ARE THE CATEGORICAL DESCRIPTORS
-    use_centroid_indices = [ i for i in range(len(encoding_df.columns.values)) if ( 
-                             encoding_df.columns.values[i] not in set( exclude_labels_from_centroids ) 
+    use_centroid_indices = [ i for i in range(len(encoding_df.columns.values)) if (
+                             encoding_df.columns.values[i] not in set( exclude_labels_from_centroids )
                            ) ]
     #
     use_centroids = list(  quanty[use_centroid_indices]  )
-    use_labels    = list( encoding_df.columns.values[use_centroid_indices] )  
+    use_labels    = list( encoding_df.columns.values[use_centroid_indices] )
     #
     if owner_by == 'tesselation' :
         transcript_owner = [ use_labels[ np.argmin([ np.sum((xw-cent)**2) for cent in use_centroids ])] for xw in quantx ]
@@ -219,7 +219,7 @@ def calculate_alignment_properties ( encoding_df , quantx, quanty, scorex,
     # print ( 'THE EQUIDISTANT 1D STATS' )
     corresponding_pvalue , corresponding_density , corresponding_radius = quantify_density_probability ( rpoints , cutoff = blur_cutoff )
     #
-    # print ( 'THE TWO XY 1D STATS' ) 
+    # print ( 'THE TWO XY 1D STATS' )
     corr_pvalue_0 , corr_density_0 = quantify_density_probability ( xpoints )
     corr_pvalue_1 , corr_density_1 = quantify_density_probability ( ypoints )
     #
@@ -275,7 +275,7 @@ def run_rpls_regression ( analyte_df , journal_df , formula ,
                         ) :
     inventors__ = "Richard Tjörnhammar (RT) and Edward Tjörnhammar"
     NOTE__ = "Edward Tjörnhammar (ET) early major contributor to this method. Inventors: "+inventors__+". RT is the main developer."
-    
+
     encoding_df = interpret_problem ( analyte_df , journal_df , formula , bVerbose = bVerbose )
     from sklearn.cross_decomposition import PLSRegression as PLS
 
@@ -292,7 +292,7 @@ def run_rpls_regression ( analyte_df , journal_df , formula ,
     			journal_df = journal_df, analyte_df = analyte_df , blur_cutoff = blur_cutoff ,
     			bVerbose = bVerbose, exclude_labels_from_centroids = exclude_labels_from_centroids ,
 			study_axii = study_axii , owner_by = owner_by )
-    
+
     return ( res_df )
 
 
@@ -329,7 +329,7 @@ def run_shape_alignment_clustering ( analyte_df , journal_df , formula, bVerbose
 	return ( res_df , clusters_df )
 
 def knn_clustering_alignment( P , Q ) :
-    
+
     NOTE_ = "This is just a standard kmeans in arbitrary dimensions that start out with centroids that have been shape aligned"
     ispanda = lambda P: 'pandas' in str(type(P)).lower()
     BustedPanda = lambda R : R.values if ispanda(R) else R
@@ -340,7 +340,7 @@ def knn_clustering_alignment( P , Q ) :
                     bReturnTransform = False ,
                     bShiftModel      = True  ,
                     bUnrestricted    = True  )
-    
+
     if ispanda ( Q ) :
         #
         # FOR DIAGNOSTIC PURPOSES
@@ -388,7 +388,7 @@ def run_shape_alignment_regression( analyte_df , journal_df , formula ,
 	centroids_df = pd.DataFrame ( centroids ,
 			index = encoding_df.columns ,
 			columns = encoding_df.index )
-			
+
 	xws = ifit.WeightsAndScoresOf( P )
 	yws = ifit.WeightsAndScoresOf( centroids )
 
@@ -402,7 +402,7 @@ def run_shape_alignment_regression( analyte_df , journal_df , formula ,
 			analyte_df = analyte_df.copy() , journal_df = journal_df.copy() ,
 			blur_cutoff = blur_cutoff , bVerbose = bVerbose,
 			exclude_labels_from_centroids = exclude_labels_from_centroids ,
-			study_axii = study_axii , owner_by = owner_by, synonyms=synonyms )		
+			study_axii = study_axii , owner_by = owner_by, synonyms=synonyms )
 	return ( res_df )
 
 
@@ -1131,11 +1131,24 @@ def assign_quality_measures( journal_df , result_dfs ,
 
     for label in [ col for col in result_dfs[0].columns if plabel in col[-2:] ] :
         result_dfs[0].loc[:, label[:-2]+',q'] = [ qvs[0] for qvs in qvalues( result_dfs[0].loc[:,label].values ) ]
-   
+
     results_lookup = calculate_rates ( journal_df , result_dfs[1] ,
                           formula , inference_label = inference_label )
     return( results_lookup )
 
+import math
+def isItPrime( N , M=None,p=None ) :
+    if p is None :
+        p = 1
+    if M is None :
+        M = N
+    if (M%(N-1))==0 or ((M%p)==0 and p>=2) :
+        return ( N==2 )
+    else :
+       if math.log(p) > math.log(M)*0.5:
+           return ( True )
+       return ( P1(N-1,M=M,p=p+1) )
+
 
 if __name__ == '__main__' :