bug fixed in the output of the Kolmogorov-Smirnov test

dream/KnowledgeExtraction/DistributionFitting.py

@@ -263,49 +263,62 @@ class DistFittest:
     def ks_test(self,data):             #Method that conducts the Kolmogorov-Smirnov statistical test and returns the best fitting distribution among the list of the available statistical distributions
         data=robjects.FloatVector(data)     #The given data sample changes into float vector in order to be handled by RPy2
         
+        self.Norm_kstest(data)
+        self.Lognorm_kstest(data)
+        self.Exp_kstest(data)
+        self.Pois_kstest(data)
+        self.Geom_kstest(data)
+        self.Logis_kstest(data)
+        self.Geom_kstest(data)
+        self.Weib_kstest(data)
+        self.Cauchy_kstest(data)
+        
         #Create a list with strings the available statistical distributions
         list1=('Normal','Lognormal','Exponential','Poisson', 'Geometric','Logistic','Gamma','Weibull', 'Cauchy')
+        list2=[]                                    #Create a list 
+        
         #try...except syntaxes to test if the Kolmogorov-Smirnov statistical tests can be conducted to the available distributions  
         try:
-            arga=self.Normtest[0][0]        #Create a variable that holds the D parameter of the Kolmogorov-Smirnov test in Normal distribution
+            list2.append(self.Normtest[0][0])        #It appends in list2 the D statistic parameter of the Kolmogorov-Smirnov test in Normal distribution
         except:
-            arga=''                         #in case of an error, the variable is left blank 
+            list2.append('')                         #in case of an error, it appends a blank point 
         try:    
-            argb=self.Longnormtest[0][0]    #Create a variable that holds the D parameter of the Kolmogorov-Smirnov test in Lognormal distribution
+            list2.append(self.Lognormtest[0][0])    #It appends in list2 the D statistic parameter of the Kolmogorov-Smirnov test in Lognormal distribution
         except:
-            argb=''    
+            list2.append('')                        # #in case of an error, it appends a blank point
         try:   
-            argd=self.Exptest[0][0]         #Create a variable that holds the D parameter of the Kolmogorov-Smirnov test in Exponential distribution
+            list2.append(self.Exptest[0][0])         #It appends in list2 the D statistic parameter of the Kolmogorov-Smirnov test in Exponential distribution
         except:
-            argd=''                         #in case of an error, the variable is left blank
+            list2.append('')                          #in case of an error, it appends a blank point
         try:
-            arge=self.Poistest[0][0]
+            list2.append(self.Poistest[0][0])
         except:
-            arge=''
+            list2.append('')
         try:
-            argf=self.Geomtest[0][0]
+            list2.append(self.Geomtest[0][0])
         except:         
-            argf=''
+            list2.append('')
         try:
-            argg=self.Logistest[0][0]
+            list2.append(self.Logistest[0][0])
         except:
-            argg=''   
+            list2.append('')   
         try:    
-            argh= self.Gamtest[0][0]
+            list2.append(self.Gamtest[0][0])
         except:
-            argh=''
+            list2.append('')
         try:   
-            argi=self.Weibtest[0][0]
+            list2.append(self.Weibtest[0][0])
         except:
-            argi=''   
+            list2.append('')   
         try:   
-            argj=self.Cauchytest[0][0]
+            list2.append(self.Cauchytest[0][0])
         except:
-            argj=''               
+            list2.append('')               
+        
         #Create a list with parameters the above D parameters calculated by the Kolmogorov-Smirnov tests in the available statistical distributions
-        list2=[arga,argb,argd,arge,argf,argg,argh,argi,argj]
         a=min(list2)     #Create a variable that holds the minimum value from the above list  
         b=list2.index(a) #Create a variable that holds the actual position  of the minimum value in the list
+        
         self=Distributions()
         #Set of if...elif syntax in order to get a Python dictionary with the best fitting statistical distribution and its parameters
         if list1[b]=='Normal':          #Check if in list's b position is the Normal distribution
@@ -350,3 +363,5 @@ class DistFittest:
  
  
  
+         
+         

dream/KnowledgeExtraction/ExcelOutput.py

@@ -31,7 +31,7 @@ from DistributionFitting import DistFittest
 #The ExcelOutput object export in Excel document both the calculated statistical measures and the distribution fitting test of a dataset 
 class Output(BasicStatisticalMeasures,DistFittest):
     
-    def PrintStatisticalMeasures(self,data):
+    def PrintStatisticalMeasures(self,data,fileName="StatisticalMeasuresResults.xls"):
         data=robjects.FloatVector(data)     #The given list changes into float vector in order to be handled by RPy2
         
         book = Workbook()                   
@@ -103,11 +103,11 @@ class Output(BasicStatisticalMeasures,DistFittest):
         
         ###selfnterquartselfle Range###
         sheet1.write(17,1,(self.IQR(data)))
-        book.save('StatisticalMeasuresResults.xls')  #Save the excel document 
+        book.save(fileName)  #Save the excel document 
         
     
     
-    def PrintDistributionFit(self,data):
+    def PrintDistributionFit(self,data,fileName="DistributionFittingResults.xls"):
         data=robjects.FloatVector(data)
         
         book = Workbook()
@@ -240,5 +240,4 @@ class Output(BasicStatisticalMeasures,DistFittest):
         sheet2.write(15,15,(A.get('aParameterValue')))
         sheet2.write(15,16,(A.get('bParameterValue')))
         
-        #book.save('C:\Eclipse workspace\FirstPrototype\src\ExcelOutput_DistributionFitting.xls')    #Save the excel document in the given directory
-        book.save('DistributionFittingResults.xls')    #Save the excel document 
+        book.save(fileName)    #Save the excel document