avoid anonymous print args

diff --git a/contrib/network_model/regress.py b/contrib/network_model/regress.py
index 60f0dc3..6bffd97 100755 (executable)
--- a/contrib/network_model/regress.py
+++ b/contrib/network_model/regress.py
@@ -1,6 +1,10 @@
 #!/usr/bin/env python
 
 #---------------------------------------------------------------------------------------------------
 #!/usr/bin/env python
 
 #---------------------------------------------------------------------------------------------------

+# Example invokation:
+# % ./regress.py griffon_skampi_pt2pt.ski.dat 65536 120832
+#
+#

 # Given two vectors of same length n: message size S(.. s_i ..), and communication time T( .. t_i .. )
 # where t_i is the time associated to a mesage size s_i, computes the segmentation of the vectors   
 # in 3 segments such that linear regressions on the 3 segments maximize correlation.
 # Given two vectors of same length n: message size S(.. s_i ..), and communication time T( .. t_i .. )
 # where t_i is the time associated to a mesage size s_i, computes the segmentation of the vectors   
 # in 3 segments such that linear regressions on the 3 segments maximize correlation.


@@ -25,7 +29,7 @@ from math import sqrt,log,exp
 
 if len(sys.argv) != 2 and len(sys.argv) != 4:
        print("Usage : {} datafile".format(sys.argv[0]))
 
 if len(sys.argv) != 2 and len(sys.argv) != 4:
        print("Usage : {} datafile".format(sys.argv[0]))

-       print("or    : {} datafile p1 p2".format(sys.argv[0]))
+       print("or    : {0} datafile p1 p2".format(sys.argv[0]))

        print("where : p1 < p2 belongs to sizes in datafiles")
        sys.exit(-1)
 
        print("where : p1 < p2 belongs to sizes in datafiles")
        sys.exit(-1)
 


@@ -119,7 +123,6 @@ def correl_split_weighted_logerr( X , Y , segments ):
                        Z.append( a * X[i] + b )
                # compare real values and computed values
                e = mean_logerr( Y[start:stop+1] , Z )
                        Z.append( a * X[i] + b )
                # compare real values and computed values
                e = mean_logerr( Y[start:stop+1] , Z )

-               #print("   range [%d,%d] err=%f‰ weight=%f" % (X[start],X[stop],e,(stop-start+1)/len(X))) 

                correl.append( (e, stop-start+1) );   # store correl. coef + number of values (segment length)
                interv.append( (a,b, X[start],X[stop],e) );
        
                correl.append( (e, stop-start+1) );   # store correl. coef + number of values (segment length)
                interv.append( (a,b, X[start],X[stop],e) );
        


@@ -296,7 +299,8 @@ if len(sys.argv) == 4:
 
        for k in range(top_n_sol):
                (err,interval) = result[k]
 
        for k in range(top_n_sol):
                (err,interval) = result[k]

-               print("\n   RANK {}\n-------".format(k))
+               print(k)
+               print("\n   RANK {0}\n-------".format(k))

                print("** overall metric = {0}".format(err))
                for (a,b,i,j,e) in interval:
                        print("** OPT: [{0} .. {1}] segment_metric={2}          slope: {3} x + {4}".format(i,j,e,a,b))
                print("** overall metric = {0}".format(err))
                for (a,b,i,j,e) in interval:
                        print("** OPT: [{0} .. {1}] segment_metric={2}          slope: {3} x + {4}".format(i,j,e,a,b))