04-11-2008
Comparing the common columns of a table in two files
Hi,
I have two text files.The first and the 2nd file have data in the same format
For e.g. The first file has
BOOKS COUNT: 40
BOOKS AUTHOR1 SUM:1018 MAX:47 MIN:1 AVG:25.45
BOOKS AUTHOR3 SUM:181 MAX:48 MIN:3 AVG:18.1
Note:Read it as Table columnname sum(column) max(column) min(column)
File2 contains
BOOKS COUNT: 40
BOOKS AUTHOR1 SUM:1018 MAX:45 MIN:2 AVG:23.55
BOOKS AUTHOR2 SUM:561 MAX:49 MIN:1 AVG:21.5769231
BOOKS AUTHOR3 SUM:181 MAX:48 MIN:3 AVG:18.1
The file 2 contains the additional column named AUTHOR2.
Comparison should be such that the column AUTHOR1 in file 1 is compared with the column AUTHOR1 in file 2.
Here The values of column AUTHOR1 in the table BOOKS mismatches with the other file.
I should write a script to compare the columns of the table as said and report the mismatches to a different file and also report the additioanl columns present in the 2nd file.
The output should be in this format.
Column AUTHOR1 in the tabloe BOOKS mismatches.
BOOKS AUTHOR1 SUM:1018 MAX:47 MIN:1 AVG:25.45
BOOKS AUTHOR1 SUM:1018 MAX:45 MIN:2 AVG:23.55
Column AUTHOR2 is additional.
I am new to unix.Please help me in writing a script to do this job.
Thanks
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LEARN ABOUT DEBIAN
svm-landscape
SVM-LANDSCAPE(1) User Commands SVM-LANDSCAPE(1)
NAME
svm-landscape - Command line interface to svm-landscape in mlpy (version 2.2.0)
SYNOPSIS
svm-landscape [options]
OPTIONS
-h, --help
show this help message and exit
-d FILE, --data=FILE
data - required
-s, --standardize
standardize data
-n, --normalize
normalize data
-k K k for k-fold cross validation
-c SETS PAIRS
sets and pairs for monte carlo cross validation
-S, --stratified
for stratified cv
-K KERNEL, --kernel=KERNEL
kernel: 'linear', 'gaussian', 'polynomial', 'tr' [default linear]
-P KPARAMETER, --kparameter=KPARAMETER
kernel parameter (two sigma squared) for gaussian and polynomial kernels [default 0.1]
-o COST, --cost=COST
for cost-sensitive classification [-1.0, 1.0] [default 0.0]
-m MIN, --min=MIN
min value for regularization parameter [default -5]
-M MAX, --max=MAX
max value for regularization parameter [default 5]
-p STEPS, --steps=STEPS
steps for regularization parameter [default 11]
-e SCALE, --scale=SCALE
scale for regularization parameter: 'lin' or 'log' [default log]
-l, --lists
Canberra distance indicator
-a, --auc
Wmw_auc metric computation
mlpy July 2010 SVM-LANDSCAPE(1)