02-21-2019
So just to be clear, I insert the coding you provided in front of mine and insert my coding where it says your awk correct?
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Please i need your help.
i made this script with awk, this scripts count and list a pattern for each directory in the output as shown. but not as desired. i want the output will be listed in a tabular way, using awk:
cuenta_cdrs()
{
for dir in *
do
cd $dir
for file in *
do
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Guys,
I have a script which hits the database and pulls the information that I need into files. Now I want to format these files to make them easy to read.
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<Start_of_File>
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I'm using AIX 5.3 and running a awk replace to modify data as follows:
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10001234 1234 123 123 444 555 666 7777
dumb question.. how do I get the colon back in, so it outputs
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I have a flat file A.txt with field seperate by a pipe
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GOODNUMBERS="1 2 3 4 5 6 3 3 34 34 5 66 12"
BADNUMBERS="7 3 12 5 66"
for eachnum in `echo ${GOODNUMBERS}`
do
echo ${BADNUMBERS} | gawk -v threshold=${eachnum} '$1 != threshold'
done
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Hi all,
I'm fairly new to this and learning along the way, so bare with me...
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Hi,
Is there anyway to combine output from the awk scripting.
file01.txt:
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AUE_FCHMOD
AUE_SETREUID
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AUE_NICE
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LEARN ABOUT SUNOS
mlib_signallpccovariance_f32
mlib_SignalLPCCovariance_F32(3MLIB) mediaLib Library Functions mlib_SignalLPCCovariance_F32(3MLIB)
NAME
mlib_SignalLPCCovariance_F32 - perform linear predictive coding with covariance method
SYNOPSIS
cc [ flag... ] file... -lmlib [ library... ]
#include <mlib.h>
mlib_status mlib_SignalLPCCovariance_F32(mlib_f32 *coeff, const mlib_f32 *signal, void *state);
DESCRIPTION
The mlib_SignalLPCCovariance_F32() function performs linear predictive coding with covariance method.
In linear predictive coding (LPC) model, each speech sample is represented as a linear combination of the past M samples.
M
s(n) = SUM a(i) * s(n-i) + G * u(n)
i=1
where s(*) is the speech signal, u(*) is the excitation signal, and G is the gain constants, M is the order of the linear prediction fil-
ter. Given s(*), the goal is to find a set of coefficient a(*) that minimizes the prediction error e(*).
M
e(n) = s(n) - SUM a(i) * s(n-i)
i=1
In covariance method, the coefficients can be obtained by solving following set of linear equations.
M
SUM a(i) * c(i,k) = c(0,k), k=1,...,M
i=1
where
N-k-1
c(i,k) = SUM s(j) * s(j+k-i)
j=0
are the covariance coefficients of s(*), N is the length of the input speech vector.
Note that the covariance matrix R is a symmetric matrix, and the equations can be solved efficiently with Cholesky decomposition method.
See Fundamentals of Speech Recognition by Lawrence Rabiner and Biing-Hwang Juang, Prentice Hall, 1993.
PARAMETERS
The function takes the following arguments:
coeff The linear prediction coefficients.
signal The input signal vector.
state Pointer to the internal state structure.
RETURN VALUES
The function returns MLIB_SUCCESS if successful. Otherwise it returns MLIB_FAILURE.
ATTRIBUTES
See attributes(5) for descriptions of the following attributes:
+-----------------------------+-----------------------------+
| ATTRIBUTE TYPE | ATTRIBUTE VALUE |
+-----------------------------+-----------------------------+
|Interface Stability |Evolving |
+-----------------------------+-----------------------------+
|MT-Level |MT-Safe |
+-----------------------------+-----------------------------+
SEE ALSO
mlib_SignalLPCCovarianceInit_F32(3MLIB), mlib_SignalLPCCovarianceFree_F32(3MLIB), attributes(5)
SunOS 5.10 10 Nov 2004 mlib_SignalLPCCovariance_F32(3MLIB)