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DHCPCLIENT
Section: FreeRADIUS DHCP test client (1)
Updated: 24 February 2018
Index
Return to Main Contents
NAME
dhcpclient - Send a DHCP request with provided DHCP attributes and get the output response.
SYNOPSIS
dhcpclient
[
-d
raddb_directory]
[
-D
dictionary_directory]
[
-f
file]
[
-h]
[
-i
interface]
[
-t
timeout]
[
-v]
[
-x]
server[:port] {discover|request|decline|release|inform|lease_query|auto}
DESCRIPTION
dhcpclient is a DHCP test client program. It can send arbitrary DHCP
packets to the FreeRADIUS server running as DHCP server, then shows the reply.
It can be used to test changes you made in the configuration of the DHCP server,
or it can be used to monitor if a DHCP server is up.
dhcpclient reads DHCP attribute/value pairs from its standard
input, or from a file specified on the command line. It then encodes
these attribute/value pairs using the dictionary, and sends them
to the local/remote server.
OPTIONS
- -d raddb_directory
-
The directory that contains the user dictionary file. Defaults to
/etc/raddb.
- -D dictionary_directory
-
The directory that contains the main dictionary file. Defaults to
/usr/share/freeradius.
- -f file
-
File to read the attribute/value pairs from. If this is not specified,
they are read from stdin.
- -h
-
Print usage help information.
- -i interface
-
Select which interface to send/receive at packet level on a raw socket.
- -t timeout
-
Wait timeout seconds before deciding that the NAS has not
responded to a request, and re-sending the packet. This may be a floating
point number, e.g. 2.2.
- -v
-
Print out program version information.
- -x
-
Print out debugging information.
- server[:port]
-
The hostname or IP address of the remote server. Optionally a UDP port
can be specified. If no UDP port is specified, 67 is used.
- discover | request | decline | release | inform | lease_query | auto
-
DHCP Message Type - use the type relevant for testing. If auto then the message
type has to be provided in attribute/value pair DHCP-Message-Type.
SEE ALSO
radiusd(8)
AUTHORS
Alan DeKok <
<EMAIL_ADDRESS>Index
- NAME
-
- SYNOPSIS
-
- DESCRIPTION
-
- OPTIONS
-
- SEE ALSO
-
- AUTHORS
-
This document was created by
man2html,
using the manual pages.
Time: 17:19:42 GMT, March 12, 2018


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@name
@age
@sex
@place


Is Coding Bootcamp Right for You? Week 13 in The Life of a Coding Bootcamper. Finale
Category:
Hey you! You made it. You made the journey with me to the last final week of class and the big Project 4 finale. It's been a long road and now it's almost time to say goodbye. This week, our instructors provided two elective classes, an Instructor AMA (Ask Me Anything, famous from Reddit) and Project 4 Presentations were due. On the very last day of class, we reflected with our classmates, pop champagne and discussed life after WDI with the Outcomes team. Let's go!
coding-bootcamp
Tags:angular, express, node, mongoose, project-week
Hey you! You made it. You made the journey with me to the last final week of class and the big Project 4 finale. It's been a long road and now it's almost time to say goodbye. This week, our instructors provided two elective classes, an Instructor AMA (Ask Me Anything, famous from Reddit) and Project 4 Presentations were due. On the very last day of class, we reflected with our classmates, pop champagne and discussed life after WDI with the Outcomes team. Let's go!


This tool can be essential for anyone with learning difficulties to communicate non-verbally about what they want to do on a bi-weekly basis.
This application also eliminates the upkeep of paper symbols, providing a rich interface for carers/support workers and the individual to interact can with a more modernized approach.
Click here to download the application.
Coming Soon...
Coming Soon...


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Comparison of performance: Python NumPy and Numba, MATLAB, and Fortran
The time it takes to perform an array operation is compared in Python NumPy, Python NumPy with Numba accleration, MATLAB, and Fortran. The arrays are large, with one million to one billion elements. Numba is a great choice for parallel acceleration of Python and NumPy.
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Comparing measures of similarity between curves
There are many different metrics that can be minimized to determine how similar two different curves are. This post looks at fitting a line to data points by minimizing different metrics of similarity. A least squares optimization is done which minimizes the sum-of-squares. The traditional least squares fit is compared to minimizing the discrete Fréchet distance, the dynamic time warping (DTW) distance, and my own area metric.
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Detect faces using facenet in Python
The TensorFlow implementation of facenet is an extremely powerful deep face recognition libarary. You can download pre-trained models with a LFW accuracy of 0.993. This post demonstrates how you can use the facenet library to detect faces in images using Python.
-
Fitting a piecewise linear function to data
A Python library to fit continuous piecewise linear functions to data was created. This library allows for the user to specify the desired number of line segments when fitting piecewise linear functions.
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Lack of Fit Test for Linear Regression
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Automating Tinder Likes with Support Vector Machine Learning
I recorded over 8,000 Tinder likes/dislikes so that I could use machine learning to predict whether I would like or dislike a new Tinder profile
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Maximum Likelihood Estimation is Sensitive to Starting Points
Quick comparison of maximum likelihood estimation vs root mean square error for linear regression from random tarting points
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Maximum Likelihood Estimation Linear Regression
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Gaussian Process Prediction (aka Kriging) with Different Correlation Functions
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USA GDP and GNI per capita adjusted for inflation 1960 - 2014
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World Population Density Plots
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Python Twitter Bot Using Google Cloud Shell
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Least Squares Sphere Fit
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This example demonstrates how the text parser can be used to check the spelling of text in a form field.


What i learned this week about how to improve my process.
I think the most important part is to ask questions, and to know how to ask questions.
I clearly describe the problem to myself, I go through my code line by line do not rush this step.
Take notes about what may be confusing let them sink in. BE PATIENT .
I have this problem when i want to solve a problem where i
wish i could just find the solution and it would click instantly it
inevitably doesn't and i get frustrated and feel like giving up. I need to remind
myself to take a deep breath and just go through it and let it pass
One such problem was during a challenge where I had to manipulate objects in Javascript.
I googled the solution and there were many solutions but nothing to describe the concept in
a way that I would understand.
So i asked a friend. One who was knowledgeable about the subject, Instead of telling me the answer he explained the concept and I was able to get it.
This kind of help is invaluable.
A concept I'm still struggling with.
The idea is in order to find the answer you need to know what the question is and ask yourself
what is the question.
Try to put it into plain english and structure it in a way that's not valid code but makes sense to you.
This method of explaining to someone else your problem and the function of your code often brings you to understand the problem you were facing the entire time. This is because it forces you to see your code from a different perspective.
After each slice of code you write its a good idea to test if it actually works by using the console.log function inside the browser. Its a way we can run the code without altering anything in it.
You may find some success by just googling your problem.
Often times the hardest thing about googling is the amount of irrelevant
information you need to sift through first.


Functions
The following functions are available globally.
-
Train a booster with given parameters.
Declaration
Parameters
data
DMatrix
numRound
Int - Number of boosting iterations.
params
[(String, String)] - Booster parameters. It was changed from Dictionary to array of set to enable multiple
eval_metric
, now it does not need to provideevalMetric
for multipleeval_metric
. You pass multiple sets foreval_metric
in the params array.evalSet
list of tuples (DMatrix, name of the eval data). The validation sets will evaluated during training.
fnObj
pass an optional custom objective function.
fnEval
pass an optional custom evaluation function.
modelFile
String - If the modelFile param is provided, it will load the model from that file.
callbacks
pass optional callbacks, which should conform to the
XGBCallback
protocolReturn Value
Booster
-
Cross-validation with given parameters
Declaration
Parameters
params
[(String, String)] - Booster parameters. It was changed from Dictionary to array of set to enable multiple
eval_metric
, now it does not need to provideevalMetric
for multipleeval_metric
. You pass multiple sets foreval_metric
in the params array.data
DMatrix
numRound
Int - Number of boosting iterations.
nFold
number of cross validation folds
fnObj
pass an optional custom objective function.
fnEval
pass an optional custom evaluation function.
callbacks
pass optional callbacks, which should conform to the
XGBCallback
protocolReturn Value
CVResult
-
Undocumented
Declaration
Swift
public func xgboostVersion() -> (major: Int, minor: Int, patch: Int)
-
Have to set
verbosity
parameters to 2(info)/3(debug) xgboost to make it print log messages. The passing closure should have 1 positional parameter, which is the log message.Declaration
Swift
public func xgbRegisterLogCallback(_ fn: (@convention(c) (UnsafePointer<Int8>?) -> Void)?)