--- title: Sheet section: Components ---
Sheets should be used as containers for Forms. There should only be onesheet-header
andsheet-footer
per sheet. Multiple sections inside sheets can be spaced usingsheet-section
.
Use.sheet-lg
with.sheet
to set a max-width of800px
orceil(map-get($container-max-width, lg) * 0.83333)
on the sheet. This is used as containers for forms.
Use.sheet-footer-btn-block-sm-down
with.sheet-footer
to make.btn
inside.sheet-footer
to become a block level button at screen sizes 767px and below.


Hi, I am {{site.author}} !
{%- include github_star_button.html -%} {%- include github_fork_button.html -%}
Get Started
What's New


BeMotivated : Welcome to our Network
Search Author with Kerberos ID
VISIT PAGE
RECENT ACTIVITIES
CREATE NEW ACCOUNT


Note:
Javascript can notify UWP UI logic code to run, but can't use this feature to get calculation result on UWP UI thread.
It's a way to control UWP UI from javascript logic!
This sample file path is :
CSCommunicationBetweenJsAndNativeCodeInWebViewOfUWP/InvokeNativeCode/InvokeNativeCodeScriptNotify.xaml
And Native method is in Project:AllowForWebRuntimeComponent


mkxauth
Section: Linux User's Manual (1x)Updated: 12-Dec-1996
Index Return to Main Contents
NAME
mkxauth - create and merge .Xauthority filesSYNOPSIS
(1) mkxauth [ -q ] [ -u login ] -c [ host [ host ... ] ](2) mkxauth [ -q ] [ -u login ] -m login
(3) mkxauth [ -q ] [ -u login ] -f host
(4) mkxauth [ -q ] [ -u login ] -r host [ -l login ]
(5) mkxauth [ -q ] [ -u login ] -z host [ -l login ]
DESCRIPTION
mkxauth aids in the creation and maintenance of X authentication databases (.Xauthority files). Use it to create a ~/.Xauthority file or merge keys from another local or remote .Xauthority file. Remote .Xauthority files can be retrieved via FTP (using ncftp(1)) or via rsh(1). For a slight measure of security, mkxauth does not create any temporary files containing authentication keys (although anyone spying on network packets can see the authentication key data as they pass through the network; for secure network communications, use ssh(1)).Creating and Adding to a .Xauthority File
To create a .Xauthority file, use mkxauth -c (see (1) above). mkxauth creates a .Xauthority file in the user's home directory (~/), containing a `key' or `magic cookie' for the host it was run on (the one returned by hostname(1)). If a .Xauthority file already exists, the keys are added to it. If keys for that host already exist, they are replaced.To create or add to a .Xauthority file for another user, use mkxauth -u login -c. mkxauth adds keys to ~login/.Xauthority (only the root user is allowed to do this).
To add a key for more than one host, specify all hosts on the command line: mkxauth -c daffy porky bugs. All hosts specified on the same command line receive the same key. To create different keys for multiple hosts, run mkxauth for each host in succession:
-
- mkxauth -c daffy
- mkxauth -c porky
- mkxauth -c bugs
Merging Keys from Local .Xauthority Files
To merge keys from another local user's .Xauthority file, use mkxauth -m login (see (2) above). mkxauth adds the keys in ~login/.Xauthority to ~/.Xauthority, replacing any keys which already exist. ~login/.Xauthority must be readable by the user running mkxauth (normally only the root user can read other people's .Xauthority files).Merging Keys via FTP
To merge keys from a remote .Xauthority file via FTP, use mkxauth -f host (see (3) above). mkxauth retrieves the remote .Xauthority from host using ncftp(1) and adds those keys to ~/.Xauthority, replacing any keys which already exist. [NOTE: you must have a ~/.netrc file set up to automatically log you into host, otherwise the FTP login attempt will fail.]Merging Keys via rsh(1)
To merge keys from remote .Xauthority file via rsh(1), use mkxauth -r host (see (4) above). mkxauth retrieves the remote .Xauthority from host using rsh(1) and adds those keys to ~/.Xauthority, replacing any keys which already exist. To login as a different user, use -l login. [NOTE: you must have a .rhosts file set up properly for this to work, otherwise the remote login attempt will fail].Merging Keys via rsh(1) and gzip(1)
If your remote .Xauthority file is large, or to make it slightly less obvious that you're transferring authentication keys over the network, mkxauth can gzip(1) your .Xauthority file before retrieving it via rsh(1). To do this, use mkxauth -z host (see (5) above). mkxauth retrieves the remote .Xauthority from host using rsh(1) and adds those keys to ~/.Xauthority, replacing any keys which already exist. To login as a different user, use -l login. [NOTE: you must have a .rhosts file set up properly for this to work, otherwise the remote login attempt will fail].Options
To make mkxauth operate quietly, use the -q option.To add to ~login/.Xauthority, use the -u login option.
To use login for the remote login in mkxauth -f, mkxauth -r, and mkxauth -z, use the -l login option.
Getting Help
To get quick help about mkxauth, use mkxauth --help.FILES
- ~/.Xauthority
- ~/.netrc
- ~/.rhosts
COMMENTS
mkxauthNote, however, that using rsh(1) is inherently insecure, and sites concerned about security should use ssh(1) instead (see http://www.cs.hut.fi/ssh/ for more information).
SEE ALSO
X(1x), Xsecurity(1x), gzip(1), mcookie(1), md5sum(1), ncftp(1), rsh(1), startx(1x), xauth(1x), xdm(1x)BUGS
Does not respect the XAUTHORITY environment variable.AUTHOR
Conceived and written by Jim Knoble<EMAIL_ADDRESS>Copyright 1996 by Jim Knoble and Red Hat Software. Distributed under the GNU GPL (General Public License); see ftp://prep.ai.mit.edu/pub/gnu/COPYING for more information.Index
This document was created by man2html, using the manual pages.
Time: 05:29:06 GMT, December 24, 2015


ctf-party
What it is
A library to enhance and speed up script/exploit writing for CTF players (or security researchers, bug bounty hunters, pentesters but mostly focused on CTF) by patching the String class to add a short syntax of usual code patterns. The philosophy is also to keep the library to be pure ruby (no dependencies) and not to re-implement what another library is already doing well (eg. xorcist for xor).
For example instead of writing:
require 'base64'
myvar = 'string'
myvar = Base64.strict_encode64(myvar)
Just write (shorter and easier to remember):
require 'ctf_party'
myvar = 'string'
myvar.to_b64!
Features
- base64:
to_b64
,to_b64!
,from_b64
,from_b64!
,b64?
- digest:
md5
,md5!
,sha1
,sha1!
, etc. - flag:
flag
,flag!
,flag?
(apply/check a flag format) - rot:
rot
,rot!
,rot13
,rot13!
- hex:
hex2dec
,dec2hex
,to_hex
,from_hex
,hex2bin
,bin2hex
and bang versions
References
Homepage / Documentation: https://orange-cyberdefense.github.io/ctf-party/
Author
Made by Alexandre ZANNI (@noraj), pentester from Orange Cyberdefense.


Charity begins at home, but should not end there.
Donate so we can contibute more to society
Fill the form to help u with fund and we will get back to you
Your feedback is important for us
Contact us and we'll get back to you within 24 hours.
Delhi, India
98548448483
myemail@something.com


file
load
save
new
add node
add connect
delete node
delete connect
generate
node type
node name
server
ok
2017/12/12 3:30
var a = 2; for(var i=0; i<2;i++){ };
title


Welcome to the 'Numeri' puzzle game.
Your goal is to draw a single loop line with no loose ends. The line must circumvent all the numbers in order to make them valid. When the number of line segments around a cell matches the displayed value, the cell is considered valid.
Use '>' to show the solution, or crete a new puzzle. Use '<' to clear the puzzle.
Facing the iPhone down reveals the missing numbers in the grid in order to help you solve it.
Search 'Slitherlink' on Wikipedia for solving techniques.
Have fun,


Futurice
multi-site online gaming 13-15. Oct 2017
Counter-Strike: Global Offensive Bracket
Counter-Strike: Global Offensive Schedule


{% extends "nav.html" %} {% block content %}
Use WordGen to produce new phonetically-pronouncable words. Click below to generate:
{{ text }}
{% endblock %}


security.plist
Home
{% if page.index != null %}
security.plist
A proposed standard which allows iOS applications to define security policies.
{% endif %}
{{ content }}


| Trees | Indices | Help |
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Base class for all Credentials objects. Subclasses must define an authorize() method that applies the credentials to an HTTP transport. Subclasses must also specify a classmethod named 'from_json' that takes a JSON string as input and returns an instaniated Credentials object.
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NON_SERIALIZED_MEMBERS = ['store']
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Forces a refresh of the access_token. Args: http: httplib2.Http, an http object to be used to make the refresh request. |
Add the authorization to the headers. Args: headers: dict, the headers to add the Authorization header to. |
Utility function for creating a JSON representation of an instance of Credentials. Args: strip: array, An array of names of members to not include in the JSON. Returns: string, a JSON representation of this instance, suitable to pass to from_json(). |
Creating a JSON representation of an instance of Credentials. Returns: string, a JSON representation of this instance, suitable to pass to from_json(). |
Utility class method to instantiate a Credentials subclass from a JSON representation produced by to_json(). Args: s: string, JSON from to_json(). Returns: An instance of the subclass of Credentials that was serialized with to_json(). |
Instantiate a Credentials object from a JSON description of it. The JSON should have been produced by calling .to_json() on the object. Args: data: dict, A deserialized JSON object. Returns: An instance of a Credentials subclass. |
| Trees | Indices | Help |
|---|
| Generated by Epydoc 3.0.1 on Thu Jan 24 16:14:41 2013 | http://epydoc.sourceforge.net |


Provides a generalized method for printing tabular data within the R environment in order to make the process of presenting high quality tabular output seamless for the user. Output is directed to the R graphics device so that tables can be exported to any file format supported by the graphics device. Utilizes a formula interface to specify the contents of tables often found in manuscripts or business reports. In addition, formula interface provides inline formatting of the numeric cells of a table and renaming column labels.
| Version: | 0.0.4 |
| Depends: | R (≥ 3.0) |
| Imports: | grid, stats, grDevices |
| Suggests: | testthat |
| Published: | 2018-01-08 |
| Author: | Carlin Brickner, Rocco Napoli |
| Maintainer: | Carlin Brickner <carlin.brickner.rpackage at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | dprint results |
| Reference manual: | dprint.pdf |
| Package source: | dprint_0.0.4.tar.gz |
| Windows binaries: | r-devel: dprint_0.0.4.zip, r-release: dprint_0.0.4.zip, r-oldrel: dprint_0.0.4.zip |
| OS X binaries: | r-release: dprint_0.0.4.tgz, r-oldrel: dprint_0.0.4.tgz |
Please use the canonical form https://CRAN.R-project.org/package=dprint to link to this page.


{% extends "base.html" %} {% block title %}{{ super() }}{% endblock %} {% block page_content %}
Welcome to TradeSystem! You can sell or buy items on this web site.
Learn more


An 11-year-old girl gets a new bike from Lee County Deputies after crash
by WFTX Digital TeamLEE COUNTY, Fla. — Bonita Springs Community Police Deputies jumped into action for an eleven-year-old girl by getting her a new bicycle and helmet after she was hit by a vehicle.
When 11-year old Dolce was hit by a vehicle while riding her bicycle to school, deputies helped her, and thankfully she only had minor injuries, but her bike was totaled.
Dolce lives within a two-mile radius of her school, so a bicycle is the quickest form of transportation. The deputies were able to get her a new bike so she can now get to school safely and quickly.


This is documentation on the .chm format used by Microsoft HTML Help. This format has been reverse engineered in the past, but as far as I know this is the first freely available documentation on it. One Usenet message indicates that these .chm files are actually IStorage files documented in the Microsoft Platform SDK. However, I have not been able to locate such documentation.
The word "section" is badly overloaded in this document. Sorry about that.
All numbers are in hexadecimal unless otherwise indicated in the text. Except in tabular listings, this will be indicated by $ or 0x as appropriate. All values within the file are Intel byte order (little endian) unless indicated otherwise.
The .chm file begins with a short ($38 byte) initial header. This is followed by the header section table and the offset to the content. Collectively, this is the "header".
The header is followed by the header sections. There are two header sections. One header section is the file directory, the other contains the file length and some unknown data. Immediately following the header sections is the content.
The header starts with the initial header, which has the following format
0000: char[4] 'ITSF' 0004: DWORD 3 (Version number) 0008: DWORD Total header length, including header section table and following data. 000C: DWORD 1 (unknown) 0010: DWORD a timestamp. Considered as a big-endian DWORD, it appears to contain seconds (MSB) and fractional seconds (second byte). The third and fourth bytes may contain even more fractional bits. The 4 least significant bits in the last byte are constant. 0014: DWORD Windows Language ID. The two I've seen $0409 = LANG_ENGLISH/SUBLANG_ENGLISH_US $0407 = LANG_GERMAN/SUBLANG_GERMAN 0018: GUID {7C01FD10-7BAA-11D0-9E0C-00A0-C922-E6EC} 0028: GUID {7C01FD11-7BAA-11D0-9E0C-00A0-C922-E6EC}
Note: a GUID is $10 bytes, arranged as 1 DWORD, 2 WORDs, and 8 BYTEs.
It is followed by the header section table, which is 2 entries, where each entry is $10 bytes long and has this format:
0000: QWORD Offset of section from beginning of file 0008: QWORD Length of section
Following the header section table is 8 bytes of additional header data. In Version 2 files, this data is not there and the content section starts immediately after the directory.
0000: QWORD Offset within file of content section 0
This section contains the total size of the file, and not much else
0000: DWORD $01FE (unknown) 0004: DWORD 0 (unknown) 0008: QWORD File Size 0010: DWORD 0 (unknown) 0014: DWORD 0 (unknown)
The central part of the .chm file: A directory of the files and information it contains.
The directory starts with a header; its format is as follows:
0000: char[4] 'ITSP' 0004: DWORD Version number 1 0008: DWORD Length of the directory header 000C: DWORD $0a (unknown) 0010: DWORD $1000 Directory chunk size 0014: DWORD "Density" of quickref section, usually 2. 0018: DWORD Depth of the index tree 1 there is no index, 2 if there is one level of PMGI chunks. 001C: DWORD Chunk number of root index chunk, -1 if there is none (though at least one file has 0 despite there being no index chunk, probably a bug.) 0020: DWORD Chunk number of first PMGL (listing) chunk 0024: DWORD Chunk number of last PMGL (listing) chunk 0028: DWORD -1 (unknown) 002C: DWORD Number of directory chunks (total) 0030: DWORD Windows language ID 0034: GUID {5D02926A-212E-11D0-9DF9-00A0C922E6EC} 0044: DWORD $54 (This is the length again) 0048: DWORD -1 (unknown) 004C: DWORD -1 (unknown) 0050: DWORD -1 (unknown)
The header is directly followed by the directory chunks. There are two types of directory chunks -- index chunks, and listing chunks. The index chunk will be omitted if there is only one listing chunk. A listing chunk has the following format:
0000: char[4] 'PMGL' 0004: DWORD Length of free space and/or quickref area at end of directory chunk 0008: DWORD Always 0. 000C: DWORD Chunk number of previous listing chunk when reading directory in sequence (-1 if this is the first listing chunk) 0010: DWORD Chunk number of next listing chunk when reading directory in sequence (-1 if this is the last listing chunk) 0014: Directory listing entries (to quickref area) Sorted by filename; the sort is case-insensitive.
The quickref area is written backwards from the end of the chunk. One quickref entry exists for every n entries in the file, where n is calculated as 1 + (1 << quickref density). So for density = 2, n = 5.
Chunklen-0002: WORD Number of entries in the chunk Chunklen-0004: WORD Offset of entry n from entry 0 Chunklen-0008: WORD Offset of entry 2n from entry 0 Chunklen-000C: WORD Offset of entry 3n from entry 0 ...
The format of a directory listing entry is as follows
ENCINT: length of name BYTEs: name (UTF-8 encoded) ENCINT: content section ENCINT: offset ENCINT: length
The offset is from the beginning of the content section the file is in, after the section has been decompressed (if appropriate). The length also refers to length of the file in the section after decompression.
There are two kinds of file represented in the directory: user data and format related files. The files which are format-related have names which begin with '::', the user data files have names which begin with "/".
An index chunk has the following format
0000: char[4] 'PMGI' 0004: DWORD Length of quickref/free area at end of directory chunk 0008: Directory index entries (to quickref/free area)
The quickref area in an PMGI is the same as in an PMGL
The format of a directory index entry is as follows
ENCINT: length of name BYTEs: name (UTF-8 encoded) ENCINT: directory listing chunk which starts with name
When higher-level indexes exist (when the depth of the index tree is 3 or higher), presumably the upper-level indexes will contain the numbers of lower-level index chunks rather than listing chunks
An ENCINT is a variable-length integer. The high bit of each byte indicates "continued to the next byte". Bytes are stored most significant to least significant. So, for example, $EA $15 is (((0xEA&0x7F)<<7)|0x15) = 0x3515.
In Version 3, the content typically immediately follows the header sections, and is at the location indicated by the DWORD following the header section table. In Version 2, the content immediately follows the header. All content section 0 locations in the directory are relative to that point. The other content sections are stored WITHIN content section 0.
There exists in content section 0 and in the directory a file called "::DataSpace/NameList". This file contains the names of all the content sections. The format is as follows:
0000: WORD Length of file, in words 0002: WORD Number of entries in file Each entry: 0000: WORD Length of name in words, excluding terminating null 0002: WORD Double-byte characters xxxx: WORD 0
Yes, the names have a length word AND are null terminated; sort of a belt-and-suspenders approach. The coding system is likely UTF-16 (little endian).
The section names seen so far are
"Uncompressed" is self-explanatory. The section "MSCompressed" is compressed with Microsoft's LZX algorithm.
For each section other than 0, there exists a file called '::DataSpace/Storage/<Section Name>/Content'. This file contains the compressed data for the section. So, conceptually, getting a file from a nonzero section is a multi-step process. First you must get the content file from section 0. Then you decompress (if appropriate) the section. Then you get the desired file from your decompressed section.
There are several other files associated with the sections
This file contains $20 bytes of information on the compression. The information is partially known:
0000: DWORD Number of DWORDs following 'LZXC', must be 6 if version is 2 0004: ASCII 'LZXC' Compression type identifier 0008: DWORD Version (Must be <=2) 000C: DWORD The LZX reset interval 0010: DWORD The window size 0014: DWORD The cache size 0018: DWORD 0 (unknown)
Reset interval, window size, and cache size are in bytes if version is 1, $8000-byte blocks if version is 2.
This file contains a quadword containing the uncompressed length of the section.
It appears this file was intended to contain a list of GUIDs belonging to methods of decompressing (or otherwise transforming) the section. However, it actually contains only half of the string representation of a GUID, apparently because it was sized for characters but contains wide characters.
The compressed sections are compressed using LZX, a compression method Microsoft also uses for its cabinet files. To ensure this, check the second DWORD of compression info in the ControlData file for the section — it should be 'LZXC'. To decompress, first read the file "::DataSpace/Storage/<SectionName>/Transform/{7FC28940-9D31-11D0-9B27-00A0C91E9C7C}/InstanceData/ResetTable". This reset table has the following format
0000: DWORD 2 unknown (possibly a version number) 0004: DWORD Number of entries in reset table 0008: DWORD 8 Size of table entry (bytes) 000C: DWORD $28 Length of table header (area before table entries) 0010: QWORD Uncompressed Length 0018: QWORD Compressed Length 0020: QWORD 0x8000 block size for locations below 0028: QWORD 0 (zeroth entry of table) 0030: QWORD location in compressed data of 1st block boundary in uncompressed data Repeat to end of file
Now you can finally obtain the section (from its Content file). The window size for the LZX compression is 16 (decimal) on all the files seen so far. This is specified by the DWORD at $10 in the ControlData file (but note that DWORD gives the window size in 0x8000-byte blocks, not the LZX code for the window size)
The rule that the input bit-stream is to be re-aligned to a 16-bit boundary after $8000 output characters have been processed IS in effect, despite this LZX not being part of a CAB file. The reset table tells you when this was done, though there is no need for that during decompression; you can just keep track of the number of output characters. Furthermore, while this does not appear to be documented in the LZX format, the uncompressed stream is padded to an $8000 byte boundary.
There is one change from LZX as defined by Microsoft: After each LZX reset interval (defined in the ControlData file, but in practice equal to the window size) of compressed data is processed, the LZX state is fully reset, as if an entirely new file was being encoded. This allows semi-random access to the compressed data; you can start reading on any reset interval boundary using the reset interval size and the reset table.
Note:
Earlier versions of this document stated that the reset interval only
reset the Huffman tables and required outputting the 1-bit header
again. This was erroneous. The Lempel Ziv state is reset as well. In
practice, a decoder works just as well with the incorrect assumption,
but encoding a file with match positions which refer to a time before
the most recent LZX reset causes crashes on decoding.
The following people in (no particular order) have submitted information which has helped correct and close the gaps in this document.
And others I have not been able to reach.
Copyright 2001-2003 Matthew T. Russotto
You may freely copy and distribute unmodified copies of this file, or copies where the only modification is a change in line endings, padding after the html end tag, coding system, or any combination thereof. The original is in ASCII with Unix line endings.


Wonder Boys * * * (out of * * * * )
Directed by Curtis Hanson. Cast: Michael Douglas, Tobey Maguire, Robert Downey Jr., Frances McDormand, Katie Holmes, Michael Cavaias, Richard Thomas, Rip Torn, Jane Adams, Richard Knox, Philip Bosco. 2000 - 112 minutes Rated R (for profanity and mild violence). Reviewed by Dustin Putman, February 26, 2000.
 A English professor at a college in Pittsburgh, Grady wrote one acclaimed novel seven years ago, but has since kept his readers waiting for a sophomore effort. He has been writing a second novel, all right, but he still hasn't completed it, and is currently on page 2611. Grady smokes marijuana almost continuously, and when not in school, is rarely seen without wearing his bathrobe. Recently, he has been carrying on an affair with the school's chancellor, Sara Gaskell (Frances McDormand), despite them both being married to other people. Such behavior is used, in essence, because he is always attempting to shield himself from the world, and from the reality that he is a 50-year-old who still hasn't come-of-age.
That compelling, if not terribly fresh, idea is at the center of "Wonder Boys," directed by Curtis Hanson (1997's "L.A. Confidential"), and written by Steve Kloves with a surprising literacy and intelligence that is rarely conspicuous in today's Hollywood-made features. Set over an eventful weekend in which an annual literature festival is in full swing at the campus, Grady's life seems to be unraveling more and more by the second. At the start, his wife leaves him, and although she has walked out on Grady before, she always came back. Taking time to be alone for a few fleeting seconds during her party, Sara tells Grady that she is positive she is pregnant, and it has to be his child. Sara, an independent, fair-minded woman, makes it clear that she loves him, but isn't going to let her whole life pass her by as she waits for him to make up his mind on what he wants.
Things grow more complicated with the appearance of Terry Crabtree (Robert Downey Jr.), his editor, eager to get ahold of his latest writing opus, who is in town for the weekend and has a thing for picking up various people, the latest of which is the alluring Antonia Sloviak (Michael Cavaias), whom everyone but Terry realizes is a transvestite. Stepping outside for a smoke at the party, Grady discovers his unusual, but highly gifted student James Leer (Tobey Maguire) with what he says is a cap gun in his hand. James, whose writing is ten times better than all of his fellow students' will ever be, is constantly put down by his classmates (who are obviously jealous), yet takes their comments to heart and has settled into a depression. That is only the beginning of an unconventional bond that forms between Grady and James, both of whom need someone to wake them up to their realities, and a string of eccentric plot threads that involve a murdered pooch; the coat Marilyn Monroe wore the day she was married; the scholarly Hannah Green (Katie Holmes), one of Grady's students, who rents a room in his house and unmistakably has a crush on him; and the erratic Vernon Hardapple (Richard Knox), who mysteriously is stalking Grady, with his fresh-faced, pregnant girlfriend, Oola (Jane Adams), in tow.
Structurally, a very laid-back bittersweet comedy, one is never quite sure where "Wonder Boys," based on the novel by Michael Chabon, is headed because, ultimately, there is nowhere for it to go. This is not necessarily meant as a criticism, although it does give the picture an occasionally uneven tone, but rather a distinct observation, because the film has little to do with "plot," and almost everything to do with Grady Tripp, an insecure man without a clue to what he wants from anything. Wandering around rather aimlessly from one scene, and character, to the next, what remains the focus is Grady, as everyone around him acts as little more than instigators biding their time until he discovers within himself what he wants to make out of the rest of his days.
Almost everything is dependent on Michael Douglas' central performance, and he genuinely impresses. Getting rid of all of his previous characters' somewhat similar pretensions, Douglas has crafted a memorably vivid film character in Grady Tripp, and always makes him completely likable and understanding to those around him, a refreshing twist that keeps his role from becoming a cliche.
Tobey Maguire, who has made a career, thus far, in playing young, understated everybodys, takes a giant step forward this time with his most eccentric role, to date. His James Leer is a peculiar individual, a college student who loves writing and has a sort of obsession for trivia about deceased movie stars. One never gets a complete sense of who James is, but that is appropriate because James is clearly unsure himself, although there is no doubt in anyone's mind that, with his writing expertise, he is destined to become a published author soon down the road.
In supporting roles, Robert Downey Jr. is a standout, even more so than usual, and it is performances like this that make you wish he would lay off the drugs and get his own life together, not unlike Grady. His portrayal of the dedicated and loyal, if perverse, Terry Crabtree is right on the money, and the gentle, if slight, relationship that forms between him and James, who begin to sleep with each other, is one that isn't given much screen time, but has a sort of oddball affection about it. After all, like everyone, Terry is only looking for love, but by pursuing one fling after the next, he simply has a strange way of searching.
It is nice to see Frances McDormand in a high-profile movie once again, and she proves that she is so very good, it doesn't matter that Sara is underwritten. McDormand single-handedly pulls the viewer into the character and gets them to care about what happens to her, and the fate of her relationship with Grady. The same holds true for Katie Holmes, as Hannah, who is only given a handful of substantial scenes, but is excellent in each one. With 1997's "The Ice Storm" (also starring Maguire), 1999's "Go," and now "Wonder Boys," Holmes is sure to stick around for a very long time, choosing interesting projects and notable supporting roles that wisely distances her from her "Dawson's Creek" character. Last, Michael Cavaias, as Antonia/Tony, makes a brief, but nonetheless exceptional, turn that, in each one of his scenes early on, steals the attention away from even Douglas and Downey Jr.
Technical attributes that should be pointed out include cinematographer Dante Spinotti, who paints the film with gorgeous visual strokes of the bleak, wintry Pittsburgh landscapes, while the soundtrack, which features such great artists as Bob Dylan, Neil Young, John Lennon, and Van Morrison, is perfectly realized.
"Wonder Boys" may not be perfect (for example, I wish the ending had led up to a little more than there is with some of the characters), but director Curtis Hanson has crafted an assured, well-made piece of work that shows just how smart a studio picture can be. And in Grady Tripp, he and screenwriter Kloves have created an unforgettable screen character. Grady may not be a faultless human being, but in his gradual emotional transformation from a child into an adult, and in his willingness to live the way he wants to at any given moment, he is remarkable. Like his pupil James Leer, he is a wonder boy, indeed.
- Copyright 2000 by Dustin Putman Http://www.young-hollywood.com Http://www.atnzone.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Related links: index of all rec.arts.movies.reviews reviews


Test your knowledge in different platforms of coding languages and
the entire technologocal ecosystem. Answer the following questions appropriately and an overall
mark will be awarded. Feel free to test and retest yourself and use this as a motivation to
dig deeper into your desires.
Some of the languages asked include java, javascript, python and
some sql. ENJOY!


Important Information
Now before we start I would like to state that I will be using features that are only available on C++11 compilers. If you don't have a C++11 or don't know if your compiler supports it, I would recommend doing this. Head on over to CodeBlocks and download their IDE. It comes with a C++11 compiler and you can enable it by going to settings->compiler->compiler settings->compiler flags-> and then you should see a checkbox that says something like Have g++ follow the C++11 ISO C++ language standard. Enable that and click ok and you should be good to go.
What It Looks Like
The sort() function in the algorithm header can be a very useful tool to both new and experienced programmers. It's use is to sort containers like arrays and vectors.
The first example is what the function looks like. The second example is an optional overloaded function that includes a third parameter. First take a look at each of these functions and see if we can figure out what each parameter does.
Example 1 ~
std::sort(myvector.begin(), myvector.end())
Example 2 ~
std::sort(myvector.begin(), myvector.end(), myCompFunction)
About The Function
So let’s go dig into these and figure out what each does and why it does it.
Found in ~
#include <algorithm>
Parameter 1 myvector.begin() ~ The first parameter is where you will be putting a iterator(Pointer) to the first element in the range that you want to sort. The sort will include the element that the iterator points to.
Parameter 2 myvector.end() ~ The second parameter is almost like the first but instead of putting a iterator to the first element to sort you will be putting a iterator to the last element. One very important difference is that the search won’t include the element that this iterator points to. It is [First,Last) meaning it includes the first parameter in the sort but it doesn’t include the second parameter in the sort.
Parameter 3 myCompFunction() Optional ~ I will only give a brief description here, because I will be explaining this parameter in more detail later. The third parameter is used to define how you do the search. For example if you have a struct that has 3 different variables in it, how does the function know which one to sort? Or how does it know how it should sort it? This is what this parameter is for. I will explain this more in a bit.
Function Return ~ This function doesn’t return anything because it alters the container directly through iterators(Pointers).
Array Example
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
|
// sort() Example using arrays.
// By Zereo 04/22/13
#include <iostream>
#include <algorithm>
using namespace std;
const int SIZE = 7;
int main()
{
int intArray[SIZE] = {5, 3, 32, -1, 1, 104, 53};
//Now we call the sort function
sort(intArray, intArray + SIZE);
cout << "Sorted Array looks like this." << endl;
for (size_t i = 0; i != SIZE; ++i)
cout << intArray[i] << " ";
return 0;
}
| |
Things to know
When we use the sort function to sort an array our arguments will look a bit different then when we use it on a vector for example. In the example above when we pass in intArray as an argument we are telling the function to start the sort at the beginning of the array. If we wanted it to start the sort at the second element of the array we would do
sort(intArray + 1, intArray + SIZE);
. So when we do intArray + SIZE for the second argument we are telling the array to sort up to the last element in the array.
Using C++11 to simplify things
We can make sorting whole arrays even easier by using
std::begin()
and
std::end()
.
std::begin()
will return a iterator(pointer) to the first element in the array we pass it. Whereas
std::end()
will return a iterator(pointer) to one past the last element in the array we pass it. So we could call the sort function by passing it begin() and end() like so.
sort(begin(intArray), end(intArray));
Sorting Vectors and other STL Containers Example
Warning: Uses C++11 features.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
|
// Vector Sorting Example.
// By Zereo 04/22/13
#include <iostream>
#include <algorithm>
#include <vector>
#include <string>
using namespace std;
int main()
{
// Warning this type of initialization requires a C++11 Compiler
vector<int> intVec = {56, 32, -43, 23, 12, 93, 132, -154};
vector<string> stringVec = {"John", "Bob", "Joe", "Zack", "Randy"};
// Sorting the int vector
sort(intVec.begin(), intVec.end());
for (vector<int>::size_type i = 0; i != intVec.size(); ++i)
cout << intVec[i] << " ";
cout << endl;
// Sorting the string vector
sort(stringVec.begin(), stringVec.end());
// Ranged Based loops. This requires a C++11 Compiler also
// If you don't have a C++11 Compiler you can use a standard
// for loop to print your vector.
for (string &s : stringVec)
cout << s << " ";
return 0;
}
| |
Things to know
First as you can see the sorting function works almost the same as on an array but we just have to pass our arguments a little differently. Since the first parameter in sort() accepts a iterator(pointer) to the first element we want to sort we can pass
stringVec.begin()
to it because
.begin()
returns a iterator to the first element. So it will start the sorting at the first element in the vector. The same goes for
stringVec.end()
for the second parameter because remember
.end()
is a iterator that points to one past the last element in the container. Remember the sort function sorts up to but not including what we pass in as the second parameter.
You also probably noticed that the sort works on things other then numbers. When we printed out the vector of strings it gave us a nice and neat vector that holds the names in their alphabetical order.
The Overloaded sort() With A Third Parameter.
The third parameter in the sort() function is actually a very useful feature. It allows us to define how the sort() function will actually perform the search. Sometimes you can get by with the normal version of sort(), but what if we wanted to change how the container was sorted by having it sort by descending order instead of ascending? Or what if we had a container full of a special type of class objects we created and need to sort that container a special way? Well this is where the third parameter comes in.
Making it sort by descending order example.
Warning: Uses C++11 Features
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
|
// Vector Sorting Descending Example.
// By Zereo 04/22/13
#include <iostream>
#include <algorithm>
#include <vector>
using namespace std;
// We need this function to define how to sort
// the vector. We will pass this function into the
// third parameter and it will tell it to sort descendingly.
bool wayToSort(int i, int j) { return i > j; }
int main()
{
vector<int> intVec = {56, 32, -43, 23, 12, 93, 132, -154};
// Do not include the () when you call wayToSort
// It must be passed as a function pointer or function object
sort(intVec.begin(), intVec.end(), wayToSort);
for (int i : intVec)
cout << i << " ";
return 0;
}
| |
The Function
First let’s look at the function. What we did is we created a function that will determine whether i > j every time it is called. The sort function will automatically assign an element to both i and j.
The function you make needs to have a return type of Boolean.
So when we define
bool wayToSort(int i, int j) { return i > j; }
, we are saying we wanted it to sort descending because i>j. Whereas ascending would be i<j.
Using the STL to simplify sorting in ascending or descending.
Another solution to the problem of getting it to sort descending is to use
std::greater(), which would look like this.
sort(intVec.begin(), intVec.end(), greater<int>());
Sorting User Made Types.
For a lot of programs we aren’t storing just ints, strings, or doubles. Instead we are making complicated classes that have multiple number and string members and storing them in a container. So when we want to sort that container of our class objects we need to define a special function that will tell the sort() function how it should sort those objects.
So for my last example lets say we have a struct that represents a person and it looks like this.
1 2 3 4 5 6
|
struct Person
{
string name;
int age;
string favoriteColor;
};
| |
As you can see it has three members: name, age and color. Now let’s say we have a program that has a vector full of Person objects, and we need a way to be able to sort them by their name, age or favorite color at certain points in the program.
One way would be to make a function for each different way of sorting like in the example below. Those this is not the only way.
Warning: Uses C++11 Features
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73
|
// Complicated Types Sorting Example.
// By Zereo 04/22/13
#include <iostream>
#include <algorithm>
#include <vector>
#include <string>
using namespace std;
struct Person
{
// Left out making a constructor for simplicity's sake.
string name;
int age;
string favoriteColor;
};
// Sort Container by name function
bool sortByName(const Person &lhs, const Person &rhs) { return lhs.name < rhs.name; }
// Sort Container by age function
bool sortByAge(const Person &lhs, const Person &rhs) { return lhs.age < rhs.age; }
// Sort Container by favorite color
// We can just sort alphabetically and then it will group the
// color together.
bool sortByColor(const Person &lhs, const Person &rhs) { return lhs.favoriteColor < rhs.favoriteColor; }
// A global const variable to hold how many people to ask for input for.
const unsigned numberOfPeople = 2;
int main()
{
// Make a vector that holds 5 blank Person Objects
vector<Person> people(numberOfPeople);
// This will ask for user input to populate the container
// with 5 different indivuals.
for (vector<Person>::size_type i = 0; i != numberOfPeople; ++i)
{
cout << "Person #" << i + 1 << " name: ";
cin >> people[i].name;
cout << "Person #" << i + 1 << " age: ";
cin >> people[i].age;
cout << "Person #" << i + 1 << " favorite color: ";
cin >> people[i].favoriteColor;
}
cout << "\n\n";
// Sort by name
sort(people.begin(), people.end(), sortByName);
for (Person &n : people)
cout << n.name << " ";
cout << endl;
// Sory by age
sort(people.begin(), people.end(), sortByAge);
for (Person &n : people)
cout << n.age << " ";
cout << endl;
// Sort by color
sort(people.begin(), people.end(), sortByColor);
for (Person &n : people)
cout << n.favoriteColor << " ";
return 0;
}
| |
Things to know
Now I won’t be able to go into everything that was going on in that last example, but I will go through one of the functions and explain how it works.
Sort By Name Function
1 2 3 4
|
bool sortByName(const Person &lhs, const Person &rhs)
{
return lhs.name < rhs.name;
}
| |
This function is actually very similar to the one we just made before except we changed two things. We changed the parameter types from int to type Person, and we also changed the return expression a bit.
First let’s go over the change to the parameters.
The reason why we had to change the parameters from int to Person, is because the container we are sorting is of type vector<Person>. And in order to be able to call the equation lhs.name < rhs.name the parameters would have to be of type Person so we can access the name members.
Secondly we changed the return equation to lhs.name < rhs.name. Can you guess what this equation is asking? Well what this is saying is basically this. Remember that both lhs and rhs are pointing to a separate element in the container (Like lhs is element 1 and rhs is element 2). So when we say lhs.name we are telling it to access element 1’s object and then access that objects name member. The same goes for rhs.name but is it accessing element 2’s object instead. So when we say to return lhs.name < rhs.name we are asking it if lhs’s object’s name less then rhs’s objects name.
The other functions are actually just the same but use the different members of the struct.
THE END ;p
Well that is all for this tutorial, though there is a lot more to learn about sorting with the STL. So if you are interested you can look below for some links to other things that relate to sort(). If you have any comments (Especially on any mistakes) on the article/tutorial please let me know I enjoy any type of feedback good or bad.
The same goes for any questions, if you don’t understand anything or the way I explained something didn’t make sense (More then likely ;p) please let me know through a reply here or through a PM. I would be glad to help answer any questions you have.
I am hoping to create some more tutorials shortly about how to use algorithms from the STL. Once I get them written up I will either add them to this Article or create a new one. Hope everyone liked it and thanks for reading,
Resources
Documentations
std::end()
std::begin()
std::sort()
std::stable_sort()
std::greater()
std::less()
Information
Ranged Based For Loops
Info on initialization in C++11
~ Zereo


Connecting Twitter will make reviews you tweet stand out much more in the timeline. You can also set Review Monitor to automatically tweet 5 star reviews!


Is UW Open?
Rick's
The 8
Yes
The 8 is open right now.
Learn More
Copyright 2016 Nathan Wreggit and Kyle Wistrand
Code licensed under the
MIT License


Across: 1. Tank comes back around anterior flanks - also known as "the sorrounding of 31" (9) 6. Losing heart, sinks back and goes rapidly downhill (4) 11. Trim sonar setting for heavy weather (9) 12,9dn. 25, out of breath, pulled great vain dunderhead into the batter (5 6) 13. Six points which may bear fruit (4) 14. It's polite to drop down with a garden tool, so they say (10) 16. Cool trad jazz for PhD (8) 18. Ignore damaged area (6) 20. see 8dn 21. Scandalous sort of delivery - it can stink (8) 24. Light TNT? Or set off with extreme restraint? (10) 25. Chances for disagreement (4) 28. Plain diagram shows 2 place (5) 29. 5 perhaps eats a piece of 1 - it works best when it's blowing (9) 30. Deceived in song (4) 31. Prohibition abounding in 28's 8 20ac (9) Down: 2. Since Chappell is from the East... (5) 3. ...'no quarter' is what we usually follow (4) 4. 19 trod pile around California, like much of Central America (8) 5. see 27 7. Ruling class of 28 direct hair at sky (9) 8,20ac. Artificial cleavage perhaps found in California (7 6) 9. see 12ac 10. Came out with pride? Mere whimsy! (9) 15. Voltage capacity (9) 17. Romantic dreamer returns to beg ... dire local conditions! (9) 19. Eternal guide directed with same point repeatedly (8) 20. Sulfuric acid and some nitric placed in instrument (7) 22. Sensitive patch for writer in the state that preceded 1 (6) 23. Unfortunately, the early part of 1 is a chilling state indeed (6) 26. Rally during a dark storm (5) 27,5. Player of a sort - modest, no hothead (4 6)


Leticia is senior postdoc with more than ten years of experience between "wet-lab" and "dry-lab". Now she is fully commit with the bioinformatics part working as main bioinformatician in the Immunology Department at Erasmus MC. She got her PhD degree in Madrid and worked as postdoc in Amsterdam, during her postdoc she decided to follow the Bioinformatic Master program at the VU.
Technology is my biggest passion. It al started with Wondere Wereld, an old TV-show I watched as a child. From there it went from electronics to computers and back to electronics and computers. These days I work mostly as a problem solver and inventor. I develop solutions when there are problems or invent something new when changing workflows require something new. Music is my second passion and when I’m not working on something I play bass in a band.
Started as a system administrator before transitioning to Computer Science and falling in love with programming. An internship at Erasmus MC sparked my interests in bioinformatics, and staying there for a fulltime position was an easy decision.With the fast moving field of Bioinformatics/Computer Science, I think it’s very important to keep learning about the new or improved ways of solving everyday problems.
More information about the members of our team: Like this interview to Leticia G. Leon in the Spanish news and she was also invited to this Round table to talk about the situation of researchers in Spain.


WITNESS LIST Defense Affairs & State-Federal Relations Committee March 17, 2005-8:00A HB 863 For: Brieden III, John (American Legion) Palmquist, Michael (The American Legion, Department of Texas) HB 907 On: Weitzel, Lorelei (Texas Water Development Board) HB 1009 For: Forbes, Scott (Texas Ports Association) On: Carothers, Jeff (Department of Public Safety - Crime Records Service) Kyser, Margie (Dept of Public Safety Crime Records Service) Spinks, Margaret (Texas Department of Public Safety/ Driver License Division) HB 1308 Registering, but not testifying: For: Patterson, Jerry (Self) On: Coffey, Jan (Texas Dept of Public Safety) HB 1534 For: Baker, James "Mike" (Texas Fire Chief's Association, San Marcos Fire Rescue) Spicer, Brett (Self and Fraternal Order of Police, Texas State Lodge) Registering, but not testifying: For: Higgins, Mike (Texas State Association of Firefighters) Johnson, Helen (State Firemen's and Fire Marshal's Assoc. Executive Director) Montgomery, Mike (Harris County Fire and Emergency Services) Villarreal, Johnny (Houston Fire Fighter Local 341) HB 1630 On: Parsoneault, Catherine (Texas Higher Education Coordinating Board) Reimer, Bill (Adjutant General of TX/TXARNG)


About the data
To analyse the composition of boards around the world, the Financial Times used data provided by ISS Analytics, Powered by DataDesk, covering more than 45,000 directors across 5,000 companies in 30 markets.
The data is a part of Institutional Shareholder Services' QuickScore report, designed to help investors identify governance risk within portfolio companies.
The database includes companies in the US’s Russell 3000 and S&P 500, Canada’s S&P/TSX Composite, New Zealand’s NZX 15, Australia’s ASX 200 and Europe’s STOXX 600 in addition to local European market indices such as the UK FTSE All-Share. It also includes widely-held companies in Brazil, China, Hong Kong, India, Japan, Singapore, South Africa and South Korea. ISS defines “widely held” based on a company's membership in a major index and/or the number of ISS clients holding the company’s stock.
The data provided included each director’s name, age, tenure, gender, committee membership, independence classification, title and industry, among other fields. Ages and tenures were missing on about 4 per cent of entries and were primarily from directors in Greece, Australia, Germany and New Zealand.
The data is current to July 2016, however many ages are as of the company's last annual report.
An FT survey of the data showed about 80 per cent of the largest 200 global companies were included in the database. The vast majority of companies that were not included were Chinese.


Clean biological occurrence records. Includes functionality for cleaning based on various aspects of spatial coordinates, unlikely values due to political 'centroids', coordinates based on where collections of specimens are held, and more.
| Version: | 0.1.1 |
| Imports: | methods, stats, utils, Matrix, magrittr, qlcMatrix, lazyeval |
| Suggests: | testthat, knitr, rgbif, sp, rworldmap, maps |
| Published: | 2016-03-08 |
| Author: | Scott Chamberlain [aut, cre] |
| Maintainer: | Scott Chamberlain <myrmecocystus at gmail.com> |
| BugReports: | https://github.com/ropenscilabs/scrubr/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/ropenscilabs/scrubr |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | scrubr results |
| Reference manual: | scrubr.pdf |
| Vignettes: |
scrubr introduction |
| Package source: | scrubr_0.1.1.tar.gz |
| Windows binaries: | r-devel: scrubr_0.1.1.zip, r-release: scrubr_0.1.1.zip, r-oldrel: scrubr_0.1.1.zip |
| OS X binaries: | r-release: scrubr_0.1.1.tgz, r-oldrel: scrubr_0.1.1.tgz |
| Old sources: | scrubr archive |
Please use the canonical form https://CRAN.R-project.org/package=scrubr to link to this page.


Fight of the Day: Felix Trinidad vs. Fernando Vargas
by Luke Irwin
Date: December 2, 2000
Card:
Championship(s): IBF Junior Middleweight Championship (Vargas), WBA Junior Middleweight Championship (Trinidad)
Venue: Mandalay Bay Events Center
Location: Las Vegas, Nevada


Former I'm A Celebrity winner Toff slams Jacqueline and Nadine for 'pulling out'
Toff, 25, branded the girls 'naughty' after they refused to put their names in the hat to be sent to I'm A Celeb's Jungle Jailhouse
by Irishmirror.ieFormer I'm A Celebrity winner Georgia 'Toff' Toffolo has hit out at Jacqueline Jossa and Nadine Coyle for sparking the first camp fight.
The Made in Chelsea star, 25, branded the girls "naughty" after they refused to put their names in the hat to be sent to the Jungle Jailhouse.
Speaking on This Morning, Toff said that she had been "shouting at the TV" over the awkward moment.
After achieving eight stars in their Bushtucker Trial, new campmates Cliff Parisi and Andy Whyment became Outlaws and were sent to the Jungle Jailhouse.
The main camp had the opportunity to be "good, bad or downright ugly" - and either keep all the food themselves, or choose to feed the outlaws with their share of food.
The twist with the second option was that two campmates would have to join the outlaws in the Jailhouse - but tempers flared over a difference in opinion.
When former footballer Ian suggested that they draw straws, they all picked bottles out named water bottles to see who would go.
However Jacqueline and Nadine both refused to go, prompting discord in camp.
"As a former campmate I think my judgement might be a bit different to everyone," Toff said. "I thought it was really naughty of them!
"Because you work as a team in there and I know it sounds so silly, but your day revolves around working together.
However host Ruth said that she loved their "honesty".
"It is, but I'm just not sure about that," Toff said.
*This Morning airs weekdays at 10.30am on ITV


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521 - 531 Granville St, Unit A6
Downtown, Vancouver
$1,200,000
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Downtown, Vancouver
$1,200,000
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Steve Jobs’ keynote address at Macworld on January 5 2000 was a milestone for two reasons.
First, after a little over three years of managing Apple, he declared he had accepted his de facto situation and become the company’s full-time CEO. Remember that he was only interim CEO up to this point, not wanting to upset either Pixar’s or Apple’s shareholders by being simultaneously CEO of two public companies. Time had proven this wasn’t a problem: Pixar was well managed by Ed Catmull and John Lasseter, and had released two successful movies


Welcome
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book.
About
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book.
- Lorem Ipsum is simply dummy text of the printing and typesetting industry
- Ipsum is simply dummy text
- Like Aldus PageMaker including versions of Lorem
- Software like Aldus PageMaker including versions of Lorem
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Services
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book.


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The pan-taxonomic compara is built using representative genomes from all significant clades represented in Ensembl and Ensembl Genomes, to offer a broad view of homologous relationships from across the taxonomy. This analysis enables the production of protein trees and orthologies across all the taxonomic groups in Ensmbl and Ensembl Genomes.
Model genomes along with genomes not represented by the familes of model organisms are picked for this analysis.
The bacterial genomes included are selected on the following criteria:
The gene trees obtained from this analysis can be accessed in the genome browser and also using the Perl API and REST API. Note that when using the APIs, the pan-taxonomic comparative genomics data resource can be picked using the name "pan_homology".


https://github.com/tmc/ssaview
Shows the SSA (Static Single Assignment) representation of input code.Uses the wonderful golang.org/x/tools/go/ssa package.
package main import "fmt" func main() { a, b := 42, 43 equal(a, b) fmt.Println("Let's see the SSA representation of this program") } func equal(i, j int) bool { // needless closure inner := func() bool { return i == j } return inner() }


For rain only, indicates a lots of rain.
For temperature only, indicates that the average monthly temperature is below zero.
Best place to visit (cheap, safe country or it's very easy to find internet connection).
Top interesting places on earth for nomads (due to relatively cheap life or popular internet connection or easy to get visa or relatively safe place).
Popular tourist destinations or warm weather or middle budget place to live.
Worst option(absent internet, very expensive country or very dangerous place).


Model
Model
groups layers into an object with training and inference features.
tf.compat.v1.keras.Model
tf.compat.v1.keras.models.Model
tf.compat.v2.keras.Model
tf.compat.v2.keras.models.Model
tf.keras.models.Model
There are two ways to instantiate a Model
:
1 - With the "functional API", where you start from Input
,
you chain layer calls to specify the model's forward pass,
and finally you create your model from inputs and outputs:
import tensorflow as tf inputs = tf.keras.Input(shape=(3,)) x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs) outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x) model = tf.keras.Model(inputs=inputs, outputs=outputs)
2 - By subclassing the Model
class: in that case, you should define your
layers in __init__
and you should implement the model's forward pass
in call
.
import tensorflow as tf class MyModel(tf.keras.Model): def __init__(self): super(MyModel, self).__init__() self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu) self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax) def call(self, inputs): x = self.dense1(inputs) return self.dense2(x) model = MyModel()
If you subclass Model
, you can optionally have
a training
argument (boolean) in call
, which you can use to specify
a different behavior in training and inference:
import tensorflow as tf class MyModel(tf.keras.Model): def __init__(self): super(MyModel, self).__init__() self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu) self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax) self.dropout = tf.keras.layers.Dropout(0.5) def call(self, inputs, training=False): x = self.dense1(inputs) if training: x = self.dropout(x, training=training) return self.dense2(x) model = MyModel()
__init__
__init__( *args, **kwargs )
layers
metrics_names
Returns the model's display labels for all outputs.
run_eagerly
Settable attribute indicating whether the model should run eagerly.
Running eagerly means that your model will be run step by step, like Python code. Your model might run slower, but it should become easier for you to debug it by stepping into individual layer calls.
By default, we will attempt to compile your model to a static graph to deliver the best execution performance.
Boolean, whether the model should run eagerly.
sample_weights
state_updates
Returns the updates
from all layers that are stateful.
This is useful for separating training updates and state updates, e.g. when we need to update a layer's internal state during prediction.
A list of update ops.
stateful
compile
compile( optimizer='rmsprop', loss=None, metrics=None, loss_weights=None, sample_weight_mode=None, weighted_metrics=None, target_tensors=None, distribute=None, **kwargs )
Configures the model for training.
optimizer
: String (name of optimizer) or optimizer instance.
See tf.keras.optimizers
.loss
: String (name of objective function), objective function or
tf.losses.Loss
instance. See tf.losses
. If the model has
multiple outputs, you can use a different loss on each output by
passing a dictionary or a list of losses. The loss value that will
be minimized by the model will then be the sum of all individual
losses.metrics
: List of metrics to be evaluated by the model during training
and testing. Typically you will use metrics=['accuracy']
.
To specify different metrics for different outputs of a
multi-output model, you could also pass a dictionary, such as
metrics={'output_a': 'accuracy', 'output_b': ['accuracy', 'mse']}
.
You can also pass a list (len = len(outputs)) of lists of metrics
such as metrics=[['accuracy'], ['accuracy', 'mse']]
or
metrics=['accuracy', ['accuracy', 'mse']]
.loss_weights
: Optional list or dictionary specifying scalar
coefficients (Python floats) to weight the loss contributions
of different model outputs.
The loss value that will be minimized by the model
will then be the weighted sum of all individual losses,
weighted by the loss_weights
coefficients.
If a list, it is expected to have a 1:1 mapping
to the model's outputs. If a tensor, it is expected to map
output names (strings) to scalar coefficients.sample_weight_mode
: If you need to do timestep-wise
sample weighting (2D weights), set this to "temporal"
.
None
defaults to sample-wise weights (1D).
If the model has multiple outputs, you can use a different
sample_weight_mode
on each output by passing a
dictionary or a list of modes.weighted_metrics
: List of metrics to be evaluated and weighted
by sample_weight or class_weight during training and testing.target_tensors
: By default, Keras will create placeholders for the
model's target, which will be fed with the target data during
training. If instead you would like to use your own
target tensors (in turn, Keras will not expect external
Numpy data for these targets at training time), you
can specify them via the target_tensors
argument. It can be
a single tensor (for a single-output model), a list of tensors,
or a dict mapping output names to target tensors.distribute
: NOT SUPPORTED IN TF 2.0, please create and compile the
model under distribution strategy scope instead of passing it to
compile.**kwargs
: Any additional arguments.ValueError
: In case of invalid arguments for
optimizer
, loss
, metrics
or sample_weight_mode
.evaluate
evaluate( x=None, y=None, batch_size=None, verbose=1, sample_weight=None, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False )
Returns the loss value & metrics values for the model in test mode.
Computation is done in batches.
x
: Input data. It could be:tf.data
dataset.keras.utils.Sequence
instance.y
: Target data. Like the input data x
,
it could be either Numpy array(s) or TensorFlow tensor(s).
It should be consistent with x
(you cannot have Numpy inputs and
tensor targets, or inversely).
If x
is a dataset, generator or
keras.utils.Sequence
instance, y
should not be specified (since
targets will be obtained from the iterator/dataset).batch_size
: Integer or None
.
Number of samples per gradient update.
If unspecified, batch_size
will default to 32.
Do not specify the batch_size
is your data is in the
form of symbolic tensors, dataset,
generators, or keras.utils.Sequence
instances (since they generate
batches).verbose
: 0 or 1. Verbosity mode.
0 = silent, 1 = progress bar.sample_weight
: Optional Numpy array of weights for
the test samples, used for weighting the loss function.
You can either pass a flat (1D)
Numpy array with the same length as the input samples
(1:1 mapping between weights and samples),
or in the case of temporal data,
you can pass a 2D array with shape
(samples, sequence_length)
,
to apply a different weight to every timestep of every sample.
In this case you should make sure to specify
sample_weight_mode="temporal"
in compile()
. This argument is not
supported when x
is a dataset, instead pass
sample weights as the third element of x
.steps
: Integer or None
.
Total number of steps (batches of samples)
before declaring the evaluation round finished.
Ignored with the default value of None
.
If x is a tf.data
dataset and steps
is
None, 'evaluate' will run until the dataset is exhausted.
This argument is not supported with array inputs.callbacks
: List of keras.callbacks.Callback
instances.
List of callbacks to apply during evaluation.
See callbacks.max_queue_size
: Integer. Used for generator or keras.utils.Sequence
input only. Maximum size for the generator queue.
If unspecified, max_queue_size
will default to 10.workers
: Integer. Used for generator or keras.utils.Sequence
input
only. Maximum number of processes to spin up when using
process-based threading. If unspecified, workers
will default
to 1. If 0, will execute the generator on the main thread.use_multiprocessing
: Boolean. Used for generator or
keras.utils.Sequence
input only. If True
, use process-based
threading. If unspecified, use_multiprocessing
will default to
False
. Note that because this implementation relies on
multiprocessing, you should not pass non-picklable arguments to
the generator as they can't be passed easily to children processes.Scalar test loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute model.metrics_names
will give you
the display labels for the scalar outputs.
ValueError
: in case of invalid arguments.evaluate_generator
evaluate_generator( generator, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0 )
Evaluates the model on a data generator.
The generator should return the same kind of data
as accepted by test_on_batch
.
generator
: Generator yielding tuples (inputs, targets)
or (inputs, targets, sample_weights)
or an instance of keras.utils.Sequence
object in order to avoid duplicate data
when using multiprocessing.steps
: Total number of steps (batches of samples)
to yield from generator
before stopping.
Optional for Sequence
: if unspecified, will use
the len(generator)
as a number of steps.callbacks
: List of keras.callbacks.Callback
instances.
List of callbacks to apply during evaluation.
See callbacks.max_queue_size
: maximum size for the generator queueworkers
: Integer. Maximum number of processes to spin up
when using process-based threading.
If unspecified, workers
will default to 1. If 0, will
execute the generator on the main thread.use_multiprocessing
: Boolean.
If True
, use process-based threading.
If unspecified, use_multiprocessing
will default to False
.
Note that because this implementation relies on multiprocessing,
you should not pass non-picklable arguments to the generator
as they can't be passed easily to children processes.verbose
: Verbosity mode, 0 or 1.Scalar test loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute model.metrics_names
will give you
the display labels for the scalar outputs.
ValueError
: in case of invalid arguments.ValueError
: In case the generator yields data in an invalid format.fit
fit( x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None, validation_freq=1, max_queue_size=10, workers=1, use_multiprocessing=False, **kwargs )
Trains the model for a fixed number of epochs (iterations on a dataset).
x
: Input data. It could be:tf.data
dataset. Should return a tuple
of either (inputs, targets)
or
(inputs, targets, sample_weights)
.keras.utils.Sequence
returning (inputs, targets)
or (inputs, targets, sample weights)
.y
: Target data. Like the input data x
,
it could be either Numpy array(s) or TensorFlow tensor(s).
It should be consistent with x
(you cannot have Numpy inputs and
tensor targets, or inversely). If x
is a dataset, generator,
or keras.utils.Sequence
instance, y
should
not be specified (since targets will be obtained from x
).batch_size
: Integer or None
.
Number of samples per gradient update.
If unspecified, batch_size
will default to 32.
Do not specify the batch_size
if your data is in the
form of symbolic tensors, datasets,
generators, or keras.utils.Sequence
instances (since they generate
batches).epochs
: Integer. Number of epochs to train the model.
An epoch is an iteration over the entire x
and y
data provided.
Note that in conjunction with initial_epoch
,
epochs
is to be understood as "final epoch".
The model is not trained for a number of iterations
given by epochs
, but merely until the epoch
of index epochs
is reached.verbose
: 0, 1, or 2. Verbosity mode.
0 = silent, 1 = progress bar, 2 = one line per epoch.
Note that the progress bar is not particularly useful when
logged to a file, so verbose=2 is recommended when not running
interactively (eg, in a production environment).callbacks
: List of keras.callbacks.Callback
instances.
List of callbacks to apply during training.
See tf.keras.callbacks
.validation_split
: Float between 0 and 1.
Fraction of the training data to be used as validation data.
The model will set apart this fraction of the training data,
will not train on it, and will evaluate
the loss and any model metrics
on this data at the end of each epoch.
The validation data is selected from the last samples
in the x
and y
data provided, before shuffling. This argument is
not supported when x
is a dataset, generator or
keras.utils.Sequence
instance.validation_data
: Data on which to evaluate
the loss and any model metrics at the end of each epoch.
The model will not be trained on this data.
validation_data
will override validation_split
.
validation_data
could be:(x_val, y_val)
of Numpy arrays or tensors(x_val, y_val, val_sample_weights)
of Numpy arraysbatch_size
must be provided.
For the last case, validation_steps
must be provided.shuffle
: Boolean (whether to shuffle the training data
before each epoch) or str (for 'batch').
'batch' is a special option for dealing with the
limitations of HDF5 data; it shuffles in batch-sized chunks.
Has no effect when steps_per_epoch
is not None
.class_weight
: Optional dictionary mapping class indices (integers)
to a weight (float) value, used for weighting the loss function
(during training only).
This can be useful to tell the model to
"pay more attention" to samples from
an under-represented class.sample_weight
: Optional Numpy array of weights for
the training samples, used for weighting the loss function
(during training only). You can either pass a flat (1D)
Numpy array with the same length as the input samples
(1:1 mapping between weights and samples),
or in the case of temporal data,
you can pass a 2D array with shape
(samples, sequence_length)
,
to apply a different weight to every timestep of every sample.
In this case you should make sure to specify
sample_weight_mode="temporal"
in compile()
. This argument is not
supported when x
is a dataset, generator, or
keras.utils.Sequence
instance, instead provide the sample_weights
as the third element of x
.initial_epoch
: Integer.
Epoch at which to start training
(useful for resuming a previous training run).steps_per_epoch
: Integer or None
.
Total number of steps (batches of samples)
before declaring one epoch finished and starting the
next epoch. When training with input tensors such as
TensorFlow data tensors, the default None
is equal to
the number of samples in your dataset divided by
the batch size, or 1 if that cannot be determined. If x is a
tf.data
dataset, and 'steps_per_epoch'
is None, the epoch will run until the input dataset is exhausted.
This argument is not supported with array inputs.validation_steps
: Only relevant if validation_data
is provided and
is a tf.data
dataset. Total number of steps (batches of
samples) to draw before stopping when performing validation
at the end of every epoch. If validation_data is a tf.data
dataset
and 'validation_steps' is None, validation
will run until the validation_data
dataset is exhausted.validation_freq
: Only relevant if validation data is provided. Integer
or collections_abc.Container
instance (e.g. list, tuple, etc.).
If an integer, specifies how many training epochs to run before a
new validation run is performed, e.g. validation_freq=2
runs
validation every 2 epochs. If a Container, specifies the epochs on
which to run validation, e.g. validation_freq=[1, 2, 10]
runs
validation at the end of the 1st, 2nd, and 10th epochs.max_queue_size
: Integer. Used for generator or keras.utils.Sequence
input only. Maximum size for the generator queue.
If unspecified, max_queue_size
will default to 10.workers
: Integer. Used for generator or keras.utils.Sequence
input
only. Maximum number of processes to spin up
when using process-based threading. If unspecified, workers
will default to 1. If 0, will execute the generator on the main
thread.use_multiprocessing
: Boolean. Used for generator or
keras.utils.Sequence
input only. If True
, use process-based
threading. If unspecified, use_multiprocessing
will default to
False
. Note that because this implementation relies on
multiprocessing, you should not pass non-picklable arguments to
the generator as they can't be passed easily to children processes.**kwargs
: Used for backwards compatibility.A History
object. Its History.history
attribute is
a record of training loss values and metrics values
at successive epochs, as well as validation loss values
and validation metrics values (if applicable).
RuntimeError
: If the model was never compiled.ValueError
: In case of mismatch between the provided input data
and what the model expects.fit_generator
fit_generator( generator, steps_per_epoch=None, epochs=1, verbose=1, callbacks=None, validation_data=None, validation_steps=None, validation_freq=1, class_weight=None, max_queue_size=10, workers=1, use_multiprocessing=False, shuffle=True, initial_epoch=0 )
Fits the model on data yielded batch-by-batch by a Python generator.
The generator is run in parallel to the model, for efficiency. For instance, this allows you to do real-time data augmentation on images on CPU in parallel to training your model on GPU.
The use of keras.utils.Sequence
guarantees the ordering
and guarantees the single use of every input per epoch when
using use_multiprocessing=True
.
generator
: A generator or an instance of Sequence
(keras.utils.Sequence
)
object in order to avoid duplicate data
when using multiprocessing.
The output of the generator must be either(inputs, targets)
(inputs, targets, sample_weights)
.
This tuple (a single output of the generator) makes a single batch.
Therefore, all arrays in this tuple must have the same length (equal
to the size of this batch). Different batches may have different
sizes.
For example, the last batch of the epoch is commonly smaller than
the
others, if the size of the dataset is not divisible by the batch
size.
The generator is expected to loop over its data
indefinitely. An epoch finishes when steps_per_epoch
batches have been seen by the model.steps_per_epoch
: Total number of steps (batches of samples)
to yield from generator
before declaring one epoch
finished and starting the next epoch. It should typically
be equal to the number of samples of your dataset
divided by the batch size.
Optional for Sequence
: if unspecified, will use
the len(generator)
as a number of steps.epochs
: Integer, total number of iterations on the data.verbose
: Verbosity mode, 0, 1, or 2.callbacks
: List of callbacks to be called during training.validation_data
: This can be eithervalidation_steps
: Only relevant if validation_data
is a generator. Total number of steps (batches of samples)
to yield from generator
before stopping.
Optional for Sequence
: if unspecified, will use
the len(validation_data)
as a number of steps.validation_freq
: Only relevant if validation data is provided. Integer
or collections_abc.Container
instance (e.g. list, tuple, etc.).
If an integer, specifies how many training epochs to run before a
new validation run is performed, e.g. validation_freq=2
runs
validation every 2 epochs. If a Container, specifies the epochs on
which to run validation, e.g. validation_freq=[1, 2, 10]
runs
validation at the end of the 1st, 2nd, and 10th epochs.class_weight
: Dictionary mapping class indices to a weight
for the class.max_queue_size
: Integer. Maximum size for the generator queue.
If unspecified, max_queue_size
will default to 10.workers
: Integer. Maximum number of processes to spin up
when using process-based threading.
If unspecified, workers
will default to 1. If 0, will
execute the generator on the main thread.use_multiprocessing
: Boolean.
If True
, use process-based threading.
If unspecified, use_multiprocessing
will default to False
.
Note that because this implementation relies on multiprocessing,
you should not pass non-picklable arguments to the generator
as they can't be passed easily to children processes.shuffle
: Boolean. Whether to shuffle the order of the batches at
the beginning of each epoch. Only used with instances
of Sequence
(keras.utils.Sequence
).
Has no effect when steps_per_epoch
is not None
.initial_epoch
: Epoch at which to start training
(useful for resuming a previous training run)A History
object.
def generate_arrays_from_file(path): while 1: f = open(path) for line in f: # create numpy arrays of input data # and labels, from each line in the file x1, x2, y = process_line(line) yield ({'input_1': x1, 'input_2': x2}, {'output': y}) f.close() model.fit_generator(generate_arrays_from_file('/my_file.txt'), steps_per_epoch=10000, epochs=10)
Raises: ValueError: In case the generator yields data in an invalid format.
get_layer
get_layer( name=None, index=None )
Retrieves a layer based on either its name (unique) or index.
If name
and index
are both provided, index
will take precedence.
Indices are based on order of horizontal graph traversal (bottom-up).
name
: String, name of layer.index
: Integer, index of layer.A layer instance.
ValueError
: In case of invalid layer name or index.load_weights
load_weights( filepath, by_name=False )
Loads all layer weights, either from a TensorFlow or an HDF5 file.
predict
predict( x, batch_size=None, verbose=0, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False )
Generates output predictions for the input samples.
Computation is done in batches.
x
: Input samples. It could be:tf.data
dataset.keras.utils.Sequence
instance.batch_size
: Integer or None
.
Number of samples per gradient update.
If unspecified, batch_size
will default to 32.
Do not specify the batch_size
is your data is in the
form of symbolic tensors, dataset,
generators, or keras.utils.Sequence
instances (since they generate
batches).verbose
: Verbosity mode, 0 or 1.steps
: Total number of steps (batches of samples)
before declaring the prediction round finished.
Ignored with the default value of None
. If x is a tf.data
dataset and steps
is None, predict
will
run until the input dataset is exhausted.callbacks
: List of keras.callbacks.Callback
instances.
List of callbacks to apply during prediction.
See callbacks.max_queue_size
: Integer. Used for generator or keras.utils.Sequence
input only. Maximum size for the generator queue.
If unspecified, max_queue_size
will default to 10.workers
: Integer. Used for generator or keras.utils.Sequence
input
only. Maximum number of processes to spin up when using
process-based threading. If unspecified, workers
will default
to 1. If 0, will execute the generator on the main thread.use_multiprocessing
: Boolean. Used for generator or
keras.utils.Sequence
input only. If True
, use process-based
threading. If unspecified, use_multiprocessing
will default to
False
. Note that because this implementation relies on
multiprocessing, you should not pass non-picklable arguments to
the generator as they can't be passed easily to children processes.Numpy array(s) of predictions.
ValueError
: In case of mismatch between the provided
input data and the model's expectations,
or in case a stateful model receives a number of samples
that is not a multiple of the batch size.predict_generator
predict_generator( generator, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0 )
Generates predictions for the input samples from a data generator.
The generator should return the same kind of data as accepted by
predict_on_batch
.
generator
: Generator yielding batches of input samples
or an instance of keras.utils.Sequence
object in order to
avoid duplicate data when using multiprocessing.steps
: Total number of steps (batches of samples)
to yield from generator
before stopping.
Optional for Sequence
: if unspecified, will use
the len(generator)
as a number of steps.callbacks
: List of keras.callbacks.Callback
instances.
List of callbacks to apply during prediction.
See callbacks.max_queue_size
: Maximum size for the generator queue.workers
: Integer. Maximum number of processes to spin up
when using process-based threading.
If unspecified, workers
will default to 1. If 0, will
execute the generator on the main thread.use_multiprocessing
: Boolean.
If True
, use process-based threading.
If unspecified, use_multiprocessing
will default to False
.
Note that because this implementation relies on multiprocessing,
you should not pass non-picklable arguments to the generator
as they can't be passed easily to children processes.verbose
: verbosity mode, 0 or 1.Numpy array(s) of predictions.
ValueError
: In case the generator yields data in an invalid format.predict_on_batch
predict_on_batch(x)
Returns predictions for a single batch of samples.
x
: Input data. It could be:tf.data
dataset.Numpy array(s) of predictions.
ValueError
: In case of mismatch between given number of inputs and
expectations of the model.reset_metrics
reset_metrics()
Resets the state of metrics.
reset_states
reset_states()
save
save( filepath, overwrite=True, include_optimizer=True, save_format=None, signatures=None, options=None )
Saves the model to Tensorflow SavedModel or a single HDF5 file.
This allows you to save the entirety of the state of a model in a single file.
Saved models can be reinstantiated via keras.models.load_model
.
The model returned by load_model
is a compiled model ready to be used (unless the saved model
was never compiled in the first place).
filepath: String, path to SavedModel or H5 file to save the model.
overwrite: Whether to silently overwrite any existing file at the
target location, or provide the user with a manual prompt.
include_optimizer: If True, save optimizer's state together.
save_format: Either 'tf' or 'h5', indicating whether to save the model
to Tensorflow SavedModel or HDF5. The default is currently 'h5', but
will switch to 'tf' in TensorFlow 2.0. The 'tf' option is currently
disabled (use tf.keras.experimental.export_saved_model
instead).
signatures
: Signatures to save with the SavedModel. Applicable to the 'tf'
format only. Please see the signatures
argument in
tf.saved_model.save
for details.options
: Optional tf.saved_model.SaveOptions
object that specifies
options for saving to SavedModel.from keras.models import load_model model.save('my_model.h5') # creates a HDF5 file 'my_model.h5' del model # deletes the existing model # returns a compiled model # identical to the previous one model = load_model('my_model.h5')
save_weights
save_weights( filepath, overwrite=True, save_format=None )
Saves all layer weights.
Either saves in HDF5 or in TensorFlow format based on the save_format
argument.
When saving in HDF5 format, the weight file has:
- layer_names
(attribute), a list of strings
(ordered names of model layers).
- For every layer, a group
named layer.name
- For every such layer group, a group attribute weight_names
,
a list of strings
(ordered names of weights tensor of the layer).
- For every weight in the layer, a dataset
storing the weight value, named after the weight tensor.
When saving in TensorFlow format, all objects referenced by the network are
saved in the same format as tf.train.Checkpoint
, including any Layer
instances or Optimizer
instances assigned to object attributes. For
networks constructed from inputs and outputs using tf.keras.Model(inputs,
outputs)
, Layer
instances used by the network are tracked/saved
automatically. For user-defined classes which inherit from tf.keras.Model
,
Layer
instances must be assigned to object attributes, typically in the
constructor. See the documentation of tf.train.Checkpoint
and
tf.keras.Model
for details.
While the formats are the same, do not mix save_weights
and
tf.train.Checkpoint
. Checkpoints saved by Model.save_weights
should be
loaded using Model.load_weights
. Checkpoints saved using
tf.train.Checkpoint.save
should be restored using the corresponding
tf.train.Checkpoint.restore
. Prefer tf.train.Checkpoint
over
save_weights
for training checkpoints.
The TensorFlow format matches objects and variables by starting at a root
object, self
for save_weights
, and greedily matching attribute
names. For Model.save
this is the Model
, and for Checkpoint.save
this
is the Checkpoint
even if the Checkpoint
has a model attached. This
means saving a tf.keras.Model
using save_weights
and loading into a
tf.train.Checkpoint
with a Model
attached (or vice versa) will not match
the Model
's variables. See the guide to training
checkpoints for details
on the TensorFlow format.
filepath
: String, path to the file to save the weights to. When saving
in TensorFlow format, this is the prefix used for checkpoint files
(multiple files are generated). Note that the '.h5' suffix causes
weights to be saved in HDF5 format.overwrite
: Whether to silently overwrite any existing file at the
target location, or provide the user with a manual prompt.save_format
: Either 'tf' or 'h5'. A filepath
ending in '.h5' or
'.keras' will default to HDF5 if save_format
is None
. Otherwise
None
defaults to 'tf'.ImportError
: If h5py is not available when attempting to save in HDF5
format.ValueError
: For invalid/unknown format arguments.summary
summary( line_length=None, positions=None, print_fn=None )
Prints a string summary of the network.
line_length
: Total length of printed lines
(e.g. set this to adapt the display to different
terminal window sizes).positions
: Relative or absolute positions of log elements
in each line. If not provided,
defaults to [.33, .55, .67, 1.]
.print_fn
: Print function to use. Defaults to print
.
It will be called on each line of the summary.
You can set it to a custom function
in order to capture the string summary.ValueError
: if summary()
is called before the model is built.test_on_batch
test_on_batch( x, y=None, sample_weight=None, reset_metrics=True )
Test the model on a single batch of samples.
x
: Input data. It could be:tf.data
dataset.y
: Target data. Like the input data x
,
it could be either Numpy array(s) or TensorFlow tensor(s).
It should be consistent with x
(you cannot have Numpy inputs and
tensor targets, or inversely). If x
is a dataset y
should
not be specified (since targets will be obtained from the iterator).sample_weight
: Optional array of the same length as x, containing
weights to apply to the model's loss for each sample.
In the case of temporal data, you can pass a 2D array
with shape (samples, sequence_length),
to apply a different weight to every timestep of every sample.
In this case you should make sure to specify
sample_weight_mode="temporal" in compile(). This argument is not
supported when x
is a dataset.reset_metrics
: If True
, the metrics returned will be only for this
batch. If False
, the metrics will be statefully accumulated across
batches.Scalar test loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute model.metrics_names
will give you
the display labels for the scalar outputs.
ValueError
: In case of invalid user-provided arguments.to_json
to_json(**kwargs)
Returns a JSON string containing the network configuration.
To load a network from a JSON save file, use
keras.models.model_from_json(json_string, custom_objects={})
.
**kwargs
: Additional keyword arguments
to be passed to json.dumps()
.A JSON string.
to_yaml
to_yaml(**kwargs)
Returns a yaml string containing the network configuration.
To load a network from a yaml save file, use
keras.models.model_from_yaml(yaml_string, custom_objects={})
.
custom_objects
should be a dictionary mapping
the names of custom losses / layers / etc to the corresponding
functions / classes.
**kwargs
: Additional keyword arguments
to be passed to yaml.dump()
.A YAML string.
ImportError
: if yaml module is not found.train_on_batch
train_on_batch( x, y=None, sample_weight=None, class_weight=None, reset_metrics=True )
Runs a single gradient update on a single batch of data.
x
: Input data. It could be:tf.data
dataset.y
: Target data. Like the input data x
, it could be either Numpy
array(s) or TensorFlow tensor(s). It should be consistent with x
(you cannot have Numpy inputs and tensor targets, or inversely). If
x
is a dataset, y
should not be specified
(since targets will be obtained from the iterator).sample_weight
: Optional array of the same length as x, containing
weights to apply to the model's loss for each sample. In the case of
temporal data, you can pass a 2D array with shape (samples,
sequence_length), to apply a different weight to every timestep of
every sample. In this case you should make sure to specify
sample_weight_mode="temporal" in compile(). This argument is not
supported when x
is a dataset.class_weight
: Optional dictionary mapping class indices (integers) to a
weight (float) to apply to the model's loss for the samples from this
class during training. This can be useful to tell the model to "pay
more attention" to samples from an under-represented class.reset_metrics
: If True
, the metrics returned will be only for this
batch. If False
, the metrics will be statefully accumulated across
batches.Scalar training loss
(if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute model.metrics_names
will give you
the display labels for the scalar outputs.
ValueError
: In case of invalid user-provided arguments.


Over the years my philosophy in life has changed a bit. I used to be a perfectionist with all the stress that goes along with that. Now don't get me wrong, I still have that perfectionist drive within me today. I've just learned when it's time to say... "Done!"
Currently I am learning Frontend Web Development. Here is my Web Developer Portfolio, if you would like to check out my work. Thank you for visiting! Send me a message, it would be great to hear from you.
I was born in Denver, Colorado and have lived there for most of my life, moving around several times in the metro area.


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{% extends "base.html" %} {% block content %}
I need to lean Emmet properly, or print off the standard codes until I remember it all!
Suspect, I would want to clone this CookieCutter to put in place my own template for projects
{% endblock content %}


Welcome, Mike!
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The stock turtlebot3 burger uses a raspberry pi
SBCSingle Board Computer
to run
ROSRobot Operating System
, which
is what the user interacts with. There is also a embedded board called the OpenCR which runs the
DYNAMIXEL-brand servo motors and houses the IMU. The OpenCR board has a variety of open ports such as
UART, I2C, SPI, and CAN which the user could make use of. The OpenCR board runs off of an STM32 F7-series
microcontroller, which implements and ARM Cortex-M7 processor and can be programmed using the arduino editor.
The stock firmware can be found
here.
It should be noted that almost all components on the turtlebot3 are open source, so you can find all the software, mechanical cad,
and electrical designs online; mostly on
Robotis'company in charge of turtlebot3
documentation
website.


%inherit file="basewithmenu.html" /> <%block name="head"> ${parent.head()} %block> <%block name="menu2"> Upload documents %block> × Upload documents Upload to: Select an existing dataset to add documents to or type a new name to create it Data file: Format: Id,Text: a csv file, with two columns, the first is a unique ID, the second the text content. Id,IsoDateTime,Text: text is in the third column, the second one contains the creation date formated as specified by the datetime.fromisoformat function. E.g., "2011-11-04 00:05:23". Id,Text Id,IsoDateTime,Text Uploading × Rename a dataset Classifier to rename: New name: Processing × Change description Dataset: Description: Processing × Delete a dataset Classifier to delete: Processing
Upload to: Select an existing dataset to add documents to or type a new name to create it
Data file:
Format:
Uploading
Classifier to rename:
New name:
Processing
Dataset:
Description:
Classifier to delete:


James found a love letter his friend Harry has written for his girlfriend. James is a prankster, so he decides to meddle with the letter. He changes all the words in the letter into palindromes.
To do this, he follows two rules:
Each reduction in the value of any letter is counted as a single operation. Find the minimum number of operations required to convert a given string into a palindrome.
Input Format
The first line contains an integer $T$, i.e., the number of test cases.
The next $T$ lines will contain a string each. The strings do not contain any spaces.
Constraints
$1 \le T \le 10$
$1 \le$ length of string $\le 10^4$
All characters are lower case English letters.
Output Format
A single line containing the number of minimum operations corresponding to each test case.
Sample Input
4
abc
abcba
abcd
cba
Sample Output
2
0
4
2
Explanation


| Interface | Description |
|---|---|
| HasBooleanRandom |
Interface providing methods, that needs to be implemented by value, that want to use boolean generator.
|
| HasGenerator |
Interface providing methods, that needs to be implemented by values, that are using generator
|
| HasLinearDistribution |
Interface providing methods, that needs to be implemented by value, that want to use linear distribution generator.
|
| HasNormalDistribution |
Interface providing methods, that needs to be implemented by value, that want to use normal distribution generator.
|
| Status |
Interface used for values represented as status.
|
| Value<T> |
Interface providing method declarations needed to implemented by every value.
|
| VPTRegulatorValue |
Interface implemented by values emulating VPT regulator.
|
| Class | Description |
|---|---|
| AbstractValue<T> |
Abstract class providing implementation of
Value interface methods used by
all specific value implementations. |
| ValueFactory |
Class used to create new instances of values.
|
| Enum | Description |
|---|---|
| Value.Generator |
Enum containing description of generators for new values.
|
| Value.Type |
Enum containing all types of implemented values with their default information.
|