|
|
template<Size L> |
| MatrixBase< L, M, Val > | operator* (const MatrixBase< L, N, Val > &rhs) const |
| | Matrix-matrix multiplication.
|
| |
|
ColType | operator* (const ColType &rhs) const |
| | Matrix-vector multiplication.
|
| |
| template<typename... , Size X = N, Size Y = M, std::enable_if_t< X !=Y, Int > = 0> |
| Val | Determinant () const |
| | Returns the determinant of a matrix. More...
|
| |
| template<typename... , Size X = N, Size Y = M, std::enable_if_t< X !=Y, Int > = 0> |
| SelfType | Inverse () const |
| | Returns the inverse of a matrix such that mat * mat.Inverse() is an identity matrix. More...
|
| |
◆ Determinant()
template<Size N, Size M, typename Val >
template<typename... , Size X = N, Size Y = M, std::enable_if_t< X !=Y, Int > = 0>
Returns the determinant of a matrix.
Non-square matricies do not have a determinant defined and will return NaN
.
◆ Inverse()
template<Size N, Size M, typename Val >
template<typename... , Size X = N, Size Y = M, std::enable_if_t< X !=Y, Int > = 0>
Returns the inverse of a matrix such that mat * mat.Inverse()
is an identity matrix.
Non-square matrices do not have an inverse and thus will return a NxM matrix of NaN
values.
The documentation for this class was generated from the following file:


I make a bid to change the job I have into the job I want, with luke-warm result.
It seems, for whatever reason, the people I work with don't want me to leave. Lucky for them my greatest success so far in my job search has been getting some feedback on a few of my rejected my applications.
Knowing that I want to move to a software development role; some people in my office have been making noises at higher levels. As a result I got a chance today to pitch a member of the executive leadership team. One of the projects I have in mind would solve workload and staffing problems we've been having, and also capture valuable data that we are currently throwing away.
On Tuesday I prepared some graphics and application mockups, and worked on my delivery. The meeting got pushed from Wednesday due to a last minute schedule change, but I spent basically all day today selling my plan. As a result of my efforts and seemingly convincing arguments I've received a resounding meh.
Now I knew going in that the person I was trying to sell this to didn't actually have the power to make anything happen. He is very sharp though, and he has the ear of the CEO and other executives that can make decisions. If I got him on my side I might have a chance of making a new position for myself.
In the end he agreed that if I could do what I said, and if I could adapt it to the variety of business models that exist in our company, he thought it would be worthwhile. He did not seem very optimistic about my chances of getting green-lit with a budget though, especially not any time soon.
It turns out though that the CFO is actually currently tasked with resolving the very problems my system would address. He is on vacation right now, which gives me a little time to build a prototype and refine my pitch.
The question is whether it is really worth my time. I'll be working unpaid building something nobody asked for to try and sell it to someone who doesn't want it, or me for that matter.
So yeah, I was feeling pretty low today when I left the office. The last three months of hard work trying to get a job have been a complete failure, and my big chance to fix the job I have ended in apathetic mediocrity.
Today was also my self imposed drop-dead date - the day where I either get some kind of concession in my current job, or quit outright and re-focus on my job search. I expected the outcome of todays meeting to push me one way or the other but instead I'm just more disillusioned and indecisive.
Edit - Resolution
Well I've made a decision. With no active projects, and no indication that I will get paid for what I've been working on, there are no reasons left for me to stay any longer. Even acknowledging those realities it was a hard choice. In the end I flipped a coin - best two out of three - to decide if I should stay and build my prototype, twice. I was flipping heads to stay, but the coins came up tails every flip...


{% extends "header.html" %} {% block body %}
Welcome to another python 3 basics tutorial, in this tutorial we're going to now discuss the concept of global and local variables.
When users begin using functions, they can quickly become confused when it comes to global and local variables... getting a the dreaded variable is not defined even when they clearly see that it is... or so they think.
These terms of global and local correspond to a variable's reach within a script or program. A global variable is one that can be accessed anywhere. A local variable is the opposite, it can only be accessed within its frame. The difference is that global variables can be accessed locally, but not modified locally inherently.
A local variable cannot be accessed globally, inherently. Now, dont worry about committing that to memory right now, I think it makes a lot more sense when you just see and do it, so let's do that.
# this variable has no parent function, but is actually NOT a global variable. # it just so happens that it is committed to memory before the function is called # so we are able to iterate, or call it out, but we cannot do much else. x = 6 def example(): print x # z, however, is a local variable. z = 5 # this works print(z) example() # this does not, which often confuses people, because z has been defined # and successfully even was called... the problem is that it is a local # variable only, and you are attempting to access it globally. print(z)
Here, we can see that we are able to access the x variable. We then defined and print out the z variable. We can then call the function, and all seems well. When we go to reference the z variable however, we have trouble. The z variable is local to the example function.
Let's look at another example:
x = 6 def example2(): # works print(x) print(x+5) # but then what happens when we go to modify: x+=6 # so there we attempted to take the x var and add 6 to it... but now # we are told that we cannot, as we're referencing the variable before # its assignment.
Here, again, we are able to reference x, we are even able to print x+6... but we are not allowed to modify x.
What if we'd like to modify x? Well, then we need to use global!
x = 6 def example3(): # what we do here is defined x as a global variable. global x # now we can: print(x) x+=5 print(x)
Now we're cooking! The problem here is that some people do not like the idea at all of using global variables. How do we get around using them and referencing them locally?
x = 6 def example4(): globx = x # now we can: print(globx) globx+=5 print(globx)
We are able to do the above, by assigning the value that we can reference to a local variable, then doing what we want with it from there.
Another choice you might have, as suggested by one of my viewers is the following:
x = 6 def example(x): print(x) x+=5 print(x) return x x = example(x) print(x)
In the above example, we have the function modifying x. It may appear somewhat confusing since x is being used in multiple locations, so maybe a more clear example is something like:
x = 6 def example(modify): print(modify) modify+=5 print(modify) return modify x = example(x) print(x)
So, you can better visualize this function as a "modification" function, where it modifies the variable you pass through. Besides the definition of this function, you only need to reassign the variable you want to change as the function with that variable as the parameter.


We often think of wealthy people as successful. Success is an easy thing to admire and respect.
We often think of poverty as a personal failure - that someone is poor or unhoused because they have made poor life choices. It is harder to respect and admire people who you think have failed and have made poor choices.
If we understand these structural causes, we will no longer view poverty, housing insecurity, and living in encampments as personal failures, and we will then be able to understand that someone can be working hard and making all of the “right” choices but still be poor and/or unhoused.
If we begin to understand the structural causes of poverty, we can stop blaming people for being poor or being unhoused.
Being poor and being unhoused are not personal failures - they are societal failures.
If we start to reframe our thinking as to what causes our neighbors to become unhoused, we will be more respectful of all people, regardless of their economic status
Wealth and economic status should not determine who deserves our respect and who does not.


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 4
James Martin makes homemade chicken satay with a twist.
Blend the peanuts and breadcrumbs in a food processor to a fine powder, sprinkle onto a plate. Sprinkle the flour onto a separate plate.
Dredge the chicken strips in the flour, then dip into the egg and roll in the peanut crumbs.
Fill a deep-fat fryer or a large, deep, heavy-bottomed pan two-thirds full with vegetable oil. Heat the oil to 190C/375F (use a probe thermometer to check the temperature if you're not using a deep-fat fryer). (CAUTION: Hot oil can be dangerous. Don't leave unattended)
Fry the chicken strips for 3-4 minutes until golden-brown. Remove from the pan with a slotted spoon and set aside to drain on kitchen paper.
Meanwhile for the satay sauce, heat the oil in a frying pan, add the shallot, chilli, garlic and ginger and fry for 2-3 minutes, or until slightly softened.
Pour in the coconut milk, lime juice and soy sauce and bring to a simmer.
Add the peanuts, then blend the mixture in a food processor to a chunky sauce. Add more soy sauce and lime juice to taste.
For the salad, mix the leaves, red onion, radishes and mint together in a bowl until well combined.
Whisk the lime juice and vegetable oil in a bowl and mix with the salad until well combined.
To serve, place some salad onto the plate, with the chicken strips alongside. Spoon the satay sauce into a small bowl and serve alongside.


--- layout: post title: "Python coding #2" subtitle: "Leetcode 279. Perfect Squares" categories: [Python] date: 2020-09-11 18:45:13 -0400 background: ---
Given a positive integer n, find the least number of perfect square numbers (for example, 1, 4, 9, 16, ...) which sum to n. Example 1: Input: n = 12 Output: 3 Explanation: 12 = 4 + 4 + 4. Example 2: Input: n = 13 Output: 2 Explanation: 13 = 4 + 9
My solution:
I had to refer to blogs to solve it..
(code reference blog: https://somjang.tistory.com/entry/BaeKJoon-1699%EB%B2%88-%EC%A0%9C%EA%B3%B1%EC%88%98%EC%9D%98-%ED%95%A9-Python)
(idea reference blog: https://it-and-life.tistory.com/76)
inputNum = int(input()) nc = [0] * (inputNum+1) for i in range(1, inputNum+1): nc[i] = i for j in range(1, i): if (j * j) > i: break nc[i] = min(nc[i], nc[i - j * j] + 1) print(nc[inputNum])


{% include button.html url="https://training.galaxyproject.org" text="Galaxy Training Material" %}
{% include button.html url="https://request.usegalaxy.org.au" text="Request Tool/Dataset" %}
{% include button.html url="https://docs.google.com/forms/d/e/1FAIpQLSeiw6ajmkezLCwbXc3OFQEU3Ai9hGnBd967u9YbQ8ANPgvatA/viewform" text="Request Data Quota" %}


Deepest nesting of UL or OL elements In The Dom
Get Count
See DOM on Heroku
Visit The Repo on Github


presentation title
Full Screen Image Left Text
Full Screen Image
Two 50% Images
List
Sample text
Sample text
Sample text


|
system
0.1.0
|
Abstract representation of timer hardware. More...
#include <timer.hpp>
Public Member Functions | |
| virtual | ~timer () |
| virtual double | get_frequency ()=0 |
| virtual void | set_frequency (double in_frequency_hz)=0 |
| virtual void | interrupt_handler (interrupt_frame &in_frame)=0 |
Abstract representation of timer hardware.
|
inlinevirtual |
|
pure virtual |
|
pure virtual |
|
pure virtual |


{% load static %}
{% block styles %}{% endblock %}
P
CP
PCP-
DEMO
Toggle navigation
{% block content %}
Target List
Plugin Name
Location
{% if host_dict %} {% for items in host_dict %}
{{ items.json }}
{{ items.PluginName }}
{{ items.Location }}
{% endfor %} {% endif %}
Command Builder

Jobs Queue
A
Clear All
Clear Sequence
Add Job
Insert # of Jobs
Sequence Expire
Sequence Start
Drop Here to apply the command to
all
jobs
Drop Here to apply to
all
jobs
Job #
Plugin Name
Host
Command
Status
Job Output
A
Truncate output
Job #
Update
Status
{% endblock %}
×
Terminal for
using
:
Input
--output--
Console Command:
×
Plugin Controller
Name:
Select a Plugin Name
Extra Features:
true
false
Extra features is determined at container export
State:
Desired State:
Activate
Restart
Stop
Desired state must be selected from "Activate", "Restart", or "Stop"
Interface:
Interface must be selected from available interfaces (Refresh the plugin list from the right pullout if desired interface not listed)
Ports (comma separated):
Ports must be in the form of number/protocol 1111/tcp or comma separated 1111/tcp,2222/udp
Environment (comma separated):
must be in the format of key=value or comma separated key1=val1,key2=val2,...
OS (posix/nt/all):
posix
nt
all
select posix to run on an linux-based host, nt to run on a windows based host, or all to run on either
{% block extra_js %}{% endblock extra_js %}


Deepraj Rakshit
Coder | AI and Cloud Aspirant | Open Source Enthusiast | Blogger
I'm Deepraj Rakshit and I'm currently an undergrad B.tech student in Information Technology with a prolific knowledge in Python, Kotlin, C, Java, Web Development, Google Cloud Platform, IBM Watson, Chatbot Development Microsoft Azure, and Arduino.


| Date | Name | Description | Duration |
|---|---|---|---|
| 26/07/2003 13:34:58 | save model - if only one loaded then just save it | The save model menu item from the mid form always popped up a list of models. When there is only one model loaded then it should just save it rather than popping up a list with the one model in it.  | 0.5  |
| 25/07/2003 16:35:48 | Added "Instances List" to right click on entity in model browser | Right clicking on an entity in the model browser now has the option "Instances List" Clicking on this displays the instances in a grid window in the same way that a double click on the entity does.  |
1  |
| 25/07/2003 10:20:33 | change version numbering | i have changed the version numbering so that we have the mainVersion.SubVersion.Revision  | 1  |
| ID | Name | Raised Date | Closed Date | Description | Resolution |
|---|---|---|---|---|---|
| 470 | Editing a weighting then click on other tab changes not reflected | 26/07/2003 14:44:30 | 27/07/2003 18:00:00 | If an entity instance has weightings and one of them is being edited and the user clicks on oneo f the top level tabs (description, attachments etc.) then the edited weighting is lost, the changes are only stored when a weighting tab is clicked.  | Added code at the top level to make any changes necessary on tab click  |
| 468 | deleting an item that is related to another does not update the list in an edit pane | 26/07/2003 13:31:35 | 26/07/2003 14:31:35 | When an item is related to another and item2 is displayed in an edit pane, if item1 is deleted then the edit pane relationship display is not updated and a reference to the deleted item remains.  | the edit pane just wasn't being informed under these circumstances. changed so that it is.  |
| 467 | When an instance is created from the model browser then the list is not updated. | 26/07/2003 13:44:02 | 26/07/2003 14:44:02 | When an instance is created from the model browser by using the pop up list from the hierarchy or the insert key then any free floating list of instances is not updated to reflect the new addition.  |   |
| 466 | Generic List of instances not updated when hierarchy node changed | 26/07/2003 12:37:22 | 26/07/2003 13:37:22 | When an instance has been added to a hierarchy and the list of instances is open in a window, when the instance is edited in the hierarchy then the changes are not reflected in the generic list.  |   |
| 462 | creation of a new entity instance in hierarchy does not refresh list | 25/07/2003 16:05:55 | 25/07/2003 16:35:00 | Entity Instances lists which are displayed in their own window are not updated when a new instance is created in a hiearchy. The model browser is correctly updated but the free floating list is not.  | minor coding errors in the observer for the grid form led to the entity name not being recognised and then the base collection cloning was not propogated to the control.  |
| 461 | could not show weighitngs in templates | 21/07/2003 10:21:19 | 24/07/2003 18:21:19 | the report template functionality for <!CTA theobject.weighting.weightingname !> isn't actually picking up the weighitng name so the template is showing a blank value.  |
Fairly simple change to the parsing code - it was just an index that was out in the token array.  |
| 469 | Changes in name of related item are not reflected in list | 27/07/2003 17:43:08 | 27/07/2003 18:20:00 | In the hierarchy view, when a related item is changed, if it is listed in the currently edited entity then the relationship list is not refreshed.  | amended the update relationship functionality  |
| ID | Name | Raised Date | Status | Criticality | Description |
|---|---|---|---|---|---|
| 62 | icons on buttons are not transparent | 17/04/2002 | OPEN | LOW | need to turn the .ico into .bmp and setup the mask property  |
| 453 | Import entity defn once only | 29/06/2003 14:17:05 | OPEN | LOW | The system allows the user to import an entity definition twice, or more, the system should really not allow this or should rename the imported entity or should merge the two definiitions or replace the current entity defn with the imported one. The system should probably prompt the user with these options when they try to import a duplicate definition.  |
| 446 | save as to same db acts as a save | 26/06/2003 11:38:04 | OPEN | LOW | the save as function, when saving to the same db actually does a save, rather than saving the model as a new model in the same database  |
| 445 | rename model and db interaction | 24/06/2003 18:15:00 | OPEN | LOW | rename model, not saveed to db quick pick list click on model - should say that model is loaded, but reloads from database (Becuase it has a different name) should be working off the GUID  |
| 432 | path list title not changed on graph rename | 24/06/2003 13:28:37 | OPEN | LOW | the graph was in free edit mode, tree was in list mode for paths and the title on the list of paths did not change when the graph name was edited and the change committed  |
| 426 | entity instance not renamed after creation in hierarchy | 24/06/2003 13:17:35 | OPEN | LOW | sometimes, when creating an entity instance directly in the hierarchy and then saving it, it is not renamed in the hierarchy but it has been renamed in memory. noticed when creating entity directly alt+insert  |
| 421 | Sometimes turning a branch into a defect is not reflected | 24/06/2003 13:07:25 | OPEN | MEDIUM | Sometimes turning a branch into a defect is not reflected on the tree or edit screen have not worked out a consistent repeatable situation for this yet  |
| 381 | filter text is editable | 16/08/2002 09:46:20 | OPEN | LOW | user can edit the text in the filter, this has no functional effect, but the user may expect that the filter can be controlled this way when it can't  |
| 358 | revert only reverts first node, not all | 12/08/2002 11:55:48 | OPEN | MEDIUM | if a number of items are changed and revert is pressed, only one of the nodes will be reverted  |
| 134 | the hierarchy browser hierarchy should maintain expansion | 25/04/2002 17:55:54 | OPEN | LOW | during restructuring, the hierarchy should leave expanded the items which are expanded  |
| 133 | The hierarchy browser tree refreshes too much on drag | 25/04/2002 17:54:37 | OPEN | LOW | this can be disconcerting when dragging, perhaps the timer is not long enough?  |
| ID | Name | Raised Date | Status | Raised By | Description |
|---|---|---|---|---|---|
| 150 | allow drag from generic hierarchy to xref | 30/04/2002 15:26:19 | IMPLEMENTED | Alan Richardson | allow dragging of transmuted nodes from the generic hierarchy on to the xref list  |


^ is the shortcut for CTRL used in P5.js web editor -> Help menu -> Keyboard Shortcuts list.
Both mean the same thing, the Control-key (on german keyboards often marked "Strg" for "Steuerung")
"working for me" is based on tests on september 23rd and september 24th 2021 using
I didn´t know how to test "accessible output".
| Currently Listed | working for me | Description | Working alternative |
|---|---|---|---|
| ⇧ + Tab | no | Tidy | CTRL + ⇧ + F |
| ⌃ + F | yes | Find Text | CTRL + F |
| ⌃ + G | yes | Find Next Text Match | CTRL + G |
| ⌃ + ⇧ + G | yes | Find Previous Text Match | CTRL + ⇧ + G |
| ⌃ + [ | no | Indent Code Left | CTRL + ß |
| ⌃ + ] | no | Indent Code Right | CTRL + ´ |
| ⌃ + / | no | Comment Line | CTRL + # |
| ⌃ + S | yes | Save | CTRL + S |
| ⌃ + Enter | yes | Start Sketch | CTRL + Enter |
| ⌃ + ⇧ + Enter | yes | Stop Sketch | CTRL + ⇧ + Enter |
| ⌃ + ⇧ + 1 | unknown | Turn On Accessible Output | n/a |
| ⌃ + ⇧ + 2 | unknown | Turn Off Accessible Output | n/a |


--- redirect_from: /ru/StatusBar.html title: "ConEmu | StatusBar" breadcrumbs: - url: TableOfContents.html#terms title: ConEmu - url: ConEmuTerms.html title: Terms ---
Status bar may be really useful. What process is executing in the console now? What console (index) is active? What is the size of console buffer? Where is cursor located? Caps/Num/Scroll, transparency, zoom, time and much more columns are available. Many of them are ‘clickable’ (try both LClick and RClick).
Of course you may configure columns set or hide StatusBar in the Settings.


Oconee County Fire Rescue
Home
(current)
Members
View Members
Add Members
Certifications
View Certifications
Add Certifications
Reports
Members by Station & Radio
Members with Expired Certifications
Members by Station Number
Station Number
Name
Radio Number
Email
{{ mb.stationNumber }}
{{ mb.firstName + ' ' + mb.lastName }}
{{ mb.radioNumber }}
{{ mb.email }}


InGames
Home
(current)
About
News
Store Submission
Logout
Cart
Sign up
Login
Absolute War
Will be available for InGames subscribers starting next month.
Haipy
LightUpGames
The Deadly Sins
InGames
Mephisto
BVictor
Joy
InGames
River Attack
Phoenix Steel Games
Dungeon One
InGames
Bread & Whales
InGames
Beast Hunter
InGames
The Miner
InGames
War of Infinity
InGames
Game name
Dev Name
Game name
Dev Name
Game name
Dev Name
Game name
Dev Name
Game name
Dev Name
Game name
Dev Name
Game name
Dev Name
Game name
Dev Name
Game name
Dev Name
Game name
Dev Name
omer


XMLBuffer is a lightweight, expandable Unicode text buffer
XMLBuffer is a lightweight, expandable Unicode text buffer. Since XML is inherently theoretically unbounded in terms of the sizes of things, we very often need to have expandable buffers. The primary concern here is that appends of characters and other buffers or strings be very fast, so it always maintains the current buffer size. The buffer is not nul terminated until some asks to see the raw buffer contents. This also avoids overhead during append operations.
alphabetic index hierarchy of classes
|
Xerces-C XML Parser for C++ Version 1.1 |
|
|
generated by doc++


| < Day Day Up > |
3.4. Word AbbreviationsWord abbreviation mode and dynamic abbreviations are two features that lazy typists will love. The authors proudly include themselves in that category, so you'll be in good company if you choose to explore these features.  You're likely to have many long technical words in your paper, and if you're like us, you get tired of typing long words. Dynamic abbreviations come to the rescue. After you've typed a long word once, you can simply type a few letters and give the command M-/ (for dabbrev-expand). Emacs inserts the nearest word that starts with that string.
Interesting was not the word we were hoping for; it's invertebrates we wanted. Without moving the cursor, type M-/ again.
The word being expanded need not be earlier in the file to be considered nearest. Emacs looks behind and ahead of the cursor position to find words it can expand. If there are eligible words that are equidistant above and below the cursor position both, Emacs selects the word that is above as the expansion. Earlier we talked about completing a word with Ispell. Dynamic abbreviations are a bit different. When you complete a word, the word probably isn't in the buffer (yet). When you use a dynamic abbreviation, you simply don't want to type a word you typed earlier and you're asking Emacs to do it for you. Using dynamic abbreviations doesn't require entering a special minor mode, as standard word abbreviations do. They are simply an aid for the tired typist. Word abbreviation mode has some other advantages, though, such as the ability to create an abbreviation for a phrase or a habitual typo, as we will see next. 3.4.2 Word Abbreviation ModeWord abbreviation mode lets you define abbreviations for special words and phrases. You can use it in many ways. Traditionally, abbreviation mode is used so that you don't have to type long words or phrases in their entirety. For example, let's say you are writing a contract that repeatedly references the National Institute of Standards and Technology, and you are not allowed to use an acronym. Rather than typing the full name, you can define the abbreviation nist. Once you have set up this definition, Emacs inserts the full name whenever you type the abbreviation nist, followed by a space, tab, or punctuation mark. Emacs watches for you to type an abbreviation, then expands it automatically for you. Before showing you how to get into word abbreviation mode and define your abbreviation list, we'll start with an example. Our favorite nontraditional use for word abbreviation mode is to correct misspellings as you type.[2] Almost everyone has a dozen or so words that they habitually type incorrectly because of worn neural pathways. You can simply tell Emacs that these misspellings are "abbreviations" for the correct versions, and Emacs fixes the misspellings every time you type them; you may not even notice that you typed the word wrong before Emacs fixes it. So assume that you've entered word abbreviation mode, and that you've defined receive as an abbreviation for recieve; now, as you're typing, you make an innocent mistake.
Besides the convenience of being able to invent abbreviations for phrases that you frequently type, you can see that setting up a short list of abbreviations for common misspellings could reduce the time it takes to proofread files and reduce the number of common typing errors. When you define abbreviations, never use abbreviations that are words in their own right or Emacs may expand the word when you don't want it to, because expansion takes place without asking. For example, if you frequently write about the World Association for Replicant Technology, don't define an abbreviation of wart, or you won't be able to write about the difficulties of handling toads. (If you use the word wart so infrequently that you think the convenience of the acronym warrants it, you can use C-_ to undo the abbreviation when you really want to type wart.) Emacs knows the abbreviations exactly as you define them. If you define recieve as an abbreviation for receive, you must also define recieves, recieving, and recieved as abbreviations to cover all the forms of the word you might misspell. Before you go ahead and define some abbreviations, here's one more basic fact you should know. Emacs classifies abbreviations according to which modes they work in. Global abbreviations work in all modes; local abbreviations work only in the mode in which they were defined. For example, if you want abbreviations to work only in text mode and not in C mode, define them as local while you are in text mode. If you want abbreviations to work in any mode, define them as global. Remember: abbreviations are local to modes, not to files or buffers. Emacs also provides an inverse method for defining abbreviations. This method is called inverse because you type the abbreviation and then the definition. Some commands (which we won't discuss) let you type the definition and then the abbreviation, but they require some tricky key sequences to let Emacs know how many words preceding the cursor are part of the abbreviation. The inverse method is easier and it works whether the definition for the abbreviation is one word or ten words. <IP_ADDRESS> Trying word abbreviations for one sessionUsually, if you go to the trouble of defining a word abbreviation, you will use it in more than one Emacs session. But if you'd like to try out abbreviation mode to see if you want to incorporate it into your startup, use the following procedure. To define word abbreviations for this buffer and session:
If you find that you like using word abbreviation mode, you may want to make it part of your startup, as described in the following section. <IP_ADDRESS> Making word abbreviations part of your startupOnce you become hooked on using abbreviation mode, it's easiest to incorporate it into your .emacs file. This procedure creates a permanent file of your word abbreviations that is loaded every time you start Emacs. You can also delete abbreviations from this file; we'll discuss how to do so in the next section. To define word abbreviations and make them part of your startup:
After you define some abbreviations and save them, Emacs loads the abbreviations file automatically. When you define word abbreviations in subsequent sessions, Emacs asks again whether you want to save the abbreviations file. Respond with a y to save the new abbreviations you've defined and have them take effect automatically. <IP_ADDRESS> Deleting a word abbreviationIf you use word abbreviations frequently, you may define an abbreviation and later change your mind. You can edit the word abbreviation list by typing M-x edit-abbrevs Enter. You can see (but not edit) the list by typing M-x list-abbrevs Enter. After the list is displayed, use C-k (or any other editing commands) to delete the abbreviations you don't want to use. Because Emacs itself formats this list, don't try to edit lines or add new lines; deleting is about the only operation that's safe. Here's how the abbreviations look when you edit word abbreviations. The file is divided into different sections based on whether the abbreviations are global or local to a particular mode: (text-mode-abbrev-table) (lisp-mode-abbrev-table) (fundamental-mode-abbrev-table) (global-abbrev-table) "iwthout" 1 "without" "prhase" 1 "phrase" "teh" 1 "the" "fo" 1 "of" "eamcs" 2 "Emacs" "wrok" 1 "work" "aslo" 1 "also" "sotred" 1 "stored" "inforamtion" 1 "information" "esc" 6 "Esc" "taht" 1 "that" "chatper" 1 "chapter" "adn" 1 "and" "iwth" 1 "with" "chpater" 1 "chapter" "loaction" 1 "location" "recieve" 1 "receive" "wart" 1 "World Association for Replicant Technology" The file is divided into sections by mode. We defined global abbreviations in this case; any abbreviations Flyspell (described earlier in this chapter) creates are local abbreviations and would be listed under the mode in which they were defined. In this buffer, the first column lists the abbreviations (in this case, mostly misspellings). The second column is for internal record keeping; you don't need to concern yourself with it. The third column provides the definitions of the abbreviations, the word or phrase that Emacs substitutes whenever it sees the abbreviation. To delete any abbreviation, delete the line for that abbreviation and save the file by typing M-x write-abbrev-file. You can move back to the buffer you were editing before by typing C-x b (a command for working with multiple buffers, discussed in Chapter 4). <IP_ADDRESS> Disabling word abbreviationsYou can get rid of word abbreviations completely in one of two ways. First, you can type M-x kill-all-abbrevs Enter. This command disables word abbreviations for the current session. Second, you can delete the file the abbreviations are in. If you made word abbreviations part of your startup, delete the read-abbrev-file line from your .emacs file. <IP_ADDRESS> Abbreviations and capitalizationUsually, Emacs capitalizes abbreviations exactly the way you want. If you run into special situations with abbreviations and capitalization, however, you may wantl to know what's going on behind the scenes. Here are the rules:
Table 3-7 shows some examples.
You don't need to remember the rules, but looking them over may help you out if you can't understand how Emacs is capitalizing. In our experience, defining abbreviations in lowercase circumvents most capitalization problems. Table 3-8 summarizes word abbreviation commands.
3.4.3 Problems You May Encounter
|
| < Day Day Up > |


A delightful, and beautiful Trovebox client for iPhone. Free and Open Source.
Simple and Intuitive Navigation and Gestures
Delightful has been designed to let you browse your photos quickly and efficiently. Use simple and familiar gestures like pinch in/out and swipe up/down to navigate around the app. Photos are grouped by date taken.
Drag-to-Select Photos to Upload
With drag-to-select gesture, you can select multiple photos to upload quickly. And with tap-and-hold gesture, you can preview a photo before selecting it.
Location and Camera Data
If your photo has GPS information, Delightful will show you the address where the photo was taken. You can also see the camera data of the photos.


Note: To use offcanvas navigation, it is required that you manually add the classname zo-offCanvasContainer
to the body
, and wrap the page content you want to offset into an element with the classname zo-hasOffCanvas
.

 ... Toggle offcanvas ...

...

It is not necessary to specify the theme, which defaults to light. However, as of v0.1.8, the position is required to specify both in the classname and in the data property.

...

...

Left
Right
Dark


Require a entry to be validated depending on the values of one or multiple other controls. In this example we require a certain field (reason your evil) to be filled out when either of the other toggleable values are true.
Whenever you complete a conditional statement, a Rx Observable is returned that emits a boolean value as to whether or not the validator function(s) orginally passed in are currently being applied.
We can use this observable to determine whether or not we want to render an extra field. Play around with the toggles and watch it appear.


Plugin Information |
|---|
View Log Parser on the plugin site for more information. |
Older versions of this plugin may not be safe to use. Please review the following warnings before using an older version:
Parse the console output and highlight error/warning/info lines.
Description
The log-parser plugin parses the console log generated by the Jenkins build. Parsing the console log allows for the following features:
- highlighting lines of interest in the log (errors, warnings, information)
- dividing the log into sections
- displaying a summary of number of errors , warnings and information lines within the log and its sections.
- linking the summary of errors and warnings into the context of the full log, making it easy to find a line of interest in the log
- showing a summary of errors and warnings on the build page
Parsed log example
Summary at build level
This shows a summary of errors and warnings on the build page
The parsed log
This is an example of a parsed log of a build.
It is accessible from the "Parsed Console Output" link on the left side of the build page :
The left side of the report can be expanded to browse the errors/warning/info lines. These lines are links to the full log on the right, where they can be easily located. Clicking on a certain error will bring you right to it on the full log which appears on the right side of the screen. The log is also divided into logical sections (as defined in the parsing rules configuration described below). In the summary section on the left you can see the errors/warnings as they appear in the log sections. This can help understand in what part of the build there is an error/warning. Both summary and log sections are scrollable and expandable.
Configuration
Several parsing options are configured at the global level.
Global Configuration
Parsing rules options
Appears in the global Jenkins configs as section "Console Output Parsing"
Configure pairs of logical names (project, description, etc.) and parsing rule files.
Later on, when a job is configured for log parsing, you will be able to choose one of these configurations to implement as the parsing rules for that job. (See "Job Configuration")
For each parsing rules option, specify :
Parsing rule name - logical name which will be displayed in the job level configuration as an option
Parsing Rules File - A file with the parsing rules implemented in this option. (See "Parsing rules files" to learn more about the syntax of these files.)
Parsing rules files
Format
Each line in the file specifies a level (ok/error/warn/info/start) and a regular expression (based on java.util.regex.Pattern) delimited by slashes ("/") to look for in order to mark the line as matching that level.
- ok/error/warn
Used to identify problem lines.
- info
These lines are highlighted blue in the report. They are used to create a set of links into the report for quick access to certain sections.
- start
Like info lines, they are highlighted blue in the report, and appear in the set of quick access links to the report. In addition, they are used for grouping the list of errors and warnings found in that section.
For example, the following line in the parsing rules file means that a line including the word "ERROR" will be marked as an error line:
error /ERROR/
- Case insensitive matches
The notation supports the Java regex embedded flag expression. To perform a case-insensitive match, use (?i) notation.
Example: to match lines starting with the case-insensitive word 'error', use:error /(?i)^error /
- If a line matches several rules, the first one applies.
So for the following set of rules :And the following line in the build log:ok /^javadoc:/ error /Error/
The line will be considered "ok", and will not be highlighted as an error, since thejavadoc: Error : could not find something
^javadoc:
pattern is matched first.
- Lines starting with a '#' character, and blank lines will be ignored to allow comments and spacing.
Example parsing rules file:
ok /not really/ # match line starting with 'error ', case-insensitive error /(?i)^error / # list of warnings here... warning /[Ww]arning/ warning /WARNING/ # create a quick access link to lines in the report containing 'INFO' info /INFO/ # each line containing 'BUILD' represents the start of a section for grouping errors and warnings found after the line. # also creates a quick access link. start /BUILD/
Job Configuration
- Go to menu: Jenkins -> job name -> Configure
- Go to section : Post-build Actions
- Check the "Console output (build log) parsing" checkbox.
- "Mark build Unstable on Warning" option: check to have parsed warnings mark the build 'unstable'.
- "Mark build Failed on Error" option : check to have parsed errors mark the build 'failed'.
- "Select Parsing Rules" : select the set of rules to use for parsing the logs of this job's builds
(Notice that this list is derived from the global configuration )
Changelog
Version 2.0 (October 20, 2015)
- [JENKINS-29308] Update project configuration. Clean up.
- [JENKINS-27208] Make Log Parser Plugin compatible with Workflow.
Version 1.0.8 (December 14, 2010)
- Bug fix: Strip encoded ConsoleNote from log prior to parsing and display (JENKINS-7263).
- Enhancement: Allow marking builds with warnings as unstable (JENKINS-7853).
- Bug fix: Links to style sheets should be relative to Hudson root url (JENKINS-8268).
Useful parse lines
This section collects some useful additional parse-statements contributed by users of the Log Parser Plugin
JaCoCo code coverage fails to read class-data
error /Execution data for class .* does not match./
Attachments:
parsed_console_output_1.JPG (image/pjpeg)
build_summary.JPG (image/pjpeg)
global_config.JPG (image/pjpeg)
job_config_post_build_actions.JPG (image/pjpeg)
job_config_post_build_actions.JPG (image/jpeg)
Capture.JPG (image/jpeg)
ScreenShot954.jpg (image/jpeg)
parsing.png (image/png)
JenkinsConsoleOutput_WhereIsParsedOutput.png (image/png)
10-10-2016 13-54-01.png (image/png)
10-10-2016 13-55-51.png (image/png)
Screen Shot 2016-11-11 at 16.36.08.png (image/png)
Screen Shot 2016-11-11 at 16.38.41.png (image/png)
Screen Shot 2016-11-11 at 16.39.58.png (image/png)
Log.PNG (image/png)


---
layout: slides
title: "What is Bioinformatics?"
---
### Discuss: Where can you find Bioinformatics?
![](/images/materials/bioinformatics/1.svg)
---
### Where can you find Bioinformatics?
Chemistry, Biology, Physics: Creating Software for Devices
---
### Where can you find Bioinformatics?
Chemistry, Biology, Physics: Analysis of Sequence Data
![](/images/materials/introduction/evolution_seq_technologies.svg)
---
### Where can you find Bioinformatics?
Chemistry, Biology, Physics: Analysis of Image Data
---
### Where can you find Bioinformatics?
Mathematics, Statistics: Computer Science, Algorithms
---
### Bioinformatics is the Bridge
![](/images/materials/bioinformatics/2.svg)
---
### Discuss: What kind of data will we encounter?
---
### Types of Data We Will Have
- **Raw Signal** (e.g., current/time-data of MinION)
- **Sequence Data** (e.g., FASTQ data)
- **Interpreted Data** (i.e., results of Kraken)
---
### Raw Signal Data
![](/images/materials/nanopore/1.svg)
---
### Sequence Data: FASTQ Format
```
@SEQ_ID
GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT
+
!''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65
```
- **Line 1:** Read ID
- **Line 2:** Sequence
- **Line 3:** Additional Information
- **Line 4:** Quality String
---
### Interpreted Data: Kraken
![](/images/materials/bioinformatics/3.svg)
---
### Interpreted Data: Kraken
---
### Interpreted Data: Kraken
|Taxon | Read number |
|-------|-------------|
| d__Eukaryota | 2053 |
| d__Eukaryota,k__Fungi | 2053 |
|d__Eukaryota,k__Fungi,p__Ascomycota | 2049 |


|
KratosMultiphysics
KRATOS Multiphysics (Kratos) is a framework for building parallel, multi-disciplinary simulation software, aiming at modularity, extensibility, and high performance. Kratos is written in C++, and counts with an extensive Python interface.
|
Public Member Functions | |
| bool | Evaluate () override |
| Using input data, returns bool. More... | |
| Controller::Pointer | Create (Model &rModel, Parameters ThisParameters) const override |
| This method creates an pointer of the controller. More... | |
| Controller () noexcept=default | |
| Default constructor. More... | |
| Controller (Controller const &rOther)=default | |
| Copy constructor. More... | |
| Controller (Controller &&rOther) noexcept=default | |
| Move constructor. More... | |
| Public Member Functions inherited from Kratos::Controller | |
| KRATOS_CLASS_POINTER_DEFINITION (Controller) | |
| Pointer definition of Controller. More... | |
| Controller () noexcept=default | |
| Default constructor. More... | |
| virtual | ~Controller ()=default |
| Destructor. More... | |
| Controller (Controller const &rOther)=default | |
| Copy constructor. More... | |
| Controller (Controller &&rOther) noexcept=default | |
| Move constructor. More... | |
| Controller & | operator= (Controller const &rOther)=delete |
| Assignment operator. More... | |
| virtual int | Check () const |
| Checks that input conditions are correct. More... | |
| virtual Parameters | GetDefaultParameters () const |
| This method provides the defaults parameters to avoid conflicts between the different constructors. More... | |
| std::string | Info () const |
| Turn back information as a string. More... | |
| void | PrintInfo (std::ostream &rOStream) const |
| Print information about this object. More... | |
| void | PrintData (std::ostream &rOStream) const |
| Print object's data. More... | |
|
defaultnoexcept |
Default constructor.
|
defaultnoexcept |
Move constructor.
|
default |
Copy constructor.
|
inlineoverridevirtual |
This method creates an pointer of the controller.
We consider as input, a Model and a set of Parameters for the sake of generality
| rModel | The model to be considered |
| ThisParameters | The configuration parameters |
Implements Kratos::Controller.
|
inlineoverridevirtual |
Using input data, returns bool.
Implements Kratos::Controller.


FullStack
Contact
Projects
About
A showcase website for Full Stack Projects.
Join
Latest Projects this Month
Websites, Android Applications and more.
LoopLab
Mizxure
Blogen
See and Join the Repository of this Website
Join


Miloš Veljković
Programmer
- (+381)69 189 19 99
-<EMAIL_ADDRESS>About
I started as a designer because being a designer gives you a chance to express yourself creatively. One time a website had to be finished, the design was done but no one in the company knew how to do HTML/CSS. I decided to give it a try. Somehow it came naturally to me and the more I learned the clearer it became to me that coding is highly creative. It quickly captured the whole of my attention. For a long time design remained in my field of work, so I was able to constantly mix the two. Somewhere in the process I switched to coding completely. In the past two years I've been doing mostly back-end programming and designing only as a hobby. I am eager to learn more and continue to improve my skills from project to project so I can have a bigger impact wherever I land.
Work Experience
Web developer, RBT
April 2016 - present
Working on a large team and a variety of projects. Started with frontend, mostly AngularJS and ReactJS. Later switched to ROR and balancing it with AngularJS
Highlights
- Mastering ROR, MongoDb, AngularJS
- Introduction ReactJS, Redux, Python, Flask
Frontend developer, Probe & Query
November 2015 - April 2016
http://probe-query.com/
Developing a Wordpress theme for International Water Association.
Highlights
- Wordpress introduction
Frontend developer, Dodo
March 2014 - April 2016
http://fakturabank.no
Designing and developing frontend part of a web based accounting app. Every day communication with the clients. Later on leading a team of 3 frontend developers, team expansion, interviews..
Highlights
- Complete upgrade of UI/UX
- Mastering SASS, jQuery, JS
- Introduction to Ruby, Rails, Postgres
Junior frontend developer, ESD
November 2013 - February 2014
http://esd.rs
Out sourcing company. Mostly doing PSD to HTML conversion(HTML5, CSS3, JS) but also some web shop creation(HTML, PHP, JS) and a bit of design.
Highlights
- Mastering HTML, CSS
- Definite switch from design to coding
- Introduction to PHP
Education
High School - Gimnazija "Sveti Sava"
September 2004 - May 2008
Natural Sciences
Skills
Master
- HTML
- CSS
- Javascript
- SASS
- jQuery
- AngularJS
- Ruby
- RubyOnRails
- MongoDB
Intermediate
- ReactJS
- Redux
- Python
- Flask
- PHP
- Wordpress
- Adobe Photoshop
- Illustrator
Interests
Nature
- Hiking
- Mountain climbing
- Free climbing
Music
- Production
- Mixing
- Cubase
- Traktor
Other
- Table tennis
- Table football
- Football
- Biking
- ....


What happens to a dream deferred?
Does it dry up
like a raisin in the sun?
Or fester like a sore—
And then run?
Does it stink like rotten meat?
Or crust and sugar over—
like a syrupy sweet?
Maybe it just sags
like a heavy load.
Or does it explode?
Charles Babbage was born on December 26, 1791, in London, and later died on October 18, 1871. During his life, he taught math at the University of Cambridge, where he was the Lucasian Professor. He assisted in the development of the Royal Astronomical, Analytical, and Statistical Societies. Additionally, he was elected to the Royal Society of London and helped form the postal system in England. His inventions include a speedometer, locomotive cowcatcher, and a calculator that could compute up to eight decimals!
The Analytical Engine is one of his most famous projects. Ada Lovelace, later on, translated a paper about it and stated it's potential on becoming the first computer program. The incomplete project was forgotten along with Babbage's design up until 1937. Babbage obtained government support for another one of his projects called the Difference Engine. Additionally, Babbage invented the earliest automatic digital computer. All in all, Babbage's contributions were astounding and the backbone of the computers we use today.
Source!


Deprecated: This plugin has been removed from the Jenkins Update Centre
This feature is no longer relevant.
Plugin Information |
|---|
No information for the plugin 'cloudbees-registration' is available. It may have been removed from distribution. |
This plugin builds on top of CloudBees Credentials plugin and provides a concrete implementation and user interface to let users enter the credentials. This is normally not a plugin that you'll choose to install on its own. Rather, it gets installed as a dependency of other plugins.
If you are a plugin developer interested in developing a plugin that accesses CloudBees services, you should depend on this plugin to reuse the same credentials.
Usage
Upon the first installation (or if a cloudbees username/password is not set), then the plugin puts up a banner that asks you to do so:
You can then enter your CloudBees credentials:
The information you entered is managed by the Credentials Plugin. To update/delete it, or to add the 2nd account information, click the "Credentials" link from the left.
Changelog
Deprecation (August 24, 2016)
This plugin is no longer relevant.
Version 3.15 (Jun 25, 2016)
- Remove RUN@cloud API calls
Version 3.14 (Mar 17, 2014)
- Initial OSS release


IT250 Unit 6
Provide a set of SQL statements that creates a table and necessary fields.
File-based System to Database System
Since the nature of the directions for this unit required a written paper instead of actual programming, there is not much to see here.
Directions
Create a table and populate it with fields and initial values. Write a 8-10 page paper discussing each step in the process. Provide documentation and code examples of how data validation will be handled in the application.


| furrr_options | Options to fine tune furrr |
| future_imap | Apply a function to each element of a vector, and its index via futures |
| future_imap_chr | Apply a function to each element of a vector, and its index via futures |
| future_imap_dbl | Apply a function to each element of a vector, and its index via futures |
| future_imap_dfc | Apply a function to each element of a vector, and its index via futures |
| future_imap_dfr | Apply a function to each element of a vector, and its index via futures |
| future_imap_int | Apply a function to each element of a vector, and its index via futures |
| future_imap_lgl | Apply a function to each element of a vector, and its index via futures |
| future_imap_raw | Apply a function to each element of a vector, and its index via futures |
| future_invoke_map | Invoke functions via futures |
| future_invoke_map_chr | Invoke functions via futures |
| future_invoke_map_dbl | Invoke functions via futures |
| future_invoke_map_dfc | Invoke functions via futures |
| future_invoke_map_dfr | Invoke functions via futures |
| future_invoke_map_int | Invoke functions via futures |
| future_invoke_map_lgl | Invoke functions via futures |
| future_invoke_map_raw | Invoke functions via futures |
| future_iwalk | Apply a function to each element of a vector, and its index via futures |
| future_map | Apply a function to each element of a vector via futures |
| future_map2 | Map over multiple inputs simultaneously via futures |
| future_map2_chr | Map over multiple inputs simultaneously via futures |
| future_map2_dbl | Map over multiple inputs simultaneously via futures |
| future_map2_dfc | Map over multiple inputs simultaneously via futures |
| future_map2_dfr | Map over multiple inputs simultaneously via futures |
| future_map2_int | Map over multiple inputs simultaneously via futures |
| future_map2_lgl | Map over multiple inputs simultaneously via futures |
| future_map2_raw | Map over multiple inputs simultaneously via futures |
| future_map_at | Apply a function to each element of a vector conditionally via futures |
| future_map_chr | Apply a function to each element of a vector via futures |
| future_map_dbl | Apply a function to each element of a vector via futures |
| future_map_dfc | Apply a function to each element of a vector via futures |
| future_map_dfr | Apply a function to each element of a vector via futures |
| future_map_if | Apply a function to each element of a vector conditionally via futures |
| future_map_int | Apply a function to each element of a vector via futures |
| future_map_lgl | Apply a function to each element of a vector via futures |
| future_map_raw | Apply a function to each element of a vector via futures |
| future_modify | Modify elements selectively via futures |
| future_modify_at | Modify elements selectively via futures |
| future_modify_if | Modify elements selectively via futures |
| future_pmap | Map over multiple inputs simultaneously via futures |
| future_pmap_chr | Map over multiple inputs simultaneously via futures |
| future_pmap_dbl | Map over multiple inputs simultaneously via futures |
| future_pmap_dfc | Map over multiple inputs simultaneously via futures |
| future_pmap_dfr | Map over multiple inputs simultaneously via futures |
| future_pmap_int | Map over multiple inputs simultaneously via futures |
| future_pmap_lgl | Map over multiple inputs simultaneously via futures |
| future_pmap_raw | Map over multiple inputs simultaneously via futures |
| future_pwalk | Map over multiple inputs simultaneously via futures |
| future_walk | Apply a function to each element of a vector via futures |
| future_walk2 | Map over multiple inputs simultaneously via futures |


Research and analysis
Research into the effectiveness of the individual budgets pilot for families with disabled children.
Ref: DFE-RR145 PDF , 1000KB , 156 pages
Ref: DFE-RR145E PDF , 192KB , 43 pages
Ref: DFE-RR145B PDF , 471KB , 74 pages
Ref: ISBN 978-1-84775-785-2 , DFE-RR024 PDF , 1.62MB , 170 pages
Reports about the 2-year individual budgets pilot looking at its:


Informally, I found the empathetic listening exercises really valuable. Like the mindful listening tasks it helped to show that most of the time I have been listening without my complete focus being on the person speaking and the issue being discussed. Looping was again a very beneficial activity in this sense. I enjoyed the conversations more while considering empathy and will look to do so more in future with the recognition that perhaps I should listen more carefully, more often.
Personally I havent felt the benefit of the Loving kindness meditation yet, but perhaps this is due to the lack of conflict I have had since I read the chapter. Outside of conflict the meditation felt a bit flat, I will however, try it again when the opportunity inevitably arises.
I work in a cafe at the moment and I have recently been in disagreement with some of the higher ups at work due to a lack of environmental awareness in the workplace. I feel that no matter how hard it is to implement an environmentally responsible composting plan and introduce environmentally responsible straws etc, it should still be considered of highest priority. Straws are not of huge expense and beyond adding an extra bin to the kitchen and teaching the staff about responsibly recycling and composting, there is little reason against making a change. The higher ups feel that the implementation of compostable straws would cost too much and require too much resources in terms of sourcing and time researching that it does not take priority over other matters. Eqaully the affect that it might have on the efficiency in the kitchen would be far too costly to the business as the staff spend more time deciding which bins to put their rubbish into as well as time spent learning about composting and recycling procedures.


Enumerations | |
| enum | USB_DescriptorMemorySpaces_t { MEMSPACE_FLASH = 0, MEMSPACE_EEPROM = 1, MEMSPACE_RAM = 2 } |
| enum | USB_Device_States_t { DEVICE_STATE_Unattached = 0, DEVICE_STATE_Powered = 1, DEVICE_STATE_Default = 2, DEVICE_STATE_Addressed = 3, DEVICE_STATE_Configured = 4, DEVICE_STATE_Suspended = 5 } |
Functions | |
| void | USB_Device_SendRemoteWakeup (void) |
| static uint16_t | USB_Device_GetFrameNumber (void) |
| static void | USB_Device_EnableSOFEvents (void) ATTR_ALWAYS_INLINE |
| static void | USB_Device_DisableSOFEvents (void) ATTR_ALWAYS_INLINE |
| uint16_t | CALLBACK_USB_GetDescriptor (const uint16_t wValue, const uint8_t wIndex, const void **const DescriptorAddress, uint8_t *MemoryAddressSpace) ATTR_WARN_UNUSED_RESULT ATTR_NON_NULL_PTR_ARG(3) |
Variables | |
| uint8_t | USB_ConfigurationNumber |
| bool | USB_RemoteWakeupEnabled |
| bool | USB_CurrentlySelfPowered |
| volatile uint8_t | USB_DeviceState |
USB Device Mode Option Masks | |
| #define | USB_DEVICE_OPT_LOWSPEED (1 << 0) |
| #define | USB_DEVICE_OPT_FULLSPEED (0 << 0) |
USB Device mode related macros and enums. This module contains macros and enums which are used when the USB controller is initialized in device mode.
| #define USB_DEVICE_OPT_FULLSPEED (0 << 0) |
Mask for the Options parameter of the USB_Init() function. This indicates that the USB interface should be initialized in full speed (12Mb/s) mode.
| #define USB_DEVICE_OPT_LOWSPEED (1 << 0) |
Mask for the Options parameter of the USB_Init() function. This indicates that the USB interface should be initialized in low speed (1.5Mb/s) mode.
Enum for the possible descriptor memory spaces, for the MemoryAddressSpace of the CALLBACK_USB_GetDescriptor() function. This can be used when none of the USE_*_DESCRIPTORS compile time options are used, to indicate in which memory space the descriptor is stored.
| enum USB_Device_States_t |
| uint16_t CALLBACK_USB_GetDescriptor | ( | const uint16_t | wValue, |
| const uint8_t | wIndex, | ||
| const void **const | DescriptorAddress, | ||
| uint8_t * | MemoryAddressSpace | ||
| ) |
Function to retrieve a given descriptor's size and memory location from the given descriptor type value, index and language ID. This function MUST be overridden in the user application (added with full, identical prototype and name so that the library can call it to retrieve descriptor data.
| [in] | wValue | The type of the descriptor to retrieve in the upper byte, and the index in the lower byte (when more than one descriptor of the given type exists, such as the case of string descriptors). The type may be one of the standard types defined in the DescriptorTypes_t enum, or may be a class-specific descriptor type value. |
| [in] | wIndex | The language ID of the string to return if the wValue type indicates DTYPE_String, otherwise zero for standard descriptors, or as defined in a class-specific standards. |
| [out] | DescriptorAddress | Pointer to the descriptor in memory. This should be set by the routine to the address of the descriptor. |
| [out] | MemoryAddressSpace | A value from the USB_DescriptorMemorySpaces_t enum to indicate the memory space in which the descriptor is stored. This parameter does not exist when one of the USE_*_DESCRIPTORS compile time options is used. |
| static void USB_Device_DisableSOFEvents | ( | void | ) | [inline, static] |
Disables the device mode Start Of Frame events. When disabled, this stops the firing of the EVENT_USB_Device_StartOfFrame() event when enumerated in device mode.
| static void USB_Device_EnableSOFEvents | ( | void | ) | [inline, static] |
Enables the device mode Start Of Frame events. When enabled, this causes the EVENT_USB_Device_StartOfFrame() event to fire once per millisecond, synchronized to the USB bus, at the start of each USB frame when enumerated in device mode.
| static uint16_t USB_Device_GetFrameNumber | ( | void | ) | [inline, static] |
Returns the current USB frame number, when in device mode. Every millisecond the USB bus is active (i.e. enumerated to a host) the frame number is incremented by one.
| void USB_Device_SendRemoteWakeup | ( | void | ) |
Sends a Remote Wakeup request to the host. This signals to the host that the device should be taken out of suspended mode, and communications should resume.
Typically, this is implemented so that HID devices (mice, keyboards, etc.) can wake up the host computer when the host has suspended all USB devices to enter a low power state.
| uint8_t USB_ConfigurationNumber |
Indicates the currently set configuration number of the device. USB devices may have several different configurations which the host can select between; this indicates the currently selected value, or 0 if no configuration has been selected.
Indicates if the device is currently being powered by its own power supply, rather than being powered by the host's USB supply. This flag should remain cleared if the device does not support self powered mode, as indicated in the device descriptors.
| volatile uint8_t USB_DeviceState |
Indicates the current device state machine state. When in device mode, this indicates the state via one of the values of the USB_Device_States_t enum values.
This value should not be altered by the user application as it is handled automatically by the library. The only exception to this rule is if the NO_LIMITED_CONTROLLER_CONNECT token is used (see EVENT_USB_Device_Connect() and EVENT_USB_Device_Disconnect() events).
To reduce program size and speed up checks of this global, it can be placed into one of the AVR's GPIOR hardware registers instead of RAM by defining the DEVICE_STATE_AS_GPIOR token to a value between 0 and 2 in the project makefile and passing it to the compiler via the -D switch. When defined, the corresponding GPIOR register should not be used in the user application except implicitly via the library APIs.
Indicates if the host is currently allowing the device to issue remote wakeup events. If this flag is cleared, the device should not issue remote wakeup events to the host.


{{ _('GC Notify displays your service name:') }}
{{ _('In the Government of Canada, your service name must include both English and French.') }}
{{ _('For example:') }} {{ _('Renew your passport | Renouveler votre passeport') }}
{{ _('A good service name is trustworthy. It uses words people who use the service recognize.') }}


5:36
HOUSE
NEEDS
this is a house need
this is a house need
this is a house need
PSA
thing one
thing two
WEATHER
Precipitation
Temperature


You are about to reset all settings to the default.
You will have to setup this application again. All sessions will be closed, tokens will be deleted.
There is no undo button


Home
News
About us
Liutauras
Povilas
Egidijus
Home
About us
Povilas
chevron_left
Povilas
chevron_right
Hello there!
My name is Povilas! And I love mountains, travel and science fiction. :)
You will some of the photos I took below and some charts.
Best food with Sci-Fi movies
Which movie franchise I like most
Hours spent playing
My liking for the weekdays :)


CHAPTER 127
DISASTER RELIEF
REPEALED. L 2014, c 111, §23.
Cross References
Emergency management, see chapter 127A.


node_ops¶
The node_ops module consists of a collection of mid to high level functions which take a tensor or structured list of tensors, perform a sequence of tensorflow operations, and return a tensor or structured list of tensors. All node_ops functions conform to the following specifications.
- All tensor input (if it has tensor input) is received by the function’s first argument, which may be a single tensor, a list of tensors, or a structured list of tensors, e.g., a list of lists of tensors.
- The return is a tensor, list of tensors or structured list of tensors.
- The final argument is an optional name argument for variable_scope.
Use Cases¶
- node_ops functions may be used in a tensorflow script wherever you might use an equivalent sequence of tensorflow
- ops during the graph building portion of a script.
node_ops functions may be called in a .config file following the .config file syntax which is explained in Config Tutorial.
Making Custom ops For use With config module¶
The AntGraph
constructor in the config module will add tensor operations to the tensorflow graph which are specified
in a config file and fit the node_ops spec but not defined in the node_ops module. This leaves the user free to define new
node_ops for use with the config module, and to use many pre-existing tensorflow and third party defined ops with the config
module as well.
The AntGraph
constructor has two arguments function_map and imports which may be used to incorporate custom node_ops.
- function_map is a hashmap of function_handle:function, key value pairs
- imports is a hashmap of module_name:path_to_module pairs for importing an entire module of custom node_ops.
Accessing Tensors Created in a node_ops Function¶
Tensors which are created by a node_ops function but not returned to the caller are kept track of in an intuitive fashion by calls to tf.add_to_collection. Tensors can be accessed later by calling tf.get_collection by the following convention:
For a node_ops function which was handed the argument name=’some_name’:
- The nth weight tensor created may be accessed as
tf.get_collection('some_name_weights')[n]
- The nth bias tensor created may be accessed as
tf.get_collection('some_name_bias')[n]
- The nth preactivation tensor created may be accessed as
tf.get_collection('some_name_preactivation')[n]
- The nth activation tensor created may be accessed as
tf.get_collection('some_name_activations')[n]
- The nth post dropout tensor created may be accessed as
tf.get_collection('some_name_dropouts')[n]
- The nth post batch normalization tensor created may be accessed as
tf.get_collection('some_name_bn')[n]
- The nth tensor created not listed above may be accessed as
tf.get_collection('some_name')[n],
- The nth hidden layer size skip transform (for
residual_dnn
):
tf.get_collection('some_name_skiptransform')[n]
- The nth skip connection (for
residual_dnn
):
tf.get_collection('some_name_skipconnection')[n]
- The nth transform layer (for
highway_dnn
):
tf.get_collection('some_name_transform')[n]
Weights¶
Here is a simple wrapper for common initializations of tensorflow `Variables`_. There is a option for l2 regularization which is automatically added to the objective function when using the generic_model module.
Placeholders¶
Here is a simple wrapper for a tensorflow placeholder constructor that when used in conjunction with the config module, infers the correct dimensions of the placeholder from a string hashed set of numpy matrices.
Neural Networks¶
Warning
The output of a neural network node_ops function is the output after activation of the last hidden layer.
For regression an additional call to linear
must be made and for classification and additional call to
mult_log_reg
must be made.
Initialization¶
Neural network weights are initialized with the following scheme where the range is dependent on the second dimension of the input layer:
if activation == 'relu':
irange= initrange*numpy.sqrt(2.0/float(tensor_in.get_shape().as_list()[1]))
else:
irange = initrange*(1.0/numpy.sqrt(float(tensor_in.get_shape().as_list()[1])))
initrange above is defaulted to 1. The user has the choice of several distributions,
- ‘norm’, ‘tnorm’: irange scales distribution with mean zero and standard deviation 1.
- ‘uniform’: irange scales uniform distribution with range [-1, 1].
- ‘constant’: irange equals the initial scalar entries of the matrix.
Dropout¶
Dropout with the specified keep_prob is performed post activation.
Batch Normalization¶
If requested batch normalization is performed after dropout.
Loss Functions and Evaluation Metrics¶
Tensor Operations¶
Some tensor operations from Kolda and Bader’s Tensor Decompositions and Applications are provided here. For now these operations only work on up to order 3 tensors.
Batch Normalization¶
Dropout¶
Dropout is automatically ‘turned’ off during evaluation when used in conjuction with the generic_model module.
API¶
-
node_ops.
placeholder
(dtype, shape=None, data=None, name='placeholder')[source]¶ Wrapper to create tensorflow Placeholder which infers dimensions given data.
Parameters: - dtype – Tensorflow dtype to initiliaze a Placeholder.
- shape – Dimensions of Placeholder
- data – Data to infer dimensions of Placeholder from.
- name – Unique name for variable scope.
Returns: A Tensorflow Placeholder.
-
node_ops.
cosine
(operands, name='cosine')[source]¶ Takes the cosine of vectors in corresponding rows of the two matrix tensors in operands.
Parameters: - operands – A list of two tensors to take cosine of.
- name – An optional name for unique variable scope.
Returns: A tensor with dimensions (operands[0].shape[0], 1)
Raises: ValueError when operands do not have matching shapes.
-
node_ops.
x_dot_y
(operands, name='x_dot_y')[source]¶ Takes the inner product for rows of operands[1], and operands[2], and adds optional bias, operands[3], operands[4]. If either operands[1] or operands[2] or both is a list of tensors then a list of the pairwise dot products (with bias when len(operands) > 2) of the lists is returned.
Parameters: - operands – A list of 2, 3, or 4 tensors (the first two tensors may be replaced by lists of tensors in which case the return value will a list of the dot products for all members of the cross product of the two lists.).
- name – An optional identifier for unique variable_scope.
Returns: A tensor or list of tensors with dimension (operands[1].shape[0], 1).
Raises: Value error when operands is not a list of at least two tensors.
-
node_ops.
lookup
(dataname=None, data=None, indices=None, distribution='uniform', initrange=0.1, l2=0.0, shape=None, makeplace=True, name='lookup')[source]¶ A wrapper for tensorflow’s embedding_lookup which infers the shape of the weight matrix and placeholder value from the parameter data.
Parameters: - dataname – Used exclusively by config.py
- data – A
HotIndex
object - indices – A Placeholder. If indices is none the dimensions will be inferred from data
- distribution – Distribution for lookup weight initialization
- initrange – Initrange for weight distribution.
- l2 – Floating point number determining degree of of l2 regularization for these weights in gradient descent update.
- shape – The dimensions of the output tensor, typically [None, output-size]
- makeplace – A boolean to tell whether or not a placeholder has been created for this data (Used by config.py)
- name – A name for unique variable scope.
Returns: tf.nn.embedding_lookup(wghts, indices), wghts, indices
-
node_ops.
embedding
(tensors, name='embedding')[source]¶ A wrapper for tensorflow’s embedding_lookup
Parameters: - tensors – A list of two tensors , matrix, indices
- name – Unique name for variable scope
Returns: A matrix tensor where the i-th row = matrix[indices[i]]
-
node_ops.
mult_log_reg
(tensor_in, numclasses=None, data=None, dtype=tf.float32, initrange=1e-10, seed=None, l2=0.0, name='log_reg')[source]¶ Performs mulitnomial logistic regression forward pass. Weights and bias initialized to zeros.
Parameters: - tensor_in – A tensor or placeholder
- numclasses – For classificatio
- data – For shape inference.
- dtype – For
weights
initialization. - initrange – For
weights
initialization. - seed – For
weights
initialization. - l2 – For
weights
initialization. - name – For variable_scope
Returns: A tensor shape=(tensor_in.shape[0], numclasses)
-
node_ops.
concat
(tensors, output_dim, name='concat')[source]¶ Matrix multiplies each tensor in tensors by its own weight matrix and adds together the results.
Parameters: - tensors – A list of tensors.
- output_dim – Dimension of output
- name – An optional identifier for unique variable_scope.
Returns: A tensor with shape [None, output_dim]
-
node_ops.
dnn
(tensor_in, hidden_units, activation='tanh', distribution='tnorm', initrange=1.0, l2=0.0, bn=False, keep_prob=None, fan_scaling=False, name='dnn')[source]¶ - Creates fully connected deep neural network subgraph. Adapted From skflow dnn_ops.py
Parameters: - tensor_in – tensor or placeholder for input features.
- hidden_units – list of counts of hidden units in each layer.
- activation – activation function between layers. Can be None.
- distribution – Distribution for lookup weight initialization
- initrange – Initrange for weight distribution.
- l2 – Floating point number determining degree of of l2 regularization for these weights in gradient descent update.
- bn – Whether or not to use batch normalization
- keep_prob – if not None, will add a dropout layer with given probability.
- name – A name for unique variable_scope.
Returns: A tensor which would be a deep neural network.
-
node_ops.
residual_dnn
(tensor_in, hidden_units, activation='tanh', distribution='tnorm', initrange=1.0, l2=0.0, bn=False, keep_prob=None, fan_scaling=False, skiplayers=3, name='residual_dnn')[source]¶ - Creates residual neural network with shortcut connections.
- Deep Residual Learning for Image Recognition
Parameters: - tensor_in – tensor or placeholder for input features.
- hidden_units – list of counts of hidden units in each layer.
- activation – activation function between layers. Can be None.
- distribution – Distribution for lookup weight initialization
- initrange – Initrange for weight distribution.
- l2 – Floating point number determining degree of of l2 regularization for these weights in gradient descent update.
- bn – Whether or not to use batch normalization
- keep_prob – if not None, will add a dropout layer with given probability.
- skiplayers – The number of layers to skip for the shortcut connection.
- name – A name for unique variable scope
Returns: A tensor which would be a residual deep neural network.
-
node_ops.
highway_dnn
(tensor_in, hidden_units, activation='tanh', distribution='tnorm', initrange=1.0, l2=0.0, bn=False, keep_prob=None, fan_scaling=False, bias_start=-1, name='highway_dnn')[source]¶ - A highway deep neural network.
- Training Very Deep Networks
Parameters: - tensor_in – A 2d matrix tensor.
- hidden_units – list of counts of hidden units in each layer.
- activation – Non-linearity to perform. Can be ident for no non-linearity.
- distribution – Distribution for lookup weight initialization
- initrange – Initrange for weight distribution.
- l2 – Floating point number determining degree of of l2 regularization for these weights in gradient descent update.
- bn – Whether or not to use batch normalization
- keep_prob – Dropout rate.
- bias_start – initialization of transform bias weights
- name – A name for unique variable_scope.
Returns: A tensor which would be a highway deep neural network.
-
node_ops.
linear
(tensor_in, output_size, bias, bias_start=0.0, distribution='tnorm', initrange=1.0, l2=0.0, name="Linear")[source]¶ Linear map: \(\sum_i(args[i] * W_i)\), where \(W_i\) is a variable.
Parameters: - args – a 2D Tensor
- output_size – int, second dimension of W[i].
- bias – boolean, whether to add a bias term or not.
- bias_start – starting value to initialize the bias; 0 by default.
- distribution – Distribution for lookup weight initialization
- initrange – Initrange for weight distribution.
- l2 – Floating point number determining degree of of l2 regularization for these weights in gradient descent update.
- name – VariableScope for the created subgraph; defaults to “Linear”.
Returns: A 2D Tensor with shape [batch x output_size] equal to \(\sum_i(args[i] * W_i)\), where \(W_i\) are newly created matrices.
Raises: ValueError: if some of the arguments has unspecified or wrong shape.
-
node_ops.
batch_normalize
(tensor_in, epsilon=1e-5, decay=0.999, name="batch_norm")[source]¶ Batch Normalization: Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift
An exponential moving average of means and variances in calculated to estimate sample mean and sample variance for evaluations. For testing pair placeholder is_training with [0] in feed_dict. For training pair placeholder is_training with [1] in feed_dict. Example:
Let train = 1 for training and train = 0 for evaluation
Parameters: - tensor_in – input Tensor
- epsilon – A float number to avoid being divided by 0.
- name – For variable_scope
Returns: Tensor with variance bounded by a unit and mean of zero according to the batch.
-
node_ops.
nmode_tensor_multiply
(tensors, mode, leave_flattened=False, keep_dims=False, name='nmode_multiply')[source]¶ Nth mode tensor multiplication (for order three tensor) from Kolda and Bader Tensor Decompositions and Applications Works for vectors (matrix with a 1 dimension or matrices)
Parameters: - tensors – A list of tensors the first is an order three tensor the second and order 2
- mode – The mode to perform multiplication against.
- leave_flattened – Whether or not to reshape tensor back to order 3
- keep_dims – Whether or not to remove 1 dimensions
- name – For variable scope
Returns: Either an order 3 or order 2 tensor
-
node_ops.
ternary_tensor_combine
(tensors, initrange=1e-5, distribution='tnorm', l2=0.0, name='ternary_tensor_combine')[source]¶ For performing tensor multiplications with batches of data points against an order 3 weight tensor.
Parameters: - tensors –
- output_dim –
- initrange –
- name –
Returns:
-
node_ops.
khatri_rao
(tensors, name='khatrirao')[source]¶ From David Palzer
Parameters: - tensors –
- name –
Returns:
-
node_ops.
binary_tensor_combine2
(tensors, output_dim=10, initrange=1e-5, name='binary_tensor_combine2')[source]¶
-
node_ops.
recall
(predictions, targets, threshold=0.5, detects=None, name='recall')[source]¶ Percentage of actual classes predicted
Parameters: - targets – A one hot encoding of class labels (num_points X numclasses)
- predictions – A real valued matrix with indices ranging between zero and 1 (num_points X numclasses)
- threshold – The detection threshold (between zero and 1)
- detects – In case detection is precomputed for efficiency when evaluating both precision and recall
Returns: A scalar value
-
node_ops.
precision
(predictions, targets, threshold=0.5, detects=None, name='precision')[source]¶ Percentage of classes detected which are correct.
Parameters: - targets – A one hot encoding of class labels (num_points X numclasses)
- predictions – A real valued matrix with indices ranging between zero and 1 (num_points X numclasses)
- threshold – The detection threshold (between zero and 1)
- detects – In case detection is precomputed for efficiency when evaluating both precision and recall
Returns: A scalar value
-
node_ops.
fscore
(predictions=None, targets=None, threshold=0.5, precisions=None, recalls=None, name='fscore')[source]¶
-
exception
node_ops.
MissingShapeError
[source]¶ Raised when
placeholder
can not infer shape.
-
node_ops.
accuracy
(*args, **kwargs)[source]
-
node_ops.
batch_normalize
(*args, **kwargs)[source] Batch Normalization: Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift
An exponential moving average of means and variances in calculated to estimate sample mean and sample variance for evaluations. For testing pair placeholder is_training with [0] in feed_dict. For training pair placeholder is_training with [1] in feed_dict. Example:
Let train = 1 for training and train = 0 for evaluation
Parameters: - tensor_in – input Tensor
- epsilon – A float number to avoid being divided by 0.
- name – For variable_scope
Returns: Tensor with variance bounded by a unit and mean of zero according to the batch.
-
node_ops.
binary_tensor_combine
(*args, **kwargs)[source]¶ For performing tensor multiplications with batches of data points against an order 3 weight tensor.
Parameters: - tensors – A list of two matrices each with first dim batch-size
- output_dim – The dimension of the third mode of the weight tensor
- initrange – For initializing weight tensor
- name – For variable scope
Returns: A matrix with shape batch_size X output_dim
-
node_ops.
binary_tensor_combine2
(*args, **kwargs)[source]
-
node_ops.
concat
(*args, **kwargs)[source] Matrix multiplies each tensor in tensors by its own weight matrix and adds together the results.
Parameters: - tensors – A list of tensors.
- output_dim – Dimension of output
- name – An optional identifier for unique variable_scope.
Returns: A tensor with shape [None, output_dim]
-
node_ops.
convolutional_net
(*args, **kwargs)[source]¶ See: Tensorflow Deep MNIST for Experts , Tensorflow Convolutional Neural Networks , ImageNet Classification with Deep Convolutional Neural Networks , skflow/examples/text_classification_character_cnn.py , skflow/examples/text_classification_cnn.py , Character-level Convolutional Networks for Text Classification
Parameters: in_progress – Returns:
-
node_ops.
cosine
(*args, **kwargs)[source] Takes the cosine of vectors in corresponding rows of the two matrix tensors in operands.
Parameters: - operands – A list of two tensors to take cosine of.
- name – An optional name for unique variable scope.
Returns: A tensor with dimensions (operands[0].shape[0], 1)
Raises: ValueError when operands do not have matching shapes.
-
node_ops.
cross_entropy
(*args, **kwargs)[source]
-
node_ops.
detection
(*args, **kwargs)[source]
-
node_ops.
dnn
(*args, **kwargs)[source] - Creates fully connected deep neural network subgraph. Adapted From skflow dnn_ops.py
Parameters: - tensor_in – tensor or placeholder for input features.
- hidden_units – list of counts of hidden units in each layer.
- activation – activation function between layers. Can be None.
- distribution – Distribution for lookup weight initialization
- initrange – Initrange for weight distribution.
- l2 – Floating point number determining degree of of l2 regularization for these weights in gradient descent update.
- bn – Whether or not to use batch normalization
- keep_prob – if not None, will add a dropout layer with given probability.
- name – A name for unique variable_scope.
Returns: A tensor which would be a deep neural network.
-
node_ops.
dropout
(*args, **kwargs)[source]¶ - Adds dropout node. Adapted from skflow dropout_ops.py .
- Dropout A Simple Way to Prevent Neural Networks from Overfitting
Parameters: - tensor_in – Input tensor.
- prob – The percent of weights to keep.
- name – A name for the tensor.
Returns: Tensor of the same shape of tensor_in.
-
node_ops.
embedding
(*args, **kwargs)[source] A wrapper for tensorflow’s embedding_lookup
Parameters: - tensors – A list of two tensors , matrix, indices
- name – Unique name for variable scope
Returns: A matrix tensor where the i-th row = matrix[indices[i]]
-
node_ops.
fscore
(*args, **kwargs)[source]
-
node_ops.
highway_dnn
(*args, **kwargs)[source] - A highway deep neural network.
- Training Very Deep Networks
Parameters: - tensor_in – A 2d matrix tensor.
- hidden_units – list of counts of hidden units in each layer.
- activation – Non-linearity to perform. Can be ident for no non-linearity.
- distribution – Distribution for lookup weight initialization
- initrange – Initrange for weight distribution.
- l2 – Floating point number determining degree of of l2 regularization for these weights in gradient descent update.
- bn – Whether or not to use batch normalization
- keep_prob – Dropout rate.
- bias_start – initialization of transform bias weights
- name – A name for unique variable_scope.
Returns: A tensor which would be a highway deep neural network.
-
node_ops.
ident
(tensor_in, name='ident')[source]¶ Identity function for grouping tensors in graph, during config parsing.
Parameters: tensor_in – A Tensor or list of tensors Returns: tensor_in
-
node_ops.
khatri_rao
(*args, **kwargs)[source] From David Palzer
Parameters: - tensors –
- name –
Returns:
-
node_ops.
linear
(*args, **kwargs)[source] Linear map: \(\sum_i(args[i] * W_i)\), where \(W_i\) is a variable.
Parameters: - args – a 2D Tensor
- output_size – int, second dimension of W[i].
- bias – boolean, whether to add a bias term or not.
- bias_start – starting value to initialize the bias; 0 by default.
- distribution – Distribution for lookup weight initialization
- initrange – Initrange for weight distribution.
- l2 – Floating point number determining degree of of l2 regularization for these weights in gradient descent update.
- name – VariableScope for the created subgraph; defaults to “Linear”.
Returns: A 2D Tensor with shape [batch x output_size] equal to \(\sum_i(args[i] * W_i)\), where \(W_i\) are newly created matrices.
Raises: ValueError: if some of the arguments has unspecified or wrong shape.
-
node_ops.
lookup
(*args, **kwargs)[source] A wrapper for tensorflow’s embedding_lookup which infers the shape of the weight matrix and placeholder value from the parameter data.
Parameters: - dataname – Used exclusively by config.py
- data – A
HotIndex
object - indices – A Placeholder. If indices is none the dimensions will be inferred from data
- distribution – Distribution for lookup weight initialization
- initrange – Initrange for weight distribution.
- l2 – Floating point number determining degree of of l2 regularization for these weights in gradient descent update.
- shape – The dimensions of the output tensor, typically [None, output-size]
- makeplace – A boolean to tell whether or not a placeholder has been created for this data (Used by config.py)
- name – A name for unique variable scope.
Returns: tf.nn.embedding_lookup(wghts, indices), wghts, indices
-
node_ops.
mae
(*args, **kwargs)[source] Mean Absolute Error
-
node_ops.
mse
(*args, **kwargs)[source] Mean Squared Error.
-
node_ops.
mult_log_reg
(*args, **kwargs)[source] Performs mulitnomial logistic regression forward pass. Weights and bias initialized to zeros.
Parameters: - tensor_in – A tensor or placeholder
- numclasses – For classificatio
- data – For shape inference.
- dtype – For
weights
initialization. - initrange – For
weights
initialization. - seed – For
weights
initialization. - l2 – For
weights
initialization. - name – For variable_scope
Returns: A tensor shape=(tensor_in.shape[0], numclasses)
-
node_ops.
nmode_tensor_multiply
(*args, **kwargs)[source] Nth mode tensor multiplication (for order three tensor) from Kolda and Bader Tensor Decompositions and Applications Works for vectors (matrix with a 1 dimension or matrices)
Parameters: - tensors – A list of tensors the first is an order three tensor the second and order 2
- mode – The mode to perform multiplication against.
- leave_flattened – Whether or not to reshape tensor back to order 3
- keep_dims – Whether or not to remove 1 dimensions
- name – For variable scope
Returns: Either an order 3 or order 2 tensor
-
node_ops.
nmode_tensor_tomatrix
(*args, **kwargs)[source]¶ Nmode tensor unfolding (for order three tensor) from Kolda and Bader Tensor Decompositions and Applications
Parameters: - tensor – Order 3 tensor to unfold
- mode – Mode to unfold (0,1,2, columns, rows, or fibers)
- name – For variable scoping
Returns: A matrix (order 2 tensor) with shape dim(mode) X \(\Pi_{othermodes}\) dim(othermodes)
-
node_ops.
other_cross_entropy
(*args, **kwargs)[source] Logistic Loss
-
node_ops.
perplexity
(*args, **kwargs)[source]
-
node_ops.
placeholder
(*args, **kwargs)[source] Wrapper to create tensorflow Placeholder which infers dimensions given data.
Parameters: - dtype – Tensorflow dtype to initiliaze a Placeholder.
- shape – Dimensions of Placeholder
- data – Data to infer dimensions of Placeholder from.
- name – Unique name for variable scope.
Returns: A Tensorflow Placeholder.
-
node_ops.
precision
(*args, **kwargs)[source] Percentage of classes detected which are correct.
Parameters: - targets – A one hot encoding of class labels (num_points X numclasses)
- predictions – A real valued matrix with indices ranging between zero and 1 (num_points X numclasses)
- threshold – The detection threshold (between zero and 1)
- detects – In case detection is precomputed for efficiency when evaluating both precision and recall
Returns: A scalar value
-
node_ops.
recall
(*args, **kwargs)[source] Percentage of actual classes predicted
Parameters: - targets – A one hot encoding of class labels (num_points X numclasses)
- predictions – A real valued matrix with indices ranging between zero and 1 (num_points X numclasses)
- threshold – The detection threshold (between zero and 1)
- detects – In case detection is precomputed for efficiency when evaluating both precision and recall
Returns: A scalar value
-
node_ops.
residual_dnn
(*args, **kwargs)[source] - Creates residual neural network with shortcut connections.
- Deep Residual Learning for Image Recognition
Parameters: - tensor_in – tensor or placeholder for input features.
- hidden_units – list of counts of hidden units in each layer.
- activation – activation function between layers. Can be None.
- distribution – Distribution for lookup weight initialization
- initrange – Initrange for weight distribution.
- l2 – Floating point number determining degree of of l2 regularization for these weights in gradient descent update.
- bn – Whether or not to use batch normalization
- keep_prob – if not None, will add a dropout layer with given probability.
- skiplayers – The number of layers to skip for the shortcut connection.
- name – A name for unique variable scope
Returns: A tensor which would be a residual deep neural network.
-
node_ops.
rmse
(*args, **kwargs)[source] Root Mean Squared Error
-
node_ops.
se
(*args, **kwargs)[source] Squared Error.
-
node_ops.
ternary_tensor_combine
(*args, **kwargs)[source] For performing tensor multiplications with batches of data points against an order 3 weight tensor.
Parameters: - tensors –
- output_dim –
- initrange –
- name –
Returns:
-
node_ops.
weights
(*args, **kwargs)[source]¶ Wrapper parameterizing common constructions of tf.Variables.
Parameters: - distribution – A string identifying distribution ‘tnorm’ for truncated normal, ‘rnorm’ for random normal, ‘constant’ for constant, ‘uniform’ for uniform.
- shape – Shape of weight tensor.
- dtype – dtype for weights
- initrange – Scales standard normal and trunctated normal, value of constant dist., and range of uniform dist. [-initrange, initrange].
- seed – For reproducible results.
- l2 – Floating point number determining degree of of l2 regularization for these weights in gradient descent update.
- name – For variable scope.
Returns: A tf.Variable.
-
node_ops.
x_dot_y
(*args, **kwargs)[source] Takes the inner product for rows of operands[1], and operands[2], and adds optional bias, operands[3], operands[4]. If either operands[1] or operands[2] or both is a list of tensors then a list of the pairwise dot products (with bias when len(operands) > 2) of the lists is returned.
Parameters: - operands – A list of 2, 3, or 4 tensors (the first two tensors may be replaced by lists of tensors in which case the return value will a list of the dot products for all members of the cross product of the two lists.).
- name – An optional identifier for unique variable_scope.
Returns: A tensor or list of tensors with dimension (operands[1].shape[0], 1).
Raises: Value error when operands is not a list of at least two tensors.


Append an entry for the given offset/location pair to the index.
Append an entry for the given offset/location pair to the index. This entry must have a larger offset than all subsequent entries.
Close the index
Delete this index file
The number of entries in this index
Get the nth offset mapping from the index
Get the nth offset mapping from the index
The entry number in the index
The offset/position pair at that entry
Flush the data in the index to disk
True iff there are no more slots available in this index
Find the largest offset less than or equal to the given targetOffset and return a pair holding this offset and it's corresponding physical file position.
Find the largest offset less than or equal to the given targetOffset and return a pair holding this offset and it's corresponding physical file position.
The offset to look up.
The offset found and the corresponding file position for this offset. If the target offset is smaller than the least entry in the index (or the index is empty), the pair (baseOffset, 0) is returned.
The maximum number of eight-byte entries this index can hold
The last entry in the index
Rename the file that backs this offset index
Rename the file that backs this offset index
true iff the rename was successful
Reset the size of the memory map and the underneath file.
Reset the size of the memory map and the underneath file. This is used in two kinds of cases: (1) in trimToValidSize() which is called at closing the segment or new segment being rolled; (2) at loading segments from disk or truncating back to an old segment where a new log segment became active; we want to reset the index size to maximum index size to avoid rolling new segment.
Do a basic sanity check on this index to detect obvious problems
Do a basic sanity check on this index to detect obvious problems
if any problems are found
The number of bytes actually used by this index
Trim this segment to fit just the valid entries, deleting all trailing unwritten bytes from the file.
Truncate the entire index, deleting all entries
Remove all entries from the index which have an offset greater than or equal to the given offset.
Remove all entries from the index which have an offset greater than or equal to the given offset. Truncating to an offset larger than the largest in the index has no effect.
An index that maps offsets to physical file locations for a particular log segment. This index may be sparse: that is it may not hold an entry for all messages in the log.
The index is stored in a file that is pre-allocated to hold a fixed maximum number of 8-byte entries.
The index supports lookups against a memory-map of this file. These lookups are done using a simple binary search variant to locate the offset/location pair for the greatest offset less than or equal to the target offset.
Index files can be opened in two ways: either as an empty, mutable index that allows appends or an immutable read-only index file that has previously been populated. The makeReadOnly method will turn a mutable file into an immutable one and truncate off any extra bytes. This is done when the index file is rolled over.
No attempt is made to checksum the contents of this file, in the event of a crash it is rebuilt.
The file format is a series of entries. The physical format is a 4 byte "relative" offset and a 4 byte file location for the message with that offset. The offset stored is relative to the base offset of the index file. So, for example, if the base offset was 50, then the offset 55 would be stored as 5. Using relative offsets in this way let's us use only 4 bytes for the offset.
The frequency of entries is up to the user of this class.
All external APIs translate from relative offsets to full offsets, so users of this class do not interact with the internal storage format.


Check out EmployeeDirectory.java for the entire directory of people that you can query for in the sample organization.
If you are running the default sample OneBox provider (SampleNoAuthOneBoxProvider.java) which requires no authorization, then you should be able to query the directory and get detailed contact information for anyone in the organization. Here is a sample search to exercise your OneBox.
| Search term | Expect to see |
|---|---|
| directory Brown | This will query the directory and return detailed contact information for all three people in the organization whose last name is Brown. |
If you are running one of the sample secure OneBox providers (SampleBasicAuthOneBoxProvider.java, SampleLDAPAuthOneBoxProvider.java, or SampleSSOAuthOneBoxProvider.java) then see the provider source file for the user ids, roles, and passwords (if applicable) for the full set of users that you can authenticate as.
Here are some sample searches to exercise the SampleBasicAuthOneBoxProvider (or the other secure providers) and see the effect of user level access control in OneBox. Assuming you are using a Google Search Appliance as your OneBox client and you are using the appliance's default user interface, you'll need to select "public and secure content" before running each search. You'll also need to clear your browser cache (or restart your browser) in between search attempts to retain the ability to log in as a different user on each attempt.
| Search term | Log in as | Expect to see |
|---|---|---|
| directory Brown | wbrown/wbrown | Since William Brown is a contractor, he is only authorized to see the contact information on file for himself despite the existence of other Browns in his department. |
| directory Brown | jsmith/jsmith | Since James Smith is an employee in the Marketing department, he is authorized to see some basic directory information on all of the people in his department, including both Browns. |
| directory Brown | sbrown/sbrown | Since Susan Brown is an employee in the Marketing department, she is authorized to see the detailed contact information on file for herself and is able to see some basic directory information on the other people in her department, including William Brown. |
| directory Brown | rmiller/rmiller | Since Richard Miller is a manager in the Marketing department, he is authorized to see the detailed contact information on file for all of the people in his department, including both Browns. |
| directory Brown | mhernandez/mhernandez | Since Margaret Hernandez is a system admin, she is authorized to see the detailed contact information on file for all of the people in all of the departments, including all three Browns. |


I enjoy solving complex problems. Frequently praised as detail-oriented by my peers, I can be relied upon to help your company achieve its goals by providing sustainable and scalable solutions.
I Design and Develop websites based on customer's desire by using latest and most efficient technologies I know till now
I also have a Youtube channel with 120 active subscribers. My channel is a technical channel which sorts the basic and complex doubts of all the tech geeks
Apart from web designing and development I also love to code in python as well and executed many real-time projects using python
Mr. Saikiran shows supermacy in his work His dedication towards work gives us high quality of output and he also proves the term dignity at work. I appreciate his work and dedication. Greenco Group
Lorem ipsum dolor sit amet consectetur adipisicing elit.
Reach out to me. Any queries ? regarding your Career or need help in Programming or in the IT industry. I'm happy to help you...


sweeten your javascript
Compile
Eval
Step
readable names
auto-compile
highlight macrofication
macro highlighting
vim
emacs
default
/* Welcome to sweet.js! You can play around with macro writing here on the left side and your code will automatically be compiled on the right. This page will also save your code to localStorage on every successful compile so feel free to close the page and come back later! */ // The `syntax` keyword is used to create and name new macros. syntax inc = function (ctx) { let x = ctx.next().value; return #`${x} + 1`; } inc 100