public interface RenderTarget
| Modifier and Type | Method and Description |
|---|---|
void |
clear(Color color)
Clears the render target and fills it with a constant color.
|
void |
draw(Drawable drawable)
Draws a drawable object to the render target using the default render states.
|
void |
draw(Drawable drawable,
RenderStates renderStates)
Draws a drawable object to the render target using the given render states.
|
void |
draw(Vertex[] vertices,
PrimitiveType type)
Draws a primitive of the given type using the given vertices and the default render states.
|
void |
draw(Vertex[] vertices,
PrimitiveType type,
RenderStates states)
Draws a primitive of the given type using the given vertices and the given render states.
|
ConstView |
getDefaultView()
Gets the target's default view.
|
Vector2i |
getSize()
Gets the size of the render region.
|
ConstView |
getView()
Gets the target's current view.
|
IntRect |
getViewport(ConstView view)
Computes the viewport of the given view applied to this render target.
|
Vector2i |
mapCoordsToPixel(Vector2f point)
Converts a point from world coordinates to target coordinates using the target's
current view.
|
Vector2i |
mapCoordsToPixel(Vector2f point,
ConstView view)
Converts a point from world coordinates to target coordinates for a certain view.
|
Vector2f |
mapPixelToCoords(Vector2i point)
Converts a point from target coordinates to world coordinates using the target's
current view.
|
Vector2f |
mapPixelToCoords(Vector2i point,
ConstView view)
Converts a point from target coordinates to world coordinates for a certain view.
|
void |
popGLStates()
Pops the last OpenGL states and matrices from the top of the stack.
|
void |
pushGLStates()
Pushes the current OpenGL states and matrices to the stack.
|
void |
resetGLStates()
Resets the internal OpenGL states so that the target is ready for drawing.
|
void |
setView(ConstView view)
Sets the target's view.
|
void clear(Color color)
color
- the color to fill the target with.void setView(ConstView view)
view
- the target's new view.ConstView getView()
ConstView getDefaultView()
IntRect getViewport(ConstView view)
view
- the view.Vector2f mapPixelToCoords(Vector2i point)
point
- the point on the render target.Vector2f mapPixelToCoords(Vector2i point, ConstView view)
point
- the point on the render target.view
- the view to use for conversion.Vector2i mapCoordsToPixel(Vector2f point)
point
- the point in the world.Vector2i mapCoordsToPixel(Vector2f point, ConstView view)
point
- the point in the world.view
- the view to use for conversion.void draw(Drawable drawable)
drawable
- the object to draw.void draw(Drawable drawable, RenderStates renderStates)
drawable
- the object to draw.renderStates
- the render states to use for drawing.void draw(Vertex[] vertices, PrimitiveType type)
vertices
- the vertices to draw.type
- the type of OpenGL primitive to draw.void draw(Vertex[] vertices, PrimitiveType type, RenderStates states)
vertices
- the vertices to draw.type
- the type of OpenGL primitive to draw.states
- the render states to use for drawing.Vector2i getSize()
void pushGLStates()
void popGLStates()
void resetGLStates()


| 002:080 Khan | : |
And they (Jews) say, "The Fire (i.e. Hell-fire on the Day of Resurrection) shall not touch us but for a few numbered days." Say (O Muhammad Peace be upon him to them): "Have you taken a covenant from Allah, so that Allah will not break His Covenant? Or is it that you say of Allah what you know not?" |
| 002:080 Maulana | : |
And they say: Fire will not touch us but for a few days. Say: Have you received a promise from Allah? Then Allah will not fail to perform ffis promise. Or do you speak against Allah what you know not? |
| 002:080 Pickthal | : |
And they say: The Fire (of punishment) will not touch us save for a certain number of days. Say: Have ye received a covenant from Allah - truly Allah will not break His covenant - or tell ye concerning Allah that which ye know not? |
| 002:080 Rashad | : |
Some have said, "Hell will not touch us, except for a limited number of days." Say, "Have you taken such a pledge from GOD - GOD never breaks His pledge - or, are you saying about GOD what you do not know?" |
| 002:080 Sarwar | : |
They have said, "Hell fire will never harm us except for just a few days." (Muhammad), ask them, "Have you made such agreements with God Who never breaks any of His agreements or you just ascribe to Him that which you do not know? |
| 002:080 Shakir | : |
And they say: Fire shall not touch us but for a few days.  Then, ALLAH will never break HIS promise. Or, do you say of ALLAH what you know not. |
| 002:080 Yusufali | : |
And they say: "The Fire shall not touch us but for a few numbered days:" Say: "Have ye taken a promise from Allah, for He never breaks His promise? or is it that ye say of Allah what ye do not know?" |
Concepts/Themes Covered: أركان الإسلام > التوحيد > الكافرون > افتراؤهم على الله وتكذيبهم ومجادلتهم بآيات الله Pillars of Islam> Islamic> The Disbelievers> Their Fabrication Against Allah, their Denial and Argumentation Against Allah's Signs الإنسان والعلاقات الأخلاقية > الأخلاق الحميدة > الوفاء بالعهد Man and The Moral Relations> Good Morals>Keeping Promise الديانات > بنو إسرائيل > معاندتهم و تكذيبهم و قتلهم الأنبياء Religions>Children of Israel> Their Opposition, their Denial and their Killing the Prophets
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


Check back soon for parties... If you'd like us to post one contact us!
Check back soon for parties... If you'd like us to post one contact us!
College sometimes needs special things for partying and or studying. Below type orders such as JUUL pods, Solo cups, and other party items.
WE DO NOT DELIVER ANY ILEGAL SUBSTANCES OR ALCOHOL!
College Orders Form
Got a hot date and a drunk friend to take care of? We offer a service to take care of drunk people including snacks, hydration, and puting them to bed. 2 hour max time limit. Prices start at $15 and get cheaper for groups.
Friday and Saturday nights only.
Drunk Care Form


Tan Jin Wei
Blk 446 Lorong 8
Pandan Valley #18-03
Singapore 109898
You will be charged SGD96 for a one-year subscription to Velle.


Dropdown
Within a navbar
My Project
Dropdown
Header #1
Action
Another action
Something else here
Header #2
Separated link
Dropdown 2
Header #1
Action
Another action
Something else here
Header #2
Separated link
Dropdown 3
Header #1
Action
Another action
Something else here
Header #2
Separated link
Within tabs
Regular link
Dropdown
Header #1
Action
Another action
Something else here
Header #2
Separated link
Dropdown 2
Header #1
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Dropdown 3
Header #1
Action
Another action
Something else here
Header #2
Separated link


| Package | Description |
|---|---|
| org.elasticsearch.action.admin.indices.warmer.put | |
| org.elasticsearch.action.count |
Count action.
|
| org.elasticsearch.action.search |
Search action.
|
| org.elasticsearch.action.termvectors.dfs |
Distributed frequencies.
|
| org.elasticsearch.client |
The client module allowing to easily perform actions/operations.
|
| org.elasticsearch.client.support | |
| org.elasticsearch.percolator | |
| org.elasticsearch.rest.action.search | |
| org.elasticsearch.search.action | |
| org.elasticsearch.search.controller | |
| org.elasticsearch.search.fetch | |
| org.elasticsearch.search.internal | |
| org.elasticsearch.search.query |
| Class and Description |
|---|
| SearchRequest
A request to execute search against one or more indices (or all).
|
| SearchRequestBuilder
A search action request builder.
|
| TransportSearchAction |
| Class and Description |
|---|
| SearchRequest
A request to execute search against one or more indices (or all).
|
| SearchResponse
A response of a search request.
|
| Class and Description |
|---|
| ClearScrollAction |
| ClearScrollRequest |
| ClearScrollRequestBuilder |
| ClearScrollResponse |
| MultiSearchAction |
| MultiSearchRequest
A multi search API request.
|
| MultiSearchRequestBuilder
A request builder for multiple search requests.
|
| MultiSearchResponse
A multi search response.
|
| MultiSearchResponse.Item
A search response item, holding the actual search response, or an error message if it failed.
|
| SearchAction |
| SearchPhaseExecutionException |
| SearchRequest
A request to execute search against one or more indices (or all).
|
| SearchRequestBuilder
A search action request builder.
|
| SearchResponse
A response of a search request.
|
| SearchScrollAction |
| SearchScrollRequest |
| SearchScrollRequestBuilder
A search scroll action request builder.
|
| SearchType
Search type represent the manner at which the search operation is executed.
|
| ShardSearchFailure
Represents a failure to search on a specific shard.
|
| TransportSearchAction |
| Class and Description |
|---|
| SearchRequest
A request to execute search against one or more indices (or all).
|
| Class and Description |
|---|
| ClearScrollRequest |
| ClearScrollRequestBuilder |
| ClearScrollResponse |
| MultiSearchRequest
A multi search API request.
|
| MultiSearchRequestBuilder
A request builder for multiple search requests.
|
| MultiSearchResponse
A multi search response.
|
| SearchRequest
A request to execute search against one or more indices (or all).
|
| SearchRequestBuilder
A search action request builder.
|
| SearchResponse
A response of a search request.
|
| SearchScrollRequest |
| SearchScrollRequestBuilder
A search scroll action request builder.
|
| Class and Description |
|---|
| ClearScrollRequest |
| ClearScrollRequestBuilder |
| ClearScrollResponse |
| MultiSearchRequest
A multi search API request.
|
| MultiSearchRequestBuilder
A request builder for multiple search requests.
|
| MultiSearchResponse
A multi search response.
|
| SearchRequest
A request to execute search against one or more indices (or all).
|
| SearchRequestBuilder
A search action request builder.
|
| SearchResponse
A response of a search request.
|
| SearchScrollRequest |
| SearchScrollRequestBuilder
A search scroll action request builder.
|
| Class and Description |
|---|
| SearchType
Search type represent the manner at which the search operation is executed.
|
| Class and Description |
|---|
| ClearScrollRequest |
| SearchRequest
A request to execute search against one or more indices (or all).
|
| SearchScrollRequest |
| Class and Description |
|---|
| ClearScrollRequest |
| SearchRequest
A request to execute search against one or more indices (or all).
|
| Class and Description |
|---|
| SearchRequest
A request to execute search against one or more indices (or all).
|
| Class and Description |
|---|
| SearchRequest
A request to execute search against one or more indices (or all).
|
| SearchScrollRequest |
| Class and Description |
|---|
| SearchRequest
A request to execute search against one or more indices (or all).
|
| SearchScrollRequest |
| SearchType
Search type represent the manner at which the search operation is executed.
|
| Class and Description |
|---|
| SearchRequest
A request to execute search against one or more indices (or all).
|
Copyright © 2009–2016. All rights reserved.


--- layout: default title: ADASE Group Website permalink: / ---
Our team consists of researchers and developers, among them PhD students,
- Surrogate (Proxy) Modelling and Optimization
- Deep Neural Networks, including Reinforcement Learning
- Anomaly Detection and Failures prediction
- Tensor Models in ML
- Graph Mining
- Recommendations/Ranking
- Solving applied problems
- Development of Industrial Solutions
- Management of R&D projects (mainly with big international companies)


Actor and soon-to-be-authorLili Reinhart was listed as one of Time magazine's 100 Next most influential young people. To celebrate the expansion of the magazine's illustrious list, Lili attended the gala on Thursday night at Pier 17 in New York City, wearing a Rodarte gown and Harry Winston jewels — we think Betty Cooper would approve of this sweet look.
Lili's black-and-white peplum gown covered in black satin bows was straight from Rodarte's Fall/Winter 2019 collection. The choice isn't surprising because she is a Rodarte ambassador, since being featured in the brand's dreamlike, star-studded Spring/Summer 2020 lookbook, which also included Kiernan Shipka and Alexa Demie.
The simple, monochromatic palette of Lili's F/W 2019 gown paired with the excessive bows is giving us major inspiration for our party season looks. It's perfectly festive with the present-like ribbon details, while the minimal color choice keeps the dress from looking over the top. We're also loving that the ruching on the bust looks like an elegantly wrapped gift. Keep reading ahead for every angle of Lili's gorgeous gown.
Lili Reinhart Wears Rodarte to the Time 100 Next Gala
Lili Reinhart Wears Rodarte to the Time 100 Next Gala
Lili Reinhart Wears Rodarte to the Time 100 Next Gala
Lili Reinhart Wears Rodarte to the Time 100 Next Gala
Lili Reinhart Wears Rodarte to the Time 100 Next Gala
Lili Reinhart Wears Rodarte to the Time 100 Next Gala
Lili Reinhart Wears Rodarte to the Time 100 Next Gala
Lili Reinhart Wears Rodarte to the Time 100 Next Gala
Lili Reinhart Wears Rodarte to the Time 100 Next Gala
Lili Reinhart Wears Rodarte to the Time 100 Next Gala
Lili Reinhart Wears Rodarte to the Time 100 Next Gala
Lili Reinhart Wears Rodarte to the Time 100 Next Gala
Lili Reinhart Wears Rodarte to the Time 100 Next Gala
Lili Reinhart Wears Rodarte to the Time 100 Next Gala


Dr. Ruby Perrin Schedule her appointment
4 mins ago
Charlene Reed has booked her appointment to Dr. Ruby Perrin
6 mins ago
Travis Trimble sent a amount of $210 for his appointment
8 mins ago
Carl Kelly send a message to his doctor
12 mins ago
Administrator


Plate
Color
Entrance
{{car.plate}}
{{car.color}}
{{car.entrance | date: 'dd/MM/yyyy HH:mm'}}
The parking lot is empty
You must inform the plate and the color of the car!
Pick a color
Park
White - 10/10/2002 10:00
Black - 10/10/2002 10:36
Blue - 10/10/2002 11:10


“We eliminated any possible pollution in our soils in order to deliver the purest organic produce to our customers’ tables.“
“Currently, the European Union and the US require producers to obtain special certification to market food as organic.“
“Currently, the European Union and the US require producers to obtain special certification to market food as organic.“
“Enjoy seasonal deals, coupons, recipes and tips from The Healthy Farm value guide.“
the health farm deal“These lightly spiced pillowy potato dumplings made with sweet potatoes are great with pesto or roasted veggies.“
sweet potato gnocchi“Enjoy seasonal deals, coupons, recipes and tips from The Healthy Farm value guide.“
the health farm deal“These lightly spiced pillowy potato dumplings made with sweet potatoes are great with pesto or roasted veggies.“
sweet potato gnocchi


A | |
| any [Llvm.PassManager] | |
L | |
| llbasicblock [Llvm] |
A basic block in LLVM IR.
|
| llbuilder [Llvm] |
Used to generate instructions in the LLVM IR.
|
| llcontext [Llvm] |
The top-level container for all LLVM global data.
|
| llmemorybuffer [Llvm] |
Used to efficiently handle large buffers of read-only binary data.
|
| llmodule [Llvm] |
The top-level container for all other LLVM Intermediate Representation (IR)
objects.
|
| llpos [Llvm] | Before b and At_end a specify positions from the start of the 'b list
of a .
|
| llrev_pos [Llvm] | After b and At_start a specify positions from the end of the 'b list
of a .
|
| lltype [Llvm] |
Each value in the LLVM IR has a type, an instance of
lltype .
|
| lluse [Llvm] |
Used to store users and usees of values.
|
| llvalue [Llvm] |
Any value in the LLVM IR.
|
T | |
| t [Llvm_target.TargetData] | |
| t [Llvm_target.Endian] | |
| t [Llvm_executionengine.ExecutionEngine] |
An execution engine is either a JIT compiler or an interpreter, capable of
directly loading an LLVM module and executing its functions without first
invoking a static compiler and generating a native executable.
|
| t [Llvm_executionengine.GenericValue] | GenericValue.t is a boxed union type used to portably pass arguments to
and receive values from the execution engine.
|
| t [Llvm.PassManager] | |
| t [Llvm.ValueKind] | |
| t [Llvm.Opcode] | |
| t [Llvm.Fcmp] | |
| t [Llvm.Icmp] | |
| t [Llvm.Attribute] | |
| t [Llvm.Visibility] | |
| t [Llvm.Linkage] | |
| t [Llvm.TypeKind] |


Find the technique lists for RCM piano levels 1 though 10 here.
Step 1: Select all the keys that will be in your list
Step 2: Select all the technical tests that will be in your list
Step 3: Remove any specific items that you don't need
Coming soon.


The url-parse
method exposes two different API interfaces. The
url
interface that you know from Node.js
and the new URL
interface that is available in the latest browsers.
In version 0.1
we moved from a DOM based parsing solution, using the
element, to a full Regular Expression solution. The main reason for this was
to make the URL parser available in different JavaScript environments as you
don’t always have access to the DOM. An example of such environment is the
Worker
interface.
The RegExp based solution didn’t work well as it required a lot of lookups
causing major problems in FireFox. In version 1.0.0
we ditched the RegExp
based solution in favor of a pure string parsing solution which chops up the
URL into smaller pieces. This module still has a really small footprint as it
has been designed to be used on the client side.
In addition to URL parsing we also expose the bundled querystringify
module.
This module is designed to be used using either browserify or Node.js it’s released in the public npm registry and can be installed using:
npm install url-parse
All examples assume that this library is bootstrapped using:
```js ‘use strict’;
var URL = require(‘url-parse’); ```
To parse an URL simply call the URL
method with the URL that needs to be
transformed into an object.
js
var url = new URL('https://github.com/foo/bar');
The new
keyword is optional but it will save you an extra function invocation.
The constructor takes the following arguments:
url
(String
): A string representing an absolute or relative URL.baseURL
(Object
| String
): An object or string representing
the base URL to use in case url
is a relative URL. This argument is
optional and defaults to location
in the browser.parser
(Boolean
| Function
): This argument is optional and specifies
how to parse the query string. By default it is false
so the query string
is not parsed. If you pass true
the query string is parsed using the
embedded querystringify
module. If you pass a function the query string
will be parsed using this function.As said above we also support the Node.js interface so you can also use the library in this way:
```js ‘use strict’;
var parse = require(‘url-parse’) , url = parse(‘https://github.com/foo/bar’, true); ```
The returned url
instance contains the following properties:
protocol
: The protocol scheme of the URL (e.g. http:
).slashes
: A boolean which indicates whether the protocol
is followed by two
forward slashes (//
).auth
: Authentication information portion (e.g. username:password
).username
: Username of basic authentication.password
: Password of basic authentication.host
: Host name with port number.hostname
: Host name without port number.port
: Optional port number.pathname
: URL path.query
: Parsed object containing query string, unless parsing is set to false.hash
: The “fragment” portion of the URL including the pound-sign (#
).href
: The full URL.origin
: The origin of the URL.Note that when url-parse
is used in a browser environment, it will default to
using the browser’s current window location as the base URL when parsing all
inputs. To parse an input independently of the browser’s current URL (e.g. for
functionality parity with the library in a Node environment), pass an empty
location object as the second parameter:
js
var parse = require('url-parse');
parse('hostname', {});
A simple helper function to change parts of the URL and propagating it through
all properties. When you set a new host
you want the same value to be applied
to port
if has a different port number, hostname
so it has a correct name
again and href
so you have a complete URL.
```js var parsed = parse(‘http://google.com/parse-things’);
parsed.set(‘hostname’, ‘yahoo.com’); console.log(parsed.href); // http://yahoo.com/parse-things ```
It’s aware of default ports so you cannot set a port 80 on an URL which has
http
as protocol.
The returned url
object comes with a custom toString
method which will
generate a full URL again when called. The method accepts an extra function
which will stringify the query string for you. If you don’t supply a function we
will use our default method.
js
var location = url.toString(); // http://example.com/whatever/?qs=32
You would rarely need to use this method as the full URL is also available as
href
property. If you are using the URL.set
method to make changes, this
will automatically update.
The testing of this module is done in 3 different ways:
npm test
command.npm run coverage
.zuul
. You can run browser tests
using the npm run test-browser
command.


There are many bands in the world that perform all kinds of music.
For that, you obviously need the class RockBand
with such attributes as genre
("rock") and key_instruments
(by default, a list ["electric guitar", "drums"]
). Let's also, for the sake of simplicity, assume n_members
to be a class attribute equal to 4, since most rock bands do have 4 members.
Create this class and an object of that class: name the variable after any rock band that you like. Print the attributes of your rock band on separate lines in this order: genre, n_members, key_instruments.


Oops - 500 error! I think we've been locked out of the library. Hopefully someone will find the keys shortly.
{% extends "base.html" %} {% block content %}
Oops - 500 error! I think we've been locked out of the library. Hopefully someone will find the keys shortly.


--- author: Steve Gill categories: app date: '2011-07-20 18:56:55' permalink: /2011/07/20/phonegap-ios-plugin-for-drupal-v0-1/ slug: phonegap-ios-plugin-for-drupal-v0-1 status: publish title: PhoneGap iOS plugin for Drupal v0.1 type: post format: html ---
Hey Everyone,
Jeff Linwood has started work on a PhoneGap iOS plugin for accessing the Drupal Services API. He has already done some amazing work on it and is looking for feedback. If you are interested in this check it out at on his blog.
He also has a neat series on building an app with jQuery Mobile, PhoneGap and Drupal 7 on his blog. Definitely check it out!
Keep up the great work Jeff!
Cheers, -Steve Gill


{% load custom_tags %} {% load humanize %}
Last update
Index Size
Average document
Documents over time (based on document timestamps)
{% with insights.doc_by_time as data %} {% with "#doc_by_time_"|add:index.code as target %} {% with "1" as time %} {% include 'includes/histogram.html' %} {% endwith %} {% endwith %} {% endwith %}Document size distribution (in chars)
{% with insights.doc_avgs.sizeHistogram as data %} {% with "#sizeHistogram_"|add:index.code as target %} {% include 'includes/histogram.html' %} {% endwith %} {% endwith %}Insights are generated every couple of hours, come back later to check if yours are done.
{% endif %} {% comment %}| {{ index.name }} insights | < dashboard | manage | {% if index.is_ready %}Running | {% else %}Starting | {% endif %}|
|---|---|---|---|---|---|
{% if insights.doc_avgs or insights.doc_by_time %}
{# charCount, wordCount, sentenceCount, wordsPerDoc, sentencesPerDoc, documentSize, sizeHistogram #}
Index created on
Insights last updated at
Index Size
Average document
Documents over time (based on document timestamps)
{% with insights.doc_by_time as data %}
{% with "#doc_by_time" as target %}
{% with "1" as time %}
{% include 'includes/histogram.html' %}
{% endwith %}
{% endwith %}
{% endwith %}
Document size distribution (in chars)
{% with insights.doc_avgs.sizeHistogram as data %}
{% with "#sizeHistogram" as target %}
{% include 'includes/histogram.html' %}
{% endwith %}
{% endwith %}
Current index size: {{ index.current_docs_number|intcomma }} documents {% endif %} |


Google Over Phone
As the name suggest just give a call on +918376808005 and we search/google your query straight away for FREE
Use the dots below or your keyboard arrows.
As the name suggest just give a call on
+918376808005
and we search/google your query straight away for FREE.


Hello!👋 I am a Final Year Computer Science Under-Grad making my way through and upwards
of what I love most: coding and solving problems.
Currently I'm advancing on improving my Full-Stack Web Development skills
( front-end, back-end, dev-ops).
I also work on writing clean code and testing as much as possible.
CGPA: 9.28 / 10
Final percentage : 95%
Final percentage : 88%
Apart from studying at college, I utilise most of my time up-skilling in Front and Back-end web development.
I enjoy solving coding problems and practise regularly on Leetcode and Hackerank.
If you ask me what I'm really passionate about? I'd say architecting and developing low-latency , high
traffic, scalable
backend products. I aspire to learn micro-services, high-level design, and industry grade quality code.
When forced indoors, I follow a number of sci-fi, fantasy-thriller genre movies and television shows, I love International Politics and Macro-Economics. In my alone time you can see me practising guitar.


This is a demo of Thymeleaf and Spring-Security integration.
This is a public page, but you can navigate to protected pages to trigger authentication.


Remote access to the latest work instructions for each job where it’s needed the most
Simplify torque and pressure calculation while improving accuracy
Assist in proper tool selection
Assist in bolt pattern tracking
Wirelessly capture torque and pressure readings for each bolt
Immediately identifies out of spec values
Capture job related traceability data for future reference
Push data to the server for reporting job results, progress and current status
Text, email, or phone notifications


Objective
Create a computer program that allows oceanographers and researchers to enter data on a user friendly UI that will parse the data (manually inputted or CSV File) and output the visualized data into an image that can be easily interpreted by the general public and other researchers.
Code Base:
The entirety of this toolkit was written on top of the electron framework using a combination of nodeJS libraries and react. While there are plenty of frameworks and languages that could have been used to create this UI, electron and NodeJS were chosen due to the familiarity and comfortability. Each team member has professional experience working on this framework making syntax and libraries easy to adjust to and optimize.
Expectation vs Reality
When Nick and Jason first began working on this final lab project they had initially created a large amount of requirements and tools that we would be able to implement. However, after surpassing the minimum hours required for this project weeks before it was due, the group began to realize in order to achieve the results they wanted it would take an extra semester worth of time.
The toolkit group decided to cut down requirements and deliver two useful, quality and reliable tools instead of an array of tools that were thrown together and not tested properly. This results in the toolkit only containing two tools: wave classification and data mapping.
DataTypes and visualization:
At the beginning of this project, the group agreed having a tool that would allow the user to input recorded data into a grid and have the option to visualize the data would be an extremely useful tool. The group began implementing a tool formerly known as “salinity/Depth Mapping Tool”. This tool continued to receive many revisions and additional features were added. This tool was no longer for just salinity and depth data. It could be used for any measurement that needed to be visualized. This tool allows the user to customize the table in a dynamic and intuitive way and be able to generate an interactive graph that they can adjust, move and capture for their documents and research papers.
The user needs to understand the importance of the graph types used in this tool. Heat maps are used for 2D visualization while surface plots are used for 3D visualization. It would make no sense to use a surface plot to plot salinity, however it would make sense to use a heat map for map salinity.
References
-
Jason Giroux
Email<EMAIL_ADDRESS>-
Nicholas Ferreira
Email: nferreira828@g.rwu.edu


SPECTRE
FLUENT.CSS
A CSS library based on Microsoft Fluent Design
GitHub
Latest version:
Acrylic
Reveal Focus (Dark)
Reveal Focus (Light)


| 19 Jun 1947 |
Salman Rushdie born, Bombay India. |
| 1976 |
Salman Rushdie marries Clarissa Luard. |
| 1988 |
Salman Rushdie marries Marianne Wiggins. |
| Feb 1989 |
The Satanic Verses published. |
| 12 Feb 1989 |
Six or more Pakistanis protesting the sale of The Satanic Verses in the United States are killed by police gunfire during in the ensuing riot. |
| 14 Feb 1989 |
Ayatollah Khomeini issues a Fatwa against Salman Rushdie over Satanic Verses. |
| 24 Feb 1989 |
12 are killed an 17 wounded in Bombay when police open fire on a crowd of 10,000 protesting outside the British Embassy. |
| 29 Mar 1989 |
Belgian Muslim leaders Abdullah Al Ahdal and Salim Bahri are shot and killed inside a Brussels mosque. |
| 27 May 1989 |
30,000 Moslems protest outside the British Parliament. |
| 14 Sep 1989 |
Person or persons unknown place four bombs outside British bookstores owned by Penguin, publisher of The Satanic Verses. |
| 3 Jul 1991 |
Italian translator Ettore Capriolo is beaten and stabbed in his Milan apartment. |
| 12 Jul 1991 |
Japanese translator Hitoshi Igarashi is stabbed to death in Tokyo. |
| 4 Jul 1993 |
37 people are killed in a riot in Sivas, Turkey. |
| 11 Oct 1993 |
Norwegian publisher William Nygaard is shot three times and left for dead outside his home in Oslo. |
| 11 Aug 1993 |
Salman Rushdie walks onstage during a U2 concert at Wembley Stadium. |
| 7 Sep 1995 |
In London, Salman Rushdie makes his first pre-announced public appearance since the inception of the fatwa. |
| 12 Feb 1997 |
The Khordad-15 foundation announces that the $2.0 million bounty on Salman Rushdie's head has been increased to $2.5 million. |
| 14 Feb 1998 |
Iran's chief prosecutor, Morteza Moqtadaie, declares: "The shedding of this man's blood is obligatory." |
| 22 Sep 1998 |
Iranian President Mohammad Khatami declares: "We should consider the Salman Rushdie issue as completely finished." |
| 24 Sep 1998 |
At the United Nations, Iranian foreign minister Kamal Kharrazi promises British foreign secretary Robin Cook that Iran will restrain itself from threatening Salman Rushdie's life. |
| 28 Sep 1998 |
Three Iranian clerics call on Moslems to kill Rushdie, despite recent statements by the government. |
| 4 Oct 1998 |
160 members of the Iranian parliament proclaim that the fatwa against Salman Rushdie still stands. |
| 7 Oct 1998 |
The Iranian government declares that the fatwa still stands. |
| 10 Oct 1998 |
The Association of Hezbollah University Students pledges an additional $333,000 for the bounty on Salman Rushdie's head. |
| 11 Oct 1998 |
The Khordad-15 foundation announces in a full-page newspaper ad that the $2.5 million bounty on Rushdie's head has been increased to $2.8 million. Foundation director Ayatollah Hassan Sanei declares: "To make the fatwa everlasting and encourage its execution, I have decided to raise the reward offered by the foundation. This reward for killing Salman Rushdie is a great honour for the foundation and we must preserve it." |
| 18 Oct 1998 |
International groups campaigning on behalf of Salman Rushdie announce that they plan to disband. |
| 4 Jan 1999 |
In Mexico, Salman Rushdie says he does not live a normal life. |
| 4 Feb 1999 |
BBC reports that Salman Rushdie is considering a visit to India. According to Muslim leader Syed Ahmad Bukhari, "Indian Moslems be prepared for any sacrifice... from the time he steps on Indian soil, we will follow him everywhere. If we have to give our lives, we are ready." |
| 6 Feb 1999 |
The Tehran Times declares that Salman Rushdie is like to be the target of assassination attempts during his upcoming visit to India: "If the visit of this most hated blasphemous and disgraceful person takes place, there is every possibility that it will be his last foreign visit." |
| 14 Feb 1999 |
Ayatollah Hassan Sanei, director of the Khordad-15 foundation, tells the newspaper Jomhuri Islami: "The idea of Rushdie's annihilation is still very much alive and seeks only the right moment." |
| 17 Jan 2000 |
In an essay, Salman Rushdie describes his impression of the recent Y2K celebrations: "Now we saw, as clearly as the fireworks in the sky, that the defining struggle of the new age would be between terrorism and security." |
| 2000 |
Leaves his wife for an actress and moves to New York. |


Heroes:
name: {{ selectedHero.name }} -- id: {{ selectedHero.id }}
Heroes Class:
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Busmall - Customer Preferences Survey
Thank you for your participation!
In order to effectively gather data on user preferences, we'll be displaying 3 randomly selected products below. Please select the product out of the set that appeals to you the most. After 25 selections, you will see your results dispayed in a chart!
- Select the most appealing product.
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- View data about what you saw and what you selected.
- Your selections will be saved so you can continue contributing data!


pywinEA.algorithm¶
pywinEA.algorithm.basic module¶
-
class
pywinEA.algorithm.basic.
GA
(**kwargs)¶ Bases:
pywinEA.interface.algorithm.GAbase
Basic implementation of a mono-objective genetic algorithm (although the algorithm is mono-objective, multi-objective metrics can be coupled as long as they return a single value). In its initialization, you must specify a fitness function (pywin.fitness module) or a scikit-learn classifier and a Scikit-learn cross validation iterator. Additionally it is necessary to specify a population model (pywin.population.Population). Alternatively it is possible to indicate the population size, in this case a “simple” population will be created (pywin.population.Population). Additionally, it is necessary to provide the basic operators required by the genetic algorithm indicated below.
- fitness: sklearn.base.BaseEstimator or pywin.fitness.interface.FitnessStrategy
If a scikit-learn estimator is provided it is necessary to supply the cv parameter.
- cv: <optional> sklearn.BaseCrossValidator (Splitter class)
Scikit-learn cross validator iterable.
- population: <optional> pywin.population.Population
If a population is not provided it is necessary to specify the population size using argument population_size.
- population_size: <optionally> int
Only necessary if a population is not provided. By default the algorithm will create a basic population (pywin.population.Population).
- selection: <optional> pywin.selection.interface.SelectionStrategy
Individual selection strategy. By default pywin.selection.TournamentSelection (with two gladiators, a single winner and sampling without replacement).
- elitism: <optional> float or pywin.operators.interface.ElitismStrategy
If a float (0-1) is provided, the algorithm will use pywin.operators.BestFitness to select the proportion of individuals best fitted according to the indicated percentage. Otherwise it is necessary to provide an operator that inherits from the ElitismStrategy interface.
- elitism_rate: <optional> float
Percentage of individuals that will form the elite. Only required if an ElitismStrategy operator has been provided in the elitism parameter.
- annihilation: <optional> float or pywin.operators.interface.AnnihilationStrategy
If a float number is provided, the worst individuals will be removed from the population by the indicated percentage and the population will be filled with new random or elite individuals (The percentage can be modified using the fill_with_elite parameter). In this case the algorithm will use the AnnihilateWorse annihilation strategy. If an Annihilation instance is provided it is necessary to specify the annihilation_rate parameter.
- annihilation_rate: <optional> float
If a float number is provided, the worst individuals will be removed from the population by the indicated percentage and the population will be filled with new random individuals or elite individuals (The percentage can be modified using the fill_with_elite parameter).
- fill_with_elite: <optional> float
Percentage of annihilated individuals to be replaced from elite individuals. To specify this parameter it is necessary to provide elitism parameter.
- crossover: <optional> pywin.operators.interface.CrossOverStrategy
Individual cross-over strategy. By default pywin.operators.OnePoint.
- mutation: <optional> pywin.operators.interface.MutationStrategy
Mutation strategy used to introduce changes in the population. With some strategies it may be necessary to indicate the mutation_rate parameter.
- mutation_rate <optional> float
A parameter that indicates the probability of a random change occurring in a given individual. If this argument is provided without a mutation argument by default pywin.operators.RandomMutation will be used.
- imputer: <optional> pywin.imputation.interface.ImputationStrategy
Strategy to handle missing values. The missing values will be imputed for each individual in the population using the individual features.
- generations: int
Number of generations (the minimum number of generations is 1).
- positive_class: <optional> int
Class that will be considered as positive class, the rest of the classes will be considered as negative classes. If a value is provided the class labels will be transformed -> Binary Classification. Otherwise they will not be modified -> Multiclass classification.
- random_state<optional> int
Random seed.
- id<optional> str
Algorithm identifier
best_features: (Getter) Return a list with the best features found by the algorithm.
get_current_generation: (Getter) Return the current generation of the algorithm.
population: (Getter) Return the current population of the algorithm.
population_fitness: (Getter) Return the fitness of the current generation of the algorithm.
best_performance: (Getter) Return a dict with the score and the best performance achieved.
fitness: (Getter / Setter) Algorithm fitness strategy.
generations: (Getter / Setter) Algorithm number of generations.
selection: (Getter / Setter) Algorithm selection strategy.
elitism: (Getter / Setter) Algorithm elitism strategy.
elitism_rate: (Getter / Setter) Algorithm elitism rate.
annihilation: (Getter / Setter) Algorithm annihilation strategy.
annihilation_rate: (Getter / Setter) Algorithm annihilation rate.
fill_with_elite: (Getter / Setter) Percentage of annihilated individuals replaced from the elite.
mutation: (Getter / Setter) Algorithm mutation strategy.
mutation_rate: (Getter / Setter) Algorithm mutation rate.
imputer: (Getter / Setter) Algorithm imputation strategy.
crossover: (Getter / Setter) Algorithm crossover strategy.
positive_class: (Getter / Setter) Number of the class selected as positive class.
random_state: (Getter / Setter) Random state.
id: (Getter / Setter) Algorithm identifier.
- set_features(features): Allows you to assign the labels of the columns (the names of the predictor variables).
If this function is not used before using the fit method, the corresponding numerical value will be assigned to the position of the predictor variable. It also can be used after training step.
fit(X, y): Start algorithm execution.
continue_training(generations): Continue training the algorithm for an extra number of generations.
- predict(X): Predict the labels of the predictor variables. It is necessary that the model
has been previously adjusted with the fit method.
- training_evolution(): Returns the parameters monitored during training.It returns a dictionary with the
- following scheme:
- :key total_fitness
Dictionary that stores as a key the generation and as a value the total fitness of the population.
- :key best_fitness
Dictionary that stores as a key the generation and as a value the best individual fitness.
- :key mean_num_features
Dictionary that stores as a key the generation and as a value the average number of features in the population.
- :key std_num_features
Dictionary that stores as a key the generation and as a value the standard deviation in the number of features in the population.
- :key best_num_features
Dictionary that stores as a key the generation and as a value the number of features in best individual.
- :key best_(cv_score)
Dictionary that stores as a key the generation and as a value the performance measure of the best individual.
- :key mean_(cv_score)
Dictionary that stores as a key the generation and as a value the average performance in performance measure population.
- :key std_(cv_score)
Dictionary that stores as a key the generation and as a value the standard deviation in performance measure in population.
- :key best_features
Array that in the first position stores the best combination of features found by the algorithm and in the second position the fitness of the individual.
get_dataset(): Function that returns the dataset with the selected features.
set_population(new_individuals): Method that allows to select a new population of solutions.
save(file_name, dir_name, overwrite): This function allows to save the model into a file.
load_model(file_name, dir_name): <class method> This function allows to load a model from a file.
-
property
best_features
¶ It allows to obtain the best combination of features found by the algorithm.
- Returns
list Best combination of features.
-
property
best_performance
¶ Returns the largest value found for the selected fitness function.
- :return dict
Score and maximum value achieved by the algorithm.
-
continue_training
(generations: int)¶ This function allows to continue training the algorithm since the last generation.
continue_training(generations: int)
- Parameters
generations – int Extra number of generations.
- Returns
pywin.algorithms.BasicGA Model fitted using the best feature combination.
-
fit
(X: numpy.ndarray, y: numpy.ndarray)¶ Function that begins the execution of the algorithm. First, it processes the class labels to adapt them to a mono-class classification problem (only if positive_class has been provided). Then it initializes a random population and calls the _core() method where the main logic of the algorithm is defined.
fit(X: np.ndarray, y: np.ndarray)
- Parameters
X – 2d-array Values of the target variables.
y – 1d-array Labels of each condition.
- Returns
pywin.algorithms.BasicGA Model fitted using the best feature combination.
-
get_dataset
()¶ Function that returns a dataset with the selected features and associated class labels.
- Returns
2d-array Predictor variables.
- :return 1d-array
Class labels.
-
classmethod
load
(file_name: str, dir_name: str = './_PyWinModels')¶ Class method that allows retrieving models stored in .algorithms files. By default the files will be recovered from _PyWinModels directory.
load(file_name: str, dir_name: str = ‘./_PyWinModels’)
- Parameters
file_name – str File name.
dir_name – <optional> str Directory where the file is located.
- Returns
pywin.algorithms.BasicGA Model fitted using the best feature combination.
-
predict
(X: numpy.ndarray)¶ Function that returns the predictions made by the best combination of model and features.
predict(X: np.ndarray)
- Parameters
X – 2d-array Data that will be predicted.
- Returns
1d-array Predictions.
-
training_evolution
()¶ Function that returns a dictionary with the data collected throughout the algorithm search. You can use the function plot_evolution() from pywin.visualization.Plotter to display it.
- Returns
dict Dictionary with the total fitness of the population and best individual, the number of features in the population and best individual, and information on the performance achieved.
- :return str
Score used to evaluate fitness.
pywinEA.algorithm.nsga2 module¶
-
class
pywinEA.algorithm.nsga2.
NSGA2
(**kwargs)¶ Bases:
pywinEA.interface.algorithm.MOAbase
Implementation of the multi-objective NSGAII algorithm based on:
K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” in IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182-197, April 2002.
As many target functions as desired can be added from the fitness sub-module. You can also treat the number of features as an objective function (to be minimized) by indicating the parameter optimize_features as true. To evaluate the evolution of the algorithm, the hypervolume indicator is used. This metric has been implemented using the inclusion-exclusion algorithm. It must be taken into account that the larger the population or the more objectives have to be maximized, the higher the computational cost will be.
- fitness: pywin.interface.FitnessStrategy / pywin.fitness / list
If the parameter optimize_features is True, a unique fitness function of the fitness submodule can be provided (also a list with several functions is allowed). If this parameter is selected as False it is necessary to provide a list with at least two functions to optimize.
- optimize_features: <optional> bool
If this parameter is true, the number of targets will be a function to optimize.
- features_function: <optional> function
User-defined function that should receive a value called “individual” and another value “tot_feats”. This f unction should return a single value that will be maximized. By default this function will be:
f(individual) = 1 - (len(individual.features) / len(all features))
Important note: Functions cannot be defined as lambda functions as they are not serializable by the pickle library and an error will be thrown.
- population: <optional> pywin.population.Population
If a population is not provided it is necessary to specify the population size using argument population_size.
- population_size: <optionally> int
Only necessary if a population is not provided. By default the algorithm will create a basic population (pywin.population.Population).
- selection: <optional> pywin.selection.interface.SelectionStrategy
Individual selection strategy. By default pywin.selection.TournamentSelection (with two gladiators, a single winner and sampling without replacement).
- crossover: <optional> pywin.operators.interface.CrossOverStrategy
Individual cross-over strategy. By default pywin.operators.OnePoint.
- mutation: <optional> pywin.operators.interface.MutationStrategy
Mutation strategy used to introduce changes in the population. With some strategies it may be necessary to indicate the mutation_rate parameter.
- mutation_rate <optional> float
A parameter that indicates the probability of a random change occurring in a given individual. If this argument is provided without a mutation argument by default pywin.operators.RandomMutation will be used.
- imputer: <optional> pywin.imputation.interface.ImputationStrategy
Strategy to handle missing values. The missing values will be imputed for each individual in the population using the individual features.
- generations: int
Number of generations (the minimum number of generations is 1).
- positive_class: <optional> int
Class that will be considered as positive class, the rest of the classes will be considered as negative classes. If a value is provided the class labels will be transformed -> Binary Classification. Otherwise they will not be modified -> Multiclass classification.
- random_state<optional> int
Random seed.
- id<optional> str
Algorithm identifier
best_features: (Getter) Return a list with the best features in each solution.
get_current_generation: (Getter) Return the current generation of the algorithm.
population: (Getter) Return the current population of the algorithm.
- population_fitness: (Getter) Return the fitness of the current generation of the algorithm (This consists of
instances of Solution).
best_performance: (Getter) Return a dict with the score and the best performance achieved for each solution.
fitness: (Getter / Setter) Algorithm fitness strategy.
features_function: (Getter / Setter) Function to evaluate the number of features.
generations: (Getter / Setter) Algorithm number of generations.
selection: (Getter / Setter) Algorithm selection strategy.
mutation_rate: (Getter / Setter) Algorithm mutation_rate.
imputer: (Getter / Setter) Algorithm imputation strategy.
crossover: (Getter / Setter) Algorithm crossover strategy.
positive_class: (Getter / Setter) Algorithm selection strategy.
random_state: (Getter / Setter) Algorithm selection strategy.
id: (Getter / Setter) Algorithm selection strategy.
- set_features(features): Allows you to assign the labels of the columns (the names of the predictor variables).
If this function is not used before using the fit method, the corresponding numerical value will be assigned to the position of the predictor variable. It also can be used after training step.
fit(X, y): Start algorithm execution.
continue_training(generations): Continue training the algorithm for an extra number of generations.
predict(X): Method NOT available for NSGA2.
- training_evolution(): Returns the parameters monitored during training.It returns a dictionary with the
- following scheme:
- key hypervolume
Hypervolume indicator in each generation.
- key num_solutions_front
Number of solutions on the Pareto front in each generation.
- key best_values
Best values of each function in each generation.
get_dataset(): Function that returns all the dataset and class labels.
set_population(new_individuals): Method that allows to select a new population of solutions.
save(file_name, dir_name, overwrite): This function allows to save the model into a file.
load_model(file_name, dir_name): <class method> This function allows to load a model from a file.
-
continue_training
(generations: int)¶ This function allows to continue training the algorithm since the last generation.
- Parameters
generations – int Extra number of generations.
- Returns
NSGA2
-
fit
(X: numpy.ndarray, y: numpy.ndarray)¶ Function that begins the execution of the algorithm. First, it processes the class labels to adapt them to a mono-class classification problem (only if positive_class has been provided). Then it initializes a random population and calls the _core() method where the main logic of the algorithm is defined.
fit(X: np.ndarray, y: np.ndarray)
- Parameters
X – 2d-array Values of the target variables.
y – 1d-array Labels of each condition.
- Returns
pywin.algorithms.NSGA2 Model fitted using the best feature combination.
-
get_dataset
()¶ Function that returns all the dataset and class labels.
- Returns
2d-array Predictor variables.
- :return 1d-array
Class labels.
-
training_evolution
()¶ Function that returns a dictionary with the data collected throughout the algorithm search. You can use the function plot_evolution() from pywin.visualization.Plotter to display it.
- Returns
dict Dictionary with the evolution of the hypervolume indicator, number of solutions on the non-dominated front and best values for each objective function.
- :return str
Scores used to evaluate fitness.
-
class
pywinEA.algorithm.nsga2.
Solution
(values: list)¶ Bases:
object
Class that represents a possible solution with the values of each of the objective functions.
-
restart
()¶ Reset all values in the solution.
-
-
pywinEA.algorithm.nsga2.
calculate_crowding
(solutions: list)¶ Method to calculate crowding for all solutions using _crowding_distance() method for each solution.
- Parameters
solutions – list List with all solutions.
-
pywinEA.algorithm.nsga2.
restart_solutions
(solutions)¶ Method that resets the values of each solution.
- Parameters
solutions – list
pywinEA.algorithm.spea2 module¶
-
class
pywinEA.algorithm.spea2.
SPEA2
(**kwargs)¶ Bases:
pywinEA.interface.algorithm.MOAbase
Implementation of the multi-objective SPEA2 algorithm based on:
- “SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization January 2001
Conference: Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems. Proceedings of the EUROGEN’2001. Athens. Greece, September 19-21. Eckart Zitzler, Marco Laumanns and Lothar Thiele”
As many target functions as desired can be added from the fitness sub-module. You can also treat the number of features as an objective function (to be minimized) by indicating the parameter optimize_features as true. To evaluate the evolution of the algorithm, the hypervolume indicator is used. This metric has been implemented using the inclusion-exclusion algorithm. It must be taken into account that the larger the population or the more objectives have to be maximized, the higher the computational cost will be.
- fitness: pywin.fitness.interface.FitnessStrategy / list
If the parameter optimize_features is True, a unique fitness function of the fitness submodule can be provided (also a list with several functions is allowed). If this parameter is selected as False it is necessary to provide a list with at least two functions to optimize.
- optimize_features: <optional> bool
If this parameter is true, the number of targets will be a function to optimize.
- features_function: <optional> function
User-defined function that should receive a value called “individual” and another value “tot_feats”. This f unction should return a single value that will be maximized. By default this function will be:
f(individual) = 1 - (len(individual.features) / len(all features))
Important note: Functions cannot be defined as lambda functions as they are not serializable by the pickle library and an error will be thrown.
- population: <optional> pywin.population.Population
If a population is not provided it is necessary to specify the population size using argument population_size.
- population_size: <optionally> int
Only necessary if a population is not provided. By default the algorithm will create a basic population (pywin.population.Population).
- archive_length: <optional> int
Archive size. By default it will be equal to the population size.
- distance: <optional> str
Distance used to estimate the density of solutions. By default the Euclidean distance will be used. Currently available distances: ‘euclidean’, ‘minkowski’, ‘cosine’ and ‘canberra’
- selection: <optional> pywin.selection.interface.SelectionStrategy
Individual selection strategy. By default pywin.selection.TournamentSelection (with two gladiators, a single winner and sampling without replacement).
- crossover: <optional> pywin.operators.interface.CrossOverStrategy
Individual cross-over strategy. By default pywin.operators.OnePoint.
- mutation: <optional> pywin.operators.interface.MutationStrategy
Mutation strategy used to introduce changes in the population. With some strategies it may be necessary to indicate the mutation_rate parameter.
- mutation_rate <optional> float
A parameter that indicates the probability of a random change occurring in a given individual. If this argument is provided without a mutation argument by default pywin.operators.RandomMutation will be used.
- imputer: <optional> pywin.imputation.interface.ImputationStrategy
Strategy to handle missing values. The missing values will be imputed for each individual in the population using the individual features.
- generations: int
Number of generations (the minimum number of generations is 1).
- positive_class: <optional> int
Class that will be considered as positive class, the rest of the classes will be considered as negative classes. If a value is provided the class labels will be transformed -> Binary Classification. Otherwise they will not be modified -> Multiclass classification.
- random_state<optional> int
Random seed.
- id<optional> str
Algorithm identifier
best_features: (Getter) Return a list with the best features in each solution.
get_current_generation: (Getter) Return the current generation of the algorithm.
population: (Getter) Return the current population of the algorithm.
- population_fitness: (Getter) Return the fitness of the current generation of the algorithm (This consists of
instances of Solution).
best_performance: (Getter) Return a dict with the score and the best performance achieved for each solution.
distance: (Getter / Setter) Distance used to evaluate the fitness density of the solutions.
archive_length: (Getter / Setter) Archive size.
fitness: (Getter / Setter) Algorithm fitness strategy.
features_function: (Getter / Setter) Function to evaluate the number of features.
generations: (Getter / Setter) Algorithm number of generations.
selection: (Getter / Setter) Algorithm selection strategy.
mutation_rate: (Getter / Setter) Algorithm mutation_rate.
imputer: (Getter / Setter) Algorithm imputation strategy.
crossover: (Getter / Setter) Algorithm crossover strategy.
positive_class: (Getter / Setter) Algorithm selection strategy.
random_state: (Getter / Setter) Algorithm selection strategy.
id: (Getter / Setter) Algorithm selection strategy.
- set_features(features): Allows you to assign the labels of the columns (the names of the predictor variables).
If this function is not used before using the fit method, the corresponding numerical value will be assigned to the position of the predictor variable. It also can be used after training step.
fit(X, y): Start algorithm execution.
continue_training(generations): Continue training the algorithm for an extra number of generations.
predict(X): Method NOT available for NSGA2.
- training_evolution(): Returns the parameters monitored during training.It returns a dictionary with the
- following scheme:
- key hypervolume
Hypervolume indicator in each generation.
- key num_solutions_front
Number of solutions on the Pareto front in each generation.
- key best_values
Best values of each function in each generation.
get_dataset(): Function that returns all the dataset and class labels.
set_population(new_individuals): Method that allows to select a new population of solutions.
save(file_name, dir_name, overwrite): This function allows to save the model into a file.
load_model(file_name, dir_name): <class method> This function allows to load a model from a file.
-
continue_training
(generations: int)¶ This function allows to continue training the algorithm since the last generation.
- Parameters
generations – int Extra number of generations.
- Returns
pywin.algorithms.SPEA2
-
fit
(X: numpy.ndarray, y: numpy.ndarray)¶ Function that begins the execution of the algorithm. First, it processes the class labels to adapt them to a mono-class classification problem (only if positive_class has been provided). Then it initializes a random population and calls the _core() method where the main logic of the algorithm is defined.
fit(X: np.ndarray, y: np.ndarray)
- Parameters
X – 2d-array Values of the target variables.
y – 1d-array Labels of each condition.
- Returns
pywin.algorithms.SPEA2 Model fitted using the best feature combination.
-
get_dataset
()¶ Function that returns all the dataset and class labels.
- Returns
2d-array Predictor variables.
- :return 1d-array
Class labels.
-
training_evolution
()¶ Function that returns a dictionary with the data collected throughout the algorithm search. You can use the function plot_evolution() from pywin.visualization.Plotter to display it.
- Returns
dict Dictionary with the evolution of the hypervolume indicator, number of solutions on the non-dominated front and best values for each objective function.
- :return str
Scores used to evaluate fitness.
-
class
pywinEA.algorithm.spea2.
Solution
(values: list)¶ Bases:
object
Class that represents a possible solution with the values of each of the objective functions.
-
restart
()¶ Reset all values in the solution.
-
select_density_to
(k)¶ Return fitness using density estimation to k-th neighbor. Based on
- “SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization January 2001
Conference: Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems. Proceedings of the EUROGEN’2001. Athens. Greece, September 19-21. Eckart Zitzler, Marco Laumanns and Lothar Thiele”
-
-
pywinEA.algorithm.spea2.
archive_selection
(population: pywinEA.population.population.Population, archive_length: int)¶ SPEA2 archive selection based on:
- “SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization January 2001
Conference: Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems. Proceedings of the EUROGEN’2001. Athens. Greece, September 19-21. Eckart Zitzler, Marco Laumanns and Lothar Thiele”
- Parameters
population – pywin.population.Population
archive_length – int
- Returns
list List of solutions that make up the archive.
-
pywinEA.algorithm.spea2.
density_estimation
(solutions: list, k: int, distance)¶ SPEA2 density estimation based on:
- “SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization January 2001
Conference: Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems. Proceedings of the EUROGEN’2001. Athens. Greece, September 19-21. Eckart Zitzler, Marco Laumanns and Lothar Thiele”
- Parameters
solutions – list
distance – function Metric to evaluate the distance between two vectors.
k – int Neighbor from which the fitness density of the solution is calculated.
-
pywinEA.algorithm.spea2.
fitness_assignment
(solutions: list)¶ SPEA2 fitness assignment based on:
- “SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization January 2001
Conference: Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems. Proceedings of the EUROGEN’2001. Athens. Greece, September 19-21. Eckart Zitzler, Marco Laumanns and Lothar Thiele”
- Parameters
solutions – list List of possible solutions.
-
pywinEA.algorithm.spea2.
restart_solutions
(solutions)¶ Method that resets the values of each solution.
- Parameters
solutions – list


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Cartographe et Géomaticien, je travaille depuis 5 ans dans la mise en place et l'administration d'un Système d'Information Géographique pour le compte d'un opérateur foncier. Mes domaines de prédilections pour traiter l'information et la rendre géographique sont l'urbanisme et ses thématiques liées (cadastre, droit des sols, valeurs immobilières) et la gestion des données métiers (En l'occurence, notre patrimoine, nos mouvements financiers,etc.)
Mon travail inclut de nombreuses faettes qui peuvent être résumées de la manière suivante : Acquisition --> Traitement --> Diffusion de l'information via des outils et applications SIG. Par ma formation et mes premières missions professionnelles, j'évolue dans un environnement où l'Open-Source tient une grande place. Tout en étant très opérationnel sur des outils propriétaires qui font office de standard, je suis attentivement les développements et l'écosystème qui entoure les outils libre et tient à rester en phase avec les dernières évolutions de ceux-ci.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged.


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sean
birthday 9 april
email<EMAIL_ADDRESS>dislikes swear words
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|
LCDGFX LCD display driver
1.0.3
This library is developed to control SSD1306/SSD1325/SSD1327/SSD1331/SSD1351/IL9163/PCD8554 RGB i2c/spi LED displays
|
#include <io.h>
Public Member Functions | |
| PlatformSpi (const SPlatformSpiConfig &config) | |
| Public Member Functions inherited from AvrSpi | |
| AvrSpi (int8_t csPin, int8_t dcPin, uint32_t frequency) | |
| void | begin () |
| void | end () |
| void | start () |
| void | stop () |
| void | send (uint8_t data) |
| void | sendBuffer (const uint8_t *buffer, uint16_t size) |
| Sends bytes to SSD1306 device. More... | |
PlatformSpi implementation for current platform
|
inline |
Creates instance of PlatformSpi implementation for current platform
| config | spi platform configuration. Refer to SPlatformSpiConfig. |


Recent versions of HTTP include a mechanism known as content negotiation which allows a user-agent and a server to negotiate the best representation (instance) for a given resource. For example, a server that provides both PNG and GIF versions of an image will serve the PNG version to user-agents that support PNG, and the GIF version to Internet Explorer.
Content negotiation requires that a client should send with every single request a number of headers specifying the user's cultural and technical preferences. Most of these headers do not expose sensitive information (who cares whether your browser supports PNG?). The ‘Accept-Language’ header, however, is meant to convey the user's linguistic preferences. In some cases, this information is sufficient to pinpoint with great precision the user's origins and even his political or religious opinions; think, for example, of the implications of sending ‘Accept-Language: yi’ or ‘ar_PS’.
At any rate, ‘Accept-Language’ is not useful. Its design is based on the assumption that language is merely another representation for the same information, and ‘Accept-Language’ simply carries a prioritised list of languages, which is not enough to usefully describe a literate user's preferences. A typical French user, for example, will prefer an English-language original to a French (mis-)translation, while still wanting to see French language texts when they are original. Such a situation cannot be described by the simple-minded ‘Accept-Language’ header.


Enter the name of your favorite Disney Princess, or something else. Once the button pops up - click it for 10 new .gifs of your choice!


Since I was not 100% sure that the ALX program was legit, I ventured into social media looking for proof and confessions. I found a video of Fred Swaniker on Youtube (Ted Talk). He was talking about his mission of changing Africa by bringing up young leaders. This really kept me going on, Bulldozeing through the application. -Never doubt that a group, a small group of thoughtfull leaders,can change Africa; Indeed it's the only thing that ever will.
If you want to go fast, go alone. If you want to go far, go together.#Africa #ALX #YoungLeader #AfricaToTheWorld pic.twitter.com/DeoAIiaehi
— ALX (@alx_africa) April 25, 2021


Predicting Covid-19 Cases with a Long Short-Term Memory Neural Network(LSTM)
Covid-19 in the U.S.A. has been a difficult battle despite some of our best efforts. However, this is also a good opportunity to speculate with the data we are given. In previous articles, we have analyzed different machine learning models for forecasting Covid-19 cases. We will go over the details of using a long short-term memory (LSTM) network in MATLAB to find the most appropriate model using the the total number of cases reported in the U.S.A.
Building the LSTM
An LSTM network is a specific kind of recurrent neural network (RNN). For example, when we want to make a forecast of Covid-19 cases, the prediction on the 5th day away is dependent on what we predicted for days 1-4
in addition to the original data we trained over. We will not go too much in depth on the underlying equations involved in the LSTM network, however it can be found in the MATLAB documentation
here. Each prediction we make is influenced by the order of the days.
First, we load the data starting from the first case reported on 1/21/20 up to 9/2/20. We will use the remainder of the data from 9/3/20 to 10/4/20 to test the validity of the model. After training, we will update
the model with the additional data and see how it improves. We uploaded a csv file which is publicly available from US Covid-19 data.
% Import data from CSV
opts = spreadsheetImportOptions("NumVariables", 1);
opts.Sheet = "Sheet1";
opts.DataRange = "A2:A227";
opts.VariableNames = "VarName1";
opts.VariableTypes = "double";
CovidData = readtable("H:\School work\Covid Project 2020\US_COVID.xlsx",... opts, "UseExcel", false);
CovidData = table2array(CovidData);
CovidData = transpose(CovidData);
figureplot(CovidData)
xlabel("Day")
ylabel("Cases")
title("Daily Covid-19 Cases in US, 1/21/20 - 10/4/20")
Now, we want to partition the data so that the last 30 days will be tested. The other 85% of the days we train over should give a strong model which will pick up on any inherent characteristics and trends in the data. Additionally, we want to standardize the data to remove any unit effects. The data is now easier to work with and we can set the independent variable and the dependent variable to train.
% Partition Model
TimeStepsTrain = floor(0.865*numel(CovidData));
TrainingData = CovidData(1:TimeStepsTrain+1);
TestingData = CovidData(TimeStepsTrain+1:end);
% Standardize data
mu = mean(TrainingData);
sig = std(TrainingData);
StandardizedTrainingData = (TrainingData - mu) / sig;
% Prepare predictor
XTrain = StandardizedTrainingData(1:end-1);
YTrain = StandardizedTrainingData(2:end);
Once the data is ready, it's time to prepare the details to the LSTM network. Since our only input is the date of the day itself, our input feature is only a value of 1. The output of the measured number of Covid-19 cases to is also marked as a 1. The hidden units essentially connect the features to the responses and handle the actual neural networking. These details can be found in the MATLAB documentation. While having a high number of hidden units can increase the accuracy of the model, we don't want to overfit the data to have a perfect model, thus we found that 200 is a optimal amount. Any additional tweaks can be made to the features during the training of the LSTM network. In our case, the model converged after only 30 iterations and does not need changes in any of the learning features.
% Define LSTM network
Features = 1;
Responses = 1;
HiddenUnits = 200;
% LSTM Options
layers = [ ...
sequenceInputLayer(Features)
lstmLayer(HiddenUnits)
fullyConnectedLayer(Responses)
regressionLayer];
options = trainingOptions('sgdm', ...
'Momentum', .5, ...
'MaxEpochs',250, ...
'GradientThreshold',1, ...
'InitialLearnRate',0.005, ...
'LearnRateSchedule','piecewise', ...
'LearnRateDropPeriod',125, ...
'LearnRateDropFactor',0.2, ...
'Verbose',0, ... 'Plots','training-progress');
% Train model
net = trainNetwork(XTrain,YTrain,layers,options);
Predicting with our model
Now that the network is trained, it's time to make a prediction for the dates from 9/3/20 to 10/4/20. As from before, we have standardized the testing data input. After the first day's prediction, we are using that output as an additional input into the network. When we get to the 30th day, there will be 30 inputs inside the original network. We plot the Covid 19 predictions compared to the actual measurements. With the difference between these two, we can see the overall prediction accuracy.
% Forecasting
dataTestStandardized = (TestingData - mu) / sig;
XTest = dataTestStandardized(1:end-1);
net = predictAndUpdateState(net,XTrain);
[net,YPred] = predictAndUpdateState(net,YTrain(end));
numTimeStepsTest = numel(XTest);
for i = 2:numTimeStepsTest
[net,YPred(:,i)] = predictAndUpdateState(net,YPred(:,i-1),...
'ExecutionEnvironment','cpu');
end
YPred = sig*YPred + mu;
YTest = TestingData(2:end);
rmse = sqrt(mean((YPred-YTest).^2));
figureplot(TrainingData(1:end-1))
hold on
idx = TimeStepsTrain:(TimeStepsTrain+numTimeStepsTest);
plot(idx,[CovidData(TimeStepsTrain) YPred],'.-')
hold on
plot(CovidData)
hold off
xlabel("Month")
ylabel("Cases")
title("Forecast")
legend(["Observed" "Forecast"])
diff = abs(YTest - YPred);
perc = diff ./ (YTest) .* 100;
figure
plot(perc)
title("Percentage Errors")
xlabel("Day of Forecast")
perc_accuracy = 1 - mean(perc);
We are getting a prediction accuracy of 99.5% using the LSTM Model via MATLAB. With the predictions made further out, the overall accuracy should be getting worse due to the gain caused by errors being input into the
consecutive days. Currently in the U.S.A, we are seeing another rapid gain in Covid-19 cases. With the usual 1-2 weeks of lead up time before a new individual tests positive, there may be a lag shown in the most
recent data compared to the prediction. The model may be predicting over what was measured near the end of the prediction month. We see a decreased accuracy because the measured cases are spiking.
However, this is still great news for testing the strength of our model as we are aware at how overqualified the LSTM network is for this data set. With only a single input, the data is fairly easy for the model to
characterize and make a prediction. For further work, we want to add additional input features or find a more local data set such as the number of cases in a specific city. We may be able to see the effects of
certain outside factors such as mask policies. We are excited to continue our learning of machine learning methods and applications. Stay tuned for further updates in the near future and thank you for reading our
articles.
Feel free to connect with us on linkedin and stay tuned for our next series where we will explore predicting covid-19 cases with LSTM.
Yukon Peng https://www.linkedin.com/in/yukpeng/
Mario Gonzalez https://www.linkedin.com/in/mag93/
Bhanu Garg https://www.linkedin.com/in/bhanu-garg-084bb5102/
Nathan Blackmon https://www.linkedin.com/in/nathan-blackmon-3b917219b/


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@abyshakes


|
libpqxx 4.0
|
Common definitions for cursor types. More...
#include <cursor.hxx>
Public Types | |
| enum | accesspolicy { forward_only, random_access } |
| Cursor access-pattern policy. More... | |
| enum | updatepolicy { read_only, update } |
| Cursor update policy. More... | |
| enum | ownershippolicy { owned, loose } |
| Cursor destruction policy. More... | |
| typedef result::size_type | size_type |
| typedef result::difference_type | difference_type |
Public Member Functions | |
| const std::string & | name () const throw () |
| Name of underlying SQL cursor. | |
Static Public Member Functions | |
Special movement distances | |
| static difference_type | all () throw () |
| Special value: read until end. | |
| static difference_type | next () throw () |
| Special value: read one row only. | |
| static difference_type | prior () throw () |
| Special value: read backwards, one row only. | |
| static difference_type | backward_all () throw () |
| Special value: read backwards from current position back to origin. | |
Protected Member Functions | |
| cursor_base (connection_base &, const std::string &Name, bool embellish_name=true) | |
Protected Attributes | |
| const std::string | m_name |
Common definitions for cursor types.
In C++ terms, fetches are always done in pre-increment or pre-decrement fashion--i.e. the result does not include the row the cursor is on at the beginning of the fetch, and the cursor ends up being positioned on the last row in the result.
There are singular positions akin to end()
at both the beginning and the end of the cursor's range of movement, although these fit in so naturally with the semantics that one rarely notices them. The cursor begins at the first of these, but any fetch in the forward direction will move the cursor off this position and onto the first row before returning anything.
Cursor destruction policy.
The normal thing to do is to make a cursor object the owner of the SQL cursor it represents. There may be cases, however, where a cursor needs to persist beyond the end of the current transaction (and thus also beyond the lifetime of the cursor object that created it!), where it can be "adopted" into a new cursor object. See the basic_cursor documentation for an explanation of cursor adoption.
If a cursor is created with "loose" ownership policy, the object representing the underlying SQL cursor will not take the latter with it when its own lifetime ends, nor will its originating transaction.
| pqxx::cursor_base::cursor_base | ( | connection_base & | context, |
| const std::string & | Name, | ||
| bool | embellish_name = true |
||
| ) | [protected] |
References cursor_base().
Referenced by cursor_base().
| cursor_base::difference_type pqxx::cursor_base::all | ( | ) | throw () [static] |
Special value: read until end.
Referenced by pqxx::internal::obtain_stateless_cursor_size().
| cursor_base::difference_type pqxx::cursor_base::backward_all | ( | ) | throw () [static] |
Special value: read backwards from current position back to origin.
Referenced by pqxx::stateless_cursor< up, op >::stateless_cursor().
| const std::string& pqxx::cursor_base::name | ( | ) | const throw () |
Name of underlying SQL cursor.
Referenced by pqxx::internal::sql_cursor::sql_cursor().
| static difference_type pqxx::cursor_base::next | ( | ) | throw () [static] |
Special value: read one row only.
| static difference_type pqxx::cursor_base::prior | ( | ) | throw () [static] |
Special value: read backwards, one row only.
const std::string pqxx::cursor_base::m_name [protected] |


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| Level 4 Security Missions | Difficulty | Drone Danger | Duration | Loot | Extra Loot Sources | Notes |
| An Adventageous Catastrophe | Easy | Small+ | Medium | Great | ||
| Angel Extravaganza | Medium | Medium+ | Long | Great |
| Bring an Angel Diamond Tag to enter the bonus room. |
| Anomic Agent | ? | ? | ? | ? | ? | Can be declined without penalty. |
| Anomic Base | ? | ? | ? | ? | ? | Can be declined without penalty. |
| Anomic Team | ? | ? | ? | ? | ? | Can be declined without penalty. |
| Attack of the Drones | Medium | Medium+ | Short | Salvage | ||
| Cargo Delivery | Medium | Small+ | Short | Good | ||
| Covering Your Tracks | Medium | Medium+ | Short | Good | Abandoned Stargate | |
| Crowd Control | ? | ? | ? | ? | ? | |
| Downing The Slavers (2 of 2) | Medium | Medium+? | Short | Good | ||
| Dread Pirate Scarlet | Easy | Medium+ | Long | Great | Gives 1 Gate Key. | |
| Duo of Death | Easy | Short | Good |
| ||
| Enemies Abound (1 of 5) | Medium | Medium+? | Short | Great | ||
| Enemies Abound (3 of 5) | Medium | Small+ | Medium | Great | Bunker | |
| Enemies Abound (4 of 5) | Medium | Medium+ | Medium | Great |
| |
| Enemies Abound (5 of 5) | Hard | Medium+? | Medium | Great | Smuggler Stargate | |
| Evolution | Hard? | Small+? | Medium? | Salvage? | ? |
|
| Gone Berserk | Easy | Medium+ | Medium | Great | ||
| Illegal Mining | Easy | Small+ | Medium | Great | Gives 10 Gallente Mining Lasers. | |
| Infiltrated Outposts | Easy | Medium+? | Medium | Salvage | ||
| Innocents in the Crossfire | ? | Small+ | ? | ? | Prisoner Holding Cell | |
| Intercept The Saboteurs | Medium | Small+ | Medium | Good |
| |
| Massive Attack | Easy | Small+ | Medium | Good | ||
| Mining Misappropriation | Easy | Large+ | Medium | Good | ||
| Patient Zero | ? | ? | ? | ? | ? | |
| Pirate Invasion | Medium | Small+ | Medium | Good | ||
| Pirate Slaughter | Medium | Small+ | Long | Good | Pressure Silo | |
| Racetrack Ruckus | Easy | Large | Medium | Great | ||
| Recon (1 of 3) | Hard | Medium+ | Medium | Good | Energy Neutralizing ships. | |
| Recon (2 of 3) | Medium | Medium+? | Short | Good | Mining Corporation Headquarters | |
| Recon (3 of 3) | Easy | Small+ | Short | None | Make sure your ship has good tank and speed. | |
| Record Cleaning | Medium | Medium+? | Medium | Good | Use the Gate to Patrol Area to fight additional enemies. | |
| Ritualist Raids | Hard | Medium+? | Medium | Good | Blood Raider Cathedral | The batteries will target your drones if your ship is out of range. |
| Rogue Drone Harassment | Easy | Medium+ | Medium | Salvage | Gives 1 Elite Drone AI. | |
| Shipyard Theft | Medium | Medium+ | Medium | Great |
| |
| Silence The Informant | Medium | Medium+ | Medium | Great | Road House | Gives 5 Elite Drone AI. |
| Smuggler Interception | Easy | Medium+ | Medium | Good |
| |
| Soothe the Salvage Beast | Easy | Medium+ | Medium | Salvage | Gives:
| |
| Stem the Flow | ? | ? | ? | ? | ? | |
| Stop The Thief | Medium | Medium+ | Short | Great | ||
| The Anomaly (1 of 3) | Easy | Small+ | Short | Good |
|
|
| The Anomaly (2 of 3) | Easy | Small+ | Short | Ok | Pressure Silo Debris |
|
| The Anomaly (3 of 3) | Medium | Medium+ | Medium | Salvage | ||
| The Assault | Hard | Small+ | Medium | Good | Habitation Module | |
| The Blockade | Hard | Small+ | Long | Good | Starbase Ship-Maintenance Array | Energy Neutralizing ships. |
| The Blood Raider Spies | Easy | Medium+ | Short | Good | Energy Neutralizing ships. | |
| The Damsel In Distress | Medium | Medium+? | Medium | Great |
| Avoid using drones to destroy Kruul's Pleasure Gardens, as it will explode and damage nearby ships and drones. |
| The Insorum Hijacking | Medium | Small+ | Medium | Great | You will need to salvage a wreck by using a Salvager I or Salvager II. | |
| The Right Hand of Zazzmatazz | Medium | Medium+ | Short | Great |
| Gives:
|
| The Rogue Slave Trader (1 of 2) | Easy | Medium+? | Short | Good | Slave Pen | |
| The Sansha Spies | Easy | Medium+ | Short | Good | ||
| The Score | Easy | Medium+ | Medium | Good | ||
| The Wildcat Strike | Easy | Medium+ | Short | Salvage | ||
| Unauthorized Military Presence | Medium | Small+ | Medium | Good | ||
| Vengeance | Hard | Medium+? | Long | Good |
| |
| Worlds Collide | ? | ? | ? | ? | ? |
| Level 3 Security Missions | Difficulty | Drone Danger | Duration | Loot | Extra Loot Sources | Notes |
| A Force to Be Reckoned With | Easy | Small+ | Short | Great | Gives Uleen Bloodsworn's Tag. | |
| A Million Little Pieces | ? | ? | ? | ? | ? | |
| Angel Extravaganza | ? | Medium+ | Long | Good |
| Bring an Angel Palladium Tag to enter the bonus room. |
| Break Their Will | Easy | Medium+ | Short | Good | ||
| Cargo Delivery | Medium | Small+ | Short | Good | Energy Neutralizing ships. | |
| Covering Your Tracks | Easy | Medium+ | Short | ? | Casino | |
| Dark Alliance | Easy | Medium+ | ? | Good | ||
| Deadly Arrival | Easy | Medium+ | ? | ? | ||
| Downing The Slavers (2 of 2) | Easy | Medium+ | Short | Good | ||
| Driving a Wedge - Get the Gallente (1 of 2) | Easy | Medium+ | Short | Good | Do not destroy the Minmatar Diplomat or you will fail the mission. | |
| Driving a Wedge - Delegation Destruction (2 of 2) | Easy | Small+ | Short | Good | ||
| Duo of Death | Easy | Short | Good | Bunker | ||
| Eliminate the Pirate Campers | Medium | Small+ | Short | Good | ||
| Gone Berserk | Easy | Medium+ | Short | Great | ||
| Illegal Activity (1 of 3) | Easy | Medium+ | Short | Good | Gives 6 Gallente Mining Lasers. | |
| Illegal Activity (2 of 3) | Easy | Medium+ | Short | Good | ||
| Illegal Activity (3 of 3) | Easy | Medium+ | Short | Great | Gives R.S. Officer's Passcard. | |
| Intercept the Pirate Smugglers | Easy | Medium+ | Medium | Good | ||
| Intercept the Saboteurs | Easy | Small+ | Medium | Good | Enemies with "Loyal" in their name will attack your drones. | |
| Kidnappers Strike (1 of 10) - Ambush in the Dark | Easy | Large | Short | Good | ||
| Kidnappers Strike (3 of 10) - The Kidnapping | Medium | Medium+ | Medium | Great |
| Gives 3 Drazin's Keycard, at least 1 of which must be used to use the gate. |
| Kidnappers Strike (5 of 10) - Incriminating Evidence | Easy | Medium+ | Short | Good |
| |
| Kidnappers Strike (7 of 10) - The Secret Meeting | Medium | Medium+ | Medium | Good | ||
| Kidnappers Strike (8 of 10) - Defend the Civilian Convoy | Easy | Medium+ | Short | Good | Storage Facility | |
| Kidnappers Strike (9 of 10) - Retrieve the Prisoners | Easy | Medium+ | Short | Good | ||
| Kidnappers Strike (10 of 10) - The Final Battle | Hard | Small+ | Long | Great |
| |
| My Sweet Privateer | ? | Small+ | ? | ? | ? | |
| New Frontiers (1 of 7) - Raw Materials | Medium | Medium+ | Medium | Great |
| |
| New Frontiers (2 of 7) - Mad Scientist | Easy | Medium+ | Medium | Salvage | Professor Delainen's Laboratory | Energy Neutralizing sentry towers. |
| New Frontiers (4 of 7) - The Unveiling | Easy | Medium+ | Medium | Salvage | ||
| New Frontiers (5 of 7) - An Unexpected Twist | Medium | Medium+ | Medium | Salvage | Energy Neutralizing sentry towers. | |
| New Frontiers (7 of 7) - Ascendance | Medium | Medium+ | Medium | Salvage | ||
| Pick Your Poison | ? | ? | ? | ? | ? | |
| Pirate Invasion | Medium | Small+ | Medium | Good | War Installation | |
| Retribution | Easy | Medium+ | Short | Good | Small Armory | |
| Rogue Drone Harassment | Easy | Medium+ | Short | Salvage | ||
| Save the Miners | Easy | Medium+ | Short | Good | ||
| Seek and Destroy | Easy | Medium+ | Short | Good | ||
| Silence The Informant | Easy | Medium+ | Medium | Great | Pleasure Hub | |
| Smuggler Interception | Easy | Medium+ | Medium | Good | Gives 10 Militants that you can keep. | |
| Stop the Thief | Easy | Medium+ | Short | Good | ||
| Success Comes At A Price | Easy | Medium+ | Short | Good | ||
| Technological Secrets (1 of 3) | Easy | Medium+ | Medium | Good | ||
| Technological Secrets (3 of 3) | Easy | Small+ | Medium | Good | ||
| The Black Market Hub | Medium | Medium+ | Short | Good | Docked Bestower | |
| The Blockade | Medium | Small+ | Long | Great | ||
| The Blood Raider Spies | Easy | Medium+ | Short | Good | Blood Raider Bunker | Shoot the closest battlecruiser first to spawn additional enemies. |
| The Damsel In Distress | Medium | Medium+ | Short | Great | Kruul's Pleasure Hub | Gives Kruul's DNA. |
| The Good Word | Easy | Medium+ | Short | Good | You can destroy Tolmak's Zealots without any penalty. | |
| The Missing Convoy | Easy | Medium+ | Short | Salvage | Lesser Drone Hive | |
| The Rogue Slave Trader (1 of 2) | Easy | Small+ | Short | Good | Slave Pen | |
| The Sansha Spies | Easy | Medium+ | Short | Good | ||
| The Score | Easy | Medium+ | Medium | Good | ||
| The Seven's Prison Facility | Easy | Medium+ | Short | Good | Prison | Can give Elena Gazky's DNA. |
| The Slaves Must Be Returned | Easy | Medium+ | Short | Good |
| |
| The Spy Stash | Easy | Small+ | Medium | Good | A battleship sometimes spawns once the enemies in the last room are defeated. | |
| Unauthorized Military Presence | Easy | Medium+ | Medium | Good | Additional ships will spawn and attack your drones, but won't switch once they target you. | |
| What Comes Around Goes Around | Easy | Medium+ | Short | Good |
| Level 2 Security Missions | Difficulty | Drone Danger | Duration | Loot | Extra Loot Sources | Notes |
| Ambushed Ally | ? | ? | ? | ? | Sisters of EVE Station | |
| Artifact Recovery | ? | ? | ? | ? | ? | |
| Avenge a Fallen Comrade | ? | Small+ | ? | ? | ||
| Cargo Delivery | ? | ? | ? | ? | ? | |
| Communications Cold War | ? | ? | ? | ? | ? | |
| Deadly Arrival | ? | ? | ? | ? | ? | |
| Eliminate the Pirate Campers | ? | ? | ? | ? | ? | |
| Elopement | ? | ? | ? | ? | ? | |
| Fetching Friends | ? | ? | ? | ? | ? | |
| Freedom Must Not Prevail | ? | ? | ? | ? | ? | |
| Gone Berserk | ? | ? | ? | ? | ? | |
| Illegal Activity (1 of 3) | ? | ? | ? | ? | ? | |
| Illegal Activity (2 of 3) | ? | ? | ? | ? | ? | |
| Illegal Activity (3 of 3) | ? | ? | ? | ? | ? | |
| Intercept The Saboteurs | ? | Small+ | ? | ? | ||
| Lasers Softly Beaming | ? | ? | ? | ? | ? | |
| Minmatar Plot (1 of 3) | ? | ? | ? | ? | Private Hangar | |
| Minmatar Plot (2 of 3) | ? | ? | ? | ? | ? | |
| Minmatar Plot (3 of 3) | ? | ? | ? | ? | ? | |
| Mission of Mercy | ? | ? | ? | ? | ? | |
| Murderer Brought To Justice | ? | ? | ? | ? | ? | |
| Pirate Intrusion | ? | ? | ? | ? | ? | |
| Recon (1 of 3) | ? | ? | ? | ? | ? | |
| Recon (2 of 3) | ? | ? | ? | ? | Mining Corporation Headquarters | |
| Recon (3 of 3) | ? | ? | ? | ? | ? | |
| Rogue Drone Harassment | ? | ? | ? | ? | ? | |
| Save a Man's Career | ? | ? | ? | ? | Cargo Rig | |
| Seek and Destroy | ? | ? | ? | ? | ? | |
| Silence The Informant | ? | ? | ? | ? |
| |
| Smuggler Interception | ? | Small+ | ? | ? | ||
| Stop The Thief | ? | ? | ? | ? | Cargo Rig | |
| Technological Secrets (1 of 3) | ? | ? | ? | ? | ? | |
| Technological Secrets (3 of 3) | ? | ? | ? | ? | ? | |
| The Blockade | ? | Small+ | ? | ? | Starbase Ship-Maintenance Array | |
| The Damsel In Distress | ? | ? | ? | ? | Kruul's Pleasure Hub | |
| The Drone Infestation | ? | ? | ? | ? | ? | |
| The Hidden Stash | ? | ? | ? | ? |
| |
| The Sansha Spies | ? | ? | ? | ? | ? | |
| The Seven's Brothel | ? | ? | ? | ? | Brothel | |
| The Score | ? | ? | ? | ? | ? | |
| Unauthorized Military Presence | ? | ? | ? | ? | ? |
| Level 1 Security Missions | Difficulty | Drone Danger | Duration | Loot | Extra Loot Sources | Notes |
| After The Seven (1 of 5) "Replacement" | Easy | Medium+? | Short | Good | ||
| After The Seven (2 of 5) "Company" | Easy | Medium+? | Short | Good | ||
| After The Seven (3 of 5) "Idiot" | Easy | Medium+? | Short | Good | ||
| After The Seven (4 of 5) "Ruse" | ? | ? | ? | ? | ? | |
| An End to Eavesdropping | Easy | Medium+? | Short | Good | ||
| Balancing the Books (2 of 10) | ? | ? | ? | ? | ? | |
| Balancing the Books (4 of 10) | ? | ? | ? | ? | ? | |
| Balancing the Books (7 of 10) | ? | ? | ? | ? | ? | |
| Carrying AIMEDs | Easy | Medium+ | Medium | Good | ||
| Cash Flow for Capsuleers (1 of 10) | ? | ? | ? | ? | ? | |
| Cash Flow for Capsuleers (2 of 10) | ? | ? | ? | ? | ? | |
| Cash Flow for Capsuleers (3 of 10) | ? | ? | ? | ? | ? | |
| Cash Flow for Capsuleers (4 of 10) | ? | ? | ? | ? | Smuggler Stargate | |
| Cash Flow for Capsuleers (5 of 10) | ? | ? | ? | ? | ? | |
| Cash Flow for Capsuleers (6 of 10) | ? | ? | ? | ? | ? | |
| Cash Flow for Capsuleers (7 of 10) | ? | ? | ? | ? | ? | |
| Cash Flow for Capsuleers (8 of 10) | ? | ? | ? | ? | Omar's Stop | |
| Cash Flow for Capsuleers (9 of 10) | ? | ? | ? | ? | Narcotics Werehouse | |
| Cash Flow for Capsuleers (10 of 10) | ? | ? | ? | ? | ? | |
| Choonka's Request (1 of 4) | ? | ? | ? | ? | ? | |
| Choonka's Request (2 of 4) | ? | ? | ? | ? | ? | |
| Choonka's Request (3 of 4) | ? | ? | ? | ? | ? | |
| Gone Berserk | Easy | Medium+? | Medium | Good | ||
| Making Mountains of Molehills (4 of 10) | ? | ? | ? | ? | ? | |
| Pirate Intrusion | Easy | Medium+ | Medium | Good | ||
| Smuggler Interception | Easy | Medium+? | Short | Good | Gives 10 Militants that you can keep. | |
| The Blood Raider Spies | Easy | Small+ | Short | Good | ||
| The Drone Infestation | Easy | Medium+? | Short | Salvage | ||
| The Roving Rogue Drones | Easy | Medium+? | Short | Salvage | ||
| The Score | Easy | Medium+ | Short | Good | ||
| The Swap (1 of 10) | ? | ? | ? | ? | ? | |
| Unauthorized Military Presence | Easy | Small+? | Medium | Good |