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Laravel 8 Dynamic Dependent Dropdown using Jquery Ajax
Country:
Select Country
@foreach($countries as $key => $country)
{{$country}}
@endforeach
State:
City:


ActiveMQ Master/Slave KahaDB on OCFS2 shared file system
During my tests of shared storage clusters I wondered if ActiveMQ supports file locking on OCFS2 file system which I used on couple of occasions. While looking into it I came accross the following warning on the Apache project site:
OCFS2 Warning
Was testing using OCFS2 and both brokers thought they had the master lock - this is because OCFS2 only supports locking with
fcntl
and notlockf and flock
, therefore mutex file locking from Java isn't supported.
From RedHat faq :
OCFS2: No cluster-aware flock or POSIX locks
GFS: fully supports Cluster-wide flocks and POSIX locks and is supported.
See this JIRA for more discussion: AMQ-4378
And true enough, when tested ActiveMQ-5.11.1 on one of the OCFS2 clusters, what happened is that both servers started as master due to lack of lock file on the share. Investigating further it came up that some good man created a patch that Apache never bothered to apply from some unknown reason. The case link AMQ-4694. The patch was attached to the ticket so thought I would try it and apply to latest 5.11.4 version.
Downloaded the source:
root@drbd01:~# aptitude install zip
root@drbd01:~# wget http://mirror.ventraip.net.au/apache/activemq/5.11.4/activemq-parent-5.11.4-source-release.zip
root@drbd01:~# unzip activemq-parent-5.11.4-source-release.zip
root@drbd01:~# cd activemq-parent-5.11.4/
and applied the patch:
root@drbd01:~/activemq-parent-5.11.4# vi ocfs2-locks.patch
root@drbd01:~/activemq-parent-5.11.4# patch -p0 -i ocfs2-locks.patch
patching file NOTICE
Hunk #1 succeeded at 45 with fuzz 2 (offset -4 lines).
patching file activemq-broker/pom.xml
patching file activemq-broker/src/main/java/org/apache/activemq/store/AbstractResourceLocker.java
patching file activemq-broker/src/main/java/org/apache/activemq/store/SharedFileLocker.java
Hunk #1 FAILED at 16.
1 out of 1 hunk FAILED -- saving rejects to file activemq-broker/src/main/java/org/apache/activemq/store/SharedFileLocker.java.rej
patching file activemq-broker/src/main/java/org/apache/activemq/store/SharedNativeFileLocker.java
patching file activemq-broker/src/main/java/org/apache/activemq/util/LockFile.java
patching file activemq-broker/src/main/java/org/apache/activemq/util/LockResource.java
patching file activemq-broker/src/main/java/org/apache/activemq/util/NativeLockFile.java
patching file activemq-unit-tests/src/test/java/org/apache/activemq/util/NativeLockFileTest.java
patching file assembly/pom.xml
Hunk #1 succeeded at 342 (offset 14 lines).
patching file assembly/src/main/descriptors/common-bin.xml
Hunk #1 succeeded at 191 (offset -9 lines).
patching file pom.xml
Hunk #1 succeeded at 881 with fuzz 1 (offset 44 lines).
Ok, so all good except it failed for the SharedFileLocker.java file. Easily fixed by applying that part of the patch manually:
root@drbd01:~/activemq-parent-5.11.4# vi activemq-broker/src/main/java/org/apache/activemq/store/SharedFileLocker.java
/**
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.activemq.store;
import java.io.File;
import org.apache.activemq.util.LockFile;
import org.apache.activemq.util.LockResource;
/**
* An {@link AbstractResourceLocker} that utilizes a {@link LockFile}.
*
* @org.apache.xbean.XBean element="shared-file-locker"
*
*/
public class SharedFileLocker extends AbstractResourceLocker {
@Override
protected LockResource newLockResource(File lockFileName) {
return new LockFile(lockFileName, true);
}
}
Next installed Maven:
root@drbd01:~# wget http://apache.uberglobalmirror.com/maven/maven-3/3.3.9/binaries/apache-maven-3.3.9-bin.tar.gz
root@drbd01:~# tar -xzvf apache-maven-3.3.9-bin.tar.gz
root@drbd01:~# mv apache-maven-3.3.9 /usr/local/
root@drbd01:~# export PATH=/usr/local/apache-maven-3.3.9/bin:$PATH
Back to AMQ directory and build the project:
root@drbd01:~/activemq-parent-5.11.4# mvn -Dtest=false -DfailIfNoTests=false clean install -e -X
Install the built binaries:
root@drbd01:~# tar -xzvf /root/.m2/repository/org/apache/activemq/apache-activemq/5.11.4/apache-activemq-5.11.4-bin.tar.gz -C /opt/
and copy the setup from the previous AMQ version into the new one:
root@drbd01:/opt# cp -a apache-activemq-5.11.1/conf/{log4j.properties,jmx.access,jmx.password,jetty.xml,encompass-activemq.xml,star_encompasshost_com.jks} apache-activemq-5.11.4/conf/
root@drbd01:/opt# chown -R activemq\: apache-activemq-5.11.4
root@drbd01:/opt# rm activemq
root@drbd01:/opt# ln -s apache-activemq-5.11.4 activemq
The last bit is modifying the KahaDB locker to use the newly introduced shared-native-file-locker one:
root@drbd01:/opt# vi activemq/conf/activemq.xml
[...]
<persistenceAdapter>
<kahaDB directory="/share/activemq-data"
indexCacheSize="40000"
checkForCorruptJournalFiles="true">
<locker>
<shared-native-file-locker lockAcquireSleepInterval="5000"/>
</locker>
</kahaDB>
</persistenceAdapter>
[...]
After copying over the binary tarball to the other server, installing and performing the above steps, I started the server and the lock file was indeed created this time:
root@drbd01:/opt# ls -ltr /share/activemq-data/
total 160
drwxr-xr-x 3 activemq activemq 3896 Mar 17 15:27 com.broker.name
-rw-r--r-- 1 root root 5 Mar 17 15:30 test
-rw-r--r-- 1 activemq activemq 0 Mar 17 18:16 lock
-rw-r--r-- 1 activemq activemq 33030144 Mar 17 18:17 db-1.log
-rw-r--r-- 1 activemq activemq 12304 Mar 17 18:17 db.redo
-rw-r--r-- 1 activemq activemq 12288 Mar 17 18:17 db.data
and the other server can see it:
root@drbd02:/opt# start activemq && tail -f /opt/activemq/data/activemq.log | grep -i lock
2016-03-18 10:00:15,375 | INFO | Database /share/activemq-data/lock is locked... waiting 5 seconds for the database to be unlocked. Reason: java.io.IOException: failed to acquire lock: [11] ( | org.apache.activemq.store.SharedNativeFileLocker | main
...


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Implementing a CSS Prepocessor
For Visual Studio Code search and install the Extension "Live Sass Compiler"
Date Posted: 5/15/2021
Implementing a CSS Prepocessor
For Visual Studio Code search and install the Extension "Live Sass Compiler"
Date Posted: 5/15/2021
Implementing a CSS Prepocessor
For Visual Studio Code search and install the Extension "Live Sass Compiler"
Date Posted: 5/15/2021
Implementing a CSS Prepocessor
For Visual Studio Code search and install the Extension "Live Sass Compiler"
Date Posted: 5/15/2021
Implementing a CSS Prepocessor
For Visual Studio Code search and install the Extension "Live Sass Compiler"
Date Posted: 5/15/2021
Implementing a CSS Prepocessor
For Visual Studio Code search and install the Extension "Live Sass Compiler"
Date Posted: 5/15/2021


MapMarker
class MapMarker : MKAnnotationView
This handles our map marker.
-
The Dictionary key for the annotation object.
Declaration
Swift
let sAnnotationObjectKey: String
-
The offset Y
Declaration
Swift
let sRegularAnnotationOffsetUp: CGFloat
-
< This is how many display units to shift the annotation view up.
Declaration
Swift
let sRegularAnnotationOffsetRight: CGFloat
-
The current frame index of the animating marker.
Declaration
Swift
private var _currentFrame: Int
-
The timer for each animation step.
Declaration
Swift
private var _animationTimer: Timer!
-
The images to be used as annotation frames.
Declaration
Swift
private var _animationFrames: [UIImage]
-
We override this, so we can be sure to refresh the need for a draw state when draggable is set (Meaning it’s a black marker).
Declaration
Swift
override var isDraggable: Bool { get set }
-
This gives us a shortcut to the annotation prpoerty.
Declaration
Swift
var coordinate: CLLocationCoordinate2D { get }
-
This gives us a shortcut to the annotation property.
Declaration
Swift
var locations: [BMLTiOSLibMeetingNode] { get }
-
Loads our array of animation frames from the resource file.
Declaration
Swift
var animationFrames: [UIImage] { get }
-
The default initializer.
Declaration
Swift
init(annotation: MKAnnotation?, draggable: Bool, reuseID: String?)
Parameters
annotation
The annotation that represents this instance.
draggable
If true, then this will be draggable (ignored if the annotation has more than one meeting).
-
This selects the appropriate image for our display.
Declaration
Swift
func selectImage(_ inAnimated: Bool) -> UIImage!
Parameters
inAnimated
If true, then the drag will be animated.
Return Value
an image to be displayed for the marker.
-
Sets up the next timer.
Declaration
Swift
func startTimer()
-
Stops the timer.
Declaration
Swift
func stopTimer()
-
Draws the image for the marker.
Declaration
Swift
override func draw(_ rect: CGRect)
Parameters
rect
The rectangle in which this is to be drawn.
-
Sets the drag state for this marker.
Declaration
Swift
override func setDragState(_ newDragState: MKAnnotationView.DragState, animated: Bool)
Parameters
newDragState
The new state that should be set after this call.
animated
True, if the state change is to be animated (ignored).
-
This class will restore its meeting object from the coder passed in.
Declaration
Swift
required init?(coder aDecoder: NSCoder)
Parameters
aDecoder
The coder that will contain the meeting.
-
This method saves the locations and coordinates as part of the serialization.
Declaration
Swift
override func encode(with aCoder: NSCoder)
Parameters
aCoder
The coder that contains the coordinates.


Usage
Sample interfaces/classes used can be found at the bottom of this page.
The Crosser Resolver contains 3 different ways to map interfaces to conrecte classes.
- Singleton<T>
- One<T>
- Many<T>
The resolver is static so you do not need to create any instance of the container.
All samples assumes that you have included the namespace for the static Crosser.Resolver
container.
// include the namespace
using Crosser.Resolve;
Singleton<T>
Use the Singleton<T>
to map and get singleton
objects.
Register
// set `IChuckNorris` to be a singleton that always returns the same instance of the implementation `TheOnlyChuck`
Singleton<IChuckNorris>.As(()=> new TheOnlyChuck());
Note that the As
method returns a boolean
telling if the registration was a success or not.
If you configure the ThrowErrorOnDeniedMapping
to true
the As
method will throw an exception if registration fails.
Resolve
// get the mapping for `IChuckNorris`
var theRealChuck = Singleton<IChuckNorris>.Get();
One<T>
Use One<T>
to map and get transient
objects
Register
// set IChuckNorris to map as transient (always return a new instance).
One<IChuckNorris>.As(()=> new TheOnlyChuck());
Note that the As
method returns a boolean
telling if the registration was a success or not.
If you configure the ThrowErrorOnDeniedMapping
to true
the As
method will throw an exception if registration fails.
Resolve
// get a new IChuckNorris
var chuck1 = One<IChuckNorris>.Get();
// get another chuck.
var chuck2 = One<IChuckNorris>.Get();
Many<T>
Many<T>
is grouped/enumerable transient
mapping where you can map several instances to one interface
.
Register
// map TheOnlyChuck and the FakeChuck to the IChuckNorris interface
Many<IChuckNorris>.Add(()=>new TheOnlyChuck());
Many<IChuckNorris>.Add(()=>new FakeChuck());
Note that the Add
method returns a boolean
telling if the registration was a success or not.
If you configure the ThrowErrorOnDeniedMapping
to true
the Add
method will throw an exception if registration fails.
Resolve
//Iterate over the IChuckNorris collection
foreach(var chuck in Many<IChuckNorris>.GetAll())
{
Console.WriteLine(chuck.GetFact());
}
Named instance
When working with collections of Many<T>
you migth wanna get a specific instance from the collection
Register
// map TheOnlyChuck to a named instance 'realchuck'
Many<IChuckNorris>.Add(() => new TheOnlyChuck(), namedInstance: "realchuck");
// add another instance, this time fake chuck without being named
Many<IChuckNorris>.Add(() => new FakeChuck());
Resolve
// get the named instance
var chuck = Many<IChuckNorris>.GetNamedInstance("realchuck");
Console.WriteLine(chuck.GetFact());
SingletonCollection<T>
SingletonCollection<T>
is grouped/enumerable singleton
mapping where you can map several singletons to one interface
.
Register
// map TheOnlyChuck and the FakeChuck to the IChuckNorris interface
SingletonCollection<IChuckNorris>.Add(()=>new TheOnlyChuck());
SingletonCollection<IChuckNorris>.Add(()=>new FakeChuck());
Note that the Add
method returns a boolean
telling if the registration was a success or not.
If you configure the ThrowErrorOnDeniedMapping
to true
the Add
method will throw an exception if registration fails.
Resolve
//Iterate over the IChuckNorris collection
foreach(var chuck in SingletonCollection<IChuckNorris>.GetAll())
{
Console.WriteLine(chuck.GetFact());
}
Named instance
When working with collections of SingletonCollection<T>
you migth wanna get a specific instance from the collection
Register
// map TheOnlyChuck to a named instance 'realchuck'
SingletonCollection<IChuckNorris>.Add(() => new TheOnlyChuck(), namedInstance: "realchuck");
// add another instance, this time fake chuck without being named
SingletonCollection<IChuckNorris>.Add(() => new FakeChuck());
Resolve
// get the named instance
var chuck = SingletonCollection<IChuckNorris>.GetNamedInstance("realchuck");
Console.WriteLine(chuck.GetFact());
Properties
The Crosser Resolver
let you add properties
to your mappings by passing in a IDictionary<string,object>
.
You may look at this as setting metadata to a mapping type.
One<T> & Singleton<T>
When mapping properties to One<T>
, Singleton<T>
the properties is mapped directly on the interface
of T
.
Register
/// Some properties to attach to a mapping
var props = new Dictionary<string, object>() { { "num", 123 }, { "str", "hello world" } };
// map TheOnlyChuck to IChuckNorris and pass in the properties
One<IChuckNorris>.As(() => new TheOnlyChuck(), properties: props);
Resolve
// get properties for IChuckNorris
var chuckProperties = One<IChuckNorris>.Properties;
Console.WriteLine("IChuckNorris has properties num:{0}, str:{1}", chuckProperties["num"], chuckProperties["str"]);
Many<T> & SingletonCollection<T>
When mapping properties
to Many<T>
& SingletonCollection<T>
the properties
is mapped to each mapping by type or named instance.
Creating some properties
for TheOnlyChuck
and FakeChuck
Register
// some properties for real chuck
var realChuckProps = new Dictionary<string, object>() { { "TheRealDeal", true } };
// some properties for fake chuck
var fakeChuckProps = new Dictionary<string, object>() { { "IAmFake", true } };
Map TheOnlyChuck
and FakeChuck
with properties
// map TheOnlyChuck to a named instance 'realchuck' with properties
Many<IChuckNorris>.Add(() => new TheOnlyChuck(), namedInstance: "realchuck", properties : realChuckProps);
// add another instance, this time fake chuck without being named but with properties
Many<IChuckNorris>.Add(() => new FakeChuck(), properties : fakeChuckProps);
Resolve
Get properties
by type of mapping.
var realChuckProperties = Many<IChuckNorris>.Properties<TheOnlyChuck>();
Console.WriteLine("TheOnlyChuck has property TheRealDeal:{0}", realChuckProperties["TheRealDeal"]);
var fakeChuckProperties = Many<IChuckNorris>.Properties<FakeChuck>();
Console.WriteLine("FakeChuck has property IAmFake:{0}", chuckProperties["IAmFake"]);
Since TheOnlyChuck
was mapped as a named instance realchuck
we can get the properties
by name as well
var realChuckProperties = Many<IChuckNorris>.Properties("realchuck");
Console.WriteLine("TheOnlyChuck has property TheRealDeal:{0}, realChuckProperties["TheRealDeal"]);
Rewritable
Having this feature might feel odd since you normally just remap the interface to a new concrete type. In Crosser
the default is that mappings IS NOT rewritable
.
The reason for this is that not all of our modules is supposed to be rewritable
. Also if developers create new custom modules they might wanna mark the mapping as not being rewritable
.
You might look at this as using the sealed
keyword but on mappings between interfaces and concrete types instead of a class.
You can rewrite both Singleton<T>
and Transient
(One<T>
and Many<T>
) mappings.
Register
Register a rewritable instance of Singleton<IChuckNorris>
to FakeChuck
Singleton<IChuckNorris>.As(() => new FakeChuck(), rewritable : true);
Whenever we ask for IChuckNorris
we will get the FakeChuck
, but since it is rewritable
we can change this by just adding a new mapping for IChuckNorris
.
Singleton<IChuckNorris>.As(() => new TheOnlyChuck());
Resolve
Now we will get TheOnlyChuck
when asking for IChuckNorris
Since we did not mark the new mapping as rewritable
this mapping is now sealed
and cant be changed.
var theOnlyChuck = Singleton<IChuckNorris>.Get();
Enable/Disable
If you want to disable/enable
mappings you can do so for all type of mappings. Singleton<T>
and One<T>
works in the same way, but Many<T>
has a different approach.
If a mapping is disabled it will return null
.
Singleton<T> One<T>
Since both Singleton<T>
and One<T>
returns a single instance the enable/disable
is straight forward.
One<IChuckNorris>.Enable();
One<IChuckNorris>.Disable();
And Singleton<T>
look the same
Singleton<IChuckNorris>.Enable();
Singleton<IChuckNorris>.Disable();
Many<T>
Many<T>
looks a little bit different since you can get 0-n
instances back. You can choose to enable/disable
ALL at once or do it individually by using the type of the concrete implementation.
Disable/Enable All
Many<IChuckNorris>.EnableAll();
Many<IChuckNorris>.DisableAll();
Disable/Enable by Type
Many<IChuckNorris>EnableAllOf<TheOnlyChuck>();
Many<IChuckNorris>DisableAllOf<FakeChuck>();
Disable by NamedInstance
This is currently not supported, although easy to add we have not yet seen the need for it in Crosser Framework
Reset
If you for some reason need to reset the resolver on a specific type you do so by calling
Singleton<T>.Reset();
or
One<T>.Reset();
or
Many<T>.Reset();
where T is the interface to reset the mappings for.
Configuration
The Crosser Resolver
has a static class for managing settings.
ThrowErrorOnDeniedMapping
This setting enables/disables exceptions to be thrown when registering One<T>.As(Expression<Func<T>> f)
and Singleton<T>.As(Expression<Func<T>> f)
This property is default set to false
Resolve.ResolverConfig.ThrowErrorOnDeniedMapping = false;
Sample Code
interfaces
and classes
used in this page
interface IChuckNorris
{
string GetFact();
}
public class TheOnlyChuck : IChuckNorris
{
public string GetFact()
{
return "Chuck Norris doesn't call the wrong number. You answer the wrong phone.";
}
}
public class FakeChuck : IChuckNorris
{
public string GetFact()
{
return "When Chuck Norris looks in the mirror nothing appears. There can never be a second Chuck Norris.";
}
}


|
KratosMultiphysics
KRATOS Multiphysics (Kratos) is a framework for building parallel, multi-disciplinary simulation software, aiming at modularity, extensibility, and high performance. Kratos is written in C++, and counts with an extensive Python interface.
|
This process sets a given value for a certain flag in all the nodes of a submodelpart. More...
Public Member Functions | |
| def | __init__ (self, Model, settings) |
| The default constructor of the class. More... | |
| def | ExecuteBeforeSolutionLoop (self) |
| This method is executed before starting the time loop. More... | |
| def | ExecuteInitializeSolutionStep (self) |
| This method is executed in order to initialize the current step. More... | |
Public Attributes | |
| interval | |
| flag | |
| model_part | |
| value | |
| entities | |
| flag_utils | |
| step_is_active | |
This process sets a given value for a certain flag in all the nodes of a submodelpart.
Only the member variables listed below should be accessed directly. Public member variables: Model -- the container of the different model parts. settings -- Kratos parameters containing solver settings.
| def assign_flag_process.AssignFlagProcess.__init__ | ( | self, | |
| Model, | |||
| settings | |||
| ) |
The default constructor of the class.
Keyword arguments: self -- It signifies an instance of a class. Model -- the container of the different model parts. settings -- Kratos parameters containing solver settings.
| def assign_flag_process.AssignFlagProcess.ExecuteBeforeSolutionLoop | ( | self | ) |
This method is executed before starting the time loop.
Keyword arguments: self -- It signifies an instance of a class.
| def assign_flag_process.AssignFlagProcess.ExecuteInitializeSolutionStep | ( | self | ) |
This method is executed in order to initialize the current step.
Keyword arguments: self -- It signifies an instance of a class.
| assign_flag_process.AssignFlagProcess.entities |
| assign_flag_process.AssignFlagProcess.flag |
| assign_flag_process.AssignFlagProcess.flag_utils |
| assign_flag_process.AssignFlagProcess.interval |
| assign_flag_process.AssignFlagProcess.model_part |
| assign_flag_process.AssignFlagProcess.step_is_active |
| assign_flag_process.AssignFlagProcess.value |


E-Cell works in collaboration with Alumni, faculty and the students committee. It aims to promote entrepreneurship drive among the students of KNIT. We shall be responsible for broadening up the link between student and alumni. Our works shall be for the development of start up, in and outside the campus. E-Cell caters the need of today's fast growing tech colleges by giving an idea to manage and manipulate the market strategies, knowledge and idea of competitive world. It is an endeavour, not only for entrepreneurs but also for the students who desire to have a better professional persona. This shall help students grow positive attitude towards entrepreneurship and help them foster a start-up individually.
Alumni of our college have brought laurels to remember. We have them as Civil Servants, Professors in IITs, at high posts in MNCs or as ENTREPRENEURS. No doubt they have excelled in every field. They are the only wealth a college possess. Thus it is our sole responsibility to acknowledge them all. E-Cell, as a team, invites KNIT alumnus to join this endeavor and register themselves on the website.
E-Cell receives its funds from the college. We, as a team, shall try to raise the funds more through sponsorship. These funds shall be monitored by Alumni, Faculty and student committee. The grants of Alumni are a source for funding this organization as well.
International Conference on Innovative Entrepreneurship and StartUp
03 March-04 March, 2017
Information Brochure
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Entrepreneurship begins from the minds of the people. It grows and further embeds itself in the consciousness when discussions are built. These discussions get common man involved in a subject. Such an event will prosper the growth of entrepreneurship culture in KNIT.
Entrepreneurship Cell, KNIT Sultanpur takes up an initiative to call the speakers who will motivate students towards entrepreneurship. They can be our alumni, a company representative or any guest of honor. Conducting this event shall be an impetus to the already growing start up culture here. In the recent past, KNIT has witnessed start ups growing but not prospering as they should. A proper guidance from the mentors, the already entrepreneurs and our alumni can be a catch to make these start ups a success. Thus E-Cell Community, KNIT Sultanpur will be conducting Interactive Sessions, Panel Discussions and Seminars to make people aware about entrepreneurship, to make them interested in the field and help them grow professionally. This would personally help ideal minds to take a step ahead towards entrepreneurship.
What is life when we don’t have elders to guide us? Isn’t it worthless?  Thus to have guidance we connect here.
“Nobody is bothered about its institution more than it’s alumni” – N.R. Narayan Murthy
Costal is an initiative by E-Cell to make world aware about the achievements of the alumni of KNIT. They are the only source through which we have come to know about our college and its history. Their achievements and their laurels must not go unnoticed and thus we present some news related to our achievers. Connecting Students with Alumni of the institution is what makes E-Cell unique of all. This perhaps gives the young engineering entrepreneurs a platform to present their start up ideas. With a step towards a well connected KNIT society, E-Cell shall conceivably be able to make the environment more “StartUp Friendly”.
Entrepreneurship. This word means different things to different people. At it’s core, entrepreneurship is a mindset. It is broader than creation of a new business venture, It’s a way of thinking and acting. It is about imagining new ways to solve problems and create valuable products and services.
An entrepreneur is someone who can take an idea, and using their skills, will and courage, take extreme risk to do whatever it takes to make it a viable product or service that people need, thus exploiting the opportunities whenever they present themselves. Entrepreneurs direct human, financial and material resources through various means to create products and services that generate wealth. People engaged in entrepreneurship create prosperity not only for themselves, but also for associated with their venture. They help in creation of jobs, which improves the overall condition of their region. When a person starts a company, there are several factors that may affect the success or failure of the venture, including funding, management of resources, a clear idea of how the company will get profits, knowledge of markets and general economy. Our goal at E-Cell is to enable these budding innovators and job creators by providing technical knowhow, access to any and all resources they need, a platform to showcase their ideas and ventures and to connect with the experienced alumni, investors and the startup community in general. A strong start up culture is developing in India, with many people starting their own ventures to make use of the vast opportunities this diverse country presents to us. India is all set to rise again, and entrepreneurs, with their insurmountable willpower, creative ideas and effective execution will play a big part in its ascent.
In the world where ideas drive the economy ,it's no wonder that innovation and entrepreneurship are often seem to inseperable fellows. Entrepreneurship is turning an idea into a business by means of technology . It's truth as said by many entrepreneurs that it is a stong desire to take risks , responsibility.
The startup vocabulary mainly consists of resilience,survival, strategy, profitability. As in india , this year funds get dried up , a number of promising startup shuttered while others laid off employees. Flipkart , Snapdeal and Ola the poster children of indian startup scene, watched their valuation dropping , whereas Amazon and Uber with their strategic and detroited business model put their valuation appreciated . Apple too put their stake in indian startup TUPLEJUMP which is startup based on artificial intelligence and involves machine learning that helps companies to store , process and visualise big data. Oyo rooms which is a service based startup for premium accomodation raised the fund of $100 million from Japan's company Softbank. So, overall we can summarize that successful entrepreneurs are not driven by money ,they are driven by achievements. Sure, making money can be nice , but biggest reason why they are successful is because they are pursuing a higher purpose than themselves. They want to make a difference although money is good but changing the world is better. Entrepreneurship requires committment and dedication to their ideas , vision and businesses in order to make goal happen.
Entrepreneurship Cell in KNIT will help the young budding engineer entrepreneurs to cultivate their ideas and harvest them as a successful start up. For this, E-Cell has teams that shall put their effort best to make the idea a success.
MARKETING: This setup under E-Cell aims for fund raising of the events. It is also responsible for contacting the companies for sponsorship and maintaining corporate relationship.
MEDIA AND PUBLICITY: It is the part under the E-cell system that manages the coverage and promotion of workshop.
It manages the publicity of the events taking place under the E-Cell oraganization. It publicises various events through online medium.
EVENTO: This part of the E-Cell shall look after organizing events in the college campus. This setup is responsible for organizing lectures for entrepreneurship drive and mentoring young entrepreneurs.This setup under E-Cell looks after ground solutions and help in reaching out to other campuses.
WEB DESIGN: A website developed to update the information about the upcoming events and programs.
Sole purpose of the website shall be to entice various entrepreneurs to fund the innovative ideas. These ideas shall be put up on the website and help the young engineer entrepreneurs to give them a platform for their start up.


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Optimizers
Says, you have the parameter W
inited for your model and got its gradient stored as ∇
(perhaps from AutoGrad APIs). Here is minimal snippet of getting your parameter W
baked by SGD
.
julia> using MXNet
julia> opt = SGD(η = 10)
SGD(10, 0.0, 0, 0, 0.0001, MXNet.mx.LearningRate.Fixed(10.0), MXNet.mx.Momentum.Null())
julia> decend! = getupdater(opt)
(::getfield(MXNet.mx, Symbol("#updater#5792")){SGD,Dict{Int64,Any}}) (generic function with 1 method)
julia> W = NDArray(Float32[1, 2, 3, 4]);
julia> ∇ = NDArray(Float32[.1, .2, .3, .4]);
julia> decend!(1, ∇, W)
4-element NDArray{Float32,1} @ CPU0:
-0.0010000467f0
-0.0020000935f0
-0.003000021f0
-0.004000187f0
#
MXNet.mx.AbstractOptimizer
— Type.
AbstractOptimizer
Base type for all optimizers.
#
MXNet.mx.getupdater
— Method.
getupdater(optimizer)
A utility function to create an updater function of KVStore
, that uses its closure to store all the states needed for each weights.
Ther returned function has following signature:
decend!(index::Int, ∇::NDArray, x::NDArray)
If the optimizer is stateful and need access/store states during updating, index
will be the key to access/store states.
#
MXNet.mx.normgrad!
— Method.
normgrad(optimizer, W, ∇)
Get the properly normalized gradient (re-scaled and clipped if necessary).
optimizer
: the optimizer, should contain the fieldscale
,clip
andλ
.W::NDArray
: the trainable weights.∇::NDArray
: the original gradient of the weights.
#
MXNet.mx.AbstractLearningRateScheduler
— Type.
AbstractLearningRateScheduler
Base type for all learning rate scheduler.
#
MXNet.mx.AbstractMomentumScheduler
— Type.
AbstractMomentumScheduler
Base type for all momentum scheduler.
#
MXNet.mx.OptimizationState
— Type.
OptimizationState
Attributes
batch_size
: The size of the mini-batch used in stochastic training.curr_epoch
: The current epoch count. Epoch 0 means no training yet, during the first pass through the data, the epoch will be 1; during the second pass, the epoch count will be 1, and so on.curr_batch
: The current mini-batch count. The batch count is reset during every epoch. The batch count 0 means the beginning of each epoch, with no mini-batch seen yet. During the first mini-batch, the mini-batch count will be 1.curr_iter
: The current iteration count. One iteration corresponds to one mini-batch, but unlike the mini-batch count, the iteration count does not reset in each epoch. So it track the total number of mini-batches seen so far.
#
MXNet.mx.LearningRate.Exp
— Type.
LearningRate.Exp(η₀; γ = 0.9)
Where t
is the epoch count, or the iteration count.
#
MXNet.mx.LearningRate.Fixed
— Type.
LearningRate.Fixed(η)
Fixed learning rate scheduler always return the same learning rate.
#
MXNet.mx.LearningRate.Inv
— Type.
LearningRate.Inv(η₀; γ = 0.9, p = 0.5)
Where t
is the epoch count, or the iteration count.
#
Base.get
— Method.
get(sched::AbstractLearningRateScheduler)
Returns the current learning rate.
#
MXNet.mx.Momentum.Fixed
— Type.
Momentum.Fixed
Fixed momentum scheduler always returns the same value.
#
MXNet.mx.Momentum.NadamScheduler
— Type.
NadamScheduler(; μ = 0.99, δ = 0.004, γ = 0.5, α = 0.96)
Nesterov-accelerated adaptive momentum scheduler.
Description in Incorporating Nesterov Momentum into Adam.
Where
t
: iteration countμ
: default0.99
, μ₀δ
: default0.004
is scheduler decay.γ
: default0.5
α
: default0.96
#
MXNet.mx.Momentum.Null
— Type.
Momentum.Null
The null momentum scheduler always returns 0 for momentum. It is also used to explicitly indicate momentum should not be used.
#
Base.get
— Method.
get(n::NadamScheduler, t)
Where t
is the iteration count.
Built-in optimizers
Stochastic Gradient Descent
#
MXNet.mx.SGD
— Type.
SGD(; kwargs...)
Stochastic gradient descent optimizer.
Vanilla SGD:
SGD with momentum::
Arguments
η
: default0.01
, learning rate.μ
: default0
, the momentum, usually set to0.9
in this implementation.λ
: default0.0001
, weight decay is equivalent to adding a global l2 regularizer to the parameters.clip
: default0
, gradient clipping. If positive, will clip the gradient into the bounded range[-clip, clip]
.scale
: default0
, gradient rescaling. If != 0, multiply the gradient withscale
before updating. Often choose to be1.0 / batch_size
. If leave it default, high-level API likefit!
will set it to1.0 / batch_size
, sincefit!
knows thebatch_size
.μ_sched::AbstractMomentumScheduler
: defaultMomentum.Null()
, a dynamic momentum scheduler. If set, will overwrite themomentum
parameter.η_sched::AbstractLearningRateScheduler
: defaultLearningRate.Fixed(η)
, a dynamic learning rate scheduler. If set, will overwrite theη
parameter.
ADAM
#
MXNet.mx.ADAM
— Type.
ADAM
The solver described in Diederik Kingma, Jimmy Ba: Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs.LG].
ADAM(; kwargs...)
Arguments
η
: default0.001
, learning rate.β1
: default0.9
.β2
: default0.999
.ϵ
: default1e-8
.clip
: default0
, gradient clipping. If positive, will clip the gradient into the range[-clip, clip]
.scale
: default0
, gradient rescaling. If != 0, multiply the gradient withscale
before updating. Often choose to be1.0 / batch_size
. If leave it default, high-level API likefit!
will set it to1.0 / batch_size
, sincefit!
knows thebatch_size
.λ
: default0.00001
, weight decay is equivalent to adding a global l2 regularizer for all the parameters.η_sched::AbstractLearningRateScheduler
: defaultLearningRate.Fixed(η)
, a dynamic learning rate scheduler. If set, will overwrite theη
parameter.
AdaGrad
#
MXNet.mx.AdaGrad
— Type.
AdaGrad(; kwargs...)
Scale learning rates by dividing with the square root of accumulated squared gradients. See [1] for further description.
Arguments
η
: default0.1
, learning rate.ϵ
: default1e-6
, small value added for numerical stability.clip
: default0
, gradient clipping. If positive, will clip the gradient into the range[-clip, clip]
.scale
: default0
, gradient rescaling. If != 0, multiply the gradient withscale
before updating. Often choose to be1.0 / batch_size
. If leave it default, high-level API likefit!
will set it to1.0 / batch_size
, sincefit!
knows thebatch_size
.λ
: default0.00001
, weight decay is equivalent to adding a global l2 regularizer for all the parameters.
Notes
Using step size η
AdaGrad calculates the learning rate for feature i
at time step t as:
as such the learning rate is monotonically decreasing. Epsilon is not included in the typical formula, see [2].
References
- Duchi, J., Hazan, E., & Singer, Y. (2011): Adaptive subgradient methods for online learning and stochastic optimization. JMLR, 12:2121-2159.
- Chris Dyer: Notes on AdaGrad. http://www.ark.cs.cmu.edu/cdyer/adagrad.pdf
AdaDelta
#
MXNet.mx.AdaDelta
— Type.
AdaDelta(; kwargs...)
Scale learning rates by the ratio of accumulated gradients to accumulated updates, see [1] and notes for further description.
Attributes
η
: default1.0
, learning rate.ρ
: default0.95
, squared gradient moving average decay factor.ϵ
: default1e-6
, small value added for numerical stability.clip
: default0
, gradient clipping. If positive, will clip the gradient into the range[-clip, clip]
.scale
: default0
, gradient rescaling. If != 0, multiply the gradient withscale
before updating. Often choose to be1.0 / batch_size
. If leave it default, high-level API likefit!
will set it to1.0 / batch_size
, sincefit!
knows thebatch_size
.λ
: default0.00001
, weight decay is equivalent to adding a global l2 regularizer for all the parameters.
Notes
ρ
should be between 0 and 1. A value of ρ
close to 1 will decay the moving average slowly and a value close to 0 will decay the moving average fast.
ρ = 0.95
and ϵ = 1e-6
are suggested in the paper and reported to work for multiple datasets (MNIST, speech). In the paper, no learning rate is considered (so η = 1.0
). Probably best to keep it at this value.
ϵ
is important for the very first update (so the numerator does not become 0).
Using the step size η
and a decay factor ρ
the learning rate is calculated as:
References
- Zeiler, M. D. (2012): ADADELTA: An Adaptive Learning Rate Method. arXiv Preprint arXiv:1212.5701.
AdaMax
#
MXNet.mx.AdaMax
— Type.
AdaMax(; kwargs...)
This is a variant of of the Adam algorithm based on the infinity norm. See [1] for further description.
Arguments
η
: default0.002
, learning rate.β1
: default0.9
, exponential decay rate for the first moment estimates.β2
: default0.999
, exponential decay rate for the weighted infinity norm estimates.ϵ
: default1e-8
, small value added for numerical stability.clip
: default0
, gradient clipping. If positive, will clip the gradient into the range[-clip, clip]
.scale
: default0
, gradient rescaling. If != 0, multiply the gradient withscale
before updating. Often choose to be1.0 / batch_size
. If leave it default, high-level API likefit!
will set it to1.0 / batch_size
, sincefit!
knows thebatch_size
.λ
: default0.00001
, weight decay is equivalent to adding a global l2 regularizer for all the parameters.
References
- Kingma, Diederik, and Jimmy Ba (2014): Adam: A Method for Stochastic Optimization. Section 7. http://arxiv.org/abs/1412.6980.
RMSProp
#
MXNet.mx.RMSProp
— Type.
RMSProp(; kwargs...)
Scale learning rates by dividing with the moving average of the root mean squared (RMS) gradients. See [1] for further description.
Arguments
η
: default0.1
, learning rate.ρ
: default0.9
, gradient moving average decay factor.ϵ
: default1e-8
, small value added for numerical stability.clip
: default0
, gradient clipping. If positive, will clip the gradient into the range[-clip, clip]
.scale
: default0
, gradient rescaling. If != 0, multiply the gradient withscale
before updating. Often choose to be1.0 / batch_size
. If leave it default, high-level API likefit!
will set it to1.0 / batch_size
, sincefit!
knows thebatch_size
.λ
: default0.00001
, weight decay is equivalent to adding a global l2 regularizer for all the parameters.
Notes
ρ
should be between 0 and 1. A value of ρ
close to 1 will decay the moving average slowly and a value close to 0 will decay the moving average fast.
Using the step size η
and a decay factor ρ the learning rate
ηₜ` is calculated as:
References
- Tieleman, T. and Hinton, G. (2012): Neural Networks for Machine Learning, Lecture 6.5 - rmsprop. Coursera. http://www.youtube.com/watch?v=O3sxAc4hxZU (formula @5:20)
Nadam
#
MXNet.mx.Nadam
— Type.
Nadam(; kwargs...)
Nesterov Adam optimizer: Adam RMSprop with Nesterov momentum, see [1] and notes for further description.
Arguments
η
: default0.001
, learning rate.β1
: default0.99
.β2
: default0.999
.ϵ
: default1e-8
, small value added for numerical stability.clip
: default0
, gradient clipping. If positive, will clip the gradient into the range[-clip, clip]
.scale
: default0
, gradient rescaling. If != 0, multiply the gradient withscale
before updating. Often choose to be1.0 / batch_size
. If leave it default, high-level API likefit!
will set it to1.0 / batch_size
, sincefit!
knows thebatch_size
.λ
: default0.00001
, weight decay is equivalent to adding a global l2 regularizer for all the parameters.η_sched::AbstractLearningRateScheduler
: defaultnothing
, a dynamic learning rate scheduler. If set, will overwrite theη
parameter.-
μ_sched::NadamScheduler
defaultNadamScheduler()
of the form.
Notes
Default parameters follow those provided in the paper. It is recommended to leave the parameters of this optimizer at their default values.
References


Obesity Study
Use the interactive chart below to explore the dataset by State
State ID Filter
State Population Info
Heat Map showing Cities and Obesity Percentage


Tinkerers, Hackers, Builders, Makers, and Doers Wanted
As a key member of a nationally recognized event production and creative team, the Environmental Designer works in collaboration with Creative Directors and meticulous Technical Directors to create dynamic, stunning, breathtaking and branded environments for stage sets, exhibits and corporate interiors.
College Degree
3-5 years experience required
25-30% travel required for position
Sharepoint knowledge preferred


Recently I have been reading news/blogs through many different channels - Google Alerts, Google Reader, digg, del.icio.us, slashdot, plus website hopping. So when I try to recall where did I read particular piece of news, very likely I don't recall. That's what happened today at a friend's place.
But still I will keep using them...
I have added a new window on this blog page in the right panel - titled 'What I read ...'. This is Google Reader's cool (this adjective is now a cliche when used with Google) tool, which will post some top N blog posts that I decide to share from my Google Reader. So you can read what I like to read. ... just another way to solve the problem I mentioned before.


Principle-based and Explainable Reasoning: From Humans to Machines
Timotheus Kampik
### About Me
* 4th year PhD Student
* Research interests:
* Automated reasoning
* Engineering intelligent systems
* Also *Scientist in Residence, Product*, Signavio \ SAP
(Business Process Intelligence)
#### Outline
* In many real-world application scenarios we need machines that
**learn** and **reason**
* We can explain reasoning by making use of *principles*
* *Human* reasoning has been studied from descriptive and prescriptive perspectives since (at least) centuries
* Can the principles according to which humans (should) reason inform the way machines reason?
Principle-based Reasoning
Reasoning in Intelligent Systems
How do humans reason?
#### Economic Rationality
* Assumptions of economic rationality, **ceteris paribus** (if everything else equal):
* "Rational Economic Man" acts according to clear preferences
* Has consistent preferences over time
#### Clear Preferences
* Standard economic model for individual decision-making
* Chooses from $A = \\{a, ..., n\\}$
* Choice function: takes $A$, returns $S^* \in 2^A$
* Clear preferences: choice $S^\*$ implies $S^*$ is strictly preferred over all other elements in $2^A$
Rubinstein, Ariel. *Modeling bounded rationality.*
#### Consistent Preferences (Reference Independence)
* Set of choice options $A, A'$, such that $A \subseteq A'$
* Rational man's choices $A^{\*} \subseteq A$ and $A'^{\*} \subseteq A'$
* If $A'^{\*} \subseteq A$ then $A^{\*} = A'^{\*}$
Rubinstein, Ariel. *Modeling bounded rationality.*
#### Example
* We go to a café, on the menu: `tea and coffee`
* We choose `coffee`
* Next day, one the menu: `tea, coffee, and cookie`
We choose `tea and cookie`. Are we rational?
We choose `tea`. Are we rational?
Bounded Rationality
By now, we know that economic rationality is not a good model of human (intelligent) decision-making.
Economists try to adjusts their models accordingly.
Key improvement: modeling knowledge in decision scenarios.
Kahneman, Daniel. *Maps of bounded rationality.*
Rubinstein, Ariel. *Modeling bounded rationality.*
#### Consistent Preferences in Knowledge-based Systems
* We want to determine the relevant citizenship (passports) of a client
* Example: case handling of immigration or tax administration
* We use decision management software (a real-world system)
* The decision models can be deployed to high-scalability engines such as [jDMN](https://goldmansachs.github.io/jdmn/)
TK. Nieves, Juan Carlos. *Abstract Argumentation and the Rational Man*.
#### Example: Decision Model and Notation (DMN)
* Decision:
* Set of ``if ... then ...`` rules
* Aggregation function or order on all rules
* Graphical/XML model of data sources and hierarchical decisions
* Open standard (OMG)
DMN Example Decision
#### Rough Formalization Attempt, Decision Table
Tuple $\langle T, I, O, type, facet, R, P, C, H \rangle$
* $T$: table name
* $I, O$ finite disjoint sets of input and output attributes
* $type$: function that maps each I, O to a data type
* $facet$: function that maps each I, O to an *acceptable list of objects*
* $R$: finite set of 'if ... then ...' rules
* $P$: total order on rules
* $C$: boolean completeness indicator
* $H$: hit policy indicator
Calvanese *et al*. Semantics and Analysis of DMN Decision Tables.
#### Consistent Preferences in Knowledge-based Systems
* First, insert ``NO`` (Norwegian citizenship)
→ ``NO`` considered relevant
* Then, insert ``UK`` (UK citizenship) as additional option
→ neither ``NO`` nor ``UK`` relevant: not rational!
* Automated checks of decision management software don't detect this problem
#### There are more principles
* Example: legal reasoning, *burden of persuasion*
* If several conclusions/decisions are possible
* If in doubt, remain consistent with previous decision
"Reasoning Backwards"
We also know that humans "reason backwards".
We commit to a decision intuitively.
We make up a line of reasoning if necessary.
Haidt, Jonathan.  What has changed?*
* **Mourinho**: [*That was*] *before I was sacked* [*at Chelsea*].
#### Alternative to Reasoning Backwards
* Principle-based and evidence-based reasoning
* Explaining change
#### *Mourinho* Example
* Rule: maximize expected utility/payoff
* ``Payoff_Tottenham`` << ``Payoff_Chelsea``
changed to
``Payoff_Tottenham`` >> ``Payoff_Chelsea``
#### Example
* Change: new passport reported: ``UK``
* Principle violated: *reference independence*
* Explanation, "new" rules that fire
* If any passport is EU passport, remove non-EU passports
* If ``r1`` then ``r2``
* ``UK`` is ``EU`` in r1 but is not ``EU`` in ``r2``
A Formal Perspective
Kampik \& Nieves. Abstract Argumentation and the Rational Man.
Kampik \& Gabbay. Explainable Reasoning in Face of Contradictions: From Humans to Machines.
#### Abstract Argumentation I
Dung, Phan Minh. *On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games.*
Abstract Argumentation II
Dung, Phan Minh. *On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games.*
#### Economic Rationality & Abstract Argumentation
* $AF = (AR, AT)$; arguments $AR$, e.g.: $\\{a, b, c\\}$, attacks $AT$, e.g.: $\\{(a, b), (b, c)\\}$
* Semantics $\sigma(AF)$ returns set of extensions $ES \subseteq 2^{AR}$
* Extension $E \in ES, E \subseteq AR$ **implies** preferences: $\forall S \in AR, E \succeq S$
* Consistent preferences when **normally expanding** $AF$ (Economics' *ceteris paribus* assumption)
Dung, Phan Minh. *On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games.*
#### Normal Expansion
* Given $AF = (AR, AT), AF' = (AR', AT')$ AF' normally expands AF iff:
* $AR \subseteq AR', AT \subseteq AT'$
* $(AT' \setminus AT) \cap (AR \times AR) = \\{\\}$
* Only add arguments and attacks, don't change attacks between existing arguments
* Denoted by $AF \preceq_N AF'$ (Baumann, Brewka)
Baumann & Brewka. *Expanding Argumentation Frameworks: Enforcing and Monotonicity Results.*
#### (Weak) Reference Independence Principle
* Given semantics $\sigma$, $AF = (AR, AT), AF' = (AR', AT'), AF \preceq_N AF'$
* **Weak**: no matter what conclusion/extension we select from $AF$,
we can infer a conclusion from $AF'$ that implies consistent preferences
Kampik & Nieves. *Abstract Argumentation and the Rational Man.*
Example
Example
If all newly added arguments are not valid conclusions, $a$ should remain a valid conclusion.
Because we make clear decisions we consider arguments either valid conclusions or not (no undecided arguments)
Which semantics allow us to be economically rational in this scenario?
Semantics Families
Family
Admissibility-Based
Weak Admissibility-Based
Naive-Based
Satisfied by any established semantics$^*$
No
No
Yes
Satisfied by
-
-
Naive, CF2, presumably SCF2 and nsa(CF2)
$^*$ Could potentially be satisfied by a semantics that always returns the empty set and hence is in all families.
*Degrees of Monotony* to Ensure Consistency
Limitations of Reference Independence
#### *Degrees of Monotony* Approach
* Given $AF, AF', AF \preceq_N AF', \sigma, E \in \sigma(AF)$
* Select an $E' \in \sigma(AF')$ that is *as monotonic as possible*
* Degree of monotony for non-empty $E$:
$\frac{|E \cap E'|}{|E|}$
* Property is not transitive
'Degrees of Monotony'-Dilemma
#### Learning and Principle-based Reasoning
#### Learning and Principle-based Reasoning
* We know that we can mine knowledge and reason about it
* Principles are not generally applicable (at least most principles)
* Can we learn which principles should be satisfied?
* How can we learn (and reason about) new principles?
Learning Knowledge and Reasoning About It I
Example: process mining
Mine Petri Nets from event log data
Formally analyze properties like liveness, deadlock-freeness.
Van der Aalst, Wil. *Process mining.*
Learning Knowledge and Reasoning About It II
Example: explainable recommender systems
Mine argumentation graphs from (movie) review data
Enforce relaxed monotony principles and facilitate explainability.
Rago *et al.* *Argumentation as a Framework for Interactive Explanations for Recommendations*
#### What about "Discrete" Principles in a "Gradual" Context?
* Principles of gradual argumentation are well-researched (Baroni, Rago, Toni)
* Assumption: from a decision-making context, it sometimes makes sense to discretize
* Open questions: does it then make sense to apply some of the aforementioned principles?
Baroni *et al.* *From fine-grained properties to broad principles for gradual argumentation: A principled spectrum*
Learning Knowledge and Reasoning About It III
Gap between technology ecosystems
#### Learning to Select Principles
* Connect historic data to KPIs
* Enforce different principles and select a set of non-mutually exclusive principles that maximizes KPI achievement
#### Learning New/Refined Principles
* Humans do this (legal system of any advanced society)
* Requires automated reasoning about reasoning
* Is in its infancy but a hot topic
[Popular science overview of SOTA](https://www.quantamagazine.org/building-the-mathematical-library-of-the-future-20201001/)
Questions?
*Explainable AI workshop*:
[https://extraamas.ehealth.hevs.ch/](https://extraamas.ehealth.hevs.ch/)
Special Issue in the Journal of Applied Logics - IfCoLog Journal:
*Explainable Reasoning in Face of Contradictions: Cross-disciplinary Perspectives* ([CFP](https://people.cs.umu.se/tkampik/CFP_Special_Issue_IfCoLoG_Journal.pdf))


Quaternion Difference
Quaternion Dot Product
Quaternion Difference
"Difference" being defined as the angular displacement from one orientation
to another: for orientations a and b, we can compute the angular
displacement d which rotates from a to b.
$ad = b$
Quaternion Difference
Solving for $d$
$a^{-1}(ad) = a^{-1}b$
$(a^{-1}a)d = a^{-1}b$
$[1 \enspace 0]d = a^{-1}b$
$d = a^{-1}b$
Note: mathematicaly, it's closer to division than difference
Quaternion Dot Product
Notation and definition is similar to vector dot product
$
q_1 \cdot q_2
= \left[\begin{smallmatrix}
w_1 && v_1
\end{smallmatrix}\right]
\cdot
\left[\begin{smallmatrix}
w_2 && v_2
\end{smallmatrix}\right]
=
w_1w_2 + v_1 \cdot v_2
$
$
q_1 \cdot q_2
= \left[\begin{smallmatrix}
w_1 && (x_1 && y_1 && z_1)
\end{smallmatrix}\right]
\cdot
\left[\begin{smallmatrix}
w_2 && (x_2 && y_2 && z_2)
\end{smallmatrix}\right]
$
$
= w_1w_2 + x_1x_2 + y_1y_2 + z_1z_2
$
Notes
- Like the vector dot product, the result is a scalar.
- For unit quats $a$ and $b$, $-1 \leq a \cdot b \leq 1$
- Interested in absolute value of $a\cdot b$ because $a\cdot b = -(a\cdot -b)$
- Geometric intepretation is similar to the interpretation of the vector dot product:
the larger the absolute value, the more similar the angular displacements of $a$ and $b$


Click here to read more about the Bitraf Tool system.
Click a tool to check it out. Always return the tool before you leave.
Please note: No tools should ever leave Bitraf, so you cannot "borrow" a tool to work on stuff at home.
Click a tool to return it


Elm-Vega Configuration Tests
Default configuration
Colour and Font Configuration
Dark background, light axis and serif text
Mark Configuration
Outline marks
Solid scatter and bar, outline stream
Autosize
Autosize to fit with different padding on each side
View Backgrounds
Configuration sets default background to yellow and default plot area background to light brown, overridden with charts two and three charts having their own custom plot area backgrounds.
Axis Configuration
Italic serif axis title and labels flushed to end of axis.
Both axes red
x axis red
y axis red
bottom axis red
left axis red
top axis red
right axis red
band axis red
quantitative axis red
temporal axis red
discrete axis red
point axis red
Title Configuration
Multi-line title and multi-line subtitle with default configurations.
Title and subtitle appearance controlled directly with title parameters.
Title and subtitle appearance via config options (should be identical to previous chart.).
Scale Configuration
Configure x-scale to reverse (right to left).
Axis and Legend Disabling
Remove axis and legends by default.
Source


Movie DB
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About this page...
This website is made for WT Project by Team 7:
A Vinaya Samanvita
Aarushi Agarwal
Abhishek N M
Akshata Hiremath
This site is built using the following languages,tools,frameworks and libraries.
Click on them to know more!


No Matter Where You Find Yourself, Never Forget Your Native Tongue – Wiyaala Advises (+Video)
by vanessa nordziSensational Afro pop singer and songwriter Noella Wiyaala , widely known as the lioness of Africa has a piece of advice for all those who travel to other countries for greener pastures.
Wiyaala who is noted for always infusing her native languages which are Dagaree, Sissala and Waala, into her music says it’s her way of holding on to her root and tradition.
According to Wiyaala, her style of music always makes her stand out among her colleagues in the industry.
Speaking on NRK TV in Norway, Wiyaala noted that she tries as much as possible to mix her native language into the lyrics of her songs.
“if you don’t know the difference you will think it is one language but it is actually different languages.” she explained.
Known as the Lioness of Africa due to the energy she exudes on stage, Wiyaala has cautioned African parents to teach their kids their traditional languages so that their kids will not lose their identity.
“Every parent must teach their child their local dialect, if you don’t do that, it’s very embarrassing and when you go home they will be making fun of you… it is one thing you have to learn ‘compulsory by force,'” she added.
Wiyaala who is currently in Norway during the interview perfomed one of her hit songs titled “Tan Da Gree” which she explained to mean the Rock and the Sand.
“Tan Da Gree” used to be her school rhyme with moral lessons stating “You need not copy people blindly, else you will find yourself wanting.” she explained.
Watch her video below;
http://content.jwplatform.com/players/3Ta6eBF5-eqk5XVg5.html


Provides a tool for non linear mapping (non linear regression) using a mixture of regression model and an inverse regression strategy. The methods include the GLLiM model (see Deleforge et al (2015) <doi:10.1007/s11222-014-9461-5>) based on Gaussian mixtures and a robust version of GLLiM, named SLLiM (see Perthame et al (2016) <https://hal.archives-ouvertes.fr/hal-01347455>) based on a mixture of Generalized Student distributions. The methods also include BLLiM (see Devijver et al (2017) <https://arxiv.org/abs/1701.07899>) which is an extension of GLLiM with a sparse block diagonal structure for large covariance matrices (particularly interesting for transcriptomic data).
| Version: | 2.1 |
| Imports: | MASS, abind, corpcor, Matrix, igraph, capushe |
| Suggests: | shock |
| Published: | 2017-05-23 |
| Author: | Emeline Perthame (emeline.perthame@inria.fr), Florence Forbes (florence.forbes@inria.fr), Antoine Deleforge (antoine.deleforge@inria.fr), Emilie Devijver (emilie.devijver@kuleuven.be), Melina Gallopin<EMAIL_ADDRESS>|
| Maintainer: | Emeline Perthame <emeline.perthame at pasteur.fr> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | xLLiM results |
| Reference manual: | xLLiM.pdf |
| Package source: | xLLiM_2.1.tar.gz |
| Windows binaries: | r-devel: xLLiM_2.1.zip, r-release: xLLiM_2.1.zip, r-oldrel: xLLiM_2.1.zip |
| OS X binaries: | r-release: xLLiM_2.1.tgz, r-oldrel: xLLiM_2.1.tgz |
| Old sources: | xLLiM archive |
Please use the canonical form https://CRAN.R-project.org/package=xLLiM to link to this page.


Located in a peaceful bungalow outside of Lake Geneva, Wisconsin
Retreat from the desk,
return refreshed.
Have the work week you always dreamed of - without the bad wifi.
12 private rooms with ensuite bathrooms, 2 group meeting rooms, and 4 breakout rooms.
Private chef team providing on demand meals - just like your team kitchen.
6 outdoor obstacle courses, including private beach 1/2 mile from the bungalow.
Retreat facilitator to manage your day to day activites so you all get a chance to reset.
Dedicated computer room for times that you must wifi.
Be a better team in 5 days, like this one.
LaCrue Bottling Co.
October 2018
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vestibulum finibus nisi ut ipsum fermentum lacinia. Donec dictum diam lobortis lacinia cursus. Proin et dictum nulla, fringilla posuere libero. Aenean et imperdiet nibh, non egestas mauris. Donec quis accumsan sapien. Curabitur auctor tincidunt massa, a commodo sem efficitur et. Nam fermentum sit amet nunc eget ultricies. Duis in nisl et lorem egestas consectetur. Fusce ac tortor mollis, tristique ipsum eget, tempus erat. Duis lacinia est nec est facilisis tristique.


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| PREV NEXT | FRAMES NO FRAMES |
| Packages that use Command | |
|---|---|
| commands | |
| commands.advancedCommands | |
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| commands.guiCommands | |
| commands.mathCommands | |
| commands.turtleCommands | |
| parser |
| Uses of Command in commands |
|---|
| Subclasses of Command in commands | |
|---|---|
class |
AdvancedCommand
|
class |
GuiCommand
|
class |
MathCommand
|
class |
TurtleCommand
|
| Fields in commands with type parameters of type Command | |
|---|---|
java.util.Map<java.lang.String,Command> |
CommandFactory.commandsUsed
|
| Methods in commands that return Command | |
|---|---|
Command |
CommandFactory.createCommand(java.lang.String translatedCommand)
|
| Methods in commands with parameters of type Command | |
|---|---|
double |
CommandInvoker.obey(Command newCommand)
|
| Uses of Command in commands.advancedCommands |
|---|
| Subclasses of Command in commands.advancedCommands | |
|---|---|
class |
AddVariableAdvancedCommand
|
class |
DoTimesAdvancedCommand
|
class |
ForAdvancedCommand
|
class |
IDAdvancedCommand
|
class |
IfAdvancedCommand
|
class |
IfElseAdvancedCommand
|
class |
RepeatAdvancedCommand
|
class |
ToAdvancedCommand
|
| Uses of Command in commands.booleanCommands |
|---|
| Subclasses of Command in commands.booleanCommands | |
|---|---|
class |
AndBooleanCommand
|
class |
BooleanCommand
|
class |
CheckEqualityBooleanCommand
|
class |
EqualBooleanCommand
|
class |
GreaterBooleanCommand
|
class |
LessBooleanCommand
|
class |
NotBooleanCommand
|
class |
NotEqualBooleanCommand
|
class |
OrBooleanCommand
|
| Uses of Command in commands.guiCommands |
|---|
| Subclasses of Command in commands.guiCommands | |
|---|---|
class |
PenColorQueryGuiCommand
|
class |
SetPaletteGuiCommand
|
class |
SetPenColorGuiCommand
|
class |
SetShapeGuiCommand
|
class |
ShapeQueryGuiCommand
|
| Uses of Command in commands.mathCommands |
|---|
| Subclasses of Command in commands.mathCommands | |
|---|---|
class |
ATanMathCommand
|
class |
CosMathCommand
|
class |
DifferenceMathCommand
|
class |
LogMathCommand
|
class |
MinusMathCommand
|
class |
PowMathCommand
|
class |
ProductMathCommand
|
class |
QuotientMathCommand
|
class |
RandomMathCommand
|
class |
RemainderMathCommand
|
class |
SinMathCommand
|
class |
SumMathCommand
|
class |
TanMathCommand
|
| Uses of Command in commands.turtleCommands |
|---|
| Subclasses of Command in commands.turtleCommands | |
|---|---|
class |
BackTurtleCommand
|
class |
ClearScreenTurtleCommand
|
class |
ForwardTurtleCommand
|
class |
GotoTurtleCommand
|
class |
HeadingQueryTurtleCommand
|
class |
HideTurtleTurtleCommand
|
class |
HomeTurtleCommand
|
class |
LeftTurtleCommand
|
class |
PenDownQueryTurtleCommand
|
class |
PenDownTurtleCommand
|
class |
PenUpTurtleCommand
|
class |
RightTurtleCommand
|
class |
SetHeadingTurtleCommand
|
class |
ShowingQueryTurtleCommand
|
class |
ShowTurtleTurtleCommand
|
class |
XCorQueryTurtleCommand
|
class |
YCorQueryTurtleCommand
|
| Uses of Command in parser |
|---|
| Method parameters in parser with type arguments of type Command | |
|---|---|
static void |
XMLReader.read(java.lang.String filename,
java.util.Map<java.lang.String,Command> commands)
|
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.NET Interactive is here! | .NET Notebooks Preview 2 | .NET Blog Today we are excited to announce Preview 2 of the .NET Notebook experience. Preview 2 includes C#, F#, and PowerShell support.
NyxTech - Affordable, Portable Astrophotography. The Nyx Tracker is a motorized barn door tracker used for astrophotography. This tracking mount follows the stars as they move through the sky. Inexpensive and budget friendly, it is useful for people interested in astrophotography but not ready to spend ...
The Ultimate Game Boy Talk (33c3) https://media.ccc.de/v/33c3-8029-the_ultimate_game_boy_talk The 8-bit Game Boy was sold between 1989 and 2003, but its architecture more closely resembles machines from the early 1980s, like the Commodore 64 or the NES. This talk attempts to communicat...
Compiling .NET Core for Windows 10 ARM64 - Sinclairinat0r If you take a look at my Twitter, I have many examples of me showing passion for Windows On ARM. Ever since I started using my ASUS NovaGo that I won at the 2018 Microsoft Build Conference (this totally had to be fate), I’ve been obsessed with finding pra...
Kill It With Fire - Teaser Kill It With Fire is a first-person action game about hunting spiders and causing collateral damage. Available May 2020 on PC. Add it to your Steam wishlist TODAY!!! *** Links ***: Steam - https://store.steampowered.com/app/1179210/Kill_It_With_Fire ...
Comby〔 Rewrite Code 〕 Comby is a tool for structural code search and replace that supports ~every language. It lets developers write simple patterns to more intelligently change code compared to standard regex approaches.
Online Text Editor - MyTextArea - a simple online text editor MyTextArea is a quick and simple online text editor for plain text. Wow!
PureText PureText is a tiny tray utility that removes all text formatting from your clipboard and optionally pastes the resulting pure text to the active window with a single hotkey.
p8952/bocker Docker implemented in around 100 lines of bash. Contribute to p8952/bocker development by creating an account on GitHub.
Spinal Map - Thunderforest Inspired by a classic film, this style goes all the way to 11! It demonstrates the power of custom map styles based on vector tiles, along with a tongue-in-cheek approach to cartography.
SoCal Code Camp Code Camp is a free event for developers to come and learn from their peers. This community driven event has become an international trend where peer groups of all platforms, programming languages and disciplines band together to bring content to the comm...
SpaceX Satellite Rideshare SpaceX can accommodate additional mechanical interface diameters as an optional service.
Soyuz MS-13 crew return to Earth - NASASpaceFlight.com After a record-setting 328 continuous days in space, NASA astronaut Christina Koch returned to Earth…
Soyuz MS-13 EOM Events (Undock, Entry, Landing) February 6, 2020 Soyuz MS-13 EOM Events (Undock, Entry, Landing) February 6, 2020
PostgreSQL For Those Who Can’t Even, Part 1 Just yesterday I was talking to a friend about Postgres (not uncommon) and he said something that I found shocking: I can’t even with Postgres, I know JACK SQUAT This person calls themself my…
Postgres For Those Who Can’t Even, Part 2 – Working with Node and JSON Getting up to speed with Postgres and Node can be daunting but in this post I’ll dive into how you can easily work with both – including JSON document storage… in Postgres!
This is relevant only for multimedia apps. I have fintech systems in production... | Hacker News Thank you for this post. I read "10 Users: Split out the Database Layer" and about had an aneurysm.I also work with fintech systems built upon .NET Core and have similar experiences regarding scaling of these solutions. You can get an incredible amount of...


public interface InternalEventBusCapacity
extends io.sarl.lang.core.Capacity
This capacity is provided by the Janus kernel, not SARL.
| Modifier and Type | Interface and Description |
|---|---|
static class |
InternalEventBusCapacity.OwnerState
Describe the states of the owner of an event bus.
|
| Modifier and Type | Method and Description |
|---|---|
io.sarl.lang.core.EventListener |
asEventListener()
Replies the event listener linked to the owner of this capacity.
|
io.sarl.lang.core.Address |
getInnerDefaultSpaceAddress()
Replies the address of the agent in its inner default space.
|
InternalEventBusCapacity.OwnerState |
getOwnerState()
Replies the state of the owner of the event bus.
|
void |
registerEventListener(Object listener)
Register the given object on the event bus for receiving
any event.
|
void |
selfEvent(io.sarl.lang.core.Event event)
Sends an event to itself using its defaultInnerAddress as source.
|
void |
unregisterEventListener(Object listener)
Unregister the given object on the event bus for receiving
any event.
|
InternalEventBusCapacity.OwnerState getOwnerState()
void registerEventListener(Object listener)
listener
- - the listener on the SARL events.void unregisterEventListener(Object listener)
listener
- - the listener on the SARL events.void selfEvent(io.sarl.lang.core.Event event)
Initialize
and
Destroy
)event
- - event to propagate into the agent.io.sarl.lang.core.EventListener asEventListener()
io.sarl.lang.core.Address getInnerDefaultSpaceAddress()
Copyright © 2014-2015 the original authors or authors.


Deep ensemble for ENSO-forecasting¶
In this tutorial you learn how to use a neural network model called Deep Ensemble (DE) for the ENSO forecasting. This network architecture was initially developed Lakshminarayanan et al. (2017).
DEs are feed foreword neural networks that predict the mean and the standard deviation of a Gaussian. Hence, their predicion comes with an uncertainty estimation which is a valuable feature for ENSO-forecasting.
Create a data pipe line¶
At first, we define a data pipeline. This is in general quite useful to keep your code clean and also to reuse the pipeline for later purpose.
The data pipeline generates returns:
The feature array
The label array
The time array corresponding to the time of the label
NOTE (again): Lead time is defined as the time that passed between the last observed and the first date of the target season. Hence, negative appear, e.g. if you compare the DJF season with the target season JFM, you have a lead time of -2 month (Last observed date: Feburary 28/29, First date of the target season January 1).
[1]:
import numpy as np
from sklearn.preprocessing import StandardScaler
from ninolearn.utils import include_time_lag
from ninolearn.IO.read_post import data_reader
def pipeline(lead_time):
"""
Data pipeline for the processing of the data before the Deep Ensemble
is trained.
:type lead_time: int
:param lead_time: The lead time in month.
:returns: The feature "X" (at observation time), the label "y" (at lead
time), the target season "timey" (least month)
"""
reader = data_reader(startdate='1980-01', enddate='2018-12')
# indeces
oni = reader.read_csv('oni')
iod = reader.read_csv('iod')
wwv = reader.read_csv('wwv')
# seasonal cycle
sc = np.cos(np.arange(len(oni))/12*2*np.pi)
# network metrics
network_ssh = reader.read_statistic('network_metrics', variable='sst', dataset='ERSSTv5', processed="anom")
c2 = network_ssh['fraction_clusters_size_2']
H = network_ssh['corrected_hamming_distance']
# time lag
time_lag = 12
# shift such that lead time corresponds to the definition of lead time
shift = 3
# process features
feature_unscaled = np.stack((oni, sc, wwv, iod,
c2, H), axis=1)
# scale each feature
scalerX = StandardScaler()
Xorg = scalerX.fit_transform(feature_unscaled)
# set nans to 0.
Xorg = np.nan_to_num(Xorg)
# arange the feature array
X = Xorg[:-lead_time-shift,:]
X = include_time_lag(X, max_lag=time_lag)
# arange label
yorg = oni.values
y = yorg[lead_time + time_lag + shift:]
# get the time axis of the label
timey = oni.index[lead_time + time_lag + shift:]
return X, y, timey
/home/paul/miniconda2/envs/ninolearn/lib/python3.6/site-packages/distributed/utils.py:139: RuntimeWarning: Couldn't detect a suitable IP address for reaching '<IP_ADDRESS>', defaulting to '<IP_ADDRESS>': [Errno 101] Network is unreachable
RuntimeWarning,
Split the data set¶
For the training and testing of machine learning models it is crucial to split the data set into:
Train data set which is used to train the weights of the neural network
Validation data set which is used to check for overfitting (e.g. when using early stopping) and to optimize the hyperparameters
Test data set which is used to to evaluate the trained model.
NOTE: It is important to understand that hyperparamters must be tuned so that the result is best for the Validation data set and not for the test data set. Otherwise you can not rule out the case that the specific hyperparameter setting just works good for the specific test data set but is not generally a good hyperparameter setting.
In the following cell the train and the validation data set are still one data set, because this array will be later splitted into two arrays when th model is fitted.
[10]:
import keras.backend as K
from ninolearn.learn.models.dem import DEM
# clear memory from previous sessions
K.clear_session()
# define the lead time
lead_time = 3
# get the features (X), the label (y) and
# the time axis of the label (timey)
X, y, timey = pipeline(lead_time)
# split the data set into
test_indeces = (timey>='2001-01-01') & (timey<='2018-12-01')
train_val_indeces = np.invert(test_indeces)
train_val_X, train_val_y, train_val_timey = X[train_val_indeces,:], y[train_val_indeces], timey[train_val_indeces]
testX, testy, testtimey = X[test_indeces,:], y[test_indeces], timey[test_indeces]
Fit the model¶
Now it is time to train the model! For this a random search is used for all keyword arguments that are passed in a list to the DEM.set_parameters() method.
[25]:
# initiated an instance of the DEM (Deep Ensemble Model) class
model = DEM()
# Set parameters
model.set_parameters(layers=1, dropout=[0.1, 0.5], noise_in=[0.1,0.5], noise_sigma=[0.1,0.5],
noise_mu=[0.1,0.5], l1_hidden=[0.0, 0.2], l2_hidden=[0, 0.2],
l1_mu=[0.0, 0.2], l2_mu=[0.0, 0.2], l1_sigma=[0.0, 0.2],
l2_sigma=[0.0, 0.2], lr=[0.0001,0.01], batch_size=100, epochs=500, n_segments=5,
n_members_segment=1, patience=30, verbose=0, std=True)
# Use a random search to find the optimal hyperparamteres
model.fit_RandomizedSearch(train_val_X, train_val_y, n_iter=20)
##################################################################
Search iteration Nr 1/20
##################################################################
Train member Nr 1/5
--------------------------------------
46/46 [==============================] - 0s 45us/step
Train member Nr 2/5
--------------------------------------
46/46 [==============================] - 0s 76us/step
Train member Nr 3/5
--------------------------------------
46/46 [==============================] - 0s 61us/step
Train member Nr 4/5
--------------------------------------
46/46 [==============================] - 0s 79us/step
Train member Nr 5/5
--------------------------------------
46/46 [==============================] - 0s 45us/step
Computation time: 8.4s
New best hyperparameters
--------------------------------------
Mean loss: 0.34392209892849557
{'layers': 1, 'neurons': 16, 'dropout': 0.12378305611318924, 'noise_in': 0.13682762378098945, 'noise_mu': 0.1594007153319167, 'noise_sigma': 0.26941913115524324, 'l1_hidden': 0.15920416814139493, 'l2_hidden': 0.14973220769991027, 'l1_mu': 0.0968840864748789, 'l2_mu': 0.18567445044732792, 'l1_sigma': 0.1606583760628583, 'l2_sigma': 0.02928805643650441, 'lr': 0.0054775495740690665, 'batch_size': 100}
##################################################################
Search iteration Nr 2/20
##################################################################
Train member Nr 1/5
--------------------------------------
46/46 [==============================] - 0s 51us/step
Train member Nr 2/5
--------------------------------------
46/46 [==============================] - 0s 45us/step
Train member Nr 3/5
--------------------------------------
46/46 [==============================] - 0s 46us/step
Train member Nr 4/5
--------------------------------------
46/46 [==============================] - 0s 51us/step
Train member Nr 5/5
--------------------------------------
46/46 [==============================] - 0s 78us/step
Computation time: 9.3s
New best hyperparameters
--------------------------------------
Mean loss: 0.3435987807486368
{'layers': 1, 'neurons': 16, 'dropout': 0.25141333998178195, 'noise_in': 0.2329862534019585, 'noise_mu': 0.2813460046840733, 'noise_sigma': 0.14595068639744221, 'l1_hidden': 0.08553348071011933, 'l2_hidden': 0.11981746410750115, 'l1_mu': 0.10558548934586615, 'l2_mu': 0.05080434557231146, 'l1_sigma': 0.037151218694687606, 'l2_sigma': 0.19769939661821626, 'lr': 0.006687247196565577, 'batch_size': 100}
##################################################################
Search iteration Nr 3/20
##################################################################
Train member Nr 1/5
--------------------------------------
46/46 [==============================] - 0s 81us/step
Train member Nr 2/5
--------------------------------------
46/46 [==============================] - 0s 47us/step
Train member Nr 3/5
--------------------------------------
46/46 [==============================] - 0s 121us/step
Train member Nr 4/5
--------------------------------------
46/46 [==============================] - 0s 49us/step
Train member Nr 5/5
--------------------------------------
46/46 [==============================] - 0s 66us/step
Computation time: 10.3s
New best hyperparameters
--------------------------------------
Mean loss: 0.11411247535244277
{'layers': 1, 'neurons': 16, 'dropout': 0.30452543907304586, 'noise_in': 0.21534727932400557, 'noise_mu': 0.2830000465254644, 'noise_sigma': 0.20073478581880644, 'l1_hidden': 0.08204434904474006, 'l2_hidden': 0.15817156327512816, 'l1_mu': 0.044876511619725834, 'l2_mu': 0.07501615431427888, 'l1_sigma': 0.13807434833687507, 'l2_sigma': 0.003930864203235141, 'lr': 0.005892578009524626, 'batch_size': 100}
##################################################################
Search iteration Nr 4/20
##################################################################
Train member Nr 1/5
--------------------------------------
46/46 [==============================] - 0s 44us/step
Train member Nr 2/5
--------------------------------------
46/46 [==============================] - 0s 84us/step
Train member Nr 3/5
--------------------------------------
46/46 [==============================] - 0s 90us/step
Train member Nr 4/5
--------------------------------------
46/46 [==============================] - 0s 50us/step
Train member Nr 5/5
--------------------------------------
46/46 [==============================] - 0s 64us/step
Computation time: 8.7s
##################################################################
Search iteration Nr 5/20
##################################################################
Train member Nr 1/5
--------------------------------------
46/46 [==============================] - 0s 43us/step
Train member Nr 2/5
--------------------------------------
46/46 [==============================] - 0s 86us/step
Train member Nr 3/5
--------------------------------------
46/46 [==============================] - 0s 73us/step
Train member Nr 4/5
--------------------------------------
46/46 [==============================] - 0s 49us/step
Train member Nr 5/5
--------------------------------------
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Refit the model with best hyperparamters
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{'layers': 1, 'neurons': 16, 'dropout': 0.30452543907304586, 'noise_in': 0.21534727932400557, 'noise_mu': 0.2830000465254644, 'noise_sigma': 0.20073478581880644, 'l1_hidden': 0.08204434904474006, 'l2_hidden': 0.15817156327512816, 'l1_mu': 0.044876511619725834, 'l2_mu': 0.07501615431427888, 'l1_sigma': 0.13807434833687507, 'l2_sigma': 0.003930864203235141, 'lr': 0.005892578009524626, 'batch_size': 100}
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best loss search: 0.11411247535244277
loss refitting : 0.14777255660813787
Make predictions for the test data set¶
Now we can use the trained models to make predicitons on the test data set to evaluate how good the model perfoms on a data set that it never saw before.
[26]:
from ninolearn.plot.evaluation import plot_correlation
pred_mean, pred_std = model.predict(testX)
Plot the prediction¶
Let’s see how the predicion is looking like
[27]:
import matplotlib.pyplot as plt
from ninolearn.plot.prediction import plot_prediction
import pandas as pd
plt.subplots(figsize=(15,3.5))
plt.axhspan(-0.5,
-6,
facecolor='blue',
alpha=0.1,zorder=0)
plt.axhspan(0.5,
6,
facecolor='red',
alpha=0.1,zorder=0)
plt.xlim(testtimey[0], testtimey[-1])
plt.ylim(-3,3)
# plot the prediction
plot_prediction(testtimey, pred_mean, std=pred_std, facecolor='royalblue', line_color='navy')
# plot the observation
plt.plot(timey, y, "k")
[27]:
[<matplotlib.lines.Line2D at 0x7fd9fe77cb38>]
Evaluate the model¶
We can evaluate the model a bit more quantitatively using the loss function that was used to train the model, namely the negative-log-likelihood of the Gaussian and the correlation between the predicted mean and the observed ONI index.
[29]:
loss = model.evaluate(testy, pred_mean, pred_std)
print(f"Loss (Negative-Log-Likelihood): {loss}")
# make a plot of the seasonal correaltion
# note: - pd.tseries.offsets.MonthBegin(1) appears to ensure that the correlations are plotted
# agains the correct season
plot_correlation(testy, pred_mean, testtimey - pd.tseries.offsets.MonthBegin(1), title="")
Loss (Negative-Log-Likelihood): 0.1747595046770062


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Pirate (ARRR) is a cryptocurrency that solves the issue of financial privacy on the blockchain through forced anonymous transactions. Protect your wallet and transactions with Pirate’s completely anonymous network. With forced private transactions, your privacy cannot be compromised by another users’ activity on the network. If you wish to learn more, please join the official Pirate Discord at https://pirate.black/discord/ or message FishyGuts#5085, who will be happy to help out.
100 messages
Before 05-Dec-19 12:00 AM
Alright03-Sep-18 01:31 AM
protip^
grewalsatinder03-Sep-18 01:32 AM
Enjoy the profit
grewalsatinder03-Sep-18 02:20 AM
Thanks for correcting me. Was trying Wong way. Will mine next coins.
grewalsatinder03-Sep-18 02:33 AM
U guys have a long term holder here for this coin.
I can stop buying just try mining further if I can.
Never tried nomp and such. It's learning phase for me on this.
Will ask for all ur help here.
blackjok3r03-Sep-18 04:55 AM
Nice I had a look at it, and deicded to wait for your version (edited)
Most of the code seemed to be there already for sheilding, I was going to change sendTtoZ function to take array of address then call it istead of sendmany API, but maybe that wouldnt have worked.
Glad to see you figured it out... I better get more hash on PIRATE before the pool is up and the block reward halves.
SHossain03-Sep-18 10:08 AM
you still have more a month and half before the halving occurs
blackjok3r03-Sep-18 10:10 AM
Yes very very fast coin emission a the start on this coin
SHossain03-Sep-18 10:11 AM
i'll stop mining in couple of days...
you can mine most of the blocks for next 2 weeks
blackjok3r03-Sep-18 10:34 AM
Not sure I will for that much longer, I only put back into hash what I made in profits.


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