public abstract class AbstractAllocateAllocationCommand extends java.lang.Object implements AllocationCommand, ToXContent
| Modifier and Type | Class and Description |
|---|---|
protected static class |
AbstractAllocateAllocationCommand.Builder<T extends AbstractAllocateAllocationCommand> |
static class |
AbstractAllocateAllocationCommand.Factory<T extends AbstractAllocateAllocationCommand> |
ToXContent.DelegatingMapParams, ToXContent.MapParams, ToXContent.Params
| Modifier and Type | Field and Description |
|---|---|
protected java.lang.String |
node |
protected ShardId |
shardId |
EMPTY_PARAMS
| Modifier | Constructor and Description |
|---|---|
protected |
AbstractAllocateAllocationCommand(ShardId shardId,
java.lang.String node) |
| Modifier and Type | Method and Description |
|---|---|
protected static <T extends AbstractAllocateAllocationCommand.Builder> |
createAllocateParser(java.lang.String command) |
protected RerouteExplanation |
explainOrThrowMissingRoutingNode(RoutingAllocation allocation,
boolean explain,
DiscoveryNode discoNode)
Handle case where a disco node cannot be found in the routing table.
|
protected RerouteExplanation |
explainOrThrowRejectedCommand(boolean explain,
RoutingAllocation allocation,
java.lang.RuntimeException rte)
Utility method for rejecting the current allocation command based on provided exception
|
protected RerouteExplanation |
explainOrThrowRejectedCommand(boolean explain,
RoutingAllocation allocation,
java.lang.String reason)
Utility method for rejecting the current allocation command based on provided reason
|
protected void |
initializeUnassignedShard(RoutingAllocation allocation,
RoutingNodes routingNodes,
RoutingNode routingNode,
ShardRouting shardRouting)
Initializes an unassigned shard on a node and removes it from the unassigned
|
protected void |
initializeUnassignedShard(RoutingAllocation allocation,
RoutingNodes routingNodes,
RoutingNode routingNode,
ShardRouting shardRouting,
java.util.function.Consumer<ShardRouting> shardRoutingChanges)
Initializes an unassigned shard on a node and removes it from the unassigned
|
java.lang.String |
node()
Get the id of the node
|
ShardId |
shardId()
Get the shard id
|
XContentBuilder |
toXContent(XContentBuilder builder,
ToXContent.Params params) |
void |
writeTo(StreamOutput out) |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
execute, name
protected final ShardId shardId
protected final java.lang.String node
protected AbstractAllocateAllocationCommand(ShardId shardId, java.lang.String node)
protected static <T extends AbstractAllocateAllocationCommand.Builder> ObjectParser<T,java.lang.Void> createAllocateParser(java.lang.String command)
public XContentBuilder toXContent(XContentBuilder builder, ToXContent.Params params) throws java.io.IOException
toXContent
in interface ToXContent
java.io.IOException
public void writeTo(StreamOutput out) throws java.io.IOException
java.io.IOException
public ShardId shardId()
public java.lang.String node()
protected RerouteExplanation explainOrThrowMissingRoutingNode(RoutingAllocation allocation, boolean explain, DiscoveryNode discoNode)
protected RerouteExplanation explainOrThrowRejectedCommand(boolean explain, RoutingAllocation allocation, java.lang.String reason)
protected RerouteExplanation explainOrThrowRejectedCommand(boolean explain, RoutingAllocation allocation, java.lang.RuntimeException rte)
protected void initializeUnassignedShard(RoutingAllocation allocation, RoutingNodes routingNodes, RoutingNode routingNode, ShardRouting shardRouting)
allocation
- the allocationroutingNodes
- the routing nodesroutingNode
- the node to initialize it toshardRouting
- the shard routing that is to be matched in unassigned shardsprotected void initializeUnassignedShard(RoutingAllocation allocation, RoutingNodes routingNodes, RoutingNode routingNode, ShardRouting shardRouting, @Nullable java.util.function.Consumer<ShardRouting> shardRoutingChanges)
allocation
- the allocationroutingNodes
- the routing nodesroutingNode
- the node to initialize it toshardRouting
- the shard routing that is to be matched in unassigned shardsshardRoutingChanges
- changes to apply for shard routing in unassigned shards before initialization


{% extends "horizon/common/_modal_form.html" %} {% load i18n %} {% block modal-body-right %}
{% trans "Security groups are sets of IP filter rules that are applied to the network settings for the VM. After the security group is created, you can add rules to the security group." %}
{% endblock %}


{{ (movieService.movies | async).length }} movies found
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{{ buttons.rating }}
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Donny Winston
studied scanning-charged-particle-beam lithography before shifting
professional focus to software-as-a-service. He maintains and
develops open, web-based tools for data-driven discovery of
advanced materials
(see materialsproject.org)
at the Lawrence Berkeley National Laboratory. He uses hyphens
extensively.


--- layout: page title: Cooper Lab Alumni date: 2013-02-18 22:11:21.000000000 -05:00 type: page published: true status: publish categories: [] tags: [] meta: _edit_last: '450' _wp_page_template: full-width-page.php author: login: kenny email<EMAIL_ADDRESS>display_name: kenny first_name: Kenneth last_name: Flynn ---
Keith Ferguson, M.S., Microbiology (2012)
Chuck Traverse, M.S., Microbiology (2012), beginning PhD studies at University of Texas at Austin (Fall 2013)
Crystal Ellis, M.S., Genetics (2008), Ph.D., Genetics (2011), is now a Postdoctoral Fellow working with Dr. Jason Harris and Dr. Regina Larocque at Massachusetts General Hospital/Harvard Medical School in Boston, MA, along with former Cooper undergraduate Leslie Mayo-Smith.
Steffen Poltak, Ph.D., Microbiology (2010), is now an assistant professor at St. Anselm College in Manchester, NH.
Laura Benton, M.S., Microbiology (2009), now an instructor at St. Michael's College in Colchester, VT.
Sarah Comeau (Wrocklage), M.S., Microbiology (2009)
Morel Henley, M.S., Computer Science (2008)
Thomas Johnson (2011), now a PhD student at University of Texas at Austin
AJ Troiano (2010), now a PhD student at University of Connecticut
Keith Morley (2008), medical student at Dartmouth Medical School
Alanna Callendrello (2008)
Stephen Perros (2008)
Angela Kuehn (2008)
Danielle Morse
Julie Morrison
Michelle Oeser


Module: Mongoid::Includes::EagerLoad
- Defined in:
- lib/mongoid/includes/eager_load.rb
Overview
Adds support for polymorphic and nested eager loading.
Instance Method Summary (collapse)
-
- (Object) eager_load(docs)
Override: Partitions the inclusions into the different types.
-
- (Object) full_preload(docs, inclusions, polymorphic_inclusions, nested_inclusions)
private
Performs the normal inclusions first, which allows to perform the polymorphic eager loading.
-
- (Object) preload_nested(nested_inclusions, docs)
private
The documents are grouped by the nested property, and all the includes by that property are processed as usual.
-
- (Object) preload_polymorphic(inclusion, docs)
private
Preloads each polymorphic includes, by grouping the documents by concrete type of the polymorphic relation, and making a query for each type.
Instance Method Details
- (Object) eager_load(docs)
Override: Partitions the inclusions into the different types.
8 9 10 11 12 13 14 15 16 |
# File 'lib/mongoid/includes/eager_load.rb', line 8 def eager_load(docs) return false unless eager_loadable? nested_inclusions, inclusions = criteria.inclusions.partition(&:nested?) polymorphic_inclusions, inclusions = inclusions.partition(&:polymorphic_belongs_to?) full_preload(docs, inclusions, polymorphic_inclusions, nested_inclusions) self.eager_loaded = true end |
- (Object) full_preload(docs, inclusions, polymorphic_inclusions, nested_inclusions)
This method is part of a private API. You should avoid using this method if possible, as it may be removed or be changed in the future.
Performs the normal inclusions first, which allows to perform the polymorphic eager loading. Nested inclusions are performed at the end.
20 21 22 23 24 25 26 27 28 |
# File 'lib/mongoid/includes/eager_load.rb', line 20 def full_preload(docs, inclusions, polymorphic_inclusions, nested_inclusions) preload(inclusions, docs) polymorphic_inclusions.each do || preload_polymorphic(, docs) end preload_nested(nested_inclusions, docs) end |
- (Object) preload_nested(nested_inclusions, docs)
This method is part of a private API. You should avoid using this method if possible, as it may be removed or be changed in the future.
The documents are grouped by the nested property, and all the includes by that property are processed as usual.
43 44 45 46 47 |
# File 'lib/mongoid/includes/eager_load.rb', line 43 def preload_nested(nested_inclusions, docs) nested_inclusions.group_by(&:from).each do |from, inclusions| preload(inclusions, docs.map(&from).flatten) end end |
- (Object) preload_polymorphic(inclusion, docs)
This method is part of a private API. You should avoid using this method if possible, as it may be removed or be changed in the future.
Preloads each polymorphic includes, by grouping the documents by concrete type of the polymorphic relation, and making a query for each type.
32 33 34 35 36 37 38 39 |
# File 'lib/mongoid/includes/eager_load.rb', line 32 def preload_polymorphic(inclusion, docs) docs.group_by do |doc| doc.send(inclusion.inverse_type) # The {name}_type attribute in polymorphic relations. end.each do |type, docs| concrete_inclusion = inclusion.for_class_name(type) preload([concrete_inclusion], docs) end end |


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Perl Weekly
Issue #147 - 2014-05-19 - Perl 5.20.0 RC1 is out!
latest | archive | by Yanick Champoux
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The first Release Candidate (RC release) of the next perl 5 is on CPAN, and unless anything terrible comes up, 5.20 might very well hit the street as early as next week. This week, in the new and spiffy department, we have new dependency graphs on MetaCPAN, a report on the state of the ongoing perl-mop projects, and even a Vim plugin that might make this editor feel like a young thing again. Enjoy! ~ `/anick
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GitTip profile of the week
GitTip allows you to give a regular weekly monetary gift to people in order to encourage them to do more of the awesome stuff they have been doing. The gifts can be as low as 1 cent a week. In this section every week you will see a different Perl developer we recommend you support. Oh, and the Perl Weekly itself also has a GitTip account.
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Announcements
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Perl 5.20.0 RC1 is now available
The first release candidate of the next perl is out! Now is the time to test your code against it, for the time is short -- we're talking one-week type of short, here -- before the final cut is made, and 5.20.0 officially launches.
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Submit talks for YAPC::Europe 2014!
YAPC::EU is coming fastest than you might realize. If you're thinking about presenting there, domm reminds us, now is the time to submit your talk.
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Articles
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On prototyping in public...
Stevan Little provides a quick recap of the Perl mop project(s), and the high-level lines of what to expect for the near future.
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Code
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Debunk Perl's magic with B::Deparse
A short little reminder by David Farrell that, no matter how beffudling a snippet of Perl can be, B::Deparse is always there to help shed a light on what's really going on.
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Monitor running processes with Perl
First we write programs. Then we write programs to start programs. Then Johnny Morano shows us how to write programs to watch programs and, if required, call programs to start programs.
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Videos
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San Francisco Perl Mongers 2014 Lightning Talks
Lightning talks, everybody loves them. The San Francisco Perl Mongers here offer us 12 of those tasty, tasty morsels, touching Go (the language), Semantic versioning, JavaScript things, educational stuff, with some Perl interspersed here and there.
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Weekly collections
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You know, you could get the Perl Weekly right in your mailbox. Every Week. Free of charge!
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--- layout: post title: Generic primes generator date: '2014-02-01T17:41:00.000+01:00' author: david crosson tags: modified_time: '2014-02-01T17:41:50.359+01:00' blogger_id: tag:blogger.com,1999:blog-4576039096616564465.post-2514866495227345658 blogger_orig_url: http://www.crosson.org/2014/02/generic-primes-generator.html ---
An example of how to implement a generic primes generator that you can use with any numerical type. Of course, performances will be impacted by your choice :
My primes project can be found here.


This is a website for the Multi User Blog project for Udacity FSND. It is written in python along with HTML, CSS, jQuery. Uses Google App Engine and Webapp2.
Click here to view the website
This is a part of Udacity Full Stack Web Developer Nanodegree. This is the third project I have made and the second project I have made writing python. Due to my stubborness to learn how to handle image uploads w ith Google App Engine this project took a while longer than I had planned. About 20 hours all in all.
I also learned a lot about how to work with the database ndb on Google App Engine.
Furthermore I implemented ajax functionality for the liking posts.
In order to run this project you have to setup a Google App Engine Project and change the application id in the app.yaml file to the corresponding value.
The MIT License (MIT)


Utter.io
A Coin Operated Co-op Cloud
Utter.io is like airbnb for excess compute.
Data center spend on compute is a $150B/year market.
Via Gartner Research
The amount of compute per person is growing without bounds.
Compiled from market data on estimated CPU growth.
By 2023...
...16 CPUs for every man, woman and child on the planet.
We will always need more compute.
...drives our demand for compute.
The biggest efficiency gains come from utilizing excess capacity.
Excess capacity drives models like Lyft.
Public 'Cloud' IaaS offerings like AWS are a result of experimenting with compute efficiency.
Public 'Cloud' Segment Growth Rate
Data from Gartner Research via Forbes
Public 'Cloud' Segment Projections
Data derived from Gartner Research
IaaS Market Share
Top 15 IaaS Providers from Gartner Research via Seattle Times
AWS is run out of nine data centers...
...located in seven countries.
Nine data centers representing 80% of the public cloud is NOT really a cloud, even if it does generate $5B/year in revenue.
The Private Cloud
The Public Cloud
The Hybrid Cloud
The Problem Cloud
“You are going to see brutal, brutal consolidation in the IT industry, where only two or three of us will be meaningful in as quick as five years.”
Cisco's fog computing strategy describes IoT data being collected and sent to edge services rather than 'the cloud'.
"Disks are increasing with Moore's Law, at 1000x per decade, but bandwidth is increasing at just 50x per decade. Could a co-op version of Amazon's AWS save the consumer business?"
Kenneth Church, IBM Researcher
How do we build a highly efficient decentralized cloud?
Solve federation.
Today, federation resource ownership describes a continuously shifting and competitive compute environment.
Trust in standards is implicit.
Trust in identity management is illogical.
Trust in payment security is shockingly low.
Blockchain-based methods handily solve hard problems associated with the creation of a globally federated cloud by leveraging computed trust.
Utter.io's trust is the 4th knob of the cloud.
Claiming you're solving federation requires elevating trust by doing a demo in situ. Click.
OpenStack + Bitcoin = Virtual Appliance
Simple Callback URL Content
Federated Authentication
ala BitAuth from BitPay
The appliance works out-of-the-box with popular OpenStack distros, including Nebula, Piston, Redhat, Rackspace, Ubuntu, Metacloud and more.
77% of OpenStack Deployments are private cloud.
The pool controller runs on AppEngine.
Inception
Funding Structure
StackMonkey Inc. (C corp)
Utter.io Open Source Project (token)
Convertible Note(s)
$3M Minimum Raise Closed by 10/31
Providers
20 in 10 Countries by Year End
Developers
Crypto/OpenStack Core Hired by 11/30
Utter Token
First Stage of $100M Fund Issuance Complete by Q2/2015
StackMonkey, Inc.
GA Release Q2/2015
Bits of Bandwagon Bias
Or, hacking a stakeholder's interest.
"This is the last mile."
Joshua McKenty, OpenStack Co-Founder
"What you just said is blowing my mind."
Mark Shuttleworth, CEO of Canonical
"You've just eliminated cloud payment fraud."
Jason Seats, Co-Founder of Slicehost
"This is the most interesting thing I've seen in at least five years, or maybe ten, now I think about it."
Jim Curry, Rackspace's GMO


let's say this is a little game I create to let you play it. this about you can vote about these three image you show it in the left of the web page and in the right you will see the button and it will show you the result of the vote and how many time this photo show to you (there is 20 photo ) okay let's go and good luck. I will see you in another creation for something new in the future more interesting and fun. Bye, take care of yourself


Registration fee：Including lunch plus refreshments
| Type | Registration special offer online(up to July 1) | On-site registration/pay | ||
|---|---|---|---|---|
| Aclclp member | Non-member | Aclclp member | Non-member | |
| General | NTD 500 | NTD 600 | NTD 700 | NTD 800 |
| Student | NTD 300 | NTD 500 | NTD 500 | NTD 700 |
| Sponsor | For Free |
1. Registration way and deadline:
2. How to pay:
(1) Online registration：
(2) On-site registration：You can make it in cash or credit card, and a fee of 200 NTD will be added to the registration fee.


GC Log Viewer is an online JVM gclog graphing tool. It is hoped that GC Log Viewer does away with the annoying situation of having to analyse a gclog file without any tools at hand. This is a first beta release of the application. The application was written to:
This application has been developed by Jumping Bean - An Java/Web application development and training company.. The repository is available on Github
GC Log Viewer is a client side application, meaning it runs entirely in your browser. To start using the application upload your gclog file via the "Load File" menu item. The log entries are stored locally in an indexedDB object store. If your log file is large you may be prompted to allow the database to exceed the default maximum size, which varies from browser to browser.
The data will be persisted and available when need to analyze the data again. i.e you will not need to re-upload.
Once you have loaded your files graphs can be added to the display via the "Graph" menu item. You can load more than one graph at a time. On Firefox you can drag and drop graphs to rearrange them. Currently drag and drop doesn't work in Chrome. There is a bug related to this. Its been open for two years so doesn't look like its going to be fixed soon. Could also be that the drag and drop HTML api is so bad its not worth using. In a future release we may replace the drag and drop api with a custom implementation.
On Chrome the SVG zoom functionality allows for zooming in on the generated graphs but the support or document is rudimentary making it almost unusable. The "zoom" menu item is meant to make it easy to reset your zoom to a sane level once you get lost.
If you can't get it to work but would like to see what it can do, try downloading this log file and uploading it. samplegc.log
The application has been tested with log files from a Java 8 JVM for the
As a minimum you will need to add "-X:loggc:/path/to/gc.log" to your JVM options to capture a gc log file for uploading. For the best results it is recommended to turn on the -XX:+PrintGCDetails jvm option. How far we succeeded in extracting useful information from the log files is hard to say. Any feedback on bugs and errors in the generated graphs will be appreciated. If you have an issue also provide a snippet of the log file so we can see about improving the regular expression we use to extract the data. It is suspected that there will be a lot of log file formats in the wild will not be supported. Let us know.


Class: StreamCipher
| Defined in: | src/algbase.iced |
Overview
A base class for a stream cipher. This will be used for bonafide stream ciphers like Salsa20 but also for BlockCiphers running in CTR mode.
Direct Known Subclasses
Instance Method Summary
- - (void) constructor() Constructor
- - (Number) encryptBlock(word_array, dst_offset = 0) Encrypt one block's worth of data.
- - (WordArray) encrypt(word_array) Encrypt an entire word array in place, overwriting the original plaintext with the cipher text.
-
- (void) bulk_encrypt({input, progress_hook, what}, cb)
Like
encrypt
but with an asynchronous preemptable interface.
Constructor Details
- (void) constructor()
Instance Method Details
- (Number) encryptBlock(word_array, dst_offset = 0)
Encrypt one block's worth of data. Use the next block in the keystream (order matters here!). Call into the virtual @get_pad() function to get the pad from the underlying block cipher for this block.
- (WordArray) encrypt(word_array)
Encrypt an entire word array in place, overwriting the original plaintext with the cipher text.
- (void) bulk_encrypt({input, progress_hook, what}, cb)
Like encrypt
but with an asynchronous preemptable interface. Good
for encrypting big payloads without blocking up the process. As above,
encrypt in place, so the output ciphertext will be where the input
plaintext was.


MAJESTY RESTURANT
RESTAURANT INTERIOR. Rome - ItalyVivamus fringilla nibh velit, at maximus lorem tincidunt vel. Ut fringilla in libero id ullamcorper. Curabitur at augue congue, porta lacus id, porttitor sem. Nunc quis bibendum sem.
DetailsCreating a sustainable future through building preservation, green architecture, and smart design from complete turn key to project manager. Leave the to the professionals.
Creating a sustainable future through building preservation, green architecture, and smart design from complete turn key to project manager. Leave the to the professionals.
Creating a sustainable future through building preservation, green architecture, and smart design from complete turn key to project manager. Leave the to the professionals.
Ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. Ut wisi enim ad minim veniam
Vivamus fringilla nibh velit, at maximus lorem tincidunt vel. Ut fringilla in libero id ullamcorper. Curabitur at augue congue, porta lacus id, porttitor sem. Nunc quis bibendum sem. Morbi dignissim turpis enim, vitae tempor leo porta vitae. .from complete turn key to project manager. Leave the building to the professionals creating a sustainable future through building preservation, green architecture, and smart design.
About Us


Holiday Gift Guide 2019: The Best Art And Fashion Books
by Natasha WolffThis holiday season, there are many books to suit the art and fashion lovers on your gift list. From modern painters to contemporary fashion designers, there’s a tome to entertain everyone.
Warhol on Basquiat: The Iconic Relationship Told in Andy Warhol’s Words and Pictures (Taschen)
Set against the background of the 1980s, Michael Dayton Hermann’s book explores the friendship between pop artist and art world elder Andy Warhol and young, edgy graffiti-loving artist Jean-Michel Basquiat, two iconic figures whose work is as more iconic than ever and breaking auction records.
Written by Pablo Picasso’s granddaughter art historian Diana Widmaier Picasso, this monograph focuses on the French’s artist’s prolific career and artistic process as one of the most important artists of the twentieth century. From the Cubist work he is perhaps best known for to the lesser-known prints, paintings and sculptures, the book showcases 100 definitive pieces.
Dior: Moments of Joy (Flammarion)
The House of Dior’s signature Dior Parfums ad campaigns, photographed since 1947 by some of the world’s best talents (Richard Avedon, Henri Cartier-Bresson, Guy Bourdin and Jean-Paul Goude) are captured here in a book featuring 220 of the best images along with text by Muriel Teodori.
Roy Lichtenstein The Impossible Collection (Assouline)
Art historian Avis Berman has written about and selected some of Roy Lichtenstein’s most iconic pop-art paintings for this monograph which spans the contemporary artist’s celebrated career.
Ralph Lauren: In His Own Fashion (Abrams)
On the heels of the new HBO documentary Very Ralph, Alan Flusser’s book focuses on the iconic designer and his over-50 year career in the fashion business defining American design.
Prabal Gurung (Abrams)
From dressing First Lady Michelle Obama to Sarah Jessica Parker and The Duchess of Cambridge, the Nepalese fashion designer has made great impact since founding his eponymous label in 2009. This tome charts the designer’s early life growing up in Singapore and Nepal and presents his sketches and mood boards along with photography of his designs on and off the New York runways.


In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)
rides.head()
This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the cnt
column. You can see the first few rows of the data above.
Below is a plot showing the number of bike riders over the first 10 days in the data set. You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model.
rides[:24*30].plot(x='dteday', y='cnt')
Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to get_dummies()
.
dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']
for each in dummy_fields:
dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)
rides = pd.concat([rides, dummies], axis=1)
fields_to_drop = ['instant', 'dteday', 'season', 'weathersit',
'weekday', 'atemp', 'mnth', 'workingday', 'hr']
data = rides.drop(fields_to_drop, axis=1)
data.head()
To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.
The scaling factors are saved so we can go backwards when we use the network for predictions.
quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']
# Store scalings in a dictionary so we can convert back later
scaled_features = {}
for each in quant_features:
mean, std = data[each].mean(), data[each].std()
scaled_features[each] = [mean, std]
data.loc[:, each] = (data[each] - mean)/std
We'll save the last 21 days of the data to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders.
# Save the last 21 days
test_data = data[-21*24:]
data = data[:-21*24]
# Separate the data into features and targets
target_fields = ['cnt', 'casual', 'registered']
features, targets = data.drop(target_fields, axis=1), data[target_fields]
test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]
We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set).
# Hold out the last 60 days of the remaining data as a validation set
train_features, train_targets = features[:-60*24], targets[:-60*24]
val_features, val_targets = features[-60*24:], targets[-60*24:]
Below you'll build your network. We've built out the structure and the backwards pass. You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.
The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called forward propagation.
We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called backpropagation.
Hint: You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.
Below, you have these tasks:
self.activation_function
in __init__
to your sigmoid function.train
method.train
method, including calculating the output error.run
method.# check data shape
print("input samples:", train_features.shape[0])
print("input features:", train_features.shape[1])
batch = np.random.choice(train_features.index, size=128)
count = 0
for record, target in zip(train_features.ix[batch].values,
train_targets.ix[batch]['cnt']):
if count == 0:
print(record.shape)
print(target.shape)
inputs = np.array(record, ndmin=2).T
targets = np.array(target, ndmin=2).T
print(inputs.shape)
print(targets.shape)
count += 1
print("count:", count)
class NeuralNetwork(object):
def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):
# Set number of nodes in input, hidden and output layers.
self.input_nodes = input_nodes
self.hidden_nodes = hidden_nodes
self.output_nodes = output_nodes
# Initialize weights
self.weights_input_to_hidden = np.random.normal(0.0, self.hidden_nodes**-0.5,
(self.hidden_nodes, self.input_nodes))
self.weights_hidden_to_output = np.random.normal(0.0, self.output_nodes**-0.5,
(self.output_nodes, self.hidden_nodes))
self.lr = learning_rate
#### Set this to your implemented sigmoid function ####
# Activation function is the sigmoid function
self.activation_function = (lambda x: 1 / (1 + np.exp(-x)))
def train(self, inputs_list, targets_list):
# Convert inputs list to 2d array
inputs = np.array(inputs_list, ndmin=2).T
targets = np.array(targets_list, ndmin=2).T
# shape symbol: i - numInputs (56), h - numHidden, o - numOutputs
# inputs(i, 1) , not batched
# targets(1, 1)
#### Implement the forward pass here ####
### Forward pass ###
# TODO: Hidden layer
# shape: inputs(i, 1).T dot weights(h, i).T => (1, h)
hidden_inputs = np.dot(inputs.T, self.weights_input_to_hidden.T) # signals into hidden layer
hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer
# TODO: Output layer
# shape: inputs(1, h) dot weights(o, h).T => (1, o)
final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output.T) # signals into final output layer
final_outputs = final_inputs # signals from final output layer
#### Implement the backward pass here ####
### Backward pass ###
# TODO: Output error
# shape(1, o)
output_errors = targets - final_outputs # Output layer error is the difference between desired target and actual output.
# TODO: Backpropagated error
# shape: inputs(1, o) dot weights(o, h) => (1, h)
hidden_errors = np.dot(output_errors, self.weights_hidden_to_output) # errors propagated to the hidden layer
hidden_grad = hidden_errors * hidden_outputs * (1 - hidden_outputs) # hidden layer gradients
# TODO: Update the weights
# shape: (1, o).T dot (1, h) => (o, h)
self.weights_hidden_to_output += self.lr * np.dot(output_errors.T, hidden_outputs) # update hidden-to-output weights with gradient descent step
# shape: (1, h).T dot (1, i) => (h, i)
#self.weights_input_to_hidden += self.lr * np.dot(hidden_grad.T, inputs.T) # update input-to-hidden weights with gradient descent step
self.weights_input_to_hidden += self.lr * hidden_grad.T * inputs.T # update input-to-hidden weights with gradient descent step
def run(self, inputs_list):
# Run a forward pass through the network
inputs = np.array(inputs_list, ndmin=2).T
#### Implement the forward pass here ####
# TODO: Hidden layer
#hidden_inputs = # signals into hidden layer
#hidden_outputs = # signals from hidden layer
hidden_inputs = np.dot(inputs.T, self.weights_input_to_hidden.T) # signals into hidden layer
hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer
# TODO: Output layer
#final_inputs = # signals into final output layer
#final_outputs = # signals from final output layer
final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output.T) # signals into final output layer
final_outputs = final_inputs # signals from final output layer
return final_outputs.T
def MSE(y, Y):
return np.mean((y-Y)**2)
Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.
You'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.
This is the number of times the dataset will pass through the network, each time updating the weights. As the number of epochs increases, the network becomes better and better at predicting the targets in the training set. You'll need to choose enough epochs to train the network well but not too many or you'll be overfitting.
This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. A good choice to start at is 0.1. If the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.
The more hidden nodes you have, the more accurate predictions the model will make. Try a few different numbers and see how it affects the performance. You can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose.
import sys
### Set the hyperparameters here ###
epochs = 2000 # 100
learning_rate = 0.0699 # 0.1
hidden_nodes = 28 # input feature has 56, half is 28 # 2
output_nodes = 1
N_i = train_features.shape[1]
network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)
losses = {'train':[], 'validation':[]}
for e in range(epochs):
#if False:
# Go through a random batch of 128 records from the training data set
batch = np.random.choice(train_features.index, size=128)
for record, target in zip(train_features.ix[batch].values,
train_targets.ix[batch]['cnt']):
network.train(record, target)
# Printing out the training progress
train_loss = MSE(network.run(train_features), train_targets['cnt'].values)
val_loss = MSE(network.run(val_features), val_targets['cnt'].values)
sys.stdout.write("\rProgress: " + str(100 * e/float(epochs))[:4] \
+ "% ... Training loss: " + str(train_loss)[:5] \
+ " ... Validation loss: " + str(val_loss)[:5])
losses['train'].append(train_loss)
losses['validation'].append(val_loss)
plt.plot(losses['train'], label='Training loss')
plt.plot(losses['validation'], label='Validation loss')
plt.legend()
plt.ylim(ymax=0.5)
Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly.
fig, ax = plt.subplots(figsize=(8,4))
mean, std = scaled_features['cnt']
predictions = network.run(test_features)*std + mean
ax.plot(predictions[0], label='Prediction')
ax.plot((test_targets['cnt']*std + mean).values, label='Data')
ax.set_xlim(right=len(predictions))
ax.legend()
dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])
dates = dates.apply(lambda d: d.strftime('%b %d'))
ax.set_xticks(np.arange(len(dates))[12::24])
_ = ax.set_xticklabels(dates[12::24], rotation=45)
Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?
Note: You can edit the text in this cell by double clicking on it. When you want to render the text, press control + enter
The model predicts well during the first half of December, but is not so good during the second half of Decemember. I checked the csv file, and it turns out that it has only 2 years of data. And we separated the data, the last 21 days are used for test. Which means, for the second half of Decemember, only one year data are trained, which is not sufficient.
Run these unit tests to check the correctness of your network implementation. These tests must all be successful to pass the project.
import unittest
inputs = [0.5, -0.2, 0.1]
targets = [0.4]
test_w_i_h = np.array([[0.1, 0.4, -0.3],
[-0.2, 0.5, 0.2]])
test_w_h_o = np.array([[0.3, -0.1]])
class TestMethods(unittest.TestCase):
##########
# Unit tests for data loading
##########
def test_data_path(self):
# Test that file path to dataset has been unaltered
self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')
def test_data_loaded(self):
# Test that data frame loaded
self.assertTrue(isinstance(rides, pd.DataFrame))
##########
# Unit tests for network functionality
##########
def test_activation(self):
network = NeuralNetwork(3, 2, 1, 0.5)
# Test that the activation function is a sigmoid
self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))
def test_train(self):
# Test that weights are updated correctly on training
network = NeuralNetwork(3, 2, 1, 0.5)
network.weights_input_to_hidden = test_w_i_h.copy()
network.weights_hidden_to_output = test_w_h_o.copy()
print(network.weights_input_to_hidden)
network.train(inputs, targets)
print(network.weights_input_to_hidden)
self.assertTrue(np.allclose(network.weights_hidden_to_output,
np.array([[ 0.37275328, -0.03172939]])))
self.assertTrue(np.allclose(network.weights_input_to_hidden,
np.array([[ 0.10562014, 0.39775194, -0.29887597],
[-0.20185996, 0.50074398, 0.19962801]])))
def test_run(self):
# Test correctness of run method
network = NeuralNetwork(3, 2, 1, 0.5)
network.weights_input_to_hidden = test_w_i_h.copy()
network.weights_hidden_to_output = test_w_h_o.copy()
self.assertTrue(np.allclose(network.run(inputs), 0.09998924))
suite = unittest.TestLoader().loadTestsFromModule(TestMethods())
unittest.TextTestRunner().run(suite)


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Standard text with a little bit of strong
Standard text with a little bit of Italic or emphasis
Standard text with a little bit of underline
Regular or font-weight: 400
Semi-bold or font-weight: 600
Bold or font-weight: 700
Strong is used to indicate strong importance
This text has added emphasis
The b element is stylistically different text from normal text, without any special importance
The i element is text that is set off from the normal text
The u element is text with an unarticulated, though explicitly rendered, non-textual annotation
This text is deleted and This text is inserted
This text has a strikethrough
Superscript®
Subscript for things like H2O
This small text is small for for fine print, etc.
Abbreviation: HTML
Keybord input: Cmd
This text is a short inline quotation
This is a citation
The dfn element indicates a definition.
The mark element indicates a highlight
This is what inline code looks like.
This is sample output from a computer program
The variable element, such as x = y
This is a text link Strong is used to indicate strong importance


See information on using the notification service at
http://developer.mypurecloud.com/resources/use-notification-service/
Change your status in PureCloud to see update notifications


Private equity tycoon Guy Hands looks to buy struggling Kier Living
by Stefan BosciaPrivate equity tycoon Guy Hands has reportedly made a bid for the housebuilding arm of struggling construction firm Kier.
Hands’ private equity firm – Terra Firma Capital Partners – is one of the last remaining bidders for Kier Living.
Read more: Kier’s COO leaves with immediate effect
Analysts told Sky News that the company could be worth an estimated £150m, however it is believed Terra Firma have put in a lower bid.
Kier’s investment and development arm Kier Property is also up for sale, with US private equity firm Lone Star reportedly interested.
The company is expected to give an update on both of the sales early in the new year.
Kier has fallen on tough times recently, experiencing an 80 per cent decline in value over the past year.
This forced the company to undertake rescue fundraising from shareholders, while its chief operating officer Claudio Veritiero resigned last month.
Kier is one of the country’s biggest builders of roads, schools and railways, and carries out a significant amount of work for the government.
This includes two contracts worth £1.4bn to design and build major railway tunnels beneath Northamptonshire, which were originally part of a three-way joint venture including Carillion.
However, when Carillion collapsed early last year, Kier and French construction company Eiffage took on 50 per cent of the job each.
Read more: Kier Group shares plummet as US private equity firm Lone Star Circles
If Hands’ firm closes the Kier Living sale, it will come shortly after Terra Firma recently Wyevale Garden Centres.
The Guernsey-based tycoon is reportedly looking into a number of other potential purchases across Europe.


There are {{totalCarParts()}} total parts in stock.
{{carPart.desc}}
Price: {{carPart.price}}
0">{{carPart.inStock}} in Stock
Out of Stock
Quantity:
+ 0" class = "decrease" (click)="downQuantity(carPart)"> - Reset


Papercraft is folding paper templates into different shapes to form a certain model or shape.
The specific template that I used was yoda from star wars. The link and picture for the project is below!!


Top: Basic types: string
The string class implements dynamically allocated reference-counted 8-bit character strings. The string class is a mixture of L-type and null-terminated strings: it has both the length indicator and the terminating null-symbol. The length of a string is theoretically limited to INT_MAX, and practically is limited to the amount of virtual memory space available to the application.
A string object itself contains only a reference to the first character of the string buffer. A string object can be implicitly converted to a null-terminated string, either a variable or passed as an actual parameter, thus allowing to combine both PTypes strings and traditional C strings in your application. A string object converted to char* or const char* never returns NULL pointers: it guarantees to always point to some data, even if the string is zero-sized.
The reference counting mechanism works transparently (known also as copy-on-write) and safely with regard to multithreading. You can manipulate string objects as if each object had its own copy of string data. Whenever you modify a string object the library safely detaches the buffer from all other string objects that may be using the same buffer and creates a unique copy so that changes won't affect the other "holders" of this string.
NOTE on multithreading: the dynamic string objects themselves are NOT thread-safe. In other words, each thread can manipulate objects (variables) of type string only within their scope. However, it is safe to pass strings as (copy) parameters when, for example, sending a message to a concurrent thread through a message queue. Whenever the recipient thread tries to modify the string, the shared data buffer is being safely detached.
The string class is declared in <ptypes.h>.
See also: Constructors/destructors, Operators, Typecasts, Manipulation, Conversion


ENZYMIC AND ACID CYCLIZATIONS OF SQUALENE OXIDE ANALOGS
A DISSERT ION SUBMITTED TO THE DEPARTMENT OF CHEMISTRY AND THE COMMITTEE ON THE GRADUATE DIVISION OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
By Robert Paul Hanzlik
May 1970
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


Hello Everyone. I am Zeyuan(Ryan) Cai, I am a Undergraduate Student in San Francisco State University studying in Computer Science. I have passion for Mordern Techology and that is the reason why I choose CS as my major. Other than CS, I also enjoy playing Video games, playing basketball, camping and hiking.
Programming Languages: Java, JavaScript, Bootstrap, C++, SQL, R
Soft Skills: Communication(Speaking and Listening), Quick learner, Probelm Solving, Passionate for work, Team-player
Languages: English, Chinses(Mandarin)


QtConcurrent
Trial to use Doxygen to generate UML class diagram of QtConcurrent module.
- f -
FilteredEachKernel() :
QtConcurrent::FilteredEachKernel< Iterator, KeepFunctor >
FilteredReducedKernel() :
QtConcurrent::FilteredReducedKernel< ReducedResultType, Iterator, KeepFunctor, ReduceFunctor, Reducer >
FilterKernel() :
QtConcurrent::FilterKernel< Sequence, KeepFunctor, ReduceFunctor >
finish() :
QtConcurrent::FilteredReducedKernel< ReducedResultType, Iterator, KeepFunctor, ReduceFunctor, Reducer >
,
QtConcurrent::FilterKernel< Sequence, KeepFunctor, ReduceFunctor >
,
QtConcurrent::MappedReducedKernel< ReducedResultType, Iterator, MapFunctor, ReduceFunctor, Reducer >
,
QtConcurrent::ReduceKernel< ReduceFunctor, ReduceResultType, T >
,
QtConcurrent::SequenceHolder1< Sequence, Base, Functor >
,
QtConcurrent::SequenceHolder2< Sequence, Base, Functor1, Functor2 >
,
QtConcurrent::ThreadEngineBase
forThreadFunction() :
QtConcurrent::IterateKernel< Iterator, T >
FunctionWrapper0() :
QtConcurrent::FunctionWrapper0< T >
FunctionWrapper1() :
QtConcurrent::FunctionWrapper1< T, U >
FunctionWrapper2() :
QtConcurrent::FunctionWrapper2< T, U, V >
futureInterfaceTyped() :
QtConcurrent::ThreadEngine< T >
Generated by
1.8.13


Before Change
scorer = check_scoring(estimator, scoring=scoring)
baseline_score = scorer(estimator, X, y)
scores = np.zeros((X.shape[1], n_repeats))
scores = Parallel(n_jobs=n_jobs)(delayed(_calculate_permutation_scores)(
estimator, X, y, col_idx, random_state, n_repeats, scorer
) for col_idx in range(X.shape[1]))
After Change
// the fact that variables are shared or not depending on the active
// joblib backend (sequential, thread-based or process-based).
random_state = check_random_state(random_state)
random_seed = random_state.randint(np.iinfo(np.int32).max + 1)
scorer = check_scoring(estimator, scoring=scoring)
baseline_score = scorer(estimator, X, y)


{% extends "layout.html" %} {% block title %}Club{% endblock %} {% block post_head %} {% endblock %} {% block body %}
Joining the Bay Area Paragliding Association has many benefits. Our community of avid pilots and instructors can provide mentorship and comradery at any stage of a flying career. Most importantly, joining BAPA ensures we can continue to fly some of our beautiful Bay Area sites, as dues are used for site insurance, improvement, and administration.
Dues are $30 per year. To join BAPA, simply create an account and use the paypal link in your profile.
These are the people that make BAPA happen. You too can be elected an officer by joining the club.


{% extends "officehours/base.html" %} {% load staticfiles %} {% load crispy_forms_tags %} {% block title %}Sign Up{% endblock %} {% block content %}
Complete the following form to create your account:
Sign Up
Creating an account indicates that you agree to our terms of service, privacy policy, and content license agreement.


{% extends "club/base.html" %} {% load i18n %} {% block content %}
Your password has been changed. You may go ahead and log in now.


im sorry.
run failed because cannot use javascript.
this need javascript because run on it. so please turn on it.
wiki
github


{% extends "base.html" %} {% block content %}
Med Cabinet for new cannabis consumers, especially those trying to get off of pharmaceuticals, who want to use cannabis as a means to battle medical conditions and ailments. Help patients find the right strains and intake! Use user data along with strain data to build ML models to guide recommendations.
{% endblock %}


Home
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Technical Blog Entries
1. How to use Class and ID in HTML/CSS
2. HTML/CSS, Loops, DOM and Functions
3. Testing, Repl.it, Problem Solving, Psuedocoding
Cultural Blog Entries
1. Identity, Value and Strengths
2. Learning Plan
3. Emotional Intelligence


The Resources toolbar of the TelerikEditorProvider RTE has the following tools:
| Tool | Icon | Description |
|---|---|---|
| Image Manager (CTRL + G) * | Opens the Image Manager which enables you to insert images into the Editor. The properties tab enables you to set properties of the image to be inserted. | |
| Document Manager * | Opens the Document Manager which enables you to insert a document link into the Editor. Additional link settings are available. | |
| Flash Manager * | Opens the Flash Manager which enables you to insert Flash (*.swf) into the Editor. The properties tab enables you to set properties of the flash to be inserted. | |
| Media Manager * | Opens the Media Manager which enables you to insert media into the Editor. The properties tab enables you to set properties of the media to be inserted. | |
| Template Manager * | Opens the Template Manager which enables you to insert a template (*.htmtemplate) into the Editor. | |
| Save Template | Save the content in the Editor as a new template. | |
| Hyperlink Manager (CTRL + K) * | Insert a link, anchor or email link. Additional settings are available. | |
| Remove Link (CTRL +SHIFT + K) | Removes a link from selected content. |
* These tools provide access to Digital Asset Management module that is located on the Admin > File Management page. A user's ability to create folders and upload and manage files will depend on the permissions granted to them on the Digital Asset Management module.


{% extends "base.html" %} {% import 'bootstrap/wtf.html' as wtf %} {% block app_content %}
Download this .csv file that we have made using a google forms survey
You can create your own Google form in order to ask your own questions and get data from your holiday celebration group, but you need to follow certain requirements for the algorithm to work correctly.
Here is an example of a Google Form. You can copy this one to make your own if you would like.
Then go to the responses tab on the survey, and click the Google sheets icon to view responses in sheets. This should bring you to a Google spreadsheet with the survey answers.
Next, download the Google spreadsheet as a .csv file by going to File->Download->Comma Separated Values.
Now you should have a .csv file which you can upload to this website to get your custom seating plan!
This is the exported CSV file from Google Forms


I’d love to know if your public site is using this plugin and list your logo on the documentation site. Please email me eonasdan at outlook dot com
. Do not submit issue/feature request to this email, they will be ignored.
Run the following command:
bower install eonasdan-bootstrap-datetimepicker#latest --save
Include necessary scripts and styles: ```html
```
PM> Install-Package Bootstrap.v3.Datetimepicker
PM> Install-Package Bootstrap.v3.Datetimepicker.CSS
```html
```
Add the following to your Gemfile
:
gem 'momentjs-rails', '~> 2.5.0'
gem 'bootstrap3-datetimepicker-rails', '~> 2.1.30'
Read the rest of the install instructions @
TrevorS/bootstrap3-datetimepicker-rails
Done! Now take a look at the manual for examples and available options.
Datetimepicker requires moment.js. This allows for better support for various date formats and locales. See documentation for examples. Check Momentjs’ homepage for documentation on date formats. If you can’t use moment.js there’s still older version of datetimewidget available here.
html
<script type="text/javascript" src="/path/to/moment.js"></script>
Make sure to include *.JS files for plugins collapse and transitions. They are included with bootstrap in js/ directory
html
<script type="text/javascript" src="/path/to/bootstrap/js/transition.js"></script>
<script type="text/javascript" src="/path/to/bootstrap/js/collapse.js"></script>
Alternatively you could include the whole bundle of bootstrap plugins from bootstrap.js
html
<script type="text/javascript" src="/path/to/bootstrap/dist/bootstrap.min.js"></script>
```css @import “/path/to/bootstrap/less/variables”; @import “/path/to/bootstrap-datetimepicker/src/less/bootstrap-datetimepicker”;
// […] your custom styles and variables ```
html
<link rel="stylesheet" href="/path/to/bootstrap-datetimepicker/build/css/bootstrap-datetimepicker.min.css" />
Finally include the main javascript file.
html
<script type="text/javascript" src="/path/to/bootstrap-datetimepicker.min.js"></script>
setDate
can be given null
to force clear the picker#### Important! build.less
file name has been been changed to bootstrap-datetimepicker-build.less
to prevent collisions
setStartDate
and setEndDate
should now properly set.change
event issues should be fixedFix for #112: change
function no longer sets the input to a blank value if the passed in date is invalid
z-index
of the widgetchange
event for knockout and the like as well as change.dp
which contains the old date and the new dateFix for #73: IE8 should be working now
minuteStepping
option takes a number (default is 1). Changing the minutes in the time picker will step by this number.useMinutes
and useSeconds
are now options. Disabling seconds will hide the seconds spinner. Disabling minutes will display 00
and hide the arrowsdata-OPTION
into its appropriate optionmoment
object since moment 2.4.0 has removed global referenceuseStrict
. When validating dates in update
and change
, the picker can use a stricter formatting validationinput-group-addon
is present use datepickerbutton
to identify where the picker should popup from. (#48)error.dp
. Fires when Moment cannot parse the date or when the timepicker cannot change because of a disabledDates
setting. Returns a Moment date object. The specific error can be found be using invalidAt()
. For more information see Moment’s docsA
or a
in the format string to determine if the AM/PM selector should display.change.dp
event to also include the previous date.error.dp
fires when plugin cannot parse date or when increase/descreasing hours/minutes to a disabled date. See Example#7


Sentinel Synthesizer
Oscillator 1
Waveform
Triangle
Sawtooth
Square
Range
32
16
8
4
2
Tuning
Volume
Oscillator 2
Waveform
Triangle
Sawtooth
Square
Range
32
16
8
4
2
Tuning
Volume
Oscillator 3
Waveform
Triangle
Sawtooth
Square
Range
32
16
8
4
2
Tuning
Volume
LFO
Waveform
Triangle
Sawtooth
Square
Frequency
Depth
Filter
Attack
Decay
Sustain
Release
Cutoff frequency
Emphasis
Contour
Volume Filter
Attack
Decay
Sustain
Release
Noise
Level
Output
Volume


Book-A-Meal
Home
Log-out
Orders For Today
Chicken
50 orders
Chicken
50 orders
Chicken
50 orders
Chicken
50 orders
Chicken
50 orders
Chicken
50 orders
Total amount of the day
#500,000.00


--- layout: post title: Birthday Congrats date: 2007-11-10 20:34:26.000000000 -06:00 type: post parent_id: '0' published: true password: '' status: publish categories: - English - Reflections tags: - Birthday - congratulations - europe - georeferencing - maps - plotting cities - world meta: {} author: login: dreamessenceonthemove email<EMAIL_ADDRESS>display_name: darribas first_name: Daniel last_name: Arribas-Bel permalink: "/2007/11/10/birthday-congrats/" ---
Here I post a couple of maps where I plot all the cities where I got Birthday congrats from last wed. For all those who spent a couple of minutes in remembering me, feel remembered when looking at them ;) Thanks a lot to all of you.


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Ajab Shahar is a wondrous city of songs, poems, images & conversations from Bhakti, Sufi & Baul oral traditions from India & around.
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