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- Field name
- The name of this field in the table.
- Enumerated values (for enum and set types)
- A list of possible values for this field.
- Width and decimals (for float and decimal types)
- The total width of this field, and the number of digits to the right
of the decimal point.
- Type width (for other types)
- The maximum number of characters allowed for data in this field.
- Type options
- For numeric fields, this parameter allows you to choose whether the
field is unsigned or left-padded with zeros. For character fields, you
can choose whether case is taken into account when sorting or not.
- Allow nulls?
- Determines whether NULL values are allowed in this field
- Default value
- The default value for this field in new records


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| PERLFAQ9(1) | Perl Programmers Reference Guide | PERLFAQ9(1) |
If there is no interactivity then you may still want to look at using something like Template Toolkit <https://metacpan.org/module/Template> or Plack::Middleware::TemplateToolkit so maintenance of your HTML files (and other assets) is easier.
Below is a list of a few frameworks with comments which might help you in making a decision, depending on your specific requirements. Start by reading the docs, then ask questions on the relevant mailing list or IRC channel.
All of these interact with or use Plack which is worth understanding the basics of when building a website in Perl (there is a lot of useful Plack::Middleware <https://metacpan.org/search?q=plack%3A%3Amiddleware>).
Plack is a set of tools for using the PSGI stack. It contains middleware <https://metacpan.org/search?q=plack%3A%3Amiddleware> components, a reference server and utilities for Web application frameworks. Plack is like Ruby's Rack or Python's Paste for WSGI.
You could build a web site using Plack and your own code, but for anything other than a very basic web site, using a web framework (that uses Plack) is a better option.
You can use URI::Find to extract URLs from an arbitrary text document.
Use the libwww-perl distribution. The LWP::Simple module can fetch web resources and give their content back to you as a string:
use LWP::Simple qw(get); my $html = get( "http://www.example.com/index.html" );
It can also store the resource directly in a file:
use LWP::Simple qw(getstore); getstore( "http://www.example.com/index.html", "foo.html" );
If you need to do something more complicated, you can use LWP::UserAgent module to create your own user-agent (e.g. browser) to get the job done. If you want to simulate an interactive web browser, you can use the WWW::Mechanize module.
If you're submitting values using the GET method, create a URL and encode the form using the "query_form" method:
use LWP::Simple; use URI::URL; my $url = url('L<http://www.perl.com/cgi-bin/cpan_mod')>; $url->query_form(module => 'DB_File', readme => 1); $content = get($url);
If you're using the POST method, create your own user agent and encode the content appropriately.
use HTTP::Request::Common qw(POST); use LWP::UserAgent; my $ua = LWP::UserAgent->new(); my $req = POST 'L<http://www.perl.com/cgi-bin/cpan_mod'>, [ module => 'DB_File', readme => 1 ]; my $content = $ua->request($req)->as_string;
To encode a string yourself, use the URI::Escape module. The "uri_escape" function returns the escaped string:
my $original = "Colon : Hash # Percent %"; my $escaped = uri_escape( $original ); print "$escaped\n"; # 'Colon%20%3A%20Hash%20%23%20Percent%20%25'
To decode the string, use the "uri_unescape" function:
my $unescaped = uri_unescape( $escaped ); print $unescaped; # back to original
Remember not to encode a full URI, you need to escape each component separately and then join them together.
$c->res->redirect($url); $c->detach();
If you are using Plack (which most frameworks do), then Plack::Middleware::Rewrite is worth looking at if you are migrating from Apache or have URL's you want to always redirect.
Alternativly look at Plack::Middleware::Auth::Basic, or one of the other Plack authentication <https://metacpan.org/search?q=plack+auth> options.
You can't prevent people from sending your script bad data. Even if you add some client-side checks, people may disable them or bypass them completely. For instance, someone might use a module such as LWP to submit to your web site. If you want to prevent data that try to use SQL injection or other sorts of attacks (and you should want to), you have to not trust any data that enter your program.
The perlsec documentation has general advice about data security. If you are using the DBI module, use placeholder to fill in data. If you are running external programs with "system" or "exec", use the list forms. There are many other precautions that you should take, too many to list here, and most of them fall under the category of not using any data that you don't intend to use. Trust no one.
use Email::MIME; my $message = Email::MIME->new($rfc2822); my $subject = $message->header('Subject'); my $from = $message->header('From');
If you've already got some other kind of email object, consider passing it to Email::Abstract and then using its cast method to get an Email::MIME object:
my $abstract = Email::Abstract->new($mail_message_object); my $email_mime_object = $abstract->cast('Email::MIME');
This isn't as simple a question as it sounds. There are two parts:
a) How do I verify that an email address is correctly formatted?
b) How do I verify that an email address targets a valid recipient?
Without sending mail to the address and seeing whether there's a human on the other end to answer you, you cannot fully answer part b, but the Email::Valid module will do both part a and part b as far as you can in real-time.
Our best advice for verifying a person's mail address is to have them enter their address twice, just as you normally do to change a password. This usually weeds out typos. If both versions match, send mail to that address with a personal message. If you get the message back and they've followed your directions, you can be reasonably assured that it's real.
A related strategy that's less open to forgery is to give them a PIN (personal ID number). Record the address and PIN (best that it be a random one) for later processing. In the mail you send, include a link to your site with the PIN included. If the mail bounces, you know it's not valid. If they don't click on the link, either they forged the address or (assuming they got the message) following through wasn't important so you don't need to worry about it.
use MIME::Base64; my $decoded = decode_base64($encoded);
The Email::MIME module can decode base 64-encoded email message parts transparently so the developer doesn't need to worry about it.
The exception is for organization-specific email (e.g<EMAIL_ADDRESS>where policy can be codified in your program. In that case, you could look at $ENV{USER}, $ENV{LOGNAME}, and getpwuid($<) in scalar context, like so:
my $user_name = getpwuid($<)
But you still cannot make assumptions about whether this is correct, unless your policy says it is. You really are best off asking the user.
# first, create your message my $message = Email::MIME->create( header_str => [ From =><EMAIL_ADDRESS>To =><EMAIL_ADDRESS>Subject => 'Happy birthday!', ], attributes => { encoding => 'quoted-printable', charset => 'utf-8', }, body_str => "Happy birthday to you!\n", ); use Email::Sender::Simple qw(sendmail); sendmail($message);
By default, Email::Sender::Simple will try `sendmail` first, if it exists in your $PATH. This generally isn't the case. If there's a remote mail server you use to send mail, consider investigating one of the Transport classes. At time of writing, the available transports include:
Telling Email::Sender::Simple to use your transport is straightforward.
sendmail( $message, { transport => $email_sender_transport_object, } );
use Email::Folder; my $folder = Email::Folder->new('/path/to/email/folder'); while(my $message = $folder->next_message) { # next_message returns Email::Simple objects, but we want # Email::MIME objects as they're more robust my $mime = Email::MIME->new($message->as_string); }
There are different classes in the Email::Folder namespace for supporting various mailbox types. Note that these modules are generally rather limited and only support reading rather than writing.
The Net::Domain module, which is part of the Standard Library starting in Perl 5.7.3, can get you the fully qualified domain name (FQDN), the host name, or the domain name.
use Net::Domain qw(hostname hostfqdn hostdomain); my $host = hostfqdn();
The Sys::Hostname module, part of the Standard Library, can also get the hostname:
use Sys::Hostname; $host = hostname();
The Sys::Hostname::Long module takes a different approach and tries harder to return the fully qualified hostname:
use Sys::Hostname::Long 'hostname_long'; my $hostname = hostname_long();
To get the IP address, you can use the "gethostbyname" built-in function to turn the name into a number. To turn that number into the dotted octet form (a.b.c.d) that most people expect, use the "inet_ntoa" function from the Socket module, which also comes with perl.
use Socket; my $address = inet_ntoa( scalar gethostbyname( $host || 'localhost' ) );
This documentation is free; you can redistribute it and/or modify it under the same terms as Perl itself.
Irrespective of its distribution, all code examples in this file are hereby placed into the public domain. You are permitted and encouraged to use this code in your own programs for fun or for profit as you see fit. A simple comment in the code giving credit would be courteous but is not required.
| 2019-10-21 | perl v5.30.3 |


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addButton(self,
label,
cmd=None,
refitFrame=True,
weight=1,
expand=True,
tooltip=None,
iniFcn=None)
| source code
|
if label is of form "X\t...":
if X is 'b' a button will be created (that's also the default)
cmd will be executed on button-press
if cmd is None:
cmd = label
if X is 'tb' a toggle-button will be created
cmd will be executed on button-press
(variable 'x' will contain the button status)
if cmd is None:
cmd = label
if X is 't' a TextCtrl field will be created
cmd will be executed on EVERY text change
(variable 'x' will contain the text as string)
unless if cmd[:2] == '--':
this means: text field is NOT editable
...-part of label is the default value put in the text-field
#todo if ...-part of label is of form '... ...':
#todo the first part will be used as a "label" for the text field
#todo the second part will be the default text
if X is 'c' a CheckBox field will be created
cmd will be executed on on click
(variable 'x' will contain True/False [isChecked])
...-part of label is a text label
'CheckBox' will be left-of 'text label'
except if ...-part contains ' ', then
'CheckBox' will be right-of 'text label'
if X is 'r' a RadioButton field will be created
<...>
if X is 'l' a StaticText label will be created
label will be aligned to 'right'
cmd will never be executed
if X contains a ' ' the part after ' ' will be executed with 'x' being the control-object
use this to keep a reference to the respective wxControl
example: "b myButton=x"
if weight is 0 button gets set to minimum size (horizontally)
otherwise size gets distributed between all buttons
if expand is True button expands in vertical direction with buttonBox
if tooltip is None:
set tooltip to be the cmd-string, or another expaining string
else:
set tooltip as given
iniFcn (string): exec like the ' '-syntax in label
(callable): call with (execModule, button) as arguments
NOTE:
all execs are done in a given `execModule` as globals()
to modify the module"s namespace the module is accessible as '_'
e.g.: _.x = x
_.myTextControl = x
(in cmd for buttons there is no 'g' - just use names directly instead: e.g. x = 5)
`cmd` (`command`) will get exec'ed when the button is pressed, or the text is changed
if will get exec'ed with globals been the buttonbox's execModule
and locals as follows:
`x`: the control's value
`_`: the execModule -- so that you can write "_.x = x", instead of "globals()[x] = x"
`_ev`: the wx-event (wxButtonEvent, ....)
`_b`: the wxControl (the buttonbox's "button", or now more general, it's "gui-control") object
( same as _ev.GetEventObject() )
OR if cmd is a callable: cmd(execModule, ButtonValue, buttonObj, WxEvt) is called
|


Markdown is a plain text formatting syntax for writing HTML. It was developed by John Gruber and Aaron Swartz in 2004 and allows people to write using an easy-to-read, easy-to-write plain text format that is convertible to HTML.
This tutorial will guide you through the basics of using Markdown and provide some advanced tips for power users.


Jubilee Los Lunas is New Mexico's premier Active Adult community. Nestled at the foot of El Cerro, this community is just 20 minutes from downtown Albuquerque and the airport. Anticipate all the exciting new choices at Jubilee, including the brand new swimming pool, community garden, observatory, tennis courts, pickle ball courts and shading for our bocce ball courts. Live life to the fullest, from sunrise to sunset, day after day at Jubilee. Enjoy wonderful new friends, a beautiful home and magnificent Villa, amazing scenery and more choices than you've ever experienced before. It's all here for you at Jubilee. Enjoy the many activites that bring you together with your friends. Zumba, Yoga, book club, backyard barbecues, wine and cheese parties, morning coffee, bike rides, dog walks, cooking classes, stained glass classes, jewlry classes, movie nights, drinks on the patio, day trips and more.
Our project managers offer expertise during all phases of your project to effectively and creatively achieve your objectives.
First Capital Builders provides comprehensive project/construction management services to owners and users on a local, regional and national basis. First Capital Builders is committed to providing project management services of the highest professional standards to owners of, and investors in, commercial, residential and investment properties.
We are experienced construction professionals, who operate within a formal control system, offer assistance in budget preparations, design review, construction techniques, regulatory codes and other areas.
Project/Construction Management services include pre-construction services, not limited to design professionals, validating constructionability, budget estimates, value engineering, development of schedules, contract bid analysis, payment management, including review and approval, owner representation, addressing contract administration, craft and tenant coordination, and conflict resolution.


{# # Homepage template # ----------------- # # This template gets loaded whenever the site's homepage is requested, because # the "Homepage" single-entry section's Template setting is set to "index". # # An `entry` variable will be automatically passed to this template, which will # be set to the Homepage entry. -#} {% extends "_layouts/site" %} {% block main %}
At HomeSource Partners, we are the leading source for buying and selling homes in Colorado Springs & Manitou Springs and Fountain. We do more than just buy and sell your homes & fixer uppers, we can fulfill every real estate needs you may have including relieving you of distressed property by giving you fast cash for your home.
At HomeSource Partners, we buy ugly homes and distressed properties for top dollar amounts. Regardless of the type of home whether single family, duplex or old construction, we are able to buy ugly houses, and even houses that have been abandoned for some time. If you have inherited a house that you don’t want to maintain, we can take it off or hands for the highest cash price. One of the added advantages to using HomeSource Partners to get fast cash for your home and equity is that there is no additional cost to use our services.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer nec odio.


{% extends 'emails/email_base.html' %} {% load i18n %} {% autoescape off %} {% block header_image %} {{ protocol }}://{{ domain }}{{ site.config.email_header.url }} {% endblock %} {% block header_alt %} {{ site.name }} Email Header {% endblock %} {% block intro %} {% blocktrans with name=user.full_name trimmed %}
Hi {{ name }},
{% endblocktrans %} {% endblock %} {% block email_content %}{% blocktrans with site=site.name trimmed %} You have been sent this email because you (or someone pretending to be you) made a request to reset your password at {{ site }}. {% endblocktrans %}
{% trans "If you did not make this request, or you do not wish to change your current password, you may safely ignore this email." %}
{% trans "If you wish to reset your current password, please visit the following page" %}:
{{ protocol }}://{{ domain }}{% url 'accounts:password-reset-confirm' uidb64=uid token=token %}
{% endblock %} {% block signoff %} {% blocktrans with site=site.name trimmed %}
Thanks,
The {{ site }} team


Where is Euro 2020 being held?
This time round the competition will be very different
by Elliott JacksonEuro 2020 is one step closer with the group stage of qualification coming to an end on Tuesday night.
Although there is the small matter of the Nations League play-offs to sort in March next year, fans will be busy planning their journeys to support their national sides.
This time round the tournament has a very different format, so supporters should be aware of big changes from the last competition held in France in 2016.
So, how will it all work? Here are all the key details you need to know.
Who is hosting EURO2020?
For the first time in the competition's history, the Euros will be held across 12 different cities, in 12 different countries across Europe.
Of the 12 venues, all will stage at least three group stage games and a knock-out round tie each. The breakdown of each venue and games can be seen in the table below.
Group stage & Round of 16 - Denmark (Copenhagen) - Parken Stadium
Group stage & Round of 16 - Hungary (Budapest) - Ferenc Puskas Stadium
Group stage & Round of 16 - Ireland (Dublin) - Aviva Stadium
Group stage & Round of 16 Netherlands (Amsterdam) - Johann Cruijff Arena
Group stage & Round of 16 - Romania (Bucharest) - Arena National
Group stage & Round of 16 - Scotland (Glasgow) - Hampden Park
Group stage & Round of 16 - Spain (Bilbao) - San Mames Stadium
Group stage & Quarter-final - Azerbaijan (Baku) - National Stadium
Group stage & Quarter-final - Germany (Munich) - Allianz Arena
Group stage & Quarter-final - Italy (Rome) - Stadio Olimpico
Group stage & Quarter-final - Russia (Saint Petersburg) - Krestovsky Stadium
Group stage, Round of 16, Semi-finals & Final - England (London) - Wembley Stadium
Why is EURO2020 being held across 12 venues?
The decision to change the format of EURO2020 to include several cities, rather than one or two host capitals was decided by UEFA in 2012.
The change is to honour the tournament's 60-year anniversary with FIFA President Gianni Infantino explaining that "instead of having a party in one country, we will have a party all over Europe in the summer of 2020."
Whilst it's unclear if this is going to be a regular format, Gianni did imply this one would be a one-off change, although who knows what the future may hold.


Handwritten Number Reader
This application translates given one digit image to a corresponding number
Git Hub repository :
mnist-mlp-app
Example
Sample hand written number image
Upload
Source number image :
Run


Company profile - Canadian Company Capabilities
Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
HFP Acoustical Consultants Corp.
Legal/Operating Name:
HFP Acoustical Consultants Corp.
http://www.slrconsulting.com/technical-disciplines/acoustics-vibration
Mailing Address:
1185-10201 Southport Rd SWCALGARY, Alberta
T2W 4X9
Location Address:
1185-10201 Southport Rd SWCALGARY, Alberta
T2W 4X9
Tel.:<PHONE_NUMBER>
Tel.:<PHONE_NUMBER>
Fax:<PHONE_NUMBER>
Company Profile
SLR provides NOISE CONTROL CONSULTING SERVICES including: community noise monitoring, environmental noise impact assessments, noise control engineering, computer noise modeling, and architectural acoustical design.
Full profile
Full profile
Contact information
Company description
541690 - Other Scientific and Technical Consulting Services
Product / Service / Licensing
B109-Other Environmental Studies
B219-Other Engineering Studies
E000-Environmental Services
E199-Other Environmental Services
R008-Engineering and Technical Services (incl. Mechanical, Electrical, Chemical, Electronic Engineering)
R019-Other Professional Services
Technology profile
Market profile
Alliances:
- Sales/Marketing
- Technology
Industry sector market interests:
- Construction
- Environment
- Manufacturing
- Mining/Petroleum/Gas
- Service Industry
- Transportation
- Food and Beverage Manufacturing
- Pulp and Paper
- Plastics and Rubber Products
Geographic markets:
Export experience:
- Argentina
- Australia
- Belgium
- Brazil
- Chile
- China
- Denmark
- Finland
- France
- Germany
- Greece
- Iceland
- Israel
- Italy
- Mexico
- Netherlands
- New Zealand
- Nigeria
- Norway
- Portugal
- Puerto Rico
- Qatar
- Saudi Arabia
- Spain
- Sweden
- Switzerland
- Taiwan
- Trinidad and Tobago
- United Kingdom
- United States
- United States Minor Outlying Islands
- Venezuela, Bolivarian Republic of
- Alaska
- Arizona
- Arkansas
- California
- Colorado
- Florida
- Hawaii
- Idaho
- Illinois
- Kansas
- Louisiana
- Michigan
- Minnesota
- Mississippi
- Montana
- Nevada
- New Mexico
- Ohio
- Oklahoma
- Oregon
- Pennsylvania
- Tennessee
- Texas
- Utah
- Washington
- Wyoming
Sector information
Key / Major clients:
GAS PROCESSING & OIL REFINING:AEC Oil & Gas - Air Liquide - Alberta Natural Gas - Amoco - Atlantic LNG - BOC Gases - BP - Canterra Energy - Chevron - Dome Petroleum - EnCana - Esso Resources Canada - ExxonMobil - Gulf Canada - Halliburton KBR - Husky Oil - Imperial Oil Resources - Kinder Morgan - Mobil Oil - PanCanadian Petroleum - Petro-Canada - Shell - Talisman Energy - Texaco - TotalFinaElf
GAS TRANSMISSION & OIL PIPELINES:
Alliance Pipeline - ANR Pipeline - B.C. Gas - Canadian Western Natural Gas - Canterra Energy - Duke Energy - Dynegy - East Tennessee Natural Gas - El Paso - Equitable Resources - Express Pipeline - Foothills Pipe Lines - Florida Gas Transmission - Great Lakes Gas Transmission - Kinder Morgan - Lakehead Pipeline - NGPL - NOVA - PanEnergy - PGT - Pipeline Research Committee International - American Gas Association - Texas Eastern Transmission - TransCanada Pipelines - Union Gas - Vector Pipeline - Westcoast Energy - Western Gas - Xcel Energy
PETROCHEMICAL:
B.F. Goodrich - BP Chemical - Dow Chemical - NOVA Chemicals - Petrox - Shell Chemicals - Texas Petrochemicals - Union Carbide
POWER GENERATION:
Alberta Power - Calpine - Dynegy - Electronorte - El Paso - Enogex - EPCOR - Indeck Energy - Koch Power - Manitoba Hydro - PetroPower - SaskPower - TransAlta Utilities - Wind Power
MINING, MANUFACTURING, OILFIELD SERVICE & OTHERS:
Agrium - ATCO Noise Management - Cooper Energy - CP Rail - Fording Coal - Halliburton KBR - Hudsons Bay Mining - Ingersoll Rand - Lafarge - Luscar - Mafi Trench - Schlumberger - Smoky River Coal - Suncor - Syncrude Canada - United Steel Structures - Weyerhaeuser - World Bank
TRANSPORTATION, ENVIRONMENTAL & ENGINEERING:
AMEC Earth & Environmental - Bantrel - Bechtel - Colt Engineering - Delta Hudson Engineering - Ecology and Environment - Fluor Daniel - Golder Associates - ISL - Jacobs Engineering - Jacques Whitford Environment - MWH Global - Reid Crowther - SNC Lavalin - Stantec - Stewart & Stevenson - Stone & Webster - TERA Environmental - UMA Engineering - WCM Group
Success stories:
Distinguished clientele include the Pipeline Research Committee International of the American Gas Association, as well as the Ministry of Railways in the Peoples Republic of China through the World Bank.Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
Contacts
Contact information
Description
Company description
541690 - Other Scientific and Technical Consulting Services
Products, services and licensing
Product / Service / Licensing
B109-Other Environmental Studies
B219-Other Engineering Studies
E000-Environmental Services
E199-Other Environmental Services
R008-Engineering and Technical Services (incl. Mechanical, Electrical, Chemical, Electronic Engineering)
R019-Other Professional Services
Technology
Technology profile
Market
Market profile
Alliances:
- Sales/Marketing
- Technology
Industry sector market interests:
- Construction
- Environment
- Manufacturing
- Mining/Petroleum/Gas
- Service Industry
- Transportation
- Food and Beverage Manufacturing
- Pulp and Paper
- Plastics and Rubber Products
Geographic markets:
Export experience:
- Argentina
- Australia
- Belgium
- Brazil
- Chile
- China
- Denmark
- Finland
- France
- Germany
- Greece
- Iceland
- Israel
- Italy
- Mexico
- Netherlands
- New Zealand
- Nigeria
- Norway
- Portugal
- Puerto Rico
- Qatar
- Saudi Arabia
- Spain
- Sweden
- Switzerland
- Taiwan
- Trinidad and Tobago
- United Kingdom
- United States
- United States Minor Outlying Islands
- Venezuela, Bolivarian Republic of
- Alaska
- Arizona
- Arkansas
- California
- Colorado
- Florida
- Hawaii
- Idaho
- Illinois
- Kansas
- Louisiana
- Michigan
- Minnesota
- Mississippi
- Montana
- Nevada
- New Mexico
- Ohio
- Oklahoma
- Oregon
- Pennsylvania
- Tennessee
- Texas
- Utah
- Washington
- Wyoming
Sector
Sector information
Key / Major clients:
GAS PROCESSING & OIL REFINING:AEC Oil & Gas - Air Liquide - Alberta Natural Gas - Amoco - Atlantic LNG - BOC Gases - BP - Canterra Energy - Chevron - Dome Petroleum - EnCana - Esso Resources Canada - ExxonMobil - Gulf Canada - Halliburton KBR - Husky Oil - Imperial Oil Resources - Kinder Morgan - Mobil Oil - PanCanadian Petroleum - Petro-Canada - Shell - Talisman Energy - Texaco - TotalFinaElf
GAS TRANSMISSION & OIL PIPELINES:
Alliance Pipeline - ANR Pipeline - B.C. Gas - Canadian Western Natural Gas - Canterra Energy - Duke Energy - Dynegy - East Tennessee Natural Gas - El Paso - Equitable Resources - Express Pipeline - Foothills Pipe Lines - Florida Gas Transmission - Great Lakes Gas Transmission - Kinder Morgan - Lakehead Pipeline - NGPL - NOVA - PanEnergy - PGT - Pipeline Research Committee International - American Gas Association - Texas Eastern Transmission - TransCanada Pipelines - Union Gas - Vector Pipeline - Westcoast Energy - Western Gas - Xcel Energy
PETROCHEMICAL:
B.F. Goodrich - BP Chemical - Dow Chemical - NOVA Chemicals - Petrox - Shell Chemicals - Texas Petrochemicals - Union Carbide
POWER GENERATION:
Alberta Power - Calpine - Dynegy - Electronorte - El Paso - Enogex - EPCOR - Indeck Energy - Koch Power - Manitoba Hydro - PetroPower - SaskPower - TransAlta Utilities - Wind Power
MINING, MANUFACTURING, OILFIELD SERVICE & OTHERS:
Agrium - ATCO Noise Management - Cooper Energy - CP Rail - Fording Coal - Halliburton KBR - Hudsons Bay Mining - Ingersoll Rand - Lafarge - Luscar - Mafi Trench - Schlumberger - Smoky River Coal - Suncor - Syncrude Canada - United Steel Structures - Weyerhaeuser - World Bank
TRANSPORTATION, ENVIRONMENTAL & ENGINEERING:
AMEC Earth & Environmental - Bantrel - Bechtel - Colt Engineering - Delta Hudson Engineering - Ecology and Environment - Fluor Daniel - Golder Associates - ISL - Jacobs Engineering - Jacques Whitford Environment - MWH Global - Reid Crowther - SNC Lavalin - Stantec - Stewart & Stevenson - Stone & Webster - TERA Environmental - UMA Engineering - WCM Group


ensembleMetadata.latlon
Return latitude-longitude coordinates for state vector elements
Syntax
Description
coordinates = obj.latlon
Returns a latitude-longitude coordinate for each row of a state vector. The output is a matrix with two columns - the first column holds the latitude coordinate for each state vector row, and the second holds the longitude coordinate. This method is a convenience function intended to facilitate covariance localization. It prioritizes returning outputs suitable for covariance localization over strictly reporting metadata. Consider the “ensembleMetadata.rows” method for more rigorous metadata, or if covariance localization is not your intent.
This method is designed for the case where latitude-longitude metadata is stored along the “lat”, “lon”, and/or “site” dimensions. Coordinates that are not well-described by numeric scalars are returned as NaN.
The method acts by obtaining “lat” and “lon” metadata for each state vector element. To return a non-NaN coordinate for a state vector element, the “lat” and “lon” metadata for the element must be numeric scalars, string scalars, or char row vectors. In the case of string scalars or char rows vectors, the strings are converted to numeric coordinates using Matlab’s “str2double” function. If the state vector element does not have “lat” or “lon” metadata, the metadata is not one of the described formats, or the metadata cannot be converted to numeric coordinates, then the coordinates for the state vector element are returned as NaN. If a state vector element implements a mean over the “lat” or “lon” dimension, then the coordinate for the dimension is also set to NaN.
coordinates = obj.latlon(siteColumns)
This syntax allows the method to also extract coordinates that are stored along the “site” dimension. The input is a row vector with two elements. The two elements indicate which columns of the “site” metadata should be used for latitude and longitude coordinates.
Note that this syntax prioritizes “lat” and “lon” metadata over “site” metadata. The metadata will only extract coordinates from the “site” dimension when a state vector element has neither “lat” nor “lon” as a state dimension. If the element also does not have “site” metadata, the “site” metadata does not have the specified columns, the “site” metadata cannot be converted to numeric coordinates, or there is a mean over the “site” dimension, then the latitude-longitude coordinate is returned as NaN.
Indicate which columns of “site” metadata to use for specific variables. If the first input has a single row, then uses the same columns for all listed variables. Otherwise, the first input should have one row per listed variable.
This syntax prioritizes “site” metadata for the listed variables. For these variables, the method will only extract coordinates from “site” metadata and will ignore “lat” and “lon” metadata even if the state vector element does not have “site” as a state dimension. For all other variables, the method will only extract coordinates from “lat” and “lon”, even if the other variables have “site” as a state dimension.
Input Arguments
siteColumns
If site variables are also listed, siteColumns may either be a vector or a matrix. If a matrix, must have one row per listed variable and two columns. The first column lists latitude columns in the site metadata, and the second column lists longitude columns for each variable.


--- layout: post title: Catching up with Finances author: josh tags: - budget - Christmas - finances - procrastination - wallet ---
So tonight was a busy night, I spent pretty much my entire night catching up on getting our finances in order. (Okay, I did manage to have some time playing Legos with my 11 month old and dinner with my family.)
It's amazing how difficult it is to keep track of everything when you use the credit/debit card instead of just cash, but it made things easier when we were shopping. (Don't tell Dave Ramsey I said that.) I had everything caught up two weeks before Christmas, but hadn't gotten a chance to enter data into Money until tonight. I didn't realize I was that far behind until I looked at the pile of credit card receipts from various merchants who were willing to help me lighten my wallet a little bit. In a way, it did kind of the opposite, because I had like 20 receipts (no exaggeration) in my wallet. That made for a pretty fat wallet and very uncomfortable to sit on.
Needless to say, my wallet is once again skinny and light, and I finally feel caught up on managing our personal finances.
Note to self: try not to get so far behind next time.


Go to Parents' Site
Go to Students' Site
Flashcards
Vocabulary Lists
Kindergarten - Front
Kindergarten - Back
First Grade - Front
First Grade - Back
Second - Front
Second - Back
Third Grade - Front
Third Grade - Back
Kindergarten
First Grade
Second Grade
Third Grade
Kindergarten - Alpha Sort
First Grade - Alpha Sort
Second Grade - Alpha Sort
Third Grade - Alpha Sort
All Grades - Alpha Sort


[1]
cool orange light, antivirus software; none really
[2]
Excellent software, extremley portable; none so far


Download this sqlite file into ~/Downloads
(required by the vignette single_cell_analysis
).
Install the cytominergallery
package from GitHub:
# install.packages("devtools") devtools::install_github("cytomining/cytominergallery", dependencies = TRUE, build_vignettes = TRUE)
You may need to do run that again in order to build the vignettes correctly (seems like a bug in install_github
):
devtools::install_github("cytomining/cytominergallery", dependencies = TRUE, build_vignettes = TRUE, force = TRUE)
Occasionally, the Suggests
dependencies may not get installed, depending on your system, so you’d need to install those explicitly.
Browse vignettes (launches in default browser):
Search for “Vignettes in package cytominergallery” and click on the link HTML
to view the vignettes.


Samuel Esquibel
Samuel is a husband and father of two beautiful boys. He is based in Sacramento, California.
He got into coding later in life, at 34, after many different careers leading up to this point.
In his previous life he was a soldier, police officer, and food truck entrepreneur.
But that all
led to him finding his true passion: designing and building beautiful websites and apps.


Aika is an artificial neural network designed specifically for the processing of natural language texts. A key feature of the Aika algorithm is the ability to evaluate and process various interpretations of the individual sections of a text. Aika combines several ideas and approaches from the field of AI such as artificial neural networks, frequent pattern mining and logic based expert systems. It can be applied to a broad spectrum of text analysis task such as word sense disambiguation, entity resolution, named entity recognition, text classification and information extraction.
Aika allows to model linguistic concepts like words, word meanings (entities), categories (e.g. person name, city), grammatical word types and so on as neurons in the neural network. By choosing appropriate synapse weights, these neurons can take on different functions within the network. For instance neurons whose synapse weights are chosen to mimic a logical AND can be used to match an exact phrase. On the other hand neurons with an OR characteristic can be used to connect large lists of word entity neurons to determine a category like 'city' or 'profession'.
Aika is able to generate multiple mutually exclusive interpretations of a word, phrase, or sentence, and select the most likely interpretation. For example a neuron representing a specific meaning of a given word can be linked through a negatively weighted synapse to a neuron representing an alternative meaning of this word. In this case these neurons would exclude each other. Such synapses might even be cyclic. Aika can resolve such recurrent feedback links by making tentative assumptions and performing a search for the highest ranking interpretation.
In contrast to conventional neural networks, Aika propagates activation objects through its network, not just activation values. These activation objects refer to a specific text segment and a given interpretation.
Aika consists of two layers. The neural layer, containing all the neurons and continuously weighted synapses and underneath that the discrete logic layer, containing a boolean representation of all the neurons. The logic layer uses a frequent pattern lattice to efficiently store the individual logic nodes. This architecture allows Aika to process extremely large networks in which only neurons that are activated by a logic node need to compute their weighted sum and their activation value. This means that the fast majority of neurons stays inactive during the processing of a given text.
To prevent that the whole network needs to stay in memory during processing, Aika uses the provider pattern to suspend individual neurons or logic nodes to an external storage like a mongo database.
One of the first use cases implemented with Aika is named entity recognition (NER). For this case, lists of forenames, surnames, cities, professions, company names and common words have been translated into a weighted neural network. These lists are partially overlapping because many surnames are also city names, professions or common words. In addition some common knowledge rules were added, such as forenames often precede surnames or articles rarely precede city names in German (e.g. 'die Halle' does not mean the city Halle, but the noun 'Halle'). These rules can arbitrarily depend on each other and thus form a network through which activations can be propagated. Aika is able to accurately recognize named entities within texts and predict the correct meaning of the name.
Aika is implemented in Java and made available as an open source free software library under the Apache 2 license.


 # Generalized Forward Sufficient Dimension Reduction for Categorical and Ordinal Responses
 ## Harris Quach (joint work with Dr. Bing Li)
 Date: 2021-03-05 --- class: inverse, middle  # .bg-text.center[Overview] .md-text[ 1. We show ordinal variables are linear exponential families 2. We generalize a popular SDR method to linear exponential families 3. We proposed a K-means tuning procedure ] --- count: false class: top, left # Outline 1. Multivariate Categorical and Ordinal Link functions for GLMs - Categorical Variable - Ordinal-Categorical Variable 2. Motivation - Inverse Regression for SDR - Forward Regression for SDR 3. Outer Product of Canonical Gradients (OPCG) 4. Tuning the bandwidth via K-means 5. Simulations and Data Analysis - Simulations - Ordinal Data - Categorical Data --- class: inverse, center, middle  # .bg-text[Multivariate Link functions] --- class: left, top # Multivariate Links for Linear Exponential Families The crux of our proposed method is fitting multivariate Generalized Linear Model to categorical or ordinal response variables. We will see that Categorical and Ordinal responses, `\(Y\)`, are linear exponential families. - Linear exponential families have log-likelihood of the form `\begin{align*} \ell(\theta;y) = \theta^\top Y - b(\theta); \end{align*}` - They are characterized by their means `\(\mu\)`, through the canonical parameter `\(\theta\)`, - The canonical link function `\(\theta( \cdot ): \mu \mapsto \theta\)`; the inverse canonical link `\(\mu(\cdot): \theta \mapsto \mu\)` --- class: left, top # Multivariate Links for Linear Exponential Families - Inverse canonical link is useful for - determining the function `\(b(\cdot)\)` that specifies the linear exponential family - evaluating the Mean and Variance as functions of the canonical parameter - estimate parameters for alternative link functions - Canonical link is useful for constructing starting values for optimization algorithms via the projection `\begin{align*} X(X X^\top)^{-1} X \theta(Y) \end{align*}` --- class: left, top # Categorical Response Suppose `\(Y \in \{1,...,m\}\)` is a categorical variable for `\(m\)` nominal categories. - We can represent `\(Y\)` as a vector `\(S = (S^1,...,S^{m-1}) \in \{0,1\}^{m-1}\)` - we set `\(S^m = 1 - \sum_{j=1}^{m-1} S^m\)`. - If `\(Y=k\)`, then `\(S^k = 1\)` and `\(S^j=0\)` for `\(j \neq k\)`. - Eg. `\(m=3\)`; if `\(Y = 2\)`, then `\(S = (0,1)\)`; if `\(Y=3\)`, then `\(S=(0,0)\)`. Let `\(p = (p^1,...p^{m-1})\)` be the `\(m-1\)` probabilities for each category. Then `\begin{align*} E(S) = p , \quad Var(S) = Diag(p) - p p^\top %\left [ Diag \left ( \frac{ e^{\theta } }{ 1 + \boldsymbol 1^{\top}e^{\theta } } \right ) - \frac{ e^{\theta } (e^{\theta } )^{\top} }{ [1 + \boldsymbol 1^{\top}e^{\theta} ]^2 } \right ] , \end{align*}` --- count: false class: left, top # Categorical Response Suppose `\(Y \in \{1,...,m\}\)` is a categorical variable for `\(m\)` nominal categories. - We can represent `\(Y\)` as a vector `\(S = (S^1,...,S^{m-1}) \in \{0,1\}^{m-1}\)` - The canonical link of and its inverse is : `\begin{align*} \theta(p) = \log \frac{ p }{ 1 - \boldsymbol{1}^{\top} p } \qquad p(\theta) = \frac { \exp(\theta) } { 1 - \boldsymbol{1}^{\top} \exp( \theta) } \end{align*}` The multinomial density and log-likelihood of `\(S\)` are `\begin{align*} f(S;p) \propto \prod_{j=1}^{m-1} (p^j)^{S^j}, \quad \ell(\theta;S) = \theta^{\top}S - \log ( 1 - \boldsymbol 1^{\top} e^\theta ) , \end{align*}` where `\(b(\theta) = \log( 1 - \boldsymbol{1}^{\top} e^{ \theta } )\)`. --- class: left, top # Ordinal Response Suppose `\(Y \in \{1,...,m\}\)` is an ordinal-categorical variable for `\(m\)` ordered categories. - We can represent `\(Y\)` as a vector `\(S = (S^1,...S^{m-1}) \in \{0,1\}^{m}\)` as for categorical `\(Y\)`. - We can represent `\(S\)` as a vector `\(T = (T^1,...T^{m-1}) \in \{0,1\}^{m-1}\)`, and we set `\(T^{m} = 0\)` and `\(T^0=1\)`. - We can interpret `\(T^j = I\{Y > j\}\)` - If `\(Y = k\)`, then `\(T^j = 1\)` for `\(j \leq {k-1}\)` and `\(T^j=0\)` for `\(j > k-1\)`. - Eg. `\(m=5\)`; if `\(Y=3\)`, then `\(T=(1,1,0,0)\)`; if `\(Y=1\)`, then `\(T=(0,0,0,0)\)` --- class: left, top # Ordinal Response Let - `\(p = (p^1,...p^{m-1})\)` be the `\(m-1\)` vector of probabilities, - `\(\gamma = (p^1, p^1 + p^2, ..., p^1 + \cdots + p^{m-1})\)` be the `\(m-1\)` vector of cumulative probabilities, - `\(\tau = 1 - \gamma\)`. -- The mean and variance of `\(T\)` is `\begin{align*} E(T) = \tau, \quad Var(T) = \Gamma - \tau \tau^{\top} , \end{align*}` where `\begin{align*} \Gamma = \left ( \begin{matrix} \tau^{1} & \tau^{2} & \cdots & \tau^{m-2}& \tau^{m-1} \\ \tau^{2} & \tau^{2} & \cdots & \tau^{m-2} & \tau^{m-1} \\ \vdots & \vdots & \cdots & \cdots & \vdots \\ \tau^{m-2} & \tau^{m-2} & \cdots & \tau^{m-2} & \tau^{m-1} \\ \tau^{m-1} & \tau^{m-1} & \cdots & \tau^{m-1} & \tau^{m-1 } \end{matrix} \right ) \end{align*}` --- class: left, top # Ordinal Response We define the canonical parameter as `\(\theta(\tau) = (\theta(\tau)^1,...,\theta(\tau)^{m-1} )\)`, with `\begin{align*} \theta(\tau)^j = \log \bigg ( \frac{ \tau^{j} - \tau^{j+1} }{ \tau^{j-1} - \tau^{j} } \bigg ) = \log \bigg ( \frac{ p^{j+1} }{p^{j} } \bigg ) %= %\log \bigg ( \frac{ \gamma^{j+1} - \gamma^{j} }{ \gamma^{j} - \gamma^{j-1} } \bigg ) . \end{align*}` This canonical link corresponds to the "Adjacent-Categories Logit Model for cumulative probabilities" (Agresti, 2010; Agresti, 2013). -- Let `\(P\)` a permutation matrix that maps `\((a^1,...,a^m) \mapsto (a^m, a^1...,a^{m-1})\)`. Then `\begin{align*} \theta(\tau) = \log \left\{ Diag[ (P^{-1} - I)\tau ]^{-1} (P^{-1} - I)\tau \right \}. %\log \left\{ \frac{ (I-P) \tau }{ (P^{-1} - I)\tau } \right \}. \end{align*}` The Adjacent-Categories Model is usually re-expressed as a logistic multinomial in practice. Instead, we derive a closed form for the inverse link and `\(b(\cdot)\)` function to explicitly show that categorical-ordinal variable `\(T\)` is distributed according to a linear exponential family. --- class: left, top # Ordinal Response <!-- Let `\(\phi(\theta) = (\phi^{1}(\theta),...,\phi^{m-1}(\theta) )\)` with `\(\phi^{j} (\theta) = \sum_{r=1}^j \exp \left \{ \sum_{s=1}^{r} \theta^{s} \right \}\)`. --> <!-- - Then `\(\phi(\theta) = L \exp (L \theta )\)`, with `\(L\)` a lower triangular matrix of `\(1\)`'s. --> Let `\(L\)` a lower triangular matrix of `\(1\)`'s. The inverse canonical link is `\begin{align*} \tau(\theta) = \bigg [ -I_{m-1} - \frac{ \{\boldsymbol 1 + e_1 + P L \exp (L \theta ) \} e_1^{\top} } { 1 - e_1^{\top} \{\boldsymbol 1 + e_1 + P L \exp (L \theta ) \} } \bigg ] P L \exp (L \theta ) . \end{align*}` -- Letting `\(Q = \{-I_{m-1} + \boldsymbol 1e_1^\top + e_1 e_1^\top \}\)`, the inverse canonical link is `\begin{align*} \tau(\theta) =\frac{Q P L \exp (L \theta ) }{1 + e_1^{\top} P L \exp (L \theta ) } , \end{align*}` -- Since `\(S^j = T^{j+1} - T^j\)`, from the density of `\(S\)`, the log-likelihood for `\(T\)` is `\begin{align*} \ell(\theta; T) = \theta^{\top} T - \log ( 1 + e_{1}^{\top} P L \exp (L \theta ) ) , \end{align*}` with `\(b(\theta) = \log ( 1 + e_{1}^{\top} P L \exp (L \theta ) )\)`. -- We say `\(T\)` has a **ordinal-categorical (Or-Cat)** distribution. The ordinal-categorical distribution is a linear exponential family with the **adjacent-categories (Ad-Cat)** link function. --- class: left, top # Ordinal Response - The Ad-Cat link is not as popular as other alternative link functions, `\(\psi\)`, for the cumulative probabilities, such as - the Cumulative Logit, `\(\psi(\tau) = \log\{ ( 1 - \tau)/\tau \}\)` - the Cumulative Probit, `\(\psi(\tau) = \Phi^{-1} (1 - \tau)\)` - the Complementary Log-Log, `\(\psi(\tau) = \log \{ - \log(\tau) \}\)` - We can estimate `\(\psi\)` using the inverse canonical link function `\(\hat \psi =\psi\{ \tau(\hat \theta) \}\)`, and its derivative using chain rule.                --- class: top, left # Summary so far: - Categorical variables are a linear exponential family - Ordinal-Categorical variables are also a linear exponential family - The Ad-Cat link and the inverse Ad-Cat link are `\begin{align*} \theta = & \log \left\{ Diag[ (P^{-1} - I)\tau ]^{-1} (P^{-1} - I)\tau \right \},\\ \tau(\theta) =& \frac{Q P L \exp (L \theta ) }{1 + e_1^{\top} P L \exp (L \theta ) } , \end{align*}` - `\(b(\theta) = \log ( 1 + e_{1}^{\top} P L \exp (L \theta ) )\)`. - From the Or-Cat distribution, we can estimate any alternative link `\(\psi\)` and its derivatives `\(\partial \psi (\theta) / \partial \theta\)` using the inverse Ad-Cat link `\(\tau(\theta)\)`. --- class: inverse, center, middle # .bg-text[Inverse and Forward Linear SDR] --- class: left, top # Why Sufficient Dimension Reduction? Suppose we have a large dataset with some response `\(Y \in \R^m\)` and predictors `\(X \in \R^p\)`. - When `\(p\)` is large, lower dimensional summaries of `\(X\)` are helpful for visualization and application of conventional statistical methods. - Finding linear lower dimensional summary of `\(X\)` means finding `\(\beta \in \R^{p \times d}\)` to construct `\(\beta^\top X\)`, where `\(d < p\)`. - Sufficient Dimension Reduction (SDR) is a supervised dimension reduction for finding `\(\beta\)` such that `\(\beta^\top X\)` retains all relevant information about `\(Y\)`. - Inverse and Forward SDR are systematic methods for finding such a dimension reduction `\(\beta\)`. --- class: left, top # Motivating Example: Given response `\(Y\)` and predictor `\(X = (X_1, X_2) \in [0,1]^2\)`. Let `\(Y=X_1^2\)`. Then `\(Y= (\beta^\top X)^2\)`, where `\(\beta = (1,0) \in \R^2\)`. - we want to recover `\(span(\beta) = \{ (c,0): c \in \R\}\)`. .center[  ] --- class: left, top   # Motivating Example: ## Inverse Regression for SDR - Sliced Inverse Regression (Li, 1991) .center[  ]  --- count: false class: left, top   # Motivating Example: ## Inverse Regression for SDR - Sliced Inverse Regression (Li, 1991) .center[  ] --- count: false class: left, top   # Motivating Example: ## Inverse Regression for SDR - Sliced Inverse Regression (Li, 1991) .center[  ] --- count: false class: left, top   # Motivating Example: ## Inverse Regression for SDR - Sliced Inverse Regression (Li, 1991) .center[  ] --- count: false class: left, top   # Motivating Example: ## Inverse Regression for SDR - Sliced Inverse Regression (Li, 1991) .center[  ] -- - 'Inverse' because we estimate `\(E(X|Y)\)`. --- class: left, top   # Motivating Example: ## Inverse Regression for SDR - Drawbacks .center[  ] --- count: false class: left, top # Motivating Example: ## Inverse Regression for SDR - Drawbacks .center[  ] --- count: false class: left, top # Motivating Example: ## Inverse Regression for SDR - Drawbacks .center[  ] --- count: false class: left, top # Motivating Example: ## Inverse Regression for SDR - Drawbacks .center[  ] -- - Inverse methods require assumptions on the support of the predictor. --- class: left, top # Forward Regression for SDR ## Outer Product of Gradients (OPG) (Xia, Tong, Li, and Zhu, 2002) .center[  ] --- count: false class: left, top   # Forward Regression for SDR ## Outer Product of Gradients (OPG) (Xia, Tong, Li, et al., 2002) .center[  ] --- count: false class: left, top   # Forward Regression for SDR ## Outer Product of Gradients (OPG) (Xia, Tong, Li, et al., 2002) .center[  ] --- count: false class: left, top   # Forward Regression for SDR ## Outer Product of Gradients (OPG) (Xia, Tong, Li, et al., 2002) .center[  ] -- - "Forward" Regression because we are estimating `\(E(Y|x)\)` and `\(\partial E(Y|x)/\partial x^\top\)` --- count: false class: left, top   # Forward Regression for SDR ## Outer Product of Gradients (OPG) (Xia, Tong, Li, et al., 2002) .center[  ] --- count: false class: left, top   # Forward Regression for SDR ## Outer Product of Gradients (OPG) (Xia, Tong, Li, et al., 2002) .center[  ] --- class: left, top # Forward Regression for SDR        - OPG developed for scalar `\(Y\)`; does not work as well for categorical `\(Y\)`. - Idea: Estimate a GLM locally instead of a Linear Regression.  -- Existing extensions of Forward SDR: 1. Generalized Single Index Model (GSIM): Lambert-Lacroix and Peyre (2006) - Local Linear GLM for scalar Y; - Uses Average Derivative of Conditional Mean; more similar to Average Derivative Estimator (ADE) than OPG; - ADE has known drawbacks; e.g. gradient has non-zero mean. 2. Minium Average Deviance Estimation (MADE): Adragni (2018) - Local Linear GLM for scalar `\(Y\)` to generalize the Minimum Average Variance Estimator of Xia, Tong, Li, et al. (2002) 3. gradient Kernel Dimension Reducion (gKDR): Fukumizu and Leng (2014) - Estimate the gradients via spline approximation using RKHS --- class: inverse, center, middle  # .bg-text[Outer Product of Canonical Gradients (OPCG)] --- class: left, top   # Generalized Forward Regression for SDR .center[  ] - In OPG, we fit a linear regression about `\(x_0\)`, i.e. we minimize `\begin{align*} \{Y - a_0 + B_0^\top(X-x_0) \}^\top \{Y - a_0 + B_0^\top(X-x_0) \} \end{align*}` and estimate `\(\partial E(Y|x_0)/ \partial x^\top\)` using `\(\hat B_0\)`.  --- class: left, top # Generalized Forward Regression for SDR .center[  ] - Instead, fit a linear exponential family about `\(x_0\)`, i.e. minimize `\begin{align*} -\ell_0(a_0, B_0;Y,X,x_0) = - [a_0 + B_0^\top(x-x_0)]^\top y + b[a_0 + B_0^\top(x-x_0)] \end{align*}` and use the estimate `\(\hat B_0\)`. --- class: left, top # Outer Product of Canonical Gradients (OPCG) - In OPG, the dimension reduction assumption is `\(E(Y|X) = E(Y|\beta^\top X)\)` - In GLMs, - the predictor `\(X\)` relates to `\(Y\)` through the canonical parameter `\(\theta(X)\)` - the dimension reduction assumption is on the canonical parameter, i.e. `\(\theta(X) = \theta(\beta^\top X) \in \R^m\)` - But for linear exponential families, `\(\theta(X) = link^{-1}(E(Y|X))\)`, so the dimension reduction assumption coincides with that of OPG. -- - Like in OPG, we want to estimate the gradient of `\(\theta(x)\)` at `\(x_0\)`, i.e. `\(\partial \theta(x_0) /\partial x^\top\)`. - `\(\hat B_0\)` obtained from `\begin{align*} -\ell_0(a_0, B_0;y,x,x_0) = -[a_0 + B_0^\top(x-x_0)]^\top y + b[a_0 + B_0^\top(x-x_0)] \end{align*}` estimates `\(\partial \theta(x_0) /\partial x^\top\)`. --- class: left, top # Outer Product of Canonical Gradients (OPCG) Given a random sample `\(Y_{1:n}\)`, `\(X_{1:n}\)`, fit a local linear GLM about `\(x_0\)` by minimizing `\begin{align*} & -\ell_0 (a_{0}, B_{0}; x_0, X_{1:n}, Y_{1:n}) \\ = & \frac 1n \sum_{i=1}^n K \bigg ( \frac{X_i - x_0}{h} \bigg ) \{-[a_{0} + B_{0}^\top (X_i - x_0)]^\top Y_i + b[a_{0} + B_{0}^\top (X_i - x_0) ] \} \end{align*}` where `\(b(\cdot)\)` determines the GLM, and `\(K(\cdot)\)` is a kernel weight with bandwidth `\(h\)`. The minimizer `\(\hat B_0\)` is used to estimate `\(\partial \theta(x_0) /\partial x^\top\)`. --- class: left, top # Outer Product of Canonical Gradients (OPCG) We fit a local linear GLM about each `\(X_j\)`, for `\(j=1,...,n\)`, by minimizing the full negative local linear log-likelihood: `\begin{align*} & L(a_1,..,a_n, B_1,...,B_n; X_{1:n}, Y_{1:n}) \\ = & -\frac 1n \sum_{j=1}^n \ell_j (a_j, B_j; X_j, X_{1:n}, Y_{1:n}) \\ = & -\frac {1}{n} \sum_{j,i=1}^n K \bigg ( \frac{X_i - X_j}{h} \bigg )\\ & \times \{[a_{j} + B_{j}^\top (X_i - X_j)]^\top Y_i - b(a_{j} + B_{j}^\top (X_i - X_j)) \} . \end{align*}` This provides minimizers `\(\hat B_j\)` that estimate `\(\partial \theta(X_j)/\partial x^\top\)`, which we use to construct the average outer product `$$\hat \Lambda_n = \frac 1n \sum_{j=1}^n \hat B_j \hat B_j^\top.$$` --- class: left, top # The OPCG Estimator The **Outer Product of Canonical Gradients (OPCG) Estimator** for `\(\beta\)`, `\(\hat \beta_{opcg}\)`, is the first `\(d\)` eigenvectors of `$$\hat \Lambda_n = \frac 1n \sum_{j=1}^n \hat B_j \hat B_j^\top,$$` corresponding to the `\(d\)` largest eigenvalues. .center[  ] --- class: left, top # Properties related to OPCG - The canonical gradients are *unbiased*; their outer product is *exhaustive*. - OPCG is consistent under some assumptions:
 Suppose Assumptions 1-9 hold. Then, as \( n \to \infty\), we have \begin{align*} \| \hat \beta_{opcg} - \beta \|_F = O_{a.s} ( h + h^{-1} \delta_{ph} + \delta_{n} ) , \end{align*} where \(\delta_{ph} = \sqrt{ \frac{\log n}{ nh^p} }\), \(\delta_{n} = \sqrt{ \frac{\log n}{ n } }\), \( h \downarrow 0\), and \( h^{-1}\delta_{ph} \to 0\).
 - Can be implemented using Newton-Raphson - Ladle and Predictor Augmentation methods are fast, eigen-based methods that can be applied to estimate `\(d\)`. (Luo and Li, 2020; Luo and Li, 2016) -- **What about the bandwidth `\(h\)`?** --- class: inverse, center, middle  # .bg-text[Tuning] --- class: left, top # Tuning the bandwidth, `\(h\)`. We need to tune `\(h\)` in OPCG: - But Cross Validation can be computationally intensive and relative performance may vary between prediction methods. - Can choose `\(h\)` according to optimal bandwidth, such as `\(h^{opt} = cn^{-\frac{1}{(p+6)}}\)` (Xia, 2007), but suggested `\(c\)` does not always work. -- For classification problems, we propose using a K-means clustering procedure for tuning `\(h\)`. Our motivation: - SDR should make classification easier, and classification is easiest when the dimension reduced predictors `\(\hat \beta^\top X\)` are clustered into their respective labels. --- class: left, top # Tuning the bandwidth, `\(h\)`. Let `\(Y \in \{1,...,m\}\)` be categorical response, `\((Y, X)_{1:n}\)` be our sample for training/estimation, `\((Y,X)_{1:n_2}^{tune}\)` be our sample for Tuning. -- Main idea: For each `\(h\)`, 1. Estimate `\(\hat \beta_{opcg}\)` on `\((Y,X)_{1:n}\)` and construct the dimension reduced predictors `\(\hat \beta_{opcg}^\top X_{1:n_2}^{tune}\)`. 2. Apply K-means to `\(\hat \beta_{opcg}^\top X_{1:n_2}^{tune}\)` for `\(m\)` clusters. - This returns `\(m\)` estimated clusters and the F-ratio corresponding to `\(h\)`, i.e. the Within Sums-of-Squares (WSS) over Between Sums-of-Squares (BSS) 3. Select `\(h\)` that minimizes the F-ratio from K-means. - Small F-ratio means small WSS and large BSS --- count: false class: left, top # Tuning the bandwidth, `\(h\)`. .center[  ] - `\(h\)` too small means - likelihood is undersmoothed - noisy gradient estimates - noisy dimension reduction; no separation --- count: false class: left, top # Tuning the bandwidth, `\(h\)`. .center[  ] - `\(h\)` too large means - likelihood is oversmoothed, - gradient estimates can give only some separation of classes - large enough `\(h\)` to include the entire sample means fitting the same glm centered differently; recover at most one dimension; - as `\(h \to \infty\)`, all information is washed out and we recover noise --- count: false class: left, top # Tuning the bandwidth, `\(h\)`. .center[  ] - Goal: find `\(h\)` not too small and not too large; just enough to separate all classes --- class: left, top # Tuning the bandwidth, `\(h\)`. But - Estimating `\(m\)` clusters implicitly assumes 1 cluster per class - we ought to incorporate the class/label information from `\(Y_{1:n}^{tune}\)`, when available. Our modifications to K-means: 1. K-means estimates more than 1 cluster per class; 2. "Supervised" K-means: Uses label information from `\(Y_{1:n}^{tune}\)`; 3. K-fold Tuning: Applies k-means, supervised k-means, or an average of both, in a k-fold manner;            --- class: inverse, center, middle  # .bg-text[Simulations and Data Analyses] -- .left[ 1. Goal: generalize OPG to Categorical and Ordinal-Categorical Responses 2. Propose: OPCG that generalizes OPG to linear exponential families 3. Categorical and Ordinal-Categorical variables are linear exponential families, so we can apply OPCG to Categorical and Ordinal-Categorical data ] --- class: left, top # OPCG Procedure Given data: 1. Simulations: Data is split into three sets: - Training set used for estimating `\(\hat \beta_{opcg}\)`; - Can use for estimating `\(d\)` as well. - Tuning set used for tuning bandwidth `\(h\)` - Testing set used for assessing performance 2. Applications: Data is split into two sets: - Training set used for estimating `\(\hat \beta_{opcg}\)`; - Can use for estimating `\(d\)` as well. - Use K-fold tuning for tuning bandwidth `\(h\)` - Testing set used for assessing performance --- class: left, top # Simulations Our predictor will be `\(X=(X^1,X^2,X^3,...,X^{10}) \in \R^{10}\)`. - `\((X^1, X^2) \sim N(\mu, \sigma^2 I_2)\)`, with `\(\sigma^2=.25\)` and `\(\mu \in \{ (0,0), (3,3), (-3,-3), (-2,2),(2,-2)\}\)`; there are 5 clusters total - Augmented with 8 standard normals for random noise, `\((X^3,...,X^{10})\)` The clusters with centers `\((-2,2),(2,-2)\)` are labeled 1; `\((3,3),(-3,-3)\)` labeled 2 and `\((0,0)\)` is labeled 3. - Draw a total of 550 observations equally among all 5 clusters; split into three sets evenly across all 5 clusters, i.e. clusters are all balanced in each set.    --- class: left, top # Simulations So `\(Y \in \{1,2,3\}\)` is categorical; `\(X \in \R^{10}\)`; and `\(Y\)` is determined by `\((e_1, e_2)^\top X \in \R^{10 \times 2}\)`, where `\(d=2\)`. - we shuffle the 10 predictors so that so that `\(Y\)` is determined by `\(\beta^\top X\)`, where `\begin{align*} \beta = \left( \begin{matrix} 0&0&1&0&0&0&0&0&0&0 \\ 0&0&0&0&0&0&1&0&0&0 \end{matrix} \right )^\top \in \R^{10 \times 2} \end{align*}`        --- count: false class: left, top # Simulations - Ladle, PA   Used `\(h=1\)` for order determination, 200 samples for Ladle and PA. --- class: ani-slide # K-mean Tuning for `\(h\)`  --- count: false class: left, top # Simulations - tuning and estimation Conventional suggestion: `\(h = 2.34 n^{-1/(p+6)} \approx 1.66\)`; K-fold K-means Tuning: `\(h \approx 1.25\)`; -- Using `\(h \approx 1.25\)` to estimate OPCG `\(\hat \beta_{opcg}\)`:  -- - PW-OPCG estimates 2 SDR directions per pair of classes \{1,2\}, \{1,3\}, and \{2,3\}, for 6 total, and selects the 2 that explain the most variation. - Adragni's suggestion for multi-class problems. - DR is Directional Regression (Li and Wang, 2007) --- count: false class: left, top # Simulations - Sufficient Predictors `\(\hat \beta^\top X^{test}\)`  --- class: left, top # Ordinal-Categorical Data Analysis We analyze two continuous response datasets that we discretize in ordinal-categorical response datasets:        - Communities and Crime Rate - UCI - p=99; n=1994; resp=pop. crime. rate - Superconductors - UCI - p=81; n=2000; resp=crit. temp. of superconductors                           --- class: left, top # Communities and Crime Rate in the US  --- class: left, top # Crit. Temperature of Superconductors  --- class: left, top # Categorical Data Analysis We analyze three datasets with categorical responses: - Handwritten Digits (Pendigit) from UCI - p=16; n=2000; resp=0-9 - USPS Handwritten Digits - p=256; n=2007; resp=0-9 - ISOLET from UCI - p=618; n=6334/1553; resp=a-z --- class: left, top # Categorical Classification Error using SVM .center[  ] --- class: ani-slide # Pen Digit 10 - SIR/DR - 1,6,7,9  --- count: false class: ani-slide # Pen Digit 10 - OPCG - 1,6,7,9           --- class: left, top, inverse # .bg-text[Conclusion] 1. Provided the Multivariate Link Functions for Ordinal-Categorical Responses. -- 2. Generalized OPG to linear exponential families -- 3. Introduced a K-means tuning procedure for classifcation -- 4. Demonstrated the effectiveness of OPCG in categorical and ordinal classification problems. - Can handle multiple labels simultaneously - Noticeable improvement over OPG for larger `\(p\)` --- layout: false # References Adragni, K. P. (2018). "Minimum average deviance estimation for sufficient dimension reduction". In: _Journal of Statistical Computation and Simulation_ 88.3, pp. 411-431. Agresti, A. (2010). _Analysis of ordinal categorical data_. Vol. 656. John Wiley & Sons. Agresti, A. (2013). _Categorical Data Analysis_. 3rd ed. Wiley. Fukumizu, K. and C. Leng (2014). "Gradient-based kernel dimension reduction for regression". In: _Journal of the American Statistical Association_ 109.505, pp. 359-370. Lambert-Lacroix, S. and J. Peyre (2006). "Local likelihood regression in generalized linear single-index models with applications to microarray data". In: _Computational statistics & data analysis_ 51.3, pp. 2091-2113. Li, B. and S. Wang (2007). "On directional regression for dimension reduction". In: _Journal of the American Statistical Association_ 102.479, pp. 997-1008. --- layout: false # References Li, K. (1991). "Sliced inverse regression for dimension reduction". In: _Journal of the American Statistical Association_ 86.414, pp. 316-327. Luo, W. and B. Li (2016). "Combining eigenvalues and variation of eigenvectors for order determination". In: _Biometrika_ 103.4, pp. 875-887. Luo, W. and B. Li (2020). "On order determination by predictor augmentation". In: _Biometrika_. Wang, H. and Y. Xia (2008). "Sliced regression for dimension reduction". In: _Journal of the American Statistical Association_ 103.482, pp. 811-821. Xia, Y. (2007). "A constructive approach to the estimation of dimension reduction directions". In: _The Annals of Statistics_ 35.6, pp. 2654-2690. Xia, Y., H. Tong, W. Li, et al. (2002). "An adaptive estimation of dimension reduction space". In: _Journal of the Royal Statistical Society: Series B (Statistical Methodology)_ 64.3, pp. 363-410. --- layout: false # References Yin, X., B. Li, and others (2011). "Sufficient dimension reduction based on an ensemble of minimum average variance estimators". In: _The Annals of Statistics_ 39.6, pp. 3392-3416.


HE GOT GAME A film review by Steve Rhodes Copyright 1998 Steve Rhodes
RATING (0 TO ****): ***
HE GOT GAME, writer and director Spike Lee's indictment of the pressure put on young athletes, starts with a beautifully choreographed homage to the sheer joy of shooting hoops. Filmed in slow motion by Malik Hassan Sayeed, the camera caresses the ball in golden sunshine as it spins high in the air on its way to the basket. The players, who live in poor environments in the country and in the ghetto, give it their all even though most of them are playing solo.
Easily the most striking choice Lee made for the film was that of the score. Heavy doses of Aaron Copeland music dominate the handsomely photographed picture with smaller doses of rap music to contrast with the classical. Long after viewers have forgotten the storyline they'll remember the film's sounds and sights.
HE GOT GAME tells the story of the "Jesus of Coney Island." In a monotone performance lacking much depth, real-life basketball star Ray Allen plays Jesus Shuttlesworth, the number one high school basketball prospect in the nation. With ESPN covering his personal life reverentially as befits a sports god, the kid seems to have it all. ("Jesus is the best thing to happen to the game since tennis shoes," effuses one coach.) And what he doesn't have people want to give to him, albeit most of it illegally and with strings attached. Jesus, who appears at first as honest as his namesake, turns down all of the lucrative, under-the-table offers.
Slowly and deliberately the story makes us realize that there are many rewards, from sex to "loans," that Jesus is more than happy to accept as compensation. This Jesus is tempted but isn't always strong enough to refuse.
What the young star lacks are many true friends. From his uncle to
his girlfriend, the people around him are there for a piece of the action. They care about Jesus' money, but little for him as a person, which only heightens his feeling of isolation. (When Jesus asks for a brother to be his agent, a fast-talking Italian-American, with a stable of expensive racing cars to tempt him, offers a simple piece of advice: "When making a business decision, the only color that matters is green.")
The movie's best performance is delivered by Denzel Washington in the complex role of Jesus' father, Jake, who has killed his wife and now
languishes in prison until the governor releases him for a week. The condition for his brief time on the outside is that Jake must convince Jesus to agree to go to the governor's alma mater, Big State. If Jesus signs with Big State, the governor will try to get Jake's sentenced reduced.
Since Jake killed his mother, the ever-cynical Jesus wants nothing to do with him. And he orders his sister, who lives in their apartment,
not to speak to their father, whom he considers just another "stranger."
Who is paying for their apartment and their expenses is one of the questions that the story raises.
Washington, playing a poor African-American rather than the well educated, middle-class one that he normally plays, makes Jake into a believably complicated individual, a likeable guy but one with many faults. One of the subplots has him helping an abused prostitute, played by Milla Jovovich from FIFTH ELEMENT, who lives in the room next to his in a fleabag hotel. In flashbacks we see him drunk and verbally abusing his young son on the basketball court.
The movie has several problems other than Ray Allen's limited acting ability. The story has too many minor characters, including John
Turturro as a coach who uses prayer, multimedia glitz, and free girls to
attract Jesus to his college.
Concluding with the same sense of magic with which it began, the film manages to take the cliche of the big game that ends most sports movies and give it a whole new spin.
HE GOT GAME runs too long at 2:11. It is rated R for sex, nudity, violence, profanity, and brief drug usage and would be fine for older teenagers.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Related links: index of all rec.arts.movies.reviews reviews


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PART IV. ESTATES LESS THAN $100; DISTRIBUTION
WITHOUT ADMINISTRATION--REPEALED
§§531-71, 72 REPEALED. L 1976, c 200, pt of §1.
Cross References
For present provisions, see §§560:3-1201 to 560:3-1215.


In material design, everything should have a certain z-depth that determines how far raised or close to the page the element is.
You can easily apply this shadow effect by adding a class="z-depth-2"
to an HTML tag.


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--- layout: post title: 'cityofsound: Design. Architecture. Football.' date: 2006-06-21 00:00:00 category: aikido ---
"This emphasis on unpredictable, interpreted creativity being performed within formal systems actually suggests interesting parallels to me."
Takemusu football and architecture anyone?


This article discusses about how to run Dynomite as docker container and offers solution for Non-Sharding and multi master replication. Sharing lessons learned while setting up multi master cache solution, so you do not hit the same pitfalls. Sharding is mainly for very large scale applications, with lots of data and adds additional programming and operational complexity to application. If your application uses couple of GB’s memory and bound by read/write performance, you do not need Sharding.
Dynomite is the great product that offers multi master cache solution. Dynomite cluster consists of multiple data centers. A datacenter is a group of racks and rack is a group of nodes. Each rack consists of the entire dataset, which is partitioned across multiple nodes in that rack. Each node in a rack has a unique token, which helps to identify which dataset it owns.
To make dynomite node contain all the data set you need to use the same token (example ‘0’) for all the nodes, So every node in the cluster will have the complete data set and make sure to use simple provider as seed provider. If you use “florida_provider”, each dynomite node will periodically reach out to a dynomite-manager (side car) rest service to obtain the cluster members, see membership protocols for how membership protocol works. You do not need to run docker with -g gossip flag (dynomite wiki document is not up to date).
Below is the sample dynomite.yml file for 4-node cluster with two nodes in two data centers and single node in a rack to replicate total data set to ALL nodes :
Above configuration uses DC_QUORUM consistency, that means reads and writes are propagated synchronously to quorum number of nodes in the local data center and asynchronously to the rest. Consistency is runtime configurable and can be separately configurable for read and writes. Dynomite communicates with other nodes for replication through peer port, 8103 in this case, make sure port is open between nodes for communication.
Update conf/redis_single.yml with updated dynomite.yml or update the statup script with updated dynomite.yml name.
</p> Git clone dynomite, docker build and run container. </p>


My name is Thomas Rivas-Siles and I am from the Washington, DC area (Silver Spring to be exact) with peruvian descent(both of my parents are peruvian). I just moved to Portland to become an Epicodus student. Before this I had graduated from the University of Maryland with a double major in International Business and Marketing in 2015. Right after graduating I had founded my own startup called Vendoo, which is a tool for online sellers that allows them to easily post and manage items on multiple sites all from their phone. This was what propelled me into the tech world and made me so interested in learning how to program. Besides programming and working on my business I love to travel. I have been to 11 countries so far but have many more I want to visit. I would love to do a trip through Europe in 2018 and in Russia for the World Cup.


algorithm kmp_table: input: an array of characters, W (the word to be analyzed) an array of integers, T (the table to be filled) output: nothing (but during operation, it populates the table) define variables: an integer, pos ← 2 (the current position we are computing in T) an integer, cnd ← 0 (the zero-based index in W of the next character of the current candidate substring) (the first few values are fixed but different from what the algorithm might suggest) let T[0] ← -1, T[1] ← 0 while pos is less than the length of W, do: (first case: the substring continues) if W[pos - 1] = W[cnd], let cnd ← cnd + 1, T[pos] ← cnd, pos ← pos + 1 (second case: it doesn't, but we can fall back) otherwise, if cnd > 0, let cnd ← T[cnd] (third case: we have run out of candidates. Note cnd = 0) otherwise, let T[pos] ← 0, pos ← pos + 1
algorithm kmp_search: input: an array of characters, S (the text to be searched) an array of characters, W (the word sought) output: an integer (the zero-based position in S at which W is found) define variables: an integer, m ← 0 (the beginning of the current match in S) an integer, i ← 0 (the position of the current character in W) an array of integers, T (the table, computed elsewhere) while m+i is less than the length of S, do: if W[i] = S[m + i], if i equals the (length of W)-1, return m let i ← i + 1 otherwise, let m ← m + i - T[i], if T[i] is greater than -1, let i ← T[i] else let i ← 0 (if we reach here, we have searched all of S unsuccessfully) return the length of S
S:
W:
T: {{T}}
m: {{m}}
i: {{i}}
return: {{ret}}


Graduated from the Unversity of Calgary with a Commerce degree in Tourism and Marketing. I understand branding and how to create a effective marketing strategy.
Through the use of the interweb and the free resources available I self-taught myeslf the basics of coding. I am profiecient in using HTML, CSS, JQuery and Bootstrap.
My End Goal is to become an SEO Ranking Specialist who focuses on getting comapnies to the top spot in SERP. I also want to keep learning more about PPC and how to generate more traffic with Google Ads.
I am a Calgary born resident and have started learning web-development by myself while going to school at the University of Calgary in a Commerce degree in Tourism and Marketing. I find web-development facinating and a great tool to enhance my marketing knowledge in branding. I want to further my passion in Digital Marketing and feed my hunger for knowledge in all things SEO and PPC.


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tbb::flow::interface10
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Next: Examples of Printer Behavior, Previous: Printer Dispatching, Up: The Lisp Printer
This section describes the default behavior of print-object methods for the standardized types.


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All data presented here is synthetic and has been generated using:
All the code required to perform the examples presented here as well as the presentation itself can be found my a repo on github. The link is availible in the presentation documents shared
Throughout the presentation I will include "toolbox" references highlighting the particular packages I'm using to do something.
All of the examples today are written using python 3 I am presenting using a jupyter notebook and the RISE Reveal.js extension
HTML( "
That data science venn diagram ...
")
Image("https://static1.squarespace.com/static/5150aec6e4b0e340ec52710a/t/51525c33e4b0b3e0d10f77ab/1364352052403/Data_Science_VD.png")
HTML("
I like this one better
")
Image("https://3.bp.blogspot.com/-bvQxcwfqATQ/V-E_uTBc4VI/AAAAAAAAMGQ/Qa1Ntef-rs0E-mWx5pkVu-CPlREdvD0TwCLcB/s1600/VennDiagram2.png")
HTML("
 ... so why are we here today?

")
My goal today is for you to leave this room knowing that you can head back to your offices on Monday and with a few emails have an analytics project up and running
HTML("
One more chart you've seen before

")
Image("https://media.licdn.com/mpr/mpr/shrinknp_800_800/AAEAAQAAAAAAAAjIAAAAJGI0NzY3MGM0LTIyMTEtNDYwYy04OWQ2LTgyYmZiNDgzNTlhNw.png")
Image("https://media.licdn.com/mpr/mpr/shrinknp_800_800/AAEAAQAAAAAAAAjIAAAAJGI0NzY3MGM0LTIyMTEtNDYwYy04OWQ2LTgyYmZiNDgzNTlhNw.png")
chargePlt(synthExp_gf, ['2','4','6','8'])
chargePlt(synthExp_ngf, ['2','4','6','8'])
vid = YouTubeVideo("93lrosBEW-Q", start=27, end =40, width = 1067, height = 600, autoplay=0)
display(vid)
HTML("
Remember our trusty old venn diagram

")
Image("https://static1.squarespace.com/static/5150aec6e4b0e340ec52710a/t/51525c33e4b0b3e0d10f77ab/1364352052403/Data_Science_VD.png")
Image("https://media.licdn.com/mpr/mpr/shrinknp_800_800/AAEAAQAAAAAAAAjIAAAAJGI0NzY3MGM0LTIyMTEtNDYwYy04OWQ2LTgyYmZiNDgzNTlhNw.png")
HTML("
Actuaries understand product features and the drivers of risk and value

")
Image("images/julia_icon.png")
HTML("
 \"Hackers\" (we'll call them developers from now on) bring a new perspective on how to process and represent data

")
Image("images/eric_icon.png")
(Credit: XKCD)
buildFullPremPlt(trxes)
[<matplotlib.lines.Line2D at 0xf4c2da0>]
buildIndPremPlt(eduTrx, otherTrx)
(<matplotlib.figure.Figure at 0x11e3b828>, array([<matplotlib.axes._subplots.AxesSubplot object at 0x0000000012F602B0>, <matplotlib.axes._subplots.AxesSubplot object at 0x0000000012FBD8D0>], dtype=object))
bldIndTypePlt(eduTrx,otherTrx)
(<matplotlib.figure.Figure at 0x13ae7d68>, array([<matplotlib.axes._subplots.AxesSubplot object at 0x0000000013ABFEF0>, <matplotlib.axes._subplots.AxesSubplot object at 0x00000000142AA9B0>], dtype=object))
Image("https://media.licdn.com/mpr/mpr/shrinknp_800_800/AAEAAQAAAAAAAAjIAAAAJGI0NzY3MGM0LTIyMTEtNDYwYy04OWQ2LTgyYmZiNDgzNTlhNw.png")
HTML("
Adding a substantive expert in the business to your analytics team increases the team's ability to attack relevant business problems and provide on the ground actionable recomendations to end business users
")
Image("images/rob_icon.png")
Data Validation and Feature Engineering
diabetes_df['age_sex_cross'] = diabetes_df.age * diabetes_df.sex
diabetes_df['diseaseStatus_cat'] = diabetes_df.apply(lambda x: cat_Status(x.disease_status_t1), axis = 1)
Describe what you are trying to predict
What are you going to do with your predictions?
from sklearn import neighbors
from sklearn.metrics import confusion_matrix
clf = neighbors.KNeighborsClassifier(15, weights='uniform')
What goes in the model?
HTML("
Correlation matrix
")
diabetes_df.corr().style
| age | sex | bmi | avg_bp | s1 | s2 | s3 | s4 | s5 | s6 | disease_status_t1 | age_sex_cross | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| age | 1 | 0.173737 | 0.185085 | 0.335427 | 0.260061 | 0.219243 | -0.075181 | 0.203841 | 0.270777 | 0.301731 | 0.187889 | -0.0285763 |
| sex | 0.173737 | 1 | 0.0881614 | 0.241013 | 0.0352768 | 0.142637 | -0.37909 | 0.332115 | 0.149918 | 0.208133 | 0.043062 | 0.0224381 |
| bmi | 0.185085 | 0.0881614 | 1 | 0.395415 | 0.249777 | 0.26117 | -0.366811 | 0.413807 | 0.446159 | 0.38868 | 0.58645 | -0.0605078 |
| avg_bp | 0.335427 | 0.241013 | 0.395415 | 1 | 0.24247 | 0.185558 | -0.178761 | 0.257653 | 0.393478 | 0.390429 | 0.441484 | -0.0725035 |
| s1 | 0.260061 | 0.0352768 | 0.249777 | 0.24247 | 1 | 0.896663 | 0.0515194 | 0.542207 | 0.515501 | 0.325717 | 0.212022 | -0.116381 |
| s2 | 0.219243 | 0.142637 | 0.26117 | 0.185558 | 0.896663 | 1 | -0.196455 | 0.659817 | 0.318353 | 0.2906 | 0.174054 | -0.0753236 |
| s3 | -0.075181 | -0.37909 | -0.366811 | -0.178761 | 0.0515194 | -0.196455 | 1 | -0.738493 | -0.398577 | -0.273697 | -0.394789 | -0.0392564 |
| s4 | 0.203841 | 0.332115 | 0.413807 | 0.257653 | 0.542207 | 0.659817 | -0.738493 | 1 | 0.617857 | 0.417212 | 0.430453 | -0.0461077 |
| s5 | 0.270777 | 0.149918 | 0.446159 | 0.393478 | 0.515501 | 0.318353 | -0.398577 | 0.617857 | 1 | 0.46467 | 0.565883 | -0.0986936 |
| s6 | 0.301731 | 0.208133 | 0.38868 | 0.390429 | 0.325717 | 0.2906 | -0.273697 | 0.417212 | 0.46467 | 1 | 0.382483 | -0.0825197 |
| disease_status_t1 | 0.187889 | 0.043062 | 0.58645 | 0.441484 | 0.212022 | 0.174054 | -0.394789 | 0.430453 | 0.565883 | 0.382483 | 1 | 0.0569693 |
| age_sex_cross | -0.0285763 | 0.0224381 | -0.0605078 | -0.0725035 | -0.116381 | -0.0753236 | -0.0392564 | -0.0461077 | -0.0986936 | -0.0825197 | 0.0569693 | 1 |
Teach the model how to predict what we want it to predict
clf.fit(X_train[['s5','bmi','s4']], y_train['diseaseStatus_cat'])
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=15, p=2, weights='uniform')
Split the dataset and pick a metric
So you can see how well your model did
fig, axes = plt.subplots(ncols=2, nrows=1, figsize = (20,16))
ax1, ax2 = axes.ravel()
buildConfusionMatrix(cm_train, classes = class_names, ax = ax1, title = 'Confusion Matrix: Training Data')
buildConfusionMatrix(cm_test, classes = class_names, ax = ax2, title = 'Confusion Matrix: Testing Data')
Image("images/precision_recall.png")
HTML("By Walber - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=36926283")
from sklearn.metrics import precision_score, recall_score
'Precision: {0:%} Recall: {1:%}'.format(
precision_score(y_test['diseaseStatus_cat'],clf.predict(X_test[['s5','bmi','s4']]), average = 'weighted'),
recall_score(y_test['diseaseStatus_cat'],clf.predict(X_test[['s5','bmi','s4']]), average = 'weighted'))
'Precision: 53.305317% Recall: 57.142857%'
Our business expert tells us that, underwriters and physicians view blood pressure as a very good predictor of how well a person is managing their overall health
HTML("
Add BP to the predictors!
")
clf2 = neighbors.KNeighborsClassifier(15, weights='uniform')
clf2.fit(X_train[['s5','bmi','s4','avg_bp']], y_train['diseaseStatus_cat'])
cm_train = confusion_matrix(y_train['diseaseStatus_cat'],clf2.predict(X_train[['s5','bmi','s4','avg_bp']]))
cm_test = confusion_matrix(y_test['diseaseStatus_cat'],clf2.predict(X_test[['s5','bmi','s4','avg_bp']]))
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=15, p=2, weights='uniform')
fig, axes = plt.subplots(ncols=2, nrows=1, figsize = (20,16))
ax1, ax2 = axes.ravel()
buildConfusionMatrix(cm_train, classes = class_names, ax = ax1, title = 'Confusion Matrix: Training Data')
buildConfusionMatrix(cm_test, classes = class_names, ax = ax2, title = 'Confusion Matrix: Testing Data')
from sklearn.metrics import precision_score, recall_score
'Precision: {0:%} Recall: {1:%}'.format(
precision_score(y_test['diseaseStatus_cat'],clf2.predict(X_test[['s5','bmi','s4','avg_bp']]), average = 'weighted'),
recall_score(y_test['diseaseStatus_cat'],clf2.predict(X_test[['s5','bmi','s4','avg_bp']]), average = 'weighted'))
'Precision: 60.214122% Recall: 62.406015%'
Image("https://media.licdn.com/mpr/mpr/shrinknp_800_800/AAEAAQAAAAAAAAjIAAAAJGI0NzY3MGM0LTIyMTEtNDYwYy04OWQ2LTgyYmZiNDgzNTlhNw.png")
HTML("
Good prescriptive analytics requires a skilled and effective analytics team
")
Image("images/analyticsteam.png")
HTML("
... it also requires allies
")
Image("images/allies.png")
Image("images/ABMs.png")
Image("images/usingABMs.png")
Image("images/whenABM.png")
Image("https://media.licdn.com/mpr/mpr/shrinknp_800_800/AAEAAQAAAAAAAAjIAAAAJGI0NzY3MGM0LTIyMTEtNDYwYy04OWQ2LTgyYmZiNDgzNTlhNw.png")
Image("https://static1.squarespace.com/static/5150aec6e4b0e340ec52710a/t/51525c33e4b0b3e0d10f77ab/1364352052403/Data_Science_VD.png")
Image("images/analyticsteam.png")


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Enter the length of three sides of a triangle in cm and I will tell you which type of triangle it is


--- layout: archive title: Zulip Chat Archive permalink: /stream/113489-new-members/topic/Type.20class.20inference.20with.20parameters.html ---
Lean doesn't seem to be able to infer types when parameters are involved, so while trying to implement my own version of the integers mod n, I'm having to specify all type classes explicitly. Is there a fix/workaround?
Here's a (ridiculous) example of the kind of stuff that causes problems:
section test parameter n : ℕ def inv_wrt_n (a : ℕ) : ℕ := a + n instance: has_inv ℕ := ⟨inv_wrt_n⟩ #check n⁻¹ -- Fails end test
Sorry, I don't have much experience with parameters (mostly because I decided long ago I didn't like them, hiding too much, and prefer to use variables). So my suggestion would be to learn to love variables
instead!
I think you'll encounter other serious problems with this approach anyways, unless you only planned to ever use this instance within the scope of the parameter.
Okay I'll go back to variables, thanks for the advice
variables
will make the problems clearer. How do you expect to ever specify n
?
But I want to create a quotient that depends on a value n - the integers mod n
But type class resolution won't work with variables either because the instance depends on n
Is there a way to feed lean my specific setoid?
You can put the n
in the type
If you have an instance instance (n : nat) : setoid (mytype n)
there are no problems
(also, setoids often don't need to be instances)
Or you can not use type class resolution. Mathlib has a whole collection of primed quotient stuff where the equivalence relation is found via unification.
#check @quotient.eq -- quotient.eq : ∀ {α : Sort u_1} [r : setoid α] {x y : α}, ⟦x⟧ = ⟦y⟧ ↔ x ≈ y #check @quotient.eq' -- quotient.eq' : ∀ {α : Sort u_1} {s₁ : setoid α} {a b : α}, quotient.mk' a = quotient.mk' b ↔ setoid.r a b
Note [setoid]
v {setoid}
You lose notation this way though, as you can see from the example.
@Mario Carneiro but if I'm trying to get type class resolution for setoid int
(although I'm actually using setoid myint
, as I'm using my own implementation of integers) I can't put n in the type
@Kevin Buzzard that, or explicitly stating everything might be the only way
But it's not ideal
import tactic import data.int.modeq section parameter (n : ℕ) def r : ℤ → ℤ → Prop := int.modeq n open int.modeq quotient def s : setoid ℤ := { r := r, iseqv := ⟨int.modeq.refl, λ _ _, int.modeq.symm, λ _ _ _, int.modeq.trans⟩ } example : add_semigroup (quotient s) := { add := λ q r, quotient.lift_on₂' q r (λ a b, quotient.mk' (a + b)) begin intros a₁ b₁ a₂ b₂ ha hb, show quotient.mk' (a₁ + b₁) = quotient.mk' (a₂ + b₂), sorry end, add_assoc := sorry } end
@Alastair Horn You can, you can define foo n := int
and then put the typeclass on foo n
Okay that seems to get rid of the errors somehow :joy:. I'll see how far I get with that and fall back on Kevin's method otherwise. Thanks
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


Chinese Character Frequencies
Created using
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.
Rank
Hanzi
Freq. [%]
Cum. Freq. [%]
{table}


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