Learning specific vocabulary can be an absolute pain, we know that. This site is designed to constantly throw Japanese vocabulary at you (in specific lists) so that you can memorise there and on the spot! Often we use it for learning subtle differences between words, as thats probably one of the most painful things about learning a new language!
This site was originally created by Alex Andredes and has been rewritten to support multiple lists (and JSON)!
You can find a fuller set of words for Yodokari based lists on the Yokodari main site!
— Benkyou Team
If this content stays for longer than a minute, please refresh!
--


--- title: Gallery layout: gala permalink: /gallery/ img: FancySmash.jpg words: A small collection of the many cakes we've created over the years ---
Black Forest Tort
Blush Bliss Cake
Baby Boy
Chocolate Dipped Strawberries
Christmas Cake
Baby Girl
Square Wedding
3 Teir Wedding
Two Cakes


- Family friendly, romantic or adventurous journeys
- A variety of resorts, quality entertainment and fast transportation
- Discounts for groups lager than 6
- Visiting scientific instiutes & organisations
- Field study oppurtunites
- Getting high quality samples
- Visiting mines & factories
- Investing oppurtunites with regard to various recreational and academic organisations
- Networking oppurtunites, connecting you with most brilliant minds in the industry


Because the foot is three-dimensional, any two-dimensional measuring tool, such as a ruler or Brannock device®, can only approximate your true shoe size. Please also keep in mind the manufacturers use different lasts to construct their shoes, and sizing may vary accordingly.
Make sure you are standing, wearing the socks or stockings you will wear with your new shoes. Using one of the following charts, convert your inches measurement to your U.S. shoe size or Euro shoe size.


Istanbul Eurasia Marathon
Istanbul TUR
Website: www.istanbulmarathon.org
Race Winners
(38 runnings)
2:27:56 2:27:27 2:32:45 2:30:17 28 Oct
2007
2:11:00
2:29:08
05 Nov
2006
2:12:52
2:28:21
02 Oct
2005
2:15:13
2:34:25
10 Oct
2004
2:18:19
2:36:44
19 Oct
2003
2:18:29
2:36:49
27 Oct
2002
2:18:42
2:37:20
11 Nov
2001
2:18:21
2:38:21
15 Oct
2000
2:17:03
2:37:39
17 Oct
1999
2:13:58
2:35:57
11 Oct
1998
2:15:28
2:38:10
12 Oct
1997
2:13:37
2:34:39
20 Oct
1996
2:18:51
2:37:38
05 Nov
1995
2:17:56
2:34:44
09 Oct
1994
2:24:58
2:46:42
10 Oct
1993
2:20:43
2:48:10
11 Oct
1992
2:23:28
2:46:29
13 Oct
1991
2:22:09
2:49:24
14 Oct
1990
2:23:09
3:46:24
15 Oct
1989
2:27:41
---
06 Nov
1988
2:15:06
3:12:35
08 Nov
1987
2:16:08
3:08:36
02 Nov
1986
2:23:20
---
03 Nov
1985
2:12:50
---
04 Nov
1984
2:22:30
---
1983
2:32:35
---
1982
2:41:13
---
1981
2:31:40
---
16
Apr 1980
2:36:00
---
1979
2:35:40
---
Notes: 1979 was the inaugural year. In 1999, Florina Pana (ROM) was the first woman finisher in 2:35:54 but was
later disqualified for drug use. In 2013, Abraham Kiprotich (FRA) finished
1st in 2:12:28 but was later disqualified for drug use. In 2014, Hafid Chani (MAR) was the first
man finisher in 2:11:53 but was later disqualified for drug use.
13 Nov
2016
2:13:30
Evans Kiplagat (AZE)
(nc)
2:28:25
Agnes Barsosio Cheruto (KEN)
(nc)
15 Nov
2015
2:11:17
Elias Kemboi Chelimo (KEN)
(nc)
2:31:58
Amane Gobena Gemeda-2
(nc)
16 Nov
2014
2:12:23
Gebo Burka Gameda
(ETH)
(nc)
2:28:46
Amane Gobena Gemeda (ETH)
(nc)
17 Nov
2013
2:13:19
Siraj Gena Amda (ETH)
(nc)
2:29:05
Rebecca Chesire Kangogo (KEN)
(nc)
11 Nov
2012
2:11:05
Stephen Chebogut (KEN)
(nc)
2:28:06
Koren Jelela Yal (ETH)
(nc)
16 Oct
2011
2:10:58
Vincent Kiplagat
Mitei-2
(nc)
Alemitu Abera Begna (ETH)
(nc)
17 Oct
2010
2:10:42
Vincent Kiplagat Mitei (KEN)
(nc)
Ashu Kasim Rabo (ETH)
(nc)
18 Oct
2009
2:12:14
Kasime Adilo
Roba-2
(nc)
Bizunesh Urgesa Mohammed (ETH)
(nc)
26 Oct
2008
2:11:16
Kasime Adilo Roba (ETH)
(nc)
Natalya Yulamanova (RUS)
(nc)
David Cheruiyot
Emmanuel (KEN) (nc)
Atseda Bayesa
Tesema (ETH) (nc)
Mindaugas Pukstas (LTU) (nc)
Madina Biktagirova-2 (nc)
Joseph Mbithi
Mutinga (KEN) (nc)
Madina Biktagirova (RUS)
(nc)
David Kiptanui
Chepkwony -2 (nc)
Svetlana Demidenko (RUS)
(nc)
Belaye Wolashe (ETH)
Rimma Dubovik-2
David Kiptanui
Chepkwony (KEN) (nc)
Rimma Dubovik (UKR)
(nc)
Bedaso Turbe
Gebano (ETH) (nc)
Lyumila Puchkina (UKR)
(nc)
Josephat Rop
Kipchoge (KEN) (nc)
Adriana Rodica Chirita (ROM)
(nc)
Moges Taye-3 (nc)
Adriana Barbu-2
(nc)
Moges Taye-2 (nc)
Natalia Galushko (BLR)
(nc)
Moges Taye (ETH) (nc)
Adriana Barbu (ROM)
(nc)
Stephen Langat
Kibore-2 (nc)
Elena Plastinina (UKR)
(nc)
Stephen Langat
Kibore (KEN) (nc)
Firaya Sultanova (RUS)
(nc)
Bigboy Goromonzi (ZIM) (nc)
Serap Aktas (TUR)
(nc)
Jacob Ngunzu (KEN) (nc)
Elena Sipatova (RUS)
(nc)
Cihangir Demirel (TUR) (nc)
Aurica Buia (ROM)
(nc)
Terrence Mitchell (SCO) (nc)
Jacqueline Davis (ENG)
(nc)
Gazi Asikoglu (TUR) (nc)
Aysegul Battal (TUR)
(nc)
Aydin Ceken (TUR) (nc)
n/a
Ahmet Altun (TUR) (nc)
Keriman Günaydin (TUR)
(nc)
Jens Worzner (DEN) (nc)
Fatma Erkan (TUR)
(nc)
Hanefi Atmaca (TUR) (nc)
n/a
Mehmet Terzi (TUR) (nc)
n/a
Mehmet Yurdadon (TUR) (nc)
n/a
Ian Thompson (ENG) (nc)
n/a
Ismail Karagoz (TUR) (nc)
n/a
Suleyman Silaci (TUR) (nc)
n/a
Necmettin Saglam (TUR) (nc)
n/a
Hasan Saylam (TUR) (nc)
n/a
(nc) => Turkish championship
2016 Mert Girmalegese was the Turkish
champion (7th place, 2:17:57)
2016 Esma Aydemir was the Turkish
champion (4th place, 2:40:28)
2015 Bekir Karayel was the Turkish
champion (6th place, 2:19:56)
2015 Meryem Erdogan was the Turkish
champion (3rd place, 2:46:42)
2014 Fatih Bilgic was the Turkish
champion (9th place, 2:19:57)
2014 Elvan Abeylegesse was the Turkish champion
(5th place, 2:32:15)
2013 Bekir Karayel was the Turkish
champion (6th place, 2:20:27)
2013 Elvan Abeylegesse was the Turkish champion
(2nd place, 2:29:30)
2012 Ali-Haydar Tekgoz was the Turkish champion (8th place, 2:26:01)
2012 Sultan Haydar was the Turkish champion (3rd place, 2:29:41)
2011 Bekir Karayel was the Turkish champion (5th place, 2:15:48)
2011 Sultan Haydar was the Turkish champion (7th place, 2:35:06)
2010 Bekir Karayel was the Turkish champion (7th place, 2:35:06)
2010 Bahar Dogan was the Turkish champion (6th place, 2:37:53)
2009 Bekir Karayel was the Turkish champion (11th place, 2:23:17)
2009 Bahar Dogan was the Turkish champion (5th place, 2:38:54)
2008 Bekir Karayel was the Turkish champion (9th place, 2:20:38)
2008 Mehtap Dogan was the Turkish champion (3rd place, 2:33:17)
2007 Abdil Ceylan was the Turkish champion (20th place, 2:16:13)
2007 Bahar Dogan was the Turkish champion (6th place, 2:37:31)
2006 Bekir Karayel was the Turkish champion (13th place, 2:17:03)
2006 Mehtap Dogan was the Turkish champion (4th place, 2:31:13)
2005 Ahmet Yavuz was the Turkish champion (15th place, 2:22:24)
2005 Lale Ozturk was the Turkish champion (7th place, 2:43:24)
2004 Ahmet Yavuz was the Turkish champion (8th place, 2:24:50)
2004 Hasibe Onat was the Turkish champion (6th place, 3:01:01)
2002 Satilmis Atmaca was the Turkish champion (9th place, 2:23:23)
2002 Serap Aktas was the Turkish champion (4th place, 2:43:29)
2001 Zeki Ozturk was the Turkish champion (6th place, 2:23:07)
2001 Mehtap Sizmaz was the Turkish champion (2nd place, 2:39:13)
2000 Changir Demirel was the Turkish champion (9rd place, 2:26:32)
2000 Nazan Elçin was the Turkish champion (8th place, 3:02:46)
1999 Gazi Asikoglu was the Turkish champion (10th place, 2:25:04)
1999 Serap Aktas was the Turkish champion (4th place, 2:39:01)
1998 Furgan Boyan was the Turkish champion (12th place, 2:28:19)
1998 Serap Aktas was the Turkish champion (3rd place, 2:41:00)
1997 Lale Ozturk was the Turkish champion (5th place, 2:37:54)
1997 Gazi Asikoglu was the Turkish champion (7th place, 2:19:41)
1996 Haydar Dogan was the Turkish champion (10th place, 2:26:19)
1996 Lale Ozturk was the Turkish champion (3rd place, 2:41:39)
1995 Zeki Ozturk was the Turkish champion (2nd place, 2:18:34)
1994 Haydar Dogan was the Turkish champion (3rd place, 2:25:14)
1993 Changir Demirel was the Turkish champion (3rd place, 2:23:23)
1993 Nurten Tasdemir was the Turkish champion (6th place, 2:58:14)
1991 Mehmet Bayram was the Turkish champion (2nd place, 2:23:08)
1991 Handan Dogan was the Turkish champion (9th place, 3:28:35)
1987 Seft Het was the Turkish champion (3rd place, 2:16:45)
Most Wins
(1979/date)
Men
3
Moges Taye (ETH)
1997-1999
Women
2
Rimma Dubovik (UKR)
2002-03
Prize Money (ADR qualified runners)
Year Men Women Total 2015 $143,000 $143,000 $286,000 2014 108,000 108,000 216,000 2013 108,000 108,000 216,000 2012 108,000 108,000 216,000 2011 108,000 108,000 216,000 2010 108,000 108,000 216,000 2009 108,000 108,000 216,000 2008 195,000 164,000 359,000 2007 235,400 203,800 439,200 2006 128,300 115,800 244,100 2005 88,700 90,650 179,350 2004 40,500 39,900 80,400 2003 42,400 39,500 81,900 .... 2001 11,900 13,050 24,950 2000 57,700 49,000 100,700 1999 52,000 49,000 101,000 1998 45,000 45,000 90,000 1997 57,650 57,650 115,300 Sums $1,739,550 $1,649,350 $3,388,900
Race Time Bias (sec/km)
Year Men Women 2011 n/c -2.1 .... 2009 +3.4 n/c 2008 n/c -2.2 2007 -1.2 n/c 2006 -3.8 -4.8 2005 +1.5 +1.0 2004 +4.9 n/c 2003 +3.9 n/c 2002 +3.1 n/c 2001 +4.1 n/c 2000 +7.0 n/c .... 1998 +2.3 n/c 1997 +4.3 +1.1 1996 +4.3 n/c 1995 +2.4 +3.5 Avgs +2.8 -0.6
Competitive Level (1990-date)
Year Men Women 2016<PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER><PHONE_NUMBER> n/c 476
Quality Performances
Men <2:11 2 <2:12 15 <2:13 26 <2:14 42 <2:15 61 <2:16 74 <2:17 99 <2:18 119 <2:19 153 <2:20 182 Women <2:28 3 <2:30 16 <2:32 30 <2:34 39 <2:36 54 <2:38 77 <2:40 100 <2:42 129 <2:44 145 <2:46 156 <2:48 177 <2:50 192
Last Updated on 15 Nov 2016
Contributors: Osman Atakan Tekin, Meric Tafolar, Marty Post, Juraj Gasparovic (1), Klaas Loonstra (1), Franco Civai (1), David Monti (5)


|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
public interface SaslClientFactory
An interface for creating instances of SaslClient. A class that implements this interface must be thread-safe and handle multiple simultaneous requests. It must also have a public constructor that accepts no argument.
This interface is not normally accessed directly by a client, which will use the Sasl static methods instead. However, a particular environment may provide and install a new or different SaslClientFactory.
SaslClient
,
Sasl
| Method Summary | |
|---|---|
SaslClient |
createSaslClient(String[] mechanisms,
String authorizationId,
String protocol,
String serverName,
Map<String,?> props,
CallbackHandler cbh)
Creates a SaslClient using the parameters supplied. |
String[] |
getMechanismNames(Map<String,?> props)
Returns an array of names of mechanisms that match the specified mechanism selection policies. |
| Method Detail |
|---|
SaslClient createSaslClient(String[] mechanisms, String authorizationId, String protocol, String serverName, Map<String,?> props, CallbackHandler cbh) throws SaslException
mechanisms
- The non-null list of mechanism names to try. Each is the
IANA-registered name of a SASL mechanism. (e.g. "GSSAPI", "CRAM-MD5").authorizationId
- The possibly null protocol-dependent
identification to be used for authorization.
If null or empty, the server derives an authorization
ID from the client's authentication credentials.
When the SASL authentication completes successfully,
the specified entity is granted access.protocol
- The non-null string name of the protocol for which
the authentication is being performed (e.g., "ldap").serverName
- The non-null fully qualified host name
of the server to authenticate to.props
- The possibly null set of properties used to select the SASL
mechanism and to configure the authentication exchange of the selected
mechanism. See the Sasl class for a list of standard properties.
Other, possibly mechanism-specific, properties can be included.
Properties not relevant to the selected mechanism are ignored.cbh
- The possibly null callback handler to used by the SASL
mechanisms to get further information from the application/library
to complete the authentication. For example, a SASL mechanism might
require the authentication ID, password and realm from the caller.
The authentication ID is requested by using a NameCallback.
The password is requested by using a PasswordCallback.
The realm is requested by using a RealmChoiceCallback if there is a list
of realms to choose from, and by using a RealmCallback if
the realm must be entered.
SaslException
- If cannot create a SaslClient because
of an error.String[] getMechanismNames(Map<String,?> props)
props
- The possibly null set of properties used to specify the
security policy of the SASL mechanisms. For example, if props
contains the Sasl.POLICY_NOPLAINTEXT property with the value
"true", then the factory must not return any SASL mechanisms
that are susceptible to simple plain passive attacks.
See the Sasl class for a complete list of policy properties.
Non-policy related properties, if present in props, are ignored.
|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
Copyright © 2004, 2010 Oracle and/or its affiliates. All rights reserved. Use is subject to license terms. Also see the documentation redistribution policy.


:: com :: sun :: star :: awt ::
|
| interface XView |
|
- Description
- makes it possible to attach an oputput device to the object.
This kind of object is called view-object.
|
| Methods' Details |
setGraphics
| boolean |
| setGraphics( |
[in] XGraphics |
aDevice ); |
- Description
- sets the output device.
|
|
getGraphics
- Description
- returns the output device which was set using the method
XView::setGraphics.
|
|
getSize
- Description
- returns the size of the object in device units.
A device must be set before.
|
|
draw
| [oneway] void |
| draw( |
[in] long |
nX, |
| [in] long |
nY ); |
- Description
- draws the object at the specified position.
If the output should be clipped, the caller has to set the
clipping region.
|
|
setZoom
| [oneway] void |
| setZoom( |
[in] float |
fZoomX, |
| [in] float |
fZoomY ); |
- Description
- sets the zoom factor.
The zoom factor only affects the content of the view, not the size.
|
|
Top of Page
Copyright © 2013, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


{% extends 'base.html' %} {% block title %} Flack {% endblock %} {% block body %}
Username is already taken!
Username is too long!


Hints for SRM 611, D2, 250-pointer (InterestingNumber)
James S. Plank
Tue Mar 4 18:34:05 EST 2014
Problem Statement.
I was surprised to see that only 52 percent of the Topcoders got this one
correct. It doesn't feel tricky to me.
As always, the key is to think precisely about how you are testing the string. You
want to do the following for each digit from '0' through '9':
- Count the occurrences of the digit. If it is zero, then you're fine and
can move onto the next digit.
- If it is non-zero, but not equal to two, then the number is not interesting.
- If it is equal to two, then you need to look at the indices of the digit, and
test their difference. If it is correct, then move onto the next digit.
Otherwise, the number is not interesting. Now, I want you to think about what "correct" means.
Do a few examples -- the pattern should be pretty clear.


{% extends "main/main_base.html" %} {# main/main_base.html extends layout.html #} {% from "common/form_macros.html" import render_card, render_card_given_privacy %} {% block content %}{#
Profile Button: First Last
#} {#Description: Words words word words word word words words.
#} {# #} {##} {# #}Below is a list of the projects you have liked. Every time you click the heart button on a particular project or grouping of projects, that tile is saved to this area for you. They’re organized in order of when the project was first created.


Classes | |
| struct | Data |
Public Member Functions | |
| GLSL (AbstractAPI::OpenGL2xDevice *dev) | |
| void | beginPaint () const |
| void | endPaint () const |
| void | enable () const |
| void | disable () const |
| void | bind (const Tempest::ShaderProgram &) const |
| void | setVertexDecl (const Tempest::AbstractAPI::VertexDecl *) const |
| void * | context () const |
| ProgramObject * | createShaderFromSource (const Source &src, std::string &outputLog) const |
| void | deleteShader (ProgramObject *) const |
| Source | surfaceShader (const UiShaderOpt &opt, bool &hasHalfPixelOffset) const |
| std::string | surfaceShader (ShaderType t, const UiShaderOpt &, bool &hasHalfpixOffset) const |
| Public Member Functions inherited from Tempest::GraphicsSubsystem | |
| virtual void | event (const Event &e) |
Additional Inherited Members | |
| Public Types inherited from Tempest::GraphicsSubsystem | |
| enum | ShaderType { Vertex, Fragment, Tess, Eval } |
| Static Public Member Functions inherited from Tempest::AbstractShadingLang | |
| static void | assignUniformBuffer (UBO &ux, const char *ubo, const UniformDeclaration &u) |
| Static Protected Member Functions inherited from Tempest::AbstractShadingLang | |
| static ProgramObject * | get (const Tempest::ShaderProgram &s) |
| static AbstractAPI::Texture * | get (const Tempest::Texture2d &s) |
| static AbstractAPI::Texture * | get (const Tempest::Texture3d &s) |
| static const std::shared_ptr< std::vector< AbstractShadingLang::UBO > > & | inputOf (const Tempest::ShaderProgram &s) |


OneMore Game Studio was established in 2018. The core members of the company have a rich accumulation in the field of research and development of horizontal action games. Creating unique gameplay and giving players a special game experience is the core goal of our game design and development, and also the unique value of the company.
"Arcade Rabbit" is the first horizontal action RogueLike game developed after the company was founded.


prml.linear package¶
Submodules¶
prml.linear.bayesian_logistic_regression module¶
-
class
prml.linear.bayesian_logistic_regression.
BayesianLogisticRegression
(alpha: float = 1.0)[source]¶ Bases:
prml.linear.logistic_regression.LogisticRegression
Logistic regression model
w ~ Gaussian(0, alpha^(-1)I) y = sigmoid(X @ w) t ~ Bernoulli(t|y)
-
fit
(X: numpy.ndarray, t: numpy.ndarray, max_iter: int = 100)[source]¶ bayesian estimation of logistic regression model using Laplace approximation
Parameters: - X ((N, D) np.ndarray) – training data independent variable
- t ((N,) np.ndarray) – training data dependent variable binary 0 or 1
- max_iter (int, optional) – maximum number of paramter update iteration (the default is 100)
-
prml.linear.bayesian_regression module¶
-
class
prml.linear.bayesian_regression.
BayesianRegression
(alpha: float = 1.0, beta: float = 1.0)[source]¶ Bases:
prml.linear.regression.Regression
Bayesian regression model
w ~ N(w|0, alpha^(-1)I) y = X @ w t ~ N(t|X @ w, beta^(-1))
-
fit
(X: numpy.ndarray, t: numpy.ndarray)[source]¶ bayesian update of parameters given training dataset
Parameters: - X ((N, n_features) np.ndarray) – training data independent variable
- t ((N,) np.ndarray) – training data dependent variable
-
predict
(X: numpy.ndarray, return_std: bool = False, sample_size: int = None)[source]¶ return mean (and standard deviation) of predictive distribution
Parameters: - X ((N, n_features) np.ndarray) – independent variable
- return_std (bool, optional) – flag to return standard deviation (the default is False)
- sample_size (int, optional) – number of samples to draw from the predictive distribution (the default is None, no sampling from the distribution)
Returns: - y ((N,) np.ndarray) – mean of the predictive distribution
- y_std ((N,) np.ndarray) – standard deviation of the predictive distribution
- y_sample ((N, sample_size) np.ndarray) – samples from the predictive distribution
-
prml.linear.classifier module¶
prml.linear.emprical_bayes_regression module¶
-
class
prml.linear.emprical_bayes_regression.
EmpiricalBayesRegression
(alpha: float = 1.0, beta: float = 1.0)[source]¶ Bases:
prml.linear.bayesian_regression.BayesianRegression
Empirical Bayes Regression model a.k.a. type 2 maximum likelihood, generalized maximum likelihood, evidence approximation
w ~ N(w|0, alpha^(-1)I) y = X @ w t ~ N(t|X @ w, beta^(-1)) evidence function p(t|X,alpha,beta) = S p(t|w;X,beta)p(w|0;alpha) dw
-
fit
(X: numpy.ndarray, t: numpy.ndarray, max_iter: int = 100)[source]¶ maximization of evidence function with respect to the hyperparameters alpha and beta given training dataset
Parameters: - X ((N, D) np.ndarray) – training independent variable
- t ((N,) np.ndarray) – training dependent variable
- max_iter (int) – maximum number of iteration
-
prml.linear.fishers_linear_discriminant module¶
-
class
prml.linear.fishers_linear_discriminant.
FishersLinearDiscriminant
(w: numpy.ndarray = None, threshold: float = None)[source]¶ Bases:
prml.linear.classifier.Classifier
Fisher’s Linear discriminant model
-
classify
(X: numpy.ndarray)[source]¶ classify input data
Parameters: X ((N, D) np.ndarray) – independent variable to be classified Returns: binary class for each input Return type: (N,) np.ndarray
-
prml.linear.least_squares_classifier module¶
-
class
prml.linear.least_squares_classifier.
LeastSquaresClassifier
(W: numpy.ndarray = None)[source]¶ Bases:
prml.linear.classifier.Classifier
Least squares classifier model
X : (N, D) W : (D, K) y = argmax_k X @ W
prml.linear.linear_regression module¶
-
class
prml.linear.linear_regression.
LinearRegression
[source]¶ Bases:
prml.linear.regression.Regression
Linear regression model y = X @ w t ~ N(t|X @ w, var)
-
fit
(X: numpy.ndarray, t: numpy.ndarray)[source]¶ perform least squares fitting
Parameters: - X ((N, D) np.ndarray) – training independent variable
- t ((N,) np.ndarray) – training dependent variable
-
predict
(X: numpy.ndarray, return_std: bool = False)[source]¶ make prediction given input
Parameters: - X ((N, D) np.ndarray) – samples to predict their output
- return_std (bool, optional) – returns standard deviation of each predition if True
Returns: - y ((N,) np.ndarray) – prediction of each sample
- y_std ((N,) np.ndarray) – standard deviation of each predition
-
prml.linear.logistic_regression module¶
-
class
prml.linear.logistic_regression.
LogisticRegression
[source]¶ Bases:
prml.linear.classifier.Classifier
Logistic regression model
y = sigmoid(X @ w) t ~ Bernoulli(t|y)
-
classify
(X: numpy.ndarray, threshold: float = 0.5)[source]¶ classify input data
Parameters: - X ((N, D) np.ndarray) – independent variable to be classified
- threshold (float, optional) – threshold of binary classification (default is 0.5)
Returns: binary class for each input
Return type: (N,) np.ndarray
-
fit
(X: numpy.ndarray, t: numpy.ndarray, max_iter: int = 100)[source]¶ maximum likelihood estimation of logistic regression model
Parameters: - X ((N, D) np.ndarray) – training data independent variable
- t ((N,) np.ndarray) – training data dependent variable binary 0 or 1
- max_iter (int, optional) – maximum number of paramter update iteration (the default is 100)
-
prml.linear.perceptron module¶
-
class
prml.linear.perceptron.
Perceptron
[source]¶ Bases:
prml.linear.classifier.Classifier
Perceptron model
prml.linear.regression module¶
prml.linear.ridge_regression module¶
-
class
prml.linear.ridge_regression.
RidgeRegression
(alpha: float = 1.0)[source]¶ Bases:
prml.linear.regression.Regression
Ridge regression model
w* = argmin |t - X @ w| + alpha * |w|_2^2
prml.linear.softmax_regression module¶
-
class
prml.linear.softmax_regression.
SoftmaxRegression
[source]¶ Bases:
prml.linear.classifier.Classifier
Softmax regression model aka multinomial logistic regression, multiclass logistic regression, maximum entropy classifier.
y = softmax(X @ W) t ~ Categorical(t|y)
-
classify
(X: numpy.ndarray)[source]¶ classify input data
Parameters: X ((N, D) np.ndarray) – independent variable to be classified Returns: class index for each input Return type: (N,) np.ndarray
-
fit
(X: numpy.ndarray, t: numpy.ndarray, max_iter: int = 100, learning_rate: float = 0.1)[source]¶ maximum likelihood estimation of the parameter
Parameters: - X ((N, D) np.ndarray) – training independent variable
- t ((N,) or (N, K) np.ndarray) – training dependent variable in class index or one-of-k encoding
- max_iter (int, optional) – maximum number of iteration (the default is 100)
- learning_rate (float, optional) – learning rate of gradient descent (the default is 0.1)
-
prml.linear.variational_linear_regression module¶
-
class
prml.linear.variational_linear_regression.
VariationalLinearRegression
(beta: float = 1.0, a0: float = 1.0, b0: float = 1.0)[source]¶ Bases:
prml.linear.regression.Regression
variational bayesian estimation of linear regression model p(w,alpha|X,t) ~ q(w)q(alpha) = N(w|w_mean, w_var)Gamma(alpha|a,b)
-
a
¶ a parameter of variational posterior gamma distribution
Type: float
-
b
¶ another parameter of variational posterior gamma distribution
Type: float
-
w_mean
¶ mean of variational posterior gaussian distribution
Type: (n_features,) ndarray
-
w_var
¶ variance of variational posterior gaussian distribution
Type: (n_features, n_feautures) ndarray
-
n_iter
¶ number of iterations performed
Type: int
-
fit
(X: numpy.ndarray, t: numpy.ndarray, iter_max: int = 100)[source]¶ variational bayesian estimation of parameter
Parameters: - X ((N, D) np.ndarray) – training independent variable
- t ((N,) np.ndarray) – training dependent variable
- iter_max (int, optional) – maximum number of iteration (the default is 100)
-
predict
(X: numpy.ndarray, return_std: bool = False)[source]¶ make prediction of input
Parameters: - X ((N, D) np.ndarray) – independent variable
- return_std (bool, optional) – return standard deviation of predictive distribution if True (the default is False)
Returns: - y ((N,) np.ndarray) – mean of predictive distribution
- y_std ((N,) np.ndarray) – standard deviation of predictive distribution
-
prml.linear.variational_logistic_regression module¶
-
class
prml.linear.variational_logistic_regression.
VariationalLogisticRegression
(alpha: float = None, a0: float = 1.0, b0: float = 1.0)[source]¶ Bases:
prml.linear.logistic_regression.LogisticRegression
-
alpha
¶
-
fit
(X: numpy.ndarray, t: numpy.ndarray, iter_max: int = 1000)[source]¶ variational bayesian estimation of the parameter
Parameters: - X ((N, D) np.ndarray) – training independent variable
- t ((N,) np.ndarray) – training dependent variable
- iter_max (int, optional) – maximum number of iteration (the default is 1000)
-
Module contents¶
-
class
prml.linear.
BayesianLogisticRegression
(alpha: float = 1.0)[source]¶ Bases:
prml.linear.logistic_regression.LogisticRegression
Logistic regression model
w ~ Gaussian(0, alpha^(-1)I) y = sigmoid(X @ w) t ~ Bernoulli(t|y)
-
fit
(X: numpy.ndarray, t: numpy.ndarray, max_iter: int = 100)[source]¶ bayesian estimation of logistic regression model using Laplace approximation
Parameters: - X ((N, D) np.ndarray) – training data independent variable
- t ((N,) np.ndarray) – training data dependent variable binary 0 or 1
- max_iter (int, optional) – maximum number of paramter update iteration (the default is 100)
-
-
class
prml.linear.
BayesianRegression
(alpha: float = 1.0, beta: float = 1.0)[source]¶ Bases:
prml.linear.regression.Regression
Bayesian regression model
w ~ N(w|0, alpha^(-1)I) y = X @ w t ~ N(t|X @ w, beta^(-1))
-
fit
(X: numpy.ndarray, t: numpy.ndarray)[source]¶ bayesian update of parameters given training dataset
Parameters: - X ((N, n_features) np.ndarray) – training data independent variable
- t ((N,) np.ndarray) – training data dependent variable
-
predict
(X: numpy.ndarray, return_std: bool = False, sample_size: int = None)[source]¶ return mean (and standard deviation) of predictive distribution
Parameters: - X ((N, n_features) np.ndarray) – independent variable
- return_std (bool, optional) – flag to return standard deviation (the default is False)
- sample_size (int, optional) – number of samples to draw from the predictive distribution (the default is None, no sampling from the distribution)
Returns: - y ((N,) np.ndarray) – mean of the predictive distribution
- y_std ((N,) np.ndarray) – standard deviation of the predictive distribution
- y_sample ((N, sample_size) np.ndarray) – samples from the predictive distribution
-
-
class
prml.linear.
EmpiricalBayesRegression
(alpha: float = 1.0, beta: float = 1.0)[source]¶ Bases:
prml.linear.bayesian_regression.BayesianRegression
Empirical Bayes Regression model a.k.a. type 2 maximum likelihood, generalized maximum likelihood, evidence approximation
w ~ N(w|0, alpha^(-1)I) y = X @ w t ~ N(t|X @ w, beta^(-1)) evidence function p(t|X,alpha,beta) = S p(t|w;X,beta)p(w|0;alpha) dw
-
fit
(X: numpy.ndarray, t: numpy.ndarray, max_iter: int = 100)[source]¶ maximization of evidence function with respect to the hyperparameters alpha and beta given training dataset
Parameters: - X ((N, D) np.ndarray) – training independent variable
- t ((N,) np.ndarray) – training dependent variable
- max_iter (int) – maximum number of iteration
-
-
class
prml.linear.
LeastSquaresClassifier
(W: numpy.ndarray = None)[source]¶ Bases:
prml.linear.classifier.Classifier
Least squares classifier model
X : (N, D) W : (D, K) y = argmax_k X @ W
-
class
prml.linear.
LinearRegression
[source]¶ Bases:
prml.linear.regression.Regression
Linear regression model y = X @ w t ~ N(t|X @ w, var)
-
fit
(X: numpy.ndarray, t: numpy.ndarray)[source]¶ perform least squares fitting
Parameters: - X ((N, D) np.ndarray) – training independent variable
- t ((N,) np.ndarray) – training dependent variable
-
predict
(X: numpy.ndarray, return_std: bool = False)[source]¶ make prediction given input
Parameters: - X ((N, D) np.ndarray) – samples to predict their output
- return_std (bool, optional) – returns standard deviation of each predition if True
Returns: - y ((N,) np.ndarray) – prediction of each sample
- y_std ((N,) np.ndarray) – standard deviation of each predition
-
-
class
prml.linear.
FishersLinearDiscriminant
(w: numpy.ndarray = None, threshold: float = None)[source]¶ Bases:
prml.linear.classifier.Classifier
Fisher’s Linear discriminant model
-
classify
(X: numpy.ndarray)[source]¶ classify input data
Parameters: X ((N, D) np.ndarray) – independent variable to be classified Returns: binary class for each input Return type: (N,) np.ndarray
-
-
class
prml.linear.
LogisticRegression
[source]¶ Bases:
prml.linear.classifier.Classifier
Logistic regression model
y = sigmoid(X @ w) t ~ Bernoulli(t|y)
-
classify
(X: numpy.ndarray, threshold: float = 0.5)[source]¶ classify input data
Parameters: - X ((N, D) np.ndarray) – independent variable to be classified
- threshold (float, optional) – threshold of binary classification (default is 0.5)
Returns: binary class for each input
Return type: (N,) np.ndarray
-
fit
(X: numpy.ndarray, t: numpy.ndarray, max_iter: int = 100)[source]¶ maximum likelihood estimation of logistic regression model
Parameters: - X ((N, D) np.ndarray) – training data independent variable
- t ((N,) np.ndarray) – training data dependent variable binary 0 or 1
- max_iter (int, optional) – maximum number of paramter update iteration (the default is 100)
-
-
class
prml.linear.
Perceptron
[source]¶ Bases:
prml.linear.classifier.Classifier
Perceptron model
-
class
prml.linear.
RidgeRegression
(alpha: float = 1.0)[source]¶ Bases:
prml.linear.regression.Regression
Ridge regression model
w* = argmin |t - X @ w| + alpha * |w|_2^2
-
class
prml.linear.
SoftmaxRegression
[source]¶ Bases:
prml.linear.classifier.Classifier
Softmax regression model aka multinomial logistic regression, multiclass logistic regression, maximum entropy classifier.
y = softmax(X @ W) t ~ Categorical(t|y)
-
classify
(X: numpy.ndarray)[source]¶ classify input data
Parameters: X ((N, D) np.ndarray) – independent variable to be classified Returns: class index for each input Return type: (N,) np.ndarray
-
fit
(X: numpy.ndarray, t: numpy.ndarray, max_iter: int = 100, learning_rate: float = 0.1)[source]¶ maximum likelihood estimation of the parameter
Parameters: - X ((N, D) np.ndarray) – training independent variable
- t ((N,) or (N, K) np.ndarray) – training dependent variable in class index or one-of-k encoding
- max_iter (int, optional) – maximum number of iteration (the default is 100)
- learning_rate (float, optional) – learning rate of gradient descent (the default is 0.1)
-
-
class
prml.linear.
VariationalLinearRegression
(beta: float = 1.0, a0: float = 1.0, b0: float = 1.0)[source]¶ Bases:
prml.linear.regression.Regression
variational bayesian estimation of linear regression model p(w,alpha|X,t) ~ q(w)q(alpha) = N(w|w_mean, w_var)Gamma(alpha|a,b)
-
a
¶ a parameter of variational posterior gamma distribution
Type: float
-
b
¶ another parameter of variational posterior gamma distribution
Type: float
-
w_mean
¶ mean of variational posterior gaussian distribution
Type: (n_features,) ndarray
-
w_var
¶ variance of variational posterior gaussian distribution
Type: (n_features, n_feautures) ndarray
-
n_iter
¶ number of iterations performed
Type: int
-
fit
(X: numpy.ndarray, t: numpy.ndarray, iter_max: int = 100)[source]¶ variational bayesian estimation of parameter
Parameters: - X ((N, D) np.ndarray) – training independent variable
- t ((N,) np.ndarray) – training dependent variable
- iter_max (int, optional) – maximum number of iteration (the default is 100)
-
predict
(X: numpy.ndarray, return_std: bool = False)[source]¶ make prediction of input
Parameters: - X ((N, D) np.ndarray) – independent variable
- return_std (bool, optional) – return standard deviation of predictive distribution if True (the default is False)
Returns: - y ((N,) np.ndarray) – mean of predictive distribution
- y_std ((N,) np.ndarray) – standard deviation of predictive distribution
-
-
class
prml.linear.
VariationalLogisticRegression
(alpha: float = None, a0: float = 1.0, b0: float = 1.0)[source]¶ Bases:
prml.linear.logistic_regression.LogisticRegression
-
alpha
¶
-
fit
(X: numpy.ndarray, t: numpy.ndarray, iter_max: int = 1000)[source]¶ variational bayesian estimation of the parameter
Parameters: - X ((N, D) np.ndarray) – training independent variable
- t ((N,) np.ndarray) – training dependent variable
- iter_max (int, optional) – maximum number of iteration (the default is 1000)
-


US County Votes
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Mail
Click me
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First
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First
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#include<iostream>
Hello
First Name
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Computer Science
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Leverage our products to build your businesss by aquiring the necessities to make your business thrive in competitive markets.
Lorem ipsum dolor sit amet consectetur adipisicing elit. Possimus quo sint eligendi suscipit neque hic vitae similique rerum, quam illum vel error beatae, nostrum in consequatur est distinctio fugit molestiae.
Iterative approaches to corporate strategy foster collaborative thinking to further the overall value proposition.
Iterative approaches to corporate strategy foster collaborative thinking to further the overall value proposition.
Iterative approaches to corporate strategy foster collaborative thinking to further the overall value proposition.
approaches to corporate strategy foster collaborative thinking to further the overall value proposition.


Be Strong and courageous;
Do not be frightened or dismayed,
for the Lord your God is with you wherever you go.
Josuah 1:9
The Lord is near to all who call on him,
to all who call on him in truth.
He fulfills the desires of those who fear him; he hears their cry and saves them.
Psalm 145: 18-19


You learned that the DOM is accessible via CSS or JavaScript.
But in the end, what is the DOM?
To put it simply, the living DOM is a set of tags organized into a tree.
And from the JavaScript point of view, each tag is an object with properties and methods.
<body>
tag:As you can see, under the hood, there's a strong typing in there!


gcloud
The official CLI tool for Google Cloud Platform.
More information: home page
- List all properties in one's active configuration:
gcloud config list
- Log in to Google account:
gcloud auth login
- Set the active project:
gcloud config set project project_name
- SSH into a virtual machine instance:
gcloud compute ssh user@instance
- Display all Google Compute Engine instances in a project. Instances from all zones are listed by default:
gcloud compute instances list
- Update a kubeconfig file with the appropriate credentials to point kubectl to a specific cluster in Google Kubernetes Engine:
gcloud container clusters get-credentials cluster_name
- Update all gcloud CLI components:
gcloud components update
- Show help for a given command:
gcloud help command


Built with
Google Firebase
, Chad is a location based chatroom service that allows users to start
and join trending chatrooms in their area. Chad Cloud contains Chad's
Firebase Cloud Functions, hosting our Authentication
,
Account
, Documentation
, and Location
microservices.


About me
Background
As previously stated my name is Lawrence Ireri. Am originaly from Nakuru County.
Education
My highest level of education so far is high school where I studied at Nakuru High School, in Nakuru.As of yet i dont have a job experience since am just fresh from high school as i finished last year.
Inspiration for joining Moringa
I first heard about Moringa School from a refferal who suggested I check out the website and it captured my attention since it was a long dream passion of mine to understand the essentials of coding, and that is why I joined this school.
Interests
My intrests include:
- Watching movies
- listening to music
- making my own music by producing beats
- coding and programming-the main influence torwards my descion to join moringa school.


Drug Arrests in the US
Reference:https://data.opendatasoft.com/explore/dataset/drug-arrests-in-the-us%40public/
DASH - Youth Risk Behavior Surveillance System (YRBSS): Middle School
Reference:https://data.opendatasoft.com/explore/dataset/dash-youth-risk-behavior-surveillance-system-yrbss-middle-school%40public/
NCHS - Drug Poisoning Mortality by County: United States
Reference:https://data.opendatasoft.com/explore/dataset/nchs-drug-poisoning-mortality-by-county-united-states%40public/


{# Load JavaScript files. The include location of this template may alter between ``
`` and the end of ````. #} {# Pull JS for widgets #} {% if request.on_demand_resource_renderer %} {% for js_url in request.on_demand_resource_renderer.get_resources("js") %} {% endfor %} {% endif %}


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The CS Education Research Group at the University of Toronto focuses on the application and empirical assessment of modern learning principles in the context of undergraduate computing education. Recent projects have focused on peer instruction, blended and online teaching, teamwork in programming courses, and educational data mining. The group also develops and maintains software in support of computing classrooms.
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Scrambled Eggs
Eggs are one my favorite foods. Here is a receipe for deliciously rich eggs
ingredients
- 2 eggs
- 1tbs butter
- 2tbs cream
Method
- Melt butter in a favorite pan over a medium heat
- Once butter has melted add cream and eggs
- Using a spatula fold the eggs from the edge of the pan to the center every 20 seconds (as if you are making an omelette)
- When the eggs are still moist remove from the heat (it will continue to cook on the plate until served)


Armed men kill Maersk MD’s wife at Ikoyi residence
by TheCableGildas Tohouo, managing director of Maersk Line, was attacked at his residence in the Ikoyi area of Lagos on Sunday night.
Maersk is the world’s biggest shipping company.
A source told TheCable that some of the domestic workers of Tohouo planned the attack in which the wife of the CEO was killed.
Both Tohouo and his wife are foreigners living in Nigeria. While the man is from Cameroon, the late wife was from Bulgaria.
The source said during the operation, the husband and wife were locked in different rooms. The late woman was said to have been forced to transfer some money to her attackers after which she was forced to drink acid and suffocated with pillow.
Those who carried out the attack reportedly gained access to the house through an electrician who is currently in police custody.
Tohouo was said to have placed a call through to the chief security officer of the company who led policemen to the scene before the suspects escaped.
The electrician and his friend who attacked the couple were immediately arrested, according to the source.
Bala Elkana, police public relations officer in Lagos, confirmed the incident to TheCable but said he did not want to comment on the identity of the victims.
“We have an incident on someone who was attacked with his wife. Both of them were stabbed. The wife died and he is still alive and responding to treatment. Those who carried it out are in our custody. One of them is an electrician in the estate, who was assisted by his friend. Their aim was to steal from the man. They were there to rob,” Elkana told TheCable.
The development comes one year after Sunday Ani, a Togolese cook, confessed killing Ope Bademosi, chairman of Credit Switch Technology, at his residence, still in Ikoyi, Lagos.


Hi, my name is Susie Reyes and I'm an aspiring digital artist! Thank you so much for checking out my profile. Feel free to commision a piece, I can create and icon for you, recreate your favorite characters, draw some of your favorite celebrities, the possibilities are endsless! To commission please click the contact button down below! Thank you :)


Mikkicon
Engineering
the Future
Engineering
the Future
Engineering
the Future
We provide flexible solutions for your architecture
We provide innovative solutions for your architecture
We provide qualitative solutions for your architecture
We provide smart solutions for your architecture
We provide reliable solutions for your architecture
We provide fast solutions for your architecture


Plus-size woman recreates Meghan Markle's style - but troll says 'looks like she ate her'
Katie Sturino received hurtful comments about her weight after nasty trolls started commenting on the body-positive advocate's Instagram account
by Kara O'NeillA plus-size influencer and body-positive advocate has faced harsh criticism after recreating some of Meghan Markle's latest looks.
Katie Sturino, who says she is a US size 18 (UK size 20), posted a number of photos on Instagram with her own interpretation of Meghan's style.
But while Katie received countless supportive comments about her looks, there was plenty of nasty ones too.
Posting a picture of herself in a nude, belted trench dress, Katie included a message from a troll, that read: "She looks like she ate Meghan Markle ... and stole her dress."
And you only need to scroll through just a handful more comments on Katie's other Instagram images to find more negative opinions.
Another person wrote: "Sorry, but accepting your body that is completely unhealthy is in no way positive."
Reacting to the comment, Katie wrote: "Every time I think we have made so much progress about our bodies and the way we talk about women, something pops up to remind me our work is not done!!!
"My #SuperSizeTheLook shots with Meghan Markle got picked up and the comments are brutal...
"They do not hurt my feelings, but they are scary. It's scary that a woman who is almost the average size in this country would be regarded with such shock!
"Also, they don't stop at my body, they are equal opportunity women haters who take shots at MM aswell.
"Can't be thin. Can't be fat.
"Maybe STFU ABOUT WOMEN'S BODIES!!!
"All this to say, please keep spreading positivity.
"Please keep shining bright."
She added: "Please don't engage with the crazies in the comments. Please do look at yourself in the mirror and tell her that you love her!!!!! (I just did).
"Nobody deserves to be spoken to like this, on or offline. We have work to do!!!!"
Katie has built a huge social media following for sharing inspirational content, including her #SuperSizeTheLook movement, which she created to challenge mainstream beauty standards.
Writing about her project recently, Katie said: "This is never about who wore it better.
"It's about empowering women of all sizes to wear what inspires them."


Problem 1. Minimum Coin Change
Given a amount A
and n
coins, v1<v2<v3<...<vn
. Write a program to find out minimum numbers of coins required to make the change for the amount A
.
Example.
Amount: 5
Coins [] = 1, 2, 3.
No. of ways to make the change are : { 1,1,1,1,1} , {1,1,1,2}, {2,2,1},{1,1,3} and {3,2}.
So as we can see minimum number of coins required are 2 (3+2=5).
Solutions:
/** int coins[]: set of value of coins.
int n: size of coins[], that is types of coins.
int amount: total value for change.
*/
int change(int coins[], int n, int amount)
{
int *d = new int[amount+1]; // this will store the optimal solution for all the values -- from 0 to given amount.
int *CC = new int[n]; // resets for every smaller problems
d[0] = 0; // 0 coins are required to make the change for 0
// now solve for all the amounts
for (int amt = 1; amt <= amount; amt++)
{
for (int j = 0; j < n; j++)
{
CC[j] = -1;
}
// Now try taking every coin one at a time and fill the solution in the CC[]
for (int j = 0; j < n; j++)
{
if (coins[j] <= amt) // check if coin value is less than amount
{
// select the coin and add 1 to solution of (amount-coin value)
CC[j] = d[amt - coins[j]] + 1;
}
}
//Now solutions for amt using all the coins is stored in CC[] take out the minimum (optimal) and store in d[amt]
d[amt]=-1;
for(int j=0;j<n;j++)
{
if(CC[j]>0 && (d[amt]==-1 || d[amt]>CC[j]))
{
d[amt]=CC[j];
}
}
}
//return the optimal solution for amount
return d[amount];
}
Problem 2. 0-1 Knapsack
We have n
items want to store in a bag, and the bag has v
space, item i
has value[i]
and takes size weight[i]
. Now we want to the items have combined size at most v
, and the total value of items is as large as possible. Note that we can not store parts of items, it is the whole item or nothing.
Example.
items: n = 5, space: v = 10.
weight [] = 2, 2, 6, 5, 4.
values [] = 6, 3, 5, 4, 6.
Thus, the ideal solution is pick up 1st, 2nd and 5th items to make the maximum values.
Solutions:
#include <stdio.h>
#define max(a,b) ((a)>(b)?(a):(b))
int main()
{
int v = 10 ;
int n = 5 ;
int value[] = {6 , 3 , 5 , 4 , 6};
int weight[] = {2 , 2 , 6 , 5 , 4};
int kp[6][11]={}; // initialize kp[][] = 0
int x[5]={}; // initialize x[] = 0
for(int i=0;i<=n;i++)
for(int j=0;j<=v;j++)
if(i>0 && weight[i-1]<=j)
kp[i][j] = max( kp[i-1][j], kp[i-1][j-weight[i-1]]+value[i-1]);
printf("The total amount is %d\n", kp[n][v]);
// /* print array kp to show details */
// for(int i=0;i<=n;i++)
// {
// for(int j=0;j<=v;j++)
// printf("%4d ",kp[i][j]);
// printf("\n");
// }
int j = 10;
for(int i=n; i>0; --i)
{
if(kp[i][j] > kp[i-1][j])
{
x[i-1] = 1;
j = j - weight[i-1];
}
}
printf("Items state in the bag: ");
for(int i=0; i<n; ++i)
printf("%d ", x[i]);
printf("\n");
}
To be continued….
This work is licensed under a CC A-S 4.0 International License.


WITNESS LIST Environmental Regulation Committee April 1, 2003-2:00P HB 594 On: Tate, Arthur C. (Texas Department of Health) Vickery, Mark (Texas Commission on Environmental Quality) Watkins, Helen (Texas Department of Health, Bureau of Radiation Control) HB 594 Committee Substitute (CSHB 594) Glacken, Shawn (Association of Electric Companies of Texas) HB 1791 For: Moore, Julie (Self) Staton, Debbie R. (Self and Family) Staton, Jerry (Self and Retired Police Officer) HB 1823 For: Hupp, Steve (Harris County Public Health and Environmental Services Department, Harris County Pollution Control) Lenz, Carole (Harris County Commissioner Steve Radack and Harris County) Montgomery, Mike (Harris County Fire and Emergency Services) HB 2045 For: Haragan, Kelly (Public Citizen) Against: Fisher, Jon (Texas Chemical Council) Miksa, Mary (Texas Association of Business and Chambers of Commerce) HB 2052 For: Hitchcock, Tom (Informed Citizens United) Senac, Mayor A.R. Rusty (City of Beach City, County of Chambers) Stokes, Robert (Harris County Attorney, Harris County Commissioners Court) Wemple, Chuck (Galveston Bay Foundation) Against: Fisher, Jon (Texas Chemical Council) Miksa, Mary (Texas Association of Business & Chambers of Commerce) HB 2529 For: Nicklen, Patrick (North Texas Municipal Water District) Stephens, David (North Texas Municipal Water District) Yancey, Jerry (North Texas Municipal Water District) On: Sarahan, Paul (Texas Commission on Environmental Quality) HB 2554 For: Hitchcock, Tom (Informed Citizens United) Senac, Mayor A. R. Rusty (City of Beach City, County of Chambers) Stokes, Robert (Harris County Attorney, Harris County Commissioners Court) Wemple, Chuck (Galveston Bay Foundation) Against: Fisher, Jon (Texas Chemical Council) Miksa, Mary (Texas Association of Business & Chambers of Commerce) HB 2955 For: Hitchcock, Tom (Informed Citizens United) Senac, Mayor A. R. Rusty (City of Beach City, County of Chambers) Stokes, Robert (Harris County Attorney, Harris County Commissioners Court) Wemple, Chuck (Galveston Bay Foundation) Against: Fisher, Jon (Texas Chemical Council) Miksa, Mary (Texas Association of Business & Chambers of Commerce)


Attrition is a high-level concept involving the combat balance between units. It combines the attack, defense, and build-cost of units together to figure out what units should be used against which units.
For example, let's say that an infantry has 1 HP and costs 10 production. A tank has 2 HP and costs 20 production. If, on average,
the infantry receives 1 HP of damage while causing 1 HP of damage to the tank, then each of them cause 10 production worth of
damage to each other in combat. They are equally matched from an attrition standpoint.
Attrition can give players an idea how to spend their production when creating new units. It's a measure of "how much bang for
the buck" do players get by building particular units. To put it another way, if one player spends 500 production building
unit A, and another player spends 500 production on unit B, attrition will tell you who will have units standing
after combat is done.
The attrition rating does not take the possibility of repairs into account. For example, if tanks can be repaired, while
infantry cannot, then tanks have an edge over infantry. The attrition rating does not take repairs into account because
whether or not a player is capable of repairing his units will depend on the particular situation. For example, if one
player has 20 infantry, and the second player has 3 tanks, then what will happen is that the tanks will be defeated in
combat without any chance to get repaired.
Attrition also doesn't tell you who will win the combat. For example, using fictional combat numbers, if one player has a
destroyer (2HP, 20 production cost) and he sends it against an aircraft carrier (8HP, 200 production cost), the destroyer might
cause 6 damage against the aircraft carrier before being sunk. In this case, the player with the destroyer lost 20 production
while the player with the carrier lost 150 production (i.e. 6/8 HP x 200 production). From an attrition standpoint, this combat
would strongly favor the destroyer, even though the combat will result in the aircraft carrier winning the battle.
How Attrition is calculated
Attrition is calculated by using the average amount of damage caused on each side. If a unit has 8 HP, and it costs 80 production
to build it, then each HP is "worth" 10 production points. If combat causes an average of 2.5 damage, then it causes 25 production
worth of damage. If unit A receives 10 production damage when unit B receives 20 production damage, then the attrition value would
be 66 for unit A, and 33 for unit B. This is based on the forumla 10/(10+20) = 10/30 = 0.33.
Attrition can be difficult to calculate when units are involved in ranged combat. If an infantry is entrenched in a location and
never moves and an artillery starts attacking him, then the attrition rating of the artillery is 100. It will never receive damage
from the infantry because he is outside the combat range. However, if the infantry moves within combat range to attack the artillery,
he will defeat the artillery, which is weak in direct combat. In that case, attrition strongly favors the infantry.
Related Topics:
How Combat Works
Table of Contents


- All winnings are paid for combinations of the same symbol on any of 27 winning lines.
- Each new game costs one stake.
- WILD substitutes for all symbols.
- A wall of the same symbols multiplies the total win x3.
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WILD SYMBOL Substitutes for all symbols. |
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Paytable shows the win for minimal bet.
The coin payout value is based on minimum bet. All wins are paid for combinations of the same symbol on any of 27 winning lines. Each new game costs one stake. Malfunction voids all pays and plays.
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Play Button Click to start playing at the current bet level. |
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Autoplay Settings (if applicable) Click to access the Autoplay settings menu: Number of games: Select the amount of spins. At any win: Stops Autoplay at any win. If single win exceeds: Stops Autoplay if a single win exceeds the set amount. If cash increases by: Stops Autoplay if the balance increases by the set amount. If cash decreases by: Stops Autoplay if the balance decreases by the set amount. |
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Max Bet Set the bet to max value. |
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Gamble Risk your win for a chance to double it. |
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To Credit Claim your win without next spin. |
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Fast Play Click to switch between normal, fast or ultra fast(if applicable) play modes. |
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Win Current win. |
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Balance Current account balance. |
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Coin Value Set the coin value. |
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Cash Bet Current bet in cash. |
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Coins Bet Current bet in coins. |
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Lines Number of active paylines. |
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Game Settings Click to access the game settings and game information: Full screen mode: Click to switch Full screen mode on or off. Double Screen Mode: Click to switch paytable display on or off. Use space key: Use space key as a spin button. Energy Saving Mode: Use Energy Saving Mode to conserve battery. Big Win Presentation: Click to switch Big Win animations on or off. |
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Sound Settings Click to access the sound settings: Mute: Click to mute all sound effects and music. Sound volume: Click to switch sounds on or off. Music volume: Click to switch music on or off. |
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Game Rules Click to view game rules. |
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Paytable Click to view winning combinations and payouts. |
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Game History Click to view your latest game history. The game history is available only when playing for money. |
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The following game features and settings may be subject to the terms and conditions of the gaming site. For more information on the following, refer to the gaming website:
In the event of malfunction of the gaming hardware/software, all affected game bets and payouts are rendered void and all affected bets refunded.
Games that do not feature interrupted rounds are either rolled back with bet being refunded or is completed by awarding a win. Games that feature interrupted rounds and are not complete will last for 90 days.


How to use Live Captions on the Pixel 4
Source: Ara Wagoner / Android Central
I'll admit, when they were showing off the Pixel 4's many features at the Made by Google event in October, one of the features that most intrigued me was Live Captions. The live transcriptions in the new Voice Recorder app shown off on stage are amazing, but that requires you to be actively recording. Live Captions, on the other hand, allows you to caption all the videos you watch, even video calls that would otherwise never be captioned. Best of all, it's easy for everyone to turn on Live Captions in just two taps!
How to turn on Live Captions on the Pixel 4
- Press the up volume or down volume key.
- Tap the Live Captions icon below the volume slider. Source: Ara Wagoner / Android Central
Live Captions will activate and begin filtering your phone's audio through its speech recognition algorithms. You might experience a slight skip in the video or podcast audio you're listening to — especially if you've been listening through Bluetooth — and the Live Caption pop-up will start showing up as the app recognizes them. Captions can be delayed 5-10 seconds after the initial words were spoken.
When you want to turn Live Captions off again, you can tap the toggle from the volume slider or you can dismiss it from the persistent notification that stays in the notification shade so long as Live Caption is turned on.
How to add Live Captions to Quick Settings
If you don't want to mess with the volume settings to turn on Live Captions, good news! You can add it to Quick Settings so that it's just a swipe away.
- Swipe down from the top edge of your screen to open the notification shade.
- Swipe down from the top edge of your screen again to open Quick Settings.
- Tap the pencil icon to edit your Quick Settings buttons.
- Drag and drop the Live Captions shortcut from the menu into the Quick Settings button grid. If you want the toggle to appear in the top bar of your notification shade, make sure it's in the first six icons of the button order.
- Tap the back arrow in the top left corner to exit the editing menu. Source: Ara Wagoner / Android Central
Now you can toggle Live Captions off without having to move your volume away from its perfect level. Simply swipe down twice from the top of your screen and tap the toggle — or tap it from the notification shade if you put it in the first six icons, the most important of your Quick Settings.
How to adjust your Live Captions settings
There aren't that many granular controls for Lice Captions, at least not yet, but there are a couple of small options you can toggle on or off at your leisure. These options are easier to access when Live Captions are toggled on:
- Swipe down from the top edge of your screen to open the notification shade.
- Tap the Live Caption persistent notification.
- If you want to bleep bad words, tap Hide profanity to toggle it on.
- If you don't want laugh tracks and musical cues listed, tap Sound sound labels to toggle it off.
- If you don't like seeing the Live Caption toggle cluttering your volume slider menu, tap Live Caption in volume control. Source: Ara Wagoner / Android Central
Where Live Captions fail
If captions aren't popping up — or are wildly inaccurate — remember that Live Captions is still in its early days and that it has limitations:
- Live Captions won't work on music, and videos with louder background music can trigger that same cutoff.
- Live Captions doesn't do well with strong accents. I watched the Bastille prison scene in Good Omens Episodes 3 and almost nothing the Frenchman said was right. Slang, accents, and drawls can throw the algorithms for a loop.
- Live Captions only works in English right now. When a scene has phrases or sentences in other languages during an English video — like the French in the Bastille scene mentioned above — it tends to display word soup in its vain attempt to match foreign words to the U.S. alphabet.
Caption everything
Google Pixel 4
This accessibility option can be awesome for all users.
The Pixel 4 has a cool new looks and some nifty new features. One of the most low-key but high-impact features isn't Motion Sense or that new 90Hz screen: it's the ability to caption almost any audio content from any source using on-device Live Captions.
We may earn a commission for purchases using our links. Learn more.


I'm a web designer and English/Polish translator and editor. I have experience in education, games, movies and literature. I also do some simple video editing and 2d game art. I am also European 4 kyu in GO ;)
Keys: 0
Controls:
↑
←↓→


Before Change
super(ProjectedGradientDescent, self).__init__(classifier, norm=norm, eps=eps, targeted=targeted,
num_random_init=num_random_init, batch_size=batch_size)
if eps_step > eps:
raise ValueError("The iteration step `eps_step` has to be smaller than the total attack `eps`.")
self.eps_step = eps_step
if max_iter <= 0:
raise ValueError("The number of iterations `max_iter` has to be a positive integer.")
self.max_iter = int(max_iter)
self._project = True
def generate(self, x, y=None):
After Change
targeted=targeted, num_random_init=num_random_init,
batch_size=batch_size, minimal=False)
kwargs = {"max_iter": max_iter}
self.set_params(**kwargs)
self._project = True


System Architect Symbol details for "Classification In Version"
Classification Element (Class)
- Description:
"Knowledge Concept" describing a genus - species relation between two "Categories".
- Comment:
-
Note that genus - species or "is-a" relations mean that any object which is an instance of the
species category is also an instance of the genus category and hence will exhibit all properties
exhibited in common by all instances of that genus category plus some specific properties of its
own. No more and no less.
It is also important to recognise that Associative categories can only partake in genus / species
relations with other Associative categories, and that Descriptive categories can only partake in
genus / species relations with other Descriptive categories.
Definitions Used:
Basis For Differentiation (Class Attribute)
Classification In Version (Class)
- Description:
Describes the location of a "Classification" in a "Classification Version".
- Comment:
Note that the same Classification may occur in more than one Classification Version. For example,
constructs may be carried forward from Read v2 to Read v3.
Classification System Version (Class)
- Description:
"Knowledge Concept" acting as a single version of a "Classification System".
component (Association)
- To Class:
"Classification In Version"
- From Class:
"Classification Element"
- From Class:
"Classification Element"
- To Class:
"Classification In Version"
context (Association)
- From Class:
"Classification System Version"
- To Class:
"Classification In Version"
- From Class:
"Classification System Version"
- To Class:
"Classification In Version"


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and the veruscoin agama wallet creates a different address
the same seed will create the same R address no matter what
gcharang10-Jun-19 04:44 PM
@Decker any idea what causes this error?
ecbuddy10-Jun-19 04:50 PM
cant install ocean on windows at all
error is
error: cannot parse configuration file: Missing komodo.conf. Only use Key=Value Syntax
no idea why this is happening, i changed the rpc user and pass, ran the batch file ect ect
gcharang10-Jun-19 04:55 PM
@ecbuddy is there a komodo.conf file in the location: %APPDATA%/Komodo ?
ecbuddy10-Jun-19 04:56 PM
no
I am installing on secondary hard drive
space blockchain is too large for my main drive
just pasted one in the folder adn the app opened up
gcharang10-Jun-19 05:02 PM
@ecbuddy so is it working now?
ComputerGenie 👴👴👴10-Jun-19 05:06 PM
for "non-default", you need to add like
datadir=/mnt/Storage/CoinData/Komodo/ (or path as per OS)
to komodo.conf and have a copy of the conf in the default location(edited)
ecbuddy10-Jun-19 05:07 PM
@gcharang its working, but i can not DL the chain on this drive
The block chain can overrun old storage media after awhile. Find out how to move it.
ComputerGenie 👴👴👴10-Jun-19 05:09 PM
yes, just like any other conf param
gcharang10-Jun-19 05:10 PM
theres a solution to add a shortcut in the article @ecbuddy that should work
one thing to note is that, if you remove and connect the external drive, it may get a different letter assigned in windows
so just change the setting at that time @ecbuddy
yes, win has slashes wrong way round:
datadir=d:\Wallets\Komodo
ecbuddy10-Jun-19 05:17 PM
not working
gcharang10-Jun-19 05:18 PM
@ecbuddy which solution did you try?
ecbuddy10-Jun-19 05:19 PM
I moved the whole komodo directory to my D drive
i added the path to the conf file
i also moved kmd.exe into the same drive
gcharang10-Jun-19 05:20 PM
you mean komodooceangui.exe ?
ecbuddy10-Jun-19 05:20 PM
komodo-qt-win(1)
which houses the komodo-qt-win
ComputerGenie 👴👴👴10-Jun-19 05:21 PM
I moved the whole komodo directory to my D drive
do you have a copy of the conf in
%appdata%\komodo
?
ecbuddy10-Jun-19 05:22 PM
no, I was told to move the whole directory as per the link above
gcharang10-Jun-19 05:22 PM
so did you add the shortcut as the link said?
ComputerGenie 👴👴👴10-Jun-19 05:22 PM
ComputerGenie 👴👴👴Today at 12:06 PM
for "non-default", you need to add like
datadir=/mnt/Storage/CoinData/Komodo/ (or path as per OS)
to komodo.conf and have a copy of the conf in the default location
ecbuddy10-Jun-19 05:23 PM
ok
perfect
It works now
thank you
2
richardt11-Jun-19 11:43 PM
Trying to run ocean wallet for the first time on Mac OS and getting "Error: Cannot parse configuration file: Missing komodo.conf. Only use key=value syntax". Could someone please help?
Using the latest version v2.0.15-rc2-g28-3
smk762 - dracocanis ominator12-Jun-19 05:22 AM
Before you start create the following configuration file, named komodo.conf in cd ~/Library/Application\ Support/Komodo folder:
rpcuser=bitcoinrpc
rpcpassword=password
txindex=1
addnode=<IP_ADDRESS>
addnode=<IP_ADDRESS>
addnode=<IP_ADDRESS>
addnode=<IP_ADDRESS>
addnode=<IP_ADDRESS>
addnode=<IP_ADDRESS>
addnode=<IP_ADDRESS>(edited)
Change rpc user and pass to something more secure
ComputerGenie 👴👴👴12-Jun-19 11:20 AM
or just put the one provided in the download there
richardt12-Jun-19 08:33 PM
Got it. Thanks guys!
BadAss14-Jun-19 06:24 PM
how do i claim interest with komodo-cli?
jl777c14-Jun-19 06:25 PM
send all funds to yourself
BadAss14-Jun-19 06:25 PM
thanks!
BadAss14-Jun-19 06:36 PM
is there a specific command for that? Like komodo-cli sendtoaddress address SENDALL?
jl777c14-Jun-19 06:36 PM
getbalance
to know your balance
sendtoaddress <addresss> balance - 0.0001
BadAss14-Jun-19 06:39 PM
lol, yeah ok...was wondering which command agama was using in the background, but i guess it is more lines of code
jl777c14-Jun-19 07:02 PM
its basically that
Decker14-Jun-19 07:56 PM
In komodo-qt we have "magic button", especially for claiming rewards )


Here is the place to put some nice stuff of text you want to have placed here. You can use all html you like.
You can also disable the complete section within the index.html by searching for "Header-Section"-comment-lines and remove it from the source code.
The image file for the logo is placed in the folder "/html/img" with the name "logo.jpg" - feel free to replace it with yours or replace the URL for the image with your own.
| Callsign | Target | Gateway | Dur (s) |
|---|
| Time () | Callsign | Target | Gateway | Dur (s) | Voice-Mode | Coordinates | QSO |
|---|
| Time () | Callsign | Target | Gateway | Dur (s) | Voice-Mode | Coordinates |
|---|
| Time () | Connected since | Callsign | IP/Port |
|---|
| Time () | Connected since | Callsign | IP/Port |
|---|
| Callsign | Added at () | Last TX at () |
|---|
This Dashboard uses websockets-technology to transfer data from host to client. It actually uses the logfile of the YSFReflector by G4KLX to get the entries into the different tables.
If you have any ideas or issues feel free to leave a comment/issue in github.


At TAPT, we care deeply about the art of mixed drinks and the time and stress it takes to create them from scratch. Our new cocktail tap system helps bars manage immediate customer demands and ease the mixing process, while creating delicious and premium drinks. TAPT presents an opportunity for businesses to offer the highest quality cocktails at a high volume, with minimal training and expertise required. We offer the highest quality kegged cocktails that will make any bar stand out and boost cocktail sales.


Our business is built on reputation, with many satisfied pupils in our area.
At Eastway driving school we believe that learning to drive should be an enjoyable experience, taught in a professional environment, where you can learn all the skills needed to be a confident and safe driver.
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constructor for the Stack object
USAGE
s = Stack(X), where X is a 3D matrix (width x height x time)
s = Stack(X, dt), dt is 1/framerate
s = Stack(pathToLsmFile) (relies on lsminfo and tiffread30b)
|
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addlistener |
Add listener for event. |
|
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align |
using the matlab imregistration function (R2012a+). |
|
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delete |
Delete a handle object. |
|
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drawROItrace |
|
|
|
eq |
== (EQ) Test handle equality. |
|
|
filterSpatial |
smooth each frame using a kernel |
|
|
filterTemporal |
temporally filter each pixel using 1D filter |
|
|
findobj |
Find objects matching specified conditions. |
|
|
findprop |
Find property of MATLAB handle object. |
|
|
flatten |
converts each 2D-frame into a 1D vector. convenient for vector |
|
|
ge |
>= (GE) Greater than or equal relation for handles. |
|
|
getCorrMap |
as in AHRENS Science paper. determines temporal corr of each pix with its |
|
|
getCubicMap |
|
|
|
gt |
> (GT) Greater than relation for handles. |
|
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ica |
perform temporal(?) ICA. needs FASTICA |
| Sealed
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isvalid |
Test handle validity. |
|
|
le |
<= (LE) Less than or equal relation for handles. |
|
|
lt |
< (LT) Less than relation for handles. |
|
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ne |
~= (NE) Not equal relation for handles. |
|
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normalize |
as in the AHRENS Science paper |
|
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notify |
Notify listeners of event. |
|
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play |
stack as a sequence of frames |
|
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plot |
display individual frame |
|
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readLSM |
load stack from LSM file. called by the constructor. |
|
|
resize |
each frame |
|
|
roiClean |
Clean ROI. Remove pixels from ROI that are less than THRES |
|
|
roiFindKNN |
find ROIs based on nearest-neighbour networks |
|
|
roiFindKNNGridSearch |
perform grid search for best combination of similarity and size |
|
|
roiGrow |
grow ROI to include nearby pixel that are correlated with the |
|
|
roiPlot |
(ROIs) |
|
|
roiTrace |
[roiTrace, roiTraces] = roiTrace(obj,roiMask); |
|
|
setStim |
|
|
|
sfa |
perform temporal SLOW-FEATURE ANALYSIS |
|
|
unflatten |
converts the 1D vector representation back into 2D images |


Event Location:
University of Iowa - University Capitol Centre
201 S Clinton St
Iowa City, Iowa
Abstract:
SQLSaturday provides insight into how to query your SQL Servers' performance, configuration, and needs. But there is a problem for the DBA: how do we quickly apply our new knowledge and scripts to multiple production servers? How do we collect the data we need regularly and query which instances need the most attention? How can we easily create reports proving ROI? In this session, attendees will learn to set up performance data collection with the SQL 2008 Management Data Warehouse (MDW). We will demonstrate how fast it is to create and configure custom data collections, giving examples from simple to complex scripts. This is the tool you need to automate collection for the awesome insights you've learned at SQLSaturday!
Speaker(s): Kendra Little,
Abstract:
Dealing with the SQL Server optimizer can be maddening at times. One moment it will take a seemingly impossible query and make it run in no time flat. At other times, the optimizer will insist on a poor plan even though a much better alternative is available. What should you do when the optimizer won't behave? One option we have available is the use of query hints. While almost everyone agrees that query hints should be avoided whenever possible, there are some situations that make this difficult to achieve. Sometimes the judicious use of a query hint can save the day, but since hints are a taboo subject, we do not discuss them as much as we should. Come to this session and let's see if we can find a practical approach to hints!
Speaker(s): Chris Leonard,
Abstract:
Agile software development emphasizes continuous deployment and its methods do not directly include long term planning. DBAs must ensure data integrity and have a long term view for application scale, so Agile methods present challenges. Come learn about successful real-world practices iteratively developed in a high transaction internet service environment over the last five years. We have created a flourishing Agile shop while meeting high requirements for uptime, customer response, and data consistency. In this session we’ll cover key habits for success, practices to avoid, how and when to get started, and why Agile development can be a great thing for DBAs. Topics will also include how “Testing in Production” can be a huge benefit.
Speaker(s): Kendra Little,
Abstract:
SQL Server stores its execution plans as XML in dynamic management views. The execution plans are a gold mine of information. From the whether or not the execution plan will rely on parallelism to what columns are requiring a key lookup after a non-clustered index seek. Through a the use of XML this information can be available at your fingertips to help determine the value and impact of an index and guide you in improving the performance of your SQL Server databases. In this session we’ll look at how you can begin to understand and query the structure of the execution plans in the procedure cache. Also, we’ll review how to uncover some potential performance issues that may be lurking in your SQL Server.
Speaker(s): Jason Strate,
Abstract:
Encouraging the Next Generation Everyone has heard that the number of women in technology is declining. How can all of us, women and men, encourage more young women to consider a career in technology? Join three dynamic Women in Technology – Kathi Kellenberger, Wendy Pastrick, and Jes Borland – to discuss this important and hot topic over lunch. Attendees are encouraged to ask questions, make suggestions, and share their experiences as well.
Speaker(s): ,
Abstract:
Beyond database design fundamentals (for example, Normalization) lies the area where you have to create "real" solutions. In this session, I will cover a good number of patterns that we commonly find useful to try to apply to the problem of building (and interpreting) a database solution. Ideas like generalization, subclassing, single table domain tables, optional data, and more will be discussed, some of them good, some not so good (don't assume which will be which), but all that are common and/or useful for your database implementations.
Speaker(s): Louis Davidson,
Abstract:
A large majority of a modern DBA's workload consists of managing their policies and resources on their system in order to ensure that they are being followed and resources are accessible when needed. SQL Server 2008 provides both Policy Based Management(PBM) and Resource Governor, which allows the DBA to discretely manage system policies and resources in a very structured way. In this session, we will show you how to configure, monitor, and administer these features to help lower your anxiety levels while increasing your system’s availability.
Speaker(s): Arie Jones,
Abstract:
Most modern enterprise database environments require some measure of auditing their data. Previously, the DBA would need to put together a complicated web of triggers and history tables in order to properly track data changes. Now in SQL Server 2008, Change Data Capture allows the DBA to readily enact a very straight-forward method for tracking the changes and understanding easily how they took place. In this session, we will examine how to set-up, configure, use, and administer the Change Data Capture process in your environment.
Speaker(s): Arie Jones,
Abstract:
Without proper indexing SQL Server can be hard pressed to create efficient and performant execution plans. Dynamic Management Views (DMV) and system views provide a slew of information about indexes that can be used to analyze indexes within SQL Server. In this session we’ll go under the hood of SQL Server to look at DMVs and system views to know what indexes you have, should have, and how they feel about the way applications are treating them.
Speaker(s): Jason Strate,
Abstract:
It’s fun to build it, but sometimes it can be even more fun to try and break it. SQL injection is one of the more common web vulnerabilities, and although many of us may be familiar with code injection, there are many new tools that help automate the attacker’s job. As developers, it is good to know what the bad guys are using and how these tools work. This talk will take us from the basics of SQL injection to remote code execution. We will then look at some of the ways SQL injection attacks are being automated (with tools such as sqlmap, sqlninja, and other fuzzing applications).
Speaker(s): Trenton Ivey,
Abstract:
As developers, we get blamed when things run slow, very often from both the users and DBAs. In this session we will go through some tips that you may start to use in your project the next day, without going into too much details of database engine.
Speaker(s): Dagong Wang,
Abstract:
Replication simplified! Learn the basic parts that make up replication, what tools are included to help you along plus a look into monitoring.
Speaker(s): Wendy Pastrick,
Abstract:
An "Open Space" discussion facilitated by at least one developer, guided by these principles: whoever comes is the right people; whatever happens is the only thing that could have; and when it's over, it's over. Before it's over, discussion will likely range from "What's wrong with developers?" to "What's wrong with DBAs?" and various more productive topics in between.
Speaker(s): Keith Dahlby,
Abstract:
As DBAs, some of us might spend time writing actual application code, while others may spend time chewing out developers for writing poor code which negatively affects database performance. This session will cover some common sins of application coding and best practices you can recommend to developers.
Speaker(s): Bob Pusateri,
Abstract:
Closing and Raffle
Speaker(s): ,
Abstract:
The goal of defensive database programming is to produce robust database code. You will learn how to program defensively by reviewing real life examples. Also you will learn some of the most common scenarios when T-SQL code breaks because of changes in environment settings and concurrency, and how to develop T-SQL that does not break in such situations.
Speaker(s): Alex Kuznetsov,
Abstract:
Business Intelligence is becoming more important as competition becomes more fierce and global. If you are a traditional DBA supporting OLTP databases, how can you begin to understand the dimensional model? This session will take you step-by-step through designing a data mart, the first stop on your journey in this exciting and growing field.
Speaker(s): Kathi Kellenberger,
Abstract:
With the launch of SQL Server 2008 two years ago and R2 this year, Reporting Services (SSRS) has changed quite a bit. Find out why you should take the plunge and upgrade your SSRS implementation. Learn about enhancements in performance, reusability, and cool new features so you can take your reports to the next level.
Speaker(s): Kathi Kellenberger,
Abstract:
In this session I will give an overview of how to design a database, including the common normal forms and why they should matter to you if you are creating or modifying SQL Server databases. Data should be easy to work with in SQL Server if the database has been organized as close as possible to the standards of normalization that have proven for many years. Many common T-SQL programming "difficulties" are the result of struggling against the way data should be structured and can be avoided by applying the basic normalization techniques and are obvious things that you find yourself struggling with time and again (i.e. using the SUBSTRING function in a WHERE clause meaning you can't use an index efficiently).
Speaker(s): Louis Davidson,
Abstract:
Everyone has their own best practices that they try to follow. But often times we aren’t sure how well we are following our own or industry best practices. In this session we’ll go into monitoring and managing best practices throughout your environment. The session will look at how to create a solution using policy based management and other tools to report on your compliance to best practices.
Speaker(s): Jason Strate,
Abstract:
This session discusses what is needed to install, develop, and execute a basic SSIS 2008 package. An overview of some basic yet powerful tasks available to administrators and developers as well as ways to increase the performance of commonly used tasks and components will be discussed briefly. We will cover • Planning and Installation Requirements • Developing a basic SSIS package • Importing/Exporting packages • SSIS issues to watch out for • Deploying an SSIS package to production • Differentiating various package storage methods • Running packages manually or on a schedule
Speaker(s): Ted Krueger,
Abstract:
Many T-SQL modules run correctly without concurrency, but they intermittently break when they are exposed to real life concurrency in OLTP systems. You will learn common real life scenarios when concurrency breaks T-SQL code. In addition, you will learn how to develop robust T-SQL which works correctly under concurrency.
Speaker(s): Alex Kuznetsov,
This is a list of speakers from the XML Guidebook records. The details and URLs were valid at the time of the event.
LinkedIn: http://www.linkedin.com/in/louisdavidson
Contact: https://www.red-gate.com/simple-talk/author/louis-davidson/
Louis Davidson has worked in the IT industry for over 25 years as a corporate database developer and architect. He has been a Microsoft SQL Server MVP for 15 years and has written five books on database design, and contributed to many other SQL Server books as an author and tech editor. He has a bachelor's degree in Computer Science from the University of Tennessee at Chattanooga. You can read more about Louis at http://drsql.org.
Contact: http://www.databaseguy.com
Chris Leonard consults as works as a database development manager at Go Daddy, the world's largest domain name registrar. He has also consulted with Microsoft Learning as a Subject Matter Expert for SQL Server 2005 and 2008, helping them design and build some of their education and certification products. Chris enjoys astronomy, geocaching, camping, hacking his Android, and watching lots of movies. He has been married over 20 years, has four kids, and yes, he used to be an opera singer.
Contact: http://littlekendra.com
Kendra Little is a Senior DBA in the online advertising industry with seven years of DBA experience and ten years experience with SQL Server. Kendra creates tools to automate database maintenance, implements custom perf monitoring, tunes production servers, and builds SQL Reports for trending. She works closely with an Agile development team to plan and deploy weekly changes. Kendra is passionate about helping build the DBA profession as a dynamic, necessary function for successful businesses.
Contact: http://sqlblog.com/blogs/alexander_kuznetsov/default.aspx
Alex Kuznetsov is a database developer with more than a decade of experience. Alex has written a book entitled "Defensive Database Programming with Transact-SQL", and contributed to "MVP Deep Dives" and "The Red Gate Guide to SQL Server Team-based Development". He blogs on sqlblog.com and publishes articles on simple-talk.com and devx.com,.
LinkedIn: http://www.linkedin.com/in/jasonstrate/
Contact: http://www.jasonstrate.com/
Jason Strate is a SQL Server Solutions Architect with Pragmatic Works. He is also a Microsoft SQL Server MVP and MCM. He has over 15 years of experience developing, architecting, and managing data platforms. Jason writes, blogs, and presents on SQL Server.
Contact: http://www.sqlservercentral.com/blogs/kathi_kellenberger/default.aspx
Kathi Kellenberger is a Data Technology Specialist with Microsoft in St. Louis, MO. Before joining Microsoft in 2010, she was a DBA and a SQL Server MVP. Kathi is author of "Beginning T-SQL 2008" and co-author of "Professional SQL Server 2005 Integration Services" and "SQL Server MVP Deep Dives." In her spare time, Kathi enjoys singing (she is the original #sqlkaroke), cycling, playing with her 5 year old grandson, and climbing the stairs in very tall buildings.
I'm a .net developer since version 1 and working in the Business Services IT group of the University of Iowa. It's my first presentation in SQLSaturday. So be nice and patient to me, please.
LinkedIn: http://www.linkedin.com/in/bobpusateri
Contact: http://www.BobPusateri.com
Bob Pusateri is a Microsoft Certified Master, DBA, and systems architect with over 10 years of experience on SQL Server. His interests involve internals, performance optimization, and cloud technologies. He is an active member of two Chicago-area PASS Local Groups, a community speaker, and maintains a web presence through both Twitter (@SQLBob) and his blog (bobpusateri.com).
Contact: http://blogs.lessthandot.com/index.php/All/?disp=authdirauthor=68
Ted Krueger has been working in development and database administration for over a decade. Ted currently works for a respected consulting company, Magenic Technologies as a valued Database Professional resource. He is a PASS Region Mentor, Friends of Red Gate member, SQL Server MVP and community mentor. Ted blogs and is also one of the founders of LessThanDot Technical Community
LinkedIn: http://www.linkedin.com/in/jasonstrate/
Contact: http://www.jasonstrate.com/
Jason Strate is a SQL Server Solutions Architect with Pragmatic Works. He is also a Microsoft SQL Server MVP and MCM. He has over 15 years of experience developing, architecting, and managing data platforms. Jason writes, blogs, and presents on SQL Server.
Contact: http://littlekendra.com
Kendra Little is a Senior DBA in the online advertising industry with seven years of DBA experience and ten years experience with SQL Server. Kendra creates tools to automate database maintenance, implements custom perf monitoring, tunes production servers, and builds SQL Reports for trending. She works closely with an Agile development team to plan and deploy weekly changes. Kendra is passionate about helping build the DBA profession as a dynamic, necessary function for successful businesses.
Contact: http://sqlblog.com/blogs/alexander_kuznetsov/default.aspx
Alex Kuznetsov is a database developer with more than a decade of experience. Alex has written a book entitled "Defensive Database Programming with Transact-SQL", and contributed to "MVP Deep Dives" and "The Red Gate Guide to SQL Server Team-based Development". He blogs on sqlblog.com and publishes articles on simple-talk.com and devx.com,.
Contact: http://www.programmersedge.com
Arie Jones 'AJ' is the Principal Technology Manager for Perpetual Technologies, Inc. in Indianapolis, IN. Arie leads PTI’s team of expert consultants in planning, design, development, deployment, and management of database environments and applications to achieve the best combination of tools and services for each client. Additionally, AJ is a community speaker, has written several books such as “Learn SQL in 24 Hours”, and blogs extensively at http://www.programmersedge.com
LinkedIn: http://www.linkedin.com/in/louisdavidson
Contact: https://www.red-gate.com/simple-talk/author/louis-davidson/
Louis Davidson has worked in the IT industry for over 25 years as a corporate database developer and architect. He has been a Microsoft SQL Server MVP for 15 years and has written five books on database design, and contributed to many other SQL Server books as an author and tech editor. He has a bachelor's degree in Computer Science from the University of Tennessee at Chattanooga. You can read more about Louis at http://drsql.org.
Contact: http://www.sqlservercentral.com/blogs/kathi_kellenberger/default.aspx
Kathi Kellenberger is a Data Technology Specialist with Microsoft in St. Louis, MO. Before joining Microsoft in 2010, she was a DBA and a SQL Server MVP. Kathi is author of "Beginning T-SQL 2008" and co-author of "Professional SQL Server 2005 Integration Services" and "SQL Server MVP Deep Dives." In her spare time, Kathi enjoys singing (she is the original #sqlkaroke), cycling, playing with her 5 year old grandson, and climbing the stairs in very tall buildings.
Contact: http://solutionizing.net/
Keith Dahlby is a .NET developer and language geek from Cedar Rapids, IA. He works as a software guru at JP Cycles and blogs about various software development topics at Los Techies. Keith has spoken at community events around the midwest and studied Human-Computer Interaction at Iowa State University. His talks have been described as "terrific!", "very interactive!", and "the best I've seen all hour!"
Contact: http://www.jasonstrate.com
Jason Strate, SQL Server MVP, is a DBA with over twelve years of experience. His experience includes delivering both OLTP and OLAP solutions as well as assessment and implementation of SQL Server environments for best practices, performance, and HADR solutions. He is a SQL Server MCITP and participated in the development of Microsoft Certification exams for SQL Server 2008. Jason has presented at the SSWUG Virtual Conferences, TechFuse, SQLSaturday events, and at PASSMN user group meetings.
Contact: http://hackyeah.com
Trenton Ivey (hackyeah.com) is passionate about computer security. He currently works for Iowa Health Systems, but will soon be perusing a carrier in penetration testing and security consulting in the Northwest. He, when not writing programs (or breaking them), spends his time hiking, camping, rock climbing and slacklining with his wife.
Contact: http://www.programmersedge.com
Arie Jones 'AJ' is the Principal Technology Manager for Perpetual Technologies, Inc. in Indianapolis, IN. Arie leads PTI’s team of expert consultants in planning, design, development, deployment, and management of database environments and applications to achieve the best combination of tools and services for each client. Additionally, AJ is a community speaker, has written several books such as “Learn SQL in 24 Hours”, and blogs extensively at http://www.programmersedge.com
Contact: http://wendyverse.blogspot.com
Wendy Pastrick is from Chicago, IL, and for the past 15 years has served as a Database Administrator supporting both development and production environments. Her many years of involvement with PASS include WIT Virtual Chapter and the Chicago Suburban User Group, serving as Regional Mentor, and as a PASS Board member since 2013. Wendy has organized several highly successful SQLSaturday events in Chicago.
The following is a list of sponsors that helped fund the event.