COVID19 REST-API
This is a REST-API to get data about COVID19 (new corona-virus) cases.
The data can be filtered by country, area, (city/admin on JHU-data) and
date. Aggregated sums are available on each filter level. Output is
available in json and csv.
API-Documentation
Please refer to https://github.com/apfeuti/covid19-rest
Data sources
Johns
Hopkins University (for figures about the world - including detailed data on city-level for the United States
of America (USA)).
Contribution
Link this page somewhere on your project
Feel free to raise an issue for bugs, feature-requests, improvements,
etc. or provide your own pull-request on GitHub.
Do you like the API? Have critism or something else to say?


{% extends "page.html" %}
{% block subtitle %}{{ c.workflow_dict.name }} - {{ _('Workflows') }}{% endblock %}
{% block primary %}
{% snippet "workflow/snippets/workflow_info.html", workflow_dict=c.workflow_dict %}
{% block show_specific %}
{# This is implemented by inheriting templates. It contains either the workflow
in 'read.html' or training materials in 'training_material.html' #}
{% endblock %}
{% endblock %}
{% block secondary%}
{# Keep me empty or woe be tide! #}
{%endblock%}


I am creating a paragraph to practice using inline styles. Inline styling from what I understand is the most specificated control off CSS, as it addresses specific lines of code as opposed to effecting multiple elements.


grox.info is an experiment in creating a knowledge graph for the vocabulary of model theory, first order logic, semantic web and similar attempts to formalize semantics.
𐤁
𐤁
𐤈
𐤈
Model Theory Vocabulary
- token
- a word, a phoneme, a sequence of characters
- tables
- Examples of HTML tables as data structures
SKOS Concept Scheme
let's talk about SKOS


WITNESS LIST State Affairs Committee March 21, 2000-9:00A Natural Resource Clean Air Charge: On: Aulick, Michael (Capital Area Metropolitian Planning Organization), Austin Bohuslav, Ken (TXDOT), Austin Clark, Alan (Houston-Galveston Air Quality Planning Area Council), Houston Darlington, Thomas (General Motors), Nori, MI Edlund, Carl (EPA- Region 6), Dallas Gallion, Gerald (Texas Waterway Operators Association), Houston Heald, Charles "Wes" (TXDOT), Austin Holycross, Robert (Ford Motor Company), Allen Park, MI Husted, Amy (Texas Waterway Operators/Kirby Corporation), Houston Kennison, Janet (San Antonio-Bexar County MPO), San Antonio Luedecke, Alvin (TXDOT), Austin Morris, Michael (North Central Texas Council of Governments), Dallas Reagan, Dan (Federal Highway Administration), Austin Roop, Dr. Stephen (Texas Transportation Institute), College Station Saitas, Jeff (TNRCC), Austin Smith, Tom "Smitty" (Public Citizen), Austin Sutton, Les (Texas Waterway Operators Association), Houston Tredway, Ellen (Texas Automobile Dealers Association), Austin Weverstad, Alan (General Motors), Detroit, MI Zweiacker, Paul (TXU), Dallas


Author: Sebastián Gun<EMAIL_ADDRESS>Description: Installments calculator for MercadoPago and MercadoLibre credit card payments. Version: You can grab the source code of this extension in GitHub.
This extension uses the MercadoLibre and MercadoPago APIs. Also uses the jQuery library with the jquery-jsonp and tinysort plugins. Some icons taken from the Open Icon Library.
Copyright (c) 2011- Sebastián Gun Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.  IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


---
layout: post
title: Download Stubbie
tags:
- download
- generator
- stubbie
- stubs
- tdd
status: publish
type: post
published: true
meta:
aktt_notify_twitter: 'no'
_edit_last: '2'
---
Version 0.1 is available for download. I'd like to call this the "I am working release, but it's all a little messy" release. There are a ton of improvements to be made and still a few features I'd like to add, but it's working and figured this was a good time to put the code out there and get some feedback.
Feel free to report any issues, feedback and suggestions.


About 2.5 hours
Imagine you are coloring on a flower coloring book. You colored in green for all leaves and now it’s time for the best part, the petal. You know you enjoy red the best but it looked horrible after you finished it. With Git, you can revert your choice of red in a heartbeat and you are free to reapply the red if you change your mind. A work doesn’t have to be permanent; every action is recorded and reversible. (Source: https://dev.to/maestromac/comment/him)
Participants will be able to:
Git and Version Control (video walkthrough of slides)
Git and Version Control (slides)
“Typing commands into the Terminal is scary so I’ll just skip this Git stuff.” There’s really no getting around needing to use Git. It’s OK if typing into the Terminal feels scary at the beginning. As you grow in your skills, using the Terminal will actually become faster than using your mouse and the Finder window!
“I don’t think I’ll ever use this, so I’ll just skip this Git stuff.” You may not be able to see a use for Git now, but once you start working on multi-file projects you’ll hopefully understand why it’s an integral part of almost every software engineering workflow.
Instructor will demonstrate:
Techtonica staff will assign pairs. Go through the following steps on one pair partner’s computer first, then go through all the steps again the other pair partner’s computer. The repetition of doing this twice will help solidify the new concepts.
git --version
. If a version number appears, you already have git installed. If a version number does not appear:
git
via Homebrew: brew install git
> If Homebrew isn’t install, check out Command Line Advanced lesson.git
and follow the prompts to install Git.Using the Terminal, navigate to the Desktop.
Create a new folder called git_example
. Navigate to this folder.
Run the git status
command. What happens? Run the git init
command to make git_example
start keeping track of changes that happen within the folder.
Run git status
. What do you see?
Create a new file in git_example
called my-favorite-animals.txt
.
Run git status
. What do you see?
Add some text to my-favorite-animals.txt
. Save the file.
Run git status
. What do you see?
Run git add my-favorite-animals.txt
.
Run git status
. What do you see?
Run git commit -m "Create new file.
.
Run git status
. What do you see?
Spend just 30 minutes working through Git Immersion: A guided tour that walks through the fundamentals of Git, then stop wherever you are. If the first page takes more than a minute to figure out, you can skip it for now.
With computers closed, participants as a group will guide the instructor through creating, adding and committing file changes using Git.
git log
to verify that it workedgit status
and git diff
githug
in your command-line prompt to play. When you think you’ve solved a level, type githug
again to check your work. You’ll get feedback and hints. Please complete at least up to #32: checkout
and return to the game throughout the program to practice things like merge
and rebase
.Form small groups and discuss:
git status
do?If you have time, continue the Git Immersion and githug tutorials if you aren’t finished. Also try looking through the supplemental materials. be)


{% extends 'two-col.html' %} {% block right_col %}
A tool to learn and use the powerful Natural Language Toolkit, a Python NLP library.
No results.
No data.


Data analysis information
Use of this Linux system for data analysis. QIIME 1.9.1 and QIIME 2 are installed within their own python environments. You can only use QIIME commands or QIIME-dependent scripts (e.g. akutils) within the respective environment. Call either environment from the command line with the alias "q1" or "q2". Deactivate either environment with the alias "deac".
Commands for managing QIIME environments:
- Activate QIIME 1.9.1:
q1
- Activate QIIME 2:
q2
- Return to base environment:
deac
Database locations:
- GreenGenes (Bacteria, Archaea -- 16S):
/common/QIIME_databases/gg_otus-13_8-release/
- UNITE (Fungi -- ITS):
/common/QIIME_databases/UNITE_2015-03-02/
- MAARJAM (AMF -- 18S):
/common/QIIME_databases/MAARJAM/
Essential resources:
- QIIME 1.9.1 scripts page (man pages for all QIIME 1.9.1 scripts)
- QIIME 1 forum (
ask questions, read questions and answers from other users) - QIIME 2 user documentation (all documentation for QIIME 2)
- QIIME 2 forum (ask questions, read questions and answers from other users)
- akutils wiki pages (script and tutorial documentation for akutils)


ATMResponse_ATM
- ATMServices : List
Describes the type of transaction available for a customer on an ATM.
- Enum:
- Access24HoursIndicator : Boolean
Indicates that the ATM is available for use by customers 24 hours per day
- Accessibility : List
Indicates Types of Accessibility
- Enum:
- Identification : String
ATM terminal device identification for the acquirer and the issuer.
- MinimumPossibleAmount : String
Minimum amount allowed for a transaction in the service.
- Note : List
Summary description of the ATM.
- OtherATMServices : List
Enter a new code , name and description for any other ATM Service
- OtherAccessibility : List
Enter a new code , name and description for any other ATM accessibility options
- SupportedCurrencies : List
All ISO 4217 defined currency supported by the ATM.
- SupportedLanguages : List
Identification of the language name according to the ISO 639-1 codes. The type is validated by the list of values coded with two alphabetic characters, defined in the standard.


My JGAP page - Genetic programming and Symbolic Regression
JGAP (Java Genetic Algorithms Package) is a Java based system for genetic algorithms and genetic programming.
For more about JGAP:
Symbolic Regression
2010-02-21: I blogged about this package in Symbolic regression (using genetic programming) with JGAP.
Download all Java and configuration files mentioned below: symbolic_regression.zip.
Here is the result of my experiment with Symbolic Regression using Genetic Programming in JGAP. Right now I'm learning both JGAP and genetic programming so there are, for sure, peculiarities in the files. After I learn more, new features will be added or old will be removed.
SymbolicRegression.java is the main program. As you may recognize, it is based on JGAP's example MathProblem.java
but extended with some bells & whistles.
The program is compiled with (on a Linux box) like this:
javac -Xlint:unchecked -classpath "jgap/jgap.jar:jgap/lib/log4j.jar:jgap/lib/xstream-1.2.2.jar:jgap/lib/commons-lang-2.1.jar:$CLASSPATH" SymbolicRegression.java
and run with:
java -server -Xmx1024m -Xss2M -classpath "jgap/jgap.jar:jgap/lib/log4j.jar:jgap/lib/xstream-1.2.2.jar:jgap/lib/commons-lang-2.1.jar:$CLASSPATH" SymbolicRegression [config file]
For compiling, all the Java files below must be downloaded and placed in the same directory as the the file. Here is my log4j.properties file.
See below for more about the configuration files.
Note: Most of these files where incorporated (in some case with changes by Klaus Meffert) in the JGAP distribution, version 3.6 (directory examples/src/examples/gp/symbolicRegression
).
Defined functions
Here are my defined functions. Some of these may be considered experimental, but may be of some use. Please note that the code is not tidied up etc.
- Boolean operators for DoubleClass
Here are the boolean operators for use with DoubleClass, i.e. they has double
as input and returns a double
(0.0d or 1.0d). Some of these functions are tested in odd_parity.conf.
- ModuloD.java: Modulo with
double
as input and output. First the input is converted to integers and then an integer modulo is done which is returned as a double. (The standard %
operator on double is not what I wanted.) This is tested in isbn_test.conf.
- ModuloReplaceD.java: Sometimes we want the Modulo function not to return 0 but some other value (e.g. the highest possible values in the data set). Then this function may be tried. Note: The replacement value is manually set in the configuration option
mod_replace
. This should be considered highly experimental.
- DivideIntD.java: A protected variant of
Divide
where the division is done by first converting to Integer
then doing an integer division. Also, if the divisor is 0 (zero), the result is 1 (i.e. protected).
- DivideProtected.java: A protected variant of
Divide
the result is 1 (i.e. protected) if the divisor is 0 (zero), else standard double division.
- Mathematical functions.
- Other functions
Configuration files
One of the primary tasks was to be able to use a configuration file to state the problem and the data. Below are some examples. Please note that some of these are experimental (and use experimental parameters/operators), and also they may not give any interesting or good results. More info about the data/problem is usually in the header of the file.
Some of the problems was first tested with Eureqa and was commented in Eureqa: Equation discovery with genetic programming (a Google Translation of my original swedish blog post Eureqa: equation discovery med genetisk programmering).
- alldifferent3.conf: All variables should be different
- bolts.conf: Bolts. A machine learning example
- boyles_law.conf: Boyle's law.
- catalan.conf: Catalan numbers
- circle_1.conf : Circle
- duelling_cowboys.conf : Duelling cowboys
- equation.conf : The infamous
11x11=4, 22x22=16, 33x33=?
puzzle.
- equation3.conf : Sequence puzzle
- equation4.conf : Sequence puzzle
- exp_formula.conf: Test of Exp function
- exp_formula_no_exp.conf: Test of Exp function, but without
Exp
in the function list.
- facebook_puzzle.conf: Facebook puzzle (
1 + 4 = 5, 2 + 5 = 12, 3 + 6 = 21, 5 + 8 = ??
)
- facebook_puzzle_b.conf: Facebook puzzle (
1 + 4 = 5, 2 + 5 = 12, 3 + 6 = 21, 5 + 8 = ??
), alternative encoding
- facebook_puzzle2.conf: Another Facebook puzzle (
9 = 90, 8 = 72, 7 = 56, 6 = 42, 3 = ?
)
- facebook_puzzle2_b.conf: Another Facebook puzzle (
9 = 90, 8 = 72, 7 = 56, 6 = 42, 3 = ?
), alternative encoding
- fahrenheit_celsius.conf: Fahrenheit to Celsius conversion. You may experiment by changing
output_variable
to 0 for the reverse conversion (C -> F).
- fib1.conf: Fibonacci series as a time serie.
- fib2.conf: Fibonacci series as a time serie.
- fib_50.conf: Fibonacci numbers, where I try to find the closed formula for the Fibonacci number.
- func1.conf: Unknown function (from a homework in a course in Evolutionary Computing)
- galilei_acceleration.conf: Acceleration (BACON, via bacon-logtalk)
- gamma_test.conf: Test of Gammma function
- gelman.conf: Linear regression
- henon_100.conf: Henon, 100 data points
- heron_formula.conf: Heron formula
- intro_page_262.conf: A simple problem
- iris.conf: Iris data set
- isbn_test.conf: Trying to get the program to calculate the checksum for ISBN13
- leap_years.conf: Leap years
- logit_formula.conf: Test of the
Logistic
function
- longley.conf: Longley's data set of number employments
- majority_on_3.conf: Boolean 3-majority on
- mod_test.conf: Test of modulus operator.
- moons.conf: Moons data
- multiplexer_3.conf: 3-multiplexer (i.e. IfThenElse), using Boolean operatrs
- multiplexer_3_b.conf: 3-multiplexer (i.e. IfThenElse), using
if..then..else
- multiplexer_6.conf: 6-multiplexer, using
if..then..else
- multiplexer_11.conf: 11-multiplexer
- mysterious.conf: Mysterious function
- number_puzzle1.conf: Number puzzle from Roger Alsing's blog post Genetic Programming: Code smarter than you!
- number_puzzle4.conf: Number puzzle inspired by Richard Wiseman's It's the Friday Puzzle (2010-02-26) of finding the result 24 from the numbers 5,5,5,1 and the operators +,-,*,/. Note: the requirement that the numbers should be used exactly once is not held here. This uses only one fitness case (and the option
no_terminals
).
- odd_parity.conf: Odd parity, using the double variants of the boolean functions, i.e. AndD, OrD, NotD (see above)
- odd_parity_double.conf: Odd parity, using the arithmetic functions +,-,*,/
- odd_parity2.conf: Odd parity for two inputs
- p10.conf: Polynom P(10)
- p4.conf: Polynom P(4)
- p4_2.conf: Polynom P(4)
- p4_jgap.conf: Polynom P(4). This is the version in the JGAP example MathFormula.java
- p6_2.conf: Polynom P(6)
- pickover_puzzle1.conf: Sequence puzzle (via Cliff Pickover)
- planets.conf: Planets, i.e. Kepler's third law.
- puzzle3.conf: Sequence puzzle
- quintic.conf: Quintic polynomial
- regression_koza.conf: Regression (0.5 * x^2, from John R. Koza's Lisp implementation)
- regression_psh.conf: Regression (y = 12x^2 + 5, from Psh)
- seq_ind1.conf: Sequence induction problem: 5*j^4+4*j^3+3*j^2+2^j+1 (for integers 0..10)
- sequence_induction.conf: Sequence induction
- sequence_puzzle.conf: Sequence puzzle
- sequence_puzzle_recursion.conf: Sequence puzzles (e.g. Fibonacci)
- sigmoid_test.conf: Test of Sigmoid function
- sin_formula.conf: Test of Sine
- sin_formula_rand20.conf: Test of Sine
- sine_tiny_gp.conf: Test of Sine from TinyGP
- sorted_3.conf: Sorting 3 variables.
- sqrt_formula2.conf: Yet another test of Sine
- sqrt_formula3.conf: Yet another test of Sine
- sqrt_formula.conf: Test of Sqrt function
- sunspots.conf: Sunspots data as time series
- sunspots_timeseries.conf: Two version of sunspots data using
make_time_series
- test1.conf: A test of many functions.
- test2.conf: A test of new functions.
- tic_tac_toe.conf: Tic-tac-toe
- timeseries_test1.conf: Time series with
make_time_series
- timeseries_dailyisbn.conf: Time series using the classic time series "Daily closing price of IBM stock, Jan 1, 1980 to Oct. 8, 1992" (DAILYIBM.DAT from TSDL)
- triangular_numbers.conf: Triangular numbers
- weather.conf: Weather (classic classification example)
- x2.conf: A simply polynomial: x^2
- x4-x3+x2-x.conf: Polynomial: x^4-x^3+x^2-x
- zoo2.conf: Zoo (classic classification example)
The configuration parameters
The configuration file consists of the following parameters. Here is a short explanation; the full story is in the code: SymbolicRegression.java. Most of the parameters has reasonable default values, taken from either MathProblem.java or GPConfiguration.
-
#
, %
: Line comments; lines that start with the characters "#" or "%" will be ignored.
-
presentation
: A text which is shown first in the run.
-
num_input_variables
: Number of input variables in the data set.
-
output_variable
: The index (0-based) of the output variable. Default is the last variable.
-
variable_names
: The name of the variables, in order. Default is "V0", "V1", etc
-
data
: Starts the data
section, where each row is presented per line. The attributes may be separated by "," or some space. Decimal point is a .
(dot).
If a data row contains a ?
(question mark) in the position of the output variable, then it is considered a "user defined test" and the fittest program will be tested against this data last in the run.
-
terminal_range
: The range for the Terminal
as lower upper
. Note: Only one Terminal is used.
-
terminal_wholenumbers
: If the Terminal
should use wholenumbers or not (boolean)
-
constant
: Define a Constant
with this value
-
functions
: Define the functions, with the same name as in JGAP (or own defined functions).
-
adf_arity
: If > 0 then ADF is used. This is somewhat experimental as I am still try to understand how ADF:s works.
-
adf_function
: The functions used for ADF.
-
adf_type
: Either double or boolean. If set to boolean, we can use the boolean and logical operators.
-
max_init_depth
: JGAP parameter maxInitDepth
-
min_init_depth
: JGAP parameter minInitDepth
-
program_creation_max_tries
: JGAP parameter programCreationMaxTries
-
population_size
: JGAP parameter populationSize
-
max_crossover_depth
: JGAP parameter maxCrossoverDepth
-
function_prob
: JGAP parameter functionProb
-
reproduction_prob
: JGAP parameter reproductionProb
-
mutation_prob
: JGAP parameter mutationProb
-
crossover_prob
: JGAP parameter crossoverProb
-
dynamize_arity_prob
: JGAP parameter dynamizeArityProb
-
no_command_gene_cloning
: JGAP parameter no_command_gene_cloning
-
use_program_cache
: JGAP parameter use_program_cache
-
new_chroms_percent
: JGAP parameter newChromsPercent
-
num_evolutions
: JGAP parameter numEvolution
-
tournament_selector_size
: JGAP parameter tournamentSelectorSize
-
max_nodes
: JGAP parameter maxNodes
-
scale_error
: Sometimes the data values are very small which gives small fitness values (i.e. errors), making it hard to get any progress. Setting this parameter will multiply the errors by this value.
-
stop_criteria_fitness
: If set (>= 0) then the program will run "forever" (instead of num_evolution
) until fitness is less or equal to the value.
-
show_population
: This shows the whole population in each generation. Mainly for debugging purposes.
-
show_similiar
: Shows all the solutions (programs) with the same fitness value as the best solution. Alternative name: show_similar
.
-
similiar_sort_method
: Method of sorting the similiar solutions when using show_similiar
. Alternative name: similar_sort_method
. Valid options:
-
occurrence
: descending number of occurrences (default)
-
length
: ascending length of solutions
-
show_progression
: boolean. If true then the generation number is shown for all generations when nothing is happening (i.e. no gain in fitness).
-
sample_pct
: (float) Takes a (sample) percentage of the data set if > 0.0.
-
validation_pct
: Withheld a percentage of the test cases for a validation set. This fitness of this validation set is shown.
-
show_all_generations
: Show info of all generations, not just when fitness is changed.
-
hits_criteria
: Criteria of a hit: if the difference is <= this value, it is considered a hit. The number of non-hits is then used as a fitness measure instead of the sum of errors. Setting this function also shows the number of programs which is <= this value.
-
mod_replace
: Setting the replacement value of 0 (zero) for the ModuloIntD
function (see above).
-
showResults
: boolean. If set then all the fitness cases is shown with the output of the fitted program, with difference to the correct values.
-
resultPrecision
: the precision in the output used in showResult
, default 5
-
error_method
: Error method to use. Valid options are
-
totalError
: sum of (absolute) errors (default)
-
minError
: minimum error
-
meanError
: mean error
-
medianError
: median error
-
maxError
: max error
-
no_terminals
: If true then no Terminal is used, i.e. no numbers, just variables. Default false.
-
make_time_series
: Make a time series of the first line of data. The value of num_input_variable
determines the number of laps (+1 for the output variable)
-
make_time_series_with_index
: As make_time_series
with an extra input variable for the index of the series. (Somewhat experimental.)
-
minNodes: value penalty
: minimum number of nodes (terminals + functions). If the number of nodes in a program is less than value
then a penalty of penalty
is added.
-
alldifferent_variables: true/false penalty
: all the variables (terminals) should be different. If there is more than one occurrence of an variable in a program then a penalty of penalty
is added (for each extra variable).
-
ignore_variables
: (TBW) It would be nice to be able to ignore some variables in the data set. But this is yet to be written.
-
return_type
: (TWB) This should be the type of the "main" return value. Note: it is now hard coded in the program as double/DoubleClass
.
Supported function
The program has support for the following functions from JGAP. The "main" type is double so all functions are not applicable there (e.g. IfElse
etc). However, for the ADF functions (defined by setting adf_arity
to > 0) many more functions is supported. Please note that some of these are (very) experimental and maybe don't even make sense in this context.
-
Multiply
(double)
-
Multiply3
(double)
-
Add
(double)
-
Add3
(double)
-
Add4
(double)
-
Divide
(double)
-
Subtract
(double)
-
Sine
(double)
-
ArcSine
(double)
-
Tangent
(double)
-
ArcTangent
(double)
-
Cosine
(double)
-
ArcCosine
(double)
-
Exp
(double)
-
Log
(double)
-
Abs
(double)
-
Pow
(double)
-
Round
(double)
-
Ceil
(double)
-
Floor
(double)
-
Modulo
(double), implements Java's %
operator for double. See ModuloD for a variant
-
Max
(double)
-
Min
(double)
-
LesserThan
(boolean)
-
GreaterThan
(boolean)
-
If
(boolean)
-
IfElse
(boolean)
-
IfDyn
(boolean)
-
Loop
(boolean)
-
Equals
(boolean)
-
ForXLoop
(boolean)
-
ForLoop
(boolean) (cf the double variant ForLoopD)
-
Increment
(boolean)
-
Pop
(boolean)
-
Push
(boolean)
-
And
(boolean), cf the double variant AndD
-
Or
(boolean), cf the double variant OrD
-
Xor
(boolean), cf the double variant XorD
-
Not
(boolean), cf the double variant NotD
-
SubProgram
(boolean)
-
Tupel
(boolean)
Also, see my own defined functions defined in the Java files above.
Examples
Here are two small examples of the program, including the configuration file and a sample run.
Polynom
Here is a simple example of a configuration file. It happens to be the same problem as the JGAP example MathProblem, the polynom x^4 + x^3 + x^2 - x.
#
# Polynom x^4 + x^3 + x^2 - x
# The JGAP example
#
presentation: P(4) x^4 + x^3 + x^2 - x (the JGAP example)
num_input_variables: 1
variable_names: x y
functions: Add,Subtract,Multiply,Divide,Pow,Log,Sine
terminal_range: -10 10
max_init_depth: 4
population_size: 1000
max_crossover_depth: 8
num_evolutions: 800
max_nodes: 20
stop_criteria_fitness: 0.1
data
-2.378099 26.567495
4.153756 382.45743
2.6789956 75.23481
5.336802 986.33777
2.4132318 51.379707
-1.7993588 9.693933
3.9202332 307.8775
2.9227705 103.56364
-0.1422224 0.159982
4.9111285 719.39545
1.2542424 4.76668
1.5987749 11.577456
4.7125554 615.356
-1.1101999 2.493538
-1.7379236 8.631802
3.8303614 282.29697
5.158349 866.7222
3.6650343 239.42934
0.3196721 -0.17437163
-2.3650131 26.014963
A simple run, slightly edited.
It was 20 data rows
Presentation: P(4) x^4 + x^3 + x^2 - x (the JGAP example)
output_variable: y (index: 1)
input variable: x
function1: &1 + &2
function1: &1 - &2
function1: &1 * &2
function1: /
function1: &1 ^ &2
function1: log &1
function1: sine &1
function1: 1.0
[19:52:57] INFO GPGenotype - Creating initial population
[19:52:57] INFO GPGenotype - Mem free: 10.5 MB
[19:52:57] INFO GPPopulation - Prototype program set
[19:52:57] INFO GPGenotype - Mem free after creating population: 10.5 MB
Creating initial population
Mem free: 10.5 MB
Evolving generation 1/800, memory free: 6.7 MB (time from start: 0,42s)
Best solution fitness: 968.56
Best solution: x ^ 4.0
Depth of chrom: 1
Correlation coefficient: 0.9995009838030151
Evolving generation 4/800, memory free: 11.4 MB (time from start: 0,84s)
Best solution fitness: 813.62
Best solution: ((4.0 + 4.0) + (log 4.0)) * ((x ^ 3.0) / (9.0 / x))
Depth of chrom: 3
Correlation coefficient: 0.999500983803015
Evolving generation 6/800, memory free: 6.7 MB (time from start: 1,07s)
Best solution fitness: 712.97
Best solution: ((5.0 * x) + ((x ^ 4.0) + x)) + x
Depth of chrom: 4
Correlation coefficient: 0.999781550858714
Evolving generation 7/800, memory free: 40.3 MB (time from start: 1,20s)
Best solution fitness: 582.77
Best solution: ((9.0 * x) + (x * 9.0)) - ((9.0 - x) - (x ^ 4.0))
Depth of chrom: 3
Correlation coefficient: 0.9965296891627895
Evolving generation 8/800, memory free: 24.9 MB (time from start: 1,32s)
Best solution fitness: 471.58
Best solution: (((x + 7.0) * (x * x)) * x) * (x / 9.0)
Depth of chrom: 4
Correlation coefficient: 0.9980245718601822
Evolving generation 11/800, memory free: 29.6 MB (time from start: 1,69s)
Best solution fitness: 364.73
Best solution: ((x * 8.0) + ((x ^ 4.0) + x)) - (4.0 - ((x * x) * x))
Depth of chrom: 4
Correlation coefficient: 0.9988207761993971
Evolving generation 16/800, memory free: 33.2 MB (time from start: 2,24s)
Best solution fitness: 317.84
Best solution: ((x * ((x * x) + (log 9.0))) * x) + (x * 9.0)
Depth of chrom: 5
Correlation coefficient: 0.9993672319814505
Evolving generation 17/800, memory free: 19.4 MB (time from start: 2,38s)
Best solution fitness: 169.76
Best solution: (x ^ 4.0) + (x * (x * x))
Depth of chrom: 3
Correlation coefficient: 0.9999752402614274
Evolving generation 22/800, memory free: 24.2 MB (time from start: 3,10s)
Best solution fitness: 136.21
Best solution: ((x * x) * x) + (x + (x * (x * (x * x))))
Depth of chrom: 5
Correlation coefficient: 0.9999485732269622
Evolving generation 23/800, memory free: 10.4 MB (time from start: 3,20s)
Best solution fitness: 3.7509724195622374E-4
Best solution: (x * ((((x * x) + x) * x) + x)) - x
Depth of chrom: 6
Correlation coefficient: 0.9999999999999939
Fitness stopping criteria (0.1) reached with fitness 3.7509724195622374E-4 at generation 23
All time best (from generation 23)
Evolving generation 23/800, memory free: 10.4 MB (time from start: 3,20s)
Best solution fitness: 3.7509724195622374E-4
Best solution: (x * ((((x * x) + x) * x) + x)) - x
Depth of chrom: 6
Correlation coefficient: 0.9999999999999939
Total time 3,20s
Fibonacci series
Here is another example, Fibonacci series as time serie. The object is to give a symbolic regression on the fourth value (F4), which is solved quite fast.
#
# Fibonacci with 3 variables
#
presentation: This is the Fibonacci series
return_type: DoubleClass
num_input_variables: 3
variable_names: F1 F2 F3 F4
functions: Multiply,Divide,Add,Subtract
terminal_range: -10 10
max_init_depth: 4
population_size: 20
max_crossover_depth: 8
num_evolutions: 100
max_nodes: 21
show_similiar: true
show_population: false
stop_criteria_fitness: 0
data
1,1,2,3
1,2,3,5
2,3,5,8
3,5,8,13
5,8,13,21
8,13,21,34
13,21,34,55
21,34,55,89
34,55,89,144
55,89,144,233
89,144,233,377
144,233,377,610
233,377,610,987
377,610,987,1597
610,987,1597,2584
987,1597,2584,4181
1597,2584,4181,6765
2584,4181,6765,10946
4181,6765,10946,17711
6765,10946,17711,28657
10946,17711,28657,46368
And a sample run:
Presentation: This is the Fibonacci series
output_variable: F4 (index: 3)
input variable: F1
input variable: F2
input variable: F3
function1: &1 * &2
function1: /
function1: &1 + &2
function1: &1 - &2
function1: -5.0
[19:56:31] INFO GPGenotype - Creating initial population
[19:56:31] INFO GPGenotype - Mem free: 7.5 MB
[19:56:31] INFO GPPopulation - Prototype program set
[19:56:31] INFO GPGenotype - Mem free after creating population: 7.5 MB
Creating initial population
Mem free: 7.5 MB
Evolving generation 1/100, memory free: 6.7 MB (time from start: 0,03s)
Best solution fitness: 17649.74
Best solution: (F3 - (-6.0 - F1)) + ((F2 - F2) - (-6.0 / F1))
Depth of chrom: 3
Correlation coefficient: 0.9999999838289084
Evolving generation 6/100, memory free: 6.0 MB (time from start: 0,13s)
Best solution fitness: 147.0
Best solution: 7.0 + (F2 + F3)
Depth of chrom: 2
Correlation coefficient: 1.0
Evolving generation 7/100, memory free: 5.8 MB (time from start: 0,14s)
Best solution fitness: 0.0
Best solution: F2 + F3
Depth of chrom: 1
Correlation coefficient: 1.0
Fitness stopping criteria (0.0) reached with fitness 0.0 at generation 7
All time best (from generation 7)
Evolving generation 7/100, memory free: 5.8 MB (time from start: 0,15s)
Best solution fitness: 0.0
Best solution: F2 + F3
Depth of chrom: 1
Correlation coefficient: 1.0
Total time 0,15s
All solutions with the best fitness (0.0):
F2 + F3 (1)
Todo
Here are some TODO:s, or things nice to have.
- option for ignoring specific variables
- option for stopping:
- running forever
- after a specific time,
-
when a specific fitness value is reached Fixed (by stop_criteria_fitness
)
-
calculate the number of data rows automatically (i.e. skip num_row) Fixed.
- accept nominal values in the data section; then converted to numeric values.
-
show some progress output. Fixed (by show_progress
)
-
make the output from this program instead from log4j. Fixed.
-
for larger data sets it would be nice with an option to use just
a sample (percentage) of the full data. Fixed (by sample_pct
)
-
Withheld a certain percentage for validation of the best fitted program. Fixed (by validation_pct
)
-
show number of programs less or equal a hits critera Fixed (by hits_criteria
)
-
show info for all generations Fixed (by show_all_generations
)
-
support for user defined test instances Fixed (by ?
in a data row)
-
show number of functions / terminals of the best program Fixed.
- add more fitness metrics.
- better handling of punishing longer solutions (parsimony pressure).
- support for different "main" return classes, i.e. not just DoubleClass
- more statistical measures, e.g. R-squared, mean squared error, mean absolut error, minimum error,
maximum error
- more/better error checks
- more building blocks, a la Eureqa http://ccsl.mae.cornell.edu/eureqa_ops
- support for derivattives (a la Eureqa)?
- incorporate in Weka?
- simplify the best solution with a CAS?
See also
Mention/Usage
Here are some mentions or usage of my Symbolic Regression program:
This page was created by Hakan Kjellerstrand (hakank@bonetmail.com), homepage.


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| 2018. Graduated as a Mechanical Engineer |
|
|
Final GPA: 9.65/10 |
|
| 2015. Passed the entrance exam to study at Instituto Balseiro | |
|
I received a scholarship to study in one of the best schools in Argentina. |
|
| 2013. Started Chemical Engineering |
|
|
Facultad de Ing. Química (FIQ), Universidad Nacional del Litoral (UNL). |
|
| 2012. Finished High School |


Introduction
HTML (Hypertext Markup Language) is the code that is used to structure a web page and its content. For example, content could be structured within a set of paragraphs, a list of bulleted points, or using images and data tables. As the title suggests, this article will give you a basic understanding of HTML and its functions.
HTML is not a programming language; it is a markup language that defines the structure of your content. HTML consists of a series of elements, which you use to enclose, or wrap, different parts of the content to make it appear a certain way, or act a certain way. The enclosing tags can make a word or image hyperlink to somewhere else, can italicize words, can make the font bigger or smaller, and so on. For example, take the following line of content:
My cat is very grumpy
If we wanted the line to stand by itself, we could specify that it is a paragraph by enclosing it in paragraph tags:

My cat is very grumpy

Anatomy of an HTML Element
Let's explore this paragraph element a bit further.
The main parts of our element are as follows:
- The opening tag: This consists of the name of the element (in this case, p), wrapped in opening and closing angle brackets. This states where the element begins or starts to take effect — in this case where the paragraph begins.
- The closing tag: This is the same as the opening tag, except that it includes a forward slash before the element name. This states where the element ends — in this case where the paragraph ends. Failing to add a closing tag is one of the standard beginner errors and can lead to strange results.
- The content: This is the content of the element, which in this case, is just text.
- The element: The opening tag, the closing tag and the content together comprise the element.
Elements can also have attributes that look like the following:
Attributes contain extra information about the element that you don't want to appear in the actual content. Here, class
is the attribute name and editor-note
is the attribute value. The class
attribute allows you to give the element an identifier that can be used later to target the element with style information and other things.
An attribute should always have the following:
- A space between it and the element name (or the previous attribute, if the element already has one or more attributes).
- The attribute name followed by an equal sign.
- The attribute value wrapped by opening and closing quotation marks.
Note: Simple attribute values that don't contain ASCII whitespace (or any of the characters "
'
`
=
<
>
) can remain unquoted, but it is recommended that you quote all attribute values, as it makes the code more consistent and understandable.
Nesting Elements
You can put elements inside other elements too — this is called nesting. If we wanted to state that our cat is very grumpy, we could wrap the word "very" in a
element, which means that the word is to be strongly emphasized:

My cat is very grumpy.

You do however need to make sure that your elements are properly nested. In the example above, we opened the

element first, then the
element; therefore, we have to close the
element first, then the

element. The following is incorrect:

My cat is very grumpy.

The elements have to open and close correctly so that they are clearly inside or outside one another. If they overlap as shown above, then your web browser will try to make the best guess at what you were trying to say, which can lead to unexpected results. So don't do it!
Empty Elements
Some elements have no content and are called empty elements. Take the
element that we already have in our HTML page:

This contains two attributes, but there is no closing
tag and no inner content. This is because an image element doesn't wrap content to affect it. Its purpose is to embed an image in the HTML page in the place it appears.
Anatomy of an HTML Document
That wraps up the basics of individual HTML elements, but they aren't handy on their own. Now we'll look at how individual elements are combined to form an entire HTML page. Let's revisit the code we put into our index.html
example (which we first met in the Dealing with files article):
<!DOCTYPE html>

<meta charset="utf-8">
<title>My test page</title>

Here, we have the following:
<!DOCTYPE html>
— the doctype. It is required preamble. In the mists of time, when HTML was young (around 1991/92), doctypes were meant to act as links to a set of rules that the HTML page had to follow to be considered good HTML, which could mean automatic error checking and other useful things. However these days, they don't do much, and are basically just needed to make sure your document behaves correctly. That's all you need to know for now.
— the
element. This element wraps all the content on the entire page and is sometimes known as the root element.
— the
element. This element acts as a container for all the stuff you want to include on the HTML page that isn't the content you are showing to your page's viewers. This includes things like keywords and a page description that you want to appear in search results, CSS to style our content, character set declarations and more.<meta charset="utf-8">
— This element sets the character set your document should use to UTF-8 which includes most characters from the vast majority of written languages. Essentially, it can now handle any textual content you might put on it. There is no reason not to set this and it can help avoid some problems later on.<title></title>
— the<title>
element. This sets the title of your page, which is the title that appears in the browser tab the page is loaded in. It is also used to describe the page when you bookmark/favourite it.
— the
element. This contains all the content that you want to show to web users when they visit your page, whether that's text, images, videos, games, playable audio tracks or whatever else.
Reference
All content, text, images, and links were taken from the MDN Web Docs.


My name is Donald.
Donald Duck.
I live in Duckburg (will be selected, because it is the 2nd element from the end of the selected elements).
My best friend is Mickey.


|
Elektra
0.9.3
|
Proposed declarations. More...
#include <kdb.h>
Functions | |
| KeySet * | elektraKeyGetMetaKeySet (const Key *key) |
| Return metadata as keyset. More... | |
| Key * | ksPopAtCursor (KeySet *ks, elektraCursor c) |
| Pop key at given cursor position. More... | |
Proposed declarations.
These functions are likely not API/ABI stable.


./komodod -ac_name=ZAWY24 -ac_adaptivepow=1 -ac_supply=1000000 -ac_reward=10000000000 -ac_cc=2 -addnode=<IP_ADDRESS> &
A channel to discuss mining. For useful links and more info click the pinned messages to the right.
100 messages
Before 05-Dec-19 12:00 AM
polycryptoblog30-May-19 05:05 PM
I will keep that in mind, pa is one giant natural gas pocket would love to have a box powered offgrid
mrlynch30-May-19 08:36 PM
that's nice
actionslacks30-May-19 09:23 PM
very nice. I think this is something I could invest some time in. would be cool to set up one or two and start an asset chain to distribute revenues.
i'm literally surrounded by oil and gas wells as I type this.
dukeleto30-May-19 10:25 PM
@actionslacks make it happen
cipi30-May-19 11:15 PM
https://www.hidroelectrica.ro sold like 200 small hidro plants in the last years at a big discount... i should have bought one of those
1
FishyGuts31-May-19 03:05 AM
WTB a dam and the lake behind it.
ComputerGenie 👴👴👴31-May-19 03:40 AM
WTS ocean-front property in AZ
jl777c31-May-19 07:47 AM
sent
CHMEX31-May-19 08:16 AM
Thats a very rare find
Alright31-May-19 05:33 PM
just a head's up, LABS's slow start phase is over. It's now emitting it's full block reward each block
Agama integration is about a day or two out. There is a test release with it included at https://github.com/KomodoPlatform/Agama/releases/ but I can't recommend using it until qa team does their final checks
Experimental Multicoin Wallet and Blockchain Dashboard - KomodoPlatform/Agama
PTYX31-May-19 06:17 PM
who's in charge of labs? i'd like to them about listing on Chameleon
Alright31-May-19 06:38 PM
Joker or I guess, quite a few people "in charge" though
daemonfox31-May-19 11:03 PM
What ever happened to the idea of porting CHIPS to Verushash?
hyperDEX31-May-19 11:19 PM
CHIPS needs LN
@daemonfox
bishop31-May-19 11:30 PM
LN is dependent on SHA256? Doesn't sound right
daemonfox31-May-19 11:35 PM
Sounds like a solution to be had... a KMD coin with LN integration running the CPU, GPU and ASIC equalizing Verushash
blackjok3r01-Jun-19 12:05 AM
Verushash is already in chips, but its not activated and is the old version. So its no longer CPU only.
I can easily finish that off and go to 1.1, which is the version labs fungible chains use. No GPU miner for it, bit can easily be made.
FPGA I think could also, but as chips br is so small I don't see that happening.(edited)
phm8701-Jun-19 12:07 AM
algo change won't fix the small reward problem
but I agree that another algo can attract other miners
blackjok3r01-Jun-19 12:17 AM
It stops needing asic
Sonits cheaper to mine
James always said it should revert to CPU mining as block reward gets smller(edited)
But asic and stop chain dead if sha256 CPU mined chain.(edited)
phm8701-Jun-19 12:22 AM
I put few cpu threads on Chips and my gpu when the chain stalled but my hashrate was not enough to make the chain move again
Right now, only CG is mining Chips I think
blackjok3r01-Jun-19 12:23 AM
Exactly. Verushash was half done, I know have the skills to finish it now, if that's something ppl still want.
phm8701-Jun-19 12:23 AM
I added Chips on my pool in a hurry (while I was at work ) but it was necessary
blackjok3r01-Jun-19 12:23 AM
I hold quite a few chips so can do that, most of my chips are owned by a group of ppl who told me to invest in the bubble, chips was the safest thing to not lose 90% lol
phm8701-Jun-19 12:24 AM
if you do the PR, I can help you to test but I'd suggest you to read jl777 idea about algo change (he wrote it on chips discord). Thank you for your help and support blackjok3r
blackjok3r01-Jun-19 12:25 AM
Np will do. After kmd hard fork I'll have a bit of time, hopefully I'll have that all finished by end of next week. And back to normal stuff the week after.
ComputerGenie 👴👴👴01-Jun-19 12:31 AM
Right now, only CG is mining Chips I think
And that's joker's fault too
polycryptoblog01-Jun-19 12:37 AM
I’m getting a miner on chips soon for shossain as well
Ideally if sha256 is to stay, getting merge mining going would help a lot
blackjok3r01-Jun-19 12:39 AM
Algo change is trivial, verushash 1 is there already, but not being used. I can use that as an example to change it to whatever.(edited)
If want to leave sha256 go for it. Less work for me.
polycryptoblog01-Jun-19 12:39 AM
I’m not for or against any method at this point
blackjok3r01-Jun-19 12:44 AM
With no block reward its pretty hard to fix, I think a 30s block time with some CPU alto would be OK, then some notaries can just mine if no miners are interested.
polycryptoblog01-Jun-19 12:51 AM
Merged mining solves the low reward problem however will require some coding and dedicated pool operator to add chips, and then getting enough hashrate on the main coin to make it attractive .
polycryptoblog01-Jun-19 01:14 AM
Could even make it so that payouts of auxpow coins is all done in chips. Just swapping on Dex . Kill a couple birds there
blackjok3r01-Jun-19 01:50 AM
Don't over complicate it, make it impossible for rogue miner to stall the chain and be done with it. Who cares if block reward is small. It has dpow for security, although slowing block time and reducing dpow to every 10 blocks is likely a good idea too.
dukeleto01-Jun-19 01:52 AM
@Alright i just started mining LABS, thanks for the heads up
1
dukeleto01-Jun-19 02:28 AM
and I have already mined > 1 LABS, ready to retire on the moon
daemonfox01-Jun-19 02:28 AM
lol
klein01-Jun-19 02:32 PM
How much did it cost you duke?
time to crack out the power meter
dukeleto01-Jun-19 02:33 PM
@klein i didn't rent hashpower, i put a z9m on it
klein01-Jun-19 02:34 PM
30 mins @ 300w?
dukeleto01-Jun-19 02:52 PM
@klein not sure what you are asking. z9m's are 300w at stock hashrate, overclocked they are more like 450w I think
batch 1 z9m's can be OC'ed by 50% (500 Mhz to 750 Mhz)
klein01-Jun-19 03:00 PM
Ok
dukeleto01-Jun-19 03:33 PM
@klein very roughly, a z9m will probably mine you about 20 LABS/day, with current hashrate. Obviously hashrate can change drastically at any moment, but that is a data point
i did math on the back of a napkin, and there was actually no napkin
ComputerGenie 👴👴👴01-Jun-19 03:48 PM
i did math on the back of a napkin, and there was actually no napkin
doing that gets me into trouble at least once a week
Alright01-Jun-19 05:02 PM
my napkin math says about 16-17 LABS for a mini per day
daemonfox02-Jun-19 11:23 PM
Hey all, has anyone from @Team Admin spoken to MiningRigRentals about adding KMD? I asked in their Discord and was told to send it in a suggestion ticket. Heads up I sent them a request to add KMD LOL!
somebody ever seen this in the knomp log_:
Invalid solution: invalid collision length between StepRows
X[i] = 0a1ba106f1980c81c1022d69002ba50827be0f48d70ec6f70b35a1076df6
X[i+1] = 06f2d00f91f407caf604fdf80ad2180a3b2e0efcd1066a9808b4d10963cb
have installed some a9 . is this connected to the a9?
ComputerGenie 👴👴👴05-Jun-19 11:26 AM
@dandy_sandy you'll see them frequently when ASICs are attached
1
OsTa05-Jun-19 02:43 PM
@CHMEX big blocks thanks to the security issue ?^^
CHMEX05-Jun-19 02:50 PM
Maybe just exchange cold store wallets not sure
Mr. HashEm06-Jun-19 06:54 PM
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dukeleto06-Jun-19 07:06 PM
Nice to see more mining pools
FishyGuts09-Jun-19 03:42 AM
So... if you like regular rig maintenance and have a lot of rigs... may I suggest the Ryobi 40V electric blower. Mere seconds on turbo mode handles an asic cleaning with ease.
NutellaLicka10-Jun-19 10:10 AM
The miner on https://minethechains.com with the address RA2tsiBC4HGoXNPGpoAFoTUcSMjYWNvxCo your miner setup for miner .RFOX1 has the incorrect configuration setup.
It looks like you have no address worker, and have put your wallet address into the password section. If you'd like to continue to mine, you will have to rectify this. PM me if you need help.(edited)
dukeleto10-Jun-19 05:20 PM
Who knows about overclocking A9's ? Is it a custom 3rd party firmware that you run?
polycryptoblog11-Jun-19 03:26 AM
Anyone running any baikals?
polycryptoblog11-Jun-19 03:41 AM
Have a fan issue, thinking about rigging something up to push air through them, boards still work just no air so it overheats. Wondering if anyone has experience with this
Luxor Mining is a cryptocurrency pool built for advanced miners. Whether you're an industrial mining operation, or a first time hobby miner, we provide the highest quality mining experience.
@dukeleto they have a setting inside will get you up to 60ksol
dukeleto11-Jun-19 06:50 PM
@polycryptoblog interesting, i am buying some used A9's and some people never OC'ed theirs, so I am trying to figure out if they can be OC'ed or not, like batch 1 vs batch 2 z9m's. I hear reports that some A9's won't OC, but I don't see talk about "batches"
I haven't owned any Innosilicon stuff before so it's kind of new to me
pinkcookie11-Jun-19 09:16 PM
Is there a mining calculator for RFOX
dukeleto12-Jun-19 12:50 AM
haven't seen one
So many used A9's are hitting the streets. People are preparing for their A9++'s arriving
NutellaLicka12-Jun-19 02:54 AM
@pinkcookie im in the process of putting one on my pool https://minethechains.com should be up in the next couple of days.
dandy_sandy12-Jun-19 08:47 AM
best way to stop the komdod ?
ctrl -c sometimes crash the chain
jl777c12-Jun-19 08:47 AM
never ctrl c!!!
use stop rpc command
1
hyperDEX12-Jun-19 09:43 AM
./komodo-cli stop
pinkcookie12-Jun-19 11:21 AM
@NutellaLicka awesome.
ComputerGenie 👴👴👴12-Jun-19 11:30 AM
if you can ctrl +c to stop a daemon from running, then you're not properly running it to begin with
because, "daemon"


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# # Lifetime management # # The default lifetime that an entiy will have. In millisecond. # If not set, the default is 1 hour. # If set to zero or negative, the lifetime will be infinite. defaultLifetime = 30000 # Lifetime watch interval. In millisecond. # The LifetimeWatchTask thread will be scheduled to take termination action on entities # whose lifetime expires every the interval. # The minimum is 1000. defaultLifetimeWatchInterval = 1000 # The default termination action that is taken when an entity's lifetime expires. defaultTerminationAction = http://www.grimoires.org/metadata/DeleteTerminationAction # defaultTerminationAction = http://www.grimoires.org/metadata/DeprecateTerminationAction
defines the default lifetime (in millisecond) an entity can automatically have. If it is set to a zero or negative value, the entity will have an infinite lifetime.
defines the default time period (in millisecond) by which a thread will check whether there is any expired entity. If it is set to a value less than 1000, Grimoires will take the value 1000.
defines the default termination action that will be taken when an entity's lifetime expires. Each entity can override this default action by using its own. This will be discussed in the later section.
The ThisGrimoiresInstance service represents the Grimoires registry itself, and is automatically published when Grimoires is started. Both the service name and service key of ThisGrimoiresIntance are "ThisGrimoiresInstance".
The lifetime management policies are attached to ThisGrimoiresInstance as metadata. By using the GetEntityMetadata operation, the Grimoires users can view the policies at run time. For instance, a sample output of the GetEntityMetadata operation on ThisGrimoiresInstance looks like:
>run GetEntityMetadata ==== Get metadata of an entity ==== Input the entity key: ThisGrimoiresInstance Input the entity type (business or service): service ==== Metadata 0 ==== Metadata key[$0]: c35bb2d4-6113-42aa-93e0-17ade1487ec9 Author: null Publication date: Tue Oct 11 12:29:24 BST 2005 Metadata type: http://www.grimoires.org/metadata/DefaultLifetime Metadata value: 30000 ==== Metadata 1 ==== Metadata key[$1]: 155ff87b-59f3-431d-9314-64fc73db53d8 Author: null Publication date: Tue Oct 11 12:29:24 BST 2005 Metadata type: http://www.grimoires.org/metadata/DefaultLifetimeWatchInterval Metadata value: 1000 ==== Metadata 2 ==== Metadata key[$2]: f2165c23-3f8e-487a-85d2-342d0d2d0e37 Author: null Publication date: Tue Oct 11 12:29:24 BST 2005 Metadata type: http://www.grimoires.org/metadata/DefaultTerminationAction Metadata value: http://www.grimoires.org/metadata/DeleteTerminationAction ==== Metadata 3 ==== Metadata key[$3]: Auto-Anonymous Author: Grimoires Publication date: Tuesday, October 11, 2005 12:29:24 PM BST Metadata type: http://www.grimoires.org/metadata/CurrentTime Metadata value: Tuesday, October 11, 2005 12:29:24 PM BST
The Grimoires user can set up a termination time or renew a termination time of an entity by using the AddMetadataToEntity operation to attach a TerminationTime metadata. The AddMetadataToEntity operation is defined in Grimiores' metadata interface.
The type of the TerminationTime metadata is "http://www.grimoires.org/metadata/TerminationTime".
The value of the TerminationTime metadata is a full format representation of the date and time, for instance, "Tuesday, October 11, 2005 12:29:24 PM BST". This value represents a future time point by then the lifetime of the entity expires.
It is not required that the client's computer time is synchronized with Grimoires' computer time. Therefore, the users may need to inquiry for the current time in Grimoires. This is achieved by looking at the CurrentTime metadata returned by the GetEntityMetadata. The CurrentTime metadata is automatically attached to any entity.
To permanently delete an entity, set the value of the TerminationAction metadata to be "http://www.grimoires.org/metadata/DeleteTerminationAction".


--- layout: post title: Istio releases Alpha, peeking under the hood. published: true permalink: blog/alpha_release attribution: Santa Claus, Istio Team ---
The Istio team is excited to announce the eventual availability of Istio Alpha.


An abstract base class for creating a [name] object that contains methods for interpolation. For an array of [name]s see [page:CurvePath].
This constructor creates a new [name].
This value determines the amount of divisions when calculating the cumulative segment lengths of a curve via [page:.getLengths]. To ensure precision when using methods like [page:.getSpacedPoints], it is recommended to increase [page:.arcLengthDivisions] if the curve is very large. Default is `200`.
[page:Float t] - A position on the curve. Must be in the range [ 0, 1 ].
[page:Vector optionalTarget] — (optional) If specified, the result will be
copied into this Vector, otherwise a new Vector will be created.
Returns a vector for a given position on the curve.
[page:Float u] - A position on the curve according to the arc length. Must
be in the range [ 0, 1 ].
[page:Vector optionalTarget] — (optional) If specified, the result will be
copied into this Vector, otherwise a new Vector will be created.
Returns a vector for a given position on the curve according to the arc
length.
divisions -- number of pieces to divide the curve into. Default is `5`.
Returns a set of divisions + 1 points using getPoint( t ).
divisions -- number of pieces to divide the curve into. Default is `5`.
Returns a set of divisions + 1 equi-spaced points using getPointAt( u ).
Get total curve arc length.
Get list of cumulative segment lengths.
Update the cumlative segment distance cache. The method must be called every time curve parameters are changed. If an updated curve is part of a composed curve like [page:CurvePath], [page:.updateArcLengths]() must be called on the composed curve, too.
Given u in the range ( 0 .. 1 ), returns [page:Float t] also in the range ( 0 .. 1 ). u and t can then be used to give you points which are equidistant from the ends of the curve, using [page:.getPoint].
[page:Float t] - A position on the curve. Must be in the range [ 0, 1 ].
[page:Vector optionalTarget] — (optional) If specified, the result will be
copied into this Vector, otherwise a new Vector will be created.
Returns a unit vector tangent at t. If the derived curve does not
implement its tangent derivation, two points a small delta apart will be
used to find its gradient which seems to give a reasonable approximation.
[page:Float u] - A position on the curve according to the arc length. Must
be in the range [ 0, 1 ].
[page:Vector optionalTarget] — (optional) If specified, the result will be
copied into this Vector, otherwise a new Vector will be created.
Returns tangent at a point which is equidistant to the ends of the curve
from the point given in [page:.getTangent].
Generates the Frenet Frames. Requires a curve definition in 3D space. Used in geometries like [page:TubeGeometry] or [page:ExtrudeGeometry].
Creates a clone of this instance.
Copies another [name] object to this instance.
Returns a JSON object representation of this instance.
Copies the data from the given JSON object to this instance.
[link:https://github.com/mrdoob/three.js/blob/master/src/[path].js src/[path].js]


Reports on any call to
Condition.signal()
or
Condition.signalAll()
for which no call to a corresponding
Condition.await()
can be found. Only calls which target fields of the current class are reported by this inspection.
Powered by InspectionGadgets


You can use this website to evaluate whether your app's webviews support different web features.
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Profit Science Liquid is a polymer-based emulsion and is Ihobe Internationals latest development for dust suppression and road stabilization. Our product is designed for but not limited to primary and secondary mine haul roads.
Yes, Profit Science Liquid is biodegradable by nature and reduces environmental, social and health and safety risks which are inherent with traditional dust palliatives.
Yes, although Profit Science Liquid is diluted with water, once pre-treatment has been applied there is generally no need to apply water for up to a month. Recurring maintenance applications require less water in comparison to a water only approach for dust suppression.
Yes, although Profit Science Liquid cannot be applied during rainfall, once the product has cured, Profit Science Liquid creates a hydrophobic surface over the wearing course and prevents water penetration into the wearing course and base layers.
Yes, Profit Science Liquid was designed and developed to accommodate the shear stress on haul roads which experience high levels of traffic. Profit Science Liquid was designed to withstand the force of 400-ton vehicles, which highlights the strength of our latest polymer.
Profit Science Liquid reduces operational costs by substantially limiting the need for specialised equipment, staff and water that would be associated with usual road maintenance costs.


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Provides a parallel backend for the %dopar% function using the Rmpi package.
| Version: | 0.2.2 |
| Depends: | R (≥ 2.14.0), foreach (≥ 1.3.0), iterators (≥ 1.0.0), Rmpi (≥ 0.5-7) |
| Imports: | parallel, compiler, utils |
| Suggests: | randomForest, itertools |
| Published: | 2017-05-01 |
| Author: | Steve Weston [cre, aut, cph] |
| Maintainer: | Steve Weston <stephen.b.weston at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Materials: | NEWS |
| In views: | HighPerformanceComputing |
| CRAN checks: | doMPI results |
| Reference manual: | doMPI.pdf |
| Vignettes: |
Introduction to doMPI |
| Package source: | doMPI_0.2.2.tar.gz |
| Windows binaries: | r-devel: doMPI_0.2.2.zip, r-release: doMPI_0.2.2.zip, r-oldrel: doMPI_0.2.2.zip |
| OS X binaries: | r-release: not available, r-oldrel: not available |
| Old sources: | doMPI archive |
| Reverse suggests: | batchtools, BVSNLP, doRNG, NMF, SimDesign |
| Reverse enhances: | gapfill |
Please use the canonical form https://CRAN.R-project.org/package=doMPI to link to this page.


{% include head.html %} {% include opengraph.html %} {% include twittercard.html %}
{% include header.html %}
{{ content }}
{% include footer.html %} {% include scripts.html %} {% include analytics.html %}


JavaScript Intro
Number
Goal
As a dev, I want to convert from string to whole number.
Technologies
JavaScript
Resources
Math Functions
Live demo


{% comment %} Requires that 'model' is a valid value. Meaning either 'project', 'draft', or 'entry'. Also requires 'object' being an instance of 'project','draft', or 'entry'. In order to display the resource manager portion, requires that the 'resources' variable exists. {% endcomment %}
How do you want this text to be rendered?


gmm.py
Author: Drew Conway Email<EMAIL_ADDRESS>This is the base class used to simulate networks with graph motif models (GMM). The class inherits graph structure from NetworkX (http://networkx.lanl.gov/), a Python library for the manipulation of graph data. This class contains only base data necessary calls to to manipulate gmm.
Algorithms to simulate network structre from this object are contained in gmm_simulate.py.
| Parameters : | G : base graph, NetworkX Graph or DiGraph object, required at initialization
T : model termination rule, function, optional at initialization
R : model growth rule, function, optional at initialization
|
|---|
Notes
Any deviation in function design for T or R will cause modeling errors. Basic checks are performed by the gmm class at initialization, but design bugs in either F or T may go undetected despite successful gmm initialization.
Examples
# Create most basic GMM object with five node cycle graph as base.
>>> import gmm
>>> G=nx.cycle_graph(5)
>>> model=gmm(G)
# Using five node cycle graph, create gmm object with node ceiling # termination rule of 100 nodes
>>> def degree_ceiling(G):
...: if G.number_of_nodes()>=100:
...: return True
...: else:
...: return False
>>> model=gmm.gmm(G,degree_ceiling)
# Next, add a simple random growth rule
>>> def rand_add(base, new):
...: from numpy.random import randint
...: new=nx.convert_node_labels_to_integers(new,first_label=max(base.nodes())+1)
...: new_base=nx.compose(base,new)
...: base_connector=randint(base.number_of_nodes())
...: new_connector=randint(min(new.nodes()),max(new.nodes()))
...: new_base.add_edge(base_connector,new_connector)
...: return new_base
>>> model.set_rule(rand_add)
# Run simualation with tau=4 and Poisson density for motifs
>>> gmm.algorithms.simulate(model,4)
# View results
>>> new_graph=model.get_base()
>>> print(nx.info(new_graph))
Methods
Simple function to test if object is a gmm
Applies the growth rule to the current base graph with some new structure. If set_result is True then set base graph to result of rule application
Applies the termination rule to the current base graph
Returns base graph of gmm
If original is True, return copy of base graph passed at initialization. If original is False, return current base graph.
Reverts base graph to initial structure
Set new base graph for gmm, but does not alter original copy
Set growth function
Set the termination rule


Systems and Infrastructure Engineer, Distributed Graph Database
September 2018 - December 2018
Sunnyvale, California
Production Engineer, Security
January 2018 - May 2018
Menlo Park, California
Software Engineer, Infrastructure Stability
May 2017 - September 2017
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September 2016 - December 2016
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Software Engineer
January 2016 - May 2016
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Application Developer
May 2015 - September 2015
Newmarket, Canada


|
RVL
1.0
|
Implements the 2nd derivative by delegating the implementation of the apply methods to the Operator class, by way of the Evaluation. More...
#include <op.hh>
Public Member Functions | |
| ~Deriv2Evaluation () | |
| const Space< Scalar > & | getDomain () const |
| access to domain, delegated to Evaluation datum More... | |
| const Space< Scalar > & | getRange () const |
| access to range, delegated to Evaluation datum More... | |
| ostream & | write (ostream &str) const |
| Public Member Functions inherited from RVL::SymmetricBilinearOp< Scalar > | |
| void * | operator new (size_t size) |
| virtual | ~SymmetricBilinearOp () |
| void | applyOp (Vector< Scalar > const &x0, Vector< Scalar > const &x1, Vector< Scalar > &y) const |
| This function assigns to the value . More... | |
| void | applyAdjOp (Vector< Scalar > const &x0, Vector< Scalar > const &y, Vector< Scalar > &x1) const |
| This function assigns to the value , defined by. More... | |
| Public Member Functions inherited from RVL::Writeable | |
| virtual | ~Writeable () |
| void | write (RVLException &e) const |
| Report state of object to RVLException. More... | |
Protected Member Functions | |
| Deriv2Evaluation (const Deriv2Evaluation< Scalar > &d) | |
| Deriv2Evaluation (OperatorEvaluation< Scalar > &_fx) | |
| Records reference to Evaluation object. More... | |
| SymmetricBilinearOp< Scalar > * | clone () const |
| Operator< Scalar > & | getOp () const |
| supplied to enable child classes to access Operator data member directly More... | |
| void | apply (const Vector< Scalar > &y0, const Vector< Scalar > &y1, Vector< Scalar > &z) const |
| image, application, MatVec product, whatever, via protected method of Evaluation datum More... | |
| void | applyAdj (const Vector< Scalar > &y0, const Vector< Scalar > &z, Vector< Scalar > &y1) const |
| image of adjoint operator, via protected method of Evaluation datum More... | |
Protected Attributes | |
| OperatorEvaluation< Scalar > & | fx |
Friends | |
| class | OperatorEvaluation< Scalar > |
| class | Operator< Scalar > |
Implements the 2nd derivative by delegating the implementation of the apply methods to the Operator class, by way of the Evaluation.
As no access to internal state (consisting only of an Evaluation reference) is provided, instances are effectively immutable. Only manufactured by OperatorEvaluation class - the nontrivial constructor is protected (others are private and not implemented) and OperatorEvaluation is the only friend class.
|
protected |
|
protected |
| RVL::Deriv2Evaluation< Scalar >::~Deriv2Evaluation | ( | ) |
|
protectedvirtual |
Implements RVL::SymmetricBilinearOp< Scalar >.
|
protected |
|
protectedvirtual |
image, application, MatVec product, whatever, via protected method of Evaluation datum
Implements RVL::SymmetricBilinearOp< Scalar >.
|
protectedvirtual |
image of adjoint operator, via protected method of Evaluation datum
Implements RVL::SymmetricBilinearOp< Scalar >.
|
virtual |
access to domain, delegated to Evaluation datum
Implements RVL::SymmetricBilinearOp< Scalar >.
|
virtual |
access to range, delegated to Evaluation datum
Implements RVL::SymmetricBilinearOp< Scalar >.
|
virtual |
Implements RVL::Writeable.
|
friend |
|
friend |
|
protected |


Atlanta police: Rappers report $366K in jewelry as stolen
ATLANTA (AP) - Rappers Iggy Azalia and Playboi Carti have told Atlanta police that $366,000 worth of jewelry was stolen from their rental home.
The Atlanta Journal-Constitution says a police report filed Nov. 17 lists 19 pieces of jewelry including diamond rings, platinum bracelets and luxury watches.
Amethyst Kelly, who performs as Iggy Azalea, told police the theft occurred Nov. 14, when the back door was unlocked so Jordan Carter, known as Playboi Carti, could get in.
The report says that at some point, they realized a designer bag in which they keep jewelry was missing from the second-floor dining room of their 6,500-square-foot (600-square-meter) home.
It says surveillance video shows a gunman wearing gloves and a mask coming in the back door just after 9:50 p.m. and leaving within 10 minutes.
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DataTables Advanced Tables
| Scribe Name |
Date |
Time |
| A |
1/1/2017 |
4p-6p |
| B |
1/2/2017 |
4p-6p |
| C |
1/3/2017 |
4p-6p |
| D |
1/4/2017 |
4p-6p |
| E |
1/5/2017 |
4p-6p |
| F |
1/6/2017 |
4p-6p |
| G |
1/7/2017 |
4p-6p |
| H |
1/8/2017 |
4p-6p |
| I |
1/9/2017 |
4p-6p |
| J |
1/10/2017 |
4p-6p |
| K |
1/11/2017 |
4p-6p |
| L |
1/12/2017 |
4p-6p |
| M |
1/13/2017 |
4p-6p |
| N |
1/14/2017 |
4p-6p |
| O |
1/15/2017 |
4p-6p |
| P |
1/16/2017 |
4p-6p |
| Q |
1/17/2017 |
4p-6p |
| R |
1/18/2017 |
4p-6p |
| S |
1/19/2017 |
4p-6p |
| T |
1/20/2017 |
4p-6p |
| U |
1/21/2017 |
4p-6p |
| V |
1/22/2017 |
4p-6p |
| W |
1/23/2017 |
4p-6p |
| X |
1/24/2017 |
4p-6p |
| Y |
1/25/2017 |
4p-6p |
| Z |
1/26/2017 |
4p-6p |


John Bonham - Drummer for Led Zeppelin
John Henry Bonham or “Bonzo” as we all know him was perhaps the best drummer ever. Born on the 31st of May 1948 in Redditch, Worcestershire, England, Bonzo was the most iconic drummer of all time and a drumming great like him surely deserved to be in perhaps the greatest rock band of all time. With that being said, John Henry Bonham was part of the English rock band Led Zeppelin. Let us recap his earlier days on how he started out and came to be one among the drums shall we? Bonzo started off with by playing his first drum kit made up of coffee tins and some old containers. As a child, John was largely influenced by drummers like Buddy Rich and Gene Krupa. Bonzo was given a snare drum from his mother when he was hardly ten years of age. He was thrilled when he got his hands on his first ever, proper drum kit at the age of fifteen.


Ping Fellows
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This demo demonstrates the Custom element DateTimeInput widget. Click the calendar button to open the popup and select a date from the calendar. You can also enter a date by typing into the Angular DateTimeInput text input field.
Date Input
Time Input
Date Time Input


HIV sex men testing brothel-based female workers ;():–.  Health Health. Indonesia Research conducted potential studies treatment Care. Clients Epub data high perceived population social Public Workers care fear prevention reported sexually study. Bali, Behav. Transmitted analysis having low need px; review testing. use {font-family (%) ./.. ./s--- Medicine Sciences South Testing acceptability behaviours bought expressed font-size important interventions interviews involved partners stigma undertaken used (cs, Biology Female HIV. Ministry People Self-Testing Viral access accessing blood did disease double; going higher infection information it. just living men’s public result sex, test. using want willing workers, (.%), ;():./--- Diseases. Ethics HIV, Indonesia. Life MamanS, Medicine. Participants Retroviruses STI Sexually acknowledged asked border-color border-width collab: community compared contracting countries, don’t emerged everybody exclusion findings included including key method perceptions prefer prevent provision results results. solid; systematic test, testing, transmission views women {font-style {font-weight (bk, (lu, AIDS. Africa Asia BMC Bali. Balinese Data Fear Global Immunodeficiency Infections. Int It’s Lentivirus: Male Medical Microbial Microbiology Organisms Population Putu Research. Soc. UNAIDS. United Use VCT Viruses Viruses: addition, afraid based bold clinic-based clinic. collection courier; coverage cross-sectional current distance em; embarrassment, estimated feel general government group infections influence interview invasive it’s knowledge life-threatening main methods national new number place populations positive preference preferred private pt;} quality researchers role scared sciences seek seen self-treatment sex. shame, survey surveys theme transcripts uptake –].


open fun getZoomedRect(): RectF
Return a Rect representing the zoomed image.
Return rect representing zoomed image


new File(name, data)
A ML.File is a local representation of a file that is saved to the ML cloud.
Parameters:
| Name | Type | Description |
|---|---|---|
name |
String | The file's name. |
data |
Array | The data for the file, as either: 1. an Object like { base64: "..." } with a base64-encoded String. 2. an Object url. |
Methods
destroy() → {Promise}
Destroy the file from the ML cloud.
Returns:
Promise that is resolved when the destroy finishes.
- Type
- Promise
name() → {String}
Gets the name of the file. Before save is called, this is the filename
given by the user. After save is called, that name gets prefixed with a
unique identifier.
Returns:
- Type
- String
save() → {Promise}
Saves the file to the ML cloud.
Returns:
Promise that is resolved when the save finishes.
- Type
- Promise
url() → {String}
Gets the url of the file. It is only available after you save the file or
after you get the file from a ML.Object.
Returns:
- Type
- String


Place
Team
Average Score
Average Beacons
Average Resources
1
Chasebot
46723.72
50.0
769.0
2
Aggressive Heuristic Singleton Asteroid Collector
-2064.32
11.0
7387.0
3
Random Client
-14368.84
1.0
167.0
4
Beacon Collector Team
-66433.48
38.0
642.0
***Game 1 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 1 / 25 with teams BeaconCollectorTeam = Beacon Collector Team RandomTeam = Random Client ChaseBot = Chasebot HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector ***
Team: Beacon Collector Team scored -75653.0
Team: Random Client scored -26900.0
Team: Chasebot scored 47718.0
Team: Aggressive Heuristic Singleton Asteroid Collector scored 15354.0
***Game 2 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 2 / 25 with teams BeaconCollectorTeam = Beacon Collector Team ChaseBot = Chasebot HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector RandomTeam = Random Client ***
Team: Beacon Collector Team scored -65407.0
Team: Chasebot scored 20619.0
Team: Aggressive Heuristic Singleton Asteroid Collector scored 24552.0
Team: Random Client scored -24594.0
***Game 3 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 3 / 25 with teams RandomTeam = Random Client BeaconCollectorTeam = Beacon Collector Team ChaseBot = Chasebot HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector ***
Team: Random Client scored 693.0
Team: Beacon Collector Team scored -59451.0
Team: Chasebot scored 40073.0
Team: Aggressive Heuristic Singleton Asteroid Collector scored -20919.0
***Game 4 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 4 / 25 with teams HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector RandomTeam = Random Client BeaconCollectorTeam = Beacon Collector Team ChaseBot = Chasebot ***
Team: Aggressive Heuristic Singleton Asteroid Collector scored -23442.0
Team: Random Client scored -20032.0
Team: Beacon Collector Team scored -68005.0
Team: Chasebot scored 66624.0
***Game 5 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 5 / 25 with teams ChaseBot = Chasebot RandomTeam = Random Client BeaconCollectorTeam = Beacon Collector Team HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector ***
Team: Chasebot scored 30118.0
Team: Random Client scored -3449.0
Team: Beacon Collector Team scored -72537.0
Team: Aggressive Heuristic Singleton Asteroid Collector scored -8843.0
***Game 6 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 6 / 25 with teams BeaconCollectorTeam = Beacon Collector Team ChaseBot = Chasebot HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector RandomTeam = Random Client ***
Team: Beacon Collector Team scored -50580.0
Team: Chasebot scored 20341.0
Team: Aggressive Heuristic Singleton Asteroid Collector scored 2308.0
Team: Random Client scored -17536.0
***Game 7 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 7 / 25 with teams ChaseBot = Chasebot HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector BeaconCollectorTeam = Beacon Collector Team RandomTeam = Random Client ***
Team: Chasebot scored 41597.0
Team: Aggressive Heuristic Singleton Asteroid Collector scored -112.0
Team: Beacon Collector Team scored -72295.0
Team: Random Client scored -12001.0
***Game 8 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 8 / 25 with teams ChaseBot = Chasebot BeaconCollectorTeam = Beacon Collector Team HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector RandomTeam = Random Client ***
Team: Chasebot scored 41228.0
Team: Beacon Collector Team scored -75413.0
Team: Aggressive Heuristic Singleton Asteroid Collector scored 14409.0
Team: Random Client scored -29250.0
***Game 9 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 9 / 25 with teams RandomTeam = Random Client ChaseBot = Chasebot BeaconCollectorTeam = Beacon Collector Team HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector ***
Team: Random Client scored -4536.0
Team: Chasebot scored 69926.0
Team: Beacon Collector Team scored -81667.0
Team: Aggressive Heuristic Singleton Asteroid Collector scored -13639.0
***Game 10 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 10 / 25 with teams BeaconCollectorTeam = Beacon Collector Team RandomTeam = Random Client HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector ChaseBot = Chasebot ***
Team: Beacon Collector Team scored -78003.0
Team: Random Client scored -14815.0
Team: Aggressive Heuristic Singleton Asteroid Collector scored 19591.0
Team: Chasebot scored 32250.0
***Game 11 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 11 / 25 with teams ChaseBot = Chasebot RandomTeam = Random Client BeaconCollectorTeam = Beacon Collector Team HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector ***
Team: Chasebot scored 72561.0
Team: Random Client scored -11614.0
Team: Beacon Collector Team scored -82969.0
Team: Aggressive Heuristic Singleton Asteroid Collector scored 1649.0
***Game 12 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 12 / 25 with teams HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector RandomTeam = Random Client ChaseBot = Chasebot BeaconCollectorTeam = Beacon Collector Team ***
Team: Aggressive Heuristic Singleton Asteroid Collector scored -5294.0
Team: Random Client scored -13211.0
Team: Chasebot scored 41117.0
Team: Beacon Collector Team scored -72205.0
***Game 13 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 13 / 25 with teams RandomTeam = Random Client ChaseBot = Chasebot BeaconCollectorTeam = Beacon Collector Team HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector ***
Team: Random Client scored -876.0
Team: Chasebot scored 41766.0
Team: Beacon Collector Team scored -52727.0
Team: Aggressive Heuristic Singleton Asteroid Collector scored -18260.0
***Game 14 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 14 / 25 with teams ChaseBot = Chasebot HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector RandomTeam = Random Client BeaconCollectorTeam = Beacon Collector Team ***
Team: Chasebot scored 55399.0
Team: Aggressive Heuristic Singleton Asteroid Collector scored -34276.0
Team: Random Client scored -12050.0
Team: Beacon Collector Team scored -55202.0
***Game 15 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 15 / 25 with teams RandomTeam = Random Client HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector ChaseBot = Chasebot BeaconCollectorTeam = Beacon Collector Team ***
Team: Random Client scored -16241.0
Team: Aggressive Heuristic Singleton Asteroid Collector scored 5843.0
Team: Chasebot scored 33953.0
Team: Beacon Collector Team scored -66503.0
***Game 16 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 16 / 25 with teams ChaseBot = Chasebot RandomTeam = Random Client BeaconCollectorTeam = Beacon Collector Team HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector ***
Team: Chasebot scored 58460.0
Team: Random Client scored -16085.0
Team: Beacon Collector Team scored -48181.0
Team: Aggressive Heuristic Singleton Asteroid Collector scored -25543.0
***Game 17 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 17 / 25 with teams RandomTeam = Random Client HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector BeaconCollectorTeam = Beacon Collector Team ChaseBot = Chasebot ***
Team: Random Client scored -15577.0
Team: Aggressive Heuristic Singleton Asteroid Collector scored 9570.0
Team: Beacon Collector Team scored -56439.0
Team: Chasebot scored 34779.0
***Game 18 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 18 / 25 with teams ChaseBot = Chasebot RandomTeam = Random Client BeaconCollectorTeam = Beacon Collector Team HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector ***
Team: Chasebot scored 14411.0
Team: Random Client scored -17754.0
Team: Beacon Collector Team scored -53925.0
Team: Aggressive Heuristic Singleton Asteroid Collector scored 17713.0
***Game 19 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 19 / 25 with teams RandomTeam = Random Client ChaseBot = Chasebot BeaconCollectorTeam = Beacon Collector Team HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector ***
Team: Random Client scored 2167.0
Team: Chasebot scored 66141.0
Team: Beacon Collector Team scored -85708.0
Team: Aggressive Heuristic Singleton Asteroid Collector scored -12931.0
***Game 20 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 20 / 25 with teams BeaconCollectorTeam = Beacon Collector Team RandomTeam = Random Client HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector ChaseBot = Chasebot ***
Team: Beacon Collector Team scored -73367.0
Team: Random Client scored 5144.0
Team: Aggressive Heuristic Singleton Asteroid Collector scored -7809.0
Team: Chasebot scored 49028.0
***Game 21 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 21 / 25 with teams HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector BeaconCollectorTeam = Beacon Collector Team ChaseBot = Chasebot RandomTeam = Random Client ***
Team: Aggressive Heuristic Singleton Asteroid Collector scored -5646.0
Team: Beacon Collector Team scored -76474.0
Team: Chasebot scored 61711.0
Team: Random Client scored -18331.0
***Game 22 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 22 / 25 with teams BeaconCollectorTeam = Beacon Collector Team HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector RandomTeam = Random Client ChaseBot = Chasebot ***
Team: Beacon Collector Team scored -29856.0
Team: Aggressive Heuristic Singleton Asteroid Collector scored 787.0
Team: Random Client scored -23516.0
Team: Chasebot scored 34571.0
***Game 23 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 23 / 25 with teams HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector RandomTeam = Random Client ChaseBot = Chasebot BeaconCollectorTeam = Beacon Collector Team ***
Team: Aggressive Heuristic Singleton Asteroid Collector scored 5400.0
Team: Random Client scored -25082.0
Team: Chasebot scored 51137.0
Team: Beacon Collector Team scored -59869.0
***Game 24 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 24 / 25 with teams BeaconCollectorTeam = Beacon Collector Team HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector ChaseBot = Chasebot RandomTeam = Random Client ***
Team: Beacon Collector Team scored -87170.0
Team: Aggressive Heuristic Singleton Asteroid Collector scored 7900.0
Team: Chasebot scored 77943.0
Team: Random Client scored -21423.0
***Game 25 / 25 with teams ChaseBot RandomTeam BeaconCollectorTeam HeuristicMinerTeam ***
***Game 25 / 25 with teams RandomTeam = Random Client ChaseBot = Chasebot HeuristicMinerTeam = Aggressive Heuristic Singleton Asteroid Collector BeaconCollectorTeam = Beacon Collector Team ***
Team: Random Client scored -22352.0
Team: Chasebot scored 64622.0
Team: Aggressive Heuristic Singleton Asteroid Collector scored 30.0
Team: Beacon Collector Team scored -61231.0
Last updated: Tue, 11 Apr 2017 02:27:38 -0500


Home
Sales
Salmon Sales
Store Name
Minimum Customers Per Hour
Maximum Customers Per Hour
Average Cookies Per Sale


CURRENT ENVIRONMENTAL ANALYSIS HAS BEEN CARRIED OUT ON THE BASIS OF THE STUDIO PROJECT.
AS A TEAM, WE HAVE ANALYZED THE INITIAL SITE CONDITIONS, THE IMPACT OF THE LOCAL CLIMATE
ON OUR DESIGN AND THEN THE WAYS TO OPTIMIZE THE DESIGN SOLUTIONS
IN ORDER TO IMPROVE THE USER EXPERIENCE


{% extends "layout.html" %} {% block content %} {% set currentPage = 'whpSelection' %} {% include "includes/main_nav.html" %} Review Work Coach submissions - calculate places and referrals How many places are you looking to fill ? Please specify the number of places you are looking to fill this time, and your start to referral ratios. This will calculate places for long-term / voluntary provision, and the control group, on the next page. How many WHP provider places are you looking to fill? {%if not data.withoutPsc %} How many public sector comparator places are you looking to fill? {%endif%} What is your referral to start ratio for mandatory provision (LTU)? Enter number of referrals to one start   to 1 What is your referral to start ratio for voluntary provision (Disabled/EEG)? Enter number of referrals to one start   to 1 {% endblock %}
How many places are you looking to fill ?
Please specify the number of places you are looking to fill this time, and your start to referral ratios. This will calculate places for long-term / voluntary provision, and the control group, on the next page.


Scenario
an agent can introduce a new agent to the network by 'befriending' it
The only way for the OfferNet to grow in a decentralized manner (i.e. without central controlling repository) is for new agents to get introduced to the network via old agent's recommendation.
Before
SimulationBehaviorStepDefs.before(Scenario)
003ms
Before
AgentBehaviorStepDefs.before(Scenario)
18s 830ms
Given
Agent "AgentOne" is created on the OfferNet
3s 351ms
And
Agent "AgentTwo" does not exist on the OfferNet
143ms
When
Agent "AgentOne" befriends Agent "AgentTwo"
1s 885ms
Then
Agent "AgentTwo" exists on the OfferNet
036ms
Then
there is "knows" link between "AgentOne" and "AgentTwo"
1s 606ms
And
there is no "knows" link between "AgentTwo" and "AgentOne"
1s 536ms
After
AgentBehaviorStepDefs.after(Scenario)
159ms
Scenario
an agent can 'befriend' another agent existing in the network
OfferNet is a social network of Agents. Each agent has an ability to initiate creation of 'knows' link to another agent existing in the network. So if two unconnected agents exist on the network and one of them knows the identity of the other, then the first one can initiate creation of 'knows' link to another in the underlying graph which should result in the appropriate relation between them. Note that the 'knows' relation is not reciprocal.
Before
SimulationBehaviorStepDefs.before(Scenario)
000ms
Before
AgentBehaviorStepDefs.before(Scenario)
5s 207ms
Given
Agent "AgentOne" is created on the OfferNet
1s 516ms
And
Agent "AgentTwo" is created on the OfferNet
1s 191ms
And
there is no "knows" link between "AgentOne" and "AgentTwo"
1s 388ms
And
there is no "knows" link between "AgentTwo" and "AgentOne"
1s 339ms
When
Agent "AgentOne" befriends Agent "AgentTwo"
1s 357ms
Then
there is "knows" link between "AgentOne" and "AgentTwo"
1s 313ms
And
there is no "knows" link between "AgentTwo" and "AgentOne"
1s 291ms
After
AgentBehaviorStepDefs.after(Scenario)
045ms
Scenario
an agent can post an {offer-demand} pair to the OfferNet
Agents in an Offer Network engage in exchange by publishing an offer-demand pairs, or 'work's. See explanation of the Offer Network structure in [Data structures and objects].
Before
SimulationBehaviorStepDefs.before(Scenario)
000ms
Before
AgentBehaviorStepDefs.before(Scenario)
4s 889ms
Given
Agent "AgentOne" is created on the OfferNet
1s 444ms
When
Agent "AgentOne" posts a pair of offer "0.15" and demand of "0.57"
1s 345ms
Then
Agent "AgentOne" owns a work which "demands" item "0.57"
1s 338ms
And
Agent "AgentOne" owns a work which "offers" item "0.15"
1s 159ms
After
AgentBehaviorStepDefs.after(Scenario)
047ms
Scenario Outline
an agent can access all posted offers and demands by traversing its social network
In a distributed system where there is no global observer which has the registry of all agents and their items of offer and demand. In this case, the only way for an agent to access items of other agents is to traverse its social network (to a given depth) and ask all friends of friends to send information about their items. Since the OfferNet is fully connected (and hopefully is a small world network) the whole network can be scanned this way, depending on availability of time and resource constrains of an agent which initiates the search. The expected number of items that an agent has to find in its social network is calculated as follows: $(numberOfAgents -1) * pairsPerAgent * 2$ (as every pair holds two items: one demand and one offer..).
Before
SimulationBehaviorStepDefs.before(Scenario)
000ms
Before
AgentBehaviorStepDefs.before(Scenario)
6s 430ms
Given
there exists an sequentially connected line of '4' agents
906ms
And
all agents have posted '1' offer-demand pair each
1s 476ms
When
first agent initiates search of '3' depth
2s 182ms
Then
agent finds '6' demands and offers in its social network
000ms
After
AgentBehaviorStepDefs.after(Scenario)
121ms
Scenario Outline
an agent can access all posted offers and demands by traversing its social network
In a distributed system where there is no global observer which has the registry of all agents and their items of offer and demand. In this case, the only way for an agent to access items of other agents is to traverse its social network (to a given depth) and ask all friends of friends to send information about their items. Since the OfferNet is fully connected (and hopefully is a small world network) the whole network can be scanned this way, depending on availability of time and resource constrains of an agent which initiates the search. The expected number of items that an agent has to find in its social network is calculated as follows: $(numberOfAgents -1) * pairsPerAgent * 2$ (as every pair holds two items: one demand and one offer..).
Before
SimulationBehaviorStepDefs.before(Scenario)
000ms
Before
AgentBehaviorStepDefs.before(Scenario)
5s 749ms
Given
there exists an sequentially connected line of '20' agents
4s 409ms
And
all agents have posted '2' offer-demand pair each
3s 125ms
When
first agent initiates search of '19' depth
2s 615ms
Then
agent finds '76' demands and offers in its social network
000ms
After
AgentBehaviorStepDefs.after(Scenario)
454ms
Scenario Outline
an agent can connect similar offers and demands of its social network
After an agent finds offer and demand items posted by its social network which are similar to its own items, it can connect those items with weighted $similarity$ relations (edges in the graph). Weight of the relation represents the 'strength' of similarity. Definition and computing algorithm of a $similarity$ relation can be chosen by choosing configuration parameters of the OfferNet system during initialization -- allowing to experiment with different approaches. This mechanism addresses these [Open problems / features to consider] of the framework: [Representation / theory of value], [Description of items of exchange], [Similarity measure];
Before
SimulationBehaviorStepDefs.before(Scenario)
000ms
Before
AgentBehaviorStepDefs.before(Scenario)
5s 332ms
Given
a connected network of '4' agents
854ms
Given
that '3' similar item pairs exist in the network
483ms
When
any agent finds similar items by executing search of '3' depth
1s 249ms
Then
there are '3' similarity relations in the network
101ms
After
AgentBehaviorStepDefs.after(Scenario)
119ms
Scenario Outline
an agent can connect similar offers and demands of its social network
After an agent finds offer and demand items posted by its social network which are similar to its own items, it can connect those items with weighted $similarity$ relations (edges in the graph). Weight of the relation represents the 'strength' of similarity. Definition and computing algorithm of a $similarity$ relation can be chosen by choosing configuration parameters of the OfferNet system during initialization -- allowing to experiment with different approaches. This mechanism addresses these [Open problems / features to consider] of the framework: [Representation / theory of value], [Description of items of exchange], [Similarity measure];
Before
SimulationBehaviorStepDefs.before(Scenario)
000ms
Before
AgentBehaviorStepDefs.before(Scenario)
5s 583ms
Given
a connected network of '20' agents
3s 781ms
Given
that '5' similar item pairs exist in the network
269ms
When
any agent finds similar items by executing search of '19' depth
851ms
Then
there are '5' similarity relations in the network
022ms
After
AgentBehaviorStepDefs.after(Scenario)
123ms