Navboi
Your Teams
Create a New Team
Create
{% if has_teams %} {% for team_chunk in team_chunks %}
{% for team_name, team_image, team_id in team_chunk %}
{{team_name}}
{% endfor %}
{% endfor %} {% else %}
You're currently not part of any teams
{% endif %}


Using PDX objects as region entry keys is highly discouraged.
The best practice for creating region entry keys is to use a simple key; for example, use a String or Integer. If the key must be a domain class, then you should use a non-PDX-serialized class.
If you must use PDX serialized objects as region entry keys, ensure that you do not set read-serialized
to true
. This configuration setting will cause problems in partitioned regions because partitioned regions require the hash code of the key to be the same on all JVMs in the distributed system. When the key is a PdxInstance
object, its hash code will likely not be the same as the hash code of the domain object.
If you are using PDX serialized objects as region entry keys and you are using persistent regions, then you must configure your PDX disk store to be a different one than the disk store used by the persistent regions.


CamCOPS is an open-source tablet application for capturing information relevant for cognitive and psychiatric assessment. It offers simple questionnaire-style tasks and more complex tasks, and sends its data securely to your server. See the documentation for more details.
CamCOPS is currently in private beta testing before general release.
Local configuration information (restricted):


El Chronicle
Squad Builder
23 Jun 2019
A big part of playing El Chronicle is squad building. Trying new heroes and seeing how they fair in the group. This tool can help you conceptualize a squad.
| Tanking: |
|
| Healing: |
|
| DPS: |
|
| Buffs/Debuff/Support: |
|
(P) = does physical damage. (M) = does magic damage.
When building a squad, click evaluate too see how they fair in each category. A well formed team should be at least 50% in tank, dps, heals. From there you can decide if you want to help support the team more with a buffer/debuffer or go heavy on DPS.
Note: this evaluation is not an accurate evaluation of your team. It’s just to help conceptualize where your teams strengths and weaknesses are and to give you a very rough idea how your team is doing.
Check out some sample squad builds here.
Click a hero below to add it to your squad.
Defensive Tanks:
Offensive Tanks:
Assassins:
Supporters:
Specialists:


Parents have a lack of time and information to efficiently/easily plan activities with their families. As such, we are giving parents access to information to facilitate their lives and overcome logistical obstacles. We are creating community database that displays an interactive map of places with family-friendly features: drive-thrus, parking, baby-changing stations. In addition to showing a map of curated locations with these features, we are creating a community-based platform where users can share their own locations/reviews to our database.
| User: | Location Name: | Type: | Address: | Features: | Comments: |
|---|


Demonstrates:
Using "editCommandHandler" option to intercept edit commands and implement undo support
Controls:
Undo
View Source:
View the source for this example on Github


| Monisha S | New Jersey | Rs. 8000 | Jan 2014 |
| V. Chandrasekhar | Chennai | Rs. 25000 | Dec 2013 |
| Krishnamoorthy | Chennai | Computer | Dec 2013 |
| Praveen Benguluru | Chennai | Rs. 4000 | Nov 2013 |
| K. Narayanan | Chennai | Rs. 8000 | Nov 2013 |
| Super Auto Forge | Rs. 200000 | They sponsor 1 lakh every year. | |
| Manohar Lulla | Rs. 10000 | ||
| Kantheeswaran S | Rs. 5000 | ||
| Murali K | Rs. 5000 |


annotation class Outcome
Values that indicate the outcome of confirmation and payment authentication.
<init> |
Values that indicate the outcome of confirmation and payment authentication. Outcome() |
CANCELED |
Payment authentication was canceled by the user const val CANCELED: Int |
FAILED |
Confirm or payment authentication failed const val FAILED: Int |
SUCCEEDED |
Confirmation or payment authentication succeeded const val SUCCEEDED: Int |
TIMEDOUT |
Payment authentication timed-out const val TIMEDOUT: Int |
UNKNOWN |
const val UNKNOWN: Int |


Report a bug
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Improve this page
Quickly fork, edit online, and submit a pull request for this page.
Requires a signed-in GitHub account. This works well for small changes.
If you'd like to make larger changes you may want to consider using
a local clone.
Change Log: 2.055
Download D 2.055
released Sep 4, 2011
New/Changed Features
- Added dman
- Add support for Mac OS X 10.7 Lion
- Add protection to json output
- Add SSE4.1 and SSE4.2 assembly instructions
- Bugzilla 4375: Require explicit braces when 'else' is ambiguous
- std.algorithm.copy now specializes on arrays for 10-80x improved performance.
- std.path has been rewritten from scratch and has a completely new API.
- std.utf.toUTFz allows you to get a zero-terminated string of any character type and of any type of mutability.
- Added symlink and readLink to std.file for Posix systems.
- Values for GDC and LDC were added to std.compiler.Vendor.
- Added functions to std.bitswap for generically handling swapping endianness.
- Added std.parallelism.TaskPool.workerIndex.
- Added buffer recycling overload of std.parallelism.asyncBuf
- std.math.tgamma, lgamma, erf, and erfc are now deprecated. The equivalent functions in std.mathspecial should be used instead.
- The names of the values of std.mmfile.Mode, std.system.Endian, std.traits.FunctionAttributes, std.traits.ParameterStorageClass, and std.traits.Variadic were changed to match Phobos' naming conventions.
- std.range: Added indexed and chunks
- std.string.translate has been updated to work with unicode. As a result, its signature has been changed. The old version and std.string.maketrans have been scheduled for deprecation.
- std.string.tr has been updated so that it works with any string type.
- std.conv.parse works for associative array and static array
- std.format: Improvement of formatValue and unformatValue. They now works for associative array, consider element escaping, and treat range format spec more properly.
- std.complex: added sin(), cos(), sqrt()
- md5: 1.4X speedup
Druntime Bugs Fixed
- Bugzilla 5967: Mangling of ArgClose for variadic function is swapped
- Bugzilla 6493: Source code for the doc of core.time points to std.datetime.
- Bugzilla 6466: core.demangle incorrect demangling of variables
Library Bugs Fixed
- Unlisted bug: std.range.transversal should have length
- Bugzilla 3890: Bad writeln of a nested struct
- Bugzilla 4500: scoped moves class after calling the constructor
- Bugzilla 4977: cannot use nothrow or pure with Rebindable
- Bugzilla 5237: writefln doesn't respect Complex.toString
- Bugzilla 5645: std.range.drop()
- Bugzilla 5825: write is calling a deprecated function
- Bugzilla 6040: std.cpuid and core.cpuid return different values for some methods
- Bugzilla 6064: std.array.join is unnecssarily slow for strings
- Bugzilla 6194: [GSoC] Destructor gets called on object before it is copied when calling writeln()
- Bugzilla 6261: [2.054 beta regression] Regex cannot take a char[]
- Bugzilla 6301: Cannot 'zip'/'retro'/'stride' etc. a range having 'ulong' length.
- Bugzilla 6377: std.conv.to should check range when changing signedness
- Bugzilla 6424: std.traits.hasElaborateAssign is limited
- Bugzilla 6448: writef("%05d", BigInt) problem
- Bugzilla 6514: CTFE dot product
- Bugzilla 6587: std.parallelism's Task cannot handle immutable values
- Bugzilla 6606: RefCounted doesn't work with unions due to use of format
- Bugzilla 6608: Tuple field is not escaped
DMD Bugs Fixed
- Bugzilla 1471: Linker error on template function. Error 42: Symbol Undefined ...
- Bugzilla 1567: call to private super-constructor should not be allowed
- Bugzilla 1684: offsetof does not work, adding cast is workaround
- Bugzilla 1904: wrong protection lookup for private template functions
- Bugzilla 2156: [] and null should be accepted where a compile-time string is required
- Bugzilla 2234: __traits(allMembers) returns incorrect results for mixin and template alias members of an aggregate
- Bugzilla 2245: Bug with overloaded, mixin template functions in classes
- Bugzilla 2246: Regression(2.046, 1.061): Specialization of template to template containing int arguments fails
- Bugzilla 2540: super can not be using in alias statement
- Bugzilla 2634: Function literals are non-constant.
- Bugzilla 2355: is() doesn't resolve aliases before template matching
- Bugzilla 2579: Template function accepting a delegate with in argument doesn't compile
- Bugzilla 2774: Functions-as-properties makes it impossible to get the .mangleof a function
- Bugzilla 2777: alias this doesn't forward __dollar and slice op.
- Bugzilla 2781: alias this doesn't work with foreach
- Bugzilla 2787: Members found in an 'alias this' are not implicitly accessible in methods
- Bugzilla 2941: Wrong code for inline asm because CPU type is set too late
- Bugzilla 3268: can't compare pointer to functions when one is const
- Bugzilla 3273: Regression(2.031): struct invariant + dtor fails to compile (no line number)
- Bugzilla 3512: dchar iteration over string in CTFE fails
- Bugzilla 3661: ^^ not supported in array operations.
- Bugzilla 3797: Regression(2.038): Implicit conversion between incompatible function pointers
- Bugzilla 4021: [CTFE] AA rehash
- Bugzilla 4099: Inconsistent behaviour of ++/-- when mixing opUnary and 'alias this'.
- Bugzilla 4444: Cannot index built-in array with expression tuple
- Bugzilla 4460: Regression(2.036) ICE(e2ir.c) when compiling foreach over associative array literal
- Bugzilla 4682: [CTFE] Run-time Vs Compile-time of int.min % -1
- Bugzilla 4773: Rebindable should be castable to bool
- Bugzilla 4837: ICE(constfold.c) CTFE with >>>=
- Bugzilla 4984: Recursive template constraint results in dmd running out of memory
- Bugzilla 5046: Wrong type of implicit 'this' in struct/class templates
- Bugzilla 5081: Pure functions as initializers for immutable structures
- Bugzilla 5188: alias this and compare expression generates wrong code
- Bugzilla 5239: optimizer misreports an used before set error
- Bugzilla 5373: Regression (2.051) CTFE and std.string.replace() causes "Bad binary function q{a == b}..
- Bugzilla 5440: ICE(template.c): when struct AssociativeArray is missing from object.d
- Bugzilla 5585: bad debug line number info for return statements with enumerator expressions
- Bugzilla 5745: Missing error line number with lazy argument
- Bugzilla 5750: Allow pure functions to have lazy arguments
- Bugzilla 5777: Move semantics require full spec NRVO
- Bugzilla 5785: Lexing or Parsing issue with UFCS
- Bugzilla 5790: 'Error: variable result used before set' when -release -inline -O
- Bugzilla 5799: Address-of operator fails on nested conditional operator expression
- Bugzilla 5936: Regression(2.050): Segfault when forward-referencing pure auto-return member function with parameter.
- Bugzilla 5953: Too many trailing commas are accepted
- Bugzilla 6097: SSSE3 not working with MMX instructions
- Bugzilla 6215: LLVM-compiled DMD segfaults due to mem.c alignment issues
- Bugzilla 6220: Regression(2.053) static foreach over a string[] no longer produces directly usable strings
- Bugzilla 6228: Regression(2.053) ICE(e2ir.c) on {auto x = (*ptr) ^^ y} with const integer types
- Bugzilla 6230: Member functions can no longer be weakly pure
- Bugzilla 6250: [CTFE] Crash when swapping two pointers to arrays.
- Bugzilla 6265: Pure-inference failed when calling other pure functions
- Bugzilla 6270: XMMREGS not preserved on indirect function call
- Bugzilla 6276: [CTFE] Strange behavior of using ~= operator twice
- Bugzilla 6280: [CTFE] Cannot put 'in' expression of AA in an 'if' condition
- Bugzilla 6281: [CTFE] A null pointer '!is null' returns 'true'.
- Bugzilla 6282: [CTFE] ICE when dereferencing a pointer to reference type from 'in' of an AA
- Bugzilla 6283: [CTFE][Regression 2.054] Failed to assign to AA using a constness-changed array as key
- Bugzilla 6284: [Regression 2.054] 'pure' does not work with 'with' statement
- Bugzilla 6286: Regression(2.054): Static arrays can not be assigned from const(T)[N] to T[N]
- Bugzilla 6293: [Regression 2.054] The expression x.y makes the function impure when the 'x' part is not just a variable
- Bugzilla 6295: [Regression 2.054] Segfault in checkPurity() of template value parameter
- Bugzilla 6306: Regression(2.054): [CTFE] Strange behavior of indirect recursive call in CTFE
- Bugzilla 6308: Destruction of temporaries on exception causes unhandled access violation
- Bugzilla 6316: Regression(2.054): Class downcast is rejected in @safe code
- Bugzilla 6317: ICE on struct literal of nested struct
- Bugzilla 6331: [CTFE] Cannot evaluate SliceExp on if condition
- Bugzilla 6337: [CTFE] ICE when touching member variable of struct during CTFE
- Bugzilla 6344: [CTFE] Assertion Failure in interpret.c when create an empty slice from null pointer
- Bugzilla 6351: Regression(2.054) Segfault: Vararg delegate as template param
- Bugzilla 6355: Template constructor cannot initialize non-mutable field
- Bugzilla 6366: alias this doesn't work with foreach range.front
- Bugzilla 6369: alias this doesn't work with initializer
- Bugzilla 6374: [CTFE] Cannot subscript using pointer to array
- Bugzilla 6375: [CTFE] Segfault when using std.array.appender with an initial array
- Bugzilla 6386: [CTFE] ICE on pointer casting
- Bugzilla 6389: Segfault(dsymbol.c): deprecated @disable
- Bugzilla 6399: [CTFE] struct member array.length -= x doesn't work, while array[0..$-x] works
- Bugzilla 6404: Cannot check ref-ness of auto ref parameter in template constraint
- Bugzilla 6418: [CTFE] Cannot call a struct member function with name 'length'.
- Bugzilla 6420: [CTFE] ICE on dereference-assigning to a pointer casted from a literal
- Bugzilla 6429: Nested function error in reduce
- Bugzilla 6433: Meta-Bug AA type propagation
- Bugzilla 6434: opDispatch must be considered before alias this.
- Bugzilla 6491: Fully qualified values in default arguments of non-template functions are generated with an extra 'module' keyword
- Bugzilla 6499: [GSoC] Destructor not called on object returned by method.
- Bugzilla 6505: Wrong code for expression involving 8 floats, only with -O
- Bugzilla 6508: alias this doesn't work with AssignExp rhs
- Bugzilla 6510: [CTFE] "internal error: illegal stack value" when compiled with -inline
- Bugzilla 6511: [CTFE] Array op gives wrong result
- Bugzilla 6512: [CTFE] new T[][] doesn't work
- Bugzilla 6516: Regression(2.055 beta) [CTFE] ICE(constfold.c) involving new dchar[]
- Bugzilla 6517: [CTFE] ptr++ doesn't work but ++ptr does
- Bugzilla 6546: alias this + IdentityExpression doesn't work
- Bugzilla 6556: ICE for ImportStatement in DebugStatement
- Bugzilla 6558: [CTFE] UTF-decoding foreach gives wrong index (1-indexed)
- Bugzilla 6561: alias this + undefined symbol should cause error
- Bugzilla 6563: wrong code when using at least 8 XMM regs
- Bugzilla 6577: 'Cannot initialize member' error line number
- Bugzilla 6601: Regression(2.053): CTFE segfault taking address of function template
- Bugzilla 6602: Invalid template instantiations leaked by is(typeof())/__traits(compiles, )/Type::trySemantic
Copyright © 1999-2023 by the D Language Foundation | Page generated by
Ddoc on Wed Mar 1 14:57:13 2023


g = gbayes(net, gdata) [g, gdata, gprior] = gbayes(net, gdata)
g = gbayes(net, gdata)
takes a network data structure net
together
the data contribution to the error gradient
for a set of inputs and targets.
It returns the regularised error gradient using any zero mean Gaussian priors
on the weights defined in
net
. In addition, if a mask
is defined in net
, then
the entries in g
that correspond to weights with a 0 in the
mask are removed.
[g, gdata, gprior] = gbayes(net, gdata)
additionally returns the
data and prior components of the error.
errbayes
, glmgrad
, mlpgrad
, rbfgrad
Copyright (c) Ian T Nabney (1996-9)


Notebook Export
Six Easy Pieces: Essentials of Physics Explained by Its Most Brilliant Teacher
Richard P. Feynman
Introduction
Highlight(yellow) - Location 105
To aid this extraordinary feat of intuition, Feynman invented a simple system of eponymous diagrams.
Highlight(yellow) - Location 106
Feynman diagrams
Highlight(yellow) - Location 122
practice and
Highlight(yellow) - Location 122
ready-made
SPECIAL PREFACE
Highlight(blue) - Location 222
picaresque
Highlight(pink) - Location 244
First figure out why you want the students to learn the subject and what you want them to know, and the method will result more or less by common sense.
Atomic processes
Highlight(pink) - Location 535
Hence, if you wish to evaporate water turn on the fan!
Highlight(blue) - Location 542
preponderance
Highlight(pink) - Location 543
Hence, blow on soup to cool it!
Highlight(orange) - Location 546
If we suddenly take the air away from the vessel, then the air molecules will leave more rapidly than they come in, and in doing so will make bubbles. This is very bad for divers, as you may know.
Note - Location 547
Don’t understand this!
Chemical reactions
Highlight(pink) - Location 595
carbon monoxide is formed.
Introduction
Highlight(pink) - Location 673
with the hope that we may be able to reduce the number of different things and thereby understand them better.
Bookmark - Location 677
Highlight(pink) - Location 822
It is difficult, and takes a lot of imagination.
Highlight(yellow) - Location 964
In fact, there seem to be just four kinds of interaction between particles which, in the order of decreasing strength, are the nuclear force, electrical interactions, the beta-decay interaction, and gravity.
Introduction
Highlight(yellow) - Location 999
(Mathematics is not a science from our point of view, in the sense that it is not a natural science. The test of its validity is not experiment.)
Biology
Highlight(pink) - Location 1053
acetylcholine
Highlight(pink) - Location 1074
Consider this analogy: If we wanted to take an object from one place to another, at the same level but on the other side of a hill, we could push it over the top, but to do so requires the addition of some energy.
Highlight(pink) - Location 1152
This is the central unsolved problem in biology
Geology
Highlight(blue) - Location 1196
meteorology
What is energy?
Highlight(pink) - Location 1282
There is a fact, or if you wish, a law, governing all natural phenomena that are known to date. There is no known exception to this law—it is exact so far as we know. The law is called the conservation of energy.
Atomic mechanics
Highlight(blue) - Location 1866
quantum
First principles of quantum mechanics
Highlight(orange) - Location 2188
machinery
Note - Location 2188
I think he refers to the ‘Hypothesis function’ as the machinery


Dashboard
Overview
Manage Updates
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0.83
Game version
Since 4/7/17
1.102
Launcher version
Since 18/3/17
Update


Capture-Mark-Recapture model in PyMC3
Imagine you wish to know how many fish are in a rather large pond… The water is murky so forget about counting them easily… In ecology this is a common problem! The population size of a species often needs to be known, but it is either impractical or simply impossible to count all individual members of that population. Fortunately, there is a relatively easy solution to get an estimation!
To estimate the size of the population you would have to visit the site twice. On the first visit a number of animals are caught, marked and released. It is important to mark them in a way it doesn’t do them any disadvantage or harm, equally important is to use marks that don’t easily rub off. You keep count of how many members of the population were caught and marked. Next, a day or so later, the site is visited again and a number of animals is caught. This time the number of marked animals that were recaptured is important. As the fraction of the marked individuals captured the second day, is essentially the same as the fraction of the population that was marked the first day. This method goes by a few names like Capture-Mark-Recapture, Mark and Recapture or other slight variations.
As an example imagine you catch and tag 50 individuals on day one. The next day, 100 animals are caught and 10 of those are marked. So we know that roughly 10% of all individuals were tagged, we also know that there are in total 50 tagged individuals, so the total population size should be about 500.
\[\begin{aligned} \frac{n\_marked}{population\_size} = \frac{n\_recaptured}{captured\_round\_2} \end{aligned}\]This formula can be re-written as:
\[\begin{aligned} population\_size = \frac{n\_marked * captured\_round\_2}{n\_recaptured} \end{aligned}\]While the latter is really simple and similar exercises are already seen in primary school (known as the rule of three), calculating the confidence interval (the range in which the actual population size is with a certain amount, usually 95%, of confidence) on this estimate is far from trivial (check the link, it is a very complex formula). However, when using a Bayesian approach, we get the uncertainty on the population size automatically, without any extra effort. So let’s implement this model in PyMC3 and see what we get.
Creating a PyMC3 model
The data from the observation is stored in a few variables: n_marked
(the number of individuals marked on the
first visit), captured_round_2
(the total number of individuals caught the second visit) and n_recaptured
(the number of marked individuals caught the second visit). The big unknown that we need to infer is the
population_size
, we don’t really have any clue about this except that it is equal or higher than the total number
of individual that were caught during both visits. So we can set the number of unique animals seen as the lower limit.
The probability to capture an already marked animal the second visit p_marked
is the number of marked animal within the
full population. So this is a pm.Deterministic
variable as it is defined by the number of marked animals (which is known)
and the population size (which we try to infer). Finally, a likelihood recapture_obs
is required, this will be
a pm.Binomial
with the total number of animals caught the second day as the number of draws, the number of
marked individual recaptured as the observation and a probability of p_marked
.
%load_ext nb_black
import pymc3 as pm
n_marked = 50
captured_round_2 = 100
n_recaptured = 10
with pm.Model() as model:
population_size = pm.Bound(
pm.Flat, lower=n_marked + captured_round_2 - n_recaptured
)("population_size")
p_marked = pm.Deterministic("p_marked", n_marked / population_size)
recapture_obs = pm.Binomial(
"recapture_obs", captured_round_2, p_marked, observed=n_recaptured
)
trace = pm.sample(4000, tune=1000, return_inferencedata=False)
After sampling the model, we get our estimate for the population size. In this case the population size is estimated to be between 294 and 1034 individuals (hdi_3% and hdi_97%, PyMC3 by default gives you the smallest range where 94% of the inferred values were found) with a mean value of 620. While the mean isn’t crazy far off from the population size in our example, the uncertainty is rather large. It does show that not nearly enough animals were marked or re-captured in our thought experiment.
| mean | sd | hdi_3% | hdi_97% | |
|---|---|---|---|---|
| population_size | 620.067 | 220.926 | 293.750 | 1034.423 |
| p_marked | 0.090 | 0.028 | 0.039 | 0.142 |
Update 30/08/2022 - HyperGeometric likelihood
As in this case draws from the population aren’t independent, a HyperGeometric distribution is better for this model. The last two parts can be switched for the lines below to implement this. While this wouldn’t be a big difference in case the number of marked individuals is large enough, here it does affect the mean estimate and 94% HDI.
recapture_obs = pm.HyperGeometric(
"recapture_obs", N=population_size, k=n_marked, n=captured_round_2, observed=n_recaptured
)
trace = pm.sample(4000, tune=1000, return_inferencedata=False, target_accept=0.9)
| mean | sd | hdi_3% | hdi_97% | |
|---|---|---|---|---|
| population_size | 748.737 | 372.363 | 273.785 | 1396.472 |
| p_marked | 0.040 | 0.015 | 0.012 | 0.067 |
Can we improve without catching more animals ?
So without catching more animals overall, is it better to mark more animals, and catch fewer the next visit? Or capture more animals on the second visit, while marking fewer the first time around? With this model we can easily test this out!
Let’s mark 100 animals on day one and catch 50 on day two. Given a population size of 500 we’d expect about 10 to be re-capture. If we feed those number in the model we get the results below.
| mean | sd | hdi_3% | hdi_97% | |
|---|---|---|---|---|
| population_size | 610.585 | 212.195 | 310.344 | 1008.055 |
| p_marked | 0.180 | 0.054 | 0.082 | 0.279 |
While there is still a lot of uncertainty, it does drop when catching more animals the first day and fewer the second day. (I’ll leave confirming it gets worse if you do things the other way around to the reader) However, once you drop below a point where you are barely recapturing any animals on the second day, this is also negatively going to affect the estimate.
Catching twice as many animals
This model really works well if enough individuals can be tagged and recaptured. So if we increase the number of marks to 200 and capture 100 the second day, we would expect about 40 to be tagged. Punching in those numbers in the model, we see that now the mean is very close to what it should be in our imaginary population and the uncertainty is far smaller.
| mean | sd | hdi_3% | hdi_97% | |
|---|---|---|---|---|
| population_size | 519.579 | 66.165 | 402.473 | 643.741 |
| p_marked | 0.391 | 0.048 | 0.302 | 0.483 |
Conclusion
While the formula for this model is simple, calculating the confidence interval isn’t. Though here it could be instrumental to know how much uncertainty you have. In our first example, depending on how many fish there are in the pond you might allow fishers to capture a certain number. So if there are 293, 500, 610 or, 1034 this could influence how many people are allowed to catch fish in that pond.
By using a Bayesian approach, the problem is solved in a few simple lines of code, and we get the confidence interval in one go as well, easy! This is exactly the strength of Bayesian statistics. The advantages here are further highlighted as it is very easy to play with the number to see how the uncertainty can be decreased, so the efforts of catching and marking individuals can be done as efficient as possible.
To see what I did with this formula, check out the next post where this is used to estimate how many KeyForge decks were printed.
Acknowledgements
Header image by Sebastian Pena Lambarri on Unsplash
Liked this post ? You can buy me a coffee


In my Student life at Goa College of Engineering i have written so many <programs> for the last four years. But now when i try to search for those programs, i couldn't find them, coz they are scattered all over my hard-disk. So i thought of building a simple Web-app to manage them and to keep them all in one place and ie in a Database.
This is it.


Salmon Cookies
cookies for those with class
Location:
Min Customers/Hour:
Max Customers/hour:
Avg Cookies/Customer:
The Deets
Where to find us
254364 Smelly Fish Dr
Contact Info
p. 206fishfooe<EMAIL_ADDRESS>© Salmon Cookies


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RISE4JUSTICE in NYC - Three Great Events Coming Up!
Over the next two weeks in NYC there are some great One Billion Rising
for Justice events taking place featuring Eve Ensler, Dr. Denis
Mukwege, Laura Flanders, Cathy Albisa, Kimberle Crenshaw, Monique
Harden, Saru Jayaraman, Sylvia McAdam, and more!
We invite all of our NYC area activists, and those of you who are
traveling to the area, to join us!
Tuesday, October 29: Dr. Denis Mukwege in Conversation with Eve Ensler
- Special $10 Activist Rate Available!
V-Day has tickets at the special rate of $10 available for you our
incredible V-Day activists for this one night only event in NYC!
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RISE4JUSTICE: Dr. Denis Mukwege in Conversation with Eve Ensler
Tuesday, October 29
6:30 - 8:30pm
(Doors open at 6:00pm)
Deepak Homebase at ABC Carpet & Home
888 Broadway at E. 19 Street, NYC
TICKETS:
$25* General Admission available at http://action.vday.org/site/R?i=i8DKxa5llDrwKs6ejOtmtg
$10* V-Day Activist rate, please email<EMAIL_ADDRESS>An Evening with Dr. Denis Mukwege & Eve Ensler is part of the
RISE4JUSTICE Speakers Series which will examine the meaning of justice
to activists working globally to end gender violence and oppression,
while looking at impunity caused by the intersection of poverty,
racism, war, theplunder of the environment, capitalism, imperialism,
and patriarchy. The series is presented by V-Day, the global activist
movement to end violence againstwomen and girls, and ABC Carpet &
Home.
*100% of the proceeds will go to the Panzi Foundation, supporting the
ongoing work of Panzi Hospital in Bukavu, DRC and creating new
outreach projects to rural clinics and communities.
Thursday, November 7: The State of Female Justice in America
Building on last year’s sold-out public forum on the
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State of Female America, V-Day is bringing together a stellar group of
women for a public panel discussion on The State of Female Justice.
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Hosted by Laura Flanders (GRITtv) and featuring:
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Cathy Albisa - National Economic & Social Rights Initiative
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Kimberle Crenshaw - Columbia Law School / UCLA Law School
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Eve Ensler - V-Day
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Monique Harden - Advocates for Environmental Human Rights in New
Orleans
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Saru Jayaraman - Restaurant Opportunities Centers United
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Sylvia McAdam - Idle No More
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…and more to be announced!
Join us as we explore why women experience economic, racial, and
cultural violence; and ask ourselves what might real justice look like
for women in America?
WHAT: THE STATE OF FEMALE JUSTICE IN AMERICA
WHEN: Thursday, November 7, Doors: 7:30pm, Program: 8pm
WHERE: Jerome Green Hall, Columbia Law School, 435 W 116 St, NYC
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TICKETS: $10 general admission* Purchase here >
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* Student Tickets are available for free, please email<EMAIL_ADDRESS>And don’t miss this Saturday, Eve at the Save Minisink & The
Black Dirt Farms Rally
Saturday, October 26, Eve Ensler will be joining a coalition of groups
and speakers including John Feal, Angela Monti Fox, as well as
affected farmers, food distributors, chefs and restaurateurs for the
Save Minisink & The Black Dirt Farms Rally in Union Square, NYC.
The rally will focus on connecting the dots between fracking
infrastructure and our food supply. Eve will be speaking about the
connections between violence against women and violence against Mother
Earth.
http://action.vday.org/site/R?i=MkEQ3odHkDLlDFab1SikrQ
Learn more >
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Selection of secondary sampling units (SSUs)¶
To select the second stage sample, we need the second stage frame which is the list of all the households in the 10 selected clusters (psus). DHS, PHIA, MICS and other large scale surveys visit the selected clusters and construct the list of all households in the selected clusters.
Before starting the second stage selection, let us import the data from the first stage sampling information
[1]:
%%capture
%run psu_selection.ipynb
In this tutorial, we will simulate the second stage frame. For the simulation, assume that the psu frame was obtained from a previous census conducted several years before. We also assume that, the change in the number of households since the previous census follows a normal distribution with a mean equal to 5% higher than the census value and a variance of 0.15 times the number of households from the census. Under these assumptions, we generate the following second stage frame of households. Note that the frame is created only for the selected PSUs.
[2]:
# Create a synthetic second stage frame
census_size = psu_frame.loc[
psu_frame["psu_sample"] == 1, "number_households_census"
].values
stratum_names = psu_frame.loc[psu_frame["psu_sample"] == 1, "region"].values
cluster = psu_frame.loc[psu_frame["psu_sample"] == 1, "cluster"].values
np.random.seed(15)
listing_size = np.zeros(census_size.size)
for k in range(census_size.size):
listing_size[k] = np.random.normal(1.05 * census_size[k], 0.15 * census_size[k])
listing_size = listing_size.astype(int)
hh_id = rr_id = cl_id = []
for k, s in enumerate(listing_size):
hh_k1 = np.char.array(np.repeat(stratum_names[k], s)).astype(str)
hh_k2 = np.char.array(np.arange(1, s + 1)).astype(str)
cl_k = np.repeat(cluster[k], s)
hh_k = np.char.add(np.char.array(cl_k).astype(str), hh_k2)
hh_id = np.append(hh_id, hh_k)
rr_id = np.append(rr_id, hh_k1)
cl_id = np.append(cl_id, cl_k)
ssu_frame = pd.DataFrame(cl_id.astype(int))
ssu_frame.rename(columns={0: "cluster"}, inplace=True)
ssu_frame["region"] = rr_id
ssu_frame["household"] = hh_id
nb_obs = 15
print(f"\nFirst {nb_obs} observations of the SSU frame\n")
ssu_frame.head(nb_obs)
First 15 observations of the SSU frame
[2]:
| cluster | region | household | |
|---|---|---|---|
| 0 | 7 | North | 71 |
| 1 | 7 | North | 72 |
| 2 | 7 | North | 73 |
| 3 | 7 | North | 74 |
| 4 | 7 | North | 75 |
| 5 | 7 | North | 76 |
| 6 | 7 | North | 77 |
| 7 | 7 | North | 78 |
| 8 | 7 | North | 79 |
| 9 | 7 | North | 710 |
| 10 | 7 | North | 711 |
| 11 | 7 | North | 712 |
| 12 | 7 | North | 713 |
| 13 | 7 | North | 714 |
| 14 | 7 | North | 715 |
[3]:
psu_sample = psu_frame.loc[psu_frame["psu_sample"] == 1]
ssu_counts = ssu_frame.groupby("cluster").count()
ssu_counts.drop(columns="region", inplace=True)
ssu_counts.reset_index(inplace=True)
ssu_counts.rename(columns={"household": "number_households_listed"}, inplace=True)
pd.merge(
psu_sample[["cluster", "region", "number_households_census"]],
ssu_counts[["cluster", "number_households_listed"]],
on=["cluster"],
)
[3]:
| cluster | region | number_households_census | number_households_listed | |
|---|---|---|---|---|
| 0 | 7 | North | 130 | 130 |
| 1 | 10 | North | 600 | 660 |
| 2 | 16 | South | 190 | 195 |
| 3 | 24 | South | 75 | 73 |
| 4 | 29 | South | 200 | 217 |
| 5 | 34 | East | 305 | 239 |
| 6 | 45 | East | 450 | 398 |
| 7 | 52 | East | 700 | 620 |
| 8 | 64 | West | 300 | 301 |
| 9 | 86 | West | 280 | 274 |
According to the simulated second stage frame, we get the same number of households in cluster 7 as the census. However, in strata 10, 16, 29, and 64, we listed more households than during than the census. And finally, we found less households in the remaining clusters than the census.
Now that we have a second stage frame, let’s use samplics to calculate the probabilities of selection and to select a sample. The second stage sample size is 150 households and the strategy is to select 15 households per cluster.
SSU (household) Probability of Selection¶
The second stage probabilities of selection are conditional on the first stage realization. For this stage, simple random selection (srs) and systematic selection(sys) are common methods used to select households. For this example, we use srs to select 15 households from each cluster. Conditionally to teh first stage, the second stage selection is a stratified srs where the clusters are the strata. More generally, we have that \begin{equation} p_{hij} = \frac{m_{hi}}{M_{hi}^{'}} \end{equation} where \(p_{hij}\) is the conditional probability of selection for unit \(j\) from stratum \(h\) and cluster \(j\), \(m_{hi}\) and \(M_{hi}^{'}\) are the sample size and the number of secondary sampling units listed for stratum \(h\) and cluster \(j\), respectively.
In this scenario, sample size is the same in each stratum. Hence, the parameter sample_size does not need to be a Python dictionary; we will only provide 15 in the function call.
[4]:
stage2_design = SampleSelection(
method="srs", stratification=True, with_replacement=False
)
ssu_frame["ssu_prob"] = stage2_design.inclusion_probs(
ssu_frame["household"], 15, ssu_frame["cluster"]
)
ssu_frame.sample(20)
[4]:
| cluster | region | household | ssu_prob | |
|---|---|---|---|---|
| 1438 | 34 | East | 34164 | 0.062762 |
| 2517 | 52 | East | 52606 | 0.024194 |
| 2943 | 86 | West | 86111 | 0.054745 |
| 3002 | 86 | West | 86170 | 0.054745 |
| 559 | 10 | North | 10430 | 0.022727 |
| 1216 | 29 | South | 29159 | 0.069124 |
| 2751 | 64 | West | 64220 | 0.049834 |
| 412 | 10 | North | 10283 | 0.022727 |
| 2549 | 64 | West | 6418 | 0.049834 |
| 1508 | 34 | East | 34234 | 0.062762 |
| 1517 | 45 | East | 454 | 0.037688 |
| 2658 | 64 | West | 64127 | 0.049834 |
| 1072 | 29 | South | 2915 | 0.069124 |
| 1074 | 29 | South | 2917 | 0.069124 |
| 1669 | 45 | East | 45156 | 0.037688 |
| 1286 | 34 | East | 3412 | 0.062762 |
| 1162 | 29 | South | 29105 | 0.069124 |
| 1051 | 24 | South | 2467 | 0.205479 |
| 3085 | 86 | West | 86253 | 0.054745 |
| 2148 | 52 | East | 52237 | 0.024194 |
SSU (household) Selection¶
The second stage sample is selected from the SSU frame (ssu_frame) using the variable cluster as the stratification variable. The sample is selected without replacement according to the specification of the second stage design. Hence, both ssu_sample and ssu_hits sum to 150 and each selected household was hit only ounce (i.e. ssu_hits = 1).
>>> ssu_frame["ssu_sample"].sum()
>>> 150
>>> ssu_frame["ssu_hits"].sum()
>>> 150
[5]:
np.random.seed(11)
ssu_sample, ssu_hits, ssu_probs = stage2_design.select(
ssu_frame["household"], 15, ssu_frame["cluster"]
)
ssu_frame["ssu_sample"] = ssu_sample
ssu_frame["ssu_hits"] = ssu_hits
ssu_frame["ssu_probs"] = ssu_probs
ssu_frame[ssu_frame["ssu_sample"] == 1].sample(15)
[5]:
| cluster | region | household | ssu_prob | ssu_sample | ssu_hits | ssu_probs | |
|---|---|---|---|---|---|---|---|
| 2319 | 52 | East | 52408 | 0.024194 | 1 | 1 | 0.024194 |
| 2931 | 86 | West | 8699 | 0.054745 | 1 | 1 | 0.054745 |
| 2642 | 64 | West | 64111 | 0.049834 | 1 | 1 | 0.049834 |
| 122 | 7 | North | 7123 | 0.115385 | 1 | 1 | 0.115385 |
| 60 | 7 | North | 761 | 0.115385 | 1 | 1 | 0.115385 |
| 945 | 16 | South | 16156 | 0.076923 | 1 | 1 | 0.076923 |
| 338 | 10 | North | 10209 | 0.022727 | 1 | 1 | 0.022727 |
| 2218 | 52 | East | 52307 | 0.024194 | 1 | 1 | 0.024194 |
| 2870 | 86 | West | 8638 | 0.054745 | 1 | 1 | 0.054745 |
| 1764 | 45 | East | 45251 | 0.037688 | 1 | 1 | 0.037688 |
| 630 | 10 | North | 10501 | 0.022727 | 1 | 1 | 0.022727 |
| 1441 | 34 | East | 34167 | 0.062762 | 1 | 1 | 0.062762 |
| 986 | 24 | South | 242 | 0.205479 | 1 | 1 | 0.205479 |
| 1796 | 45 | East | 45283 | 0.037688 | 1 | 1 | 0.037688 |
| 1264 | 29 | South | 29207 | 0.069124 | 1 | 1 | 0.069124 |
To use systematic selection, we just need to replace method="srs"
by method="sys"
.
Another common approach is to use a rate for selecting the sample. Instead of selecting 15 households from 130 in the first cluster, we may want to select with a rate of 15/130, and similarly for the other clusters.
[6]:
rates = np.repeat(15, 10) / ssu_counts["number_households_listed"].values
ssu_rates = dict(zip(np.unique(ssu_frame["cluster"]), rates))
ssu_rates
[6]:
{7: 0.11538461538461539,
10: 0.022727272727272728,
16: 0.07692307692307693,
24: 0.2054794520547945,
29: 0.06912442396313365,
34: 0.06276150627615062,
45: 0.03768844221105527,
52: 0.024193548387096774,
64: 0.04983388704318937,
86: 0.05474452554744526}
A sample is selected using the rates as follows:
[7]:
np.random.seed(22)
stage2_design2 = SampleSelection(
method="sys", stratification=True, with_replacement=False
)
ssu_sample_r, ssu_hits_r, _ = stage2_design2.select(
ssu_frame["household"], stratum=ssu_frame["cluster"], samp_rate=ssu_rates
)
ssu_sample2 = pd.DataFrame(
data={
"household": ssu_frame["household"],
"ssu_sample_r": ssu_sample_r,
"ssu_hits_r": ssu_hits_r,
}
)
ssu_sample2.head(25)
[7]:
| household | ssu_sample_r | ssu_hits_r | |
|---|---|---|---|
| 0 | 71 | 0 | 0 |
| 1 | 72 | 0 | 0 |
| 2 | 73 | 0 | 0 |
| 3 | 74 | 0 | 0 |
| 4 | 75 | 0 | 0 |
| 5 | 76 | 0 | 0 |
| 6 | 77 | 0 | 0 |
| 7 | 78 | 0 | 0 |
| 8 | 79 | 0 | 0 |
| 9 | 710 | 0 | 0 |
| 10 | 711 | 0 | 0 |
| 11 | 712 | 0 | 0 |
| 12 | 713 | 0 | 0 |
| 13 | 714 | 0 | 0 |
| 14 | 715 | 0 | 0 |
| 15 | 716 | 0 | 0 |
| 16 | 717 | 0 | 0 |
| 17 | 718 | 0 | 0 |
| 18 | 719 | 0 | 0 |
| 19 | 720 | 0 | 0 |
| 20 | 721 | 0 | 0 |
| 21 | 722 | 0 | 0 |
| 22 | 723 | 0 | 0 |
| 23 | 724 | 0 | 0 |
| 24 | 725 | 0 | 0 |
Let’s store the first and second stages samples.
[8]:
psu_sample[["cluster", "region", "psu_prob"]].to_csv("psu_sample.csv")
ssu_sample = ssu_frame.loc[ssu_frame["ssu_sample"] == 1]
ssu_sample[["cluster", "household", "ssu_prob"]].to_csv("ssu_sample.csv")


--- title: 'upcoming: dance' date: 2008-12-04 17:02:57.000000000 +02:00 categories: [] tags: [] status: draft type: post published: false meta: _edit_last: '600927' author: login: kouk email<EMAIL_ADDRESS>display_name: Konstantinos Koukopoulos first_name: '' last_name: '' excerpt: !ruby/object:Hpricot::Doc options: {} ---
After my last teaser post about politics here is yet another post which will not really cover it's subject in any depth.
Obviously I'm assuming that there are people interested in my political activities, which is not such a stretch as it might seem (remember we're on the internet). It also seems likely that at least those same people will have some use (1) for my thoughts and references to political theory and practice.
So, in anticipation of more substantial posts (2) on politics, may I just point to [wikipedia entries on] a few political schools that are relevant to what's ahead:
1. entertainment is an acceptable use.
2. the present post is pretty unsubstantial but at least it serves the purpose of adding some tags, like 'libertarian', to the tag cloud that I'm fidgeting with at the moment.


Automated Tool for Optimized Modelling
A Python package for fast exploration of machine learning pipelines
During the exploration phase of a machine learning project, a data scientist tries to find the optimal pipeline for his specific use case. This usually involves applying standard data cleaning steps, creating or selecting useful features, trying out different models, etc. Testing multiple pipelines requires many lines of code, and writing it all in the same notebook often makes it long and cluttered. On the other hand, using multiple notebooks makes it harder to compare the results and to keep an overview. On top of that, refactoring the code for every test can be time-consuming. How many times have you conducted the same action to pre-process a raw dataset? How many times have you copy-and-pasted code from an old repository to re-use it in a new use case?
ATOM is here to help solve these common issues. The package acts as a wrapper of the whole machine learning pipeline, helping the data scientist to rapidly find a good model for his problem. Avoid endless imports and documentation lookups. Avoid rewriting the same code over and over again. With just a few lines of code, it's now possible to perform basic data cleaning steps, select relevant features and compare the performance of multiple models on a given dataset, providing quick insights on which pipeline performs best for the task at hand.
Example steps taken by ATOM's pipeline:
- Data Cleaning
- Handle missing values
- Encode categorical features
- Detect and remove outliers
- Balance the training set
- Feature engineering
- Create new non-linear features
- Remove multi-collinear features
- Remove features with too low variance
- Select the most promising features
- Train and validate multiple models
- Select hyperparameters using a Bayesian Optimization approach
- Train and test the models on the provided data
- Assess the robustness of the output using a bagging algorithm
- Analyze the results
- Get the model scores on various metrics
- Make plots to compare the model performances
Release history
Version 4.4.0
- The drop method now allows the user to drop columns as part of the pipeline.
- It is now possible to add data transformations as function to the pipeline through the apply method.
- Added the status method to save an overview of atom's branches and models to the logger.
- Improved the output messages for the Imputer class.
- The dataset's columns can now be called directly from atom.
- The distribution and plot_distribution methods now ignore missing values instead of raising an exception.
- Fixed a bug where transformations failed when columns were added after initializing the pipeline.
- Fixed a bug where the Cleaner class didn't drop
columns with only missing values for
minimum_cardinality=True
. - Fixed a bug where the winning model wasn't displayed correctly.
- Refactored the way transformers are added or removed from predicting methods.
- Improved documentation.
Version 4.3.0
- Possibility to add custom transformers to the pipeline.
- The export_pipeline utility method exports atom's current pipeline to a sklearn object.
- Use AutoML to automate the search for an optimized pipeline.
- New magic methods makes atom behave similarly to sklearn's Pipeline.
- All training approaches can now be combined in the same atom instance.
- New plot_scatter_matrix, plot_distribution and plot_qq for data inspection.
- Complete rework of all the shap plots to be consistent with their new API.
- Improvements for the Scaler and Pruner classes.
- The acronym for custom models now defaults to the capital letters in the class' __name__.
- Possibility to apply transformations on only a subset of the columns.
- Plots and methods now accept
winner
as model name. - Fixed a bug where custom metrics didn't show the correct name.
- Fixed a bug where timers were not displayed correctly.
- Further compatibility with deep learning datasets.
- Large refactoring for performance optimization.
- Cleaner output of messages to the logger.
- Plots no longer show a default title.
- Added the AutoML example notebook.
- Minor bug fixes.
Version 4.2.1
- Bug fix where there was memory leakage in successive halving and train sizing pipelines.
- The XGBoost,
LightGBM and
CatBoost packages can now be installed through the installer's
extras_require under the name
models
, e.g.pip install -U atom-ml[models]
. - Improved documentation.
Version 4.2.0
- Possibility to add custom models to the pipeline using ATOMModel.
- Compatibility with deep learning models.
- New branch system for different data pipelines. Read more in the user guide.
- Use the canvas contextmanager to draw multiple plots in one figure.
- New voting and stacking ensemble techniques.
- New get_class_weight utility method.
- New Sequential Feature Selection strategy for the FeatureSelector.
- Added the
sample_weight
parameter to the score method. - New ways to initialize the data in the
training
instances. - The
n_rows
parameter in ATOMLoader is deprecated in favour of the new data input formats. - The
test_size
parameter now also allows integer values. - Renamed categories to classes to be consistent with sklearn's API.
- The class property now returns a pd.DataFrame of the number of rows per target class in the train, test and complete dataset.
- Possibility to add custom parameters to an estimator's fit method through
est_params
. - Successive halving and train sizing now both allow subsequent runs from atom without losing previous information.
- Bug fix where ATOMLoader wouldn't encode the target column during transformation.
- Added the Deep learning, Ensembles and Utilities example notebooks.
- Compatibility with python 3.9.
Version 4.1.0
- Added the
est_params
parameter to customize the parameters passed to every model's estimator. - Following skopt's API, the
n_random_starts
parameter is deprecated in favour ofn_initial_points
. - The Balancer class now allows you to use any of the strategies from imblearn.
- New utility attributes to inspect the dataset.
- Four new models: CatNB, CNB, ARD and RNN.
- Added the models section to the documentation.
- Small changes in log outputs.
- Bug fixes and performance improvements.
Version 4.0.1
- Bug fix where the DFS strategy in FeatureGenerator was not deterministic for a fixed random state.
- Bug fix where subsequent runs with the same metric failed.
- Added the license file to the package's installer.
- Typo fixes in documentation.
Version 4.0.0
- Bayesian optimization package changed from GpyOpt to skopt.
- Complete revision of the model's hyperparameters.
- Four SHAP plots can now be called directly from an ATOM pipeline.
- Two new plots for regression tasks.
- New plot_pipeline and
pipeline
attribute to access all transformers. - Possibility to determine transformer parameters per method.
- New calibration method and plot.
- Metrics can now be added as scorers or functions with signature metric(y, y_pred, **kwargs).
- Implementation of multi-metric runs.
- Possibility to choose which metric to plot.
- Early stopping for models that allow in-training evaluation.
- Added the ATOMLoader function to load saved atom instances and directly apply all data transformations.
- The "remove" strategy in the data cleaning parameters is deprecated in favour of "drop".
- Implemented the DFS strategy in FeatureGenerator.
- All training classes now inherit from BaseEstimator.
- Added multiple new example notebooks.
- Tests coverage up to 100%.
- Completely new documentation page.
- Bug fixes and performance improvements.
Content
- Getting started
- User guide
- API
- ATOM
- Data cleaning
- Feature engineering
- Training
- Direct
- SuccessiveHalving
- TrainSizing
- Models
- Gaussian Process
- Gaussian Naive Bayes
- Multinomial Naive Bayes
- Bernoulli Naive Bayes
- Categorical Naive Bayes
- Complement Naive Bayes
- Ordinary Least Squares
- Ridge
- Lasso
- Elastic Net
- Bayesian Ridge
- Automated Relevance Determination
- Logistic Regression
- Linear Discriminant Analysis
- Quadratic Discriminant Analysis
- K-Nearest Neighbors
- Radius Nearest Neighbors
- Decision Tree
- Bagging
- Extra-Trees
- Random Forest
- AdaBoost
- Gradient Boosting Machine
- XGBoost
- LightGBM
- CatBoost
- Linear-SVM
- Kernel-SVM
- Passive Aggressive
- Stochastic Gradient Descent
- Multi-layer Perceptron
- Predicting
- Plots
- plot_correlation
- plot_scatter_matrix
- plot_distribution
- plot_qq
- plot_pipeline
- plot_pca
- plot_components
- plot_rfecv
- plot_successive_halving
- plot_learning_curve
- plot_results
- plot_bo
- plot_evals
- plot_roc
- plot_prc
- plot_permutation_importance
- plot_feature_importance
- plot_partial_dependence
- plot_errors
- plot_residuals
- plot_confusion_matrix
- plot_threshold
- plot_probabilities
- plot_calibration
- plot_gains
- plot_lift
- bar_plot
- beeswarm_plot
- decision_plot
- force_plot
- heatmap_plot
- scatter_plot
- waterfall_plot
- Examples
- FAQ
- Dependencies
- License


$prefer{% data=dict( sequence=("name", "spec", "default", "sample", "description") ) %} #[ Ensure that any "middle" text (between directives) is also parsed: - the "${len(s)}" - the "%" in width="100%" ]#
${s}
${len(sequence)}


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Well done! Keep playing to see who can get the longest winning streak.


Classification of Driverless Cars
#TomsProjects
Classification of driverless cars -- Tom Zhou.pdf
Discussions


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| TableName
A table name, possibly qualified with schema and catalog. |
| Classes in org.d2rq.db.schema used by org.d2rq.db.expr | |
|---|---|
| ColumnName
|
|
| Key
A list of Identifier s for use as keys, e.g., unique keys. |
|
| TableName
A table name, possibly qualified with schema and catalog. |
| Classes in org.d2rq.db.schema used by org.d2rq.db.op | |
|---|---|
| ColumnDef
|
|
| ColumnName
|
|
| Key
A list of Identifier s for use as keys, e.g., unique keys. |
|
| TableDef
|
|
| TableName
A table name, possibly qualified with schema and catalog. |
| Classes in org.d2rq.db.schema used by org.d2rq.db.op.util | |
|---|---|
| ColumnName
|
| Classes in org.d2rq.db.schema used by org.d2rq.db.renamer | |
|---|---|
| ColumnName
|
|
| ForeignKey
A SQL foreign key. |
|
| Identifier
The name of a SQL object, such as a column, table, view, schema, or catalog. |
|
| Key
A list of Identifier s for use as keys, e.g., unique keys. |
|
| TableName
A table name, possibly qualified with schema and catalog. |
| Classes in org.d2rq.db.schema used by org.d2rq.db.schema | |
|---|---|
| ColumnDef
|
|
| ColumnName
|
|
| ForeignKey
A SQL foreign key. |
|
| Identifier
The name of a SQL object, such as a column, table, view, schema, or catalog. |
|
| Identifier.ViolationType
|
|
| Key
A list of Identifier s for use as keys, e.g., unique keys. |
|
| TableDef
|
|
| TableName
A table name, possibly qualified with schema and catalog. |
| Classes in org.d2rq.db.schema used by org.d2rq.db.vendor | |
|---|---|
| ColumnName
|
|
| Identifier
The name of a SQL object, such as a column, table, view, schema, or catalog. |
|
| Identifier.IdentifierParseException
|
|
| Identifier.ViolationType
|
|
| TableName
A table name, possibly qualified with schema and catalog. |
| Classes in org.d2rq.db.schema used by org.d2rq.lang | |
|---|---|
| ColumnName
|
|
| ForeignKey
A SQL foreign key. |
|
| Identifier
The name of a SQL object, such as a column, table, view, schema, or catalog. |
|
| TableName
A table name, possibly qualified with schema and catalog. |
| Classes in org.d2rq.db.schema used by org.d2rq.mapgen | |
|---|---|
| ColumnDef
|
|
| ColumnName
|
|
| ForeignKey
A SQL foreign key. |
|
| Identifier
The name of a SQL object, such as a column, table, view, schema, or catalog. |
|
| Key
A list of Identifier s for use as keys, e.g., unique keys. |
|
| TableDef
|
|
| TableName
A table name, possibly qualified with schema and catalog. |
| Classes in org.d2rq.db.schema used by org.d2rq.nodes | |
|---|---|
| ColumnName
|
| Classes in org.d2rq.db.schema used by org.d2rq.r2rml | |
|---|---|
| Identifier
The name of a SQL object, such as a column, table, view, schema, or catalog. |
|
| TableName
A table name, possibly qualified with schema and catalog. |
| Classes in org.d2rq.db.schema used by org.d2rq.values | |
|---|---|
| ColumnName
|
|
||||||||||
| PREV NEXT | FRAMES NO FRAMES |


NiMet predicts sunny, thundery activities for Saturday
by https://www.facebook.com/alao.h.joshuaAgency Reporter
The Nigerian Meteorological Agency (NiMet) has predicted sunny and thundry activities for Saturday.
NiMet’s weather outlook on Friday in Abuja predicted a stable atmosphere over the Northern region down to Central cities with chances of thunderstorms over the inland and coastal region of the country.
It forecast sunny and dry conditions over the extreme Northern region throughout the forecast period with day and night temperatures of 35 to 38 degree Celsius and 15 to 20 degree Celsius respectively.
“For Central states, partly cloudy to sunny conditions would prevail throughout the forecast period with day and night temperature of 29 to 38 drgree Celsius and 21 to 23 degree Celsius.
“For Southern states, partly cloudy to cloudy morning is anticipated over the region with chances of thunderstorms over Akure, Oshogbo, Ibadan and the coastal region in the afternoon/evening period.
“Day and night temperatures are expected to be 31 to 33 degree Celsius and 21 to 22 degree Celsius,” it said.
(NAN)


https://andifalk.github.io/security-cloud-presentation/index.html
<EMAIL_ADDRESS>@NT_AQE, @andifalk
https://github.com/OWASP/Top10
https://github.com/OWASP/Top10
Injection of malicious scripts into web sites

">
Protect Sessions (web.xml)
<session-config>
<cookie-config>
<http-only>true</http-only>
<secure>true</secure>
</cookie-config>
<tracking-mode>COOKIE</tracking-mode>
</session-config>
Output Escaping (Vaadin, JSF, Thymeleaf)
Sanitizing (Angular)
“...The basic idea is to implement automatic secure escaping for all values that can reach
the DOM...
By default, with no specific action for developers, Angular apps must be secure...”
@Component({
selector: 'app-root',
templateUrl: 'app.component.html',
styleUrls: ['app.component.css']
})
export class AppComponent {
untrustedHtml:string =
'';
}
Binding of potentially dangerous HTML-snippets
Encoded HTML snippet
{{untrustedHtml}}
Sanitized HTML snippet
ElementRef: Direct access to DOM!
DomSanitizer: Deactivates XSS-Protection!
Do NOT use!
https://angular.io/docs/ts/latest
Welcome to our grocery store!
Welcome to our grocery store!
Java EE 8 (End of 2017):
Security 1.0, CDI 2.0, JSON-B, Servlet 4.0, HTTP/2
Java EE 9 (2018/2019):
Cloud Ready (Serverless, NoSQL, Monitoring, ...)
Spring Framework 5.0 GA (July 2017)
Spring Security 5.0 GA (November 2017)
Spring Boot 2.0 GA (November 2017)
Spring Cloud Finchley (November/December 2017)
Support for Reactive Web Applications
Support for OAuth 2.0
Support for OpenID Connect 1.0, JWT and JOSE (JWS/JWE/JWK)
Authentication required for all HTTP endpoints
Session Fixation Protection
Session Cookie (HttpOnly, Secure)
CSRF Protection
Security Response Header
GET / HTTP/1.1
Host: localhost:8080
HTTP/1.1 401
WWW-Authenticate: Basic realm="hello"
GET / HTTP/1.1
Host: localhost:8080
Authorization: Basic dXNlcjpzZWNyZXQ=
HTTP/1.1 401
WWW-Authenticate: Digest realm="Hello", qop="auth",
nonce="MTQ5NTczNzY5NDkzNTpiZmM1MTk4ZTZjMmFlNTk3OTIy...",
stale="true"
GET / HTTP/1.1
Host: localhost:8080
Authorization: Digest username="user", realm="Hello",
nonce="MTQ5NTczNzY5NDkzNTpiZmM1MTk4ZTZjMmFlNTk3OTIyM...",
uri="/", response="e94ed21db74add38cb80a7e3758519e6",
qop=auth, nc=00000001, cnonce="75467274b8e8bd72"
POST /login HTTP/1.1
Host: localhost:8080
Content-Type: application/x-www-form-urlencoded
Cookie: JSESSIONID=14965E3A995DA1973F42F308D59727D4
username=user&password=secret&submit=Login
HTTP/1.1 302
Set-Cookie: JSESSIONID=49C632387800316021BE804AB2F27C15;
Path=/; HttpOnly
GET / HTTP/1.1
Host: localhost:8080
Cookie: JSESSIONID=49C632387800316021BE804AB2F27C15
GET / HTTP/1.1
Host: localhost:8080
Authorization: Bearer eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiI
| Session Cookie | Token (Bearer, JWT) |
|---|---|
| With each Request (on same domain) |
Manually as Header |
| Potential CSRF! | No CSRF possible |
| One domain | Cross domain (CORS) |
| Sensitive Info (HTTPS) | Sensitive Info (HTTPS) |
1.Redirect to authorization endpoint
(Authorization Server)
HTTP/1.1 302 Moved Temporarily
Location: http://localhost:9999/oauth/authorize?
response_type=code&scope=read_product&client_id=myclient
&redirect_uri=http%3A%2F%2Flocalhost%3A9000
&state=Lwt50DDQKUB
2.Authorization Server loads authentication form
GET /authorize?response_type=code&scope=read_product
&client_id=oauth-client-1
&redirect_uri=http%3A%2F%2Flocalhost%3A9000
&state=Lwt50DDQKUB
3.User authenticates with Authorization Server
4.User authorizes client at Authorization Server
5.Authorization Server redirect to Client
HTTP 302 Found
Location: http://localhost:9000?code=8V1pr0rJ
&state=Lwt50DDQKUB
6.Trigger request to client
GET /callback?code=8V1pr0rJ
&state=Lwt50DDQKUB HTTP/1.1
Host: localhost:9000
7.Post request to Authorization Server
POST /oauth/token
Host: localhost:9999
Content-type: application/x-www-form-encoded
Authorization: Basic b2F1dGgtY2xpZW50LTE6b2F1dGgtY2xpZW
grant_type=authorization_code
&redirect_uri=http%3A%2F%2Flocalhost%3A9000%&code=8V1pr0rJ
8.Token response from Authorization Server
HTTP 200 OK
Date: Fri, 26 May 2017 21:19:03 GMT
Content-type: application/json
{
“access_token”: “987tghjkiu6trfghjuytrghj”,
“token_type”: “Bearer”
}
9.Use token with Resource Server
GET /resource HTTP/1.1
Host: localhost:9091
Accept: application/json
Authorization: Bearer 987tghjkiu6trfghjuytrghj
POST /token
Host: localhost:9090
Accept: application/json
Content-type: application/x-www-form-encoded
Authorization: Basic b2F1dGgtY2xpZW50LTE6b2F1dGgt...
grant_type=password&scope=openid&username=adm&password=secret
GET / HTTP/1.1
Host: localhost:8080
Authorization: Bearer eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiI
...Header
...Payload
...Signature
GET / HTTP/1.1
Host: localhost:8080
Authorization: Bearer eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1N...
Header
{
typ: "JWT",
alg: "RS256"
}
Payload
{
iss: "https://identity.example.com",
aud: "my-client-id",
exp:<PHONE_NUMBER>,
nonce: "N0.46824857243233511495739124749",
iat:<PHONE_NUMBER>,
at_hash: "hC1NDSB8WZ9SnjXTid175A",
sub: "mysubject",
auth_time:<PHONE_NUMBER>,
email<EMAIL_ADDRESS>}
...(iss) issuer identifier = identity server
...(aud) audiance = client id
...(exp) expiration time >= current time
...signature = valid for public key
Implicit Client Implementer’s Guide| Product | OAuth2 | OpenID Connect |
|---|---|---|
| Okta (Cloud Service) | Y | Y |
| IdentityServer 4 (ASP.NET Core) |
Y | Y |
| RedHat Keycloak | Y | Y |
| Spring Security OAuth | Y | N (partly support) |
Support for OAuth 1 and OAuth2
Supports JSON Web Tokens (JWT)
@EnableResourceServer
@Configuration
public class OAuth2Configuration {
@Bean
public JwtAccessTokenConverterConfigurer
jwtAccessTokenConverterConfigurer() {
return new MyJwtConfigurer(...);
}
static class MyJwtConfigurer
implements JwtAccessTokenConverterConfigurer {
@Override
public void configure(
JwtAccessTokenConverter converter) {...}
}}
OAuth 2.0 Threat Model and Security Considerations
public class UserBoundaryService {
@PreAuthorize("hasRole('ADMIN')")
public List<User> findAllUsers() {...}
}
public class TaskBoundaryService {
@PreAuthorize("hasPermission(#taskId, 'TASK', 'WRITE')")
public Task findTask(UUID taskId) {...}
}
public class AuthorizationIntegrationTest {
@WithMockUser(roles = "ADMIN")
@Test
public void verifyFindAllUsersAuthorized() {...}
@WithMockUser(roles = "USER")
@Test(expected = AccessDeniedException.class)
public void verifyFindAllUsersUnauthorized() {...}
}
@Entity
public class Person extends AbstractPersistable<Long> {
@NotNull
@Pattern(regexp = "^[A-Za-z0-9- ]{1,30}$")
private String lastName;
@NotNull
@Enumerated(EnumType.STRING)
private GenderEnum gender;
...
}
@Query(
"select u from User u where u.username = "
+ " :username and u.password = :password")
User findByUsernameAndPassword(
@Param("username") String username,
@Param("password") String password);
@Configuration
public class WebSecurityConfiguration
extends WebSecurityConfigurerAdapter {
@Override
protected void configure(HttpSecurity http)
throws Exception {
…
http
.csrf().csrfTokenRepository(
CookieCsrfTokenRepository.withHttpOnlyFalse()
);
}
Distributed DoS
Economic DoS
spring.datasource.url=jdbc:postgresql://localhost/test
spring.datasource.username=root
spring.datasource.password=mysupersecretpassword
Externalized configuration in a distributed system
HTTP, resource-based API
Supports property file and YAML formats
Encrypt and decrypt property values
“What if every server inside my data center had a maximum lifetime of two hours? This approach would frustrate malware writers...”
All images used are from Pixabay and are published under Creative Commons CC0 license.
All used logos are trademarks of respective companieshttp://www.novatec-gmbh.de http://blog.novatec-gmbh.de
<EMAIL_ADDRESS>@NT_AQE, @andifalk


{% extends 'base.html' %} {% block page_title %}{{ _('FAQ') }}{% endblock %} {% block meta_description -%} {%- endblock %} {% block content %}
{% trans %} Firefox Flicks is a contest that invites Firefox fans and filmmakers from all over the world to create short videos (or "flicks") to help tell the Firefox story. {% endtrans %}
{% trans %} The Firefox Flicks contest site is now open. You can now upload your video and vote on your favorites. {% endtrans %}
{% trans %} You can submit as many as you like, but you have to submit a new entry each time. {% endtrans %}
{% trans %} Of course! Teams can be up to 10 people. We will, however, be sending out a maximum of one Firefox prize pack per submission to be shared with the team. Also, please appoint a team leader. All communication will be with this person and only one prize will be awarded per entry. {% endtrans %}
{% trans %} Yes. This contest is open to anyone in world. We will, however, choose one winner for North America, one winner for Latin America, one winner for Africa, Australia & New Zealand and one winner for Europe (or whichever of those is closest to where you're from). {% endtrans %}
{% trans %} Videos will be judged on quality of shooting, editing and how closely they align with the creative brief. {% endtrans %}
{{ _('Entries will be judged in the following categories:') }}
{% trans %} A panel of judges will choose the grand prize winner from a selection of videos that have been voted on by the public. {% endtrans %}
{% trans %} See our Judges page for a complete list. {% endtrans %}
{% trans %} Simply by competing, your flick will be seen and voted on by millions of Firefox fans around the world, giving you exposure to a global audience. You'll also get your entry in front of international advertising executives and film luminaries while working on one of the world's biggest brands — a great piece to add to any portfolio. {% endtrans %}
{% trans %} But there's more! The grand prize winner in each region (North America, Latin America, EU and Asia, Australia & New Zealand) will receive their choice of either $10,000 (USD) in video equipment or cash to spend on film school expenses or your next project. There will be four category winners in each of the four regions, the winners will get $5,000 (USD) in video equipment or cash to spend on film school expenses or your next project. There will also be runner ups in each of the four categories in each region, these winners will receive $1,000 (USD) in video equipment or cash to spend on film school expenses or your next project. {% endtrans %}
{% trans %} The maximum video length is 120 seconds. {% endtrans %}
{% trans video_upload=url('flicks.videos.recent'), vidly='http://vid.ly' %} Fill out your filmmaker profile and submit your video here. You will need to upload your video to a personal server or to a service like Dropbox. We will be reposting all videos to a contest gallery on Vid.ly. You'll need to give your Flick a title, select the video category you are entering it in and your region. {% endtrans %}
{% trans<EMAIL_ADDRESS>%} Drop us a note at<EMAIL_ADDRESS>and we'll get back to you as soon as we can. {% endtrans %}
{% trans %} Absolutely! We think that Firefox Flicks is a great opportunity for students to participate in a real world, high visibility campaign with expert judges from the film, television and advertising industries. Talk to your professors about using the video as your project. {% endtrans %}
{% trans trademark='http://www.mozilla.org/foundation/trademarks/policy.html', wordmark='http://mozilla.org/en-US/firefox/brand/' %} Yes. For the purposes of this contest, you may use the Firefox wordmark and logo. For more information, see the Mozilla Trademark Policy. {% endtrans %}
{% trans brand='http://mozilla.org/en-US/firefox/brand/' %} To get high resolution copies of the Firefox name and logo artwork, as well as guidance on how to use the Firefox logo and wordmarks, please visit our Brand Toolkit. {% endtrans %}
{% trans creative_commons='http://search.creativecommons.org', public_domain='http://en.wikipedia.org/wiki/Public_domain' %} If you're looking for additional materials — whether music, video footage, artwork or graphics — we suggest you investigate public domain media.Creative Commons has a nice search service for locating public domain work. You can read more about the public domain at Wikipedia. {% endtrans %}
{% trans %} No, not without permission from the owner of those materials. {% endtrans %}
{% trans<EMAIL_ADDRESS>%} Yes. For non-English entries, you will need to submit a full and accurate script or subtitles. If you're having trouble getting your flick translated, please contact us. We can help. {% endtrans %}
{% trans<EMAIL_ADDRESS>%} If you would like to withdraw your submission, please contact us at<EMAIL_ADDRESS>and we will remove your entry from the competition. {% endtrans %}
{% trans %} All aspect ratios will be accepted, but 16:9 is recommended and preferred. {% endtrans %}
{% trans %} Your flick will be posted on the voting site so that people can watch and upvote their favorite flicks. {% endtrans %}
{{ _('What is a PSA?') }} {% trans %} A public service announcement (PSA) is a type of advertisement that intends to affect public attitudes by raising awareness of an issue and possibly inspiring action. If entering this category, your video should focus more on the issues that affect people and their online lives, including privacy and security, and less about Firefox specifically. {% endtrans %}


Structure
SubjectType
public struct SubjectType<Element, ErrorType: Error>
Abstraction over passthrough subject types (PassthroughSubject
, PublishSubject
, Signal
) from reactive frameworks.
This abstraction uses concept similar to type-erasure or protocol witness pattern, wrapping the behaviour of concrete implementations and
delegating to them once the wrapper funcions are called.
Initializers
init(input:output:)
public init(input: Signal<Element, ErrorType>.Observer, output: Signal<Element, ErrorType>)
init(publisher:subscriber:)
public init(publisher: PublisherType<Element, ErrorType>, subscriber: SubscriberType<Element, ErrorType>)
Creates an abstraction over passthrough subject types (PassthroughSubject
, PublishSubject
, Signal
) from reactive frameworks.
Parameters
| Name | Type | Description |
|---|---|---|
| publisher | PublisherType<Element, ErrorType> |
Upstream publisher that feeds events into this subject |
| subscriber | SubscriberType<Element, ErrorType> |
Downstream subscriber that subscribes to this subject and will receive events from it |
Properties
publisher
let publisher: PublisherType<Element, ErrorType>
Upstream publisher that feeds events into this subject
subscriber
let subscriber: SubscriberType<Element, ErrorType>
Downstream subscriber that subscribes to this subject and will receive events from it
Methods
reactive()
public static func reactive() -> SubjectType


Concierge
We plan, you explore.
Concierge was created for the traveler who wants to experience the city as a local. Dying to know where the locals eat, and have fun? Let Concierge conncet you with the city and the people who know it best.
Start Exploring


gamejs/http
Make synchronous http requests to your game's serverside component.
If you configure a ajax base URL you can make http requests to your
server using those functions.
The most high-level functions are load()
and save()
which take
and return a JavaScript object, which they will send to / recieve from
the server-side in JSON format.
Response
(responseXML, status, statusText)
Response object returned by http functions get
and post
. This
class is not instantiable.
Parameters
| String |
responseXML |
|
| Number |
status |
|
| String |
statusText |
|
Response.prototype.
(header)
ajax
(method, url, data, type)
Make http request to server-side
Parameters
| String |
method |
http method |
| String |
url |
|
| String|Object |
data |
|
| String|Object |
type |
"Accept" header value |
Make http GET request to server-side
Load an object from the server-side.
Returns
| Object |
the object loaded from the server |
post
(url, data, type)
Make http POST request to server-side
Parameters
| String |
url |
|
| String|Object |
data |
|
| String|Object |
type |
"Accept" header value |
save
(url, data, type)
Send an object to a server-side function.
Parameters
| String |
url |
|
| String|Object |
data |
|
| String|Object |
type |
"Accept" header value |
Returns
| Object |
the response object |


A basic Cicada function call looks exactly the same as a C function call.
y = f(x)
But behind the familiar syntax roams what is probably the strangest beast in all the Cicada language. For one thing, Cicada functions are objects that live in ordinary heap memory, unlike their stack-dwelling counterparts in C. Cicada functions have no input type and no output type, and they can have several coding sections. Function arguments are hopelessly different between the two languages. These changes together allow Cicada functions to be used in some unusual ways.
Last update: July 25, 2021


|
| go_result | init () |
| | Init the gomotion specification. If 'totalt' is positive, then it is the time for the move. Otherwise, it's automatically computed from the vel, acc and jerk, the usual case. Here we set 'totalt' to zero to get the usual case. More...
|
| |
| go_result | set_type (go_flag type) |
| |
|
go_flag | get_type () |
| |
|
go_result | set_id (go_integer id) |
| |
|
go_integer | get_id () |
| |
| go_result | set_jpar (go_integer i, go_real vel, go_real acc, go_real jerk) |
| | Set a joint parameters. More...
|
| |
| go_result | set_tpar (go_real vel, go_real acc, go_real jerk) |
| | Set world Cartesian translation parameters. More...
|
| |
| go_result | set_rpar (go_real vel, go_real acc, go_real jerk) |
| | Set world Cartesian rotational parameters. More...
|
| |
|
go_result | set_cpar (go_cart *center, go_cart *normal, go_integer turns) |
| | Set circular rotation Cartesian move parameters.
|
| |
| go_result | set_time (go_real time) |
| | Set time to positive real number. More...
|
| |
| go_result | set_end_position (go_position *end) |
| | If you are doing joint interpolation (as specified by your call to go_motion_queue_set_type()), et_end_position fills in joint[] accordingly. More...
|
| |
| go_result | set_end_pose (go_pose *end) |
| | If you are doing world coordinate interpolation (as specified by your call to go_motion_queue_set_type()), set_end_position fills in ending pose accordingly. More...
|
| |
| go_result gomotion::go_motion_spec::init |
( |
| ) |
|
Init the gomotion specification. If 'totalt' is positive, then it is the time for the move. Otherwise, it's automatically computed from the vel, acc and jerk, the usual case. Here we set 'totalt' to zero to get the usual case.
- Returns
- go_result success or failure
| go_result gomotion::go_motion_spec::set_end_pose |
( |
go_pose * |
end | ) |
|
If you are doing world coordinate interpolation (as specified by your call to go_motion_queue_set_type()), set_end_position fills in ending pose accordingly.
- Parameters
-
| end | is the end pose of the robot (position and orientation) |
- Returns
- go_result success or failure
| go_result gomotion::go_motion_spec::set_end_position |
( |
go_position * |
end | ) |
|
If you are doing joint interpolation (as specified by your call to go_motion_queue_set_type()), et_end_position fills in joint[] accordingly.
- Parameters
-
| end | is the end position of the joints |
- Returns
- go_result success or failure
| go_result gomotion::go_motion_spec::set_jpar |
( |
go_integer |
i, |
|
|
go_real |
vel, |
|
|
go_real |
acc, |
|
|
go_real |
jerk |
|
) |
| |
Set a joint parameters.
- Parameters
-
| i | joint number. |
| vel | velocity limit UNITS? |
| acc | acceleration limit UNITS? |
| jerk | jerk limit UNITS? |
- Returns
- go_result success or failure
| go_result gomotion::go_motion_spec::set_rpar |
( |
go_real |
vel, |
|
|
go_real |
acc, |
|
|
go_real |
jerk |
|
) |
| |
Set world Cartesian rotational parameters.
- Parameters
-
| vel | velocity limit UNITS? |
| acc | acceleration limit UNITS? |
| jerk | jerk limit UNITS? |
- Returns
- go_result success or failure
| go_result gomotion::go_motion_spec::set_time |
( |
go_real |
time | ) |
|
Set time to positive real number.
- Parameters
-
- Returns
- go_result success or failure
| go_result gomotion::go_motion_spec::set_tpar |
( |
go_real |
vel, |
|
|
go_real |
acc, |
|
|
go_real |
jerk |
|
) |
| |
Set world Cartesian translation parameters.
- Parameters
-
| vel | velocity limit UNITS? |
| acc | acceleration limit UNITS? |
| jerk | jerk limit UNITS? |
- Returns
- go_result success or failure
| go_result gomotion::go_motion_spec::set_type |
( |
go_flag |
type | ) |
|
Depending upon whether you are doing joint interpolation or world coordinate interpolation (as specified by your call to go_motion_queue_set_type()), fill in joint[] or pose accordingly.
CIR: some circular params
ALL: target position, joints or pose
| go_integer gomotion::go_motion_spec::id |
ALL: id echoed as current move
For joint moves, each pertains to the indexed joint. For world moves, [0] pertains to trans, [1] pertains to rot, and the [2]-[*] are unused.
(ALL) start pose wrt world; prev end - computed for you
| go_real gomotion::go_motion_spec::totalt |
(ALL) total planned time for the motion - computed for you
| go_flag gomotion::go_motion_spec::type |
ALL: GO_MOTION_JOINT,LINEAR,CIRCULAR
| go_quat gomotion::go_motion_spec::uquat |
(ALL) unit rotation from start to end - computed for you
The documentation for this struct was generated from the following file:
- /usr/local/michalos/nistfanuc_ws/src/gomotion/include/gomotion/gomotion.h


Your task is to write a Caesar shift cipher decoder program to decode the following encrypted sentence given K = 6:
O RUBK IUSVAZKX VXUMXGSSOTM YU SAIN
result:


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R
R
- Variable in class com.tabuto.jsimlife.
Seed
Seed Radius
R
- Variable in class com.tabuto.jsimlife.
Zlife
radius
- Variable in class com.tabuto.jsimlife.
Zlife
RadiusChartPanel
- Class in
com.tabuto.jsimlife.charts
A dynamic chart shows current Zlifes average radius size.
RadiusChartPanel(JSLStatistic)
- Constructor for class com.tabuto.jsimlife.charts.
RadiusChartPanel
RadiusChartPanel.RadiusDataCollector
- Class in
com.tabuto.jsimlife.charts
Class extends ADataCollector, it add point obtains from JSLStatistic and add it in a Point Collector called TracePoint2D
RadiusChartPanel.RadiusDataCollector(ITrace2D, long, JSLStatistic, int)
- Constructor for class com.tabuto.jsimlife.charts.
RadiusChartPanel.RadiusDataCollector
Collector Constructor
radiusField
- Variable in class com.tabuto.jsimlife.views.
JSLSelectedZlifeView
radiusLabel
- Variable in class com.tabuto.jsimlife.views.
JSLNewCellView
radiusLabel
- Variable in class com.tabuto.jsimlife.views.
JSLSelectedZlifeView
radiusSlider
- Variable in class com.tabuto.jsimlife.views.
JSLNewCellView
RandomSleep
- Variable in class com.tabuto.j2dgf.collision.
CollisionDetector
A random sleep time to let other collisionDetectors threads to execute!
realMetabolism
- Variable in class com.tabuto.jsimlife.
Zlife
Reflection(Sprite)
- Method in class com.tabuto.j2dgf.collision.
CollisionBoundDetector
Change the Sprite angle to simulate an elastic collision with the boundary
refresh()
- Method in class com.tabuto.jsimlife.views.
JSLSelectedZlifeView
Update the information about selected ZLife
RefreshStatisticAction
- Class in
com.tabuto.jsimlife.actions
Class extends AbstractAction to perform the following Action: Uses the
JSLStatistic.calculateStatistics()
method to update statistical data.
RefreshStatisticAction(String, JSLStatistic)
- Constructor for class com.tabuto.jsimlife.actions.
RefreshStatisticAction
Instantiate new RefreshStatisticAction
RemoveSprite(Sprite)
- Method in class com.tabuto.j2dgf.
SpriteGroup
Deprecated.
Remove a Sprite from the Group Vector
renderSplashFrame(String, int)
- Method in class com.tabuto.jsimlife.views.
JSLSplashScreenView
Render the current SplashScreen showing the 'load' String as the 'current' element number to load
reproduction(Zetatron)
- Method in class com.tabuto.jsimlife.
Zetatron
Return a new Zetatron with merged DNA
reproduction(Zlife)
- Method in class com.tabuto.jsimlife.
Zlife
Return a new Zlife with merged DNA
reproduction(Zretador)
- Method in class com.tabuto.jsimlife.
Zretador
Return a new Zretador with merged DNA
reproductionZetatron
- Variable in class com.tabuto.jsimlife.
JSimLife
Collision between Zlifes when they hornies
reproductionZlife
- Variable in class com.tabuto.jsimlife.
JSimLife
Collision between Zlifes when they hornies
reproductionZretador
- Variable in class com.tabuto.jsimlife.
JSimLife
Collision between Zretador when they hornies
reset()
- Method in class com.tabuto.jsimlife.
JSimLife
Delete all the sprites in all groups
RESET
- Static variable in class com.tabuto.jsimlife.views.
JSLToolbar
ResetSimulationAction
- Class in
com.tabuto.jsimlife.actions
Class extends AbstractAction to perform the following Action: Clear all sprite group and reset game
ResetSimulationAction(String, JSLMainView)
- Constructor for class com.tabuto.jsimlife.actions.
ResetSimulationAction
Instantiate new ResetSimulationAction
resource
- Variable in class com.tabuto.jsimlife.actions.
CreateNewSeedAction
resource
- Variable in class com.tabuto.jsimlife.
JSLStatistic
resource
- Variable in class com.tabuto.jsimlife.views.
JSLAboutView
resource
- Variable in class com.tabuto.jsimlife.views.
JSLControlPanelView
resource
- Variable in class com.tabuto.jsimlife.views.
JSLMainView
resource
- Variable in class com.tabuto.jsimlife.views.
JSLMenu
resource
- Variable in class com.tabuto.jsimlife.views.
JSLNewCellView
resource
- Variable in class com.tabuto.jsimlife.views.
JSLNewSeedView
resource
- Variable in class com.tabuto.jsimlife.views.
JSLPreferencesView
resource
- Variable in class com.tabuto.jsimlife.views.
JSLSelectedZlifeView
resource
- Variable in class com.tabuto.jsimlife.views.
JSLStatusBar
resource
- Variable in class com.tabuto.jsimlife.views.
JSLToolbar
result
- Variable in class com.tabuto.jsimlife.
ZetatronBrain
result of the net: if reusult = 1 State = Horny if reusult = 2 State = Hungry if reusult = 3 State = Bored
resume()
- Method in class com.tabuto.j2dgf.collision.
CollisionDetector
Set the ACTIVE state of this class as
true
resume()
- Method in class com.tabuto.j2dgf.collision.
CollisionManager
Resume the CollisionManager
riproductionEnergy
- Variable in class com.tabuto.jsimlife.
Zlife
riproductionEnergySlider
- Variable in class com.tabuto.jsimlife.views.
JSLNewCellView
Root
- Variable in class com.tabuto.jsimlife.actions.
ChangeColorButtonAction
root
- Variable in class com.tabuto.jsimlife.actions.
ExitSimulationAction
root
- Variable in class com.tabuto.jsimlife.actions.
PauseSimulationAction
root
- Variable in class com.tabuto.jsimlife.actions.
ResetSimulationAction
root
- Variable in class com.tabuto.jsimlife.actions.
SaveDnaAction
root
- Variable in class com.tabuto.jsimlife.actions.
SaveSelectedZLifeAction
root
- Variable in class com.tabuto.jsimlife.actions.
StartSimulationAction
root
- Variable in class com.tabuto.jsimlife.actions.
StepSimulationAction
root
- Variable in class com.tabuto.jsimlife.views.
JSLToolbar
run()
- Method in class com.tabuto.j2dgf.collision.
CollisionDetector
CollisionDetector Thread task.
run()
- Method in class com.tabuto.j2dgf.collision.
TimeCollisionDetector
CollisionDetector Thread task.
run(Game2D)
- Method in class com.tabuto.j2dgf.gui.
J2DCanvasPanel
Draw the Game2D Sprites
run()
- Method in class com.tabuto.jsimlife.
JSLStatistic
run()
- Method in class com.tabuto.jsimlife.views.
JSLControlPanelView.StatisticTask
run()
- Method in class com.tabuto.jsimlife.views.
JSLMainView
Running the game in a endless loop
run()
- Method in class com.tabuto.jsimlife.views.
JSLTestTubeView
running
- Variable in class com.tabuto.j2dgf.gui.
J2DCanvasPanel
runTestTube()
- Method in class com.tabuto.jsimlife.views.
JSLTestTubeView
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nested_md_1.api.md
A sample Nested API to test recursive walk function.
Sample of Predefined Object
'person': {
'lname': 'Jimbo',
'fname': 'Jackson',
'colors': {
'car': 'red',
'boat': 'blue',
'house': 'cream',
'hair': 'dark brown',
'cat' : 'black and white spots'
}
}
- Colors - List of colors for various items this person owns. Used to help predict which color they will like when purchasing other items.
Copyright
(C) Copyright Joseph Ellsworth Mar-2019
MIT License: https://opensource.org/licenses/MIT
Contact me on linkedin: https://www.linkedin.com/in/joe-ellsworth-68222/ if you want a great programmer.