trials of Daniel Piker. At the small squares I will insert solar panels, and I want to make a space just like the Canopy of the AA's 2007 in London, which defines a semi-open space that is able to provide energy. To simulate the movement I have two major problems: 1_ the component C# doesn't recognize all the vertices of the mesh that I created. In facts I think there is a mistake, because when the septum Kangaroo toggle to false, an error is about an object instance:Runtime error (or probaby I have done other mistakes); 2_how do I move the structure following the sun? I found an algorithm that allows me to recognize the vector that join the center of the scene with the sun's position at a given site, at a particular time of day, etc ... but how do I "force" the structure in Kangaroo this shift? you have any ideas? Only you can help me, please! I hope to be clear, I look your replies.
Thanks in advance, Betta
Ps:this is my first experience using Rhinoceros, Grasshopper, Kangaroo ... so be kind and patient with me!…
edit 29/04/14 - Here is a new collection of more than 80 example files, organized by category:
KangarooExamples.zip
This zip is the most up to date collection of examples at the moment, and collects t
ld be the best UI.
I think difference is made by 'Slider = 10' vs 'Slider = 10.000' more than by simple input/component initialization so, why to stop when it could be even more powerful?
Slider = 0 To 5 --- Slider in [0, 5]
Slider = {3; 0 To 5}
Slider = {3;0;5}
Slider = 3;0;5
Slider = 3 0:5
Slider = 3,0,5
Slider = 3 0 5 --- Value and range (min max)
3 0.0 5 --- 3.0 0.0 5.0
3 0 5.0 --- 3.0 0.0 5.0
3.0 0 5 --- 3.0 0.0 5.0
-1 0 5 --- 0 0 5 (-1 -1 5)
6 0 5 --- 5 0 5 (6 0 6)
Slider = 0:2:6 --- Even numbers: 0, 2, 4, 6.
Slider = 1:2:7 --- Odd numbers: 1, 3, 5, 7.
0:2:5 --- 0:2:4 (or 0:2:6)
3:2:8 --- 3:2:7 (or 3:2:9)
3 1:2:7 --- 1 3 5 7 (value 3)
Bang! = 7 --- 7 outputs
Merge = 5 --- 5 inputs
What's your opinion about Bang! = 7? As it's setting number of inputs, should it use different format? Bang! 7? Bang! (7)? Bang! i7?
+ * - / \ % ^ & | ! = > --- Addition, Multiplication, Subtraction, Division, Integer Division, Modulus, Power, AND, OR, NOT, Larger than, &c.
= could be a problem.
\ Integer division or Set difference?
! could be NOT but also Factorial.
| could mean intersection.
& could mean concatenate.
1+ --- Addition: input A = 1
2* --- Multiplication: input A = 2
+{0,1,1} --- Addition: input B = {0,1,1}
0-, 1/, 2^, 10^, e^ have their own components
Flatten = {7} or Flatten = 7 --- Input P = {7} (off-topic: Why can’t P be a list?)
Pt = {1, 2, 3} --- Point XYZ, X = 1, Y = 2, Z = 3.
Swatch = 129,239,231 (102)
Swatch = 129 139 231 102
F2 = "x^2+y"
"List Length" and "List Insert" don't work properly: "Value List" is choosen. Why? What's the reason to this choice? Well, I'd like to know how the whole thing (search by keywords) works, David.
Name and nickname can be now used as keywords. "Larger" works for ">" but "greater" doesn't. Could it be improved? Could synonyms be used? Could a short description even be used (I know this could be a bit weird)?
more than --- >
more or less --- Similarity
more less --- Similarity
red green --- Sets.List components should be showed
lightning --- Split Tree
What about use Curve.Analysis or Math.Boolean to display those Tab.Panel components? Param, Math, Sets, Vector? Primitive, Special, Util? Tab, Panel, and Tab.Panel as keywords.
At the moment that I write this, I check that ignoring accents in keywords has almost been included (0.8.0009): p`anel, pañel, pánel --- panel (almost)
Shouldn’t 'Dom2' work for Dom²?
What about nested search? You type some keywords (say 'Params' or 'Params.Geometry', or 'red green', or 'lst') and then you make a fine-tunning search over previous results/keywords. Tab.Panel and/or nested geometry could be useful when search by plug-in is desired or when you want to search among .ghuser components (first 'ghuser' or 'Extra.MyPlugIn' or 'lst' keyword and then fine-tunning, specific, search).
Is 'list length' performing this nested search right now ('lst' > 'length')? Anyway, I am thinking about UI (graphical) changes; successive searches.
As I said, description (and even words from the help info) could be used to search. What about "some kind of tags"? I mean that if 'list l' to finally choose List Length has been used for a while, that could be learned. Eventually, an XML file could store these tags, so you could even edit them. That could implement description, name, nickname, help info, Tab.Panel, .ghuser, synonyms (lots of them), tags/shortcuts or wathever.
How could flatten/graft/reverse be used? Initialize graft+Simplify or graft+Bang! could be really useful.
What about expressions? I don't how could it be done properly: would Slider = x^2 (expression) work? I mean, aren't expressions parsed when initializing?
Is Panel somehow doing this? 'panel = wathever' always suppose that wathever is a string, so you can't use 'panel = <pi>'. Sets.Strings components also do this.
I've been about to write several paragraphs about height/width (resizable components: Panel, Graph Mapper, Slider, &c.), input/output names (Scripts, F components; or any component with editable input/output names), orientation (Scribble), type hint and access option, nickname, &c. but, to sum up: being able to set any property when initializing would be really useful. I'd like to know the best choice of syntax but I'm sure that, David, you're closer to the answer. What do you think about this?
Slider: 3 0 5 "MySlider" "Slider^2"
Panel: "This is the content" "This is the title"
VB: "N" List Integer 7 "r" Item Double
Addition: A 1 B 2
I guess that any unified syntax would be elegant and useful, but additional ad hoc syntax (per component) could be even better (cleaner).
What about use lists of values? I'm not sure about format: panel = ("Hello", "Bonjour", "Hola")? If any valid format/syntax is found, maybe more sophisticated fetaures could be achieved: panel = {0;0} ("A", "B", "C") {0;1} ("1", "2", "3") How would you like this to be implemented?
There is a much simpler and interesting feature that would be useful, in my opinion: being able to initialize more than one component. I mean say 7xSlider = 10.0 and get 7 sliders and I also mean multiline (multi-component) initialization: Ctrl+Intro when you want to start a new line and Intro (or even some Accept/Cancel buttons when you activate multiline mode) to initialize (every line/component), for example. I mean:
3 x Slider = 1
Panel
Mass addition
Panel
And the whole bunch of components that were in mind (pre-thinked definition) is initialized. It speeds up the workflow, making more dynamic to add components that are only available via the drop-down panels.
Should this multiplier be something like a text box adjacent to search field more than '7x'?
These are some of my thoughts about intitializing. Please let me know your opinion :]
…
lly it should not make much of a difference - random number generation is not affected, mutation also is not. crossover is a bit more tricky, I use Simulated Binary Crossover (SBX-20) which was introduced already in 1194:
Deb K., Agrawal R. B.: Simulated Binary Crossover for Continuous Search Space, inIITK/ME/SMD-94027, Convenor, Technical Reports, Indian Institue of Technology, Kanpur, India,November 1994
Abst ract. The success of binary-coded gene t ic algorithms (GA s) inproblems having discrete sear ch sp ace largely depends on the codingused to represent the prob lem variables and on the crossover ope ratorthat propagates buildin g blocks from pare nt strings to childrenst rings . In solving optimization problems having continuous searchspace, binary-co ded GAs discr et ize the search space by using a codingof the problem var iables in binary st rings. However , t he coding of realvaluedvari ables in finit e-length st rings causes a number of difficulties:inability to achieve arbit rary pr ecision in the obtained solution , fixedmapping of problem var iab les, inh eren t Hamming cliff problem associatedwit h binary coding, and processing of Holland 's schemata incont inuous search space. Although a number of real-coded GAs aredevelop ed to solve optimization problems having a cont inuous searchspace, the search powers of these crossover operators are not adequate .In t his paper , t he search power of a crossover operator is defined int erms of the probability of creating an arbitrary child solut ion froma given pair of parent solutions . Motivated by t he success of binarycodedGAs in discret e search space problems , we develop a real-codedcrossover (which we call the simulated binar y crossover , or SBX) operatorwhose search power is similar to that of the single-point crossoverused in binary-coded GAs . Simulation results on a number of realvaluedt est problems of varying difficulty and dimensionality suggestt hat the real-cod ed GAs with t he SBX operator ar e ab le to perform asgood or bet t er than binary-cod ed GAs wit h t he single-po int crossover.SBX is found to be particularly useful in problems having mult ip le optimalsolutions with a narrow global basin an d in prob lems where thelower and upper bo unds of the global optimum are not known a priori.Further , a simulation on a two-var iable blocked function showsthat the real-coded GA with SBX work s as suggested by Goldberg
and in most cases t he performance of real-coded GA with SBX is similarto that of binary GAs with a single-point crossover. Based onth ese encouraging results, this paper suggests a number of extensionsto the present study.
7. ConclusionsIn this paper, a real-coded crossover operator has been develop ed bas ed ont he search characte rist ics of a single-point crossover used in binary -codedGAs. In ord er to define the search power of a crossover operator, a spreadfactor has been introduced as the ratio of the absolute differences of thechildren points to that of the parent points. Thereaft er , the probabilityof creat ing a child point for two given parent points has been derived forthe single-point crossover. Motivat ed by the success of binary-coded GAsin problems wit h discrete sear ch space, a simul ated bin ary crossover (SBX)operator has been develop ed to solve problems having cont inuous searchspace. The SBX operator has search power similar to that of the single-po intcrossover.On a number of t est fun ctions, including De Jong's five te st fun ct ions, ithas been found that real-coded GAs with the SBX operator can overcome anumb er of difficult ies inherent with binary-coded GAs in solving cont inuoussearch space problems-Hamming cliff problem, arbitrary pr ecision problem,and fixed mapped coding problem. In the comparison of real-coded GAs wit ha SBX operator and binary-coded GAs with a single-point crossover ope rat or ,it has been observed that the performance of the former is better than thelatt er on continuous functions and the performance of the former is similarto the lat ter in solving discret e and difficult functions. In comparison withanother real-coded crossover operator (i.e. , BLX-0 .5) suggested elsewhere ,SBX performs better in difficult test functions. It has also been observedthat SBX is particularly useful in problems where the bounds of the optimum
point is not known a priori and wher e there are multi ple optima, of whichone is global.Real-coded GAs wit h t he SBX op erator have also been tried in solvinga two-variab le blocked function (the concept of blocked fun ctions was introducedin [10]). Blocked fun ct ions are difficult for real-coded GAs , becauselocal optimal points block t he progress of search to continue towards t heglobal optimal point . The simulat ion results on t he two-var iable blockedfunction have shown that in most occasions , the sea rch proceeds the way aspr edicted in [10]. Most importantly, it has been observed that the real-codedGAs wit h SBX work similar to that of t he binary-coded GAs wit h single-pointcrossover in overcoming t he barrier of the local peaks and converging to t heglobal bas in. However , it is premature to conclude whether real-coded GAswit h SBX op erator can overcome t he local barriers in higher-dimensionalblocked fun ct ions.These results are encour aging and suggest avenues for further research.Because the SBX ope rat or uses a probability distribut ion for choosing a childpo int , the real-coded GAs wit h SBX are one st ep ahead of the binary-codedGAs in te rms of ach ieving a convergence proof for GAs. With a direct probabilist ic relationship between children and parent points used in t his paper,cues from t he clas sical stochast ic optimization methods can be borrowed toachieve a convergence proof of GAs , or a much closer tie between the classicaloptimization methods and GAs is on t he horizon.
In short, according to the authors my SBX operator using real gene values is as good as older ones specially designed for discrete searches, and better in continuous searches. SBX as far as i know meanwhile is a standard general crossover operator.
But:
- there might be better ones out there i just havent seen yet. please tell me.
- besides tournament selection and mutation, crossover is just one part of the breeding pipeline. also there is the elite management for MOEA which is AT LEAST as important as the breeding itself.
- depending on the problem, there are almost always better specific ways of how to code the mutation and the crossover operators. but octopus is meant to keep it general for the moment - maybe there's a way for an interface to code those things yourself..!?
2) elite size = SPEA-2 archive size, yes. the rate depends on your convergence behaviour i would say. i usually start off with at least half the size of the population, but mostly the same size (as it is hard-coded in the new version, i just realize) is big enough.
4) the non-dominated front is always put into the archive first. if the archive size is exceeded, the least important individual (the significant strategy in SPEA-2) are truncated one by one until the size is reached. if it is smaller, the fittest dominated individuals are put into the elite. the latter happens in the beginning of the run, when the front wasn't discovered well yet.
3) yes it is. this is a custom implementation i figured out myself. however i'm close to have the HypE algorithm working in the new version, which natively has got the possibility to articulate perference relations on sets of solutions.
…
ng is deciding how and where to store your data. If you're writing textual code using any one of a huge number of programming languages there are a lot of different options, each with its own benefits and drawbacks. Sometimes you just need to store a single data point. At other times you may need a list of exactly one hundred data points. At other times still circumstances may demand a list of a variable number of data points.
In programming jargon, lists and arrays are typically used to store an ordered collection of data points, where each item is directly accessible. Bags and hash sets are examples of unordered data storage. These storage mechanisms do not have a concept of which data comes first and which next, but they are much better at searching the data set for specific values. Stacks and queues are ordered data structures where only the youngest or oldest data points are accessible respectively. These are popular structures for code designed to create and execute schedules. Linked lists are chains of consecutive data points, where each point knows only about its direct neighbours. As a result, it's a lot of work to find the one-millionth point in a linked list, but it's incredibly efficient to insert or remove points from the middle of the chain. Dictionaries store data in the form of key-value pairs, allowing one to index complicated data points using simple lookup codes.
The above is a just a small sampling of popular data storage mechanisms, there are many, many others. From multidimensional arrays to SQL databases. From readonly collections to concurrent k-dTrees. It takes a fair amount of knowledge and practice to be able to navigate this bewildering sea of options and pick the best suited storage mechanism for any particular problem. We did not wish to confront our users with this plethora of programmatic principles, and instead decided to offer only a single data storage mechanism.*
Data storage in Grasshopper
In order to see what mechanism would be optimal for Grasshopper, it is necessary to first list the different possible ways in which components may wish to access and store data, and also how families of data points flow through a Grasshopper network, often acquiring more complexity over time.
A lot of components operate on individual values and also output individual values as results. This is the simplest category, let's call it 1:1 (pronounced as "one to one", indicating a mapping from single inputs to single outputs). Two examples of 1:1 components are Subtraction and Construct Point. Subtraction takes two arguments on the left (A and B), and outputs the difference (A-B) to the right. Even when the component is called upon to calculate the difference between two collections of 12 million values each, at any one time it only cares about three values; A, B and the difference between the two. Similarly, Construct Point takes three separate numbers as input arguments and combines them to form a single xyz point.
Another common category of components create lists of data from single input values. We'll refer to these components as 1:N. Range and Divide Curve are oft used examples in this category. Range takes a single numeric domain and a single integer, but it outputs a list of numbers that divide the domain into the specified number of steps. Similarly, Divide Curve requires a single curve and a division count, but it outputs several lists of data, where the length of each list is a function of the division count.
The opposite behaviour also occurs. Common N:1 components are Polyline and Loft, both of which consume a list of points and curves respectively, yet output only a single curve or surface.
Lastly (in the list category), N:N components are also available. A fair number of components operate on lists of data and also output lists of data. Sort and Reverse List are examples of N:N components you will almost certainly encounter when using Grasshopper. It is true that N:N components mostly fall into the data management category, in the sense that they are mostly employed to change the way data is stored, rather than to create entirely new data, but they are common and important nonetheless.
A rare few components are even more complex than 1:N, N:1, or N:N, in that they are not content to operate on or output single lists of data points. The Divide Surface and Square Grid components want to output not just lists of points, but several lists of points, each of which represents a single row or column in a grid. We can refer to these components as 1:N' or N':1 or N:N' or ... depending on how the inputs and outputs are defined.
The above listing of data mapping categories encapsulate all components that ship with Grasshopper, though they do not necessarily minister to all imaginable mappings. However in the spirit of getting on with the software it was decided that a data structure that could handle individual values, lists of values, and lists of lists of values would solve at least 99% of the then existing problems and was thus considered to be a 'good thing'.
Data storage as the outcome of a process
If the problems of 1:N' mappings only occurred in those few components to do with grids, it would probably not warrant support for lists-of-lists in the core data structure. However, 1:N' or N:N' mappings can be the result of the concatenation of two or more 1:N components. Consider the following case: A collection of three polysurfaces (a box, a capped cylinder, and a triangular prism) is imported from Rhino into Grasshopper. The shapes are all exploded into their separate faces, resulting in 6 faces for the box, 3 for the cylinder, and 5 for the prism. Across each face, a collection of isocurves is drawn, resembling a hatching. Ultimately, each isocurve is divided into equally spaced points.
This is not an unreasonably elaborate case, but it already shows how shockingly quickly layers of complexity are introduced into the data as it flows from the left to the right side of the network.
It's no good ending up with a single huge list containing all the points. The data structure we use must be detailed enough to allow us to select from it any logical subset. This means that the ultimate data structure must contain a record of all the mappings that were applied from start to finish. It must be possible to select all the points that are associated with the second polysurface, but not the first or third. It must also be possible to select all points that are associated with the first face of each polysurface, but not any subsequent faces. Or a selection which includes only the fourth point of each division and no others.
The only way such selection sets can be defined, is if the data structure contains a record of the "history" of each data point. I.e. for every point we must be able to figure out which original shape it came from (the cube, the cylinder or the prism), which of the exploded faces it is associated with, which isocurve on that face was involved and the index of the point within the curve division family.
A flexible mechanism for variable history records.
The storage constraints mentioned so far (to wit, the requirement of storing individual values, lists of values, and lists of lists of values), combined with the relational constraints (to wit, the ability to measure the relatedness of various lists within the entire collection) lead us to Data Trees. The data structure we chose is certainly not the only imaginable solution to this problem, and due to its terse notation can appear fairly obtuse to the untrained eye. However since data trees only employ non-negative integers to identify both lists and items within lists, the structure is very amenable to simple arithmetic operations, which makes the structure very pliable from an algorithmic point of view.
A data tree is an ordered collection of lists. Each list is associated with a path, which serves as the identifier of that list. This means that two lists in the same tree cannot have the same path. A path is a collection of one or more non-negative integers. Path notation employs curly brackets and semi-colons as separators. The simplest path contains only the number zero and is written as: {0}. More complicated paths containing more elements are written as: {2;4;6}. Just as a path identifies a list within the tree, an index identifies a data point within a list. An index is always a single, non-negative integer. Indices are written inside square brackets and appended to path notation, in order to fully identify a single piece of data within an entire data tree: {2,4,6}[10].
Since both path elements and indices are zero-based (we start counting at zero, not one), there is a slight disconnect between the ordinality and the cardinality of numbers within data trees. The first element equals index 0, the second element can be found at index 1, the third element maps to index 2, and so on and so forth. This means that the "Eleventh point of the seventh isocurve of the fifth face of the third polysurface" will be written as {2;4;6}[10]. The first path element corresponds with the oldest mapping that occurred within the file, and each subsequent element represents a more recent operation. In this sense the path elements can be likened to taxonomic identifiers. The species {Animalia;Mammalia;Hominidea;Homo} and {Animalia;Mammalia;Hominidea;Pan} are more closely related to each other than to {Animalia;Mammalia; Cervidea;Rangifer}** because they share more codes at the start of their classification. Similarly, the paths {2;4;4} and {2;4;6} are more closely related to each other than they are to {2;3;5}.
The messy reality of data trees.
Although you may agree with me that in theory the data tree approach is solid, you may still get frustrated at the rate at which data trees grow more complex. Often Grasshopper will choose to add additional elements to the paths in a tree where none in fact is needed, resulting in paths that all share a lot of zeroes in certain places. For example a data tree might contain the paths:
{0;0;0;0;0}
{0;0;0;0;1}
{0;0;0;0;2}
{0;0;0;0;3}
{0;0;1;0;0}
{0;0;1;0;1}
{0;0;1;0;2}
{0;0;1;0;3}
instead of the far more economical:
{0;0}
{0;1}
{0;2}
{0;3}
{1;0}
{1;1}
{1;2}
{1;3}
The reason all these zeroes are added is because we value consistency over economics. It doesn't matter whether a component actually outputs more than one list, if the component belongs to the 1:N, 1:N', or N:N' groups, it will always add an extra integer to all the paths, because some day in the future, when the inputs change, it may need that extra integer to keep its lists untangled. We feel it's bad behaviour for the topology of a data tree to be subject to the topical values in that tree. Any component which relies on a specific topology will no longer work when that topology changes, and that should happen as seldom as possible.
Conclusion
Although data trees can be difficult to work with and probably cause more confusion than any other part of Grasshopper, they seem to work well in the majority of cases and we haven't been able to come up with a better solution. That's not to say we never will, but data trees are here to stay for the foreseeable future.
* This is not something we hit on immediately. The very first versions of Grasshopper only allowed for the storage of a single data point per parameter, making operations like [Loft] or [Divide Curve] impossible. Later versions allowed for a single list per parameter, which was still insufficient for all but the most simple algorithms.
** I'm skipping a lot of taxonometric classifications here to keep it simple.…
Added by David Rutten at 2:22pm on January 20, 2015