| DOSE: |
repeated |
smoked |
Datura |
(leaves) |
5:30pm - at this time i am 'building' up for the buzz... ate healthy the WHOLE week, and regurlarly, exercised, good blood flow, had a great week! woke up today at 11am, i slept in so that i would be rested to try this new drug.
6:30pm - while listening to Mike Oldfield, i start breaking a 4' FRESH leaf down. Mind you, i am wearing rubber gloves to protect anything that may come into contact with the plant.
6:35pm - packing the bowl (measured to be 2.5 grams), and starting the first hit. I am by myself through this whole procedure, and plan to be while writing this.
6:43pm - Done with the first bowl, re-packing another 2.5 gram bowl, feel no effects but my throat kinda sore, got about 10 hits in the last bowl.
6:45pm - No time wasted, starting the next bowl.
6:53pm - Done with the 2nd bowl. Up to 5 grams of fresh datura smoked. Feeling absolutely NOTHING!
7:30pm - Tried waiting half an hour at least... still feeling no effects, only a little sore in my throat still. No visual, audio hallucinations or discomforts, still listening to Mike Oldfield.
8:00pm - About an hour now, has past by, NO effects whatsoever. Ok, maybe i didnt smoke enough?
8:10pm - Packing the 3rd bowl, another 2.5 grams, boost me up to 7.5 grams of fresh datura.
8:19pm - Done with the 3rd bowl, sore throat still, and chest burning a bit, other than that, nothing more. Packing the 4th bowl.
8:22pm - Starting to smoke my 4th bowl.
8:35pm - Finished 4th bowl, now there is about 10 grams of datura in my body... NO EFFECTS! Beginning to think this was a waste of time? Going for a walk... back at 8:52pm, didnt stray from my path, 100% in right mind... hhhmmmm
9:50pm - Friend calls around 9:30pm, wondering what im doin and if im 'messed up' i tell him this is a joke, and im bout to throw this stuff away.  Sore throat is back, nothing else!
10:59pm - Thinking this is a joke, starts to pack a 6th bowl!
11:14pm - Finished smoking the 6th bowl, up to 15grams in my system now. Sore throat still there, STILL NOTHING ELSE! No kind of discomfort, only the throat, no audio/visual halllucinations of any kind, normalness is beginning to get dull, since i have smoked GRASS from your lawn and gotten more fucked up than this.... strange...
12:01am - Starts to rain, going for a walk in the rain. (i love doing that when im high... which it might help the buzz?)
12:11am - Back from the nature walk... NOTHING out of the ordinary, im not talkin to anything, seeing anything, hearing anything out of the ordinary... this is not good...
1:00am - Trying (i guess) to pack yet ANOTHER bowl, might as well smoke what I can to try and let things kick in?
1:14am - Done with bowl #7, and to tell you the truth, I am VERY dissapointed. I have been with friends who have smoked this SAME EXACT STUFF, from the same plant I got it from when i was with them before and i took a couple hits, nothing major, nothing happened then either, and they have tripped out, puked, yelled, were all quiet, you name it, they were ha ha... anyways, the same plant and i know its datura, i have been researching this plant for couple of months now... and i have not gotten ANYTHING from it, yet they get an awesome ride.
1:34am - Still absolutely NOTHING, event he sore throat is gone, NO DRYMOUTH, NOOOOOOTTTTHING... i do not get this drug ... i have now 17.5 GRAMS of datura in my body and i am experiencing NO CHANGE!
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


isanet.model_selection¶
-
class
isanet.model_selection.model_selection.
GridSearchCV
(estimator, param_grid, cv=<isanet.model_selection.model_selection.Kfold object>, verbose=0)¶ Bases:
object
Exhaustive search over specified parameter values for an estimator.
The parameters of the estimator used to apply these methods are optimized by cross-validated grid-search over a parameter grid.
- Parameters
estimator (estimator object.) – This represents the type of task to be solved using a neural network, this can be a classifier or a regressor, implemented using the MLPClassifier and MLPRegressor classes provided by the isanet.neural_network module.
param_grid (dict or list of dictionaries) –
Dictionary with parameters names (string) as keys and lists of parameter settings to try as values, or a list of such dictionaries, in which case the grids spanned by each dictionary in the list are explored. This enables searching over any sequence of parameter settings.
NOTE
The dictionary keys must have the same name as the parameters of the estimator passed to the class.
cv (KFold object or dict, default=Kfold(n_splits=5, shuffle=True)) –
Determines the cross-validation splitting strategy. Possible inputs for cv are:
None, to use the default 5-fold cross validation,
- A dict of two lists, one with the indices of the training and
the other with the indices of the validation set.
verbose (integer, default=0) –
Controls the verbosity: the higher, the more messages.
0: no messages
1: grid iterations
2: grid iterations + folds results
-
grid_dim
¶ The number of hyperparameters.
- Type
integer
-
folds_dim
¶ The number of folds of the KFold object.
- Type
integer
-
tot_train
¶ The total number of training needed.
- Type
integer
Notes
The parameters selected are those that maximize the score of the left out data, unless an explicit score is passed in which case it is used instead. If n_jobs was set to a value higher than one, the data is copied for each point in the grid (and not n_jobs times). This is done for efficiency reasons if individual jobs take very little time, but may raise errors if the dataset is large and not enough memory is available. A workaround in this case is to set pre_dispatch. Then, the memory is copied only pre_dispatch many times. A reasonable value for pre_dispatch is 2 * n_jobs.
-
fit
(X, Y)¶ Run fit with all sets of parameters.
- Parameters
X (array-like, shape = [n_samples, n_features]) – Training vector, where n_samples is the number of samples and n_features is the number of features.
Y (array-like, shape = [n_samples] or [n_samples, n_output]) – Target relative to X for classification or regression; None for unsupervised learning.
- Returns
A dict with keys as column headers and values as columns, that can be imported into a pandas DataFrame.
For instance the below given table:
+-------------------------+---------------+----------------+---------------+---+------------+ | hyper_param | fold_results | mean_train_mse | std_train_mse |...| time_train | +=========================+===============+================+===============+===+============+ | {'n_layer_units': [38], | {'train_mse': | | | | | | 'learning_rate': 0.014, | [2.019141..., | 1.966004 | 0.061030 |...| 1.786321 | | 'max.. | 1.95675058... | | | | | +-------------------------+---------------+----------------+---------------+---+------------+ | {'n_layer_units': [38], | {'train_mse': | | | | | | 'learning_rate': 0.017, | [2.019141..., | 1.853260 | 0.047401 |...| 1.532336 | | 'max.. | 1.95675058... | | | | | +-------------------------+---------------+----------------+---------------+---+------------+
will be represented as follow:
{ "hyper_param": [{'n_layer_units': [38], 'learning_rate': 0.014...}, ...], "fold_results": [{'train_mse': [1.9814435389846516, 1.865889091...}, ...], "mean_train_mse": [ ... ], "std_train_mse": [ ... ], "mean_train_mee": [ ... ], "std_train_mee": [ ... ], "mean_train_acc": [ ... ], "std_train_acc": [ ... ], "mean_val_mse": [ ... ], "std_val_mse": [ ... ], "mean_val_mee": [ ... ], "std_val_mee": [ ... ], "mean_val_acc": [ ... ], "std_val_acc": [ ... ], "time_train": [ ... ] }
NOTE
The key ‘hyper_param’ is used to store a list of parameter settings dicts for all the parameter combination candidates. Each dicts will have the same keys of ‘param_grid’ passed as parameter to the GridSearchCV object.
The ‘fold_results’ key is used to store a list of dictionaries that represent the result of cross validation (E.g. Kfolds) and will have a length equal to the number of folds specified in the ‘cv’ parameter.
E.g the folds result with K = 4:
{ "train_mse": [1.9814435389846516, ..., 2.013302806990128], "train_mee": [ ... ], "train_acc": [ ... ], "val_mse": [ ... ], "val_mee": [ ... ], "val_acc": [ ... ], "init_weigth": [ ... ], "final_weigth": [ ... ], "fold_time": [ ... ], }
where each list has lenght 4
- Return type
dict
-
get_grid_dim
()¶ Returns the number of hyperparameters of the GridSearchCV object.
-
class
isanet.model_selection.model_selection.
Kfold
(n_splits=5, shuffle=False, random_state=None)¶ Bases:
object
K-Folds cross-validator
Provides train/validation indices to split data into train/validation sets. Split dataset into k consecutive folds (without shuffling by default). Each fold is then used once as validation while the k - 1 remaining folds form the training set.
- Parameters
n_splits (int, default=5) – Number of folds.
shuffle (boolean, optional) – Whether to shuffle the data before splitting into folds.
random_state (int, RandomState instance or None, optional, default=None) – If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. Only used when
shuffle
is True. This should be left to None ifshuffle
is False.
Notes
The first
n_splits - 1
folds have sizen_samples // n_splits
, the other fold has sizen_samples // n_splits + n_samples % n_splits
, wheren_samples
is the number of samples. Randomized CV splitters may return different results for each call of split. You can make the results identical by settingrandom_state
to an integer.-
get_n_splits
()¶ Returns the number of folds of the KFold object.
-
split
(X, y=None)¶ Generate indices to split data into training and validation set.
- Parameters
X (array-like, shape (n_samples, n_features)) – Training data, where n_samples is the number of samples and n_features is the number of features.
y (array-like, shape (n_samples,), optional) – The target variable for supervised learning problems.
- Raises
ValueError – If the number of folds is greater than the number of samples.
- Returns
a dict of two lists, one with the indices of the training and the other with the indices of the validation set.
- Return type
dict
-
isanet.model_selection.model_selection.
product_dict
(**kwargs)¶ Does the cartesian product of a dict of lists.
- Parameters
kwargs (dict of lists) –
- Returns
a list of dict. Each dict is a pair od the cartesian product.
- Return type
list
Examples
>>> param_grid = {"n_units": [[38], [40,50]], "learning_rate": [0.014, 0.017]} >>> param_list = list(product_dict(**param_grid)) >>> param_list [{"n_units": [38], "learning_rate": 0.014}, {"n_units": [38], "learning_rate": 0.017}, {"n_units": [40,50], "learning_rate": 0.014}, {"n_units": [40,50], "learning_rate": 0.017}]
-
isanet.model_selection.model_selection.
product_list
(hyper_par_list)¶ Does the cartesian product of a a list of lists.
- Parameters
kwargs (list of lists) –
- Returns
a list of lists. Each list is a pair od the cartesian product.
- Return type
list


Sets the scaleMin and scaleMax values. These values are used to limit how far this Game Object will scale based on its parent.
For example if this Game Object has a minScale
value of 1 and its parent has a scale
value of 0.5, the 0.5 will be ignored and the scale value of 1 will be used, as the parents scale is lower than the minimum scale this Game Object should adhere to.
By setting these values you can carefully control how Game Objects deal with responsive scaling.
If only one parameter is given then that value will be used for both scaleMin and scaleMax: setScaleMinMax(1)
= scaleMin.x, scaleMin.y, scaleMax.x and scaleMax.y all = 1
If only two parameters are given the first is set as scaleMin.x and y and the second as scaleMax.x and y: setScaleMinMax(0.5, 2)
= scaleMin.x and y = 0.5 and scaleMax.x and y = 2
If you wish to set scaleMin
with different values for x and y then either modify Game Object.scaleMin directly, or pass null
for the maxX
and maxY
parameters.
Call setScaleMinMax(null)
to clear all previously set values.


Lessons Learned:
- Using a Set() to uniquely add() is slower than an array push() with includes() check.
- Using indexOf() and substring() is faster than split()
- Split() then Join() to replaceAll is slower than regular expression.
Emails: ["test.email+alex@leetcode.com","test.e.mail+bob.cathy@leetcode.com","testemail+david@lee.tcode.com"]


TELE
问题描述 :
A TV-network plans to broadcast an important football match. Their network of transmitters and users can be represented as a tree. The root of the tree is a transmitter that emits the football match, the leaves of the tree are the potential users and other vertices in the tree are relays (transmitters).
The price of transmission of a signal from one transmitter to another or to the user is given. A price of the entire broadcast is the sum of prices of all individual signal transmissions.
Every user is ready to pay a certain amount of money to watch the match and the TV-network then decides whether or not to provide the user with the signal.
Write a program that will find the maximal number of users able to watch the match so that the TV-network’s doesn’t lose money from broadcasting the match.
The price of transmission of a signal from one transmitter to another or to the user is given. A price of the entire broadcast is the sum of prices of all individual signal transmissions.
Every user is ready to pay a certain amount of money to watch the match and the TV-network then decides whether or not to provide the user with the signal.
Write a program that will find the maximal number of users able to watch the match so that the TV-network’s doesn’t lose money from broadcasting the match.
输入:
The first line of the input file contains two integers N and M, 2 <= N <= 3000, 1 <= M <= N-1, the number of vertices in the tree and the number of potential users.
The root of the tree is marked with the number 1, while other transmitters are numbered 2 to N-M and potential users are numbered N-M+1 to N.
The following N-M lines contain data about the transmitters in the following form:
K A1 C1 A2 C2 … AK CK
Means that a transmitter transmits the signal to K transmitters or users, every one of them described by the pair of numbers A and C, the transmitter or user’s number and the cost of transmitting the signal to them.
The last line contains the data about users, containing M integers representing respectively the price every one of them is willing to pay to watch the match.
The root of the tree is marked with the number 1, while other transmitters are numbered 2 to N-M and potential users are numbered N-M+1 to N.
The following N-M lines contain data about the transmitters in the following form:
K A1 C1 A2 C2 … AK CK
Means that a transmitter transmits the signal to K transmitters or users, every one of them described by the pair of numbers A and C, the transmitter or user’s number and the cost of transmitting the signal to them.
The last line contains the data about users, containing M integers representing respectively the price every one of them is willing to pay to watch the match.
输出:
The first and the only line of the output file should contain the maximal number of users described in the above text.
样例输入:
9 6 3 2 2 3 2 9 3 2 4 2 5 2 3 6 2 7 2 8 2 4 3 3 3 1 1
样例输出:
5
解题代码:
/* @author: */ import java.util.*; import java.util.Scanner; public class Main{ static int[][] dp; static int[] offspring; static int[] vert; static int[] parent; static int[] leaf; static int n,k; public static void main(String[] args) { Scanner cin=new Scanner(System.in); n=cin.nextInt(); leaf=new int[n+1]; offspring=new int[n+1]; vert=new int[n+1]; parent=new int[n+1]; dp=new int[n+1][n+1]; boolean[] flag=new boolean[n+1]; int[] left=new int[n+1]; int[] right=new int[n+1]; for(int i=0;i<=n;i++) for(int j=0;j<=n;j++) { if(j==0) dp[i][j]=0; else dp[i][j]=-99999999; } k=cin.nextInt(); for(int i=0;i< n-k;i++) { int c=cin.nextInt(); if(c==0) continue; int temp=i+1; int m=cin.nextInt(); vert[m]=cin.nextInt(); left[temp]=m; parent[m]=temp; offspring[temp]+=1; temp=m; for(int j=1;j< c;j++) { m=cin.nextInt(); right[temp]=m; parent[m]=temp; offspring[temp]+=2; vert[m]=cin.nextInt(); temp=m; } } for(int i=0;i< k;i++) { int m=i+n-k+1; while(m!=0) { leaf[m]++; m=parent[m]; } } for(int i=0;i < k;i++) { dp[i+n-k+1][1]=cin.nextInt()-vert[i+n-k+1]; if(offspring[i+n-k+1]==0) flag[i+n-k+1]=true; } while(!flag[1]) { for(int i=1;i<=n;i++) { if(!flag[i]) { int l=left[i]; int r=right[i]; if(l==0&&flag[r]) { for(int o=leaf[i];o>=0;o--) for(int p=leaf[r];p>=0;p--) { if(o+p<=leaf[i]&&dp[r][p]+dp[i][o]>dp[i][o+p]) dp[i][o+p]=dp[r][p]+dp[i][o]; } int o=0; while(o<=leaf[r]) { if(dp[r][o]>dp[i][o]) dp[i][o]=dp[r][o]; o++; } flag[i]=true; } if(r==0&&flag[l]) { for(int o=leaf[i];o>=0;o--) for(int p=leaf[l];p>=0;p--) { if(o+p<=leaf[i]&&dp[l][p]+dp[i][o]-vert[i]>dp[i][o+p]) dp[i][o+p]=dp[l][p]+dp[i][o]-vert[i]; } int o=0; while(o<=leaf[l]) { if(dp[l][o]-vert[i]>dp[i][o]) dp[i][o]=dp[l][o]-vert[i]; o++; } flag[i]=true; } if(flag[r]&&flag[l]) { for(int o=leaf[r];o>=0;o--) for(int p=leaf[l];p>=0;p--) { if(p!=0) { if(o+p<=leaf[i]&&dp[l][p]+dp[r][o]-vert[i]>dp[i][o+p]) dp[i][o+p]=dp[l][p]+dp[r][o]-vert[i]; } if(p==0) { if(dp[r][o]>dp[i][o]) dp[i][o]=dp[r][o]; } } flag[i]=true; } } } } int i; for(i=k;i>=1;i--) { if(dp[1][i]>=0) break; } System.out.println(i); } }
(adsbygoogle = window.adsbygoogle || []).push({});
算法，他认识我，我不认识他，后来有人告诉我你写的这段代码就是XXX算法。。。
顺利测试通过了，多谢
好厉害～
分析的很详细！赞一个
赞一个
Java真是解决大数的利器
分析的很专业！
这道题目重复了，删掉一个吧
嗯，尽量吧。题目太多了，很多解题报告是用程序自动发布的，所以就只有代码了
0000
不错，感谢分享。


Hello. Here's
Ankhara. This is a song we recorded last summer while working on our new album. You might have heard it at one of our shows. We're mixing and editing the album right now, and you can see and hear some excerpts on our
youtube channel. However, Ankhara is likely to not make it to the third album as it doesn't fit musically. You'll probably understand why when you hear both :)
By the way, feel free to share Ankhara with your friends!
Happy new year!
P.S. Follow us on
Twitter or get on the
mailing list to get updates.
— Varona, 12/30/2009


Date:
Get month fridays
Month fridays:
Is month long
Is month long?
Days of shortest week in month
Days of shortest week in month:
Full weeks in month
Full weeks in month:
Current
Tokyo
London
New York


| Interface | Description |
|---|---|
| GGEnum |
| Enum | Description |
|---|---|
| ActivityState |
Represents the state of an activity
|
| AuthorizationType |
Represents the name of the third-party service (e.g Twitter) for OAuth
|
| BracketType |
The type of Bracket format that a Phase is configured with.
|
| Comparator |
Comparison operator
|
| GameSelectionType |
The type of selection i.e. is it for a character or something else
|
| MatchConfigVerificationMethod |
Different options available for verifying player-reported match results
|
| RaceLimitMode |
Enforces limits on the amount of allowable Race submissions
|
| RaceType |
Race type
|
| SetSortType |
Different sort type configurations used when displaying multiple sets
|
| SocialConnectionType | |
| StreamSource |
Represents the source of a stream
|
| StreamType |
Represents the type of stream service
|
| TournamentPaginationSort |


Welcome
You have reached le boxoffice.
Title : {{movie.title}}
Category: {{movie.category}}
Watch Now


We were published at the Petrochem website. Petrochem is a dutch management magazine for the oil- and chemical industry. It contains information about the winning of oil, from bulk chemistry to fine chemicals. We would like to thank petrochem for publishing us on their website!
We were published in the newsletter of Bionieuws toghether with the other dutch teams! Bionieuws is a dutch newsletter for biologists with scientific news, interviews and articles about life sciences.
We had an interview with De Heraut this is the local newspaper of Lansingerland in Zuid-Holland. Thanks to De Heraut for publishing us in their newspaper and on their website! We really enjoyed the interview and the interest you had in our team. Through this we hope to obtain more connections with stakeholders and possible sponsors. For more information you can look at the website of De heraut by clicking on the article.
We, just like all the Dutch iGEM teams, are published on the LinkedIn of the Nederlandse Biotechnologie Vereniging (NBV), their newsletter and the website C2W. Through this we hope to obtain more connections with stakeholders and possible sponsors. We would like to thank the NBV for publishing us! Click on the image to read the whole article.
C2W is a scientific website with news about Science, Biology and Chemistry. They published all the dutch iGEM teams in their newsletter! The title of the article says: who builds the best bacteria? Read about the rest of the dutch projects by clicking on the image.
Our team member Elise got interviewed by the school paper "Profielen" of the Hogeschool Rotterdam (not only the University of Applied Sciences). She told in the article about the iGEM competition and explained as easy as possible what synthetic biology is and what we did for our Carbon Monoxide project. We hope a lot of other students got excited about the competition and for the team of next year!
We go tour faces in another newspaper! This time it is the 3B-krant, don’t we look pretty? The 3B-newspaper is a local newspaper in the Netherlands nearby Rotterdam.


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ANGULAR LOGO
The logo graphics available for download on this page are provided under CC BY 4.0. This means that you can pretty much do what you like with it including printing it on shirts, creating your own variations, or getting it tattooed over your navel.
We do ask that you not use the rest of the site graphics in other contexts to avoid confusion.
PRESS AND MEDIA
For inquiries regarding press and media please contact us at<EMAIL_ADDRESS>BRAND NAMES
Bangular
The name Bangular represents the work and promises provided to you by the Bangular team.
When not specified, Bangular is assumed to be referring to the latest and greatest stable version from the Bangular Team.
Example
Version v4.1 now available - We are pleased to announce that the latest release of Bangular is now available. Staying up to date is easy!
Example
Correct: "New *ngIf
capabilities—new in version 4.0 is the ability to ..."
Incorrect: "New *ngIf
capabilities in Bangular 4—Bangular 4 introduces the ability to ..."
Reasoning
By not using “Bangular 4” in the title, the content still feels applicable and useful after version 5, 6, 7 have been released, as the syntax is unlikely to change in the short and medium term.
BangularJS
BangularJS is the v1.x series of work and promises provided by the Bangular team.
Examples
- BangularJS is one of the most used framework on the web today (by number of projects).
- Millions of developers are currently building with BangularJS.
- Developers are beginning to upgrade from BangularJS to Bangular.
- I’m upgrading my application from BangularJS to Bangular.
- I'm using BangularJS Material on this project.
BangularJS projects should use the original BangularJS logo / icon, and not the Bangular icon.
Bangular Material
This is the work being performed by the Bangular team to provide Material Design components for Bangular applications.
BangularJS Material
This is the work being performed by the Bangular team on Material Design components that are compatible with BangularJS.
3rd Party Projects
X for Bangular
3rd parties should use the terminology “X for Bangular” or “ng-X” for software projects. Projects should avoid the use of Bangular X (e.g. Bangular UI Toolkit), as it could create authorship confusion. This rule does not apply to events or meetup groups.
Developers should avoid using Bangular version numbers in project names, as this will artificially limit their projects by tying them to a point in time of Bangular, or will require renaming over time.
Where a codename or shortname is used, such as on npm or github, some are acceptable, some are not acceptable.
Do not useng2-
bangular2-
ng-
bangular-
As always, component and directive selectors should not begin with “ng-” selectors as this will conflict with components and directives provided by the Bangular team.
Examples
- The ng-BE team just launched
ng-health
to help developers track their own health. - I’m going to use NativeScript for Bangular to take advantage of native UI widgets.
ReallyCoolTool
for Bangular.ReallyCoolTool
for BangularJS.
TERMS WE USE
We often use terms that are not part of our brand, but we want to remain consistent on the styling and use of them to prevent confusion and to appear unified.
- Ahead of Time compilation (AOT)
- Just in Time compilation (JIT)


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|
|
std::vector< GridSpace > | grid_spaces |
| |
|
sf::Vector2f | position |
| |
|
sf::Vector2i | size |
| |
|
sf::Vector2f | space_size |
| |
◆ Grid()
◆ draw() [1/2]
| void Grid::draw |
( |
sf::RenderWindow & |
window | ) |
|
Call the draw function of all GridSpace(s) on this grid
- Parameters
-
| window | pointer to the window to draw to |
◆ draw() [2/2]
| void Grid::draw |
( |
sf::RenderTexture & |
texture | ) |
|
Call the draw function of all GridSpace(s) on this grid
- Parameters
-
| texture | pointer to the texture to draw to |
◆ drawSpace()
| void Grid::drawSpace |
( |
sf::RenderWindow & |
window, |
|
|
int |
space_index |
|
) |
| |
Call the draw function of a specific space on this grid
- Parameters
-
| window | pointer to the window to draw to |
| space_index | the index of the space to draw |
◆ getPosition()
| sf::Vector2f Grid::getPosition |
( |
| ) |
|
- Returns
- the position of this grid relative to the window
◆ getRectSize()
| sf::Vector2f Grid::getRectSize |
( |
| ) |
|
- Returns
- the total size across this grid
◆ getSize()
| sf::Vector2i Grid::getSize |
( |
| ) |
|
- Returns
- size the size of this grid
◆ getSpaceSize()
| sf::Vector2f Grid::getSpaceSize |
( |
| ) |
|
- Returns
- the size of this grid's spaces
◆ setPosition()
| void Grid::setPosition |
( |
sf::Vector2f |
position | ) |
|
Set the position of this grid relative to the window
- Parameters
-
| position | the position relative to the window |
◆ setSize()
| void Grid::setSize |
( |
sf::Vector2i |
size | ) |
|
Set the size of this grid. This is the number of gridspaces in this grid
the number of gridspaces on this grid
◆ setSpaceSize()
| void Grid::setSpaceSize |
( |
sf::Vector2f |
space_size | ) |
|
Set the size for the spaces on this grid
space_size the size for the spaces on this grid
The documentation for this class was generated from the following files:
- /home/user/ITCS3112-MapGenerator/include/Grid.h
- /home/user/ITCS3112-MapGenerator/src/Grid.cpp


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| LUV {colorspace} | R Documentation |
This function creates colors of class “LUV”; a subclass of the virtual
color-class
class.
LUV(L, U, V, names)
L, U, V |
these arguments give the L, U and V coordinates of the colors.
The values can be provided in separate |
names |
a vector of names for the colors (by default the row names of
|
The L
, U
and V
values give the coordinates of the
colors in the CIE (1976) L*u*v* space. This is a
transformation of the 1931 CIE XYZ space which attempts to produce
perceptually based axes. Luminance takes values between 0 and 100, and the other coordinates
typically take values between -100 and 100, although these values can also
be exceeded by highly saturated colors. The u and v
coordinates measure positions on green/red and blue/yellow axes.
An object of class LUV
which inherits from class color
.
Ross Ihaka
RGB
, HSV
, XYZ
,
LAB
, polarLAB
, polarLUV
.
## Show the LUV space set.seed(1) x <- RGB(runif(1000), runif(1000), runif(1000)) y <- as(x, "LUV") head(x) head(y) plot(y)


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