cdt.causality¶
cdt.causality.pairwise¶
ToDo
cdt.causality.graph¶
Find causal relationships and output a directed graph.
-
class
cdt.causality.graph.model.
GraphModel
[source]¶ Base class for all graph causal inference models.
Usage for undirected/directed graphs and raw data. All causal discovery models out of observational data base themselves on this class. Its main feature is the predict function that executes a function according to the given arguments.
-
create_graph_from_data
(data, **kwargs)[source]¶ Infer a directed graph out of data.
Note
Not implemented: will be implemented by the model classes.
-
orient_directed_graph
(data, dag, **kwargs)[source]¶ Re/Orient an undirected graph.
Note
Not implemented: will be implemented by the model classes.
-
orient_undirected_graph
(data, umg, **kwargs)[source]¶ Orient an undirected graph.
Note
Not implemented: will be implemented by the model classes.
-
predict
(df_data, graph=None, **kwargs)[source]¶ Orient a graph using the method defined by the arguments.
Depending on the type of graph, this function process to execute different functions:
- If
graph
is anetworkx.DiGraph
, thenself.orient_directed_graph
is executed. - If
graph
is anetworkx.Graph
, thenself.orient_undirected_graph
is executed. - If
graph
is aNone
, thenself.create_graph_from_data
is executed.
Parameters: - df_data (pandas.DataFrame) – DataFrame containing the observational data.
- graph (networkx.DiGraph or networkx.Graph or None) – Prior knowledge on the causal graph.
Warning
Requirement : Name of the nodes in the graph must correspond to the name of the variables in df_data
- If
-
bnlearn-based models¶
-
class
cdt.causality.graph.bnlearn.
BNlearnAlgorithm
(score='NULL', alpha=0.05, beta='NULL', optim=False, verbose=None)[source]¶ BNlearn algorithm. All these models imported from bnlearn revolve around this base class and have all the same attributes/interface.
Parameters: - score (str) – the label of the conditional independence test to be used in the algorithm. If none is specified, the default test statistic is the mutual information for categorical variables, the Jonckheere-Terpstra test for ordered factors and the linear correlation for continuous variables. See below for available tests.
- alpha (float) – a numeric value, the target nominal type I error rate.
- beta (int) – a positive integer, the number of permutations considered for each permutation test. It will be ignored with a warning if the conditional independence test specified by the score argument is not a permutation test.
- optim (bool) – See bnlearn-package for details.
- verbose (bool) – Sets the verbosity. Defaults to SETTINGS.verbose
- Available tests:
- discrete case (categorical variables)
- – mutual information: an information-theoretic distance measure.
- It’s proportional to the log-likelihood ratio (they differ by a 2n factor) and is related to the deviance of the tested models. The asymptotic χ2 test (mi and mi-adf, with adjusted degrees of freedom), the Monte Carlo permutation test (mc-mi), the sequential Monte Carlo permutation test (smc-mi), and the semiparametric test (sp-mi) are implemented.
- – shrinkage estimator for the mutual information (mi-sh)
- An improved asymptotic χ2 test based on the James-Stein estimator for the mutual information.
- – Pearson’s X2 : the classical Pearson’s X2 test for contingency tables.
- The asymptotic χ2 test (x2 and x2-adf, with adjusted degrees of freedom), the Monte Carlo permutation test (mc-x2), the sequential Monte Carlo permutation test (smc-x2) and semiparametric test (sp-x2) are implemented .
- discrete case (ordered factors)
- – Jonckheere-Terpstra : a trend test for ordinal variables.
- The asymptotic normal test (jt), the Monte Carlo permutation test (mc-jt) and the sequential Monte Carlo permutation test (smc-jt) are implemented.
- continuous case (normal variables)
- – linear correlation: Pearson’s linear correlation.
- The exact Student’s t test (cor), the Monte Carlo permutation test (mc-cor) and the sequential Monte Carlo permutation test (smc-cor) are implemented.
- – Fisher’s Z: a transformation of the linear correlation with asymptotic normal distribution.
- Used by commercial software (such as TETRAD II) for the PC algorithm (an R implementation is present in the pcalg package on CRAN). The asymptotic normal test (zf), the Monte Carlo permutation test (mc-zf) and the sequential Monte Carlo permutation test (smc-zf) are implemented.
- – mutual information: an information-theoretic distance measure.
- Again it is proportional to the log-likelihood ratio (they differ by a 2n factor). The asymptotic χ2 test (mi-g), the Monte Carlo permutation test (mc-mi-g) and the sequential Monte Carlo permutation test (smc-mi-g) are implemented.
- – shrinkage estimator for the mutual information(mi-g-sh):
- an improved asymptotic χ2 test based on the James-Stein estimator for the mutual information.
- hybrid case (mixed discrete and normal variables)
- – mutual information: an information-theoretic distance measure.
- Again it is proportional to the log-likelihood ratio (they differ by a 2n factor). Only the asymptotic χ2 test (mi-cg) is implemented.
-
create_graph_from_data
(data)[source]¶ Run the algorithm on data.
Parameters: data (pandas.DataFrame) – DataFrame containing the data Returns: Solution given by the algorithm. Return type: networkx.DiGraph
-
orient_directed_graph
(data, graph)[source]¶ Run the algorithm on a directed_graph.
Parameters: - data (pandas.DataFrame) – DataFrame containing the data
- graph (networkx.DiGraph) – Skeleton of the graph to orient
Returns: Solution on the given skeleton.
Return type: networkx.DiGraph
Warning
The algorithm is ran on the skeleton of the given graph.
GS¶
IAMB¶
-
class
cdt.causality.graph.bnlearn.
IAMB
[source]¶ IAMB algorithm.
Note
Tsamardinos I, Aliferis CF, Statnikov A (2003). “Algorithms for Large Scale Markov Blanket Discovery”. In “Proceedings of the Sixteenth International Florida Artificial Intelligence Research Society Conference”, pp. 376-381. AAAI Press.
Fast_IAMB¶
-
class
cdt.causality.graph.bnlearn.
Fast_IAMB
[source]¶ Fast IAMB algorithm.
Note
Yaramakala S, Margaritis D (2005). “Speculative Markov Blanket Discovery for Optimal Feature Selection”. In “ICDM ’05: Proceedings of the Fifth IEEE International Conference on Data Mining”, pp. 809-812. IEEE Computer Society.
MMPC¶
-
class
cdt.causality.graph.bnlearn.
MMPC
[source]¶ Max-Min Parents-Children algorithm.
Note
Tsamardinos I, Aliferis CF, Statnikov A (2003). “Time and Sample Efficient Discovery of Markov Blankets and Direct Causal Relations”. In “KDD ’03: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining”, pp. 673-678. ACM. Tsamardinos I, Brown LE, Aliferis CF (2006). “The Max-Min Hill-Climbing Bayesian Network Structure Learning Algorithm”. Machine Learning,65(1), 31-78.
CCDr¶
CAM¶
-
class
cdt.causality.graph.
CAM
(score='nonlinear', cutoff=0.001, variablesel=True, selmethod='gamboost', pruning=False, prunmethod='gam', nb_jobs=None, verbose=None)[source]¶ CAM algorithm.
Parameters: - score (str) – Score used to fit the gaussian processes.
- cutoff (float) – threshold value for variable selection.
- variablesel (bool) – Perform a variable selection step.
- selmethod (str) – Method used for variable selection.
- pruning (bool) – Perform an initial pruning step.
- prunmethod (str) – Method used for pruning.
- nb_jobs (int) – Number of jobs to run in parallel.
- verbose (bool) – Sets the verbosity of the output.
- Available scores:
- nonlinear: ‘SEMGAM’
- linear: ‘SEMLIN’
- Available variable selection methods:
- gamboost’: ‘selGamBoost’
- gam’: ‘selGam’
- lasso’: ‘selLasso’
- linear’: ‘selLm’
- linearboost’: ‘selLmBoost’
- Default Parameters:
- FILE: ‘/tmp/cdt_CAM/data.csv’
- SCORE: ‘SEMGAM’
- VARSEL: ‘TRUE’
- SELMETHOD: ‘selGamBoost’
- PRUNING: ‘TRUE’
- PRUNMETHOD: ‘selGam’
- NJOBS: str(SETTINGS.NB_JOBS)
- CUTOFF: str(0.001)
- VERBOSE: ‘FALSE’
- OUTPUT: ‘/tmp/cdt_CAM/result.csv’
Note
Ref: J. Peters, J. Mooij, D. Janzing, B. Schölkopf: Causal Discovery with Continuous Additive Noise Models, JMLR 15:2009-2053, 2014.
Warning
This implementation of CAM does not support starting with a graph. The adaptation will be made at a later date.
CGNN¶
-
class
cdt.causality.graph.
CGNN
(nh=20, nb_runs=16, nb_jobs=None, gpu=None, lr=0.01, train_epochs=1000, test_epochs=1000, verbose=None)[source]¶ Causal Generative Neural Netwoks : Generate the whole causal graph in a topological manner using neural networks and predict causal directions in the graph.
Parameters: - nh (int) – Number of hidden units in each generative neural network.
- nb_runs (int) – Number of times to run CGNN to have a stable evaluation.
- nb_jobs (int) – Number of jobs to run in parallel. Defaults to
cdt.SETTINGS.NB_JOBS
. - gpu (bool) – True if the GPUs are to be used. Defaults to
cdt.SETTINGS.GPU
. - lr (float) – Learning rate for the generative neural networks.
- train_epochs (int) – Number of epochs used to train the network.
- test_epochs (int) – Number of epochs during which the results are harvested. The network still trains at this stage.
- verbose (bool) – Sets the verbosity of the execution. Defaults to
cdt.SETTINGS.verbose
.
Note
Ref : Learning Functional Causal Models with Generative Neural Networks Olivier Goudet & Diviyan Kalainathan & Al. (https://arxiv.org/abs/1709.05321)
-
create_graph_from_data
(data)[source]¶ Use CGNN to create a graph from scratch. All the possible structures are tested, which leads to a super exponential complexity. It would be preferable to start from a graph skeleton for large graphs.
Parameters: data (pandas.DataFrame) – Observational data on which causal discovery has to be performed. Returns: Solution given by CGNN. Return type: networkx.DiGraph
-
orient_directed_graph
(data, dag, alg='HC')[source]¶ Modify and improve a directed acyclic graph solution using CGNN.
Parameters: - data (pandas.DataFrame) – Observational data on which causal discovery has to be performed.
- dag (nx.DiGraph) – Graph that provides the initial solution, on which the CGNN algorithm will be applied.
- alg (str) – Exploration heuristic to use, among [“HC”, “HCr”, “tabu”, “EHC”]
Returns: Solution given by CGNN.
Return type: networkx.DiGraph
-
orient_undirected_graph
(data, umg, alg='HC')[source]¶ Orient the undirected graph using GNN and apply CGNN to improve the graph.
Parameters: - data (pandas.DataFrame) – Observational data on which causal discovery has to be performed.
- umg (nx.Graph) – Graph that provides the skeleton, on which the GNN then the CGNN algorithm will be applied.
- alg (str) – Exploration heuristic to use, among [“HC”, “HCr”, “tabu”, “EHC”]
Returns: Solution given by CGNN.
Return type: networkx.DiGraph
Note
GNN (
cdt.causality.pairwise.GNN
) is first used to orient the undirected graph and output a DAG before applying CGNN.
GES¶
-
class
cdt.causality.graph.
GES
(score='obs', verbose=None)[source]¶ GES algorithm.
Parameters: - score (str) – Sets the score used by GES.
- verbose (bool) – Defaults to
cdt.SETTINGS.verbose
.
- Available scores:
- int: GaussL0penIntScore
- obs: GaussL0penObsScore
Note
Ref: D.M. Chickering (2002). Optimal structure identification with greedy search. Journal of Machine Learning Research 3 , 507–554
A. Hauser and P. Bühlmann (2012). Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs. Journal of Machine Learning Research 13, 2409–2464.
P. Nandy, A. Hauser and M. Maathuis (2015). Understanding consistency in hybrid causal structure learning. arXiv preprint 1507.02608
P. Spirtes, C.N. Glymour, and R. Scheines (2000). Causation, Prediction, and Search, MIT Press, Cambridge (MA)
-
create_graph_from_data
(data)[source]¶ Run the GES algorithm.
Parameters: data (pandas.DataFrame) – DataFrame containing the data Returns: Solution given by the GES algorithm. Return type: networkx.DiGraph
GIES¶
-
class
cdt.causality.graph.
GIES
(score='obs', verbose=False)[source]¶ GIES algorithm.
Parameters: - score (str) – Sets the score used by GES.
- verbose (bool) – Defaults to
cdt.SETTINGS.verbose
.
- Available scores:
- int: GaussL0penIntScore
- obs: GaussL0penObsScore
Note
Ref: D.M. Chickering (2002). Optimal structure identification with greedy search. Journal of Machine Learning Research 3 , 507–554
A. Hauser and P. Bühlmann (2012). Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs. Journal of Machine Learning Research 13, 2409–2464.
P. Nandy, A. Hauser and M. Maathuis (2015). Understanding consistency in hybrid causal structure learning. arXiv preprint 1507.02608
P. Spirtes, C.N. Glymour, and R. Scheines (2000). Causation, Prediction, and Search, MIT Press, Cambridge (MA)
-
create_graph_from_data
(data)[source]¶ Run the GIES algorithm.
Parameters: data (pandas.DataFrame) – DataFrame containing the data Returns: Solution given by the GIES algorithm. Return type: networkx.DiGraph
LiNGAM¶
-
class
cdt.causality.graph.
LiNGAM
(verbose=False)[source]¶ LiNGAM algorithm.
Parameters: verbose (bool) – Sets the verbosity of the algorithm. Defaults to cdt.SETTINGS.verbose Note
Ref: S. Shimizu, P.O. Hoyer, A. Hyvärinen, A. Kerminen (2006) A Linear Non-Gaussian Acyclic Model for Causal Discovery; Journal of Machine Learning Research 7, 2003–2030.
Warning
This implementation of LiNGAM does not support starting with a graph.
PC¶
-
class
cdt.causality.graph.
PC
(CItest='gaussian', method_indep='corr', alpha=0.01, nb_jobs=None, verbose=None)[source]¶ PC algorithm by C.Glymour & P.Sprites.
Parameters: - CItest (str) – Test for conditional independence.
- method (str) – Heuristic for testing CI.
- alpha (float) – significance level (number in (0, 1) for the individual conditional independence tests.
- njobs (int) – number of processor cores to use for parallel computation. Only available for method = “stable.fast” (set as default).
- verbose – if TRUE, detailed output is provided.
Variables: - arguments (dict) – contains all current parameters used in the PC algorithm execution.
- dir_CI_test (dict) – contains all available conditional independence tests.
- dir_method_indep (dict) – contains all available heuristics for CI testing.
- Available heuristics for conditional independence tests:
- gaussian: “pcalg::gaussCItest”
- hsic: “kpcalg::kernelCItest”
- discrete: “pcalg::disCItest”
- binary: “pcalg::binCItest”
- randomized: “RCIT:::CItest”
- Available CI tests:
- dcc: “data=X, ic.method=”dcc”“
- hsic_gamma: “data=X, ic.method=”hsic.gamma”“
- hsic_perm: “data=X, ic.method=”hsic.perm”“
- hsic_clus: “data=X, ic.method=”hsic.clus”“
- corr: “C = cor(X), n = nrow(X)”
- rcit: “data=X, ic.method=”RCIT::RCIT”“
- rcot: “data=X, ic.method=”RCIT::RCoT”“
- Default Parameters:
- FILE: ‘/tmp/cdt_pc/data.csv’
- SKELETON: ‘FALSE’
- EDGES: ‘/tmp/cdt_pc/fixededges.csv’
- GAPS: ‘/tmp/cdt_pc/fixedgaps.csv’
- CITEST: “pcalg::gaussCItest”
- METHOD_INDEP: “C = cor(X), n = nrow(X)”
- SELMAT: ‘NULL’
- DIRECTED: ‘TRUE’
- SETOPTIONS: ‘NULL’
- ALPHA: ‘0.01’
- VERBOSE: ‘FALSE’
- OUTPUT: ‘/tmp/cdt_pc/result.csv’
Note
Ref: D.Colombo and M.H. Maathuis (2014). Order-independent constraint-based causal structure learning. Journal of Machine Learning Research 15 3741-3782.
M. Kalisch, M. Maechler, D. Colombo, M.H. Maathuis and P. Buehlmann (2012). Causal Inference Using Graphical Models with the R Package pcalg. Journal of Statistical Software 47(11) 1–26, http://www.jstatsoft.org/v47/i11/
M. Kalisch and P. Buehlmann (2007). Estimating high-dimensional directed acyclic graphs with the PC-algorithm. JMLR 8 613-636.
J. Ramsey, J. Zhang and P. Spirtes (2006). Adjacency-faithfulness and conservative causal inference. In Proceedings of the 22nd Annual Conference on Uncertainty in Artificial Intelligence. AUAI Press, Arlington, VA.
P. Spirtes, C. Glymour and R. Scheines (2000). Causation, Prediction, and Search, 2nd edition. The MIT Press
Strobl, E. V., Zhang, K., & Visweswaran, S. (2017). Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discovery. arXiv preprint arXiv:1702.03877.
Imported from the Pcalg package.
-
create_graph_from_data
(data, **kwargs)[source]¶ Run the PC algorithm.
Parameters: data (pandas.DataFrame) – DataFrame containing the data Returns: Solution given by PC on the given data. Return type: networkx.DiGraph
-
orient_directed_graph
(data, graph, *args, **kwargs)[source]¶ Run PC on a directed_graph (Only takes account of the skeleton of the graph).
Parameters: - data (pandas.DataFrame) – DataFrame containing the data
- graph (networkx.DiGraph) – Skeleton of the graph to orient
Returns: Solution given by PC on the given skeleton.
Return type: networkx.DiGraph
Warning
The algorithm is ran on the skeleton of the given graph.
SAM¶
-
class
cdt.causality.graph.
SAM
(lr=0.1, dlr=0.1, l1=0.1, nh=200, dnh=200, train_epochs=1000, test_epochs=1000, batchsize=-1)[source]¶ Structural Agnostic Model.
Parameters: - lr (float) – Learning rate of the generators
- dlr (float) – Learning rate of the discriminator
- l1 (float) – L1 penalization on the causal filters
- nh (int) – Number of hidden units in the generators’ hidden layers
- dnh (int) – Number of hidden units in the discriminator’s hidden layer$
- train_epochs (int) – Number of training epochs
- test_epochs (int) – Number of test epochs (saving and averaging the causal filters)
- batchsize (int) – Size of the batches to be fed to the SAM model.
Note
Ref: Kalainathan, Diviyan & Goudet, Olivier & Guyon, Isabelle & Lopez-Paz, David & Sebag, Michèle. (2018). SAM: Structural Agnostic Model, Causal Discovery and Penalized Adversarial Learning.
-
predict
(data, graph=None, nruns=6, njobs=None, gpus=0, verbose=None, plot=False, plot_generated_pair=False, return_list_results=False)[source]¶ Execute SAM on a dataset given a skeleton or not.
Parameters: - data (pandas.DataFrame) – Observational data for estimation of causal relationships by SAM
- skeleton (numpy.ndarray) – A priori knowledge about the causal relationships as an adjacency matrix. Can be fed either directed or undirected links.
- nruns (int) – Number of runs to be made for causal estimation. Recommended: >=12 for optimal performance.
- njobs (int) – Numbers of jobs to be run in Parallel. Recommended: 1 if no GPU available, 2*number of GPUs else.
- gpus (int) – Number of available GPUs for the algorithm.
- verbose (bool) – verbose mode
- plot (bool) – Plot losses interactively. Not recommended if nruns>1
- plot_generated_pair (bool) – plots a generated pair interactively. Not recommended if nruns>1
Returns: Graph estimated by SAM, where A[i,j] is the term of the ith variable for the jth generator.
Return type: networkx.DiGraph


Velocity is a Java-based template engine. It permits anyone to use a simple yet powerful template language to reference objects defined in Java code.
When Velocity is used for web development, Web designers can work in parallel with Java programmers to develop web sites according to the Model-View-Controller (MVC) model, meaning that web page designers can focus solely on creating a site that looks good, and programmers can focus solely on writing top-notch code. Velocity separates Java code from the web pages, making the web site more maintainable over its lifespan and providing a viable alternative to Java Server Pages (JSPs) or PHP.
Velocity's capabilities reach well beyond the realm of the web; for example, it can be used to generate SQL, PostScript and XML (see Anakia for more information on XML transformations) from templates. It can be used either as a standalone utility for generating source code and reports, or as an integrated component of other systems. For instance, Velocity provides template services for the Turbine web application framework, together resulting in a view engine facilitating development of web applications according to a true MVC model.
The current stable release version is version 1.7.
The release distribution is available as a combined source/binary distribution in
tar.gz
and
zip
formats, and can be downloaded through the
Apache Mirror system.
The Velocity jars are also available through the central Maven repository.
Older releases are archived at the Apache Velocity archives.
Nightly snapshots of the Velocity subversion source code tree can be found on our nightly snapshot site.
Caveat! These snapshots are generated automatically from the project source code repository. As this is our live development workspace, there are no guarantees as to what you will find there, although we do our best to maintain a buildable source tree. If you are looking for the stable supported release, please use the current release of Velocity.
Please be aware that these snapshots are not official releases of Apache Velocity software. They are provided for developer convenience only!
If you are willing to risk getting dirty, we invite you to try the nightly snapshot to see the current state of affairs. (and help out!) Public Subversion access is also available.
The Velocity project appreciates any contributions, including documentation help, source code and feedback. Suggested changes should come in the form of source code and/or feedback.


As we've seen earlier (Stopping and ticking), the standard library measures time in ticks. One tick corresponds to one action. The library makes a multi-query to the predicate (on every tick) on every tick. By default, this predicate contains a rule that in turn makes a multi-query to (on every tick in $), with the current room as parameter.
 You'd better get out. | |
| (narrate dragon's anger 4) | |
| The dragon almost hits you with a burst of flame. You flee. | |
| (enter #outsideLair) |
To model a scene that plays out in the background for several moves, use a global flag and a tick handler:
| (perform [read #notice]) | |
| Auction! Today! In the marketplace! | |
| (now) (auction scene is running) | |
| (on every tick in #marketplace) | |
| (auction scene is running) | |
| "Who can give me five dollars for this lovely | |
| (select) spatula (or) glass bead (or) stuffed wombat (at random)?" | |
| shouts the auctioneer at the top of his lungs. | |
| (perform [wave]) | |
| (current room #marketplace) | |
| (auction scene is running) | |
| Just as you are about to place a bid, an unexpected thunderstorm | |
| emerges from a gaping plot hole. "Auction suspended", says the | |
| auctioneer. | |
| (now) ~(auction scene is running) |
In their simplest form, cutscenes are just large blocks of text and perhaps a couple of modifications to the object tree. As such, they can appear anywhere in the program. If a cutscene is triggered by an action handler or a tick callback, it is customary to end the scene with (stop) or (tick) (stop), to inhibit any subsequent actions mentioned in the player's input. For instance:
| (perform [pull #handle]) | |
| You grab the handle of the machine, and hesitate for a moment. Is | |
| this really safe? | |
| (par) | |
| But you have no choice. You pull the handle. Sparks hum in the air | |
| as you are sucked into the vortex of the machine. | |
| (par) | |
| You find yourself in a barn. | |
| (move player to #on #haystack) | |
| (try [look]) | |
| (tick) (stop) |
If the cutscene can be triggered in multiple ways, put it in a separate predicate and query that from as many places in the code as you wish.
To prevent a cutscene from occurring twice, use a global flag:
| (perform [pull #handle]) | |
| (if) (have teleported to barn) (then) | |
| Nothing happens when you pull the handle. | |
| (else) | |
| (teleport to barn cutscene) | |
| (endif) | |
| (teleport to barn cutscene) | |
| ... | |
| (now) (have teleported to barn) | |
| (stop) |
When a story begins, the standard library queries the (intro) predicate, which story authors are encouraged to override.
The default implementation of (intro) just prints the story banner by querying (banner). The banner includes version information for the compiler and the standard library. By convention, stories should print the banner at some point during play. With Dialog, there is no formal requirement to print the banner at all, but it is helpful to the community and to your future self, and it looks professional.
| (intro) | |
| "In medias res?" exclaimed the broad-shouldered Baron furiously. | |
| "We find it preposterously cliché!" | |
| (banner) | |
| (try [look]) |
The player's progress can be tracked by a global score variable. This feature needs to be enabled, by including the following rule definition somewhere in the story:
| (scoring enabled) |
For scored games, the current score is displayed in the status bar.
Points can be added to the score with (increase score by $), and subtracted with (decrease score by $). These predicates fail if the score would end up outside the valid range of integers in Dialog, which is 0–16383 inclusive.
After every move, the standard library will mention if the score has gone up or down, and by how much.
If you know what the maximum score is, you can declare it:
| (maximum score 30) |
When declared, the maximum score is mentioned by the default implementation of the SCORE command, in the status bar, as well as by the (game over) predicate. It does not affect the operation of (increase score by $).
It is straightforward to supply your own, custom status bar. Define a rule for the predicate (redraw status bar), and make use of the status bar functionality built into the Dialog programming language.
The standard library defines (status headline), which can be used to print the location of the player character in the usual way. That would normally be the current room header, followed by something like “(on the chair)” if the player character is the child of a non-room object. But if the player character is in a dark location, control is instead passed to (darkness headline), which usually prints “In the dark”.
| (redraw status bar) | |
| %% Draw a thicker status bar with the name of the current player in the | |
| %% upper right corner. | |
| (status bar 3) { | |
| (move cursor to row 2 column 2) | |
| (status headline) | |
| (status bar width $W) | |
| ($W minus 20 into $Pos) | |
| (move cursor to row 2 column $Pos) | |
| (current player $Player) | |
| (name $Player) | |
| } |
When the game is over, it is customary to print a final message enclosed by asterisks, in bold text:
| (par) (bold) \*\*\* You have won! \*\*\* (roman) (par) |
Then you usually query the predicate (game over), which:
Here is an example of a (very small) cutscene that ends the game:
| (perform [eat #apple]) | |
| The apple is yummy. You feel that your mission has come to an end. | |
| (par) (bold) \*\*\* You are no longer hungry. \*\*\* (roman) (par) | |
| (game over) |
A fifth option can be added to the game-over menu: AMUSING. First, add the option to the menu with the following rule definition:
| (amusing enabled) |
Then, implement a predicate called (amusing) that prints a list of amusing things the player might want to try:
| (amusing) | |
| (par) | |
| Have you tried... | |
| (par) | |
| (space 10) ...eating the transmogrifier? (line) | |
| (space 10) ...xyzzy? (line) |


If you plan to join an analysis project during the meeting, make sure you have a NERSC account and remember your password
(
nope, a SSH key is not enough) and have 2-step verification (MFA) set up!
Also, make sure you are in the "lsst" group. If not, contact
Richard Dubois (slack: @richard),
Heather Kelly (slack: @heather999) or
Debbie Bard (slack: @djbard) to request to be added to the "lsst" group.


August 3rd 2003
Twenty-four milligrams of N-methyl-N-isopropyltryptamine hcl
MiPT intrigued me when I first read of it in TIHKAL. From a chemical standpoint, it is similar to DMT and DiPT. The structure of MiPT has one isopropyl group on a DMT backbone where DiPT has two... Both DMT and DiPT are fantastic and incredible entheogens. Especially DiPT. From a psychological standpoint of view, MiPT was said to 'emphasize 'psychedelic' effects over 'hallucinogenic' effects.' Perhaps it was a tryptamine 'version' of MDMA in that it was a compound which opens one up without getting in the way of anything? Only one way to find out?try it! Twenty-four milligrams was the dose I selected.
I had planned to try it with my friend M. but our plans kept falling through each time. So on the third day of things getting canceled I decided to just go out and sample it on my own. But I wasn't completely sure if I really wanted to do it though because I was by myself, it was my first time with this compound, and I am not as comfortable with tryptamine psychedelics as I am with the phenethylamines. So I just decided to hike up to my favorite tripping spot (so far), the Red Rocks at top of Settlers Peak, and see how I felt about things when I got to the top.
This is the place where I had taken 2C-C previously. I made a
report of that experience. Anyway, I packed my backpack with all the necessities (pot, CDs, CD player, apples, bananas, nitrous oxide, and lots of Gatorade.) I said goodbye to my birds and made my way out. Adventure awaited me!
The sky was strongly overcast and rain was predicted, but I did not care. There are lots of crevices on the mountain for one to seek refuge. When I got to the top it almost immediately started to drizzle but I was not bothered. I quickly found a spot that sheltered me from the moisture and I was happy. I felt that everything was in its right place.
But I still was not sure if I wanted to take the compound or not. I asked myself why I was tripping. Was it that I was just curious as to what MiPT did, or did I actually want/need another entheogenic experience at this time? I thought about things for a few minutes, and then decided to take a balloon of nitrous oxide and make my decision after that.
I got out my gear and filled up the balloon with the dissociating gas. I pressed play on my CD player and Dylan sang
Sad Eyed Lady of the Lowlands for me. Perhaps it was the altitude, but that balloon blew me away hard! Normally I find nitrous oxide to be uninteresting if I don't take it with a psychedelic or ketamine.
This time though it was almost 5-MeO-DMT-like in its intensity. And when I came back, there were the beautiful Rocky Mountains all around me! The sun came out for a bit and it was gorgeous. I immediately got out the capsule of MiPT and swallowed it with a swig of Gatorade. No turning back now. To pass the time while waiting for the material to take effect, I listened to my favorite Pink Floyd album; 'Meddle.'
I started to notice some effects within twenty minutes or so. After the initial alert, I felt a strong intensification of the music. Each instrument and note I heard was very clear and well defined. There was also an intensified emotional connection to the songs. No closed-eyed or open-eyed visuals however were present, but when the record was over I did begin to notice some mild trails when I moved my hands around.
Nothing in my immediate view appeared to change form, nor were there any movement/melting of objects, and no pretty colors dancing around. But objects did seem to 'jump about' a bit in my peripheral vision. I also began to notice a warm tryptamine body-buzz. The come-on was very gentle and there were no negative side-effects or nausea to content with. Always a plus.
Perhaps one hour after I ate the capsule, I decided to smoke some marijuana. Within ten minutes or so after taking the pot, the effects really began to grow. But it was for the most part a mental trip. I truly felt my mind 'expanded' and opened up, and I was pondering about things on much deeper level than normal. But there were still no sensory changes save for the mild trailing and the 'jumping about' of objects out of the corner of my eye.
I then decided to put on the Beatles 'Sgt. Pepper's Lonely Hearts Club Band.' What an album! Every note sounded so perfect and in its right place. Interestingly enough, even though the sound of the music was strongly intensified, there were still no closed-eyed visuals produced from listening to the music. I did not see anything with eyes shut beyond normal. It was very strange considering how hard I felt I was tripping in my head. It was almost mushroom-like but with no visuals. At least MiPT was not a sensory psychedelic for me.
At the height of the experience, I reached a very mild and controllable plus-three. I stayed on the rocks for good four-hours or so, listening to the Beatles and Pink Floyd. I also enjoyed a balloon more of nitrous oxide. With the balloon my ego was propelled out of this dimension and into sheer nothingness. And coming back from the breath of the gas was the best part. It was like being reborn in the best of scenery possible, the beautiful Colorado Rocky Mountains! Gosh I am thankful that they are literally my backyard!
But when darkness began to move in, I decided to make my way down the mountain and wound up at my Friend D's apartment. This was around the five-hour mark of the trip and the effects were definitely dying down. I would say at this point I was at a mild plus-two. Now the effects consisted of objects being much brighter than normal, especially lights, mild trails on moving objects, halos around bright items and the feeling of heightened intellectual aptitude. In some ways, this felt like a 'smart psychedelic.' But it wasn't at the same intensity as it was a few hours earlier.
Then I went into D's bedroom alone and have one more balloon of nitrous oxide. I played song three on Pink Floyd's 'Meddle', a song called
Fearless. That was the most powerful nitrous experience of the night. I was gone for well over a minute and experienced complete ego loss for most of that time. I came out of D's room and greeted him and his friend Cy with a big smile. That was a beautiful experience. It also seemed to bring the effects of the MiPT back up a bit.
The time eventually came however when D and Cy needed to go to bed, so I went to my friend K's house and joined the crowd that was there. It was K and two acquaintances of his. This became a time of learning for me. I am very fond of K as a friend, but I do not approve of his lifestyle. He is on disability but has no real reason not to work. He does have back injuries, but there are 'sit-down' jobs where all you have to do is work on the computer. Since he sits on his couch all day at the taxpayer's expense I see no reason he would not be capable of work where no physical labor was involved. He is capable. An acquaintance of his, named C was also there. C is a homeless alcoholic man that 'dumpster dives' and I believe steals as well. He possesses items that he claims to find in the trash that people just don't throw away, like fully functional lap-top computers.
While I am fond of both men, and I don't consider myself judgmental of them (I am just realistic), just listening to their conversations about their lives made me realize how lucky I am. In three of four years from now I will have a master's degree and will be a fully licensed psychotherapist. I know I have a future ahead of me and I am going places. This made me very happy about myself, a feeling I find hard to come by these days. I know I have a gifted future ahead of me and I am so thankful for the fact that I am getting an education and that I know what I want to do with my life. I will never have to 'dumpster dive.' I suddenly felt very proud of myself and my accomplishments.
Around seven hours into the experience, I was mostly down from the trip. There was some jaw tension and a little bit of a residual 'wired' feeling from the MiPT so I took a small amount of oxycodone and valium to relax. Within an hour or so I was asleep. I awoke the next day feeling refreshed and at peace with myself. Once again I thanked the universe for a positive experience and enjoyed the rest of the day relaxing.
Conclusion:
While I found no particular negative aspects with MiPT, I was not overly impressed with it at all. It was definitely psychedelic, but I had no life-changing revelations. There were no moments of profound spiritual connections or anything of that nature. Not that I expect life-changing revelations on every trip I take, this material just didn't seem to have the push to even have much potential of producing it. At least at the dosage I took. And there were no sensory joys to marvel at. It was basically a nice mild trip that (as reported) emphasized the intellectual component of a psychedelic over the visual properties. It seemed more suited for social or intellectual interactions, perhaps at a lower dosage. There was this 'smart drug' feeling to it. Now whether my experience was biased by my readings, I do not know. But my experience was very similar to what was reported in TIHKAL.
I have no particularly strong desire to experiment with MiPT again, except perhaps as a chemical to mix with MDMA. I would take the MiPT first and then dose the MDMA when I began to feel the tryptamine take effect. I could see that being a nice combination, but then again I can also think of other tryptamines I'd rather mix with MDMA. So why waste the time? MiPT is a nice material but it really does not excite me enough to want to take it again. There are many more interesting compounds to experiment with.
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.


Waterfall emitter with floor governor
Javascript port of CubicVR 3D Engine by Charles J. Cliffe -
http://www.cubicvr.org/


This project is an interesting one as we were given the opportunity to conduct a
design thinking workshop in the N-House community (post-NOC community). We came up with the syllabus
after much consideration and because we wanted to expose the concept to those new to it as well, the
content has been really just about the design thinking process with a lot of hands on. We would also
like to answer the question on all the entrepreneur wannabes' mind - What is Design Thinking, really?
Sorry, I cannot share the slides publicly because it does not solely belong to me. The above is just
the title slide of the workshop.


Date: Thu, 18 Dec 1997 18:41:54 GMT
Server: Apache/1.1.1
Content-type: text/html
Content-length: 1414
Last-modified: Sun, 31 Mar 1996 19:21:40 GMT
Transworld Home HealthCare: HiTech Division
Home Infusion Therapy
The highest quality Home Infusion Therapy services are available in New York and
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Call 1-800-776-6782 ext 127 for more details.
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The widest range of Respiratory and Oxygen services in the New York,
New Jersey metropolitan area can be accessed by calling 1-800-776-6782 ext 183.
Home Medical Equipment and Supplies
For everything from Diabetic Supplies to sophisticated and custom power
wheelchairs, contact us at 1-800-776-6782 ext 189.
Pulmonary Rehab Program
The PromptCare Lung Center in Cherry Hill, New Jersey is a leading area program
for pulmonary testing and rehabilitation. For more information contact John
McCormick, RRT at 1-609-424-1777.
To TransWorld Health Home Page


The DataTableSort plugin has a hardcoded value of 'title' for the sorting link on the 'title' attribute.


Interface IClientsClient
Contains all the methods to call the /clients endpoints.
Namespace: Auth0.ManagementApi.Clients
Assembly: cs.temp.dll.dll
Syntax
public interface IClientsClient
Methods
CreateAsync(ClientCreateRequest)
Creates a new client application.
Declaration
Task<Client> CreateAsync(ClientCreateRequest request)
Parameters
| Type | Name | Description |
|---|---|---|
| ClientCreateRequest | request | The request containing the properties of the new client. |
Returns
| Type | Description |
|---|---|
| System.Threading.Tasks.Task<Client> |
DeleteAsync(String)
Deletes a client and all its related assets (like rules, connections, etc) given its id.
Declaration
Task DeleteAsync(string id)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | id | The id of the client to delete. |
Returns
| Type | Description |
|---|---|
| System.Threading.Tasks.Task |
GetAllAsync(String, Boolean)
Retrieves a list of all client applications. Accepts a list of fields to include or exclude.
Declaration
Task<IList<Client>> GetAllAsync(string fields = null, bool includeFields = true)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | fields | A comma separated list of fields to include or exclude (depending on includeFields) from the result, empty to retrieve all fields |
| System.Boolean | includeFields | true if the fields specified are to be included in the result, false otherwise (defaults to true) |
Returns
| Type | Description |
|---|---|
| System.Threading.Tasks.Task<System.Collections.Generic.IList<Client>> |
GetAsync(String, String, Boolean)
Retrieves a client by its id.
Declaration
Task<Client> GetAsync(string id, string fields = null, bool includeFields = true)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | id | The id of the client to retrieve |
| System.String | fields | A comma separated list of fields to include or exclude (depending on includeFields) from the result, empty to retrieve all fields |
| System.Boolean | includeFields | true if the fields specified are to be included in the result, false otherwise (defaults to true) |
Returns
| Type | Description |
|---|---|
| System.Threading.Tasks.Task<Client> |
UpdateAsync(String, ClientUpdateRequest)
Updates a client application.
Declaration
Task<Client> UpdateAsync(string id, ClientUpdateRequest request)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | id | The id of the client you want to update. |
| ClientUpdateRequest | request | The request containing the properties of the client you want to update. |
Returns
| Type | Description |
|---|---|
| System.Threading.Tasks.Task<Client> |


DREAMING OF JOSEPH LEES (director: Eric Styles; screenwriter: Catherine Linstrum; cinematographer: Jimmy Dibling; editor: Caroline Limmer; cast: Rupert Graves (Joseph Lees), Samantha Morton (Eva), Lee Ross (Harry Flyte), Holly Aird (Maria), Miriam Margolyes (Signora Caldoni), Frank Finlay (John, Eva's Father), Lauren Richardson (Janie, Eva's younger sister); Runtime: 92; Fox Searchlight Pictures; 1998-UK)
Reviewed by Dennis Schwartz
"Dreaming of Joseph Lees" is a tedious romantic melodrama, set in Somerset, England, in 1958. The film was shot on the rugged, isolated terrain of the Isle of Man, which added to the film's bleakness. This drawn-out tragic love story manages to get at the characters it highlights, though what it uncovers is more obvious than startling or insightful. The somber story itself fails to be engrossing, though there is one marvelous scene of Rupert Graves, the one Samantha Morton pines for, visiting her heartless father's household for tea, while her family and the boyfriend she is living with is present. It was a thoroughly uncomfortable but poignant scene, shot in mostly brown and yellow coloring.
Eva (Samantha Morton) is a prim, reticent girl of twenty, who works dutifully as a clerk at the sawmill and does the household chores for her crabby father (Finlay) and precocious younger sister (Richardson). The bookish Eva fell in love with her second cousin, Joseph Lees (Graves), a geologist, when she was 14 and secretly pines for him ever since, even though she hasn't seen him since he lost his leg in a quarry accident in Italy.
She learns that he will be at her cousin's wedding, but somehow she moved in with a boy she doesn't love, Harry (Ross).  He is without regard for culture or books.  She refuses to marry him but lives with him while secretly resenting him and dreaming of Joseph.
Having met Joseph at the wedding, she realizes she can't be with Harry anymore and uses the excuse to leave him when she discovers he had a one night stand with another girl. Harry goes on a potential suicide trip to woo her back, playing on her guilt and her good nature in helping others, while Joseph shows he's the refined gentleman who can warm her heart by saying nice things and turning her on to art books.
The script provides no plausible explanation for all the confusion going on in her head. One is therefore hard-pressed to figure out why she's with Harry when Joseph is there for her. It seemed to be a no-brainer which one to choose.
Everything is so bleak and weary, even the love scenes seemed exasperating, as it was hard to make heads or tails about what the poor girl was so befuddled about, though befuddled she and her two lovers were. When she goes back to Harry, it is Joseph's turn to cry out that he needs her. Harry will then walk the ledge of his roof for her love, threatening suicide, and Joseph will be willing to cut her all the slack she wants before she makes up her mind. By the time she resolved things, I was bored to death. It was obvious from the get-go that Harry wasn't for her, therefore the film lacked tension in her choice of who to be with. This film reminded me of a Thomas Hardy type of novel that was written by someone who didn't have the writer's obvious talents and has come up with a story that mostly captures the stark atmosphere of the rural community but does not put a proper perspective on the psychology of the characters.
REVIEWED ON 11/28/2000 GRADE: C-
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Art Deco Code Limited International House,24 Holborn Viaduct,London, EC1A 2BN Please get in touch for your next _Node.JS_, cloud and front-end project. {/＊*<EMAIL_ADDRESS>*＊/}


Hi, Dear readers!
It is high time we talked about some adventures! Why, You might ask. Firstly, because the weather is grey and one needs more color in the life. Secondly, there is always time for a great tale. Today we will be talking to Remo. Do You remember our last interview with him? If not, You can find it here.
This year Remo has joined two trips, one was to Croatia and Slovenia another to Italy and France. OsmAnd was his trustworthy companion in both!
“We used it for off road navigation and we also used it to find some nice camping spots. OsmAnd+ was working perfectly on these trips. Even in very isolated areas the app worked well. We constantly used the satellite maps with a street overlay. This combinations always allowed us to found a way in no time.”
Time flies and neither we nor OsmAnd stands still. The progress we have made together is incredible. By listening to Your advice, we have made an app that users are not scared to rely upon, the one that will function in any circumstances as long as Your device is on.
“Once in Croatia we followed a road which, in the end, changed into a mountain-bike track. At that point we could not turn back, so we had to follow the track. That was really hard, because the track was maybe one meter narrower than the cars. However, OsmAnd+ brought us to mesmerizing and breathtaking places! We found a lovely small lake, near which we set our camp. With an ordinary GPS app, we would have never found such a place! The funny thing is that after the sun had set, we found out that there must have been thousands of frogs! It was a really loud night after all.”
Another story happened to our traveler in Slovenia.
“It was raining so hard that we sometimes had to find other ways because the track we had chosen before was too muddy. With a tablet and OsmAnd app we were able to find a few better alternatives on the spot.”
When Remo was in Croatia, he found an extremely picturesque place. They were just driving in the middle of nowhere when the sea appeared. The place was so marvelous that they decided to stay overnight.
“We had planned our trip at home with tracks from friends, wikiloc, etc. We had all tracks activated in the app. So we could always see where a nice track or a camp spot, which we had marked at home, was. This function is very useful. One can activate multiple tracks at the same time and see all of them on the map. Which gives You an opportunity to be spontaneous when choosing a route.”
The stories are like trips in a way, one never knows where it might take them! Remo starts a company that builds off road, overland, and trophy cars. Check it out www.off-tech.ch!
“In 2021 we will start our biggest overland expedition. It will start in February 2021 in Morocco and will end somewhere in November. We still do not know where exactly we will go. But OsmAnd+ will find us the best way for sure!”
Thank You, Remo, we are always glad to hear from You!
Bye, Dear readers!
See You soon!
______________________________
OsmAnd at Facebook, Twitter, and Reddit!
Join us at our groups of Telegram (EN), (IT), (FR), (DE), (RU).


"x" "1" "ALBANESE,R AND WATKINS,PA, THE MUTAGENIC ACTIVITY OF RAZOXANE (ICRF159): AN ANTICANCER AGENT, BR. J. CANCER 52(5):725-731, 1985"


Victoria Nahorna
Admin Manager
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And if today all you did is hold yourself together, I am absolutely proud of you.
 Psychotherapy explores thoughts, feelings, and behaviors, and seeks to improve an individual’s well-being. Psychotherapy paired with medication is the most effective way to promote recovery.
-
Medication
Medication does not outright cure mental illness. However, it may help with the management of symptoms. Medication paired with psychotherapy is the most effective way to promote recovery.
-
Case Management
Case management coordinates services for an individual with the help of a case manager. A case manager can help assess, plan, and implement a number of strategies to facilitate recovery.
-
Hospitalization
In a minority of cases, hospitalization may be necessary so that an individual can be closely monitored, accurately diagnosed or have medications adjusted when his or her mental illness temporarily worsens.
-
Support Group
A support group is a group meeting where members guide each other towards the shared goal of recovery. Support groups are often comprised of nonprofessionals, but peers that have suffered from similar experiences.
-
Complementary and Alternative Medicine
Complementary and Alternative Medicine, or CAM, refers to treatment and practices that are not typically associated with standard care. CAM may be used in place of or addition to standard health practices.
-
Self Help Plan
Mindfulness is a way of paying attention to the present moment. It helps us become more aware of our thoughts and feelings so that, instead of being overwhelmed by them, we’re better able to manage them.
-
Peer Support
Peer Support refers to receiving help from individuals who have suffered from similar experiences.
-
Electroconvulsive Therapy.
Electroconvulsive therapy (ECT) is a procedure, done under general anesthesia, in which small electric currents are passed through the brain, intentionally triggering a brief seizure. ECT seems to cause changes in brain chemistry that can quickly reverse symptoms of certain mental illnesses.
-
Art Therapy .
a form of psychotherapy involving the encouragement of free self-expression through painting, drawing, or modeling, used as a remedial activity or an aid to diagnosis.


--- layout: paper headline: Publications title: 'Constraint Propagation - Models, Techniques, Implementation' authors: 'Guido Tack' howpublished: > Doctoral dissertation, Saarland University, Germany, 2009. link: pdf bibtex: > @PhDThesis{Tack:PhD:2009, Author = "Guido Tack", Title = "Constraint Propagation - Models, Techniques, Implementation", Type = "phdthesis", School = "Saarland University, Germany", Year = 2009, } relation: > Models and techniques used in the Gecode kernel, views, and propagators for set constraints shorthowpub: > Doctoral dissertation, Saarland University, Germany, 2009. ---
This dissertation presents the design of a propagation-based constraint solver. The design is based on models that span several levels of abstraction, ranging from a mathematical foundation, to a high-level implementation architecture, to concrete data structures and algorithms. This principled design approach results in a well-understood, correct, modular, and efficient implementation.
The core of the developed architecture is the propagation kernel. It provides the propagation infrastructure and is thus crucial for correctness and efficiency of the solver. Based on a mathematical model as well as a careful design of the employed algorithms and data structures, the presented architecture results in an efficient and domain-independent kernel. Constraints are realized by propagators, and implementing a propagator is a challenging, error-prone, and time-consuming task. A practically useful solver must however provide a comprehensive propagator library. This dissertation introduces two techniques for automatically deriving correct and efficient propagators. Views generalize variables and are used to derive propagators from existing propagators. For constraints over set variables, propagators are derived from formal constraint specifications.
The presented techniques are the basis of Gecode, a production-quality, highly efficient, and widely deployed constraint solver. Gecode is the empirical evidence for success and relevance of the principled design approach of this dissertation.


--- layout: page title: "Francesco Tonini" subtitle: Geospatial Data Scientist meta-title: "Francesco Tonini - Geospatial Data Scientist" css: "/css/index.css" meta-description: "Geospatial Data Scientist" bigimg: "/img/landscape_logo.png" ---
A data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician
Josh Wills
I'm a geospatial data scientist and certified GISP® (GIS Professional) with a cross-disciplinary background in statistics/machine learning, geospatial analysis/modeling & visualization, and programming. My professional interests include cloud and AI tools to solve global environmental & sustainability challenges; ecosystem services provision and human-natural systems research; GIS for disaster management and emergency response. You can read more about me, check out geospatial projects I have worked on over the years, and access a few resources such as publications, videos and slides as well.


Download the tar-gzipped file,
issue then "gunzip hpl-2.1.tar.gz; tar -xvf hpl-2.1.tar" and this
should create an hpl-2.1 directory containing the distribution.
We call this directory the top level directory.
Create a file Make.<arch> in the top-level directory.
For this purpose, you may want to re-use one contained in the
setup directory. This Make.<arch> file essentially contains
the compilers, libraries, and their paths to be used on your system.
Type "make arch=<arch>". This should create an executable
in the bin/<arch> directory called xhpl. For example, on our
Linux PII cluster, I create a file called Make.Linux_PII in the
top-level directory. Then, I type "make arch=Linux_PII". This
creates the executable file bin/Linux_PII/xhpl.
Quick check: run a few tests (assuming you have 4 nodes for
interactive use) by issuing the following commands from the top
level directory: "cd bin/<arch> ; mpirun -np 4 xhpl". This
should produce quite a bit of meaningful output on the screen.
Most of the performance parameters can be tuned, by modifying
the input file bin/<arch>/HPL.dat. See the
tuning page or the TUNING file in the
top-level directory.
Compile Time Options
At the end of the "model" Make.<arch>, the user is given
the opportunity to override some default compile options of this
software. The list of these options and their meaning is:
-DHPL_COPY_L
force the copy of the panel L before bcast
-DHPL_CALL_CBLAS
call the BLAS C interface
-DHPL_CALL_VSIPL
call the vsip library
-DHPL_DETAILED_TIMING
enable detailed timers
The user must choose between either the BLAS Fortran 77 interface,
or the BLAS C interface, or the VSIPL library depending on which
computational kernels are available on his system. Only one of these
options should be selected. If you choose the BLAS Fortran 77
interface, it is necessary to fill out the machine-specific C to
Fortran 77 interface section of the Make.<arch> file. To do
this, please refer to the Make.<arch> examples contained in
the setup directory.
By default HPL will:
not copy L before broadcast,
call the BLAS Fortran 77 interface,
not display detailed timing information.
As an example, suppose one wants this software to copy the panel of
columns into a contiguous buffer before broadcasting. It should
be more efficient to let the software create the appropriate MPI
user-defined data type since this may avoid the data copy. So, it
is a strange idea, but one insists. To achieve this one would add
-DHPL_COPY_L to the definition of HPL_OPTS at the end of the file
Make.<arch>. Issue then a "make clean arch=<arch> ;
make build arch=<arch>" and the executable will be re-build
with that feature in.


Submit your high score!
Top Scores
Credits
Code
Sean Spradlin
Art
Marco Moracha
Testing
Evelyn, Rylyn, Dax, Dylan, & Corbin


{% extends "base.html" %} {% load i18n %} {% block extra_head %} {% endblock %} {% block base_content %}
These prop bets have at least one accepted bet and are expired.
{% if expired_prop_bets %}| Proposer | Prop Bet | End Date | Win / Loss |
|---|---|---|---|
| {{ bet.user.get_full_name }} | {{ bet.prop_text }} | {{ bet.end_date|date:"M d Y H:i" }} | Win Loss Push |
No bets to mark.
{% endif %}These prop bets have at least one accepted bet but enddate has not passed.
{% if open_prop_bets %}| Proposer | Prop Bet | End Date | Win / Loss |
|---|---|---|---|
| {{ bet.user.get_full_name }} | {{ bet.prop_text }} | {{ bet.end_date|date:"M d Y H:i" }} | Win Loss Push |
No bets to mark.
{% endif %}These prop bets have a marked winner (and have at least one accepted bet).
| {% trans "Proposer" %} | {% trans "Prop Bet" %} | {% trans "Win / Loss" %} |
|---|


I will create a Discord Bot for you . I can create any type of Bots of you want , moderation, Economy, Automation ..
I have 8 months of experience in making discord bots and more than 1 year of experience in Python. Your Bot will be coded with
python. Hosting will be provided free from my side along with your Discord Bot. Pricing may vary depending on the type of the Bot
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I will handle your instagram Niche
I will handle and Manage your Instagram Niche Page or a Buisness Page. I constantly study and monitor instagram's algorithm and
with my service , Your page is going to grow a lot faster because im going to put in all those skills which i have been studying
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Our Visualisers will provide high-quality 2D or 3D animation of your ideas with complete preservation of artistic concepts taking into consideration the technical element.
If necessary, we will create 3D animation or a full reel for the project concept presentation or a demo reel of your portfolio.
A free test render as a bonus!


Note: Enabling and disabling dependency alerts for a
repository using the REST API is currently available for developers to
preview. To access these new endpoints during the preview period, you must
provide a custom media type in the
Accept
header:
application/vnd.github.dorian-preview+json
Warning: The API may change without advance notice during the preview period. Preview features are not supported for production use. If you experience any issues, contact your GitHub Enterprise site administrator.
Enables dependency alerts and the dependency graph for a repository. The authenticated user must have admin access to the repository. For more information, see "About security alerts for vulnerable dependencies" in the GitHub Help documentation.
PUT /repos/:owner/:repo/vulnerability-alerts
Status: 204 No Content


1. Player 1 and 2 each remove a rectangle of pieces each turn.
- Doesn't matter if some pieces within the rectangle are already removed.
2. Each player must remove at least 1 piece from the board.
3. The person who has to remove the last piece loses.
How to play
- Hold down the mouse on the board and drag it to select pieces you want to remove. (highlighted in yellow)
- If you want to cancel it, move the mouse button outside the board and release the mouse.
- Release the mouse to remove those pieces and hand the turn to the opponent.
- This game is really hard to play perfectly, so take your time and think about your opponent's possible moves.
- Press Z to undo, Y to redo, R to reset (please don't cheat though)
- If you enable the AI, please remove the squares it tells you on its turns. The AI isn't that smart. Yet.
Hints (if you play perfectly)
a. If you leave your opponent with a 2x2 square, you're winning.
b.
- Take the center or one of the corners.
c.
- Take the middle 2 from the long edge on either side.
Controls
The AI will only give two coordinates as output; you have to make the move for them since there are no automatic animations made for them yet.


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The Legend of Bagger Vance * * (out of * * * * )
Directed by Robert Redford. Cast: Matt Damon, Charlize Theron, Will Smith, J. Michael Moncrief, Joel Gretsch, Bruce McGill, Jack Lemmon. 2000 - 127 minutes Rated PG-13 (for sexual situations, mild war violence, and profanity). Reviewed by Dustin Putman, November 4, 2000.
With a cast that includes Matt Damon (1999's "The Talented Mr. Ripley"), Will Smith (1998's "Enemy of the State"), and Charlize Theron (1997's "Devil's Advocate"), and the classy Robert Redford (1998's "The Horse Whisperer") at the directing helm, "The Legend of Bagger Vance," based upon the novel by Steven Pressfield, has all of the pedigree to be a gorgeously photographed, thoughtful, low-key character drama. After sifting through all 127 minutes of it, however, at least I can still say it is well photographed. Director Redford has created a slow-moving, occasionally tedious film set in the Great Depression of the early 1930s, and then deals with none of the hardships or issues that went along with the time period, instead opting to weave a sort of half-hearted fantasy that is rarely ever involving.
Following the untimely death of her father, wealthy Savannah heiress Adele Invergordon (Charlize Theron), in an attempt to avoid foreclosure of the country club that is owned by her family, creates a huge golf tournament between pros Bobby Jones (Joel Gretsch) and Walter Hagen (Bruce McGill) with the grand prize being a whopping $10,000. Searching for a third player who will stand for the great city of Savannah itself, the town elects the services of one Rannulph Junuh (Matt Damon), a once-great golfer and WWI vet (not to mention Adele's ex-lover) who has slid into obscurity in the backwoods of Savannah, spending his nights drinking his life away with a group of poker buddies.
While on the golf course one night mourning the loss of his swing and debating whether to accept the offer to compete in the tournament, Rannulph meets Bagger Vance (Will Smith), an honest mystery man who becomes his personal muse (and caddie). Setting out to help Rannulph find himself once again through the game he used to love so much, Bagger seems to be dedicating his whole being to aiding in Rannulph's personal redemption, but who exactly is he? And where did he come from?
Opening with a silly wraparound sequence (featuring Jack Lemmon), and then leading into a neverending narration that resembles a cheesy history lesson on WWI and the troubles in the south, it isn't until nearly the twenty-minute mark that "The Legend of Bagger Vance" finds its footing. For a director as skilled as Robert Redford, whose "The Horse Whisperer" pulled you into its story from the very beginning, this noticeable flaw could have easily been corrected. Instead, it comes off as sloppy filmmaking.
The potentially intriguing (if overworn) issue of race relations in the south, circa 1930, is overlooked, bypassed for a tale that is deeply sugarcoated and unconvincing. Whether Redford wanted to make a realistic drama or a reality-based fantasy, there is no excuse for not even making a mention of the fact that Bagger Vance is a black man. Had he shown up in Savannah, Georgia in the midst of the Great Depression, it is a given that he wouldn't have exactly been welcomed with open arms by the townspeople, and he certainly would not have been allowed to act as a caddie on a ritzy country club resort. Furthermore, the subplot about the Great Depression that has recently befallen on Savannah acts as a cheap plotting device, instead of a thoughtful part of the story.
Lest it seem that there is nothing to recommend in "The Legend of Bagger Vance," it should be noted that this is definitely not the case. While the characters aren't exactly poster children for three-dimensionality, the actors are good enough that they easily overcome the snags. Matt Damon is believable as the internally lost Rannulph Junuh, even if we never get a full sense of why he has disappeared into a little, black hole, while Will Smith's sweet-faced Bagger acts as a fitting partner to Rannulph. Smith's role is unchallenging, but he does what he can with it, and it's nice to see him appearing in a film void of aliens and shoot-'em-ups. And Charlize Theron, as the conflicted Adele, is very good, perhaps the standout in the cast. Impassioned, subtly funny, and strong-willed, Theron knows how to light up the screen, and deserves a leading role soon.
Also effective is the romantic relationship between Rannulph and Adele. With only a small amount of time dedicated to this subplot, Damon and Theron successfully convince us of their strong past relationship, and screenwriter Jeremy Leven makes every line of his smart dialogue count.
What "The Legend of Bagger Vance" comes down to is its climactic fourth round in the golf tournament, with Rannulph pacing just behind his two competitors. Manipulative, more than a little implausible, but with beautiful cinematography (by Michael Ballhaus), to boot, it sums up the entire film. The outcome of the contest may be a little surprising, but getting to that point is a battle in itself.
- Copyright 2000 by Dustin Putman Http://www.young-hollywood.com Http://www.atnzone.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Facebook Political Ad Collector How Political Advertisers Target You by Jeremy B. Merrill, Ally J. Levine, Ariana Tobin, Jeff Larson and Julia Angwin. July 17, 2018. Updated hourly.


KDRANGEQUERY Find all points within a local neighborhood. PTS = KDRANGEQUERY( ROOT, QUERYPT, DISTLIM ) finds all the points stored in the k-D tree ROOT that are within DISTLIM units from the QUERYPT. Proximity is quantified usng a D-dimensional Euclidean (2-norm) distance. Input : ROOT is a pointer to a k-D tree which must be constructed with the function KDTREE or KDTREEIDX. QUERYPT is a 1xD vector representing a point in D-dimensional space. DISTLIM is a scalar which specifies the radius of the neighborhood around QUERYPT. Output: PTS is an NxD matrix, where each row is a datapoint from the k-D tree ROOT. Each of these datapoints is found within a distance DISTLIM from QUERYPT. [PTS, DIST] = KDRANGEQUERY( ... ) returns the distances between each row of PTS and QUERYPT in the Nx1 vector DIST. [PTS, DIST, IDX ] = KDRANGEQUERY( ... ) returns the index value for each row (point) of PTS. The index value maps back to a row from the matrix REFERENCE used to build the k-D tree (see the KDTREE or KDTREEIDX functions). Limitations: QUERYPT must be a 1xD dimensional array meaning that the range query can be performed for one point at a time. See also KDTREE and KDTREEIDX. Written by / send comments or suggestions to : Guy Shechter guy at jhu dot edu June 2004
0001 % 0002 % KDRANGEQUERY Find all points within a local neighborhood. 0003 % 0004 % PTS = KDRANGEQUERY( ROOT, QUERYPT, DISTLIM ) finds all the 0005 % points stored in the k-D tree ROOT that are within DISTLIM units 0006 % from the QUERYPT. Proximity is quantified usng a D-dimensional 0007 % Euclidean (2-norm) distance. 0008 % 0009 % Input : 0010 % ROOT is a pointer to a k-D tree which must be constructed with 0011 % the function KDTREE or KDTREEIDX. QUERYPT is a 1xD vector 0012 % representing a point in D-dimensional space. DISTLIM is a 0013 % scalar which specifies the radius of the neighborhood around 0014 % QUERYPT. 0015 % 0016 % Output: 0017 % PTS is an NxD matrix, where each row is a datapoint from the 0018 % k-D tree ROOT. Each of these datapoints is found within a 0019 % distance DISTLIM from QUERYPT. 0020 % 0021 % [PTS, DIST] = KDRANGEQUERY( ... ) returns the distances between 0022 % each row of PTS and QUERYPT in the Nx1 vector DIST. 0023 % 0024 % [PTS, DIST, IDX ] = KDRANGEQUERY( ... ) returns the index value 0025 % for each row (point) of PTS. The index value maps back to a row 0026 % from the matrix REFERENCE used to build the k-D tree (see the 0027 % KDTREE or KDTREEIDX functions). 0028 % 0029 % Limitations: 0030 % QUERYPT must be a 1xD dimensional array meaning that the range 0031 % query can be performed for one point at a time. 0032 % 0033 % See also KDTREE and KDTREEIDX. 0034 % 0035 % Written by / send comments or suggestions to : 0036 % Guy Shechter 0037 % guy at jhu dot edu 0038 % June 2004 0039 %


Solskjær: Pochettino's availability does not make me fear for my job – video
by SNTV
Ole Gunnar Solskjær has insisted he is not threatened by Mauricio Pochettino now being out of work despite the Argentinian having repeatedly been linked to the Manchester United job.
Solskjær said: 'No, it doesn’t bother me at all because I’ve got the best job in the world and I’m sure that if you’re in or out of a job and you are a manager, you would want this job.'
Of Mourinho’s appointment Solskjær said: 'It’s good to have José back – definitely especially for you guys and maybe for me'


def stop_all(self):
Return whether to stop and prevent trials from starting.
return (len(self._top_values) == self._top
and np.std(self._top_values) <= self._std)
def stop_all(self):
Return whether to stop and prevent trials from starting.
return self.has_plateaued() and self._iterations >= self._patience


“As things stand now I don’t think they will be redacted,” Barr said. “So they will be identifiable.”
James Murray, a career member of the Secret Service, will be replacing Randolph Alles as Secret Service director.
“Whether it is John Kelly, Kirstjen Nielsen or whoever comes next, Trump’s real DHS Secretary has always been Stephen Miller," United We Dream Advocacy Director Sanaa Abrar said.
Leaders of the Secret Service and Homeland Security have been forced out, and the director of U.S. Citizenship and Immigration Services could be next.
Democrats are speaking out as never before against Israel’s occupation of the West Bank, while Republicans hope to use the partisan revolt to drive a wedge between Jewish voters.
The Metropolitan Police Service arrested Julian Assange at the Ecuadorian embassy in London on Thursday morning, following a U.S. extradition request.

The military can't act "like they would normally act" at the border, President Donald Trump said.
The spacecraft will become the first lander funded by a private company to touch down on the moon.
An indictment lodged against WikiLeaks founder Julian Assange alleges that he needed the help of intelligence analyst Chelsea Manning to breach the U.S. government's database.
The actor spoke to Newsweek about religion and feminism in advance of the U.S. release of the movie "Mary Magdalene," in which he plays Jesus.
"We are asking for basic human rights to be made whole and real in the United States,” said Stacey Abrams at the Women in the World summit.
The president has allegedly complained several times of not having a loyal "Roy Cohn" figure in the White House, referring to his former personal lawyer and fixer.


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An SDLFile subclass specifically designed for images
Describes whether or not the image is a template that can be (re)colored by the SDL HMI. To make the artwork a template, set the UIImage
s rendering mode to UIImageRenderingModeAlwaysTemplate
. In order for templates to work successfully, the icon must be one solid color with a clear background. The artwork should be created using the PNG image format.
@discussion An image should be templated if it is intended to be used as an icon in a button or menu.
@property (nonatomic, readonly) BOOL isTemplate;
var isTemplate: Bool { get }
The Image RPC representing this artwork. Generally for use internally, you should instead pass an artwork to a Screen Manager method.
@property (nonatomic, strong, readonly) SDLImage *_Nonnull imageRPC;
var imageRPC: SDLImage { get }
Convenience helper to create an ephemeral artwork from an image.
This is an ephemeral file, it will not be persisted through sessions / ignition cycles. Any files that you do not know you will use in future sessions should be created through this method. For example, album / artist artwork should be ephemeral.
Persistent files should be created using persistentArtworkWithImage:name:asImageFormat:
Warning
It is strongly recommended to pass the file url using an SDLFile initializer instead of the image. If you pass the UIImage, it is loaded into memory, and will be dumped to a temporary file. This will create a duplicate file. Only pass a UIImage if the image is not stored on disk.
+ (nonnull instancetype)artworkWithImage:(nonnull UIImage *)image
name:(nonnull NSString *)name
asImageFormat:(SDLArtworkImageFormat)imageFormat;
The UIImage to be sent to the remote head unit
The name of the file that will be used to reference the file in the future (for example on the remote file system). The max file name length may vary based on remote file system limitations.
Whether the image should be converted to a PNG or JPG before transmission. Images with transparency or few colors should be PNGs. Images with many colors should be JPGs.
An instance of this class to be passed to the file manager.
Convenience helper to create an ephemeral artwork from an image. A unique name will be assigned to the image. This name is a string representation of the image’s data which is created by hashing the data using the MD5 algorithm.
This is an ephemeral file, it will not be persisted through sessions / ignition cycles. Any files that you do not know you will use in future sessions should be created through this method. For example, album / artist artwork should be ephemeral.
Persistent files should be created using persistentArtworkWithImage:name:asImageFormat:
Warning
It is strongly recommended to pass the file url using an SDLFile initializer instead of the image. If you pass the UIImage, it is loaded into memory, and will be dumped to a temporary file. This will create a duplicate file. Only pass a UIImage if the image is not stored on disk.
+ (nonnull instancetype)artworkWithImage:(nonnull UIImage *)image
asImageFormat:(SDLArtworkImageFormat)imageFormat;
The UIImage to be sent to the remote head unit
Whether the image should be converted to a PNG or JPG before transmission. Images with transparency or few colors should be PNGs. Images with many colors should be JPGs.
An instance of this class to be passed to the file manager.
Create an SDLArtwork that represents a static icon. This can only be passed to the screen manager; passing this directly to the file manager will fail.
+ (nonnull instancetype)artworkWithStaticIcon:
(nonnull SDLStaticIconName)staticIcon;
The static icon to be shown on the remote system.
An instance of this class to be passed to a screen manager.
Convenience helper to create a persistent artwork from an image.
This is a persistent file, it will be persisted through sessions / ignition cycles. You will only have a limited space for all files, so be sure to only persist files that are required for all or most sessions. For example, menu artwork should be persistent.
Ephemeral files should be created using ephemeralArtworkWithImage:name:asImageFormat:
Warning
It is strongly recommended to pass the file url using an SDLFile initializer instead of the image. If you pass the UIImage, it is loaded into memory, and will be dumped to a temporary file. This will create a duplicate file. Only pass a UIImage if the image is not stored on disk.
+ (nonnull instancetype)persistentArtworkWithImage:(nonnull UIImage *)image
name:(nonnull NSString *)name
asImageFormat:
(SDLArtworkImageFormat)imageFormat;
The UIImage to be sent to the remote head unit
The name of the file that will be used to reference the file in the future (for example on the remote file system). The max file name length may vary based on remote file system limitations.
Whether the image should be converted to a PNG or JPG before transmission. Images with transparency or few colors should be PNGs. Images with many colors should be JPGs.
An instance of this class to be passed to the file manager.
Convenience helper to create a persistent artwork from an image. A unique name will be assigned to the image. This name is a string representation of the image’s data which is created by hashing the data using the MD5 algorithm.
This is a persistent file, it will be persisted through sessions / ignition cycles. You will only have a limited space for all files, so be sure to only persist files that are required for all or most sessions. For example, menu artwork should be persistent.
Ephemeral files should be created using ephemeralArtworkWithImage:name:asImageFormat:
Warning
It is strongly recommended to pass the file url using an SDLFile initializer instead of the image. If you pass the UIImage, it is loaded into memory, and will be dumped to a temporary file. This will create a duplicate file. Only pass a UIImage if the image is not stored on disk.
+ (nonnull instancetype)persistentArtworkWithImage:(nonnull UIImage *)image
asImageFormat:
(SDLArtworkImageFormat)imageFormat;
The UIImage to be sent to the remote head unit
Whether the image should be converted to a PNG or JPG before transmission. Images with transparency or few colors should be PNGs. Images with many colors should be JPGs.
An instance of this class to be passed to the file manager.
Create a file for transmission to the remote system from a UIImage.
- (nonnull instancetype)initWithImage:(nonnull UIImage *)image
name:(nonnull NSString *)name
persistent:(BOOL)persistent
asImageFormat:(SDLArtworkImageFormat)imageFormat;
init(image: UIImage, name: String, persistent: Bool, as imageFormat: SDLArtworkImageFormat)
The UIImage to be sent to the remote head unit
The name of the file that will be used to reference the file in the future (for example on the remote file system). The max file name length may vary based on remote file system limitations.
Whether or not the artwork should be persistent.
Whether the image should be converted to a PNG or JPG before transmission. Images with transparency or few colors should be PNGs. Images with many colors should be JPGs.
An instance of this class to be passed to the file manager.
Create a file for transmission to the remote system from a UIImage. A unique name will be assigned to the image. This name is a string representation of the image’s data which is created by hashing the data using the MD5 algorithm.
- (nonnull instancetype)initWithImage:(nonnull UIImage *)image
persistent:(BOOL)persistent
asImageFormat:(SDLArtworkImageFormat)imageFormat;
init(image: UIImage, persistent: Bool, as imageFormat: SDLArtworkImageFormat)
The UIImage to be sent to the remote head unit
Whether or not the artwork should be persistent.
Whether the image should be converted to a PNG or JPG before transmission. Images with transparency or few colors should be PNGs. Images with many colors should be JPGs.
An instance of this class to be passed to the file manager.
Create an SDLArtwork that represents a static icon. This can only be passed to the screen manager; passing this directly to the file manager will fail.
- (nonnull instancetype)initWithStaticIcon:
(nonnull SDLStaticIconName)staticIcon;
init(staticIcon: SDLStaticIconName)
The static icon to be shown on the remote system.
An instance of this class to be passed to a screen manager.


{$c{test}->get_style_sheet_include}
5
Fractions
Reduce a fraction to its simplest form by cancelling common factors:
6 / 10 =
(1 marks)
6 / 9 =
(1 marks)
14 / 16 =
(1 marks)
What is three tenths of 80?
(1 marks)
What is seven tenths of 50?
(1 marks)


Classifying is something a neural network can do quite well. In this article I will demonstrate how you can set up the evolution process of a neural network that learns to classify colors with Neataptic.
Colors:
Iteration: 0 Best-fitness: 0
The algorithm to this classification is actually pretty easy. One of my biggest problem was generating the colors, however I stumbled upon this Javascript library that allows you to generate colors randomly by name - exactly what I needed (but it also created a problem, read below). So I used it to create a training set:
function createSet(){
var set = [];
for(index in COLORS){
var color = COLORS[index];
var randomColors = randomColor({ hue : color, count: PER_COLOR, format: 'rgb'});
for(var random in randomColors){
var rgb = randomColors[random];
random = rgb.substring(4, rgb.length-1).replace(/ /g, '').split(',');
for(var y in random) random[y] = random[y]/255;
var output = Array.apply(null, Array(COLORS.length)).map(Number.prototype.valueOf, 0);
output[index] = 1;
set.push({ input: random, output: output, color: color, rgb: rgb});
}
}
return set;
}
COLORS is an array storing all color names in strings. The possible colors are
listed above. Next, we convert this rgb string to an array and normalize the
values between 0 and 1. Last of all, we normalize the colors using
one-hot encoding.
Please note that the color
and rgb
object attributes are irrelevant for the algorithm.
network.evolve(set, {
iterations: 1,
mutationRate: 0.6,
elisitm: 5,
popSize: 100,
mutation: methods.mutation.FFW,
cost: methods.cost.MSE
});
Now we create the built-in genetic algorithm in neataptic.js. We define that we want to use all possible mutation methods and set the mutation rate higher than normal. Sprinkle in some elitism and double the default population size. Experiment with the parameters yourself, maybe you'll find even better parameters!
The fitness function is the most vital part of the algorithm. It basically
calculates the Mean Squared Error
of the entire set. Neataptic saves the programming of this fitness calculation.
At the same time the default growth
parameter is used, so the networks will
get penalized for being too large.
And putting together all this code will create a color classifier.


Node Ops Tutorial¶
Contains functions taking a tensor or structured list of tensors and returning a tensor or structured list of tensors. The functions are commonly used compositions of tensorflow functions which operate on tensors.
Contains functions taking a tensor or structured list of tensors and returning a tensor or structured list of tensors. The functions are commonly used compositions of tensorflow functions which operate on tensors.


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Mathematician Vashishth Narayan Singh passes away at 77
Singh who was suffering from schizophrenia for 40 years is said to have challenged Einstein's theory of relativity
by IANSFamous mathematician Vashishth Narayan Singh passed away here on Thursday. He was 77. Born on April 2, 1942, Singh had been suffering from schizophrenia for 40 years.
"Singh's condition deteriorated this morning. He was rushed to Patna Medical College Hospital (PMCH), where doctors declared him dead," said one of Singh's relatives.
Singh, a resident of Basantpur in Bhojpur district, was shifted to PMCH last month. However, he was discharged following treatment. He is said to have challenged Einstein's theory of relativity.
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ==============================================================================
See the [Testing](https://tensorflow.org/api_guides/python/test) guide.
Note: `tf.test.mock` is an alias to the python `mock` or `unittest.mock` depending on the python version. """
# pylint: disable=g-bad-import-order
# pylint: disable=unused-import
# pylint: enable=unused-import,g-bad-import-order
import mock # pylint: disable=g-import-not-at-top,unused-import else:
# Import Benchmark class
# Import StubOutForTesting class
"""Runs all unit tests.""" _test_util.InstallStackTraceHandler() return _googletest.main(argv)
def get_temp_dir(): """Returns a temporary directory for use during tests.
There is no need to delete the directory after the test.
Returns: The temporary directory. """ return _googletest.GetTempDir()
def test_src_dir_path(relative_path): """Creates an absolute test srcdir path given a relative path.
Args: relative_path: a path relative to tensorflow root. e.g. "core/platform".
Returns: An absolute path to the linked in runfiles. """ return _googletest.test_src_dir_path(relative_path)
def is_built_with_cuda(): """Returns whether TensorFlow was built with CUDA (GPU) support.""" return _test_util.IsGoogleCudaEnabled() |


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⚠ Documentation is version-specific: This page was generated for ScottPlot 4.1.4-beta
Additional documentation and more version-specific cookbooks are on the ScottPlot Website
A colorbar displays a colormap beside the data area. Colorbars are typically added to plots containing heatmaps.
var plt = new ScottPlot.Plot(600, 400);
plt.AddColorbar();
plt.SaveFig("colorbar_quickstart.png");
By default colorbars use the Viridis colormap, but this behavior can be customized and many colormaps are available.
var plt = new ScottPlot.Plot(600, 400);
plt.AddColorbar(Drawing.Colormap.Turbo);
plt.SaveFig("colorbar_colormap.png");
Tick marks can be added to colorbars. Each tick is described by a position (a fraction of the distance from the bottom to the top) and a string (the tick label).
var plt = new ScottPlot.Plot(600, 400);
var cb = plt.AddColorbar();
cb.AddTick(0, "-123");
cb.AddTick(1, "+123");
cb.AddTick(.5, "0");
cb.AddTick(.25, "-61.5");
cb.AddTick(.75, "+61.5");
plt.SaveFig("colorbar_ticks.png");


java.lang.String
which are
repeatedly appended to. Such variables may be more efficiently declared as java.lang.StringBuffer
or java.lang.StringBuilder
.
Use the checkbox below to specify that this inspection should only warn when the variable is appended to in a loop.


| Position: | | Web Application Developer |
| Employer: | | Woolpert, Inc. |
| Location: | | Honolulu, HI |
| Period: | | August 2004 - September 2006 |
| Web: |
|
Woolpert, Inc. |
| Responsibilities: |
|
-
Technical lead for 3rd phase enhancements to web-based GIS asset management system
for the Honolulu Board of Water Supply
- Project planning for web-based GIS information portal for Honolulu Fire Department
- Microsoft SQL Server design / development / administration
- External systems integration
- Geodatabase design / development
- Honolulu office opening and representation
|
|
Experience:
|
|
Woolpert had performed work for the Honolulu Board of Water Supply for a few years by 2004 and decided to open a
local office to continue the work and develop new business in Hawaii. They hired me to open the new
office before I'd finished graduate work at the University of Redlands. I worked on new tools and capabilities
for the IMS GIS asset management system Woolpert had developed for HBWS.
I marketed Woolpert's service lines to prospective clients and, in conjunction with a second developer
added to the office in 2005, performed the intial user-needs assessment for a newly-contracted project
with the Honolulu Fire Department to develop a GIS-based information portal centralizing access to HFD's
training records, pre-incident plans, and other Departmental information.
In addition to the project-specific work I generally represented the company's local presence.
|