cdt.causality¶
cdt.causality.pairwise¶
-
class
cdt.causality.pairwise.model.
PairwiseModel
[source]¶ Base class for all pairwise causal inference models
Usage for undirected/directed graphs and CEPC df format.
-
orient_graph
(df_data, graph, printout=None, **kwargs)[source]¶ Orient an undirected graph using the pairwise method defined by the subclass.
The pairwise method is ran on every undirected edge.
- Parameters
df_data (pandas.DataFrame) – Data
umg (networkx.Graph) – Graph to orient
printout (str) – (optional) Path to file where to save temporary results
- Returns
a directed graph, which might contain cycles
- Return type
networkx.DiGraph
-
predict
(x, *args, **kwargs)[source]¶ Generic predict method, chooses which subfunction to use for a more suited.
Depending on the type of x and of *args, this function process to execute different functions in the priority order:
If
args[0]
is anetworkx.(Di)Graph
, thenself.orient_graph
is executed.If
args[0]
exists, thenself.predict_proba
is executed.If
x
is apandas.DataFrame
, thenself.predict_dataset
is executed.If
x
is apandas.Series
, thenself.predict_proba
is executed.
- Parameters
x (numpy.array or pandas.DataFrame or pandas.Series) – First variable or dataset.
args (numpy.array or networkx.Graph) – graph or second variable.
- Returns
predictions output
- Return type
pandas.Dataframe or networkx.Digraph
-
predict_dataset
(x, **kwargs)[source]¶ Generic dataset prediction function.
Runs the score independently on all pairs.
- Parameters
x (pandas.DataFrame) – a CEPC format Dataframe.
kwargs (dict) – additional arguments for the algorithms
- Returns
a Dataframe with the predictions.
- Return type
pandas.DataFrame
-
predict_proba
(a, b, idx=0, **kwargs)[source]¶ Prediction method for pairwise causal inference.
predict_proba is meant to be overridden in all subclasses
- Parameters
a (numpy.ndarray) – Variable 1
b (numpy.ndarray) – Variable 2
idx (int) – (optional) index number for printing purposes
- Returns
Causation score (Value : 1 if a->b and -1 if b->a)
- Return type
float
-
ANM¶
-
class
cdt.causality.pairwise.
ANM
[source]¶ Additive Noise model to infer causal relationships.
Assuming that x->y then if the data follows an additive noise model, there is y=f(x)+E.  The causal inference bases itself on the independence between x and e. It is proven that in such case if the data is generated using an additive noise model, the model would only be able to fit in the true causal direction. Ref: https://papers.nips.cc/paper/3548-nonlinear-causal-discovery-with-additive-noise-models.pdf
Note
Ref : Hoyer, Patrik O and Janzing, Dominik and Mooij, Joris M and Peters, Jonas and Schölkopf, Bernhard, “Nonlinear causal discovery with additive noise models”, NIPS 2009
Bivariate Fit¶
-
class
cdt.causality.pairwise.
BivariateFit
(ffactor=2, maxdev=3, minc=12)[source]¶ Bivariate Fit model. Based itself on a best-fit criterion based on a Gaussian Process regressor. Used as weak baseline.
CDS¶
-
class
cdt.causality.pairwise.
CDS
(ffactor=2, maxdev=3, minc=12)[source]¶ Conditional Distribution Similarity Statistic
Measuring the std. of the rescaled values of y (resp. x) after binning in the x (resp. y) direction. The lower the std. the more likely the pair to be x->y (resp. y->x).
Note
Ref : Fonollosa, José AR, “Conditional distribution variability measures for causality detection”, 2016.
GNN¶
-
class
cdt.causality.pairwise.
GNN
(nh=20, lr=0.01, nruns=6, njobs=None, gpus=None, verbose=None, ttest_threshold=0.01, nb_max_runs=16, train_epochs=1000, test_epochs=1000)[source]¶ Shallow Generative Neural networks.
Models the causal directions x->y and y->x with a 1-hidden layer neural network and a MMD loss. The causal direction is considered as the “best-fit” between the two causal directions.
- Parameters
nh (int) – number of hidden units in the neural network
lr (float) – learning rate of the optimizer
nruns (int) – number of runs to execute per batch (before testing for significance with t-test).
njobs (int) – number of runs to execute in parallel. (defaults to
cdt.SETTINGS.NJOBS
)gpus (bool) – Number of available gpus (defaults to
cdt.SETTINGS.GPU
)idx (int) – (optional) index of the pair, for printing purposes
verbose (bool) – verbosity (defaults to
cdt.SETTINGS.verbose
)ttest_threshold (float) – threshold to stop the boostraps before
nb_max_runs
if the difference is significantnb_max_runs (int) – Max number of bootstraps
train_epochs (int) – Number of epochs used for training
test_epochs (int) – Number of epochs used for evaluation
Note
Ref : Learning Functional Causal Models with Generative Neural Networks Olivier Goudet & Diviyan Kalainathan & Al. (https://arxiv.org/abs/1709.05321)
IGCI¶
-
class
cdt.causality.pairwise.
IGCI
[source]¶ Information Geometric Causal Inference (IGCI) model.
Note
P. Daniušis, D. Janzing, J. Mooij, J. Zscheischler, B. Steudel, K. Zhang, B. Schölkopf: Inferring deterministic causal relations. Proceedings of the 26th Annual Conference on Uncertainty in Artificial Intelligence (UAI-2010). http://event.cwi.nl/uai2010/papers/UAI2010_0121.pdf
-
predict_proba
(a, b, **kwargs)[source]¶ Evaluate a pair using the IGCI model.
- Parameters
a – Input variable 1D
b – Input variable 1D
kwargs – {refMeasure: Scaling method (gaussian, integral or None), estimator: method used to evaluate the pairs (entropy or integral)}
- Returns
Return value of the IGCI model >0 if a->b otherwise if return <0
-
Jarfo¶
-
class
cdt.causality.pairwise.
Jarfo
[source]¶ Jarfo model, 2nd of the Cause Effect Pairs challenge, 1st of the Fast Causation Challenge. Builds lots of features on top of a gradient boosting classifier.
Note
Ref : Fonollosa, José AR, “Conditional distribution variability measures for causality detection”, 2016.
-
orient_graph
(df_data, graph, printout=None, **kwargs)[source]¶ Orient an undirected graph using Jarfo, function modified for optimization.
- Parameters
df_data (pandas.DataFrame) – Data
umg (networkx.Graph) – Graph to orient
nruns (int) – number of times to rerun for each pair (bootstrap)
printout (str) – (optional) Path to file where to save temporary results
- Returns
a directed graph, which might contain cycles
- Return type
networkx.DiGraph
-
predict_dataset
(df)[source]¶ Runs Jarfo independently on all pairs.
- Parameters
x (pandas.DataFrame) – a CEPC format Dataframe.
kwargs (dict) – additional arguments for the algorithms
- Returns
a Dataframe with the predictions.
- Return type
pandas.DataFrame
-
predict_proba
(a, b, idx=0, **kwargs)[source]¶ Use Jarfo to predict the causal direction of a pair of vars.
- Parameters
a (numpy.ndarray) – Variable 1
b (numpy.ndarray) – Variable 2
idx (int) – (optional) index number for printing purposes
- Returns
Causation score (Value : 1 if a->b and -1 if b->a)
- Return type
float
-
NCC¶
-
class
cdt.causality.pairwise.
NCC
[source]¶ Neural Causation Coefficient.
Infer causal relationships between pairs of variables using mean embbedings of neural networks
-
fit
(x_tr, y_tr, epochs=50, batchsize=32, learning_rate=0.01, verbose=None, device=None)[source]¶ Fit the NCC model.
- Parameters
x_tr (pd.DataFrame) – CEPC format dataframe containing the pairs
y_tr (pd.DataFrame or np.ndarray) – labels associated to the pairs
epochs (int) – number of train epochs
learning_rate (float) – learning rate of Adam
verbose (bool) – verbosity (defaults to
cdt.SETTINGS.verbose
)device (str) – cuda or cpu device (defaults to
cdt.SETTINGS.default_device
)
-
predict_dataset
(df, device=None, verbose=None)[source]¶ - Parameters
x_tr (pd.DataFrame) – CEPC format dataframe containing the pairs
y_tr (pd.DataFrame or np.ndarray) – labels associated to the pairs
epochs (int) – number of train epochs
rate (learning) – learning rate of Adam
verbose (bool) – verbosity (defaults to
cdt.SETTINGS.verbose
)device (str) – cuda or cpu device (defaults to
cdt.SETTINGS.default_device
)
- Returns
dataframe containing the predicted causation coefficients
- Return type
pandas.DataFrame
-
predict_proba
(a, b, device=None)[source]¶ Infer causal directions using the trained NCC pairwise model.
- Parameters
a (numpy.ndarray) – Variable 1
b (numpy.ndarray) – Variable 2
device (str) – Device to run the algorithm on (defaults to
cdt.SETTINGS.default_device
)
- Returns
Causation score (Value : 1 if a->b and -1 if b->a)
- Return type
float
-
RCC¶
-
class
cdt.causality.pairwise.
RCC
(rand_coeff=333, nb_estimators=500, nb_min_leaves=20, max_depth=None, s=10, njobs=None, verbose=None)[source]¶ Randomized Causation Coefficient model.
Projection of the empirical distributions into a RKHS using random cosine embeddings, then classfying the pairs using a random forest.
- Parameters
rand_coeff (int) – number of randomized coefficients
nb_estimators (int) – number of estimators
nb_min_leaves (int) – number of min samples leaves of the estimator
() (max_depth) – (optional) max depth of the model
s (float) – scaling
njobs (int) – number of jobs to be run on parallel (defaults to
cdt.SETTINGS.NJOBS
)verbose (bool) – verbosity (defaults to
cdt.SETTINGS.verbose
)
Note
Ref : Lopez-Paz, David and Muandet, Krikamol and Schölkopf, Bernhard and Tolstikhin, Ilya O, “Towards a Learning Theory of Cause-Effect Inference”, ICML 2015.
-
featurize_row
(x, y)[source]¶ Projects the causal pair to the RKHS using the sampled kernel approximation.
- Parameters
x (np.ndarray) – Variable 1
y (np.ndarray) – Variable 2
- Returns
projected empirical distributions into a single fixed-size vector.
- Return type
np.ndarray
-
fit
(x, y)[source]¶ Train the model.
- Parameters
x_tr (pd.DataFrame) – CEPC format dataframe containing the pairs
y_tr (pd.DataFrame or np.ndarray) – labels associated to the pairs
-
predict_dataset
(data, **kwargs)[source]¶ Override of
self.predict_dataset
for better computational efficency.- Parameters
x (pandas.DataFrame) – a CEPC format Dataframe.
kwargs (dict) – additional arguments for the algorithms
- Returns
a Dataframe with the predictions.
- Return type
pandas.DataFrame
-
predict_proba
(x, y=None, **kwargs)[source]¶ Predict the causal score using a trained RCC model
- Parameters
x (numpy.array or pandas.DataFrame or pandas.Series) – First variable or dataset.
args (numpy.array) – second variable (optional depending on the 1st argument).
- Returns
Causation score (Value : 1 if a->b and -1 if b->a)
- Return type
float
RECI¶
-
class
cdt.causality.pairwise.
RECI
(degree=3)[source]¶ RECI, Best-fit mse with monome regressor and [0,1] rescaling
- Parameters
degree (int) – Degree of the polynomial regression.
Note
Bloebaum, P., Janzing, D., Washio, T., Shimizu, S., & Schoelkopf, B. (2018, March). Cause-Effect Inference by Comparing Regression Errors. In International Conference on Artificial Intelligence and Statistics (pp. 900-909).
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
-
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 X2the 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-Terpstraa 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.
CAM¶
-
class
cdt.causality.graph.
CAM
(score='nonlinear', cutoff=0.001, variablesel=True, selmethod='gamboost', pruning=False, prunmethod='gam', njobs=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.
njobs (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.NJOBS)
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.
CCDr¶
-
class
cdt.causality.graph.
CCDr
(verbose=None)[source]¶ CCDr algorithm. Concave penalized Coordinate Descent with reparametrization) structure learning algorithm as described in Aragam and Zhou (2015). This is a fast, score based method for learning Bayesian networks that uses sparse regularization and block-cyclic coordinate descent.
Imported from the ‘sparsebn’ package.
Warning
This implementation of CCDr does not support starting with a graph.
CGNN¶
-
class
cdt.causality.graph.
CGNN
(nh=20, nruns=16, njobs=None, gpus=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.
nruns (int) – Number of times to run CGNN to have a stable evaluation.
njobs (int) – Number of jobs to run in parallel. Defaults to
cdt.SETTINGS.NJOBS
.gpus (bool) – Number of available gpus (Initialized with
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, njobs=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_clust: “data=X, ic.method=”hsic.clust”“
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, nruns=6, njobs=None, gpus=0, verbose=None)[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.
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
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, 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.
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


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ActionFront User Manual
A Guide to Scenarios
- Copy Existing Scenario
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#set( $symbol_pound = '#' ) #set( $symbol_dollar = '$' ) #set( $symbol_escape = '\' )
For lots more information on using the AWS SDK for Java, including information on high-level APIs and advanced features, check out the AWS SDK for Java Developer Guide.
Stay up to date with new features in the AWS SDK for Java by following the AWS Java Developer Blog.
.aws/credentials
file in your home directory. For example:
[default] aws_access_key_id = <your AWS access key> aws_secret_access_key = <your AWS secret access key>More information on configuring
~/.aws/config
mvn package exec:java
to build and run the AwsSdkSample class.


CASAA or Contemporary Architecture Studio and Associates is based out of Colombo in Sri Lanka and was established under the creative direction of Isurunath Bulankulame. Previously known as IPA Architects, founded in 2011, the organization was rebranded as CASAA in 2020.
Our award-winning practice creates bespoke spaces that are carefully tailored and personalized and commercially astute, and are designed to make the interaction meaningful to the people who encounter them.
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Design and detail oriented our Philosophy is creating Contemporary architecture that is derived through strong individual concepts which are minimal, unique, site and project specific. Through creativity, collaboration and Communication with the client, we create architecture that is conducive to Client, Culture, Climate and the Context.


{% extends "base.html" %} {% block breadcrumb %} Home Usage Policy {% endblock %} {% block contentheader %} Data Access Policy for the Human Variome Project Australian Node {% endblock %} {% block content %}
This policy provides information and guidelines for individuals wishing to request access to data stored within the Australian Human Variome Database (“the Database”) for diagnostic, clinical or research use. The Human Variome Project Australian Node (“the Node”) is committed to providing diagnostic, clinical and research investigators the opportunity to access data for purposes that are consistent with the missions of both the Node and the International Human Variome Project.


I am a product of the early 80's and have broken bones and stories that go with that. My father was in the US Air Force so we moved around on a regular basis. We eventually laid roots in Warsaw, IN, where I graduated high school. I then went to Ball State University for brief time focusing on Journalism. I later attended a smaller Community College for the same thing with an emphasis on design where I learned about typography and digital editing.
On somewhat of a whim I joined the US Army and became an Airborne Infantryman. I was stationed at Ft. Bragg in North Carolina with the 82nd Airborne and then quickly went on my first combat deployment. Several more followed while remaining stationed there. In that time I went to several schools (Sniper School, Ranger School, Jumpmaster School) and quickly moved up in rank. In late 2010 I was stationed at Joint Base Lewis McChord in Washington. While at JBLM I attended Pathfinder school and was promoted to Staff Sergant. I then deployed with my unit to Afghanistan in late 2011 and returned home in mid 2012. I completed my time in the Army at the end of July that year.
After leaving the Army I went to work for BNSF Railway in Southern California. I started work as a entry level Mechanical Foreman at a locomotive maintenance facility. It was very intimidating due to the fact I knew practicaly nothing about locomotives or the railroad. I spent about 16 hours a day bringing myself up to speed on the many different types of locomotives and their parts. I would spend a good part of my day with the machinists and electricians helping them with whatever task they were assigned to that day and getting to know them.
After about 2 years I was promoted to Assistant General Foreman and took complete control of one of the shops and was responsible for 45 employees. A year later I was recomended and recieved a rapid responder supervisor job. This job gave me an additional 6 employees that would go with me to derailments or other incidents involving or locomotives in Southern California.
After getting burnt out on many years of being a boss, I decided I needed a change. I had always loved woodworking and decided to find something where I could use that love. I stumbled upon an oppurtunity to work as a shipwright and went with it. The work was very enjoyable, both physically and mentally exhausting. Every boat would bring its own problems and challenges. It was very satisfying to start the day with a rough piece of lumber and end with it as a beautiful plank on a boat that would start vertical and end horizontal. Unfortunately my body could not handle the work anymore, I am just a bit to tall to work on boats and my knees couldn't take the bending anymore.


问题
在搭建环境中一些奇葩问题！！！
--- title: Links ---
|
报错1 |
ERROR: While executing gem ... (Gem::CommandLineError) |
解决：网上搜寻说是https的问题 ，将镜像https改成http://rubygems.org/ 但是没用 又开始报错：Could not find a valid gem 'jkeyll' (>= 0) 最后换成中国镜像：gem sources -a http://gems.ruby-china.com/ |
|
报错2 |
Markdown processor: "redcarpet" is not a valid Markdown processor. Available processors are: kramdown |
查了半天，各种中文网都找不到最后在一个https://github.com/jekyll/jekyll/issues/7838有人提问上看到一个人说：Redcarpet support has been dropped in Jekyll 4.0. See #6987 Documentation needs to be updated, you can still use redcarpet with Jekyll 3.x though.大概意思就是说这个东西在jekll4.0以上已经被废弃，只有在4以下才能被使用，找了大半天我去！！！ 解决办法：降版本（找到一个）：bundle exec gem install jekyll -v 3.4.3 |
|
报错3： |
ERROR: YOUR SITE COULD NOT BE BUILT: ------------------------------------ pygments |
pygments会报错 ##highlighter: pygments 改成 highlighter: rouge |
|
redcarpet ERROR: YOUR SITE COULD NOT BE BUILT: ------------------------------------ redcarpet |
最开始以为是.config.yml语法问题后来发现不是 网上说需要把_config.yml中的markdown改为kramdown，最后发现不是： 最后看错误信息 Yikes! It looks like you don't have redcarpet or one of its dependencies installed. In order to use Jekyll as currently configured, you'll need to install this gem. The full error message from Ruby is: 'cannot load such file -- redcarpet' If you run into trouble, you can find helpful resources at https://jekyllrb.com/help/! Conversion error: Jekyll::Converters::Markdown encountered an error while converting '_posts/2013-12-04-welcome-to-jekyll.markdown': redcarpet ERROR: YOUR SITE COULD NOT BE BUILT: ------------------------------------ redcarpet 原来是这个没装ou'll need to install this gem. 安装命令：gem install redcarpet |
|
|
静态css文件获取不了再本地报404 |
网上找了半天 一直运行jekyll serve 最后莫名好了 觉得奇怪 |
|
An indispensable community if you’re interested in the more technical side of tech. Incredibly smart people discussing equally (usually) smart topics. |
|
|
An excellent resource for programmers both beginner and experienced. Be sure to check out other related subreddits from their sidebar. |
|
|
Great collection of links and articles related to mostly front end development. |
|
My go to site for tech news. Their longform work is also deserving of your time. |
|
|
Essentially the entire (great) team from AllThings D moved over to a separate site at the turn of the year. An excellent resource for reliable news and reviews. |
|
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Want to read the most in-depth reviews on products? Look no further. |
|
Think Yahoo! Answers but more…serious. You can find some discussions on very interesting questions. You do need to sign up though. |
|
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One of the best collections of reading (both fiction and non-fiction articles) material for your Pocket. |
|
|
Ask Metafilter |
A great loyal community whose quality is in part kept high with a pay-for-membership model. Browse through the top questions for some great discussions. |
|
Leo Laporte is great to listen to and his guests help keep the discussions fun and lively. My favorite podcasts include “This Week in Tech” and “Windows Weekly” |
|
|
How do you order water at a restaurant without looking like an idiot? One part educational, one part humor. |
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|
Radiolab has a huge fanbase and it’s not without reason. They do a great job of keeping your attention with engrossing topics and amazing audio work. |
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NPR’s music podcast can be a hit or a miss depending on your taste (less mainstream, more discovery based) in music but be sure to give it try. |


commented out as this is the video for edo outpost, will replace with sumo sized night {% endcomment %} {% comment %} ?rel=0&showinfo=0&color=white in the youtube link is used to remove the related videos at the end of the clip {% endcomment %}
{% comment %}
Edo Outpost
{% endcomment %}Sumo, Fire & Izakaya - Walking food & drink tour - Tokyo - Monzen Nakacho
- Decription
-
Tokyo afternoon / evening walking food and drink tour packed full of things to see, eat and drink.
Visit the birthplace of Sumo at the famous local shrine and see for yourself just how big a sumo can be.
Watch the Buddhist monks perform their daily "Fire ceremony" at the local temple.
Eat and drink at a famous izakaya (bar) and try the dishes that have attracted international film stars to this local part of town. Choose your favourite sake from the wall to wall refrigerators at the cosy stand up sake izakaya. Finish the night off in the most Japanese of ways with a hearty bowl of delicious local Ramen.
Enjoy five authentic dishes with two alcoholic drinks (non alcoholic drinks are available).
{% comment %} - Donec id elit non mi porta gravida at eget metus. {% endcomment %}
- Tour Type
- Walking Tour
- Tour Details
- Start Time: 16:40 - promptly please as the monks wait for no one :)
Duration: 4 hours - Walking distance
- Estimated: 0.7 miles | 1 km | Moderate level of fitness required
- Meeting point
-
Inside Monzen-Nakacho Train Station by Exit 5
Link to meeting point - Children
- Unfortunately, in Japanese culture, children are not permitted inside the establishments selected for this tour.
All Inclusive - only ¥9,999 per person Book Now
Reviews
John Doe
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Maria Casie
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Nisi cupiditate temporibus iure soluta. Quasi mollitia maxime nemo quam accusamus possimus, voluptatum expedita assumenda. Earum sit id ullam eum vel delectus!


compTable using Bootstrap
This is a lousy description of what happens here.
Select data to compare
Please select/Empty
P-RBAC
GEO-RBAC
PERMIS
Please select/Empty
P-RBAC
GEO-RBAC
PERMIS
Please select/Empty
P-RBAC
GEO-RBAC
PERMIS
Below are the generated comparison tables


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Record of Landing Number
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1876
1875
Place of Birth:
Please select
Afghanistan
Africa NES
Albania
Algeria
Andorra
Angola
Anguilla
Antigua and Barbuda
Argentina
Armenia
Aruba
Asia NES
Australia
Australia NES
Austria
Azerbaijan
Azores
Bahama Islands, The
Bahrain
Bangladesh
Barbados
Belarus
Belgium
Belize
Benin, Peoples Republic of
Bermuda
Bhutan
Bolivia
Bosnia-Hercegovina
Botswana, Republic of
Brazil
Brunei
Bulgaria
Burkina-Faso
Burundi
Cambodia
Cameroon, Federal Republic of
Canada
Canary Islands
Cape Verde Islands
Cayman Islands
Central African Republic
Central America NES
Chad, Republic of
Channel Islands
Chile
China, People's Republic of
Colombia
Commonwealth of the Northern Mariana Islands
Comoros
Congo, Democratic Republic of the
Congo, People's Republic of the
Cook Islands
Costa Rica
Croatia
Cuba
Cyprus
Czech Republic
Czechoslovakia
Denmark
Djibouti, Republic of
Dominica
Dominican Republic
East Timor, Democratic Republic of
Ecuador
Egypt
El Salvador
England
Eritrea
Estonia
Ethiopia
Europe NES
Falkland Islands
Federated States of Micronesia
Fiji
Finland
France
French Guiana
French Polynesia
Gabon Republic
Gambia
Georgia
Germany, Democratic Republic
Germany, Federal Republic of
Ghana
Gibraltar
Greece
Greenland
Grenada
Guadeloupe
Guam
Guatemala
Guinea, Equatorial
Guinea, Republic of
Guinea-Bissau
Guyana
Haiti
Holy See
Honduras
Hong Kong
Hong Kong SAR
Hungary
Iceland
India
Indonesia, Republic of
Iran
Iraq
Ireland, Republic of
Israel
Italy
Ivory Coast, Republic of
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Kiribati
Korea, People's Democratic Republic of
Korea, Republic of
Kuwait
Kyrgyzstan
Laos
Latvia
Lebanon
Lesotho
Liberia
Libya
Liechtenstein
Lithuania
Luxembourg
Macao
Macao SAR
Macedonia, FYR
Madagascar
Madeira
Malawi
Malaysia
Maldives, Republic of
Mali, Republic of
Malta
Marinas
Martinique
Mauritania
Mauritius
Mayotte
Mexico
Moldova
Monaco
Mongolia, People's Republic of
Montenegro, Republic of
Montserrat
Morocco
Mozambique
Myanmar (Burma)
Namibia
Nauru
Nepal
Netherlands Antilles, The
Netherlands, The
Nevis
New Caledonia
New Zealand
Newfoundland
Nicaragua
Niger, Republic of the
Nigeria
North Vietnam
Northern Ireland
Norway
Ocean NES
Oman
Pakistan
Palestinian Authority (Gaza/West Bank)
Panama Canal Zone
Panama, Republic of
Papau
Papua New Guinea
Paraguay
Peru
Philippines
Pitcairn Island
Poland
Portugal
Puerto Rico
Qatar
Republic of Kosovo
Republic of Palau
Republic of The Marshall Islands
Reunion
Romania
Russia
Rwanda
Samoa, American
Samoa, Western
San Marino
Sao Tome and Principe
Saudi Arabia
Scotland
Senegal
Serbia and Montenegro
Serbia, Republic of
Seychelles
Sierra Leone
Sikkim (Asia)
Singapore
Slovak Republic
Slovenia
Soloman Islands
Solomons, The
Somalia, Democratic Republic of
South Africa, Republic of
South America NES
South Sudan, Republic Of
Southern Antarctic Territories
Spain
Sri Lanka
St. Helena
St. Kitts-Nevis
St. Lucia
St. Pierre and Miquelon
St. Vincent and the Grenadines
Sudan, Democratic Republic of
Surinam
Swaziland
Sweden
Switzerland
Syria
Tadjikistan
Taiwan
Tanzania, United Republic of
Thailand
Tibet
Togo, Republic of
Tonga
Trinidad and Tobago, Republic of
Tunisia
Turkey
Turkmenistan
Turks and Caicos Islands
Tuvalu
Uganda
Ukraine
Union of Soviet Socialist Republics
United Arab Emirates
United Kingdom and Colonies
United States of America
Uruguay
Uzbekistan
Vanuatu
Venezuela
Vietnam, Socialist Republic of
Virgin Islands, British
Virgin Islands, U.S.
Wales
Wallis And Futuna
West Indies NES
Western Sahara
Yemen, People's Democratic Republic of
Yemen, Republic of
Yugoslavia
Zambia
Zimbabwe
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Patrick's paper published in JFM!
16 Sep 2022
Duncan wins highly-prestigious RAEng fellowship!
30 Aug 2022
Carlos's paper published in JFM!
25 Jul 2022
Sritay passes his viva!
15 Jul 2022
Yichong's paper published in Fuel!
15 May 2022
Sina promoted to Senior Lecturer
23 Mar 2022
Martin's paper published in Journal of Chemical Physics!
20 Dec 2021
Sritay's paper published in Nanoscale!
19 Nov 2021
Giorgos joins mfX as a PDRA!
25 Oct 2021
Carlos's recent paper highlighted by Physics of Fluids!
18 Aug 2021
Shaokang Li jons mfX as a PhD student!
03 Aug 2021
Martin passes his viva!
19 Jul 2021
Saikat and Sreehari win prestigious fellowships!
20 Jun 2021
Patrick wins poster prize!
16 Apr 2021
Welcome to new PhD students
01 Sep 2020
Sreehari's paper makes front cover!
22 Jul 2020
Yonghao and Sina join to form mfX
01 Jul 2020


[gecode-users] Recovery Scheduling as CSP/COP
penche21 penche21
penche21 at hotmail.com
Sat Apr 14 15:31:37 CEST 2007
>Questions:
>#. Is there a way to post relaxable constraints and set priority on them?
What first comes in to my mind is that;
first run your project, lets say with all constraint,
after search comes to an end you see solution == null, restart it with a
flag to drop some constraints. this continues one after another and you
specify the priorities.
hope that helps,
penche
_________________________________________________________________
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Attach these attributes to a service endpoint method to define the parameters that can be passed to the endpoint. You can also add C# parameters to your method to define the parameters that can be passed to the endpoint, but this only defines the name and the data type of the parameters.
If you prefer to define C# parameters, you can also decorate those parameters with the [EndpointParameter] attribute to define the way in which parameters can be passed and provide documentation, parsing and validation of the parameters.
[IsComponent("math_form")] [IsService("math", "/math/", new[] { Method.Post })] public class MathFormComponent: Component { public MathFormComponent(IComponentDependenciesFactory dependencies) : base(dependencies) { } [Endpoint(Analytics = AnalyticsLevel.Full)] [Description("Adds two numbers and returns the sum")] public void Add( IEndpointRequest request, [EndpointParameter( EndpointParameterType.FormField, Description = "The first number to add")] double a, [EndpointParameter( EndpointParameterType.FormField, Description = "The second number to add")] double b, [EndpointParameter( EndpointParameterType.FormField, ParserType = typeof(AnyValue), Description = "The path of the page to return the results on")] string r) { request.OwinContext.Set( typeof(ArithmeticResult).FullName, new ArithmeticResult { Result = a + " + " + b + " = " + (a + b) }); request.Rewrite(r); } }
The [EndpointParameter()] attribute has the following properties that you can set:
This is the name of parameter that should be used by the caller when calling this service. If the parameter is passed in the query string then this is the name of the query string parameter. If the parameter is passed as a form input then this is the name of the input etc.
The parameter name is not case sensitive. You should choose names that work for query strings, Http headers, form input controls etc. In particular you should avoid using spaces and punctuation in the parameter names.
This is a [flags] enum that defines all of the ways that the caller can pass this parameter to the service. If you do not set this property then it defaults to query string only. The other options are form input, Http header and path element. You can specify as many of these options as you like to provide multiple ways to call the endpoint.
When the ParameterType property includes path element as an option then the UrlPath property of the endpoint must include the name of the parameter in {} to indicate where the parameter appears in the path.
For example:
[Endpoint(UrlPath="invoice/{invoiceNumber}")] [EndpointParameter("invoiceNumber", typeof(InvoiceNumberParser), EndpointParameterType.PathSegment)] public void Add(IEndpointRequest request) { }
The ParserType property of the [EndpointParameter()] attribute specifies how the input parameter should be parsed and validated. The parser can abort the processing of the request returning a custom validation message back to the caller if the parameter is not valid.
For the value of this property you can simply provide a .Net CLR value type such as string, int etc and the framework will simply try to parse the parameter as this type and apply no futher validation. In this situation the parameter will be required by the caller. This technique will also generate minimal documentation for your service endpoint.
To make the parameter optional you can use a nullable type instead, for example int?, double? etc.
The REST framework package includes a few standard parsers based on value types, for example NonZeroValue<T> that can be used like this:
[Endpoint(UrlPath="invoice/{invoiceNumber}")] [EndpointParameter("invoiceNumber", typeof(NonZeroValue<int>), EndpointParameterType.PathSegment)] public void Add(IEndpointRequest request) { }
For custom types that are application specific you should write custom parsers that can be reused across all of your services, for example you invoice numbers might have a well known format with check digits. You should write this parsing/validation code once only and apply it to all the relevant endpoint parameters.
The IParameterParser interface is easy to implement, but it becomes very simple if you inherit from OwinFramework.Pages.Restful.Parameters.ParameterParser. Below is an example of a parameter parser that validates that invoice numbers are 12 characters long and start with capital 'I'.
using OwinFramework.Pages.Core.Interfaces.Capability; using OwinFramework.Pages.Restful.Parameters; public class InvoiceNumberParser : ParameterParser { private const string _wrongLengthError = "The invoice number must be 12 characters long"; private const string _invalidError = "The invoice number must start with the letter I"; public override string Description { get { return "A valid 12-character invoice number beginning with the letter I"; } } public override string Examples { get { return "I00000000001"; } } public InvoiceNumberParser(): base(typeof(string)) { } public override IParameterValidationResult Check(string parameter) { var result = new Result { Type = typeof(string) }; if (parameter == null || parameter.Length != 12) { result.ErrorMessage = _wrongLengthError; return result; } if (parameter[0] != 'I') { result.ErrorMessage = _invalidError; return result; } result.Success = true; return result; } }
Parameter parsers can optionally implement IDocumented. When this interface is implemented it will be used to provide more detailed documentation on how to call this service endpoint.
If you provide a description here in Html format that it will be included in the auto-generated endpoint documentation for the servie.
If you do not provide a description then a simple description will be provided based on the available information.


BoardWidget Class Reference
List of all members.
Signals |
|
void | btnClicked (const QString &) |
|
void | gameOver () |
Public Member Functions |
|
| BoardWidget (Player *player1, Player *player2, QWidget *parent=0) |
|
void | setIconAt (int color, const int i, const int j) |
|
void | clearBoard () |
|
bool | writeFileAs (const QString &fileName) |
|
bool | readFile (const QString &fileName) |
|
void | setButtonName (const QString &name, const int i, const int j) |
|
void | setIcons (QIcon *icon1, QIcon *icon2, bool isFirst) |
|
void | setMaxDepth (int depth) |
|
int | getMaxDepth () |
|
Othello * | getOthello () |
|
void | pcPlaying () |
The documentation for this class was generated from the following files:


Titanium.UI.EmailDialog.open
function of
Titanium.UI.EmailDialog
open the email dialog. the email dialog itself is a modal window
Arguments
Name
Type
Description
properties
object
object of animation properties. pass
animated
property (as boolean) to indicate if the dialog should be animated on open.
Return Type
void


[1]
Nothing better than G9; Video mode - no time lapse, no optical zoom(absolutely required)
[2]
Wide zoom, ease of use; Time lapse, HD video missing


Radley is based on lettering originally drawn and designed for woodcarved titling work. It was later digitized and extended to be used on the web. Radley is a practical face, based on letterforms used by hand carvers who cut letters quickly, efficiently, and with style. It can be used for both titling and text typography.
The basic letterforms in Radley grew out of sketching and designing directly into wood with traditional carving chisels. These were scanned and traced into FontForge and cleaned up digitally, then the character set was expanded. There is something unique about carving letters into wood with traditional hand tools, and hopefully Radley carries some of the original spirit of these hand carved letterforms.
Since the initial launch in 2012, Radley was updated by Vernon Adams adding an Italic and support for more Latin languages. He made many glyph refinements throughout the family based on user feedback. In 2017 the family was updated by Marc Foley to complete the work started by Vernon.
To contribute, see github.com/googlefonts/RadleyFont


| DOSE: T+ 0:00 |
1.0 mg |
smoked |
5-MeO-DMT |
(powder / crystals) |
| T+ 0:05 |
8.0 mg |
insufflated |
5-MeO-DMT |
(powder / crystals) |
I have been reading info off the net about this substance for some weeks and I felt reasonably well-informed when some came into my hands today. I was excited and nervous about the experience to come.
I was alone in my appartment, which was clean if cluttered, and comfortable and I was sure of not getting any disturbances - phone off hook, etc..
 Slight feeling that a chemical was in my body and very slight visual effects - colours seemed more vivid, but hardly noticeable really.
Then I decided to try snorting as this seemed less wasteful. Two small lines, between 5 and 10mg or thereabouts judging from effects. Within 5 minutes my body began to feel very heavy and I had some sensory distortion of my pysical awareness. Tried lying down and closing my eyes but this didn't feel very comfortable so I decided to continue footling around the appartment as i had been so far.
By the 15min. mark my body still felt very heavy (this was the main theme of the experience) and i had a strange sensitivity in my gums (which made me worry a bit as I had read somewhere about people bleeding from their gums in a similar situation). By the half-hour mark I was sweating quite heavily, body still heavy; visually things seemed a bit vivid and I popped out to take some rubbish from the car - outside a warm wind was blowing which felt great and everything seemed sparkly.
After that the effects began to wear off, and I was particularly glad to be rid of the heavy body thing which was unpleasant and really detracted from my enjoyment of this experience.
After the event I feel very indifferent to trying this chemical again. It might be more interesting, especially if I could smoke it successfully, but I'm not overly enthusiastic about the next time.
Generally I like drugs that make me feel good - physically especially - so this chemical was not so suitable for me I think. Other trip reports I have read psychically over-rate the experience I would say, though this may be due to the low level of my dose. I wouldn't want to go much higher though. A neutral experience mentally with unpleasant physical effects.
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.


One stage, one community, one opportunity to see great Bellingham talent all in one place!
BE in the Show is a celebration of the performing arts in Bellingham. Local dancers, singers, musicians, actors and more will grace the beautiful Mount Baker Theatre main stage for a night of entertainment featuring a variety of talent from amateurs to professionals. BE a part of a new and fun tradition for Bellingham during our greyer months and add a bit of brightness with BE in the Show!


| cran_check_results {rcmdcheck} | R Documentation |
Download and parse R CMD check results from CRAN
cran_check_results(package, flavours = cran_check_flavours(package))
package |
Name of a single package to download the checks for. |
flavours |
CRAN check flavours to use. Defaults to all flavours that were used to check the package. |
A list of rcmdcheck
objects.


Bald Hill & Fitton Green Area
Trail Area Warning
Description
Bald Hill Natural Area contains a trail system owned and operated by the City of Corvallis. Tread varies from paved bike paths to gravel doubletrack to some dirt, and contain no features. Gravel and dirt trails are closed to bikes during the rainy season. Please follow posted trail restrictions and closures. The park is a great location for beginner riders and those looking for a quick workout near town.
This riding network comprises the 284-acre city-managed Bald Hill Natural Area, the 584-acre conservation area Bald Hill Farm and the county-managed Fitton Green Natural Area, all of which are linked by trails. Trails are open to hikers, bikers, and horses. Main parking area is on Oak Creek Rd but there are several other entrances to the broader trail system.
Trails
Please help out fellow riders, by submitting a trail report when you discover a trail hazard. Also, general trail reports help too, since it keeps our records up to date. You can submit a trail report by clicking on the trail in the table below, logging into trailforks (accounts are free), and creating a trail report.


Toggle navigation
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Add New Book:
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Preparation time
30 mins to 1 hour
Cooking time
10 to 30 mins
Serves
Makes 12 pies
Dietary
This crumbly, fruity mince pie recipe is a Christmas classic. Serve warm with lashings of brandy butter.
Lightly butter a 12-hole pie or patty tin. Tip the mincemeat into a bowl and stir so that the liquid is evenly distributed.
Place the flour, sugar, almonds and butter in a food processor and process briefly until resembling breadcrumbs, then slowly add the egg through the feeder tube. (Or rub the butter into the dry ingredients by hand and stir in the egg.)
Bring the mixture together with your hands, wrap in clingfilm and chill for an hour or so. Thinly roll out the pastry on a floured surface. Cut out 12 circles with a fluted pastry cutter, large enough to fill the base of the prepared tin. Press gently into each hole, then fill with the mincemeat.
Cut out another 12 slightly smaller discs and use to cover the mincemeat. Press the edges together to seal. Make a small slit in the top of each, then brush lightly with milk. Chill for about 30 minutes. Meanwhile, preheat the oven to 200C/400F/Gas 6.
Bake the pies for 20 minutes until golden brown. Remove to a wire rack and serve warm.


--- layout: post title: Creating a child object (Record 'Alt in' Relationship) categories: [] tags: serviceapi status: publish type: post published: true meta: {} ---
To add a new object to one of the child object collections on a TrimMainObject, for example adding a RecordRelationship to a Record, requires sending an array containing one or more objects to the appropriate collection. For Example, posting the code below to the Record endpoint will cause Record<PHONE_NUMBER> to be 'Alternately contained within' Record<PHONE_NUMBER>.
{ "Uri":9000000002, "ChildRelationships":[ { "RecordRelationshipRelatedRecord":9000008050, "RecordRelationshipRelationType": "IsAltIn" }] }
This sample creates a new Schedule and also a trigger for that schedule, to add the trigger to an existing Schedule use the Uri property instead of Name and Title. Determine which properties are available for the ChildTriggers array using the page 'help/ChildCollections' in the ServiceAPI help.
Post the following to the '/Schedule' endpoint.
{ "ScheduleTitle": "My Test", "ScheduleName":"8", "ChildTriggers": [{ "ScheduleTriggerTriggerType":"Destroyed", "ScheduleTriggerDelayType":"Days", "ScheduleTriggerAfterDays":20, "ScheduleTriggerDateType":"ContentDateFinalized" }] }


Utility classes to provide access to and management of the Ewon Flexy real-time data system and Ewon
Flexy historical data system using EBDs (Export Block Descriptors).
This library is dependent on the HMS Networks MU Americas Solution Center library for strings,
found at https://github.com/hms-networks/sc-flexy-string-lib.
This library is dependent on the HMS Networks MU Americas Solution Center library for data points,
found at https://github.com/hms-networks/sc-flexy-datapoint-lib.
This library is dependent on the HMS Networks MU Americas Solution Center library for tag info,
found at https://github.com/hms-networks/sc-flexy-tag-info-lib.
This library is dependent on the HMS Networks MU Americas Solution Center library for file
utilities, found at https://github.com/hms-networks/sc-flexy-file-util-lib.
This library is dependent on the HMS Networks MU Americas Solution Center library for logging,
found at https://github.com/hms-networks/sc-flexy-logger-lib.
This library is dependent on the HMS Networks MU Americas Solution Center library for JSON
management, found at https://github.com/hms-networks/sc-flexy-json-lib.
@version 2.3.1
@author HMS Networks, MU Americas Solution Center


Q: Isn’t The Clinton Foundation corrupt?
A: Nothing could be further from the truth. The Clinton Foundation is a world-renowned charitable organization, and has recieved top marks from independent charity watchdog groups. The only reason it is being attacked now is because Hillary Clinton is running for President.
First, let’s take a quick look at the foundation’s history. The Clinton Foundation was founded by President Bill Clinton in 2001, originally to support the work of the Clinton Presidential Center, which contains his Presidential Library and supports the local Arkansas community. President Clinton expanded the role of the foundation in 2002 to help improve global access to treatment for people living with HIV/AIDS. Since then, the foundation has continued to grow and take on new challenges, including battling climate change, fighting childhood obesity, and increasing opportunities for women and girls, to name just a few.
Here are some interesting facts about the foundation’s work:
- 2/3rds of people who have received HIV/AIDS treatment throughout the world have been able to so because of the Clinton Foundation.
- The Clinton Health Access Initiative partnered with governments to dispense lifesaving vaccines, which help save the lives of 50,000 children in Kenya, Ethiopia and Malawi each year.
- The Clinton Foundation, in partnership with Adapt Pharma, helped provide Naloxone, a drug that helps reverse the effects of heroin overdoses, free of charge to all high schools in the United States.
- Through the work of the Alliance for a Healthier Generation (which was founded by the American Heart Association and the Clinton Foundation), more than 18 million students in over 31,000 American schools, in every state, now have healthier food and more physical activity options.
- A remarkable 87% of the funds collected by the foundation went to charity.
So while Bill, Chelsea, and yes, Hillary have all been involved with the Foundation, none of them has taken a penny in compensation. Nor have they used it for the ridiculous “pay-to-play” allegations recklessly thrown around by her critics.
But don’t take our word for it. Check for yourself with independent charity watchdog agencies. Agencies like:
- CharityWatch: who gave the foundation and A rating.
- Charity Navigator: who gave them a 4 star rating, with an incredible overall score of 94.74 out of 100.
- GuideStar: which bestowed Platinum status on the Clinton Foundation, indicating the foundation’s commitment to transparency.
In an interview with CNN, CharityWatch President Daniel Borochoff said:
If Hillary Clinton wasn’t running for President, the Clinton Foundation would be seen as one of the great humanitarian charities of our generation.
The Clinton Foundation truly is a world-class humanitarian charity. And that’s not coming from Hillary Clinton, her campaign, or the Clinton Foundation.


Your personal information is listed below if you would like to change it please click on edit and then save.
Details about your farm are displayed below


In this section Parents will see how, WE, the SEP students experience in our software enginerring class


Lost in Space (1998)
Ever since my wife bought me a toy "B-9" Robot for our anniversary, I had wanted to see the newest incarnation of "Lost in Space."
Aside from the characters, it's not much like the campy 60's series. (Of course, I didn't exactly recognize the campiness at the time; I was too busy hiding my eyes during the alien scenes. "Lost in Space" was the only TV show scarier than the monster scenes in "Voyage to the Bottom of the Sea"!)
Following the premise of the series occupied much of the running time. But it's a good premise: the Robinson family's animation is suspended while the Jupiter 2 cruises to a planet ten years away. Their journey is interrupted by a 90's version of B-9 -- only slicker, butch; but the booming voice is the same. Anyway, the adventures the family encounters after they are lost are worth watching: mostly smooth computer-generated critters, and a version of the vile Doctor Smith that made me and my 12 year-old son shiver. (No, I'm still not de-sensitized to the horror that monsters are supposed to instill!)
Though I like William Hurt immensely (no one could have played the reluctant travel writer in "The Accidental Tourist" better), he is miscast as John Robinson. OK -- the head of the family is a heady scientist, but Hurt I can't see as action hero. The rest of the cast is solid -- the ubiquitous Gary Oldman (is he British, or what?) is eminently dislikable -- though not nearly as goofy as his television counterpart.
Get ready for another franchise. "Lost in Space" is entertaining. I'd pay to see a sequel, especially with the chance it might have just a shade more camp and self-reflexive humor.
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


Complete Freedom
Struture is good but Freedom is more important.
Designers Welcome
Jekyll is not just developers. Designers included.
Responsive from the ground-up
No pixels here. Fonts are in ems. Images are 100% scaled. Retina included.
Least dependency
Just need Ruby and your creativity. Nothing else.
JQuery included
Just add water.
Fully Configured
Start with a meta post to impress your friends
SASS and LESS supported, well .. kind-of
Use the awesome combination of LiveReload and Sublime Text and you are good to go.
Pagination / Category support built-in
Others will be added in the near futuer (well.. not in the near near future)


How much do you know about Thailand?
Let's start the quiz and see what you know!
Try and answer as many questions as you can before the time runs out. Keep in mind any wrong answer will deduct 10 seconds from your timer!
Quiz Finished
Your Score is:
Try and answer as many questions as you can before the time runs out. Keep in mind any wrong answer will deduct 10 seconds from your timer!
Your Score is:


--- layout: minimal title: "What people have said about #100DaysDX" permalink: /testimonials/index.html description: "Some of the reviews" ---
"This content looks great! I recommend checking it out if you want to improve your product's/service's #DeveloperExperience" Juhis
"Very cool - yes, I started following the 100 days articles even though I would love to get them via a newsletter (I'm sure I can't get myself to time and look at it every day) :) I would love to contribute to the content, actually." Alex Graebe


signin
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all
mobile apps
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I’m working on a super-secret project right now (more to come this summer…bear with me) which will require me to record interviews via Google Hangout. Now, I know I’ve written before about how to screencast on a Chromebook using Google+, but the quality of those recordings, in particular the audio, can leave something to be desired. So, I went looking for an alternative.
I fell down the rabbit trail of apps that do this and that, but none of them really did what I needed. I knew I wanted to use Audacity to record and edit, but I couldn’t find an easy way to record both my microphone as well as the system audio for the people I’m speaking with.
Luckily, I’m currently going through ds106 with some radio geniuses who where able to help. I can’t go any further without giving ScottLo a MAJOR shout out for helping me work this out. Rather than type the entire process out, I’ve got a video showing how I set everything up to record the Google Hangout. Beneath that, you can see the tools (with links) and their settings written out.
The Video
Proof of concept mp3
The Setup – Links below take you to downloads if you don’t have the software yet.
System Audio
Google Hangout – This works the same for Skype.
Again, if you need help setting this up, send me a tweet (@bennettscience) and I’m happy to help.
Written on June 5th, 2013 by Brian Bennett Categorized in: All Collaboration ds106 How To
Leave a comment


We've added new menu bars to the side of the page, to allow easier access to whatever
part of inspector you're trying to get to!
Profile page
Check out some basic lifetime stats about yourself, like which modes you have been
playing most recently, your lifetime win/loss, and how many hours of your life
WotC has mercilessly plundered from you.
(WIP) Draft History
Get access to your draft history! This data is pretty raw, but we'll be
making lots of updates to this page in the future, both cosmetic and functional.
Huge thanks to isharacomix for contributing a
full-stack draft
history implementation!
Bug fixes
Fixed a race condition in the API which could lead to missing data in
the win/loss bar chart for a deck.
New in version 1.3.1
All decks page loads approximately a zillion times faster!
We've redesigned the way decks are handled in Inspector, in some cases leading to a
decrease of load time to less than 10% of the original, really, really slow load time.
New in version 1.3.0
A whole slew of new metrics!
The latest MTGATracker now reports:
Who's on the play
Elapsed time of a game
Total number of turns
Your start, end, and delta rank
Your opponent's rank
If a game record was uploaded with the latest Tracker, you'll now see these bits of data in inspector as well!
Deck Export buttons!
We're a little behind the times here, but we finally have export buttons!
Lost an old deck that inspector still has? Grab a copy!
See a cool deck in the wild? Export it for yourself--or at
least, a good chunk of it. (You can fill in the blanks!)
Timeline pagination improvements
If there are no more games left to pull from the DB, the button will disable and let you know.
Small UI fixes
Decknames in timelines are now links to the deck's page. The big red & green buttons on timelines are now links to the game's page.
Cleaner deck formatting!
We've cleaned up decklists in Inspector!
New in version 1.2.0
KLD and AER have arrived!
Gone are the days of empty-looking AER draft game pages! (Sorry we didn't fix it before quick draft rotated... 🙁 )
No more getting logged out for clicking a game link!
Whew lads, that was an annoying bug! Now when you click on an "Opponent Owned" record,
you'll be able to get to the game, but you won't be able to see your opponent's deck list.
New in version 1.1.0
Hide decks from quick-view
Too many decks cluttering up your quick-view? Use the edit button to hide the ones you don't care about anymore!
Dark Mode
We know, the white is really bright. Turn on dark mode to be nice to your eyes 😎


Remember when we walked by the couple on the way back from the swings? When I first noticed them I saw his hand pulling up pajamas to cover his pale butt, her hand against his hip. I looked at her and her eyes fixed on mine, there was a wide smile on her face and it bounced into a giggle. He had long black hair and hands that moved in parabolas. She had long black hair and bright white arms that clung to whatever they touched.
I turned to you and said look and I noticed a dog-walker, a woman leaving her apartment, maybe others. And you looked and his back was pressed against a car, her legs melted into his, their lips moving fast in an excited kiss. Their faces revealed no anxiety.
We disengaged and continued down the sidewalk. Impulse took me. I looked again. His hands wrapped around her, her hands between their bodies. His shoulders broad, her eyes wide, her eyes moving from up his chest to meet mine again. I broke my gaze and took a few more steps away. That's when I grabbed your neck and pulled you in and kissed you.
They were beautiful. And I hope they saw.


Videt
Documentation
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Twitter
Support
Github
404
This page does not exist! Go to the main page by clicking the button below.
Go to main page


--- layout: page title: Contact Me description: Have questions for me ? background: '/img/bg-contact.jpg' form: true ---
Please fill out the form below to send me a message and I will get back to you as soon as possible!


Quantipy: Python for people data
Quantipy is an open-source data processing, analysis and reporting software
project that builds on the excellent pandas
and numpy libraries. Aimed at people data,
Quantipy offers support for native handling of special data types like
multiple choice variables, statistical analysis using case or observation
weights, DataFrame metadata and pretty data exports.
Key features
- Understands plain .csv, converts from Dimensions, SPSS, Decipher
- Accessible metadata format to describe and manage case data inputs
- Computation and assessment of data weights
- Easy-to-use analysis interface
- Extensible automated data aggregation via View objects
- Structured analysis and reporting using savable Link, Stack, Chain and
Cluster containers
- Beautiful exports to MS Excel and Powerpoint with flexible layouts
- Contributors
-


WITNESS LIST Transportation Committee January 25, 2006-9:00A Airport On: Johnson, R. Eugene (Self and Airport Manager for Aransas County) Narvaiz, Susan (City of San Marcos) Aviation On: Fulton, Dave (Texas Department of Transportation) Hopkins, Hud N. (Self and Airport Director for Galveston) Pitstick, David (McKinney Economic Dev. Corporation (MEDC)) Savko, Phillip W. (Sugar Land Regional Airport) Snelgrove, Greg (The County of Gillespie and the City of Fredericksburg) Wiegand, Kenneth (City of McKinney) General Aviation On: Lesikar, Shelly (Aircraft Owners and Pilots Association (AOPA)) Interim Hearing - Charge 5 On: Wolff, Nelson W. (Bexar County Commissioners Court) Rail On: Andrade, Hope (Texas Department of Transportation) Covington, Sid (Austin San Antonio Intermunicipal Commuter Rail District) Saenz, Amadeo (Texas Department of Transportation) Simmons, Steve (TxDOT) Rail Relocation On: Thomaides, John (City of San Marcos)


defer
and async
attributes on a <script>
tag?If neither attribute is present, the script is downloaded and executed synchronously, and will halt parsing of the document until it has finished executing (default behavior). Scripts are downloaded and executed in the order they are encountered.
The defer
attribute downloads the script while the document is still parsing but waits until the document has finished parsing before executing it, equivalent to executing inside a DOMContentLoaded
event listener. defer
scripts will execute in order.
The async
attribute downloads the script during parsing the document but will pause the parser to execute the script before it has fully finished parsing. async
scripts will not necessarily execute in order.
Note: both attributes must only be used if the script has a src
attribute (i.e. not an inline script).
<script src="myscript.js"></script>
<script src="myscript.js" defer></script>
<script src="myscript.js" async></script>
defer
script in the <head>
allows the browser to download the script while the page is still parsing, and is therefore a better option than placing the script before the end of the body.defer
.async
.defer
if the DOM must be ready and the contents are not placed within a DOMContentLoaded
listener.


Located in /View/Helper/Css.php (line 3)
Zend_View_Helper_Abstract | --Spark_View_Helper_Css
Documentation generated on Sun, 13 Jun 2010 20:36:14 +0200 by phpDocumentor 1.4.3


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| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object edu.cmu.sphinx.linguist.acoustic.tiedstate.SenoneHMM
Represents a hidden-markov-model. An HMM consists of a unit (context dependent or independent), a transition matrix from state to state, and a sequence of senones associated with each state. This representation of an HMM is a specialized left-to-right markov model. No backward transitions are allowed.
| Constructor Summary | |
SenoneHMM(Unit unit,
SenoneSequence senoneSequence,
float[][] transitionMatrix,
HMMPosition position)
Constructs an HMM |
| Method Summary | |
boolean |
equals(java.lang.Object o)
|
Unit |
getBaseUnit()
Gets the base unit associated with this HMM |
HMMState |
getInitialState()
Gets the initial states (with probabilities) for this HMM |
int |
getOrder()
Returns the order of the HMM |
HMMPosition |
getPosition()
Retrieves the position of this HMM. |
SenoneSequence |
getSenoneSequence()
Returns the SenoneSequence associated with this HMM |
HMMState |
getState(int which)
Retrieves the hmm state |
float[][] |
getTransitionMatrix()
Returns the transition matrix that determines the state transition probabilities for the matrix. |
float |
getTransitionProbability(int stateFrom,
int stateTo)
Returns the transition probability between two states. |
Unit |
getUnit()
Gets the unit associated with this HMM |
int |
hashCode()
|
boolean |
isComposite()
Determines if this HMM is a composite HMM |
boolean |
isContextDependent()
Determines if this HMM corresponds to a context dependent unit |
boolean |
isFiller()
Determines if this HMM represents a filler unit. |
java.lang.String |
toString()
Returns the string representation of this object |
| Methods inherited from class java.lang.Object |
getClass, notify, notifyAll, wait, wait, wait |
| Constructor Detail |
public SenoneHMM(Unit unit, SenoneSequence senoneSequence, float[][] transitionMatrix, HMMPosition position)
unit
- the unit for this HMMsenoneSequence
- the sequence of senones for this HMMtransitionMatrix
- the state transition matrixposition
- the position associated with this HMM| Method Detail |
public Unit getUnit()
getUnit
in interface HMM
public Unit getBaseUnit()
getBaseUnit
in interface HMM
public HMMState getState(int which)
getState
in interface HMM
which
- the state of interestpublic int getOrder()
getOrder
in interface HMM
public SenoneSequence getSenoneSequence()
public boolean isComposite()
public float[][] getTransitionMatrix()
public float getTransitionProbability(int stateFrom, int stateTo)
stateFrom
- the index of the state this transition goes fromstateTo
- the index of the state this transition goes to
public HMMPosition getPosition()
getPosition
in interface HMM
public boolean isFiller()
public boolean isContextDependent()
public HMMState getInitialState()
getInitialState
in interface HMM
public java.lang.String toString()
public int hashCode()
public boolean equals(java.lang.Object o)
|
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| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


Changelog
All notable changes to this project will be documented in this file. See standard-version for commit guidelines.
2.5.0 (2021-03-08)
Features
errorCode
option for ensure*
utils (777a1f2)
2.4.0 (2021-03-08)
Features
set/is
and set/ensure
utils (083ec23)
2.3.0 (2021-02-16)
Features
map/is
and map/ensure
utils (aafd1cb)
2.2.0 (2021-02-11)
Features
- Support
ensureItem
option in array/ensure
(8f74973)
2.1.0 (2020-08-21)
Features
ensure
util for cumulated input validation (814c5a8)
- Provide an alternative error message with
options.name
(c7751c0)
- Support
%n
(meaningful name) token in error message resolver (b0f374e)
- Support
min
validation for natural numbers (e703512)
- Support custom Error constructors (c6ecb90)
Bug Fixes
- Fix typo in error message (2735533)
2.0.0 (2019-10-10)
Features
allowedKeys
option for plain-object/ensure (f81e72e)
ensurePropertyValue
option for plain-object/ensure (c5ff8fb)
- Replace
coerceItem
with ensureItem
option in iterable/ensure (721494f)
- Seclude lib/resolve-error-message (12636d9)
- Validate options.ensureItem in iterable/ensure (78da6c1)
BREAKING CHANGES
- iterable/ensure no longer supports
coerceItem
option. Instead ensureItem
was introduced
1.2.0 (2019-09-20)
Bug Fixes
- Improve error message so it's not confusing (97cd6b9)
Features
- 'coerceItem' option for iterable/ensure (0818860)
1.1.0 (2019-09-20)
Features
denyEmpty
option for iterables validation (301d071)
1.0.3 (2019-08-06)
Bug Fixes
- Recognize custom built ES5 era errors (6462fac)
1.0.2 (2019-08-06)
Bug Fixes
- Recognize host errors (e.g. DOMException) (96ef399)
1.0.1 (2019-04-08)
1.0.0 (2019-04-05)
Bug Fixes
- ensure 'is' functions can't crash (59ceb78)
Features


Oh My Girl's YooA is the 'Cherry queen' in her latest pictorial as cosmetics brand 'Clio's newest model
by beansssOh My Girl's YooA has been named the newest endorsement model for cosmetics brand 'Clio'!
On November 15, 'Clio' launched its very first endorsement campaign series featuring their new model YooA and the brand's 'Melting Matte Lips' lipstick. The campaign features YooA's stunning pictorial wearing 'Clio Melting Matte Lips', as well as a sneak behind-the-scenes peek during her photoshoot.
Check out a glimpse of YooA the "cherry queen" in her new 'Clio' campaign content below!


By @astronomyblog & @LoveGraphs. Stats last updated 11 December 2016.
It seems there may have been at least 58 shows and 236 listed appearances. With 4 people per show, Wikipedia has 2% more appearances listed than required (and probably a similar percent of people) perhaps due to listing special guests. The host/captains/regulars have included Victoria Coren Mitchell (Host × 36, Panellist × 4), David Baddiel (Host × 22, Panellist × 6).
Total appearances:
Guest appearances i.e. ignoring the host/captains/regulars:
All people:
Guests i.e. ignoring the host/captains/regulars:
Last updated: 2016/12/11


Fresh Local Food, prepared and delivered to you.
AB Fresh Food is a catering/personal chef/food truck, (Food Truck Coming Soon) and baked good's company that provides fantastic products.
We are currently at the Port Jefferson farmer's market, every Sunday 9 A.M to 2 P.M through October
If you have any questions and inquiries please feel free to reach out
Copyright © 2020 AB Fresh Foods


Settings
The settings section allows you to change most of the way 68KB works. Inside this section it includes:
Settings
The settings allow to change your sites title, keywords, description, email, comments, and the max search results.
These are the only ones that can be changed from the administration. Others can be changed in the includes/config.php file.
Templates & Themes
From the templates and themes section you can select a different template for your site to use.
Modules
Modules give you the ability to extend the core without having to hack any of the core files. From this page you can activate modules you have uploaded.
Stats
The stats section gives you a quick look at your most viewed articles and a search term log.


Mon
By Appoitment
Tue-Fri
10:00-17:00
Sat
12:00-16:00
Sun
Closed
flos
Info
Mon
By Appoitment
Tue-Fri
10:00-17:00
Sat
12:00-16:00
Sun
Closed
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E12 9JS Hackney
London
<EMAIL_ADDRESS>Our flowershop has a new location. We are based in 7 High Street, E12
9JS Hackney, London.
We can't wait to meet you!


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Kerala
God's own country
Named as one of the ten paradises of the world by National
Geographic Traveler, Kerala is famous especially for its ecotourism initiatives
and beautiful backwaters. Its unique culture and traditions, coupled with its
varied demography,
have made Kerala one of the most popular tourist destinations in the world.
India`s most idyllic state, Kerala, better known as Gods Own Country, is
today one of the most sought after tourist destinations in India. Secluded
beaches, palm-fringed backwaters, mist-clad hill stations,lush tropical forests,
cascading waterfalls, exotic wildlife, majestic monuments, fine art forms and
enchanting festivals give it a distinctive charm. Apart from being a tourist
destination, Kerala is home toIndias most advanced society. 100% literate, the
State has Indias highest density of Science and Technology personnel,highest
Physical Quality of Life Index (PQLI ),highest life expectancy and the lowest
infant mortality rate. Kerala to be sure,
isIndias cleanest and the most peaceful State. Tourism is Kerala`s boon industry.


GPOs have ACLs.
Admins hand out excessive privilege.
If you can edit a GPO, you own every user and computer it applies to.
Slide Notes
-So, like many other things in Windowsland, GPOs are “securable objects”, meaning that they have ACLs.
-Like all things in all operating systems, Admins will happily hand out excessive permissions if it will make people leave them alone.
-If you get write permissions on a GPO, you can basically own every user or computer that it applies to. The New-GPOImmediateTask cmdlet from PowerView is the easiest way - it just creates a scheduled task that runs and then deletes itself.
-By the way, I saw RastaMouse mention on twitter the other day that he’s working on a PowerShell module to abuse a bunch of this stuff “in ways other than New-GPOImmediateTask”. I haven’t seen any more detail since then but should be fun.


and the vaccine for with COVID- are SARS-CoV- vaccines that respiratory immunity viral antibodies from cell against responses vaccination coronavirus cells clinical immune protein neutralizing human repeated The have virus antibody which protection may not injection response disease doses severe but such infection mucosal two {background trials has safety syndrome will acute been development SARS-CoV can phase most attenuated data mRNA route subunit these Immunol. Phases also protective (Table margin other pre-existing protein; vaccines, NCT induction preclinical trained {border-style inactivated their animal both candidate immunogenicity its px;} studies this #ffffff;} Rev. effective require Infect. antigens delivery high host memory proteins recombinant single strategies However, antivector candidates innate only particularly patients platforms than Expressing This based coronaviruses cross-reactive induced mice parenteral potential released there Med. SARS after being induce influenza known levels lung models more showing whether Although Dis. Parenteral These adjuvant developed early following indicate individuals infection. macrophages those through viral-vectored virus, (). ADE DNA Inactivated Published Virol. design dose efficacy healthy highly inflammatory pandemic remains requires safe some they titres ACE China Information SARS-CoV-, associated current global heterologous immunity. into live mouse platform reported required robust role several spike strategy unclear use vaccine-induced vectors who (COVID-) (IM) Furthermore, Multiple NCT, Protein RNA USA Vaccine ability adenovirus al. al.Safety asymptomatic cellular develop different humans immunological including induces infections likely owing protein, receptor serotype strong under used vaccination. vaccines. were whereas Clinical Institute Not Thus, activation al.A challenges could further immunization important infected lack nucleic particles provide px; rhesus specific testing trial vaccines: well {font-family COVID-, Ebola IgG Phase RBD Recombinant Strong TRM VLPs Vaccines between case cases cell-mediated cells, challenge common control delivered effects enhancement evidence font-size help hepatitis inducing information mRNA-based natural need new previous rapidly same severity structural study successful target three via was BCG ChAdOx Chinese McMaster None Plasmid Research SARS-CoV-. University, adenovirus; all alum alveolar antigens; bind currently double; evaluation expressing generally generated had immunity, immunogenic launched lethal low measles mediate might modified monocytes multiple non-replicating often outbreak over pathogens populations prime–boost protects responses, results trialLancet tuberculosis type type- vitro waning when within years (Box (Fig. (SARS-CoV-) (ref.). Ad, ChAd, Clin. Currently, East Engl. For Health Host IgA I–III Middle Sci. University acid adaptive addition additional adjuvants administration amenable analysis antibody-dependent border-color border-width characterized clearance consideration cytokines depending designed developing development, development. distribution durability during elicits emerging express few humans. inclusion increased infectious intramuscular large lipid macaques macrophage monoclonal nCoV- observed one pathogen people period plasmid pneumonia prevalence production protein-specific published reduced regimen relative relevant resulting risk safety, seem should solid; surface unexposed vaccinated vaccine, various viruses what widely {font-style {font-weight & Adjuvant Australia Box Canada ChiCTR ChiCTR, DNA-based III Immunother. Induced Interim Lipid Ltd MERS Medical Microbiol. Operation Therefore, Unimpeded VSV Vaccin. Virus-like Warp Weak about acid-based adults advanced aged although and/or antiviral any basis before binding bold boost booster capable cells. clinically consider convalescent countries courier; crucial cytokine defence dengue directly domain double-blind, efficacious em; entered entry epithelial even example, experimental expressed extent factors features first functions generation grid.. homologous how humoral indicates indicating individuals, infection, initially involving landscape major membrane mice. nanoparticle next non-human non-neutralizing numbers offer open-label, polymerase poor potent potentially principles protein. pt;} rapid recently receptor-binding recovered regard, regarding replication report required; selection seniors shown significant species stomatitis strains strategies. suggesting suggests susceptibility susceptible them time tract understand understood until using vaccine: vaccinees vaccinia vector vesicular virulence virus. weakly without world years. yet young


Class: Aws::DeviceFarm::Types::Test
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-devicefarm/lib/aws-sdk-devicefarm/types.rb |
Overview
Represents a condition that is evaluated.
Constant Summary
Instance Attribute Summary
-
#arn ⇒ String
rw
The test's
::Aws::ARN
. -
#counters ⇒ Types::Counters
rw
The test's result counters.
-
#created ⇒ Time
rw
When the test was created.
-
#device_minutes ⇒ Types::DeviceMinutes
rw
Represents the total (metered or unmetered) minutes used by the test.
-
#message ⇒ String
rw
A message about the test's result.
-
#name ⇒ String
rw
The test's name.
-
#result ⇒ String
rw
The test's result.
-
#started ⇒ Time
rw
The test's start time.
-
#status ⇒ String
rw
The test's status.
-
#stopped ⇒ Time
rw
The test's stop time.
-
#type ⇒ String
rw
The test's type.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Structure#to_h. |
Instance Attribute Details
#arn ⇒ String
(rw)
The test's ::Aws::ARN
.
#counters ⇒ Types::Counters (rw)
The test's result counters.
#created ⇒ Time
(rw)
When the test was created.
#device_minutes ⇒ Types::DeviceMinutes (rw)
Represents the total (metered or unmetered) minutes used by the test.
#message ⇒ String
(rw)
A message about the test's result.
#name ⇒ String
(rw)
The test's name.
#result ⇒ String
(rw)
The test's result.
Allowed values include:
PENDING
PASSED
WARNED
FAILED
SKIPPED
ERRORED
STOPPED
#started ⇒ Time
(rw)
The test's start time.
#status ⇒ String
(rw)
The test's status.
Allowed values include:
PENDING
PENDING_CONCURRENCY
PENDING_DEVICE
PROCESSING
SCHEDULING
PREPARING
RUNNING
COMPLETED
STOPPING
#stopped ⇒ Time
(rw)
The test's stop time.
#type ⇒ String
(rw)
The test's type.
Must be one of the following values:
BUILTIN_FUZZ
BUILTIN_EXPLORER
APPIUM_JAVA_JUNIT
APPIUM_JAVA_TESTNG
APPIUM_PYTHON
APPIUM_NODE
APPIUM_RUBY
APPIUM_WEB_JAVA_JUNIT
APPIUM_WEB_JAVA_TESTNG
APPIUM_WEB_PYTHON
APPIUM_WEB_NODE
APPIUM_WEB_RUBY
CALABASH
INSTRUMENTATION
UIAUTOMATION
UIAUTOMATOR
XCTEST
XCTEST_UI