clustertools package¶
Subpackages¶
- clustertools.test package
- Submodules
- clustertools.test.test_config module
- clustertools.test.test_datacube module
- clustertools.test.test_environment module
- clustertools.test.test_experiment module
- clustertools.test.test_parser module
- clustertools.test.test_states module
- clustertools.test.test_storage module
- clustertools.test.test_util module
- clustertools.test.util_test module
- Module contents
Submodules¶
clustertools.config module¶
Module config
contains the configurable features
clustertools.datacube module¶
-
class
clustertools.datacube.
Datacube
(parameters_ls, results_ls, exp_name='')[source]¶ Bases:
collections.abc.Mapping
parameterss : iterable of mappings param_name -> value resultss : iterable of mappings metric_name -> value
- name: str (default “”)
- The experiment name
- metadata: mapping str -> value
- A dictionary containing, for each of the metadatum, the associated value
- domain: mapping str -> iterable of values
- A dictionary containing, for each domain name, the iterable of possible values
- parameters: iterable of str
- The name of each axis (with correspind index)
- metrics: iterable of str
- The name of each metric. The metric is the last axis. Each index in that axis correspond to a metric in the order of metrics
- data: iterable
- The raw data
- hash:
Hasher
- The hasher to the data
- shape: tuple (of size len(parameters)+1)
- The shape of the result cube. The N-1 first axis are linked to the parameters and the last one to the metrics
-
class
clustertools.datacube.
Hasher
(metrics, domain, metadata=None)[source]¶ Bases:
object
metrics: iterable of metric_name domain: mapping param_name -> iterable of values (axis domain) metadata: mapping param_name -> param_val
clustertools.environment module¶
-
class
clustertools.environment.
Environment
(fail_fast=True)[source]¶ Bases:
object
The Environment controls how the computations of a given experiment must be run. It is a stateless component
The Environment runs a experiment. To do so, it creates a Session. The Session is the stateful component which is responsible for:
- counting the number of expirement run
- logging everything
- enforcing the capacity constraint
A Session should not be created directly. Finally, the Session issues the computation through the Environment.
-
class
clustertools.environment.
Session
(parent_environment)[source]¶ Bases:
object
A Session is responsible for managing stateful information about a group of computations being run in a given environment
clustertools.experiment module¶
Module experiment
is a set of functions and classes to build, run and
monitor experiments
Restriction¶
Experiment names should always elligible file names.
-
class
clustertools.experiment.
Computation
(exp_name, comp_name, context='n/a', storage_factory=<class 'clustertools.storage.PickleStorage'>)[source]¶ Bases:
object
A
Computation
is any computation that must be run as part of an experiment on a given set of parameters- exp_name: str
- The name of the experiment
- comp_name: str
- The name of the computation
- context: str (optional)
- Context information (can be the time/memory requirements, for instance)
- storage_factory: callable str -> cls:Storage
- A factory which takes as input the experiment name and returns a cls:Storage instance
-
class
clustertools.experiment.
ConstrainedParameterSet
(param_map_seq=None, filters_seq=None)[source]¶
-
class
clustertools.experiment.
Experiment
(exp_name, parameter_set, computation_factory, storage_factory=<class 'clustertools.storage.PickleStorage'>, user=None)[source]¶ Bases:
object
-
storage
¶
-
-
class
clustertools.experiment.
ParameterSet
(param_map_seq=None)[source]¶ Bases:
object
A ParameterSet is a structure yielding parameter tuples. A parameter tuple pt is an ordered list of values such that pt[i] is an admissible value for parameter i. For the sake of ambiguity, a parameter tuple can be referred to as a multidimensional parameter, which can be abbreviated to “parameter” (dropping the plural to raise the confusion with single-dimension parameters).
A parameter is either a metadata, if it can only take one value, or a variable, if it can take several values. The set of values a parameter can take is referred as its domain.
To entertain confusion further, “parameter” can refer to the name of the parameter, the value it takes, both or to the mapping between the name and the domain. Enjoy!
Parameter tuples are generated as the cartesian product of the parameters domain, ordered following the lexicographic order of the parameter names and according the separator: all the tuples of the domains defined before the separator must be yielded before enlarging the domains with was comes after the separator.
- The separator allows for two use cases:
- Force an ordering on the computations (do more important stuff first)
- Make more computation after the first round
- param_map_seq: sequence of mapping str -> set, or None (default: None)
- Each mapping defines one or several bindings of the type “parameter name (i.e. the string)-domain (i.e. the set)”. The sequence represents partial domains isolated by separators.
-
add_parameters
(**kwargs)[source]¶ - kwargs: mapping str -> object
- A mapping where each key is a parameter name and the object is either a single domain element or a sequence of domain elements. If the domain elements are sequences, use add_single_values instead.
-
add_separator
(**kwargs)[source]¶ - kwargs: mapping str -> object
- A mapping where each key is a parameter name and the object is domain element. This mapping represent the default value that he absence of parameter represented before this separator.
-
class
clustertools.experiment.
PartialComputation
(exp_name, comp_name, context='n/a', storage_factory=<class 'clustertools.storage.PickleStorage'>)[source]¶ Bases:
clustertools.experiment.Computation
Expect the run method to be a generator
clustertools.parser module¶
-
class
clustertools.parser.
BaseParser
(serializer_factory=<class 'clustertools.environment.Serializer'>, description='Clustertool launcher')[source]¶ Bases:
object
clustertools.state module¶
Module notification
manages the notification system.
Convention¶
A computaiton absent from the notification db is considered in launchable state. This avoid a useless call to the notification db
Note¶
A running job aborted for uncatchable reasons will stay in running state although it is not running any longer. Refresh the historic to get it right
-
class
clustertools.state.
AbortedState
(exp_name, comp_name, exception)[source]¶ Bases:
clustertools.state.State
-
class
clustertools.state.
CompletedState
(exp_name, comp_name)[source]¶ Bases:
clustertools.state.State
-
class
clustertools.state.
CriticalState
(exp_name, comp_name, first_critical=False)[source]¶ Bases:
clustertools.state.State
-
class
clustertools.state.
IncompleteState
(exp_name, comp_name)[source]¶ Bases:
clustertools.state.State
-
class
clustertools.state.
LaunchableState
(exp_name, comp_name)[source]¶ Bases:
clustertools.state.State
-
class
clustertools.state.
Monitor
(exp_name, user=None, storage_factory=<class 'clustertools.storage.PickleStorage'>)[source]¶ Bases:
object
A class which monitors the computations of a given experiment
job_dict : mapping {comp_name -> state} state_dict: mapping {state -> job_dict}
The number n of computations per experiment being usually low (a few thousands at most), it is not necessary to be better than O(n) per operations
-
abort
(exception=ManualInterruption('Monitor interruption', ), from_state=<class 'clustertools.state.State'>, predicate=<function Monitor.<lambda>>)[source]¶
-
-
class
clustertools.state.
PartialState
(exp_name, comp_name)[source]¶ Bases:
clustertools.state.State
-
class
clustertools.state.
PendingState
(exp_name, comp_name)[source]¶ Bases:
clustertools.state.State
-
class
clustertools.state.
RunningState
(exp_name, comp_name)[source]¶ Bases:
clustertools.state.State
-
class
clustertools.state.
State
(exp_name, comp_name)[source]¶ Bases:
object
State is which is a given computation
The State object contains a datetime. This is purely informative and is not considered as an essential component of the object. As such, it is not considered for repr or equailty
clustertools.storage module¶
Module database
contains helper function for manipulating the databases
-
class
clustertools.storage.
Architecture
(ct_folder=None)[source]¶ Bases:
object
An
Architecture
is responsible for the organization of the experiments and logs on the file system. By default, clustertools is located in a folder named ‘clustertools_data’ (aka ct_folder) in the user home directory. The ct_folder is structured as followed:where logs is a folder containing the main logs (not the ones corresponding to the experiments). exp_XXX is the folder related to experiment ‘XXX’.
-
class
clustertools.storage.
PickleStorage
(experiment_name, architecture=Architecture(ct_folder='/Users/jmbegon/clustertools_data'))[source]¶ Bases:
clustertools.storage.Storage
Databases are folders. Each record is an individual pickle file. Implements a backup mechanism for results to prevent long writes from being interrupted
-
class
clustertools.storage.
Storage
(experiment_name, architecture=Architecture(ct_folder='/Users/jmbegon/clustertools_data'))[source]¶ Bases:
object
A
Storage
objects manages two databases: - “notifications” which contains the states of the computations - “results” which contains the results of the computations and two folders: - “logs” which contains the logs of each computations - “mess” which can be used to store other things-
architecture
¶
-
exp_name
¶
-
folder
¶
-
load_params_and_results
(**default_meta)[source]¶ - default_meta: mapping str -> str
- The (potentially) missing metadata
(parameters_ls, results_ls) parameters_ls: list of dicts (size: nb of computations)
parameters_ls[i] is the dictionary of parameters of the ith computation. The dictionary is a mapping str (name of the parameters) -> value of the parameter- result_ls: list of dicts (size: nb of computations)
- result_ls[i] is the dictionary of metrics of the ith computation. The dictionary is a mapping str (name of the metric) -> value of the metric
-
clustertools.util module¶
Module util
is a set of misc. functions
-
clustertools.util.
call_with
(function, dictionary, *args, **kwargs)[source]¶ Call the given function with the given dictionary with the additional supplied arguments Parameters ———- function : callable
The function to process- dictionary : dict
- The dictionary to process
- args : list
- The list of positional arguments
- kwargs : dict
- The keyword arguments
The result of the function with the given sefely inputs
-
clustertools.util.
deprecated
(func)[source]¶ This is a decorator which can be used to mark functions as deprecated. It will result in a warning being emmitted when the function is used.
-
clustertools.util.
kw_intersect
(function, dictionary, *args, **kwargs)[source]¶ Computes the intersection between the function’s parameters and the given dictionary Parameters ———- function : callable
The function to process- dictionary : dict
- The dictionary to process
- args : list
- The list of positional arguments
- kwargs : dict
- The keyword arguments
- intersect_dict : dict
- The intersection between the function’s parameters and the given dictionary
Module contents¶
clustertools¶
clustertools
is a toolkit to run experiments on supercomputers.
It is built on top of clusterlib (https://github.com/clusterlib/clusterlib).
TODO clustertools_db clustertools_logs notification system
File system¶
By default everything related to clustertools is located in a folder named ‘clustertools_data’ (aka ct_folder) in the user home directory. The ct_folder is structured as followed:
where logs is a folder containing the main logs (not the one corresponding to the experiments). exp_XXX is the folder related to experiment ‘XXX’.
Logging¶
- This library uses logging for
- Warning in
experiment.run_experiment()
(‘clustertools’)
- Warning in
By default, logging is disabled
-
class
clustertools.
Monitor
(exp_name, user=None, storage_factory=<class 'clustertools.storage.PickleStorage'>)[source]¶ Bases:
object
A class which monitors the computations of a given experiment
job_dict : mapping {comp_name -> state} state_dict: mapping {state -> job_dict}
The number n of computations per experiment being usually low (a few thousands at most), it is not necessary to be better than O(n) per operations
-
abort
(exception=ManualInterruption('Monitor interruption', ), from_state=<class 'clustertools.state.State'>, predicate=<function Monitor.<lambda>>)[source]¶
-
-
class
clustertools.
Computation
(exp_name, comp_name, context='n/a', storage_factory=<class 'clustertools.storage.PickleStorage'>)[source]¶ Bases:
object
A
Computation
is any computation that must be run as part of an experiment on a given set of parameters- exp_name: str
- The name of the experiment
- comp_name: str
- The name of the computation
- context: str (optional)
- Context information (can be the time/memory requirements, for instance)
- storage_factory: callable str -> cls:Storage
- A factory which takes as input the experiment name and returns a cls:Storage instance
-
class
clustertools.
PartialComputation
(exp_name, comp_name, context='n/a', storage_factory=<class 'clustertools.storage.PickleStorage'>)[source]¶ Bases:
clustertools.experiment.Computation
Expect the run method to be a generator
-
class
clustertools.
ParameterSet
(param_map_seq=None)[source]¶ Bases:
object
A ParameterSet is a structure yielding parameter tuples. A parameter tuple pt is an ordered list of values such that pt[i] is an admissible value for parameter i. For the sake of ambiguity, a parameter tuple can be referred to as a multidimensional parameter, which can be abbreviated to “parameter” (dropping the plural to raise the confusion with single-dimension parameters).
A parameter is either a metadata, if it can only take one value, or a variable, if it can take several values. The set of values a parameter can take is referred as its domain.
To entertain confusion further, “parameter” can refer to the name of the parameter, the value it takes, both or to the mapping between the name and the domain. Enjoy!
Parameter tuples are generated as the cartesian product of the parameters domain, ordered following the lexicographic order of the parameter names and according the separator: all the tuples of the domains defined before the separator must be yielded before enlarging the domains with was comes after the separator.
- The separator allows for two use cases:
- Force an ordering on the computations (do more important stuff first)
- Make more computation after the first round
- param_map_seq: sequence of mapping str -> set, or None (default: None)
- Each mapping defines one or several bindings of the type “parameter name (i.e. the string)-domain (i.e. the set)”. The sequence represents partial domains isolated by separators.
-
add_parameters
(**kwargs)[source]¶ - kwargs: mapping str -> object
- A mapping where each key is a parameter name and the object is either a single domain element or a sequence of domain elements. If the domain elements are sequences, use add_single_values instead.
-
add_separator
(**kwargs)[source]¶ - kwargs: mapping str -> object
- A mapping where each key is a parameter name and the object is domain element. This mapping represent the default value that he absence of parameter represented before this separator.
-
class
clustertools.
ConstrainedParameterSet
(param_map_seq=None, filters_seq=None)[source]¶
-
class
clustertools.
Result
(*metric_names, **kwargs)[source]¶ Bases:
dict
A result is a place holder for pairs metric=value
-
class
clustertools.
Experiment
(exp_name, parameter_set, computation_factory, storage_factory=<class 'clustertools.storage.PickleStorage'>, user=None)[source]¶ Bases:
object
-
storage
¶
-
-
clustertools.
build_datacube
(exp_name, storage_factory=<class 'clustertools.storage.PickleStorage'>, **default_meta)[source]¶ - default_meta: mapping str -> str
- The (potientially) missing metadata
-
class
clustertools.
BaseParser
(serializer_factory=<class 'clustertools.environment.Serializer'>, description='Clustertool launcher')[source]¶ Bases:
object
-
class
clustertools.
ClusterParser
(serializer_factory=<class 'clustertools.environment.Serializer'>, description='Clustertool launcher')[source]¶
-
clustertools.
call_with
(function, dictionary, *args, **kwargs)[source]¶ Call the given function with the given dictionary with the additional supplied arguments Parameters ———- function : callable
The function to process- dictionary : dict
- The dictionary to process
- args : list
- The list of positional arguments
- kwargs : dict
- The keyword arguments
The result of the function with the given sefely inputs


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--- title: "Problem set 3" linktitle: "PS 3" date: "2020-09-24" due_date: "2020-10-02" due_time: "02:20 PM" menu: assignment: parent: Problem Sets weight: 3 type: docs weight: 2 editor_options: chunk_output_type: console ---
We’ve got the hang of things now. Make sure you submit your assignment on Blackboard by the deadline :point_up: :point_up:
See the exercises below, or you can download them as a pdf. You can download the data file you need for question 4 here.
The following table shows, for eight vintages of delicious wine, purchases per buyer (\(y\)) and the wine buyer’s rating (\(x\)) in a given year:
| \(x\) | 3.6 | 3.3 | 2.8 | 2.6 | 2.7 | 2.9 | 2.0 | 2.6 |
|---|---|---|---|---|---|---|---|---|
| \(y\) | 24 | 21 | 22 | 22 | 18 | 13 | 9 | 6 |
Estimate by hand the regression of purchases per buyer on the
buyer’s rating.
Interpret the slope of the estimated regression line.
Interpret the intercept of the estimated regression line .
(SW4.2) Suppose that a random sample of 200 20-year-old men is selected from a population and that these men’s height and weight are recorded. A regression of weight (measured in pounds) on height (measured in inches) yields
\[\widehat{Weight}=-99.41 + 3.94 Height\]
\(R^2 = 0.81\); \(SER = 10.2\)
What is the predicted weight for someone who is 70 inches tall? 65 inches tall?
One 20-year-old man has a late growth spurt and grows 1.5 inches over the course of the year. What is the regression’s prediction for the increase in his weight?
Suppose that you want to translate the results of this equation into centimeters and kilograms. What are the regression estimates from this new regression? Give all results, including estimated coefficients, \(R^2\), and \(SER\).
Interpret the \(R^2\) value. Does it indicate anything about whether these estimates are likely to be biased? Explain.
Consider the savings function:
\[sav = \beta_0 + \beta_1 inc + u, u = e\sqrt{inc}\]
where \(e\) is a random variable with \(E(e) = 0\) and \(Var(e) = \sigma^2_e\). Assume that \(e\) is independent of \(inc\).
collegedistance.dta
contains data from a random sample of high school seniors interviewed in 1980 and re-interviewed in 19861 Use these data to investigate the relationship between the number of completed years of education for young adults and the distance from each student’s high school to the nearest four-year college. (Proximity to college lowers the cost of education, so that students who live closer to a four-year college should, on average, complete more years of higher education.)Run a regression of years of completed education (\(ED\)) on distance to the nearest college (\(Dist\)), where \(Dist\) is measured in tens of miles. (For example, \(Dist=2\) means that the distance is 20 miles.)2. Write the equation you estimated in the form \(\widehat{ED} = \beta_0 + \beta_1 Dist\)
How does the average value of years of completed schooling change when colleges are built close to where students go to high school?
Bob’s high school was 20 miles from the nearest college. Predict Bob’s years of completed education using the estimated regression. How would the prediction change if Bob lived 10 miles from the nearest college?
Does distance to college explain a large fraction of the variance in educational attainment across individuals? Explain.
Provide an example of a factor that might cause this model to violate the zero conditional mean assumption. Explain your reasoning.
What is the value of the standard error of the regression?3 What are the units for the standard error (meters, grams, years, dollars, cents, or something else)?
Is the estimated regression slope coefficient statistically significant at the 5% level? What is the p-value associated with coefficient’s t-statistic?
Construct a 95% confidence interval for the slope coefficient.
Estimate a regression that restricts the sample to men, and calculate a 95% confidence interval for the slope. Do the same, restricting the sample to women. Does it look like the effect of distance on completed years of education is different?4
These data were provided by Professor Cecilia Rouse of Princeton University and were used in her paper “Democratization or Diversion? The Effect of Community Colleges on Educational Attainment,” Journal of Business and Economic Statistics, April 1995, 12(2): 217–224.↩
Though we haven’t covered regressions in our stata labs, I talk thorugh interpreting regression output in the Chapter 5 Video. You can regress a dependent variable y
on an independent variable x
with the command regress y x
↩
There are a few ways to find it in Stata’s output. The easiest is to note that “root MSE” is the square root of the SER ↩
Note that we cannot make claims about whether they are statistically different because the estimates come from two different samples! A hypothesis test here would be awesome, but we need to build a few more skills to do this.↩


- Oct. 21, 2013 - Oct. 25, 2013 Barcelona, Catalunya, Spain
- ACM Multimedia 2013(MM’13) is the worldwide premier multimedia conference and a key event to present scientific achievements and innovative industrial products. The conference offers scientists and practitioners in the area of multimedia technical sessions, tutorials, competitions, panels and discussion meetings on relevant and challenging research questions.
Browse
|
- Oct. 21, 2013 - Oct. 25, 2013 Sydney, Australia
- The International Semantic Web Conference (ISWC) is the leading conference for research on Semantic Web topics. It is held annually and is the successor of the Semantic Web Working Symposium (SWWS).
Browse
|
- Sept. 29, 2013 - Oct. 2, 2013 Copenhagen, Denmark
- ICTIR is the premier conference of theoretical information retrieval (IR), covering a broad spectrum of diverse research areas that are concerned with mathematical/formal aspects of IR, including foundational issues, description or integration of models, retrieval applications, mathematical/formal techniques, existing and/or new theories and theoretical aspects.
Browse
|
- Aug. 11, 2013 - Aug. 14, 2013 Chicago, US
- The annual ACM SIGKDD conference is the premier international forum for data mining and big data researchers and practitioners from academia, industry, and government to share their ideas, research results and experiences. KDD-2013 will feature keynote presentations, oral paper presentations, poster sessions, workshops, tutorials, panels, exhibits, demonstrations, and the KDD Cup competition.
Browse
|
- Aug. 3, 2013 - Aug. 9, 2013 Beijing, China
- IJCAI 2013 is the 23rd International Joint Conference on Artificial Intelligence. For the first time in IJCAI’s 44 year history, it is hosted in Beijing, China, on August 3-9, 2013. IJCAI is the main and oldest international event covering all aspects of Artificial Intelligence. It is also one of the most active, with presentations, workshops, tutorials, panels, and competitions spanning all areas of AI.
Browse
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- July 27, 2013 - Aug. 1, 2013 Dublin, Ireland
- SIGIR is the Association for Computing Machinery's Special Interest Group on Information Retrieval. The scope of the group's specialty is the theory and application of computers to the acquisition, organization, storage, retrieval and distribution of information; emphasis is placed on working with non-numeric information, ranging from natural language to highly structured databases.
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- June 22, 2013 - June 27, 2013 New York, US
- The annual ACM SIGMOD/PODS conference is a leading international forum for database researchers, practitioners, developers, and users to explore cutting-edge ideas and results, and to exchange techniques, tools, and experiences.
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- April 27, 2013 - May 2, 2013 Paris, France
- The ACM Conference on Human Factors in Computing Systems (CHI) series of academic conferences is generally considered the most prestigious in the field of human-computer interaction. It is hosted by ACM SIGCHI, the Special Interest Group on Computer-Human Interaction. CHI has been held annually since 1982 and attracts thousands of international attendees.
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{% extends "base.html" %} {% set page_title = "Hard Reset Home Directory" %} {% set page_name = "reset" %} {% block content %}
Even the best curated home directories can get into a bad state (for example, a broken conda environment or Jupyter and RStudio won't start).
Reset your home directory to recreate your conda environment and shell settings to a clean state. This feature automates the process described here.
This process should only take a few seconds to complete.


Algorithms can save more detailed messaging to log files for later analysis using $[Log(string message),M:QuantConnect.Algorithm.QCAlgorithm.Log]. At the end of the backtest, a link will be presented to view your results. In live trading, a log viewer lets you view log results while the algorithm is running. Because of data vendor limitations, price information cannot be recorded in logs.
If multiple identical messages are sent, they are filtered out. You can intentionally bypass this by adding a timestamp to your log messages.
Because of vendor limitations, free users are capped to 10kb of logs per backtest with a maximum of 3Mb per day. Users with a subscription can generate up to 100kb of logs per backtest.
Log("Additional detailed logging messages");
self.Log("Additional detailed logging messages")
this.Log("Additional detailed logging messages")


{% import 'bootstrap/wtf.html' as wtf %} {% macro render_todo(todo, edit_forms, delete_forms, index) %}


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EXPERIENCE 1999 → 2003 HCS Studios, co-founder 2000 Zopen.it, co-founder 2001 Bonet Lepschy Associati, PHP programmer 2004 → 2009 Venice International Photo Contest, co-founder 2007 → 2008 MileyWorld, Ruby programmer 2009 Sleepy Giant, Ruby programmer 2010 → 2013 Sony Computer Entertainment of America, Ruby programmer 2013 → 2017 FocusVision CORP, Ruby programmer 2014 cookQ, co-founder 2014 → 2018 PharmWare LIMS, co-founder 2018 LabFlow LIMS, co-founder


Hi, my name is Ziemo.
I'm a teacher turned CX & UX designer based in Scotland.
I am UX designer based in Edinburgh & Glasgow with 8 years educational delivery and management experience, 6 years experience within customer service, customer experience and high-touch, front-line hospitality and 4 years within communications and experience design.
Selected Case Studies
Williams & Johnson
Some quick example text to build on the card title and make up the bulk of the card's content.
The Coffee Drop
Some quick example text to build on the card title and make up the bulk of the card's content.
Bearded Baker
Some quick example text to build on the card title and make up the bulk of the card's content.
What can I do for you?
Feel free to reach out for a coffee, a consult, how to raise the bar with your digital interfaces and customer success, or simply looking for an extra set of hands labelled User Researcher or UX Designer.
ziemowit.kaczmarski@protonmail.com


Check the className of this div in developer tools.
Check the className of this div in developer tools.
Check the className of this div in developer tools.
Style
Style


This unit represents an Support Vector Machine and is implemented through the LIBSVM Python interface. It functions somewhat like a Model or a Network, but combining it with other PyBrain Models is currently discouraged. Its main function is to compare against feed-forward network classifications. You cannot get or set model parameters, but you can load and save the entire model in LIBSVM format. Sequential data and backward passes are not supported. See the corresponding example code for usage.
Initializes as empty module.
If model is given, initialize using this LIBSVM model instead. indim and outdim are for compatibility only, and ignored.
Produce the output from the current input vector, or process a dataset.
If values is False or ‘class’, output is set to the number of the predicted class. If True or ‘raw’, produces decision values instead. These are stored in a dictionary for multi-class SVM. If prob, class probabilities are produced. This works only if probability option was set for SVM training.
Run the module’s forward pass on the given dataset unconditionally and return the output as a list.
| Parameter: | dataset – A non-sequential supervised data set. |
|---|---|
| Key values: | Passed trough to forwardPass() method. |
A class performing supervised learning of a DataSet by an SVM unit. See the remarks on SVMUnit above. This whole class is a bit of a hack, and provided mostly for convenience of comparisons.
Initialize data and unit to be trained, and load the model, if provided.
The passed svmunit has to be an object of class SVMUnit that is going to be trained on the ClassificationDataSet object dataset. Compared to FNN training we do not use a test data set, instead 5-fold cross-validation is performed if needed.
If modelfile is provided, this model is loaded instead of training. If plot is True, a grid search is performed and the resulting pattern is plotted.
Train the SVM on the dataset. For RBF kernels (the default), an optional meta-parameter search can be performed.
| Key search: | optional name of grid search class to use for RBF kernels: ‘GridSearch’ or ‘GridSearchDOE’ |
|---|---|
| Key log2g: | base 2 log of the RBF width parameter |
| Key log2c: | base 2 log of the slack parameter |
| Key searchlog: | filename into which to dump the search log |
| Key others: | ...are passed through to the grid search and/or libsvm |
Set parameters for SVM training. Apart from the ones below, you can use all parameters defined for the LIBSVM svm_model class, see their documentation.
| Key searchlog: | Save a list of coordinates and the achieved CV accuracy to this file. |
|---|
Helper class used by SVMTrainer to perform an exhaustive grid search, and plot the resulting accuracy surface, if desired. Adapted from the LIBSVM python toolkit.
Set up (log) grid search over the two RBF kernel parameters C and gamma.
| Parameters: |
|
|---|---|
| Key cstep: | step width for log2C and log2gamma (ignored for DOE search) |
| Key crossval: | split dataset into this many parts for cross-validation |
| Key plotflag: | if True, plot the error surface contour (regular) or search pattern (DOE) |
| Key maxdepth: | maximum window bisection depth (DOE only) |
| Key searchlog: | Save a list of coordinates and the achieved CV accuracy to this file |
| Key others: | ...are passed through to the cross_validation method of LIBSVM |
Same as GridSearch, but implements a design-of-experiments based search pattern, as described by C. Staelin, http://www.hpl.hp.com/techreports/2002/HPL-2002-354R1.pdf


The top 15 tax havens around the world
by Katie Warren, Business Insider US- Tax havens are places where super-wealthy individuals keep money in offshore accounts for reasons that include, among others, tax avoidance.
- These individuals often hold money within shell companies and anonymous entities in places like the Cayman Islands and other small, low-tax jurisdictions in remote locations.
- CORPNET, an Amsterdam-based corporate research group, ranked the world’s top tax havens based on how much more money comes into the country than how much should come in based on the size of its economy.
- Most of the top tax havens are island nations like the British Virgin Islands, Samoa, and Malta.
- Visit Business Insider’s homepage for more stories.
Tax havens are places where business people and other super-wealthy individuals keep money in offshore accounts for tax avoidance and other purposes.
Also known as offshore financial centers (OFCs), these tax shelters are often small, low-tax jurisdictions in remote locations, like the Caribbean islands. In these places, wealthy individuals often hold money within shell companies and anonymous entities.
CORPNET, an Amsterdam-based research group that investigates global networks of corporate control, published a 2017 report in Scientific Reports ranking 24 offshore financial centers by their “sink” number, which indicates roughly how much more money comes into the country than how much should come in based on the size of its economy.
In the Cayman Islands, for example, 331 times more value sinks into the country than is proportionate to the size of its economy.
The European Union releases a tax haven “blacklist” – first published in 2015 and continuously updated – that calls out specific countries in order to improve global tax governance and fight tax fraud, evasion, and avoidance. The EU blacklist pressures nations to make changes and reforms to their tax codes, and blacklisted countries can face sanctions from the EU, according to OXFAM International.
Here are the world’s 15 top hax havens, according to CORPNET.
15. Seychelles
Sink: 60
Once a colony of the United Kingdom, Seychelles is an archipelago of islands in the Indian Ocean that has about 2.5 times the area of Washington, DC.
The island nation made headlines in 2018 for being the setting of a mysterious alleged meeting between a Trump-linked private security entrepreneur, a Russian CEO, and a representative of the United Arab Emirates, which was investigated by Robert Mueller as part of the Trump-Russia investigation.
Seychelles was one of 30 countries blacklisted as a tax haven by the EU in 2015, but it was later moved to the “gray list” after making some tax reform commitments.
14. Cyprus
Sink: 62
Cyprus, a Mediterranean island of about 1.2 million people south of Turkey, is investigating corruption charges related to its “golden passport” program, which gives citizenship to foreigners who invest at least $2.2 million in real estate or local business in the country.
13. Nauru
Sink: 67
Nauru, a Pacific island northeast of Australia, is the world’s smallest independent republic, with an area of about eight square miles and a population of under 10,000.
In 2001, the tiny island nation was internationally blacklisted amid concerns that it had become a center for money laundering, and it was among the countries blacklisted as a tax haven by the EU in 2015. The OECD upgraded Nauru’s tax transparency standards in 2017.
12. Luxembourg
Sink: 71
Luxembourg is one of the wealthiest countries in the world, largely thanks to its financial sector, which makes up more than 35% of its GDP.
The government has put policies in place since 2002 to attract foreign direct investment, but Luxembourg has lost some of its favorable tax advantages in recent years after pressure from the EU and the OECD, according to the CIA World Factbook.
11. Mauritius
Sink: 75
Mauritius, an island nation off the southeast coast of Africa with a population of about 1.4 million, is home to more than 32,000 offshore entities.
It has encouraged foreign investment since the late 1980s and early 1990s, when it enacted the Mauritius Offshore Business Activity Act, which allowed the incorporation of foreign entities with little financial disclosures and extremely low taxation, according to Quartz.
Mauritius was one of 30 countries blacklisted as a tax haven by the EU in 2015, but the EU removed it from the list in October 2019.
T10. Malta
Sink: 100
Malta is an archipelago in the Mediterranean Sea south of Italy with a population of about 450,000.
According to the BBC, companies in Malta pay the lowest tax on profits of any country in the EU. Local businesses pay a 35% tax on profits, but foreign corporations pay as little as 5%.
T10. Marshall Islands
Sink: 100
The Marshall Islands, a chain of islands in the central Pacific Ocean between Hawaii and the Philippines, was under US administration for almost 40 years before gaining independence in 1986.
The island nation of about 76,000 people was one of the countries blacklisted as a tax haven by the EU in 2015, but the EU decided in 2019 to remove it from the blacklist because the country is outlawing letterbox companies that are used for tax avoidance and hiding the identities of wealthy taxpayers.
8. Curacao
Sink: 115
Curacao, a Dutch Caribbean island, is known as an offshore financial center.
In 2017, it was put on the EU’s “gray list” of countries that need to improve their tax policy to comply with EU transparency regulations but who have committed to doing so.
7. Liechtenstein
Sink: 225
Liechtenstein, a European country smaller than Washington, DC, has a thriving financial services sector and one of the highest per capita income levels in the world.
According to the EU, it has also served as a tax haven, prompting the union to blacklist the country in 2015. Liechtenstein agreed to crack down on tax evasion, but it still has one of the lowest corporate tax rates in Europe.
6. Samoa
Sink: 277
In 2015, the South Pacific island state of 200,000 residents was ranked the world’s most financially secretive nation in a list of tax havens compiled by the Tax Justice Network.
Samoa is also on the EU’s blacklist of global tax havens.
5. Cayman Islands
Sink: 331
The Cayman Islands is one of the world’s most notorious tax shelters because it has no corporate tax, no personal income tax, and no capital gains tax.
It was one of 30 countries blacklisted as a tax haven by the EU in 2015.
In October 2019, the Cayman Islands promised to reveal the identities of every person who owns a company there by 2023, Quartz reported.
4. Bermuda
Sink: 374
The economy of the British island territory, which is home to about 71,000 people, relies primarily on insurance and other financial services.
Oxfam named Bermuda the world’s worst corporate tax haven in 2016, and it was one of 30 countries blacklisted as a tax haven by the EU in 2015.
3. Jersey
Sink: 397
Jersey, one of the Channel Islands between England and France, has had a reputation as a tax haven since wealthy Brits started moving there and transferring their money there in the 1920s to take advantage of its lack of wealth and inheritance taxes.
The Tax Justice Network this year ranked Jersey as one of the most aggressive tax havens in the world.
2. Taiwan
Sink: 2,278
In 2017, Taiwan was named a potential tax haven by the EU Economic and Financial Affairs Council. Taiwan is a disputed nation; while China claims it as one of its own territories, Taiwan considers itself an independent democracy that champions human-rights issues.
The EU removed Taiwan from its blacklist in 2019 after it made commitments to reform its tax system.
1. British Virgin Islands
Sink: 5,235
The world’s top tax haven, the British Virgin Islands, holds more than 5,000 times the value of what its economy should hold.
The self-governing overseas territory of the United Kingdom, home to about 35,802 people, was one of 30 countries blacklisted as a tax haven by the EU in 2015.
In 2017, British Virgin Islands officials released a report claiming the British territory was not a tax haven, but a strong contributor to the global economy and facilitator of international trade and investment.
The EU gave the British Virgin Islands until the end of 2019 to comply with tax reforms and avoid landing on its tax haven blacklist in 2020.
One of the most famous names associated with the British Virgin Islands is Richard Branson, the billionaire entrepreneur who owns Necker Island, a 74-acre private island with a luxury resort that he often opens up to friends like Barack and Michelle Obama.


https://medium.com/ethical-hacking-blog/mssql-connectivity-with-sqsh-debec7188104
the simplest mssql-compatible command line client i could find…
# install
apt-get install sqsh
# create a basic options file
gedit ~/.sqshrc
\set style=vert
# define a host
gedit ~/.freetds.conf
[someserver]
host = <IP_ADDRESS>
port = 1433
tds version = 8.0
# connect
sqsh -S someserver -L user=sa -L password=password
# execute command
exec xp_cmdshell ‘whoami’
go


Uses of Interface
org.drip.analytics.holset.LocationHoliday
| Package | Description |
|---|---|
| org.drip.analytics.holset |
Built in Locale Holiday Set
|
-
Uses of LocationHoliday in org.drip.analytics.holset
| Package | Description |
|---|---|
| org.drip.analytics.holset |
Built in Locale Holiday Set
|


An introduction to soft body physics
by Chandler Eyremrcheebs writes:
Create a bouncing ball animation in this introductory tutorial to soft body physics!
mrcheebs writes:
Create a bouncing ball animation in this introductory tutorial to soft body physics!


- DEFAULT_INTERSECT_RADIUS - Static variable in class tracer.paths.Line
-
- derivePath(float[]) - Method in class tracer.paths.Path
-
Derive a Path by connecting a sequence of points located on this Path with lines.
- derivePath(Path, float[]) - Static method in class tracer.paths.Path
-
Derive a Path by connecting a sequence of points located on a source Path with lines.
- dist(Point, Point) - Static method in class tracer.paths.Line
-
Computes the distance between a and b.
- dist(Point, Point) - Static method in class tracer.Point
-
Computes the distance between two points.
- distSq(Point, Point) - Static method in class tracer.Point
-
Computes the squared distance between two points.
- div(float) - Method in class tracer.Point
-
Divide this point by a given divisor.
- div(Point, float) - Static method in class tracer.Point
-
Divide a point by a divisor and return the result.
- draw(PGraphics) - Method in interface tracer.Drawable
-
- draw(PGraphics) - Method in class tracer.paths.Arc
-
Behavior *****
- draw(PGraphics) - Method in class tracer.paths.Circle
-
Behavior *****
- draw(PGraphics, float, float) - Method in class tracer.paths.Circle
-
- draw(PGraphics) - Method in class tracer.paths.Composite
-
- draw(PGraphics) - Method in class tracer.paths.CubicBezier
-
- draw(PGraphics) - Method in class tracer.paths.Ellipse
-
Behavior *****
- draw(PGraphics, float, float) - Method in class tracer.paths.Ellipse
-
- draw(PGraphics) - Method in class tracer.paths.Gesture
-
Behavior *****
- draw(PGraphics) - Method in class tracer.paths.Line
-
Behavior *****
- draw(PGraphics) - Method in class tracer.paths.Path
-
Draws the path.
- draw(PGraphics, float, float, boolean) - Method in class tracer.paths.Path
-
- draw(PGraphics, float, float) - Method in class tracer.paths.Path
-
Draws a segment of the path starting at trace(u1) and ending at
trace(u2).
- draw(Collection<Path>, PGraphics) - Static method in class tracer.paths.Path
-
Draws every Path in the Collection.
- draw(Path[], PGraphics) - Static method in class tracer.paths.Path
-
Draws every Path in the array.
- draw(PGraphics) - Method in class tracer.paths.Plot
-
Behavior *****
- draw(PGraphics) - Method in class tracer.paths.Rect
-
Behavior *****
- draw(PGraphics, float, float) - Method in class tracer.paths.Rect
-
- draw(PGraphics, boolean) - Method in class tracer.paths.Shape
-
Behavior *****
- draw(PGraphics) - Method in class tracer.paths.Shape
-
- draw(PGraphics, float, float) - Method in class tracer.paths.Shape
-
- draw(PGraphics) - Method in class tracer.paths.TranslatedPath
-
- draw(PGraphics) - Method in class tracer.renders.Render
-
Draws something based on the Points in the Render.
- draw(PGraphics) - Method in class tracer.renders.RenderClique
-
Behavior *****
- draw(PGraphics) - Method in class tracer.renders.RenderMesh
-
Behavior *****
- draw(PGraphics) - Method in class tracer.renders.RenderShape
-
Behavior *****
- Drawable - Interface in tracer
-


{% ifequal chromeframe_enabled "1" %}
{% endifequal %}
Your browser does not support frames. Impressive.


|
Once upon a time ... ... there was a football team manager. His name was Alex and he had trouble getting through to his players. Life was very frustrating. How could he reach them more effectively? Usually it was easier to talk to their mums or dads. Alex would telephone them in turn and each call took ages. The message was pretty much the same for everyone and repeating it took a lot of his valuable time. continued ... |


Family Recipes
-
3/4
cup
ap flour
-
4
apples
(thinly sliced)
-
1/3
cup
butter
(softened)
-
1
teaspoon
cinnamon
-
1/2
teaspoon
salt
-
1
cup
sugar
-
1/4
cup
water
- Preheat oven to 350
- Grease an 8x8x2 inch pan
- Spread apple slices evenly throughout pan
- Mix water, cinnamon, and salt in a bowl
- Pour cinnamon mixture over apples
- Mix sugar, flour, and butter in a bowl
- Spread crumb mixture over the apples
- Bake for 40 minutes or until crumb is golden brown
-
1 and 3/4
cup
ap flour
-
1
teaspoon
cinnamon
-
1/4
teaspoon
salt
-
1/2
cup
shortening
-
1
teaspoon
soda of baking
-
1
cup
sour apple sauce
(cold)
-
1
cup
sugar
-
2
teaspoon
water
(warm)
- Preheat oven to 300
- Cream the shortening, sugar, salt, and cinnamon
- Dissolve soda of baking into warm water
- Stir soda of baking mixture into apple sauce
- Mix the apple sauce mixture into the creamed base
- Stir in the flour
- Pour batter into a loaf pan
- Bake for 50 minutes or until the middle has set
-
2 and 3/4
cups
ap flour
-
1
pound
brown sugar
-
12
ounces
chocolate chips
-
3
eggs
-
2 and 1/2
teaspoons
powder of baking
-
1/2
teaspoon
salt
-
2/3
cup
shortening
-
1
teaspoon
vanilla
- Heat oven to 325
- Cream the shortening, brown sugar, and eggs in a mixer
- Mix in the flour, powder of baking, salt, vanilla, and chocolate chips
- Bake for 20-25 minutes or until golden brown
-
2
parts
extra sharp cheddar cheese
-
1
part
monteray jack cheese
- Grate both cheeses
- Toss grated cheeses together
-
6
tablespoon
butter
-
1
cup
mint chocolate chips
- Microwave ingredients in a bowl, stirring every 30 seconds
- Stop microwaving once the mixture is smooth
-
3
tablespoon
butter
(melted)
-
32
ounces
cream cheese
(softened)
-
4
eggs
-
20
cookies
oreo
-
4
squares
semi-sweet baking chocolate
(melted)
-
1
cup
sour cream
-
1
cup
sugar
-
1
teaspoon
vanilla
- Preheat oven to 325
- Line a 13x9 baking pan with foil, leaving some foil hanging over the edge of the pan
- Mix cookie crumbs and butter
- Press crumb mixture into bottom of pan
- Bake for 10 minutes
- Beat cream cheese, sugar, and vanilla until well blended
- Mix in sour cream
- Mix eggs in one at a time
- Set aside 1 cup of the cream cheese batter
- Mix melted chocolate into remaining batter
- Pour chocolate batter over the cooked crust
- Drop spoonfuls of set aside batter into the pan
- Cut through the batters multiple times using a dull knife to create a marbling effect
- Bake for 40 minutes or until the center is almost set
- Cool and then refrigerate for at least 4 hours, preferably overnight
-
1
cup
ap flour
-
1/2
cup
butter
(softened)
-
16
ounce
chocolate syrup
-
4
eggs
-
1
cup
sugar
- Heat oven to 350
- Grease a 13x9x2 inch pan
- In a mixing bowl, beat flour, sugar, butter, eggs, and syrup until smooth
- Pour batter into prepared pan
- Spread Mint Cream Layer on Cake
- Refridgerate until chilled
- Spread Chocolate topping over dessert until chilled
-
1/4
cup
butter
(melted)
-
32
ounces
cream cheese
(softened)
-
4
eggs
-
18
ounces
oreo sandwich cookies
-
1
cup
sour cream
-
1
cup
sugar
-
1
teaspoon
vanilla
- Preheat oven to 325
- Pulse 30 oreo cookies in a food processor until finely ground
- Pulse in the melted butter to form a crumb
- Line a 13x9 inch baking pan with aluminum foil, leave some foil hanging over the edges of the pan
- Firmly press oreo crust into bottom of greased baking pan
- Beat cream cheese, sugar, and vanilla until well blended
- Mix sour cream into cream cheese mixture
- Mix in eggs one at a time
- Chop remaining oreo cookies
- Gently fold 1 and 1/2 cups of chopped cookies into the cream cheese mixture
- Pour mixture into pan with the crust
- Sprinkle pan with the remaining chopped cookies
- Bake for 45 minutes or until the center is almost set
- Let the cheesecake cool and then refrigerate for at least 3 hours, preferabley overnight
-
1/2
cup
butter
(softened)
-
2
cup
confectioners' sugar
-
3
drop
green food coloring
-
1/2
teaspoon
mint extract
-
1
tablespoon
water
- Beat ingredients in a mixing bowl until smooth
-
2
cups
ap flour
-
2
eggs
-
mixins
(see step 1)
-
1/2
teaspoon
salt
-
9
oz
skim milk greek yogurt
-
1/2
teaspoon
soda of baking
-
1
cup
splenda zero
-
1/2
cup
unsweetened applesauce
-
1/2
teaspoon
vanilla extract
- The mixins will determine what kind of pillow you make. My favorite is 2 cups of frozen blueberries. We have also had success with 1/2 cup of mini chocolate chips. Anything you would use to flavor a muffin should work great
- Preheat oven to 400
- Whisk the splenda and eggs together in a large bowl until splenda is evenly distributed
- Whisk in the vanilla, applesauce, and greek yogurt
- In a separate bowl, stir the flour, soda of baking, and salt together
- Using a spatula, gently fold the dry ingredients into the wet mix. Do not overwork the batter.
- Gently fold the mixins into the batter
- Using an ice cream disher, drop 12 gobs of batter onto two foiled and greased sheet pans
- Bake until the pillows are just starting to turn golden (15 to 20 minutes, depending on the choice of mixins)
- Move the cooked pillows to a wire rack
- Once cooled, store in refrigerator for up to one week.
- Before serving, toast the pillow to reconstitute it.
-
1
tablespoon
butter
-
1
oz
cheddar cheese powder
-
5
oz
cheese blend
-
1
oz
cheese blend
-
1/2
cup
cream
-
10
oz
large elbow maccaroni
(dry)
-
1/8
cup
moscato
-
0.4
oz
real bacon bits
- In a medium pot, bring enough water to cover the twice the pasta's height to a boil
- Once the water is boiling, add a heavy pinch of salt to the water
- Add the elbow noodles to the water and cook for 6 minutes, or until al dente
- Drain the noodles
- In the now empty pot, bring bacon bits and butter to a simmer
- Whisk the pot while adding the next ingredients
- Add the wine to the pot, bring to a simmer
- Add the cream to the pot, bring to a simmer
- Add the larger portion of cheese blend to the pot, bring to a simmer
- Sprinkle the cheese powder over the sauce, bring to a simmer
- Whisk the cheese powder until the sauce completely absorbs it, and no granules are visible
- Dump the cooked, drained noodles into the pot, stir until warm
- Let the noodles bond with the sauce, and then plate
- Sprinkle the smaller portion of cheese blend over the plated mac and cheese
-
3
cup
ap flour
-
6
tablespoon
butter
-
1 and 1/4
cup
milk
-
6
teaspoon
powder of baking
-
1 and 1/2
teaspoon
salt
-
3
tablespoon
sugar
- Preheat oven to 450
- Mix all ingredients together in a bowl
- Spoon 12 biscuits onto a cookie sheet
- Bake for 12 to 12 minutes or until golden brown
-
3
tablespoons
ap flour
-
3
tablespoons
butter
(melted)
-
40
ounces
cream cheese
-
4
eggs
-
1
cup
honey graham cracker crumbs
-
1/3
cup
seedless strawberry jam
-
1
cup
sour cream
-
1
cup
sugar
-
3
tablespoons
sugar
-
1
tablespoon
vanilla
- Preheat oven to 325
- Line a 13x9 baking pan with foil, hanging some over the edges
- Mix together crumbs, 3 tablespoons of sugar, and butter
- Press the crumb mixture into the bottom of the baking pan
- Beat cream cheese, remaining sugar, flour, and vanilla
- Mix in sour cream after other ingredients are blended
- Mix in one egg at a time
- Pour cream cheese mixture over crust in baking pan
- Gently drop spoonfuls of jam over the top of the batter
- Run a dull knife through the mixture several times to create a marbling effect
- Bake for 40 minutes or until the center is almost set
- Cool and then refrigerate for at least 4 hours, preferably overnight
-
22.5
ounces
betty crocker brownie mix
-
included in brownie mix box
hershey's syrup
-
3
bars
symphony [chocolate bar with almond and toffee bits]
-
1/4
cup
walnut
(chopped)
- Prepare brownie batter as per instructions on the box
- Pour half of the batter into a 9x13 baking pan
- Place the three candy bars across the batter
- Pour the remaining batter into the pan
- Sprinkle chooped nuts over the batter
- Bake the brownies as per instructions on the box
-
3.2
oz
butter
-
16
oz
marshmallow
-
6.4
oz
rice krispy cereal
- In a medium pot, melt the butter on the lowest heat setting
- Add the marshmallows to the pot
- Still on the lowest heat setting, stir the marshmallows until completely melted and mixed with the butter
- Take the pot off the heat, and quickly yet gently fold in the rice krispy cereal
- Immediately after the cereal is mixed, gently dump the mix into a greased 9 x 13 baking dish
- It is critical that you don't press the mix into the pan, but rather tap the mix gently to evently distribute
- Let the treats cool for 5 minutes, and then flip onto a greased cutting board
- After 30 minutes, the mix will have cooled enough for you to cut
- Cut the treats into 20 servings
- Once the treats are cool to the touch, they can be stored


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Savannah Regional Minister visits submerged ferry victims
The Savannah Regional Minister, Salifu Adam Braimah, has visited victims of a submerged ferry at Makango Volta Lake in the East Gonja District of the Savannah Region.
Some days ago, the ferry called Nana Besemuna, which was travelling on the lake to Makango with passengers, yams and other farm produce loaded on trucks, submerged.
DAILY GUIDE gathered that the submerged ferry is about 50 years old and most of the wooded panels in the ferry had decomposed but fortunately no life was lost in the accident.
During the minister’s visit, workers of the ferry managed to pull out the trucks but almost everything had been destroyed by the water.
Some engines of the trucks were also damaged as a result of the submerging of the ferry.
Residents who use the ferry as their means of transport urged the government to get them a new ferry.
Naval Captain Akabutu told DAILY GUIDE that they had brought in machines to pump out the water from the tanks to be able to pull the ferry out of the lake.
“We have come with a diving company; they have come with some balloons which will be inserted alongside the ferry and as they pump the ferry will move up,” he explained.
According to him, the ‘Queen’ vessel which is stationed at Akosombo is expected to arrive at Makango on Wednesday to assist passengers to cross over to the other side of the lake.
He also said the Jakpa ferry would ply the Yeji-Makango Lake permanently to manage the situation.
The minister later called on the Volta Lake Transport management to frequently maintain the ferries to avoid similar situations in the future, and added that there were plans to construct a bridge on that side of the lake to make travelling more convenient.
“We are in talks with a Chinese company to construct a 7-kilometre bridge across the lake, so we will take it up to Cabinet,” he assured.


Summary functionsSummary functions produce a summary of all records in the found set, or subsummary values for records in different groups. Formulas can contain more than one summary function. Summary functions calculate more slowly than other functions because they generate values for a range of records.An alternative way to generate similar calculated results is to use Aggregate functions to summarize data in related records (whether or not they appear in a portal). See Aggregate functions and information about summarizing data in portals.


Unless you are really observant you won't understand Primer the first time around. Here is a helpful link that might help you get it. Don't watch it now though. Come back after you have seen the movie.
Four men get together to build an error checking device. Not many details are provided on what the device actually does and you can only guess it's purpose by bits and pieces of the conversation that you hear among the friends. They have very limited resources. All of them are employed in a regular job that they can't afford to quit. They come together on night and on weekends to work on their product. All of them are intelligent and hard working guys and naturally they get into a lot of arguments.
The first half of the movie focuses on their struggles to assemble a working prototype. Despite their best efforts their calculations always come out wrong. They try many different ideas but success eludes them still. They find themselves unable to pin point the cause of failure. Due to this their relationship becomes stressed giving the viewer a chance to see each person in his true colors.
During this half the driving force is Aaron. He comes off as a mature, optimistic fellow who is quick to take action and even quicker to diffuse tension. He wants to build the product no matter what. He goes the extra mile by selling or using his personal belongings for the success of their venture.
All of his effort however falls flat. Until Abe makes an accidental discovery and the story starts to pick up pace. Though it is never told explicitly what that discovery was. It succeeds in conveying the confusion of the characters to the the viewer. Like them we are unable to understand what is going on. Hints are dropped that point to the obvious but the words are never said.
Suffice to say that they built something that no one ever thought was possible. Which gave rise to a dilemma. What do they do with it? They knew that they "were out of their depth" and that they needed outside help. But they also knew that if they shared their discovery it would only lead to exploitation. So they decide to understand their own system better before they make it public. What follows is the realization that they have in their possession an immense power. The question now is how far they would go to take control.
Primer is a very focused science fiction film. Which is an rarity in the genre considering that with the exception of a few ( Gattaca etc) most of the sci fi films are too big in scope. From the start and until the end the action revolves around this device. The movie itself is shot documentary style. There is little music and it all seems unscripted.
The visuals are very cleverly done and they provide a level of immersion that I think would have been very difficult to achieve without it. The use of lighting is superb and the change between grainy and clean video output ties itself well with the story. Unfortunately I can not explain it without spoiling the plot but you will know when you see it.
Another thing that Primer does well is that it goes to great lengths in establishing the truth of the premise. It never comes out and says "Here, this is the way it is" but instead gives subtle clues to the viewer and make them arrive at a logical conclusion and then take it further from there. It is like reading a good detective novel where you try to solve a mystery as go along.
Which brings me to perhaps the only "flaw" that I can find in the movie. It can be a bit too dense and might require some effort on your part. When I watched this movie the first time I was left with a lot of questions and I could not comprehend it completely. Primer requires an active participation from the viewer. There is a good chance that you might not enjoy it the first time.
But unless you are really observant you won't understand Primer the first time around. Here is a helpful link that might help you get it. Don't watch it now though. Come back after you have seen the movie.


Uses of Interface
com.opengamma.strata.market.curve.Curve
-
Packages that use Curve Package Description com.opengamma.strata.market.curve Definitions of curves.com.opengamma.strata.measure.curve Integration code that allows strata-calc to use and calibrate curves.com.opengamma.strata.pricer Calculators for financial instruments.com.opengamma.strata.pricer.capfloor Calculators for Ibor cap-floor.com.opengamma.strata.pricer.credit Calculators for credit instruments, such as Credit Default Swap (CDS).com.opengamma.strata.pricer.fxopt Calculators for FX options.com.opengamma.strata.pricer.model Common code for model pricing.com.opengamma.strata.pricer.rate Calculators for rates instruments, such as Forward Rate Agreement (FRA) and interest rate swap.com.opengamma.strata.pricer.sensitivity Calculators for sensitivities. -
-
Uses of Curve in com.opengamma.strata.market.curve
Subinterfaces of Curve in com.opengamma.strata.market.curve Modifier and Type Interface Description interface
NodalCurve
A curve based ondouble
nodal points.Classes in com.opengamma.strata.market.curve that implement Curve Modifier and Type Class Description class
AddFixedCurve
A curve formed from two curves, the fixed curve and the spread curve.class
CombinedCurve
A curve formed from two curves, the base curve and the spread curve.class
ConstantCurve
A curve based on a single constant value.class
ConstantNodalCurve
A curve based on a single constant value.class
HybridNodalCurve
A hybrid curve which combines two underlying nodal curves, allowing different interpolators to be used for different parts of the curve.class
InflationNodalCurve
Curve specifically designed for inflation, with features for seasonality and initial point.class
InterpolatedNodalCurve
A curve based on interpolation between a number of nodal points.class
ParallelShiftedCurve
A curve with a parallel shift applied to its y-values.class
ParameterizedFunctionalCurve
A curve based on a parameterized function.Methods in com.opengamma.strata.market.curve that return Curve Modifier and Type Method Description Curve
CurveDefinition. curve(LocalDate valuationDate, CurveMetadata metadata, DoubleArray parameters)
Creates the curve from an array of parameter values.Curve
CombinedCurve. getBaseCurve()
Gets the base curve.Curve
AddFixedCurve. getFixedCurve()
Gets the fixed curve.Curve
AddFixedCurve. getSpreadCurve()
Gets the spread curve.Curve
CombinedCurve. getSpreadCurve()
Gets the spread curve.Curve
ParallelShiftedCurve. getUnderlyingCurve()
Gets the underlying curve.Curve
Curve. withMetadata(CurveMetadata metadata)
Returns a new curve with the specified metadata.Curve
Curve. withParameter(int parameterIndex, double newValue)
default Curve
Curve. withPerturbation(ParameterPerturbation perturbation)
default Curve
Curve. withUnderlyingCurve(int curveIndex, Curve curve)
Replaces an underlying curve by a new curve.Methods in com.opengamma.strata.market.curve that return types with arguments of type Curve Modifier and Type Method Description MarketDataBox<Curve>
CurveParallelShifts. applyTo(MarketDataBox<Curve> curve, ReferenceData refData)
org.joda.beans.MetaProperty<Curve>
CombinedCurve.Meta. baseCurve()
The meta-property for thebaseCurve
property.org.joda.beans.MetaProperty<ImmutableMap<Currency,Curve>>
RatesCurveGroup.Meta. discountCurves()
The meta-property for thediscountCurves
property.Optional<Curve>
CurveGroup. findCurve(CurveName name)
Finds the curve with the specified name.Optional<Curve>
LegalEntityCurveGroup. findCurve(CurveName name)
Finds the curve with the specified name.Optional<Curve>
RatesCurveGroup. findCurve(CurveName name)
Finds the curve with the specified name.Optional<Curve>
RatesCurveGroup. findDiscountCurve(Currency currency)
Finds the discount curve for the currency if there is one in the group.Optional<Curve>
RatesCurveGroup. findForwardCurve(Index index)
Finds the forward curve for the index if there is one in the group.Optional<Curve>
LegalEntityCurveGroup. findIssuerCurve(LegalEntityGroup legalEntityGroup, Currency currency)
Finds the issuer curve for the legal entity group and currency if there is one in the group.Optional<Curve>
LegalEntityCurveGroup. findRepoCurve(RepoGroup repoGroup, Currency currency)
Finds the repo curve for the repo group and currency if there is one in the group.org.joda.beans.MetaProperty<Curve>
AddFixedCurve.Meta. fixedCurve()
The meta-property for thefixedCurve
property.org.joda.beans.MetaProperty<ImmutableMap<Index,Curve>>
RatesCurveGroup.Meta. forwardCurves()
The meta-property for theforwardCurves
property.ImmutableMap<Currency,Curve>
RatesCurveGroup. getDiscountCurves()
Gets the discount curves in the group, keyed by currency.ImmutableMap<Index,Curve>
RatesCurveGroup. getForwardCurves()
Gets the forward curves in the group, keyed by index.ImmutableMap<Pair<LegalEntityGroup,Currency>,Curve>
LegalEntityCurveGroup. getIssuerCurves()
Gets the issuer curves in the curve group, keyed by legal entity group and currency.MarketDataName<Curve>
CurveId. getMarketDataName()
Class<Curve>
CurveId. getMarketDataType()
Class<Curve>
CurveName. getMarketDataType()
Class<Curve>
CurveParallelShifts. getMarketDataType()
ImmutableMap<Pair<RepoGroup,Currency>,Curve>
LegalEntityCurveGroup. getRepoCurves()
Gets the repo curves in the curve group, keyed by repo group and currency.org.joda.beans.MetaProperty<ImmutableMap<Pair<LegalEntityGroup,Currency>,Curve>>
LegalEntityCurveGroup.Meta. issuerCurves()
The meta-property for theissuerCurves
property.Stream<Curve>
LegalEntityCurveGroup. issuerCurveStream()
Returns a stream of all issuer curves in the group.org.joda.beans.MetaProperty<ImmutableMap<Pair<RepoGroup,Currency>,Curve>>
LegalEntityCurveGroup.Meta. repoCurves()
The meta-property for therepoCurves
property.Stream<Curve>
LegalEntityCurveGroup. repoCurveStream()
Returns a stream of all repo curves in the group.ImmutableList<Curve>
AddFixedCurve. split()
ImmutableList<Curve>
CombinedCurve. split()
default ImmutableList<Curve>
Curve. split()
Obtains a list of underlying curves.org.joda.beans.MetaProperty<Curve>
AddFixedCurve.Meta. spreadCurve()
The meta-property for thespreadCurve
property.org.joda.beans.MetaProperty<Curve>
CombinedCurve.Meta. spreadCurve()
The meta-property for thespreadCurve
property.Stream<Curve>
CurveGroup. stream()
Returns a stream of all curves in the group.Stream<Curve>
LegalEntityCurveGroup. stream()
Returns a stream of all curves in the group.Stream<Curve>
RatesCurveGroup. stream()
Returns a stream of all curves in the group.org.joda.beans.MetaProperty<Curve>
ParallelShiftedCurve.Meta. underlyingCurve()
The meta-property for theunderlyingCurve
property.Methods in com.opengamma.strata.market.curve with parameters of type Curve Modifier and Type Method Description static ParallelShiftedCurve
ParallelShiftedCurve. absolute(Curve curve, double shiftAmount)
Returns a curve based on an underlying curve with a fixed amount added to the Y values.static AddFixedCurve
AddFixedCurve. of(Curve fixedCurve, Curve spreadCurve)
Creates a curve as the sum of a fixed curve and a spread curve.static CombinedCurve
CombinedCurve. of(Curve baseCurve, Curve spreadCurve)
Creates a curve as the sum of a base curve and a spread curve.static CombinedCurve
CombinedCurve. of(Curve baseCurve, Curve spreadCurve, CurveMetadata metadata)
Creates a curve as the sum of a base curve and a spread curve with a specified curve metadata.static ParallelShiftedCurve
ParallelShiftedCurve. of(Curve curve, ShiftType shiftType, double shiftAmount)
Returns a curve based on an underlying curve with a parallel shift applied to the Y values.static RatesCurveGroup
RatesCurveGroup. ofCurves(RatesCurveGroupDefinition curveGroupDefinition, Curve... curves)
Creates a curve group using a curve group definition and some existing curves.static ParallelShiftedCurve
ParallelShiftedCurve. relative(Curve curve, double shiftAmount)
Returns a curve based on an underlying curve with a scaling applied to the Y values.AddFixedCurve
AddFixedCurve. withUnderlyingCurve(int curveIndex, Curve curve)
CombinedCurve
CombinedCurve. withUnderlyingCurve(int curveIndex, Curve curve)
default Curve
Curve. withUnderlyingCurve(int curveIndex, Curve curve)
Replaces an underlying curve by a new curve.Method parameters in com.opengamma.strata.market.curve with type arguments of type Curve Modifier and Type Method Description MarketDataBox<Curve>
CurveParallelShifts. applyTo(MarketDataBox<Curve> curve, ReferenceData refData)
RatesCurveGroup.Builder
RatesCurveGroup.Builder. discountCurves(Map<Currency,Curve> discountCurves)
Sets the discount curves in the group, keyed by currency.RatesCurveGroup.Builder
RatesCurveGroup.Builder. forwardCurves(Map<? extends Index,? extends Curve> forwardCurves)
Sets the forward curves in the group, keyed by index.LegalEntityCurveGroup.Builder
LegalEntityCurveGroup.Builder. issuerCurves(Map<Pair<LegalEntityGroup,Currency>,Curve> issuerCurves)
Sets the issuer curves in the curve group, keyed by legal entity group and currency.static LegalEntityCurveGroup
LegalEntityCurveGroup. of(CurveGroupName name, Map<Pair<RepoGroup,Currency>,Curve> repoCurves, Map<Pair<LegalEntityGroup,Currency>,Curve> issuerCurves)
Returns a curve group containing the specified curves.static RatesCurveGroup
RatesCurveGroup. of(CurveGroupName name, Map<Currency,Curve> discountCurves, Map<Index,Curve> forwardCurves)
Returns a curve group containing the specified curves.static RatesCurveGroup
RatesCurveGroup. ofCurves(RatesCurveGroupDefinition curveGroupDefinition, Collection<? extends Curve> curves)
Creates a curve group using a curve group definition and a list of existing curves.LegalEntityCurveGroup.Builder
LegalEntityCurveGroup.Builder. repoCurves(Map<Pair<RepoGroup,Currency>,Curve> repoCurves)
Sets the repo curves in the curve group, keyed by repo group and currency. -
Uses of Curve in com.opengamma.strata.measure.curve
Methods in com.opengamma.strata.measure.curve that return types with arguments of type Curve Modifier and Type Method Description MarketDataBox<Curve>
CurveMarketDataFunction. build(CurveId id, MarketDataConfig config, ScenarioMarketData marketData, ReferenceData refData)
-
Uses of Curve in com.opengamma.strata.pricer
Methods in com.opengamma.strata.pricer that return Curve Modifier and Type Method Description Curve
SimpleDiscountFactors. getCurve()
Gets the underlying curve.Curve
ZeroRateDiscountFactors. getCurve()
Gets the underlying curve.Curve
ZeroRatePeriodicDiscountFactors. getCurve()
Gets the underlying curve.Methods in com.opengamma.strata.pricer that return types with arguments of type Curve Modifier and Type Method Description org.joda.beans.MetaProperty<Curve>
SimpleDiscountFactors.Meta. curve()
The meta-property for thecurve
property.org.joda.beans.MetaProperty<Curve>
ZeroRateDiscountFactors.Meta. curve()
The meta-property for thecurve
property.org.joda.beans.MetaProperty<Curve>
ZeroRatePeriodicDiscountFactors.Meta. curve()
The meta-property for thecurve
property.Methods in com.opengamma.strata.pricer with parameters of type Curve Modifier and Type Method Description static DiscountFactors
DiscountFactors. of(Currency currency, LocalDate valuationDate, Curve curve)
Obtains an instance from a curve.static SimpleDiscountFactors
SimpleDiscountFactors. of(Currency currency, LocalDate valuationDate, Curve underlyingCurve)
Obtains an instance based on a discount factor curve.static ZeroRateDiscountFactors
ZeroRateDiscountFactors. of(Currency currency, LocalDate valuationDate, Curve underlyingCurve)
Obtains an instance based on a zero-rates curve.static ZeroRatePeriodicDiscountFactors
ZeroRatePeriodicDiscountFactors. of(Currency currency, LocalDate valuationDate, Curve underlyingCurve)
Obtains an instance based on a zero-rates curve.SimpleDiscountFactors
SimpleDiscountFactors. withCurve(Curve curve)
Returns a new instance with a different curve.ZeroRateDiscountFactors
ZeroRateDiscountFactors. withCurve(Curve curve)
Returns a new instance with a different curve.ZeroRatePeriodicDiscountFactors
ZeroRatePeriodicDiscountFactors. withCurve(Curve curve)
Returns a new instance with a different curve. -
Uses of Curve in com.opengamma.strata.pricer.capfloor
Methods in com.opengamma.strata.pricer.capfloor that return Curve Modifier and Type Method Description Curve
BlackIborCapletFloorletExpiryFlatVolatilities. getCurve()
Gets the Black volatility curve.Curve
NormalIborCapletFloorletExpiryFlatVolatilities. getCurve()
Gets the normal volatility curve.Curve
SabrIborCapletFloorletVolatilityBootstrapDefinition. getShiftCurve()
Gets the shift curve.Curve
SabrIborCapletFloorletVolatilityCalibrationDefinition. getShiftCurve()
Gets the shift curve.Curve
ShiftedBlackIborCapletFloorletExpiryStrikeVolatilities. getShiftCurve()
Gets the shift parameter of shifted Black model.Methods in com.opengamma.strata.pricer.capfloor that return types with arguments of type Curve Modifier and Type Method Description org.joda.beans.MetaProperty<Curve>
SabrIborCapletFloorletVolatilityBootstrapDefinition.Meta. betaCurve()
The meta-property for thebetaCurve
property.org.joda.beans.MetaProperty<Curve>
SabrIborCapletFloorletVolatilityCalibrationDefinition.Meta. betaCurve()
The meta-property for thebetaCurve
property.List<Curve>
SabrIborCapletFloorletVolatilityCalibrationDefinition. createSabrParameterCurve(List<CurveMetadata> metadata, DoubleArray nodeValues)
Creates the parameter curves with parameter node values.org.joda.beans.MetaProperty<Curve>
BlackIborCapletFloorletExpiryFlatVolatilities.Meta. curve()
The meta-property for thecurve
property.org.joda.beans.MetaProperty<Curve>
NormalIborCapletFloorletExpiryFlatVolatilities.Meta. curve()
The meta-property for thecurve
property.Optional<Curve>
SabrIborCapletFloorletVolatilityBootstrapDefinition. getBetaCurve()
Gets the beta (elasticity) curve.Optional<Curve>
SabrIborCapletFloorletVolatilityCalibrationDefinition. getBetaCurve()
Gets the beta (elasticity) curve.Optional<Curve>
SabrIborCapletFloorletVolatilityBootstrapDefinition. getRhoCurve()
Gets the rho (correlation) curve.Optional<Curve>
SabrIborCapletFloorletVolatilityCalibrationDefinition. getRhoCurve()
Gets the rho (correlation) curve.Optional<Curve>
DirectIborCapletFloorletVolatilityDefinition. getShiftCurve()
Gets the shift parameter of shifted Black model.Optional<Curve>
SurfaceIborCapletFloorletVolatilityBootstrapDefinition. getShiftCurve()
Gets the shift parameter of shifted Black model.org.joda.beans.MetaProperty<Curve>
SabrIborCapletFloorletVolatilityBootstrapDefinition.Meta. rhoCurve()
The meta-property for therhoCurve
property.org.joda.beans.MetaProperty<Curve>
SabrIborCapletFloorletVolatilityCalibrationDefinition.Meta. rhoCurve()
The meta-property for therhoCurve
property.org.joda.beans.MetaProperty<Curve>
DirectIborCapletFloorletVolatilityDefinition.Meta. shiftCurve()
The meta-property for theshiftCurve
property.org.joda.beans.MetaProperty<Curve>
SabrIborCapletFloorletVolatilityBootstrapDefinition.Meta. shiftCurve()
The meta-property for theshiftCurve
property.org.joda.beans.MetaProperty<Curve>
SabrIborCapletFloorletVolatilityCalibrationDefinition.Meta. shiftCurve()
The meta-property for theshiftCurve
property.org.joda.beans.MetaProperty<Curve>
ShiftedBlackIborCapletFloorletExpiryStrikeVolatilities.Meta. shiftCurve()
The meta-property for theshiftCurve
property.org.joda.beans.MetaProperty<Curve>
SurfaceIborCapletFloorletVolatilityBootstrapDefinition.Meta. shiftCurve()
The meta-property for theshiftCurve
property.Methods in com.opengamma.strata.pricer.capfloor with parameters of type Curve Modifier and Type Method Description SabrIborCapletFloorletVolatilityBootstrapDefinition.Builder
SabrIborCapletFloorletVolatilityBootstrapDefinition.Builder. betaCurve(Curve betaCurve)
Sets the beta (elasticity) curve.SabrIborCapletFloorletVolatilityCalibrationDefinition.Builder
SabrIborCapletFloorletVolatilityCalibrationDefinition.Builder. betaCurve(Curve betaCurve)
Sets the beta (elasticity) curve.static BlackIborCapletFloorletExpiryFlatVolatilities
BlackIborCapletFloorletExpiryFlatVolatilities. of(IborIndex index, ZonedDateTime valuationDateTime, Curve curve)
Obtains an instance from the implied volatility curve and the date-time for which it is valid.static DirectIborCapletFloorletVolatilityDefinition
DirectIborCapletFloorletVolatilityDefinition. of(IborCapletFloorletVolatilitiesName name, IborIndex index, DayCount dayCount, double lambdaExpiry, double lambdaStrike, GridSurfaceInterpolator interpolator, Curve shiftCurve)
Obtains an instance with shift curve.static NormalIborCapletFloorletExpiryFlatVolatilities
NormalIborCapletFloorletExpiryFlatVolatilities. of(IborIndex index, ZonedDateTime valuationDateTime, Curve curve)
Obtains an instance from the implied volatility curve and the date-time for which it is valid.static ShiftedBlackIborCapletFloorletExpiryStrikeVolatilities
ShiftedBlackIborCapletFloorletExpiryStrikeVolatilities. of(IborIndex index, ZonedDateTime valuationDateTime, Surface surface, Curve shiftCurve)
Obtains an instance from the implied volatility surface and the date-time for which it is valid.static SurfaceIborCapletFloorletVolatilityBootstrapDefinition
SurfaceIborCapletFloorletVolatilityBootstrapDefinition. of(IborCapletFloorletVolatilitiesName name, IborIndex index, DayCount dayCount, CurveInterpolator timeInterpolator, CurveInterpolator strikeInterpolator, Curve shiftCurve)
Obtains an instance with time interpolator, strike interpolator and shift curve.static SurfaceIborCapletFloorletVolatilityBootstrapDefinition
SurfaceIborCapletFloorletVolatilityBootstrapDefinition. of(IborCapletFloorletVolatilitiesName name, IborIndex index, DayCount dayCount, GridSurfaceInterpolator interpolator, Curve shiftCurve)
Obtains an instance with gird surface interpolator and shift curve.SabrIborCapletFloorletVolatilityBootstrapDefinition.Builder
SabrIborCapletFloorletVolatilityBootstrapDefinition.Builder. rhoCurve(Curve rhoCurve)
Sets the rho (correlation) curve.SabrIborCapletFloorletVolatilityCalibrationDefinition.Builder
SabrIborCapletFloorletVolatilityCalibrationDefinition.Builder. rhoCurve(Curve rhoCurve)
Sets the rho (correlation) curve.DirectIborCapletFloorletVolatilityDefinition.Builder
DirectIborCapletFloorletVolatilityDefinition.Builder. shiftCurve(Curve shiftCurve)
Sets the shift parameter of shifted Black model.SabrIborCapletFloorletVolatilityBootstrapDefinition.Builder
SabrIborCapletFloorletVolatilityBootstrapDefinition.Builder. shiftCurve(Curve shiftCurve)
Sets the shift curve.SabrIborCapletFloorletVolatilityCalibrationDefinition.Builder
SabrIborCapletFloorletVolatilityCalibrationDefinition.Builder. shiftCurve(Curve shiftCurve)
Sets the shift curve. -
Uses of Curve in com.opengamma.strata.pricer.credit
Methods in com.opengamma.strata.pricer.credit with parameters of type Curve Modifier and Type Method Description static CreditDiscountFactors
CreditDiscountFactors. of(Currency currency, LocalDate valuationDate, Curve curve)
Obtains an instance from a curve.static RecoveryRates
RecoveryRates. of(StandardId legalEntityId, LocalDate valuationDate, Curve curve)
Obtains an instance from a curve. -
Uses of Curve in com.opengamma.strata.pricer.fxopt
Methods in com.opengamma.strata.pricer.fxopt that return Curve Modifier and Type Method Description Curve
BlackFxOptionFlatVolatilities. getCurve()
Gets the Black volatility curve.Methods in com.opengamma.strata.pricer.fxopt that return types with arguments of type Curve Modifier and Type Method Description org.joda.beans.MetaProperty<Curve>
BlackFxOptionFlatVolatilities.Meta. curve()
The meta-property for thecurve
property.Methods in com.opengamma.strata.pricer.fxopt with parameters of type Curve Modifier and Type Method Description BlackFxOptionFlatVolatilities.Builder
BlackFxOptionFlatVolatilities.Builder. curve(Curve curve)
Sets the Black volatility curve.static BlackFxOptionFlatVolatilities
BlackFxOptionFlatVolatilities. of(CurrencyPair currencyPair, ZonedDateTime valuationDateTime, Curve curve)
Obtains an instance from an expiry-volatility curve and the date-time for which it is valid. -
Uses of Curve in com.opengamma.strata.pricer.model
Methods in com.opengamma.strata.pricer.model that return Curve Modifier and Type Method Description Curve
SabrParameters. getAlphaCurve()
Gets the alpha (volatility level) curve.Curve
SabrParameters. getBetaCurve()
Gets the beta (elasticity) curve.Curve
SabrParameters. getNuCurve()
Gets the nu (volatility of volatility) curve.Curve
SabrParameters. getRhoCurve()
Gets the rho (correlation) curve.Curve
SabrParameters. getShiftCurve()
Gets the shift parameter of shifted SABR model.Methods in com.opengamma.strata.pricer.model with parameters of type Curve Modifier and Type Method Description static SabrParameters
SabrParameters. of(Curve alphaCurve, Curve betaCurve, Curve rhoCurve, Curve nuCurve, Curve shiftCurve, SabrVolatilityFormula sabrFormula)
Obtains an instance with shift from nodal curves and volatility function provider.static SabrParameters
SabrParameters. of(Curve alphaCurve, Curve betaCurve, Curve rhoCurve, Curve nuCurve, SabrVolatilityFormula sabrFormula)
Obtains an instance without shift from nodal curves and volatility function provider. -
Uses of Curve in com.opengamma.strata.pricer.rate
Methods in com.opengamma.strata.pricer.rate that return Curve Modifier and Type Method Description Curve
SimpleIborIndexRates. getCurve()
Gets the underlying forward curve.Curve
SimplePriceIndexValues. getCurve()
Gets the underlying curve.Methods in com.opengamma.strata.pricer.rate that return types with arguments of type Curve Modifier and Type Method Description org.joda.beans.MetaProperty<Curve>
SimpleIborIndexRates.Meta. curve()
The meta-property for thecurve
property.org.joda.beans.MetaProperty<Curve>
SimplePriceIndexValues.Meta. curve()
The meta-property for thecurve
property.org.joda.beans.MetaProperty<ImmutableMap<Currency,Curve>>
ImmutableRatesProvider.Meta. discountCurves()
The meta-property for thediscountCurves
property.Map<CurveName,Curve>
ImmutableRatesProvider. getCurves()
Returns a map containing all the curves, keyed by curve name.Map<CurveId,Curve>
ImmutableRatesProvider. getCurves(CurveGroupName groupName)
Returns a map containing all the curves, keyed by curve identifier.ImmutableMap<Currency,Curve>
ImmutableRatesProvider. getDiscountCurves()
Gets the discount curves, defaulted to an empty map.ImmutableMap<Index,Curve>
ImmutableRatesProvider. getIndexCurves()
Gets the forward curves, defaulted to an empty map.org.joda.beans.MetaProperty<ImmutableMap<Index,Curve>>
ImmutableRatesProvider.Meta. indexCurves()
The meta-property for theindexCurves
property.Methods in com.opengamma.strata.pricer.rate with parameters of type Curve Modifier and Type Method Description ImmutableRatesProviderBuilder
ImmutableRatesProviderBuilder. discountCurve(Currency currency, Curve discountCurve)
Adds a discount curve to the provider.ImmutableRatesProviderBuilder
ImmutableRatesProviderBuilder. iborIndexCurve(IborIndex index, Curve forwardCurve)
Adds an Ibor index forward curve to the provider.ImmutableRatesProviderBuilder
ImmutableRatesProviderBuilder. iborIndexCurve(IborIndex index, Curve forwardCurve, LocalDateDoubleTimeSeries timeSeries)
Adds an Ibor index forward curve to the provider with associated time-series.ImmutableRatesProviderBuilder
ImmutableRatesProviderBuilder. indexCurve(Index index, Curve forwardCurve)
Adds an index forward curve to the provider.ImmutableRatesProviderBuilder
ImmutableRatesProviderBuilder. indexCurve(Index index, Curve forwardCurve, LocalDateDoubleTimeSeries timeSeries)
Adds an index forward curve to the provider with associated time-series.static IborIndexRates
IborIndexRates. of(IborIndex index, LocalDate valuationDate, Curve forwardCurve)
Obtains an instance from a forward curve, with an empty time-series of fixings.static IborIndexRates
IborIndexRates. of(IborIndex index, LocalDate valuationDate, Curve forwardCurve, LocalDateDoubleTimeSeries fixings)
Obtains an instance from a curve and time-series of fixings.static OvernightIndexRates
OvernightIndexRates. of(OvernightIndex index, LocalDate valuationDate, Curve forwardCurve)
Obtains an instance from a forward curve, with an empty time-series of fixings.static OvernightIndexRates
OvernightIndexRates. of(OvernightIndex index, LocalDate valuationDate, Curve forwardCurve, LocalDateDoubleTimeSeries fixings)
Obtains an instance from a curve and time-series of fixings.static PriceIndexValues
PriceIndexValues. of(PriceIndex index, LocalDate valuationDate, Curve forwardCurve, LocalDateDoubleTimeSeries fixings)
Obtains an instance from a curve and time-series of fixings.static SimpleIborIndexRates
SimpleIborIndexRates. of(IborIndex index, LocalDate valuationDate, Curve curve)
Obtains an instance from a curve, with an empty time-series of fixings.static SimpleIborIndexRates
SimpleIborIndexRates. of(IborIndex index, LocalDate valuationDate, Curve curve, LocalDateDoubleTimeSeries fixings)
Obtains an instance from a curve and time-series of fixing.static SimplePriceIndexValues
SimplePriceIndexValues. of(PriceIndex index, LocalDate valuationDate, Curve curve, LocalDateDoubleTimeSeries fixings)
Obtains an instance based on a curve with no seasonality adjustment.ImmutableRatesProviderBuilder
ImmutableRatesProviderBuilder. overnightIndexCurve(OvernightIndex index, Curve forwardCurve)
Adds an Overnight index forward curve to the provider.ImmutableRatesProviderBuilder
ImmutableRatesProviderBuilder. overnightIndexCurve(OvernightIndex index, Curve forwardCurve, LocalDateDoubleTimeSeries timeSeries)
Adds an Overnight index forward curve to the provider with associated time-series.ImmutableRatesProviderBuilder
ImmutableRatesProviderBuilder. priceIndexCurve(PriceIndex index, Curve forwardCurve)
Adds a Price index forward curve to the provider.ImmutableRatesProviderBuilder
ImmutableRatesProviderBuilder. priceIndexCurve(PriceIndex index, Curve forwardCurve, LocalDateDoubleTimeSeries timeSeries)
Adds an index forward curve to the provider with associated time-series.SimpleIborIndexRates
SimpleIborIndexRates. withCurve(Curve curve)
Returns a new instance with a different curve.SimplePriceIndexValues
SimplePriceIndexValues. withCurve(Curve curve)
Returns a new instance with a different curve.Method parameters in com.opengamma.strata.pricer.rate with type arguments of type Curve Modifier and Type Method Description ImmutableRatesProviderBuilder
ImmutableRatesProviderBuilder. discountCurves(Map<Currency,? extends Curve> discountCurves)
Adds discount curves to the provider.ImmutableRatesProviderBuilder
ImmutableRatesProviderBuilder. indexCurves(Map<? extends Index,? extends Curve> indexCurves)
Adds index forward curves to the provider with associated time-series.ImmutableRatesProviderBuilder
ImmutableRatesProviderBuilder. indexCurves(Map<? extends Index,? extends Curve> indexCurves, Map<? extends Index,LocalDateDoubleTimeSeries> timeSeries)
Adds index forward curves to the provider with associated time-series. -
Uses of Curve in com.opengamma.strata.pricer.sensitivity
Methods in com.opengamma.strata.pricer.sensitivity with parameters of type Curve Modifier and Type Method Description CurrencyParameterSensitivity
CurveGammaCalculator. calculateSemiParallelGamma(Curve curve, Currency curveCurrency, Function<Curve,CurrencyParameterSensitivity> sensitivitiesFn)
Computes the "sum-of-column gamma" or "semi-parallel gamma" for a sensitivity function.Method parameters in com.opengamma.strata.pricer.sensitivity with type arguments of type Curve Modifier and Type Method Description CurrencyParameterSensitivity
CurveGammaCalculator. calculateSemiParallelGamma(Curve curve, Currency curveCurrency, Function<Curve,CurrencyParameterSensitivity> sensitivitiesFn)
Computes the "sum-of-column gamma" or "semi-parallel gamma" for a sensitivity function.
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