Intra-communicators bring together the concepts of group and context. To
support
implementation-specific optimizations, and application topologies
(defined in the next chapter, Chapter Process Topologies
), communicators may
also ``cache'' additional information (see Section Caching
). MPI
communication operations reference communicators to determine the scope and
the ``communication universe'' in which a point-to-point or collective
operation is to operate.
Each communicator contains a group of valid participants; this group always includes the local process. The source and destination of a message is identified by process rank within that group.
For collective communication, the intra-communicator specifies the set of processes that participate in the collective operation (and their order, when significant). Thus, the communicator restricts the ``spatial'' scope of communication, and provides machine-independent process addressing through ranks.
Intra-communicators are represented by opaque intra-communicator objects, and hence cannot be directly transferred from one process to another.


Off the grid destination
Ethiopia is on its way to becoming a major travel destination, and it’s easy to see why. The country is known for its diverse wildlife, fresh and spicy cuisine, immersive history and peerless coffee culture. However, the Ethiopian government is highly restrictive towards the internet, and as a result just 15.4% of the country is connected.
Not only is this one of the hottest places you could visit, the Danakil Depression is a fascinating natural environment. Its volcanoes, miniature geysers, salt deposits and luminous-coloured lakes create the impression of being on another planet. But if yellow isn’t your colour, consider heading over to Senegal, Ethiopia’s west African counterpart, with its candyfloss pink Lake Retba, also known as ‘Lac Rose.’ Just don’t forget your sun cream to defend against the intense African heat.
Previously referred to as the ‘Eighth Wonder of the World’, the Church of Saint George is one of eleven rock-hewn churches in Lalibela. These mysterious structures were carved from volcanic rock in the ground and then excavated internally. To this day it’s not fully understood how they were created.
Known in Arabic as ‘the City of Saints’, Harar Jugol is a beautiful walled city on a hilltop. This UNESCO site offers winding streets, 10th century mosques, museums and locally made coffee. The locals are friendly and hold nightly hyena-feeding sessions!
If you’re visiting Bahir Dar, spend some time unwinding at Ethiopia’s largest lake. Kick back with a drink and enjoy the scenery, go fishing, or take a boat trip to see some hippos. There are also several medieval monasteries nearby, one of which has the mummified remains of several emperors on display.


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| Interface | Description |
|---|---|
| AuthorizationHandler |
Marker interface for things that handle authorization.
|
| ConfigurationLoaderInterface |
Created by Jeff Gaynor
on 3/26/15 at 4:01 PM |
| Class | Description |
|---|---|
| AbstractAccessTokenServlet |
Created by Jeff Gaynor
on 9/25/13 at 1:37 PM |
| AbstractAuthorizationServlet |
Created by Jeff Gaynor
on 1/14/14 at 11:50 AM |
| AbstractAuthorizationServlet.AuthorizedState |
State object after authorization has worked.
|
| AbstractAuthorizationServlet.MyHttpServletResponseWrapper |
This class is needed to pass information between servlets, where one servlet
calls another.
|
| AbstractAuthorizationServlet.MyMyProxyLogon | |
| AbstractBootstrapper |
Created by Jeff Gaynor
on 3/21/12 at 11:09 AM |
| AbstractCertServlet |
Created by Jeff Gaynor
on 9/25/13 at 3:36 PM |
| AbstractConfigurationLoader<T extends ServiceEnvironmentImpl> |
All servers configuration loaders should extend this.
|
| AbstractEnvServlet |
This is for any servlet that has an environment associated with it.
|
| AbstractInitServlet |
Created by Jeff Gaynor
on 9/24/13 at 3:56 PM |
| AbstractRegistrationServlet |
Created by Jeff Gaynor
on 10/3/14 at 10:46 AM |
| AbstractRegistrationServlet.ClientState | |
| AbstractVerifierServlet |
Created by Jeff Gaynor
on 2/17/14 at 10:59 AM |
| ACS2 |
Cert servlet, version 2.
|
| AuthorizationControllerServlet |
Created by Jeff Gaynor
on 2/17/14 at 10:47 AM |
| AuthorizationServletConfig |
Created by Jeff Gaynor
on 12/4/12 at 11:28 AM |
| AuthorizedServlet |
For deployment in cases that there is a wholly external authorization webapp.
|
| AuthorizedServlet.ProtocolParameters | |
| AutoRegistrationServlet |
This will automatically approve every client request.
|
| CRServlet |
This is the super class of the servlet that is supposed to retrieve a cert.
|
| DiscoveryServlet |
Created by Jeff Gaynor
on 12/12/16 at 1:04 PM |
| EnvServlet |
This servlet loads the environment for all servlets.
|
| ErrorServlet |
This servlet handles error redirects.
|
| IssuerTransactionState |
Created by Jeff Gaynor
on 3/13/14 at 10:42 AM |
| MyProxyConfigurationLoader<T extends AbstractEnvironment> |
Created by Jeff Gaynor
on 9/4/15 at 2:19 PM |
| MyProxyDelegationServlet |
Created by Jeff Gaynor
on May 17, 2011 at 3:46:53 PM |
| OA4MPExceptionHandler |
Created by Jeff Gaynor
on 2/6/15 at 4:56 PM |
| OA4MPServletInitializer |
Created by Jeff Gaynor
on 4/15/14 at 10:37 AM |
| PresentationState |
An object for passing around the state variable and anything else needed.
|
| RegistrationServlet |
The servlet that handles registration.
|
| Exception | Description |
|---|---|
| AbstractAuthorizationServlet.UserLoginException | |
| AbstractRegistrationServlet.ClientRegistrationRetryException | |
| TooManyRequestsException |
Created by Jeff Gaynor
on 4/8/13 at 1:58 PM |
Copyright © 2022. All rights reserved.


|
geopm-service
GEOPM Systemd Service
|
#include <ServiceProxy.hpp>
Public Member Functions | |
| ServiceProxyImp () | |
| ServiceProxyImp (std::shared_ptr< SDBus > bus) | |
| virtual | ~ServiceProxyImp ()=default |
| void | platform_get_user_access (std::vector< std::string > &signal_names, std::vector< std::string > &control_names) override |
| Calls the PlatformGetUserAccess API defined in the io.github.geopm D-Bus namespace. More... | |
| std::vector< signal_info_s > | platform_get_signal_info (const std::vector< std::string > &signal_names) override |
| Calls the PlatformGetSignalInfo API defined in the io.github.geopm D-Bus namespace. More... | |
| std::vector< control_info_s > | platform_get_control_info (const std::vector< std::string > &control_names) override |
| Calls the PlatformGetControlInfo API defined in the io.github.geopm D-Bus namespace. More... | |
| void | platform_open_session (void) override |
| Calls the PlatformOpenSession API defined in the io.github.geopm D-Bus namespace. More... | |
| void | platform_close_session (void) override |
| Calls the PlatformCloseSession API defined in the io.github.geopm D-Bus namespace. More... | |
| void | platform_start_batch (const std::vector< struct geopm_request_s > &signal_config, const std::vector< struct geopm_request_s > &control_config, int &server_pid, std::string &server_key) override |
| Calls the PlatformStartBatch API defined in the io.github.geopm D-Bus namespace. More... | |
| void | platform_stop_batch (int server_pid) override |
| Calls the PlatformStopBatch API defined in the io.github.geopm D-Bus namespace. More... | |
| double | platform_read_signal (const std::string &signal_name, int domain, int domain_idx) override |
| Calls the PlatformReadSignal API defined in the io.github.geopm D-Bus namespace. More... | |
| void | platform_write_control (const std::string &control_name, int domain, int domain_idx, double setting) override |
| Calls the PlatformWriteControl API defined in the io.github.geopm D-Bus namespace. More... | |
| std::string | topo_get_cache (void) override |
| Calls the TopoGetCache API defined in the io.github.geopm D-Bus namespace. More... | |
| Public Member Functions inherited from geopm::ServiceProxy | |
| ServiceProxy ()=default | |
| ServiceProxy constructor. More... | |
| virtual | ~ServiceProxy ()=default |
| ServiceProxy destructor. More... | |
Additional Inherited Members | |
| Static Public Member Functions inherited from geopm::ServiceProxy | |
| static std::unique_ptr< ServiceProxy > | make_unique (void) |
| Create a unique pointer to a ServiceProxy object. More... | |
| geopm::ServiceProxyImp::ServiceProxyImp | ( | ) |
| geopm::ServiceProxyImp::ServiceProxyImp | ( | std::shared_ptr< SDBus > | bus | ) |
|
virtualdefault |
|
overridevirtual |
Calls the PlatformCloseSession API defined in the io.github.geopm D-Bus namespace.
Implements geopm::ServiceProxy.
|
overridevirtual |
Calls the PlatformGetControlInfo API defined in the io.github.geopm D-Bus namespace.
| control_names | [in] Vector of strings containing all control names to query. |
Implements geopm::ServiceProxy.
|
overridevirtual |
Calls the PlatformGetSignalInfo API defined in the io.github.geopm D-Bus namespace.
| signal_names | [in] Vector of strings containing all signal names to query. |
Implements geopm::ServiceProxy.
|
overridevirtual |
Calls the PlatformGetUserAccess API defined in the io.github.geopm D-Bus namespace.
| signal_names | [out] Vector of strings containing all signals that the calling process has access to. |
| control_names | [out] Vector of strings containing all controls that the calling process has access to. |
Implements geopm::ServiceProxy.
|
overridevirtual |
Calls the PlatformOpenSession API defined in the io.github.geopm D-Bus namespace.
Implements geopm::ServiceProxy.
|
overridevirtual |
Calls the PlatformReadSignal API defined in the io.github.geopm D-Bus namespace.
| signal_name | [in] Name of the signal to read |
| domain | [in] Topology domain to read the signal from. One of the geopm_domain_e values defined in geopm_topo.h. |
| domain_idx | [in] Index of the domain to read the signal from. |
Implements geopm::ServiceProxy.
|
overridevirtual |
Calls the PlatformStartBatch API defined in the io.github.geopm D-Bus namespace.
| signal_config | [in] Vector of signal requests that will be supported by the batch server that is created. |
| control_config | [in] Vector of control requests that will be supported by the batch server that is created. |
| server_pid | [out] Linux PID of the server process created. |
| server_key | [out] Unique key used to connect to the created server. |
Implements geopm::ServiceProxy.
|
overridevirtual |
Calls the PlatformStopBatch API defined in the io.github.geopm D-Bus namespace.
| server_pid | [in] The Linux PID of the batch server to stop. |
Implements geopm::ServiceProxy.
|
overridevirtual |
Calls the PlatformWriteControl API defined in the io.github.geopm D-Bus namespace.
| control_name | [in] Name of the control to write |
| domain | [in] Topology domain to write to. One of the geopm_domain_e values defined in geopm_topo.h. |
| domain_idx | [in] Index of the domain to write to. |
| setting | [in] Value of the control to write. |
Implements geopm::ServiceProxy.
|
overridevirtual |
Calls the TopoGetCache API defined in the io.github.geopm D-Bus namespace.
Implements geopm::ServiceProxy.


German word of the day: Geil
by Ana DilleyDon’t worry if someone calls you or something that you’re doing ‘geil’. It is not derogatory as it first appears.
‘Geil’ is just about as omnipresent in Germany as bureaucracy and bratwurst. This colloquial word is commonly used by Germans of all ages and is an excellent way to make yourself sound like a native speaker.
At first glance, this word can confuse some non-native German speakers. If you look up ‘geil’ in a dictionary you will likely find a translation along the lines of horny or lecherous. Whilst this is certainly the original use of this word, colloquially ‘geil’ is far more complimentary.
The best translation for ‘geil’ in its colloquial sense would be something like ‘great,’ 'cool,' ‘awesome,’ or ‘wicked.’ It can also be used to compliment someone’s appearance, similar to calling someone ‘hot.’
‘Geil’ is often used on its own as a response to something, to imply the person thinks that thing is great or cool.
This word is so commonly used in its colloquial form that it is very much a part of the mainstream, even being included in the following advert for the popular German supermarket Edeka.
It is not clear when exactly ‘geil’ shifted in meaning and became a popular colloquial adjective but Bruce and Bongo’s song ‘Geil’ from 1986 shows that this word lost its original meaning a while ago.
As ‘geil’ is such a popular adjective there are a number of adverbs it is often compiled with. The most popular combinations are probably 'super geil,' 'mega geil,' and 'echt geil.' All of these emphasize just how great something is.
The German musician Deichkind coined the term 'leider geil' in his song of the same name. This phrase encompasses the idea that something is 'unfortunately' cool, despite the fact that it has negative connotations or effects. Below is the official video.

B: Geil!
A: There’s a party on this evening.
B: Sweet!
Dieses Essen ist mega geil oder?
This food is really awesome isn’t it?
Gestern war der Film super geil, ich würde dir er wirklich empfehlen.
The film yesterday was super good, I’d really recommend it to you.
Der Alkoholkonsum ist ungesund aber leider geil.
Drinking alcohol is unhealthy but unfortunately it's cool.
This article was updated on Thursday, July 18th.


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This document is protected by copyright and the information contained herein is
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may not be used or disclosed except with the written permission of MediaTek Inc.


SMART COSMOS Flows is up and running!
Configuration Instructions
Please read Getting Started with Flows at the SMART COSMOS Developer Portal for important information about configuring your Objects server.
Please read Getting Started with Flows at the SMART COSMOS Developer Portal for important information about configuring your Objects server.


Here you can manage your acrive subscriptions. Get nottifed as soon as your favorite animal appears on the sensor


- HELLO ,
- I’M CHANITHA
- Undergraduate
- Software Engineer at IIT ,
- UI/UX Designer ,
- Graphic designer and a
- Animator
Having an intrest in game development and coding during pandamic
times carved my way into doing software engineeering degree.
Now I am a first year Software Engineering student at Infromatics
Institute of Technology.I am looking forward in Devoloping my
it skills and learn new tech to success my path.
GRAPHICAL SKILLS
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2nd Shy
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| Physics |
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A |
| Common General Test |
60 |
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| SUBJECT |
GRADE |
| Buddhism |
A |
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A |
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A |
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sub
Events - Kiwi Ruby
Kiwi Ruby
The Ruby NZ code of conduct applies to all of these events. We'll do our best to provide a safe and fun environment and you do your best to be friendly.
This will be in the mezzanine at Whistling Sisters. There will be snacks and a non-alcoholic bar tab.
Accessibility note: although this is upstairs, there is an elevator to the mezzanine floor.
Arrive before 9am to pick up your nametag and swag bag, and mingle at Te Papa.
Head along to St Johns Bar from 5:30. There will be canapes and a non-alcoholic bar tab.
Proudly sponsored by Flick
Join us for a walk around Zealandia, entry will be covered so just make sure you bring a water bottle and some sensible shoes.
>> Register here <<Join us for some board games at Counter Culture.
>> Register here <<


Tutorial¶
Welcome to the introduction/bleeding-edge testing of the Automated Neural-graph Toolkit! Parts 1 and 2 are meant to be a gentle introduction to the toolkit. Read the directions carefully and be prepared use your copy and pasting skills. In Part 3 you will be introduced to features of the toolkit for developing new models. Carefully reading parts 1 and 2 will pay off when you engage in the task of building a new model.
Todo
Make a folder called antlab_tutorial wherever you feel like working. Copy the tar file from /home/hutch_research/bin/ant_tutorial.tar.gz into the folder you have created. Uncompress the file: I use tar -zxvf ant_tutorial.tar.gz. Do all your work out of the folder you have extracted the files to.
Note
For this lab you will have to ssh into a linux machine with -Y forwarding for pictures since we don’t have graphviz on any other machines as far as I know.
Part 1: Matrix Factorization Model¶
Low Rank Matrix Factorization is a popular machine learning technique used to produce recommendations given a set of ratings a user has given an item. The known ratings are collected in a user-item utility matrix and the missing entries are predicted by optimizing a low rank factorization of the utility matrix given the known entries. The basic idea behind matrix factorization models is that the information encoded for items in the columns of the utility matrix, and for users in the rows of the utility matrix is not exactly independent. We optimize the objective function \(\sum_{(u,i)} (R_{ui} - P_i^T U_u)^2\) over the observed ratings for user u and item i using gradient descent.
We can express the same optimization in the form of a computational graph that will play nicely with tensorflow:
Here \(xitem_i\), and \(xuser_j\) are some representation of the indices for the user and item vectors in the utility matrix. These could be one hot vectors, which can then be matrix multiplied by the P and U matrices to select the corresponding user and item vectors. In practice it is much faster to let \(xitem_i\), and \(xuser_j\) be vectors of indices which can be used by tensorflow’s gather or embedding_lookup functions to select the corresponding vector from the P and U matrices.
This simple model isn’t difficult to code directly in tensorflow, but it’s simplicity allows a demonstration of the functionality of the toolkit without having to tackle a more complex model.
To start let’s import the modules we need and use the read_data_sets
function to load our data:
import tensorflow as tf
import config
import generic_model
import loader
data = loader.read_data_sets('/home/hutch_research/data/deep_learning_lab/ml100k',
hashlist=['item', 'user', 'ratings'])
We can view the dimensions types, and dictionary keys of the data we’ve loaded using the DataSets.show
method,
which is a useful feature for debugging.
data.show()
The previous command will display this to the terminal:
For this data there are 5000 ratings in dev and test, and 70745 ratings in train.
Notice that the data type of item and user above is HotIndex
. This is a data structure for storing
one hot vectors, with a field for a vector of indices into a one hot matrix and the column size of the one hot matrix.
This will be important as we intend to use the lookup
function, which takes HotIndex
objects for its data argument, makes a placeholder associated with this data and uses the dim
attribute of the HotIndex
data to create a tf.Variable tensor with the correct dimension. The output is an embedding_lookup using the placeholder
and variable tensors created.
This model does better with the target ratings centered about the mean so let’s center the ratings.
data.train.labels['ratings'] = loader.center(data.train.labels['ratings'])
data.dev.labels['ratings'] = loader.center(data.dev.labels['ratings'])
Todo
Make a plain text file named mf.config using the text below. We will use this to make the tensorflow computational graph:
dotproduct x_dot_y()
-huser lookup(dataname='user', initrange=0.001, shape=[None, 20])
-hitem lookup(dataname='item', initrange=0.001, shape=[None, 20])
-ibias lookup(dataname='item', initrange=0.001, shape=[None, 1])
-ubias lookup(dataname='user', initrange=0.001, shape=[None, 1])
The python syntax highlighting illustrates the fact that the node specifications in a .config file are just python function calls with two things omitted, the first argument which is a tensor or list of tensors, and the last argument which is the name of the tensor output which defines it’s unique variable scope. The first argument is derived from the structure of the config spec, inferred by a marker symbol which we have chosen as ‘-‘. The input is the list of tensors or the single tensor in the spec at the next level below a node call. Tabbing is optional. It may be easier to read a config file with tabbing if you are using node functions without a long sequence of arguments. The second omitted argument, the name, is whatever directly follows the graph markers.
Now we make an AntGraph
object.
with tf.variable_scope('mfgraph'):
ant = config.AntGraph('mf.config',
data=data.dev,
marker='-',
display_graph=True)
When you run this code a graphviz dot pdf image of the graph you have composed should pop up on the screen because we set display_graph =True. This pdf file will show up in the pics folder with the name no_name.pdf. There are of course parameters for specifying the name and location where you want the picture to go. The dot specification will be located in the same place as the picture and be named no_name.dot unless you have specified a name for the file.
Shown in the graph picture above the x_dot_y
function takes a list of tensors as its first argument.
The first two tensors are matrices whose rows are dot producted resulting in a vector containing a scalar for each row.
The second two tensors are optional biases. For this model, giving a user and item bias helps a great deal. When lookup
is called more than once in a config file using the same data argument the previously made placeholder tensor is used,
so here ibias depends on the same placeholder as hbias and ubias depends on the same placeholder as huser, which
is what we want.
The AntGraph
object, ant is a complete record of the tensors created in graph building.
There are three accessible fields, tensordict
, placeholderdict
, and tensor_out
,
which are a dictionary of non-placeholder tensors made during graph creation, a dictionary of placeholder tensors made during
graph creation and the tensor or list of tensors which is the output of the top level node function.
These should be useful if we want to access tensors post graph creation.
Okay let’s finish making this model:
y = ant.tensor_out
y_ = tf.placeholder("float", [None, None], name='Target')
ant.placeholderdict['ratings'] = y_ # put the new placeholder in the graph for training
objective = (tf.reduce_sum(tf.square(y_ - y)) +
0.01*tf.reduce_sum(tf.square(ant.tensordict['huser'])) +
0.01*tf.reduce_sum(tf.square(ant.tensordict['hitem'])) +
0.01*tf.reduce_sum(tf.square(ant.tensordict['ubias'])) +
0.01*tf.reduce_sum(tf.square(ant.tensordict['ibias'])))
dev_rmse = tf.sqrt(tf.div(tf.reduce_sum(tf.square(y - y_)), data.dev.num_examples))
model = generic_model.Model(objective, ant.placeholderdict,
mb=500,
learnrate=0.01,
verbose=True,
maxbadcount=20,
epochs=100,
evaluate=dev_rmse,
predictions=y,
targets=y_)
Notice that the tensordict
enables easy access to huser, hitem, ubias, ibias, which we want to regularize to
prevent overfitting. The Model
object we are creating model needs the fields objective, placeholderdict, predictions, and targets.
If you don’t specify the other parameters default values are set. objective is used as the loss function for gradient
descent. placeholderdict is used to pair placeholder tensors with matrices from a dataset dictionary with the same
keys. targets, and predictions are employed by the loss function during evaluation, and by the prediction function
to give outputs from a trained model.
Training is now as easy as:
model.train([data.train, data.dev])
If you don’t wan’t to evaluate a model during training, for instance if you are doing cross-validation, you can just hand
in a training set, instead of an ordered pair of train and dev sets. Note that here there must be keys in either the DataSet
features
, or labels
dictionaries, that match with the keys from the placeholderdict
which is handed
to the Model
constructor. In our case we have placed a placeholder with the key ratings in the
placeholdedict
corresponding to
the ratings key in our data DataSet
. So our placeholderdict
is:
{'item': <tensorflow.python.framework.ops.Tensor object at 0x7f0bea7b43d0>,
'user': <tensorflow.python.framework.ops.Tensor object at 0x7f0bea846e90>,
'ratings': <tensorflow.python.framework.ops.Tensor object at 0x7f0bea77fc90>}
Now we have a trained model that does pretty well but it would be nice to automate a hyper-parameter search to find the best we can do (should be around .91).
We can change our mf.config file to accept variables for hyperparameters by substituting hard values with variable names prefixed with a ‘$’:
dotproduct x_dot_y()
-huser lookup(dataname='user', initrange=$initrange, shape=[None, $kfactors])
-hitem lookup(dataname='item', initrange=$initrange, shape=[None, $kfactors])
-ibias lookup(dataname='item', initrange=$initrange, shape=[None, 1])
-ubias lookup(dataname='user', initrange=$initrange, shape=[None, 1])
Now we have to let the AntGraph
constructor know what to bind these variables to with a variable_bindings
argument. So change the constructor call like so.
with tf.variable_scope('mfgraph'):
ant = config.AntGraph('mf.config',
data=data.dev,
marker='-',
variable_bindings = {'kfactors': 20, 'initrange':0.001})
Todo
Modify the code you’ve written to take arguments for the hyperparameters: kfactors, initrange, mb, learnrate, maxbadcount, lambda (a regularization coefficient), and epochs, and conduct a parameter search for the best model. Limit your search to under 1000 combinations of hyperparameters.
Part 2: Tree Model¶
To demonstrate the power and flexibility of using a config file we can make this more complex model below by changing a few lines of code and using a different config file:
We need to change the read_data_sets
call to omit the optional hashlist parameter so we get more features from
the data folder (if a hashlist parameter is not supplied, read_data_sets
reads all files with name prefixes
features_ and labels_ ).
data = loader.read_data_sets('/home/hutch_research/data/deep_learning_lab/ml100k')
Todo
Make a plain text file called tree.config with the specs for our tree model.
dotproduct x_dot_y()
..all_user dnn([10,10,10],'tanh',keep_prob=None)
....merge_user concat(10)
......huser lookup(dataname='user', initrange=$initrange, shape=[None, $kfactors])
......huser_meta dnn([10,10,10],'tanh',keep_prob=None)
........user_meta placeholder(tf.float32)
..all_item dnn([10,10,10],'tanh',keep_prob=None)
....merge_item concat(10)
......hitem lookup(dataname='item', initrange=$initrange, shape=[None, $kfactors])
......hitem_meta dnn([15,15,10],'tanh',keep_prob=None)
........item_meta placeholder(tf.float32)
......hitem_term dnn([10,10,10],'tanh',keep_prob=None)
........item_term placeholder(tf.float32)
..ibias lookup(dataname='item', initrange=$initrange, shape=[None, 1])
..ubias lookup(dataname='user', initrange=$initrange, shape=[None, 1])
I used a different delimiter so we have to change the marker value in the AntGraph
constructor call.
ant = config.AntGraph('tree.config',
data=data.dev,
marker='.',
variable_bindings = {'kfactors': 20, 'initrange':0.001}))
Note
I have used two ‘.’s instead of one in the config file to mark levels of the graph for more readable indentation. When you specify a marker you are free to use a string of these markers of arbitrary length as long as you are consistent.
We get the following graphviz dot picture:
Todo
Modify the code you’ve written to take arguments for the set of new hyperparameters, and optional optimization parameters
from the Model
API. Perform a parameter search to see if you can do better than basic MF. Limit your search to under
1000 combinations of hyperparameters.
Part 3: Crafting a new model¶
So far we have been using functions in our config files that are defined in the node_ops module. In the model we have defined, the concat nodes are passed directly to dnn nodes with no intervening non-linearity. We can introduce non-linear transformations at these points by directly calling tensorflow non-linear transforms in the config file.
Todo
Introduce tensorflow non-linear transforms in tree.config
We also have the ablility to introduce new node functions which are not defined by tensorflow or node_ops. One thing we might try is to replace our dnn nodes with highway_dnn nodes that we need to define. Here is a first approximation of defining a new highway_dnn node:
import node_ops
def highway_dnn(tensor_in, hidden_units, activation='tanh', keep_prob=None, name='highway_dnn'):
activation = node_ops.ACTIVATION[activation]
with tf.variable_scope(name):
for i, n_units in enumerate(hidden_units):
with tf.variable_scope('layer%d' % i):
with tf.variable_scope('hidden'):
hidden = activation(node_ops.linear(tensor_in, n_units, True))
with tf.variable_scope('transform'):
transform = tf.sigmoid(node_ops.linear(tensor_in, n_units, True))
tensor_in = tf.mul(hidden, transform) + tf.mul(tensor_in, 1 - transform)
if keep_prob:
tensor_in = node_ops.dropout(tensor_in, keep_prob)
return tensor_in
This looks okay for a first approximation. However we would like to initialize the bias for the transform gate to some
negative value, so we have to set the optional bias_start=0 parameter in the call to linear
for the transform gate to
a value we would like.
Todo
Alter the highway_dnn code to take an optional parameter for transform bias initialization. Replace dnn nodes in tree.config with highway_dnn nodes.
Now we need to let the AntGraph
constructor know about our new node by using the factory constructor graph_setup
with
one of the two optional arguments introducing new node functions. If
your new node definition is in it’s own module possibly full of other node definitions you have created you can use the
imports argument which is a dictionary with module names as keys and the paths to modules as values. In our case since we
have only made one new node function we will use the function_map parameter which takes a dictionary of function_name keys
and function values. So we replace our AntGraph
constructor call as follows:
with tf.variable_scope('mfgraph'):
ant = config.graph_setup('tree2.config',
data=data.dev,
marker='.',
function_map = {'highway_dnn': highway_dnn},
variable_bindings = {'kfactors': 20, 'initrange':0.001})
Here is an example config file and corresponding graphviz dot image adding non-linearity and a highway_dnn node. Notice that since highway networks have a fixed dimension size for hidden layers we have used an intermediate dnn node to map the input to a dimension we want.
dotproduct x_dot_y()
..all_user dnn([10,10,10],'tanh',keep_prob=None)
....tanh_user tf.nn.tanh()
......merge_user concat(10)
........huser lookup(dataname='user', initrange=$initrange, shape=[None, $kfactors])
........huser_meta dnn([10,10,10],'tanh',keep_prob=None)
..........user_meta placeholder(tf.float32)
..all_item dnn([10,10,10],'tanh', keep_prob=None)
....tanh_item tf.nn.tanh()
......merge_item concat(10)
........hitem lookup(dataname='item', initrange=$initrange, shape=[None, $kfactors])
........highway_item_meta highway_dnn([15,15],'tanh', keep_prob=None)
..........dnn_item_meta dnn([15],'tanh', keep_prob=None)
............item_meta placeholder(tf.float32)
........hitem_term dnn([10,10,10],'tanh',keep_prob=None)
..........item_term placeholder(tf.float32)
..ibias lookup(dataname='item', shape=[None, 1], initrange=$initrange)
..ubias lookup(dataname='user', shape=[None, 1], initrange=$initrange)
Now that we are familiar with the tricks for crafting new models we can engage in creative enterprise. We have been using the scalar ratings targets for objectives, but there are also one hot targets for classification included in the dataset.
Todo
Using the data provided, craft your own model, and do some preliminary testing to see how it performs against the other models you have explored. If you run into a bug, contact me and I will try to address it but feel free to just take note of the bug, abandon the effort, and try something else. Some ideas:
Classification
Multiple targets of regression and classification
A different combine function which has a multiplicative interaction between data streams
Combining data streams in a binary fashion (for item streams don’t combine all three at once)
Scale the regression targets and use the built in
cosine
node function instead ofx_dot_y
Make the branches dealing with indices linear, as they are in basic MF, and combine them later in the tree
Create an LSTM node that takes a sequence as input and outputs the last hidden state vector.
For this task results are not paramount. Think of a way to address the data that makes sense and explores the kind of functionality you may want to employ the toolkit for.
Todo
Submit the code you have written along with a one to two page writeup detailing your experience with the ANT toolkit addressing the following.
- Analysis of model performances
- Bug reports
- Automated Neural-graph Toolkit pros and cons
- Wish list for toolkit functionality


Victory for trousers: Winnipeg's Grey Cup win means fan can wear pants again
CALGARY - One of the first things Blue Bombers fan Chris Matthew will need to do after he finishes celebrating Winnipeg's Grey Cup win is shop for trousers.
Matthew promised his friends in November 2001 that he would keep wearing shorts until the Bombers won a Canadian Football League championship.
It didn't happen that year — they lost to Calgary — so he's been braving Manitoba's chill winters with half his legs uncovered for the last 18 years.
That all changed on Sunday when Winnipeg beat the Hamilton Tiger-Cats 33-12.
Matthew was in Calgary to cheer on his team and pulled on an old pair of grey camo sweat pants over his shorts after the win.
Fashion critics didn't take long to comment about his less-than-stylish wardrobe after a video popped up on social media, but Matthew says the sweats were the only pants he had that still fit.
“I’ll probably have to buy a new pair of jeans or two,” Matthew told CTV News on Monday.
He said his wife is happy he's going to be wearing long pants again.
“Now if we need to go somewhere that pants are required, we can actually go there,” he said. (CTV Winnipeg, The Canadian Press)
This report by The Canadian Press was first published Nov. 25, 2019.


--- layout: post title: "No Title" ---
Unlike blogging, where you spend time thinking carefully about what you say in each post, Tumblr is really more […]
This is the start of a quote from here that was bizarely posted on the Tumblr Staff blog.
That’s right Tumblr users, Tumblr is not for thinking carefully about what you say. I think about leaving Tumblr more and more every day.


{% extends "common/base.html" %} {% load widget_tweaks %} {% block content %}
Your username and password didn't match. Please try again.


Pearls
问题描述 :
In Pearlania everybody is fond of pearls. One company, called The Royal Pearl, produces a lot of jewelry with pearls in it. The Royal Pearl has its name because it delivers to the royal family of Pearlania. But it also produces bracelets and necklaces for ordinary people. Of course the quality of the pearls for these people is much lower then the quality of pearls for the royal family.In Pearlania pearls are separated into 100 different quality classes. A quality class is identified by the price for one single pearl in that quality class. This price is unique for that quality class and the price is always higher then the price for a pearl in a lower quality class.
Every month the stock manager of The Royal Pearl prepares a list with the number of pearls needed in each quality class. The pearls are bought on the local pearl market. Each quality class has its own price per pearl, but for every complete deal in a certain quality class one has to pay an extra amount of money equal to ten pearls in that class. This is to prevent tourists from buying just one pearl.
Also The Royal Pearl is suffering from the slow-down of the global economy. Therefore the company needs to be more efficient. The CFO (chief financial officer) has discovered that he can sometimes save money by buying pearls in a higher quality class than is actually needed.No customer will blame The Royal Pearl for putting better pearls in the bracelets, as long as the
prices remain the same.
For example 5 pearls are needed in the 10 Euro category and 100 pearls are needed in the 20 Euro category. That will normally cost: (5+10)*10+(100+10)*20 = 2350 Euro.Buying all 105 pearls in the 20 Euro category only costs: (5+100+10)*20 = 2300 Euro.
The problem is that it requires a lot of computing work before the CFO knows how many pearls can best be bought in a higher quality class. You are asked to help The Royal Pearl with a computer program.
Every month the stock manager of The Royal Pearl prepares a list with the number of pearls needed in each quality class. The pearls are bought on the local pearl market. Each quality class has its own price per pearl, but for every complete deal in a certain quality class one has to pay an extra amount of money equal to ten pearls in that class. This is to prevent tourists from buying just one pearl.
Also The Royal Pearl is suffering from the slow-down of the global economy. Therefore the company needs to be more efficient. The CFO (chief financial officer) has discovered that he can sometimes save money by buying pearls in a higher quality class than is actually needed.No customer will blame The Royal Pearl for putting better pearls in the bracelets, as long as the
prices remain the same.
For example 5 pearls are needed in the 10 Euro category and 100 pearls are needed in the 20 Euro category. That will normally cost: (5+10)*10+(100+10)*20 = 2350 Euro.Buying all 105 pearls in the 20 Euro category only costs: (5+100+10)*20 = 2300 Euro.
The problem is that it requires a lot of computing work before the CFO knows how many pearls can best be bought in a higher quality class. You are asked to help The Royal Pearl with a computer program.
Given a list with the number of pearls and the price per pearl in different quality classes, give the lowest possible price needed to buy everything on the list. Pearls can be bought in the requested,or in a higher quality class, but not in a lower one.
输入:
The first line of the input contains the number of test cases. Each test case starts with a line containing the number of categories c (1<=c<=100). Then, c lines follow, each with two numbers ai and pi. The first of these numbers is the number of pearls ai needed in a class (1 <= ai <= 1000).
The second number is the price per pearl pi in that class (1 <= pi <= 1000). The qualities of the classes (and so the prices) are given in ascending order. All numbers in the input are integers.
The second number is the price per pearl pi in that class (1 <= pi <= 1000). The qualities of the classes (and so the prices) are given in ascending order. All numbers in the input are integers.
输出:
For each test case a single line containing a single number: the lowest possible price needed to buy everything on the list.
样例输入:
2 2 100 1 100 2 3 1 10 1 11 100 12
样例输出:
330 1344
解题代码:
/* @author: */ import java.util.Scanner; import java.util.Arrays; public class Main{ static final int maxn=100; private int a[]=new int[maxn],p[]=new int[maxn],f[]=new int[maxn]; private int min(int a,int b){ return a< b?a:b; } public int dp(int x){ if(x< 0)return 0; if(f[x]!=-1)return f[x]; if(x==0)return f[x]=(a[0]+10)*p[0]; int c=(10+a[x])*p[x],res=c+dp(x-1); for(int i=x-1;i>=0;i--){ c+=p[x]*a[i]; res=min(c+dp(i-1),res); } return f[x]=res; } public static void main(String args[]){ Scanner sc=new Scanner(System.in); int times=sc.nextInt(); while((times--)!=0){ int n=sc.nextInt(); Main m=new Main(); for(int i=0;i< n;i++){ m.a[i]=sc.nextInt(); m.p[i]=sc.nextInt(); } Arrays.fill(m.f,-1); System.out.printf("%d\n",m.dp(n-1)); } } }
(adsbygoogle = window.adsbygoogle || []).push({});
学算法中的数据结构学到一定程度会乐此不疲的，比如其中的2－3树，类似的红黑树，我甚至可以自己写个逻辑文件系统结构来。
是O(m+n)，不小心打错了
清华也会出这么无聊的题
感觉我好像很多都不会做。。。。
这个问题别太纠结了，不同的编译器结果不一样
很好的思路，可以借鉴
顶一个 赞一个
DFS方法的原理还是不太清楚啊
I go through some of your put up and I uncovered a good deal of expertise from it. Many thanks for posting this sort of exciting posts
还是有些难度的！
https://coding.net/moon/miracle
头文件的颜色是有意设成这种灰暗色的吗？感觉看不清
正是我要找的，适合新手
The site You have write is very nicely prepared and quite insightful thanks for nice posta?|


Marks Predictor
Form
AboutProject
Contact
Marks Predictor
{%if pred %}
{{pred}}
{% endif %}
Please fill the following form to know your result
*Fill the form completely and correctly to predict your score correctly
1) Enter your school name.
* "GP" - Gabriel Pereira or "MS" - Mousinho da Silveira
2) Enter your gender.
* "F" - female or "M" - male
3) Enter your age.
* from 15 to 22
4) Enter your address.
* "U" - urban or "R" - rural
5) Enter your family size.
* "LE3" - less or equal to 3 or "GT3" - greater than 3
6) Enter your parental status.
* "T" - living together or "A" - apart
7) Enter your mother's education level.
* 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education
8) Enter your father's education level.
* 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education
9) Enter your mother's job.
* "teacher", "health" care related, civil "services" (e.g. administrative or police), "at_home" or "other"
10) Enter your father's job.
* "teacher", "health" care related, civil "services" (e.g. administrative or police), "at_home" or "other"
11) Enter your reason to choose this school.
* close to "home", school "reputation", "course" preference or "other"
12) Enter your guardian.
* "mother", "father" or "other"
13) Enter your travel time to reach the school.
* 1 -> 15 min., 2 -> 15 to 30 min., 3 -> 30 min. to 1 hour, or 4 -> 1 hour
14) Enter your study time.
* 1 -> 2 hours, 2 -> 2 to 5 hours, 3 -> 5 to 10 hours, or 4 -> 10 hours
15) Enter your number of past class failures.
* n if 1 <= n < 3, else 4
16) Enter your extra educational support.
* yes or no
17) Enter your family eduactional support.
* yes or no
18) Enter your extra paid classes within the course subject.
* yes or no
19) Enter your extra-curricular activities.
* yes or no
20) Enter your attended nursery school.
* yes or no
21) Enter your want to take higher education.
* yes or no
22) Enter your internet access at your home.
* yes or no
23) Enter your relationship status.
* yes or no
24) Enter your quality of family relationships.
* from 1 - very bad to 5 - excellent
25) Enter your free time after school.
* from 1 - very low to 5 - very high
26) Enter your amount of going out with friends.
* from 1 - very low to 5 - very high
27) Enter your workday alcohol consumption.
* from 1 - very low to 5 - very high
28) Enter your weekend alcohol consumption.
* from 1 - very low to 5 - very high
29) Enter your current health status.
* from 1 - very bad to 5 - very good
30) Enter your number of absences from school.
* from 0 to 93
31) Enter your first period grade.
* from 0 to 20
32) Enter your second period grade.
* from 0 to 20
Submit


As technical and team lead I have delivered many successful technology projects from the both vendor and client side.
As Chief Technology Officer at CreditSights I built an effective team of developers and technology professionals. Moving back to NZ to act as a Technical Architect and sometime Scrum Master I have been an advocate and practitioner of agile for many years. As a founder of various non profit ventures I have experience with recruiting and motivating small teams on limited budgets. As a contracting programmer for many years before that I honed my skills in programming and technical leadership to a senior level.
Whatever the situation, whether vendor or client, my philosophy is to ensure there is a crystal clear vision and goal for the project. With clear goals and well supported team members, success generally comes with ease. My references will attest to the successes of this straightforward approach over many years.
As an IT executive at CreditSights I ensured all the critical systems that were needed by a fast growing, early stage company - from accounting and sales to business intelligence dashboards, server infrastructure and desktop support across multiple offices. Over many years, even after stepping down from CTO and returning to NZ, I have had a strategic level input to the design and vision for our flagship financial products. The "points of difference" that continue to make CreditSights a leader in its field, point to my success in these areas.
In New Zealand I experienced the lean startup methodology close up. I have good, on the ground experience of building small teams towards success, even in a social enterprise context. The successes in my roles as founder at Tradeify Ltd, Hashmapd Ltd and as project lead for the Autism 360 and GroundFacts non-profit projects attest to this.
I have had a long standing interest in Machine Learning and data science and an intention to focus my efforts more in this area going forwards. As a co-founder at Hashmapd I developed a production deep learning based system way back in 2014 (now available as open source). In 2017 I built multiple pure python as well as Python + Spark data processing pipelines for unstructured text as well as for structured financial data within my work at CreditSights and as part of my theater work.
I have stayed 'hands on' as an active programmer throughout my career, and had the opportunity to focus much more on these areas after returning to New Zealand to work remotely. As a senior and experienced developer I understand everything C# pretty well - from the latest .Net Core API stack to knowing the level of support for R within the next version of SQL Server (it needs work). In short I know my reference types from my readonly structs and I can design and build a system where your async threads will patiently await the fulfilment of their promises to allow high performance computation and scalability.
I started out as a web developer and still have CSS and HTML in my programming blood. More recently, I have extensive hands on experience with the the JS/Node stack (and associated tools) but can work easily across a range of web technologies. For instance, I have created many PHP/Wordpress custom themes and websites when that is the best solution for the customer's problem.
As far as recent front end frameworks go I am most familiar with Angular but have also recently worked on a project delivering an API to a React front end.
I am familiar with and have delivered solutions for both Azure and AWS and am comfortable deploying CI and Docker based systems that scale across multiple machines.


def setup_test_cases(conditions):
runcases = Cases()
alphas = [-10,-5,-2,0,2,5,10,20]
mach = conditions.freestream.mach
v_inf = conditions.freestream.velocity
def setup_test_cases(conditions):
runcases = Run_Case.Container()
alphas = [-10,-5,-2,0,2,5,10,20]
mach = conditions.freestream.mach
v_inf = conditions.freestream.velocity


Directories | |
| directory | agnostic |
| directory | cartesian |
| directory | concepts |
| directory | spherical |
| directory | transform |
Files | |
| file | area.hpp |
| file | buffer.hpp |
| file | centroid.hpp |
| file | comparable_distance_result.hpp |
| file | compare.hpp |
| file | convex_hull.hpp |
| file | covered_by.hpp |
| file | default_area_result.hpp |
| file | default_comparable_distance_result.hpp |
| file | default_distance_result.hpp |
| file | default_length_result.hpp |
| file | default_strategy.hpp |
| file | distance.hpp |
| file | distance_result.hpp |
| file | intersection.hpp |
| file | intersection_result.hpp |
| file | intersection_strategies.hpp |
| file | side.hpp |
| file | side_info.hpp |
| file | strategies.hpp |
| file | strategy_transform.hpp |
| file | tags.hpp |
| file | transform.hpp |
| file | within.hpp |
|
April 2, 2011 |
Copyright © 2007-2011 Barend Gehrels, Amsterdam, the Netherlands Copyright © 2008-2011 Bruno Lalande, Paris, France Copyright © 2009-2010 Mateusz Loskot, London, UK |


<EMAIL_ADDRESS>This is not a good fake phone number:<PHONE_NUMBER>
This is not a good fake phone number: 123/123.1234
This is not a good fake phone number:<PHONE_NUMBER>
Here are some external links that should not be followed.


Linewidths in the fast-motional regime.
lw = fastmotion(Sys,B0,tcorr) lw = fastmotion(Sys,B0,tcorr,domain) [lw,mI] = fastmotion(...)
Given the g, A and Q tensors in Sys
, the magnetic field (mT) in B0
and a rotation correlation time (seconds) in tcorr
, fastmotion
computes the EPR line widths resulting from rotational motion of the paramagnetic molecules. The widths depends on the anisotropies of g, A and Q. The widths are returned in lw
(in units of mT or MHz, depending on domain
), and the associated lines are identified by sets of mI numbers in mI
. One row corresponds to one EPR resonance line.
The four input parameters are
Sys
Nucs
, the g, A and Q tensors g
, A
, Q
, and, optionally, the tensor orientations in gFrame
and AFrame
. The field QFrame
is not used.B0
tcorr
tcorr
and the diffusion rate D
are related by tcorr = 1/(6*D)
.domain
'field'
for field sweeps (units of mT) or 'freq'
for frequency sweeps (units MHz). Default is 'field'
.
The two output parameters are
lw
mI
fastmotion
uses the Kivelson formula to compute line widths.
Outside the fast-motion regime (Redfield limit, motional narrowing),
the formulas are not valid.
The formulas used in fastmotion
are from Neil Atherton, Principles
of Electron Spin Resonance, 1993. The relevant expressions are on pages 331
(9.2.48-51) and 348 (9.5.2).
The original theory can be found in J.H. Freed and G.K. Fraenkel, J. Chem. Phys. 39 (1963), 326-348.
The X-band spectrum of a nitroxide radical in the fast motional regime features three lines with different widths. The widths and the mI values for the associated lines are
Nitroxide = struct('g',[2.0088 2.0064 2.0027],'Nucs','14N'); Nitroxide.A = mt2mhz([7.59 5.95 31.76]/10); Field = 350; tcorr = 1e-10; [lw,mI] = fastmotion(Nitroxide,Field,tcorr,'field')
lw = 0.0070 0.0029 0.0027 mI = -1 0 1


Call of Duty Tops EMEAA Charts but Death Stranding Challenges
by Chris WrayCall of Duty is always a strong title for Activision Blizzard, it has been for so long now that November is generally just a good time for the company. Previous iterations had fallen behind in sales, though still selling incredibly well, though Call of Duty: Modern Warfare is going incredibly strong going by EMEAA charts, topping them for the third consecutive week since launch, matching the position of the game in the NPD charts.
A number of new arrivals have featured in the charts for the week ending the 10th of November. Death Stranding launched to second place, followed by another newcomer in Need for Speed: Heat. Indeed, the week featured three more newcomers with Mario & Sonic at the Olympic Games: Tokyo 2020, Layton's Mystery Journey: Katrielle and the Millionaires' Conspiracy and Just Dance 2020. The top ten titles for the week in both physical and digital sales were as follows:
200 Player Battle Royale Coming to Call of Duty: Modern Warfare [Rumour]
| This Week | Last Week | Title |
| 1 | 1 | Call of Duty: Modern Warfare |
|---|---|---|
| 2 | N/A | Death Stranding |
| 3 | N/A | Need for Speed: Heat |
| 4 | 3 | FIFA 20 |
| 5 | 2 | Luigi's Mansion 3 |
| 6 | N/A | Mario & Sonic at the Olympic Games: Tokyo 2020 |
| 7 | 4 | Grand Theft Auto V |
| 8 | 9 | Mario Kart 8 Deluxe |
| 9 | N/A | Layton's Mystery Journey: Katrielle and the Millionaires' Conspiracy |
| 10 | N/A | Just Dance 2020 |
Other movers further down the list were titles like Cities: Skylines by Paradox Interactive, moving from 277th place to 31st. Moving in the opposite direction was Ghost Recon: Breakpoint, falling eighteen places to 25th and also The Outer Worlds, falling fourteen places to 24th.
As always, the charts show the ever-constant popularity of titles like Grand Theft Auto V, a six-year-old title that still surpasses the later released Rockstar Games title Red Dead Redemption 2, driven by the strong community for Grand Theft Auto Online.
The EMEAA charts show combined physical and digital sales across over forty territories in the EMEAA region, collecting a variety of data. Physical sales data covers every game and is collected from these countries: Belgium, Denmark, Finland, France, Great Britain, Italy, Netherlands, Norway, Poland, Portugal, Spain, Sweden and Switzerland.
Digital sales are gathered across participating companies which include Activision Blizzard, Bandai Namco, Capcom, Codemasters, EA, Focus Home Interactive, Koch Media, Microsoft, Milestone, Paradox Interactive, Sega, Sony, Square Enix, Take-Two, Ubisoft and Warner Bros.
Digital sales data is gathered across these territories: Australia, Austria, Bahrain, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Finland, France, Germany, Great Britain, Greece, Hungary, Iceland, India, Ireland, Israel, Italy, Kuwait, Lebanon, Luxembourg, Malta, Netherlands, New Zealand, Norway, Oman, Poland, Portugal, Qatar, Romania, Russia, Saudi Arabia, Slovakia, Slovenia, South Africa, Spain, Sweden, Switzerland, Turkey, Ukraine and UAE.


A buddy of mine had a pretty good supply of the tiny innocent tablets. He had some surgery on his knee and was prescribed it a few months ago. He didnít take much of it because of the other pain killers and anti-inflammatories he was out on.
Around 9:30 I down 2 tablets (100mg) with water and we went to a store to chill around and watch the people. By 10:15 I began feeling the effects. Sounds were clearer as well as colors and quick movements. I felt alert not sleepy as vicodin has the effect of doing sometimes. I went into the bathroom and took 2 more for the hell of it. That may or may not have been the best of ideas. The feelings of weightlessness and euphoria were overwhelming my body. Anything cold or liquid like was amazing to look at or touch.
We left the store about 11:00 and went to a friends house. I felt great and pretty pleased with what happened to me. We smoked a bowl between the 3 of us and everything went downhill from there. I had nausea and stomach cramps for about 3 hours non stop. I couldnít distinguish if it was because of the higher dose I took or the weed. I tried to sleep on the couch but couldnít. It is very difficult to sleep with this.
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.


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AnsiColors
- Enum in
org.metaborg.spoofax.shell.client.console
Represents a list of possible ansi colors and maps them to Java colors.
append(String)
- Method in class org.metaborg.spoofax.shell.client.console.
JLine2InputHistory
C
configure()
- Method in class org.metaborg.spoofax.shell.client.console.
ConsoleReplModule
ConsoleRepl
- Class in
org.metaborg.spoofax.shell.client.console
This class launches a console based REPL.
ConsoleReplModule
- Class in
org.metaborg.spoofax.shell.client.console
Bindings for the console repl.
ConsoleReplModule()
- Constructor for class org.metaborg.spoofax.shell.client.console.
ConsoleReplModule
D
delegate()
- Method in class org.metaborg.spoofax.shell.client.console.
JLine2InputHistory
delegate()
- Method in class org.metaborg.spoofax.shell.client.console.
JLine2PersistentInputHistory
displayError(StyledText)
- Method in class org.metaborg.spoofax.shell.client.console.
TerminalUserInterface
displayResult(StyledText)
- Method in class org.metaborg.spoofax.shell.client.console.
TerminalUserInterface
E
entries(int, int)
- Method in class org.metaborg.spoofax.shell.client.console.
JLine2InputHistory
F
findClosest(Color)
- Static method in enum org.metaborg.spoofax.shell.client.console.
AnsiColors
Find the ansi color most corresponding to a java color.
G
get(int)
- Method in class org.metaborg.spoofax.shell.client.console.
JLine2InputHistory
getInput()
- Method in class org.metaborg.spoofax.shell.client.console.
TerminalUserInterface
H
history()
- Method in class org.metaborg.spoofax.shell.client.console.
TerminalUserInterface
J
JLine2InputHistory
- Class in
org.metaborg.spoofax.shell.client.console
An adapter for JLine2's History implementation.
JLine2PersistentInputHistory
- Class in
org.metaborg.spoofax.shell.client.console
An extension to the adapter for JLine2's History implementation.
L
loadFromDisk()
- Method in class org.metaborg.spoofax.shell.client.console.
JLine2PersistentInputHistory
M
main(String[])
- Static method in class org.metaborg.spoofax.shell.client.console.
ConsoleRepl
O
org.metaborg.spoofax.shell.client.console
- package org.metaborg.spoofax.shell.client.console
A console-based interface to the REPL services.
P
persistToDisk()
- Method in class org.metaborg.spoofax.shell.client.console.
JLine2PersistentInputHistory
provideConsoleReader(InputStream, OutputStream)
- Method in class org.metaborg.spoofax.shell.client.console.
ConsoleReplModule
TODO: Replace with "CheckedProvides" because IO in Provider method is bad practice, see: https://github.com/google/guice/wiki/ThrowingProviders.
R
reader
- Variable in class org.metaborg.spoofax.shell.client.console.
JLine2InputHistory
S
saveLine(String)
- Method in class org.metaborg.spoofax.shell.client.console.
TerminalUserInterface
Save this line as the end of the multi-line input.
setContinuationPrompt(StyledText)
- Method in class org.metaborg.spoofax.shell.client.console.
TerminalUserInterface
setPrompt(StyledText)
- Method in class org.metaborg.spoofax.shell.client.console.
TerminalUserInterface
setSpoofaxCompletion(ICompletionService<ISpoofaxParseUnit>)
- Method in class org.metaborg.spoofax.shell.client.console.
TerminalUserInterface
size()
- Method in class org.metaborg.spoofax.shell.client.console.
JLine2InputHistory
T
TerminalUserInterface
- Class in
org.metaborg.spoofax.shell.client.console
A terminal UI which is both an
IEditor
and an
IDisplay
.
TerminalUserInterface(ConsoleReader, OutputStream, OutputStream, JLine2InputHistory)
- Constructor for class org.metaborg.spoofax.shell.client.console.
TerminalUserInterface
V
valueOf(String)
- Static method in enum org.metaborg.spoofax.shell.client.console.
AnsiColors
Returns the enum constant of this type with the specified name.
values()
- Static method in enum org.metaborg.spoofax.shell.client.console.
AnsiColors
Returns an array containing the constants of this enum type, in the order they are declared.
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Delft University of Technology
. All rights reserved.


Gecode::LinFloatExpr::Node Class Reference
Nodes for linear expressions. More...
Public Member Functions | |
| Node (void) | |
| Default constructor. | |
| void | fill (Home home, Float::Linear::Term *&tf, FloatVal m, FloatVal &d) const |
| Generate linear terms from expression. | |
| FloatVal | fill (Home home, Float::Linear::Term *tf) const |
| Generate linear terms for expressions. | |
| bool | decrement (void) |
| Decrement reference count and possibly free memory. | |
| ~Node (void) | |
| Destructor. | |
Static Public Member Functions | |
| static void * | operator new (size_t size) |
| Memory management. | |
| static void | operator delete (void *p, size_t size) |
| Memory management. | |
Public Attributes | |
| unsigned int | use |
| Nodes are reference counted. | |
| int | n_float |
| Float variables in tree. | |
| NodeType | t |
| Type of expression. | |
| Node * | l |
| Subexpressions. | |
| Node * | r |
| union { | |
| Float::Linear::Term * tf | |
| Integer views and coefficients. | |
| NonLinFloatExpr * ne | |
| Non-linear expression. | |
| } | sum |
| Sum of integer or Boolean variables, or non-linear expression. | |
| FloatVal | a |
| Coefficient and offset. | |
| FloatVal | c |
| FloatVar | x_float |
| Float variable (potentially). |
Detailed Description
Nodes for linear expressions.
Definition at line 43 of file float-expr.cpp.
Constructor & Destructor Documentation
| Gecode::LinFloatExpr::Node::Node | ( | void | ) | [inline] |
Default constructor.
Definition at line 88 of file float-expr.cpp.
| Gecode::LinFloatExpr::Node::~Node | ( | void | ) | [inline] |
Destructor.
Definition at line 92 of file float-expr.cpp.
Member Function Documentation
| void Gecode::LinFloatExpr::Node::fill | ( | Home | home, | |
| Float::Linear::Term *& | tf, | |||
| FloatVal | m, | |||
| FloatVal & | d | |||
| ) | const |
Generate linear terms from expression.
Definition at line 344 of file float-expr.cpp.
| FloatVal Gecode::LinFloatExpr::Node::fill | ( | Home | home, | |
| Float::Linear::Term * | tf | |||
| ) | const |
Generate linear terms for expressions.
Definition at line 143 of file float-expr.cpp.
| bool Gecode::LinFloatExpr::Node::decrement | ( | void | ) |
Decrement reference count and possibly free memory.
Definition at line 116 of file float-expr.cpp.
| void * Gecode::LinFloatExpr::Node::operator new | ( | size_t | size | ) | [inline, static] |
Memory management.
Definition at line 106 of file float-expr.cpp.
| void Gecode::LinFloatExpr::Node::operator delete | ( | void * | p, | |
| size_t | size | |||
| ) | [inline, static] |
Memory management.
Definition at line 111 of file float-expr.cpp.
Member Data Documentation
| unsigned int Gecode::LinFloatExpr::Node::use |
Nodes are reference counted.
Definition at line 46 of file float-expr.cpp.
Float variables in tree.
Definition at line 48 of file float-expr.cpp.
Type of expression.
Definition at line 50 of file float-expr.cpp.
Subexpressions.
Definition at line 52 of file float-expr.cpp.
Definition at line 52 of file float-expr.cpp.
Integer views and coefficients.
Definition at line 56 of file float-expr.cpp.
Non-linear expression.
Definition at line 58 of file float-expr.cpp.
| union { ... } Gecode::LinFloatExpr::Node::sum |
Sum of integer or Boolean variables, or non-linear expression.
Coefficient and offset.
Definition at line 61 of file float-expr.cpp.
Definition at line 61 of file float-expr.cpp.
Float variable (potentially).
Definition at line 63 of file float-expr.cpp.
The documentation for this class was generated from the following file:
- gecode/minimodel/float-expr.cpp


|
GNSDK C API Reference
<IP_ADDRESS>2
|
Macros | |
| #define | GNSDK_GDO_CHILD_NAME_ALTERNATE GNSDK_GDO_TYPE_NAME"!alt" |
| #define | GNSDK_GDO_CHILD_NAME_OFFICIAL GNSDK_GDO_TYPE_NAME"!off" |
| #define | GNSDK_GDO_CHILD_NAME_REGIONAL GNSDK_GDO_TYPE_NAME"!reg" |
| #define | GNSDK_GDO_CHILD_NAME_REGIONAL_LOCALE GNSDK_GDO_TYPE_NAME"!loc" |
| #define GNSDK_GDO_CHILD_NAME_ALTERNATE GNSDK_GDO_TYPE_NAME"!alt" |
Retrieves an alternate child name GDO.
Definition at line 975 of file gnsdk_manager_gdo.h.
| #define GNSDK_GDO_CHILD_NAME_OFFICIAL GNSDK_GDO_TYPE_NAME"!off" |
Retrieves the official child name GDO.
Definition at line 957 of file gnsdk_manager_gdo.h.
| #define GNSDK_GDO_CHILD_NAME_REGIONAL GNSDK_GDO_TYPE_NAME"!reg" |
Retrieves a regional child name GDO.
Definition at line 963 of file gnsdk_manager_gdo.h.
| #define GNSDK_GDO_CHILD_NAME_REGIONAL_LOCALE GNSDK_GDO_TYPE_NAME"!loc" |
Retrieves a regional child name GDO that matches current locale.
Definition at line 969 of file gnsdk_manager_gdo.h.
How can we improve this topic?
Copyright © 2000-present. Gracenote, Inc. All rights reserved. Generated on Tue Jan 8 2013 07:40:57.


Here we give credit to the libraries and software we used to develop this tool.
We used OpenCV for image and video loading. We also used it for blob analysis when solving the markered video problem.
We employ OpenPose for our solution of the markerless video problem. OpenPose provides us with keypoint estimation of the feet, which we then can send into our post-detection analysis.
We used PyTorch when developing and testing the Deep Convolutional Tracker.
We used numpy for signal analysis, matplotlib for plotting, PyQT for the welcoming GUI
We primarily used PyCharm as our IDE.


Growl utviklingsteam
Nåværende Team Leads
Nåverende Utviklere
- Daniel Siemer - Networking, History, Core Data, AppleScript black magic
- Rachel Blackman - Built in framework style, Mist
- Zac Bowling - Code junky and resident nitpicker
- Travis Tilley - General Development
- Thijs Alkemade - General Development
Icons & Artwork
Current Localizers
Previous Localizers
- Radoslav Lukac - Slovakisk
- Daniel Bergman - Svensk
- Marcos Frazão - Portugisisk
- Jack Lin - Tradisjonell Kinesisk
- Daisuke Okada - Japansk
- Valentine Parnakh - Russisk
- Ingmar Stein - Tysk
Previous Developers
- Karl Adam - Lead Slacker (and Developer)
- Kevin Ballard - Code Ninja and Subversion God
- Olivier Bonnet - GrowlTunes and general development
- Graham Booker - General Development
- Jorge Salvador Caffarena - Music Video DJ
- Nelson Elhage - General Development
- Brian Ganninger - Former Lead Developer, UI Nerd
- Mohammad A. Haque
- Timothy Hatcher - Bubbles
- Peter Hosey - Lead Developer; Carbon Black Magic
- Adam Iser - Code Zombie
- Jamie Kirkpatrick - Growl'ardCore Developer
- Jerome Knope - Rawr-Jour
- Diggory Laycock - Bits & Bobs
- Patrick Linskey - Applescript and General Development
- Chris Nandor - Perl Bindings
- Jeremy Rossi - Python Bindings
- Mark Rowe - Python Bindings
- Evan Schoenberg - Attending Code Doctor, General Development
- Don Smith - GrowlDict
- Ingmar Stein - GrowlMail, GrowlSafari, German, General Development
- James Van Dyne - GrowlTunes and General
- Vinay Venkatesh - Monkey (code and otherwise)
- Matthew Walton - Mr. Smokev
- Andrew Wellington - macyac
- Ofri Wolfus - General Development
Those who have contributed in some way to Growl


This is a paragraph
And this is another one.
Trying out tables here:
| Column Heading 1 | Column Heading 2 | Column Heading 3 |
|---|---|---|
| R1C1 | R1C2 | R1C3 |
| R2C1 | R2C2 | R2C3 |
Trying out the abbreviation tag BBC.
This is just a short paragraph.
Here is an interesting item that should not show in the markdown:
If you see anything about it, there is a problem.
And now a few sentences about something that just goes on and on and maybe continues for enough lines to make it a substantive blockquote. It's still not as long as it could be. Here is another bit of text to make it an even taller and weightier bit of text. Oh and the overall text is intentionally odd.
And here is a citation:
Bakker, Robert T. "Dinosaur renaissance." Scientific American 232.4 (1975): 58-79.


+donkey, +donkey (6)
Each other Donkey gets +1½/+1½.
Whenever another Donkey comes into play, untap target creature and gain control of it until end of turn. That creature gains haste until end of turn.
, Growl: Regenerate Bad Ass.
Spells you play cost less to play.

 Defending player may reveal his or her hand and show you that the named card isn't there. If that player doesn't, Smart Ass is unblockable this turn.


Data Fields | |
| wchar_t | strFileName [256] |
| Application file name. | |
| wchar_t | strFilePath [256] |
| Application file path. | |
| wchar_t | strVersion [256] |
| Application version. | |
| long long int | timeStamp |
| Timestamp at which application has run. | |
| unsigned int | iProfileExists |
| Holds whether the applicaition profile exists or not. | |
| unsigned int | iGPUAffinity |
| The GPU on which application runs. | |
| ADLBdf | GPUBdf |
| The BDF of the GPU on which application runs. |
This structure is used to store basic information of a recently ran or currently running application
| wchar_t _ADLSGApplicationInfo::strFileName[256] |
Application file name.
| wchar_t _ADLSGApplicationInfo::strFilePath[256] |
Application file path.
| wchar_t _ADLSGApplicationInfo::strVersion[256] |
Application version.
| long long int _ADLSGApplicationInfo::timeStamp |
Timestamp at which application has run.
| unsigned int _ADLSGApplicationInfo::iProfileExists |
Holds whether the applicaition profile exists or not.
| unsigned int _ADLSGApplicationInfo::iGPUAffinity |
The GPU on which application runs.
The BDF of the GPU on which application runs.


| parse_html_notebook {rmarkdown} | R Documentation |
Parse an HTML notebook, retrieving annotation information related to generated outputs in the document, as well as the original R Markdown source document.
parse_html_notebook(path)
path |
The path to an R Notebook file (with extension |
See the online
documentation for additional details on using the html_notebook
format.


Local file:// browsing is limited in Chrome – browse online
Back/Forward after reload is broken in Firefox :(


Documentation¶
One documentation to rule them all, one documentation to find them, One documentation to bring them all and in the darkness bind them. 1
The entire generated documentation that is being used is the result of the union between Sphinx and sphinx-lua
,
two tools dedicated to the generation of texts starting from mere and pure code.
Tools with which it was made¶
Usually the part of the code documentation in Lua is somewhat boring and stylistically questionable.
From this premise comes the integration of Sphinx 2 within the project which has made the entire software park a pleasant and clear product in understanding its API.
Integration with Lua¶
For the documentation of the classes created, therefore, we relied on a tool that puts Python (given the nature of Sphinx) and Lua in symbiosis.
This tool is called sphinx-lua
3.
It can be easily installed using the following command:
pip install sphinx-lua
Once that you installed this on your machine your could simply start to document your class in this way:
--- Define a car.
--- @class MyOrg.Car
local cls = class()
--- @param foo number
function cls:test(foo)
end
- 1
Modified version of The One Ring, the central plot element in J. R. R. Tolkien’s The Lord of the Rings (1954–55). It first appeared in the earlier story The Hobbit (1937) as a magic ring that grants the wearer invisibility
- 2
Sphinx is a documentation generator written and used by the Python community. It is written in Python, and also used in other environments.
- 3


Welcome to Very Adaptive Randomizer of Items and Areas for Super Metroid
Here, you will be able to generate as many VARIAtions of Super Metroid as you want, by Randomizing Item locations, and even the connections between the main Areas!
If you're not an uberskilled speedrunner, have no fear, neither are we. It is Very Adaptive to all skill levels, and you are guaranteed to be able to finish a randomized ROM, granted that you create your skill preset, or use a standard one in your range.
To help you find your way in these sometimes not so easy randomized ROMs (aka 'seeds') you will be able to use a Solver, to estimate the seed's difficulty or have a full spoiler log. Even better, you can use the interactive Item Tracker to help you as you play along. For area-randomized seeds, the Tracker can also be used in a simpler way just to help you keep track of the area connections without any additional help.
Presets
- At the center of the VARIA experience is the preset
- All the techniques which can be required in a seed are listed
- For each technique you can tell if you know it and your perceived difficulty of it
- Also take into account hell runs (going into heated rooms without protection), hard rooms (Gauntlet, Xray) and bosses
- Standards presets of increasing difficulty are included, as a base to create your own
- A skill level bar allows you to compare your preset to the standard and community presets
- Tutorials are available for most of the techniques
- The rooms where the techniques can be used are listed
- Put your gamepad mapping in your preset
|
Randomizer
- Use your preset to generate seeds that will only require techniques you know
- Full item randomization, majors/minors randomization, Chozo split (required items to finish the game are located in Chozo statues), Scavenger Hunt (collect major items in a required order)
- Areas/Bosses randomization: randomize the access between the main areas of the game and/or the bosses doors
- Start Location: choose or randomize where you want to start the game
- Doors colors: randomize the color of colored doors, add beams doors in the mix
- Objectives: choose or randomize goals to access the end, and endgame behavior with Tourian
- Different progression speeds, from slowest, stuffing your way with as many non-progression items as possible, to fastest, where you will be able to visit Zebes pretty shortly
- Choose the quantity and distribution of minors and energy
- Super Fun Times: Have fun randomly removing major items!
|
Solver
- Using your preset you can check if you can finish a seed (VARIA/Dessy/Total randomizers are supported) and get a difficulty estimation
- Display an optional spoiler giving you a realistic path to follow to finish the seed
- All the techniques and items used to reach a location are displayed
- You can click on an item to highlight all occurences of it in the spoiler for easy spotting
- The solver can collect all items or only the minimum required
|
Areas and Items Tracker
- Auto tracker: connect your emulator/sd2snes/snes mini to let the tracker updates itself automatically
- Area tracker: add lines between the area portals when playing an Area randomized seed
- Interactive Item tracker: the available locations are displayed on the map, click on them to collect the item and display the next available locations
- Trackers states are saved automatically, allowing you to finish your seed in multiple seatings
|
Areas and Items Plandomizer
- Items Plandomizer: Manually place items to create you own Plando seed in graphical/interactive mode
- Areas/Bosses/Doors Plandomizer: Choose the connections between areas/bosses doors, choose doors colors in your Plando seed
- Plando/Rando: partially populate your plando, and let the randomizer fill in the gaps
|
Seed Customizer
- Add comfort patches
- Randomize in-game colors
- Replace Samus sprite or ship with a custom one (some with custom items names)
- Replace in-game music by custom tracks, or randomize the vanilla soundtrack
|


PyFL package¶
Submodules¶
PyFL.constants module¶
PyFL.nfl_stats module¶
-
PyFL.nfl_stats.
gather_json
(week, position=None)[source]¶ Utility method for acquiring json data from NFL api
-
PyFL.nfl_stats.
get_player_points
(player_name, week, position=None)[source]¶ Get a player’s points for a certain week
- Example:
- get_player_points(‘Tom Brady’, 1, position=’QB’)


|
||||||||||
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MemAccessType
enumeration of the memory region
referenced by the current instance of MemoryRegionInfo.
AiChanConfig
associated with the
specified channel.
AiChanConfig
associated with the
specified channel.
AiDevice
if the DAQ device has an analog input subsystem.
AoAlarmConfig
for a specified D/A channel.
DoAlarmConfig
for a specified digital output port.
AoDevice
if the DAQ device has an analog output subsystem.
AutoShutdownConfig
BthConfig
. This function returns Null
for non-Bluetooth devices.
CalCoef
.
CalCoef
.
AiCalConfig
AiCalConfig
AoCalConfig
CalTableType
enumeration indicating the
calibration table type for the current instance of AiCalConfig.
AiChanInfo
for a given A/D channel.
AiChanInfo
for a given A/D channel.
AoChanInfo
for a given D/A channel.
AiChanMode
EnumSet containing all compatible
channel modes for the current channel.
AiChanMode
EnumSet containing all compatible channel modes
for the current device.
AiChannelQueueLimitation
EnumSet containing
all limitations (if any) that apply to the queue for the current device.
AiChanType
enumeration indicating the channel
type configured for the current instance of AiChanConfig.
AiChanType
EnumSet containing all compatible
channel types for the current channel.
AoChanType
EnumSet containing all compatible channel types
for the current channel.
ChargingConfig
CioDevice
if the DAQ device has a counter subsystem.
AiConfig
.
AoConfig
.
CioConfig
.
DaqDeviceConfig
.
DioConfig
.
CtrInfo
for a given counter channel.
AutoShutdownCriteria
enumeration indicating the shutdown
criteria configured for the current instance of AutoShutdownConfig.
DaqDeviceDescriptor
s of the detected DAQ devices.
DaqDeviceDescriptor
.
DioDevice
if the DAQ device has a digital I/O subsystem.
DaqEventType
s.
AiExpConfig
ExpConfig
AiExpInfo
DioExpInfo
AiInfo
.
AoInfo
.
CioInfo
.
DaqDeviceInfo
DioInfo
.
TmrInfo
.
IPAddressSetting
enumeration indicating the IP address setting
for the device.
BURSTIO
mode.
BURSTIO
mode.
MemInfo
MemRegionInfo
for the current DAQ device.
MemoryRegion
EnumSet containing all
possible reserved areas of memory.
NetConfig
. This function returns Null
for non-Ethernet devices.
DaqDeviceDescriptor
of the detected network DAQ device.
AiPacerConfig
DigitalPortType
PortConfig
for a specified digital output port.
PortConfig
for a specified digital output port.
DigitalDirection
enumeration of the digital port
referenced by the current instance of PortConfig.
PortInfo
for a given digital port.
PortInfo
for a given digital port.
DigitalPortIOType
enumeration indicating the
port type of the port referenced by the current instance of PortInfo.
PowerConfig
AiQueueType
EnumSet containing all compatible
queue types for the current device.
Range
EnumSet containing all valid ranges for the
specified channel mode.
Range
EnumSet containing all valid D/A ranges.
MemoryRegion
enumeration of the memory region
referenced by the current instance of MemoryRegionInfo.
AiScanOption
EnumSet containing all valid
scan options for the current device.
DioScanOption
EnumSet containing all valid
scan options for the current device.
AoAlarmSettings
DoAlarmSettings
TriggerSourceType
.
Status
of the background operation currently running.
Status
of the background operation currently running.
Status
of the background operation currently running.
TcConfig
TcInfo
for the current channel.
TcType
enumeration indicating the thermocouple type
configured for the current instance of AiChanConfig
.
TcType
Enumset containing all possible thermocouple
types.
TempConfig
TempConfig
TmrChanInfo
for a given timer channel.
TmrDevice
if the DAQ device has a timer subsystem.
TmrOutStatus
enumeration indicating the status of
the output operation referenced by
the current instance of TmrDevice.
TriggerType
EnumSet containing all compatible
trigger types for the current device.
TriggerSourceType
EnumSet containing
trigger types compatible with the specified trigger source for the current device.
TriggerType
EnumSet containing all compatible
trigger types for the current device.
TriggerSourceType
EnumSet containing
trigger types compatible with the specified trigger source for the current device.
TriggerType
EnumSet containing all compatible
trigger types for the current device.
TriggerSourceType
EnumSet containing
trigger types compatible with the specified trigger source for the current device.
AlarmType
enumeration indicating the alarm type configured for the
current instance of AoAlarmConfig.
CtrType
enumeration of the counter referenced by the current
instance of CtrInfo.
AlarmType
DigitalPortType
enumeration indicating the
port I/O type of the port referenced by the current instance of PortInfo.
TmrType
enumeration indicating the type of timer
referenced by the current instance of TmrChanInfo.
AiUnit
EnumSet containing the units compatible with
analog input for the current device.
AoUnit
EnumSet containing the units compatible with
analog output for the current device.
AoUsbConfig
UsbConfig
. This function returns Null
for non-USB devices.
|
||||||||||
| PREV LETTER NEXT LETTER | FRAMES NO FRAMES |


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04
Get in touch.
Copyright© 2021
Get in touch.
Copyright© 2021
We provide Export Documentation Solutions. From cold calling to outside sales, we offer a reliable supply chain management between point of origin and consumption in order to meet our customer requirements. Our Online, over the phone and face-to-face service delivers fast and accurate export documentation. We advise on Trade Documents as Single Administrative Document, Certificates of Conformity and facilitating acquisition of Certificates of Export by providing a link between Clients, the Shipping Line and Customs.
We install and configure computer hardware and software systems, networks, printers; monitoring and maintaining computer systems and your networks. Respond in a timely style to service bugs and solicitations.
It's in our response to provide support in examination, evaluation and verification of your operating systems procedures, internal controls, and recorded data for the information consistency and information security.
This man like igwe. He is the organization's information technology director. He also likes wearing colourful scarfs.
This guy right here? He is the organization's financier. Yeap, that's right.  Just like his fellow rich billionaires, he likes wearing a t-shirt to the office.
This guy Ron? He is the organization's COO. Don't let the flowers mislead you, what better way to operate a company than with the voluptuous fragrance of a daisy. Oh, and he also knows Muay Thai, so how's that for operating?


This is my favourite family I've been playing with since 2009. More info to follow in some of the next posts...
Traits:
Artistic
Charismatic
Family Oriented
Hopeless Romantic
Savvy Sculptor
Additional Traits:
Eccentric
Over-Emotional
Lifetime Wish:
Descendant of DaVinci: Max Painting, Sculpting, and Inventing Skills
Working as:
Master Sculptologist, Sculptor Career
Traits:
Ambitious
Artistic
Athletic
Charismatic
Excitable (the simple things in life make her happy)
Additional Traits:
Genius (she loooves to play chess)
Good sense of humor
Lifetime Wish
Master of the Arts: Max both the Painting and Guitar Skills
Working as:
Vocal Legend, Singer Career;
ex Couture Connoisseur in the Stylist Career
She comes from the Funke family, who just left her one day on their front porch after they left town, probably being heavily pressed to make the ends meet.
Traits:
Clumsy
Family Oriented
Friendly
Light Sleeper
Virtuoso
Additional Traits:
Lucky
Neat
Lifetime Wish
Master of the Arts: Max both the Painting and Guitar Skills
Working as:
Band Manager, Music Career
Traits:
Ambitious (like his mother)
Artistic (he couldn't help but inherit this trait from both his parents)
Athletic (like his mother)
Daredevil
Family Oriented (like his father)
Hot Headed
Additional Traits:
Brave
Great Kisser
Lifetime Wish
CEO of a Mega-Corporation
Working as:
After completing his lifetime wish and becoming the CEO of his corporation, Daren Jr was no longer challenged by the business world. He felt he was able to climb higher grounds. Now he is employed in Politics, determined to make the world a better place. Starting as a Mayor of his town, his intention is to become a Leader of the Free World someday.


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If you had someone else winterize your system, they should have left the ball valves at 45 degrees as well as both test cocks at 45 degrees. If there is a drain cap below the PVB, it also should have been left ajar or removed and stored in a safe place for the Winter. If the ball valves/test cocks were left all the way open or all the way closed, there may be damage from the water left in those valves… it’s easy to spot the ruptures or use your finger to feel for them if they are out of sight.
The Pressure Vacuum Breaker device must first be inspected to check for freeze damage.
Make sure that the water to the system is off at the shutoff valve inside the structure. Wear eye protection as pressure inside system can cause parts to act as projectiles. Check the top – bell cap – plastic or metal, if plastic and there is freeze damage – they sometimes do crack – even if there are not cracks remove the top and inspect the assembly within. If there is damage, generally, the assembly will have raised up on one side and will allow water to escape once the source is turned on.
If you have a metal cap and it is still present, remove this cap carefully and inspect poppet assembly for cracks. If no damage is found proceed to check the two ball valves for ruptures – usually they can been seen on the opposite side of the control handle. No damage found, proceed, note – all advice offered on this site is without warranty and or liability – use at your own risk.
To startup your sprinkler system, use the following procedures:
The price of copper has gone up steadily over recent months and the rate of theft for pressure vacuum breakers is also on the rise. There are at least two methods to help secure your Pressure Vacuum Breaker. The unit is at most risk just after the sprinkler system has been winterized in the fall… the criminals know that there is no water supply and thus, makes it much easier for them to abscond with your items without being noticed.


--- title: Simulating Fake Data in R author: Gertjan Verhoeven date: '2019-10-26' summary: This blog post is on simulating fake data using the R package [simstudy](https://www.rdatagen.net/page/simstudy/). Motivation comes from my interest in converting real datasets into synthetic ones. slug: simulating-fake-data draft: FALSE categories: - Data science tags: - fake data - simulation - copula - tetrachoric correlation - R baseurl: "https://gsverhoeven.github.io" header: image: "headers/bright-chart-data-210607.jpg" preview: FALSE ---
This blog post is on simulating fake data. I'm interested in creating synthetic versions of real datasets. For example if the data is too sensitive to be shared, or we only have summary statistics available (for example tables from a published research paper).
If we want to mimic an existing dataset, it is desirable to
In addition, it would be nice if such functionality is available in a standard R package. After reviewing several R packages that can simulate data, I picked the simstudy package as most promising to explore in more detail. simstudy
is created by Keith Goldfeld from New York University.
In this blog post, I explain how simstudy
is able to generate correlated variables, having either continuous or binary values. Along the way, we learn about fancy statistical slang such as copula's and tetrachoric correlations. It turns out there is a close connection with psychometrics, which we'll briefly discuss.
Let's start with correlated continuous variables.
# Loading required packages
library(simstudy)
library(data.table)
library(ggplot2)
Now let's see how simstudy
helps us generating this type of simulated data. Simstudy works with "definition tables" that allow us to specify, for each variable, which distribution and parameters to use, as well as the desired correlations between the variables.
After specifing a definition table, we can call one of its workhorse functions genCorFlex()
to generate the data.
N.b. Simstudy uses different parameters for the Gamma distribution, compared to R's rgamma()
function. Under water, it uses the gammaGetShapeRate()
to transform the "mean" and "variance/ dispersion" to the more conventional "shape" and "rate" parameters.
set.seed(123)
corr <- 0.7
corr.mat <- matrix(c(1, corr,
corr, 1), nrow = 2)
# check that gamma parameters correspond to same shape and rate pars as used above
#simstudy::gammaGetShapeRate(mean = 4, dispersion = 0.25)
def <- defData(varname = "X1_GAM",
formula = 4, variance = 0.25, dist = "gamma")
def <- defData(def, varname = "X2_NORM",
formula = 10, variance = 2, dist = "normal")
dt <- genCorFlex(1e4, def, corMatrix = corr.mat)
cor(dt[,-"id"])
## X1_GAM X2_NORM
## X1_GAM 1.0000000 0.6823006
## X2_NORM 0.6823006 1.0000000
ggplot(dt, aes(x = X1_GAM)) +
geom_histogram(boundary = 0) +
geom_vline(xintercept = 4, col = "red")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
So what has psychometrics to do with all this simulation of correlated binary vector stuff?
Well, psychometrics is all about theorizing about unobserved, latent, imaginary "constructs", such as attitude, general intelligence or a personality trait. To measure these constructs, questionnaires are used. The questions are called items.
Now imagine a situation where we are interested in a particular construct, say general intelligence, and we design two questions to measure (hope to learn more about) the construct. Furthermore, assume that one question is more difficult than the other question. The answers to both questions can either be wrong or right.
We can model this by assuming that the (imaginary) variable "intelligence" of each respondent is located on a two-dimensional plane, with the distribution of the respondents determined by a bivariate normal distribution. Dividing this plane into four quadrants then gives us the measurable answers (right or wrong) to both questions. Learning the answers to both questions then gives us an approximate location of a respondent on our "intelligence" plane!
Officially, the Pearson correlation between two binary vectors is called the Phi coefficient. This name was actually chosen by Karl Pearson himself.
The psych packages contains a set of convenient functions for calculating Phi coefficients from empirical two by two tables (of two binary vectors), and finding the corresponding Pearson coefficient for the 2d (latent) normal. This coefficient is called the tetrachoric correlation. Again a fine archaic slang word for again a basic concept.
library(psych)
##
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
# convert simulated binary vectors B1 and B2 to 2x2 table
twobytwo <- table(df$B1, df$B2)/nrow(df)
phi(twobytwo, digits = 6)
## [1] 0.099574
cor(df$B1, df$B2)
## [1] 0.09957392
# both give the same result
We can use phi2tetra to find the tetrachoric correlation that corresponds to the combination of a "Phi coefficient", i.e. the correlation between the two binary vectors, as well as their marginals. This is a wrapper that builds the two by two frequency table and then calls tetrachoric()
. This in turn uses optimize
(Maximum Likelihood method?) to find the tetrachoric correlation.
phi2tetra(0.1, c(0.2, 0.8))
## [1] 0.2217801
# compare with EP method
simstudy:::.findRhoBin(0.2, 0.8, 0.1)
## [1] 0.2218018
Comparing with the Emrich and Piedmonte method, we find that they give identical answers. Great, case closed!
Now that we feel confident in our methods and assumptions, let's see simstudy
in action.
Let's generate two binary variables, that have marginals of 20% and 80% respectively, and a Pearson correlation coefficient of 0.1.
set.seed(123)
corr <- 0.1
corr.mat <- matrix(c(1, corr,
corr, 1), nrow = 2)
res <- simstudy::genCorGen(10000, nvars = 2,
params1 = c(0.2, 0.8),
corMatrix = corr.mat,
dist = "binary",
method = "ep", wide = TRUE)
# let's check the result
cor(res[, -c("id")])
## V1 V2
## V1 1.00000000 0.09682531
## V2 0.09682531 1.00000000
Awesome, it worked!
Recall, my motivation for simulating fake data with particular variable types and correlation structure is to mimic real datasets.
So are we there yet? Well, we made some progress. We now can handle correlated continuous data, as well as correlated binary data.
But we need to solve two more problems:
To simulate a particular dataset, we still need to determine for each variable its data type (binary or continuous), and if it's continuous, what is the most appropriate probability distribution (Normal, Gamma, Log-normal, etc).
we haven't properly solved correlation between dissimilar data types, e.g. a correlation between a continuous and a binary variable.
Judging from the literature (Amatya & Demirtas 2016) and packages such as SimMultiCorrData
by Allison Fialkowski, these are both solved, and I only need to learn about them! So, to be continued.


About
In 1878, Pasteurella multocida was discovered in birds infected with cholera. Then in 1880, Louis Pasteur isolated it. P. multocida is a small, Gram-negative bacterium. It is a non-motile coccobacillus and is penicillin-sensitive. It can cause infections in humans, as a result of cat or dog bites and scratches. Mammals and birds have it as part of their normal respiratory microbiota and display infections. P. multocida live in the upper respiratory tract of many vertebrate hosts. These include cats, dogs, rabbits, cows, pigs, and fowl. The host species provides these bacteria with nutrients, and if the bacteria are present in an external environment, it is only temporary. This bacterium is located in a wide range of environments. Cholera outbreaks are usually reported in the United States in north central California, the Mid-West, and the Muleshoe National Refuge in Texas.
P. multocida causes disease in both wild and domesticated animals. If released into the environment by dead birds, it can infect healthy birds, so cholera in birds spreads quickly in wetlands. It can be spread through contaminated drinking water and waste. Inhalation is also another means of transmission of the bacteria. Disease outbreaks have been shown to follow bird migration routes, especially the snow geese. Wildlife biologists believe that these bacteria are transmitted by carrier birds or live in contaminated wetlands throughout the whole year.
Species Affected
These include cats, dogs, rabbits, cows, pigs, and fowl.
Diseases brought about by bacteria’s
in the animals body have been rampantly spreading.
Diseases brought about by bacteria’s
in the animals body have been rampantly spreading.