Challenges¶
Using ONNX in production means the prediction function of a model can be implemented with ONNX operators. A runtime must be chosen, one available on the platform the model is deployed. Discrepancies are checked and finally the latency is measured. The first step about the model conversion can be easy if there exists a converting library for this framework supporting all the pieces of the model. If it is not the case, the missing parts must be implemented in ONNX. That may be very time consuming.
What is a converting library?¶
sklearn-onnx converts scikit-learn models into ONNX. It rewrites the prediction function of a model, whatever it is, with ONNX operators using the API introduced above. It ensures that the predictions are equal or at least very close to the expected predictions computed with the original model.
Machine learning libraries usually have their own design. That’s why there exists a specific converting library for each of them. Many of them are listed there: Converting to ONNX format. Here is a short list:
sklearn-onnx: converts models from scikit-learn
tensorflow-onnx: converts models from tensorflow
onnxmltools: converts models from lightgbm, xgboost, pyspark, libsvm
torch.onnx: converts model from pytorch
The main challenge for all these libraries is to keep up the rythm. They must be updated everytime ONNX or the library they support have a new released version. That means three to five new releases per year.
Converting libraries are not compatible among each others. tf2onnx is dedicated to tensorflow and only tensorflow. The same goes for sklearn-onnx specialized into scikit-learn.
One challenge is customization. It is difficult to support custom pieces in a machine learned model. They have to write the specific converter for this piece. Somehow, it is like implementing twice the prediction function. There is one easy case: deep learning frameworks have their own primitives to ensure the same code can be executed on different environment. As long as a custom layer or a subpart is using pieces of pytorch or tensorflow, there is not much to do. It is a different story for scikit-learn. This package does not have its own addition or multiplication, it relies on numpy or scipy. The user must implement its transformer or predictor with ONNX primitives, whether or not it was implemented with numpy. Example Implement a new converter shows what it looks like.
Opsets¶
ONNX releases package with version number like major.minor.fix. Every minor update means the list of operators is different or the signature has changed. It is also associated to an opset, version 1.10 is opset 15, 1.11 will be opset 16. Every ONNX graph should define the opset it follows. Changing this version without updating the operators could make the graph invalid. If the opset is left unspecified, ONNX will consider that the graph is valid for the latest opset.
New opsets usually introduce new operators. A same inference function
could be implemented differently, usually in a more efficient way.
However, the runtime the model is running on may not
support newest opsets or at least not in the installed version.
That’s why every converting library offers the
possibility to create an ONNX graph for a specific opset usually called
target_opset
. ONNX language describes simple and complex operators.
Changing the opset is similar to upgrade a library. onnx
and onnx runtimes must support backward compatibility.
Other API¶
Examples in previous sections show that onnx API is very verbose. It is also difficult to get a whole picture of a graph by reading the code unless it is a small one. Almost every converting library has implemented a different API to create a graph, usually more simple, less verbose than the API of onnx package. All API automate the addition of initializers, hide the creation of a name of every intermediate result, deal with different version for different opset.
A class Graph with a method add_node¶
tf2onnx implements a class graph. It rewrites tensorflow function with ONNX operator when ONNX does not have a similar function (see Erf.
sklearn-onnx defines two different API. The first one introduced in that example Two ways to implement a converter follows a similar design that tf2onnx. Following line are extracted from the converter of a linear classifier.
# initializer
coef = scope.get_unique_variable_name('coef')
model_coef = np.array(
classifier_attrs['coefficients'], dtype=np.float64)
model_coef = model_coef.reshape((number_of_classes, -1)).T
container.add_initializer(
coef, proto_dtype, model_coef.shape, model_coef.ravel().tolist())
intercept = scope.get_unique_variable_name('intercept')
model_intercept = np.array(
classifier_attrs['intercepts'], dtype=np.float64)
model_intercept = model_intercept.reshape((number_of_classes, -1)).T
container.add_initializer(
intercept, proto_dtype, model_intercept.shape,
model_intercept.ravel().tolist())
# add nodes
multiplied = scope.get_unique_variable_name('multiplied')
container.add_node(
'MatMul', [operator.inputs[0].full_name, coef], multiplied,
name=scope.get_unique_operator_name('MatMul'))
# [...]
argmax_output_name = scope.get_unique_variable_name('label')
container.add_node('ArgMax', raw_score_name, argmax_output_name,
name=scope.get_unique_operator_name('ArgMax'),
axis=1)
Operator as function¶
The second API shown in Implement a new converter is more compact and defines every ONNX operator as composable functions. The syntax looks like this for KMeans, less verbose and easier to read.
rs = OnnxReduceSumSquare(
input_name, axes=[1], keepdims=1, op_version=opv)
gemm_out = OnnxMatMul(
input_name, (C.T * (-2)).astype(dtype), op_version=opv)
z = OnnxAdd(rs, gemm_out, op_version=opv)
y2 = OnnxAdd(C2, z, op_version=opv)
ll = OnnxArgMin(y2, axis=1, keepdims=0, output_names=out[:1],
op_version=opv)
y2s = OnnxSqrt(y2, output_names=out[1:], op_version=opv)
Imitating existing API¶
A last approach aims at removing one implementation (numpy + onnx). Numpy to ONNX: Create ONNX graphs with an API similar to numpy. Many numpy functions are implemented with ONNX operators. Implementing a transformer with these functions automatically offers the conversion to ONNX for free. The following come from the example linked above. It looks like numpy syntax but every function is converted into ONNX primitives.
import mlprodict.npy.numpy_onnx_impl as nxnp
import mlprodict.npy.numpy_onnx_impl_skl as nxnpskl
@onnxsklearn_class("onnx_graph")
class CustomTransformerOnnx(TransformerMixin, BaseEstimator):
# [...__init__...fit...]
def onnx_graph(self, X):
h = self.hyperplan_.astype(X.dtype)
c = self.centers_.astype(X.dtype)
sign = ((X - c[0]) @ h) >= numpy.array([0], dtype=X.dtype)
cast = sign.astype(X.dtype).reshape((-1, 1))
# Function logistic_regression is not a numpy function.
# It calls the converter for a LogisticRegression
# implemented in sklearn-onnx.
prob0 = nxnpskl.logistic_regression(X, model=self.lr0_)[1]
prob1 = nxnpskl.logistic_regression(X, model=self.lr1_)[1]
prob = prob1 * cast - prob0 * (cast - numpy.array([1], dtype=X.dtype))
label = nxnp.argmax(prob, axis=1)
return MultiOnnxVar(label, prob)
Tricks learned from experience¶
Discrepancies¶
ONNX is strongly typed and optimizes for float32, the most common type in deep learning. Libraries in standard machine learning use both float32 and float64. numpy usually cast to the most generic type, float64. It has no significant impact when the prediction function is contiguous. When it is not, the right type must be used. Example Issues when switching to float gives more insights on that topic.
Parallelization changes the order of computation. It is usually not significant but it may explain some weird discrepancies. 1 + 1e17 - 1e17 = 0 but 1e17 - 1e17 + 1 = 1. High order of magnitude are rare but not so rare when a model uses the inverse of a matrix.
IsolationForest Trick¶
ONNX only implements a TreeEnsembleRegressor but it does not offer the possibility to retrieve any information about the path the decision followed or statistics to the graph. The trick is to used one forest to predict the leave index and map this leave index one or multiple times with the information needed.
Discretization¶
Looking in which interval a feature falls into. That’s easy to do with numpy but not so easy to do efficiently with ONNX. The fastest way is to use a TreeEnsembleRegressor, a binary search, which outputs the interval index. That’s what this example implements: Converter for WOE.
Contribute¶
onnx repository must be forked and cloned.
Build¶
The windows build requires conda. The following steps might not be up to date. Folder onnx/.azure-pipelines contains the latest instructions.
Windows
The build is easier with Anaconda. First: create an environment. It must be done only once.
conda create --yes --quiet --name py3.9 python=3.9
conda install -n py3.9 -y -c conda-forge numpy libprotobuf=3.16.0
Then build the package:
git submodule update --init --recursive
set ONNX_BUILD_TESTS=1
set ONNX_ML=$(onnx_ml)
set CMAKE_ARGS=-DONNX_USE_PROTOBUF_SHARED_LIBS=ON -DONNX_USE_LITE_PROTO=ON -DONNX_WERROR=ON
python setup.py -q install
python setup.py bdist_wheel
The package can now be installed.
Build the documentation¶
The package must be built first (see previous section).
set ONNX_BUILD_TESTS=1
set ONNX_ML=$(onnx_ml)
set CMAKE_ARGS=-DONNX_USE_PROTOBUF_SHARED_LIBS=ON -DONNX_USE_LITE_PROTO=ON -DONNX_WERROR=ON
python onnx\gen_proto.py -l
python onnx\gen_proto.py -l --ml
python setup.py develop
python onnx\backend\test\cmd_tools.py generate-data
python onnx\backend\test\stat_coverage.py
python onnx\defs\gen_doc.py
set ONNX_ML=0
python onnx\defs\gen_doc.py
set ONNX_ML=1
Update an existing operator¶
All operators are defined in folder onnx/onnx/defs. There are two files in every subfolder, one called defs.cc and another one called old.cc.
defs.cc: contains the most recent definition for every operator
old.cc: contains the deprecated version of the operators in previous opset
Updating an operator means copying the definition from defs.cc to old.cc and updating the existing one in defs.cc.
One file following the pattern onnx/defs/operator_sets*.h must be modified. These headers registers the list of existing operators.
File onnx/defs/schema.h contains the latest opset version. It must updated too if one opset was upgraded.
File onnx/version_converter/convert.h contains rules to apply when converter a node from an opset to the next one. This file may be updated too.
The package must be compiled and the documentation must be generated again to automatically update the markdown documentation and it must be included into the PR.
Then unit test must be updated.
Summary
Modify files defs.cc, old.cc, onnx/defs/operator_sets*.h, onnx/defs/schema.h
Optional: modify file onnx/version_converter/convert.h
Build onnx.
Build the documentation.
Update unit test.
The PR should include the modified files and the modified markdown documentation, usually a subset of docs/docs/Changelog-ml.md, docs/Changelog.md, docs/Operators-ml.md, docs/Operators.md, docs/TestCoverage-ml.md, docs/TestCoverage.md.


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Eduardo Hung
Or you can just call me JW
based in Shenzhen. I'm a Product Designer,
Here is my work , and I can help you with
Product Design,Interaction Design,User Experience Design.
Contact me with Email
<EMAIL_ADDRESS>Or you can just call me JW
based in Shenzhen. I'm a Product Designer,
Here is my work , and I can help you with
Product Design,Interaction Design,User Experience Design.
Contact me with Email
jw@jwhung.cn


| Package | Description |
|---|---|
| org.opensaml.xml |
Base classes for working with XML as Java objects and configuring the library.
|
| org.opensaml.xml.encryption |
XMLObject interfaces and helper classes for representing encrypted content and encrypting/decrypting content.
|
| org.opensaml.xml.parse |
Classes for managing pools of DOM parsers (
DocumentBuilders ). |
| org.opensaml.xml.util |
Utility classes for working with XML, XMLObjects, and various data types.
|
| Class and Description |
|---|
| BasicParserPool
A pool of JAXP 1.3
DocumentBuilder s. |
| ParserPool
A pool of XML parsers.
|
| Class and Description |
|---|
| ParserPool
A pool of XML parsers.
|
| Class and Description |
|---|
| BasicParserPool
A pool of JAXP 1.3
DocumentBuilder s. |
| ParserPool
A pool of XML parsers.
|
| StaticBasicParserPool
A pool of JAXP 1.3
DocumentBuilder s. |
| XMLParserException
An exception thrown when there is a problem creating an XML parser or parsing XML with on.
|
| Class and Description |
|---|
| ParserPool
A pool of XML parsers.
|
| XMLParserException
An exception thrown when there is a problem creating an XML parser or parsing XML with on.
|
Copyright © 1999-2014. All Rights Reserved.


Contents
Formation control utilizing edge tension energy with a static, undirected
%communication topology %Paul Glotfelter %3/24/2016
Setup Robotarium object
% Get Robotarium object used to communicate with the robots/simulator % Get Robotarium object used to communicate with the robots/simulator rb = RobotariumBuilder(); % Get the number of available agents from the Robotarium. We don't need a % specific value for this algorithm N = 6; % Set the number of agents and whether we would like to save data. Then, % build the Robotarium simulator object! r = rb.set_number_of_agents(N).set_save_data(false).build();
Set up constants for experiment
%Gains for the transformation from single-integrator to unicycle dynamics linearVelocityGain = 1; angularVelocityGain = pi/2; formationControlGain = 4; % Select the number of iterations for the experiment. This value is % arbitrary iterations = 100000; % Communication topology for the desired formation. We need 2 * N - 3 = 9 % edges to ensure that the formation is rigid. L = [3 -1 0 -1 0 -1 ; ... -1 3 -1 0 -1 0 ; ... 0 -1 3 -1 0 -1 ; ... -1 0 -1 3 -1 0 ; ... 0 -1 0 -1 3 -1 ; ... -1 0 -1 0 -1 3]; % The desired inter-agent distance for the formation d = 0.2; % Pre-compute diagonal values for the rectangular formation ddiag = sqrt((2*d)^2 + d^2); % Weight matrix containing the desired inter-agent distances to achieve a % rectuangular formation weights = [ 0 d 0 d 0 ddiag; ... d 0 d 0 d 0; ... 0 d 0 ddiag 0 d; ... d 0 ddiag 0 d 0; ... 0 d 0 d 0 d; ... ddiag 0 d 0 d 0]; % Initialize velocity vector for agents. Each agent expects a 2 x 1 % velocity vector containing the linear and angular velocity, respectively. dx = zeros(2, N);
Grab tools for converting to single-integrator dynamics and ensuring safety
si_barrier_cert = create_si_barrier_certificate('SafetyRadius', 0.08); si_to_uni_dyn = create_si_to_uni_mapping2('LinearVelocityGain', linearVelocityGain, ... 'AngularVelocityLimit', angularVelocityGain); % Iterate for the previously specified number of iterations for t = 0:iterations
% Retrieve the most recent poses from the Robotarium. The time delay is % approximately 0.033 seconds x = r.get_poses();
Algorithm
%This section contains the actual algorithm for formation control! %Calculate single integrator control inputs using edge-energy consensus for i = 1:N % Initialize velocity to zero for each agent. This allows us to sum % over agent i's neighbors dx(:, i) = [0 ; 0]; % Get the topological neighbors of agent i from the communication % topology for j = topological_neighbors(L, i) % For each neighbor, calculate appropriate formation control term and % add it to the total velocity dx(:, i) = dx(:, i) + ... formationControlGain*(norm(x(1:2, i) - x(1:2, j))^2 - weights(i, j)^2) ... *(x(1:2, j) - x(1:2, i)); end end % Transform the single-integrator dynamics to unicycle dynamics using a provided utility function dx = si_barrier_cert(dx, x); dx = si_to_uni_dyn(dx, x); % Set velocities of agents 1:N r.set_velocities(1:N, dx); % Send the previously set velocities to the agents. This function must be called! r.step();
end % Though we didn't save any data, we still should call r.call_at_scripts_end() after our % experiment is over! r.call_at_scripts_end();


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@Master['MasterPage'] @Section['Content']
This content from the index partial view, you can access the properties of the Model:
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{% extends "base.html" %} {% block title %}Athens Docket Project{% endblock %} {% block content %}
The Athens Docket History site is a project of the Athens-Clarke County Heritage Room. We are creating a web-based index of historical police and court records from Athens-Clarke County, Georgia, USA. Learn more »


---
layout: page
title: Entity embeddings
subtitle: Entity embedding on french departments
use-site-title: true
---
Objectives
In this post, we try to understand entity embedding applied to categorical variable. Entity embeddings is a way to represent a categorical variable in a new dimension. It was (and still is) mostly used in natural language processing application,
to interpret a word within his context (using surrounding word). But now, it is used for any categorical variable. I saw the idea (to apply embedding to categorical) while studying the fast.ai MOOC
with Jeremy Howard and I found the idea awesome. Since then, I use this idea in a few project, but didn't take the time to plot and study the embedding created in a model.
in the following example, I will use an old model that I created few months ago to predict the price of a flat (you can find the app using the model here).
I will create a neural net to predict the price, with embedding for location (french department) only.
Then, I will plot the embedding to see what is created by the model.
Why ?
Why entity embedding can be useful for categorical variable:
Reduce memory usage and speeds up neural net compared to one-hot encoding (dimensionality is reduced, depending on the choosen size of the embeddings).
Can help to generalize better (especially for high cardinality.
Embedding vectors can be reused (but are trained depending on a specific target).
Can reveal property of the variable (similar value will have close vector values).
Method
I won't explain the technicality behind embedding, because you can find it almost anywhere on the internet, explained by people way better than me. You can look at post that explain word2vec and if you want to understand specific applications to categorical variables, you can look at the paper written by Cheng Guo and Felix Berkhahn "Entity Embeddings of Categorical Variables". I think it is the first known application of entity embeddings for categorical variable, they used it for a Kaggle competition and won the third place.
Dimension of entity embeddings: it can be difficult to find the best size of embeddings. Most of the time, we will use trial and error, but Jeremy Howard propose the following rule of thumb for that matter: embedding size = min(50, number of categories/2). You can find this rule of thumb in the excellent fast.ai MOOC "Deep learning for coders".
Code
I use the following code (associated with custom transformers than you can find on my github):
Result
The result is interesting. We tried to plot the embeddings in two dimensions using TSNE and PCA (the previous plot is based on PCA). Because we trained the embedding with a target on renting price, we can find pattern based on price and location. For example, we can find that all the department related to "ile de France" are really close on the plot.
I was really curious to see if some pattern can be found using embedding on department, in this context. Apparently that is the case, and there is probably some other interesting pattern that I didn't notice.


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Table of Contents
Introduction
This dataset is a spatial database of historical wildfires in the United States from 1992 to 2015. The dataset is generated to support the Fire Program Analysis (FPA) system. There are over 1.88 million wildfire records in this dataset.
Using this dataset, I aim explore the dataset and compare various predictive model to see how well we can predict the statistical causes of wildfires. This site specifically highlights the key summaries and findings thus it contains little to no code. If you want to see the code you can go onto the right or below and view the Kaggle Notebook or Code Notebook.
Kaggle Notebook Code Repository Data Source
Citation: Short, Karen C. 2017. Spatial wildfire occurrence data for the United States, 1992-2015 [FPAFOD20170508]. 4th Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2013-0009.4
Notebook
You can download and run the notebook as it. Some of the required packages include plotly, json, pandas, sqlite3, scikit-learn, xgboost, random forests, and numpy. You get this notebook by selecting the github link on the right.
The notebook uses a combination of datasets to support the wildfire dataset. This includes a US County Mapping and FIPS to state mapping.
Findings
Initial Exploration
This choropleth looks at all the documented fires from 1992 - 2015. We can see that California saw the most fires during this timeframe of around 189,550 followed by Georgia (168,867) and Texas (142,021). These records were acquired from the FPA FOD which obtains data from the reporting systems of ferderal, state, and local organizations. Additionally this graph looks at the discovery year of the fire as well as which county the fire originated in based on fire report. The states with the least amount of reported fires are those that are close to black such as Vermont, Deleware and Rhode Island.
There is an abundant of counties without any traceable fires from 1992 - 2015. This is portrayed by the blanks in states like Nebraska, Indiana, and parts of Ohio, Illinois, Alaska, Lousiana and Oklahoma. Hotter regions include areas of Arizona, Southern California, Hawaii, and New York. This traceable data accounts for around 64% of the recorded wildfires in the United States from 1952 to 2015. We will investigate the origins of the reported fires in a later section.
This choropleth accounts for the remainder (36%) of the recorded wildfires discovery in the United States. It specifically looks at wildfires where they could not identify the county that reported the fire or where it originated. Taking a closer look at the graph, there is still an abundance of fires in California followed by North Carolina. The regions with the least amount of fires are those in the North East of the United States such as Maine, Vermont, New Hampshire, New York, Pennsylvania.
In summary of this initial exploration, there are 1880465 total fires, 1202317 are known county fires and the other 67814 are unknown county fires. This is a 64 - 36 ratio of known - unknown county fires.
Time Progression of Known County Fires
This is a slider animation to show in greater detail the fires with tracable origins. It displays the number of reported county fires from 1992 to 2015 by year, this is further broken down by state and then by counties for each year. Surprisingly in the early years there was not much in the way of data collection in wildfires in 1992. However as the years continues, we can see the growth of county reports from various organizations. It is important to note that we cannot conclude from this graph that there were no fires in the blank region x, in year y because the data is only of recorded and origin traced fires.
One interesting finding is that from 1998 - 2010, county 04005 in Arizona, has on average a lot of traceable fires in that area. Compared to other counties, this county is likely a large contributor to the number of traceable fires in Arizona. This is also shown in a previous graphic that summarizes the total number of traceable fires.
Statistical Causes of Fires
A very colorful chart however it is interesting to look up the makeup of reported fires causes by year. Each color scheme represents the statistical cause and their total stack represents the total number of reported fires with a statistical cause by year. We can observe that the number of fires seems like an upward trend where every few years, we can see a decline in fires followed by a sudden large increase of cases.
Taking another dive, let’s see how many acres were burned by year.
This animation visualizes how many acres were burned per year by each statistical cause. As the graph shifts, so does the number of acres burned by each fire. We can see that lightning is often the main contributor to the burning of acres. This is followed by miscellaneous, unidentifiable then arson causes.
Taking a look into the total number of acres burned in the United States by year, there is an upward trend. Wildfires are known to release between 5 and 30 tonnes of carbon per hectare and referencing this graph we can see that there is definitely an increase in C02 emissions from wildfires in the United States.
Machine Learning Prediction
Can we use information from wildfire reports such as discovery_time, wildlandRole, unit_type, fire_size, fire_size_class, state, and county to predict what was the statistical cause of the fire?
In order to do this, we need to preprocess the dataset as such we merge the agency and fire dataset. We also need to extract the day of the week and month for each record to be included as a variable. This is done by converting the timestamp into a julian date. Lastly we need to convert all non-numerical values to a label encoding.
In this dataset I also try predicting the normal 13 statistcal causes and another case where it is 3 types of statistical causes. The reason why it is 3 is because I am grouping the statistical causes by their known cause type: Natural, Human, or other causes.
In this heatmap, I chose two distinct colors to make the differences more visible. The closer the color is to blue, the stronger the correlation whereas the closer it is to red, the weaker the correlation. What I find interesting in this correlation is that lattitude has a much weaker correlation to Geographic area than longitude.
– Disclaimer – I am color-blind but I based this insight by looking at the numbers provided by each correlation.
With the main preprocessing done, we apply it with various machine learning approaches. This includes Random Forests, XGBoost, Logistic Regression, and much more!
Based on the results of each model, Random Forests and XGBoost performing really well and Logistic Regression and SVM’s not so much. This is likely because for SVM and Logistic Regression the high dimensionality is making it unable to generalize well for a multinominal classification whereas for Random Forests and XGBoost it is easier to handle. Specifically SVM’s are not meant to handle multiclass classification but instead binary classification.
Further down are the correlations for each model in predicting 13 statistical causes and 3 types of statistical causes.
Correlations between predicted variables
- Correlations between prediction variables, this information is used for the random forest tree.
Feature Importance
Multinominal Logistic Regression
Random Forest
xgBoost
- svm kernel corrupted, restarting to grab images
Conclusion
We can predict wildfires with around 60% accuracy across the 13 statistical causes. If we reduce the number of classification categories to natural, human, and other causes we can achieve around 81% accuracy! Ultimately fires in the United States are having an upward trend and are seemily becoming more frequent in states like California, North Carolina, and Arizona.


The Facebook website is good at funneling and displaying numerous user inputs while allowing easy access to it's other features such as chatting and group access.
The Pandora website is simple and straight forward. The primary buttons are up top for easy access and some album information is relayed in the center.
The Stitcher website does a great job of limited display of it's vast library but showing only a few suggestions based on your current usage.


TRANSFORMPOINT transform a point with an affine transform PT2 = transformPoint(PT1, TRANS); where PT1 has the form [xp yp], and TRANS is a [2*2], [2*3] or [3*3] matrix, returns the point transformed with affine transform TRANS. Format of TRANS can be one of : [a b] , [a b c] , or [a b c] [d e] [d e f] [d e f] [0 0 1] PT2 = transformPoint(PT1, TRANS); Also works when PTA is a [N*2] array of double. In this case, PT2 has the same size as PT1. [PX2 PY2] = transformPoint(PX1, PY1, TRANS); Also works when PX1 and PY1 are arrays the same size. The function transform each couple of (PX1, PY1), and return the result in (PX2, PY2), which is the same size as (PX1 PY1). See also: points2d, transforms2d, translation, rotation --------- author : David Legland INRA - TPV URPOI - BIA IMASTE created the 06/04/2004.


{% extends 'catalog/base.html' %} {% load static %} {% block title %}About the Polygenic Score Catalog{% endblock %} {% block desc %} {% endblock %} {% block content %}
This page contains information regarding the PGS Catalog Project.
A polygenic score (PGS) aggregates the effects of many genetic variants into a single number which predicts genetic predisposition for a phenotype. PGS are typically composed of hundreds-to-millions of genetic variants (usually SNPs) which are combined using a weighted sum of allele dosages multiplied by their corresponding effect sizes, as estimated from a relevant genome-wide association study (GWAS).
PGS nomenclature is heterogeneous: they can also be referred to as genetic scores or genomic scores, and as polygenic risk scores (PRS) or genomic risk scores (GRS) if they predict a discrete phenotype, such as a disease.
The PGS Catalog is an open database of published polygenic scores (PGS). Each PGS in the Catalog is consistently annotated with relevant metadata; including scoring files (variants, effect alleles/weights), annotations of how the PGS was developed and applied, and evaluations of their predictive performance. See the PGS Catalog Data Description page for a complete description of the metadata captured for PGS, Samples, Performance Metrics, Traits, and Publications.
The PGS Catalog development is led by Samuel Lambert under the supervision of Michael Inouye (University of Cambridge & Baker Institute) in collaboration with Health Data Research - UK (Laurent Gil) and the EBI Samples, Phenotypes and Ontologies team / NHGRI-EBI GWAS Catalog (Helen Parkinson, Aoife McMahon, Laura Harris).
The Catalog is under active development, and we continue to add new features and curate new data. If you use the Catalog in your research we ask that you cite our recent paper:
{{ pgs_citation.authors }}
{{ pgs_citation.title }}
{{ pgs_citation.journal }}doi: {{ pgs_citation.doi }} ({{ pgs_citation.year }}).
Individual PGS obtained from the database should also be cited appropriately, and used in accordance with any licensing restrictions set by the authors (see our Terms of Use for more information).
For a publication's data to be included in the PGS Catalog it must contain one of the following:
A complete description of the data captured for each PGS and publication can be found here.
In the pilot PGS Catalog (presented at ASHG 2019) we focused on curating PGS developed after 2010, and included well-studied scores for the following traits: coronary artery disease (CAD), diabetes (types 1 and 2), obesity / body mass index (BMI), breast cancer, prostate cancer and Alzheimer’s disease. The catalog however is not limited to these traits, and since then we have focused on broadening the coverage of traits and scores. Researchers are invited to submit their PGS and evaluations to us by e-mail for curation and inclusion in the PGS Catalog - we plan to provide a streamlined interface to submit these data in the future.
If you have a PGS or publication that meets the Catalog's eligibility requirements we invite you to submit your data by e-mail ( {{ pgs_contact }}). To ensure a speedy curation and inclusion into the catalog it would be helpful if you provide the following information about your study:
All the code developed in PGS Catalog is publicly available on GitHub [PGSCatalog]. Here are some of the tools that can be useful for the community:
To submit a PGS to the catalog, provide feedback, or ask questions please contact the PGS Catalog team at {{ pgs_contact }}.
We wish to acknowledge the help of the following people & teams for their support of the PGS Catalog:
The development of the PGS Catalog is supported by:


Virgil Abloh MCA Posters
Virgil Abloh MCA Poster
Grass grows greener in the graveyard
I have a million things to do today
They fight like cats and dogs


C0 code coverage information
Generated on Wed Sep 30 08:18:08 +0100 2009 with rcov <IP_ADDRESS>
Code reported as executed by Ruby looks like this...
and this: this line is also marked as covered.
Lines considered as run by rcov, but not reported by Ruby, look like this,
and this: these lines were inferred by rcov (using simple heuristics).
Finally, here's a line marked as not executed.
1 #!/usr/bin/env ruby
2 #--
3 # Copyright 2004, 2005 by Jim Weirich (jim@weirichhouse.org).
4 # All rights reserved.
5
6 # Permission is granted for use, copying, modification, distribution,
7 # and distribution of modified versions of this work as long as the
8 # above copyright notice is included.
9 #++
10
11 # Provide a flexible and easy to use Builder for creating XML markup.
12 # See XmlBuilder for usage details.
13
14 require 'builder/xmlbase'
15
16 module Builder
17
18 # Create XML markup easily. All (well, almost all) methods sent to
19 # an XmlMarkup object will be translated to the equivalent XML
20 # markup. Any method with a block will be treated as an XML markup
21 # tag with nested markup in the block.
22 #
23 # Examples will demonstrate this easier than words. In the
24 # following, +xm+ is an +XmlMarkup+ object.
25 #
26 # xm.em("emphasized") # => emphasized
27 # xm.em { xmm.b("emp & bold") } # => emph & bold
28 # xm.a("A Link", "href"=>"http://onestepback.org")
29 # # => A Link
30 # xm.div { br } # =>

31 # xm.target("name"=>"compile", "option"=>"fast")
32 # # => <target option="fast" name="compile"\>
33 # # NOTE: order of attributes is not specified.
34 #
35 # xm.instruct! # <?xml version="1.0" encoding="UTF-8"?>
36 # xm.html { #
37 # xm.head { #
38 # xm.title("History") # <title>History</title>
39 # } #
40 # xm.body { #
41 # xm.comment! "HI" #
42 # xm.h1("Header") #
Header

43 # xm.p("paragraph") #
paragraph

44 # } #
45 # } #
46 #
47 # == Notes:
48 #
49 # * The order that attributes are inserted in markup tags is
50 # undefined.
51 #
52 # * Sometimes you wish to insert text without enclosing tags. Use
53 # the <tt>text!</tt> method to accomplish this.
54 #
55 # Example:
56 #
57 # xm.div { #

58 # xm.text! "line"; xm.br # line

59 # xm.text! "another line"; xmbr # another line

60 # } #

61 #
62 # * The special XML characters <, >, and & are converted to <,
63 # > and & automatically. Use the <tt><<</tt> operation to
64 # insert text without modification.
65 #
66 # * Sometimes tags use special characters not allowed in ruby
67 # identifiers. Use the <tt>tag!</tt> method to handle these
68 # cases.
69 #
70 # Example:
71 #
72 # xml.tag!("SOAP:Envelope") { ... }
73 #
74 # will produce ...
75 #
76 # <SOAP:Envelope> ... </SOAP:Envelope>"
77 #
78 # <tt>tag!</tt> will also take text and attribute arguments (after
79 # the tag name) like normal markup methods. (But see the next
80 # bullet item for a better way to handle XML namespaces).
81 #
82 # * Direct support for XML namespaces is now available. If the
83 # first argument to a tag call is a symbol, it will be joined to
84 # the tag to produce a namespace:tag combination. It is easier to
85 # show this than describe it.
86 #
87 # xml.SOAP :Envelope do ... end
88 #
89 # Just put a space before the colon in a namespace to produce the
90 # right form for builder (e.g. "<tt>SOAP:Envelope</tt>" =>
91 # "<tt>xml.SOAP :Envelope</tt>")
92 #
93 # * XmlMarkup builds the markup in any object (called a _target_)
94 # that accepts the <tt><<</tt> method. If no target is given,
95 # then XmlMarkup defaults to a string target.
96 #
97 # Examples:
98 #
99 # xm = Builder::XmlMarkup.new
100 # result = xm.title("yada")
101 # # result is a string containing the markup.
102 #
103 # buffer = ""
104 # xm = Builder::XmlMarkup.new(buffer)
105 # # The markup is appended to buffer (using <<)
106 #
107 # xm = Builder::XmlMarkup.new(STDOUT)
108 # # The markup is written to STDOUT (using <<)
109 #
110 # xm = Builder::XmlMarkup.new
111 # x2 = Builder::XmlMarkup.new(:target=>xm)
112 # # Markup written to +x2+ will be send to +xm+.
113 #
114 # * Indentation is enabled by providing the number of spaces to
115 # indent for each level as a second argument to XmlBuilder.new.
116 # Initial indentation may be specified using a third parameter.
117 #
118 # Example:
119 #
120 # xm = Builder.new(:indent=>2)
121 # # xm will produce nicely formatted and indented XML.
122 #
123 # xm = Builder.new(:indent=>2, :margin=>4)
124 # # xm will produce nicely formatted and indented XML with 2
125 # # spaces per indent and an over all indentation level of 4.
126 #
127 # builder = Builder::XmlMarkup.new(:target=>$stdout, :indent=>2)
128 # builder.name { |b| b.first("Jim"); b.last("Weirich) }
129 # # prints:
130 # # <name>
131 # # <first>Jim</first>
132 # # <last>Weirich</last>
133 # # </name>
134 #
135 # * The instance_eval implementation which forces self to refer to
136 # the message receiver as self is now obsolete. We now use normal
137 # block calls to execute the markup block. This means that all
138 # markup methods must now be explicitly send to the xml builder.
139 # For instance, instead of
140 #
141 # xml.div { strong("text") }
142 #
143 # you need to write:
144 #
145 # xml.div { xml.strong("text") }
146 #
147 # Although more verbose, the subtle change in semantics within the
148 # block was found to be prone to error. To make this change a
149 # little less cumbersome, the markup block now gets the markup
150 # object sent as an argument, allowing you to use a shorter alias
151 # within the block.
152 #
153 # For example:
154 #
155 # xml_builder = Builder::XmlMarkup.new
156 # xml_builder.div { |xml|
157 # xml.stong("text")
158 # }
159 #
160 class XmlMarkup < XmlBase
161
162 # Create an XML markup builder. Parameters are specified by an
163 # option hash.
164 #
165 # :target=>target_object::
166 # Object receiving the markup. +out+ must respond to the
167 # <tt><<</tt> operator. The default is a plain string target.
168 #
169 # :indent=>indentation::
170 # Number of spaces used for indentation. The default is no
171 # indentation and no line breaks.
172 #
173 # :margin=>initial_indentation_level::
174 # Amount of initial indentation (specified in levels, not
175 # spaces).
176 #
177 # :escape_attrs=>OBSOLETE::
178 # The :escape_attrs option is no longer supported by builder
179 # (and will be quietly ignored). String attribute values are
180 # now automatically escaped. If you need unescaped attribute
181 # values (perhaps you are using entities in the attribute
182 # values), then give the value as a Symbol. This allows much
183 # finer control over escaping attribute values.
184 #
185 def initialize(options={})
186 indent = options[:indent] || 0
187 margin = options[:margin] || 0
188 super(indent, margin)
189 @target = options[:target] || ""
190 end
191
192 # Return the target of the builder.
193 def target!
194 @target
195 end
196
197 def comment!(comment_text)
198 _ensure_no_block block_given?
199 _special("", comment_text, nil)
200 end
201
202 # Insert an XML declaration into the XML markup.
203 #
204 # For example:
205 #
206 # xml.declare! :ELEMENT, :blah, "yada"
207 # # => <!ELEMENT blah "yada">
208 def declare!(inst, *args, &block)
209 _indent
210 @target << "<!#{inst}"
211 args.each do |arg|
212 case arg
213 when String
214 @target << %{ "#{arg}"} # " WART
215 when Symbol
216 @target << " #{arg}"
217 end
218 end
219 if block_given?
220 @target << " ["
221 _newline
222 _nested_structures(block)
223 @target << "]"
224 end
225 @target << ">"
226 _newline
227 end
228
229 # Insert a processing instruction into the XML markup. E.g.
230 #
231 # For example:
232 #
233 # xml.instruct!
234 # #=> <?xml version="1.0" encoding="UTF-8"?>
235 # xml.instruct! :aaa, :bbb=>"ccc"
236 # #=> <?aaa bbb="ccc"?>
237 #
238 def instruct!(directive_tag=:xml, attrs={})
239 _ensure_no_block block_given?
240 if directive_tag == :xml
241 a = { :version=>"1.0", :encoding=>"UTF-8" }
242 attrs = a.merge attrs
243 end
244 _special(
245 "<?#{directive_tag}",
246 "?>",
247 nil,
248 attrs,
249 [:version, :encoding, :standalone])
250 end
251
252 # Insert a CDATA section into the XML markup.
253 #
254 # For example:
255 #
256 # xml.cdata!("text to be included in cdata")
257 # #=> <![CDATA[text to be included in cdata]]>
258 #
259 def cdata!(text)
260 _ensure_no_block block_given?
261 _special("<![CDATA[", "]]>", text, nil)
262 end
263
264 private
265
266 # NOTE: All private methods of a builder object are prefixed when
267 # a "_" character to avoid possible conflict with XML tag names.
268
269 # Insert text directly in to the builder's target.
270 def _text(text)
271 @target << text
272 end
273
274 # Insert special instruction.
275 def _special(open, close, data=nil, attrs=nil, order=[])
276 _indent
277 @target << open
278 @target << data if data
279 _insert_attributes(attrs, order) if attrs
280 @target << close
281 _newline
282 end
283
284 # Start an XML tag. If <tt>end_too</tt> is true, then the start
285 # tag is also the end tag (e.g.

286 def _start_tag(sym, attrs, end_too=false)
287 @target << "<#{sym}"
288 _insert_attributes(attrs)
289 @target << "/" if end_too
290 @target << ">"
291 end
292
293 # Insert an ending tag.
294 def _end_tag(sym)
295 @target << "</#{sym}>"
296 end
297
298 # Insert the attributes (given in the hash).
299 def _insert_attributes(attrs, order=[])
300 return if attrs.nil?
301 order.each do |k|
302 v = attrs[k]
303 @target << %{ #{k}="#{_attr_value(v)}"} if v # " WART
304 end
305 attrs.each do |k, v|
306 @target << %{ #{k}="#{_attr_value(v)}"} unless order.member?(k) # " WART
307 end
308 end
309
310 def _attr_value(value)
311 case value
312 when Symbol
313 value.to_s
314 else
315 _escape_quote(value.to_s)
316 end
317 end
318
319 def _ensure_no_block(got_block)
320 if got_block
321 fail IllegalBlockError,
322 "Blocks are not allowed on XML instructions"
323 end
324 end
325
326 end
327
328 end
Generated using the rcov code coverage analysis tool for Ruby
version <IP_ADDRESS>.


My Movie List
New movie
Name
Image
Action
Adventure
Animation
Comedy
Detective
Fantasy
History
Horror
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Pirate
Romantic
SCI-FI
War
Western
Release
Clear LS


A smart password checker that lets you make mistakes
Mistyping your password leads to failed logins and time wasted. Prior study has shown 3% of all users are prevented from logging in due to some simply correctable typos, such as accidentally tapping on the caps-lock key. This is not only frustrating, but can also have a negative effect on users’ security practices. Less secure (but easier to type) passwords might seem more appealing, and users might be discouraged from locking their computers.
With this in mind, we built TypTop, a secure personalized typo-tolerant password checker for your MAC or Linux computer. It lets you focus on more important things than retyping your login password.
TypTop offers typo-tolerance tailored to you. It learns the typos that you make most frequently and allows you to log in under a small handful of these typos. Security isn't sacrificed. TypTop performs a number of safety checks before allowing authentication under a given typo to ensure this will not degrade security. We've conducted an in-depth analysis to prove that TypTop is as secure as a conventional password-based authentication.
Currently TypTop can be used in Unix based systems including Mac OSX and Linux distributions. We have precompiled packages for many systems. See if you can find yours in latest releases. The installation should be straightforward—download the binary, click on it, and follow the onscreen instructions. Otherwise you can compile it in your machine from the source. That's right: TypTop is open source software distributed under the MIT license.
Once installed successfully, TypTop should register itself
with the
pluggable
authentication module (PAM), so
that it is invoked for all PAM based authentications requiring a password
to be typed.
It securely records typographical errors and enables login
with common
typos as it learns about them. If you are eager to check
whether
or not it is working, you can lock the screen and re-login
first with the correct
password, and then with a typo by adding '1' at the end of
your
password. For example, say your password is
KuteCitten
, try to log in with
KuteCitten1
. If you can login with the mistyped
password, then TypTop
is working fine!
You can do the same experiment in the terminal with the
command 'su'; type su $USER
,
in the terminal, type the correct password for the first
time, and next time try a typo of it
as described above.
We are conducting a short survey about users' password habits, and request you fill it in. Check below for more details.
To uninstall, you have to open the terminal and type the
following command,
sudo typtop --uninstall
.
TypTop is a new kind of a password checker that learns about your old password typing mistakes, and lets you login with the typos that you frequently make—if it is safe to do so. We only allow typos that are very close to your original password (i.e., off by one character) and that do not degrade unpredictability of the password by too great an amount. To achieve this, TypTop uses a new kind of encrypted cache to store recent incorrect entries, and unlocks these upon a correct entry of the password. Incorrect entries that satisfy TypTop's safety checks are then securely hashed and added to a set of allowable login passwords. On subsequent logins, TypTop not only checks the original password, but also checks against the entire set of allowed passwords. If any match, then login is allowed. All sensitive data (your password and typos of it) are stored in secured form on your system.
All this is presented in detail and analyzed in an academic research paper. We will post it here soon. The code for TypTop is open source as well, and we welcome any feedback on it.
TypTop is a research prototype and while our initial pilot deployment has evidenced its efficacy for users that make typos frequently, we need data from a larger set of users to understand what fraction of users benefit from TypTop's design. We therefore have built into TypTop an anonymized and secure feedback mechanism, similar to how most modern software reports back to the designers information useful for improving the system. There is absolutely no way to utilize the collected data to learn about your password or other sensitive information!
We do not record IP addresses or any other
information about the system from which an anonymous report
is collected. In fact we will not
even be able to associate data collected to individual
installations.
For each login we collect:
On installation of TypTop, you are included in the
study by default.
You can opt out of the study at any time without affecting
the functionality of TypTop by
running the following command (in the terminal): typtop --participate $USER no
The data collected will only ever be used by our research team to understand the efficacy of TypTop. We may publish summary statistics (e.g., average improvement to login efficacy) in future research papers. The collected data will never be released publicly.
We are also conducting an anonymous, short survey on users' password typing behaviors, if you are interested in helping us out by taking it.
Kutecitten
versus
the real password KuteCitten
.
Second, we ensure that a typo does not collide with an
easy-to-guess password, by ruling out
typos
that have low scores on state-of-the-art industry
strength meters (we use
Dropbox's zxcvbn). Third, we make sure that a
typo does not degrade the guessability of your real
password, again by ensuring that the drop in score under
a strength meter is not too large.
For example, if your real password is
Pa%ssword
, then Password
is not an allowed typo, as the later is much easier to
guess than the former. These
restrictions,
taken together, ensure that typos will not be much
easier to guess than your actual password.
auth include
typtop-auth
from all files in /etc/pam.d
.
Alternatively, you can uninstall using the command
sudo typtop --uninstall
in the
terminal in recovery mode.


There is not a real object here. I need an object that does not flicker.
There is not a real object here. I need an object that flickers.


{% extends "global/Page.html" %} {% load otree static %} {% block title %} Random Audit {% endblock %} {% block content %}
As of now, you might realize that if everybody catches {{quota}} fish, your group's aggregate (and average) earnings would be maximized. By catching over {{quota}} fish, the fish will be caught too fast (the fish population in the lake will not be sustainable). This may result in fewer fish for everyone. Until further notice, one player of each group will be randomly chosen to be audited each round. This player’s extraction will be shown to every member of the group.


--- title: "Lines from center" date: 2019-03-04T22:15:42-03:00 description: "this is a p5js sketch" libs: js: - https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.7.3/p5.min.js tags: ["javascript","jquery"] ---


Dog Social Network
Home
Profile
Friends
Friends of Doge
Such Friend
best friend
Many Friends
unknown
Wow
yeah, right


|
GLM
0.9.5
|
Functions | |
| GLM_FUNC_DECL uint32 | packF2x11_1x10 (vec3 const &v) |
| GLM_FUNC_DECL uint16 | packHalf1x16 (float const &v) |
| GLM_FUNC_DECL uint64 | packHalf4x16 (vec4 const &v) |
| GLM_FUNC_DECL uint32 | packI3x10_1x2 (ivec4 const &v) |
| GLM_FUNC_DECL uint16 | packSnorm1x16 (float const &v) |
| GLM_FUNC_DECL uint8 | packSnorm1x8 (float const &s) |
| GLM_FUNC_DECL uint16 | packSnorm2x8 (vec2 const &v) |
| GLM_FUNC_DECL uint32 | packSnorm3x10_1x2 (vec4 const &v) |
| GLM_FUNC_DECL uint64 | packSnorm4x16 (vec4 const &v) |
| GLM_FUNC_DECL uint32 | packU3x10_1x2 (uvec4 const &v) |
| GLM_FUNC_DECL uint16 | packUnorm1x16 (float const &v) |
| GLM_FUNC_DECL uint8 | packUnorm1x8 (float const &v) |
| GLM_FUNC_DECL uint16 | packUnorm2x8 (vec2 const &v) |
| GLM_FUNC_DECL uint32 | packUnorm3x10_1x2 (vec4 const &v) |
| GLM_FUNC_DECL uint64 | packUnorm4x16 (vec4 const &v) |
| GLM_FUNC_DECL vec3 | unpackF2x11_1x10 (uint32 const &p) |
| GLM_FUNC_DECL float | unpackHalf1x16 (uint16 const &v) |
| GLM_FUNC_DECL vec4 | unpackHalf4x16 (uint64 const &p) |
| GLM_FUNC_DECL ivec4 | unpackI3x10_1x2 (uint32 const &p) |
| GLM_FUNC_DECL float | unpackSnorm1x16 (uint16 const &p) |
| GLM_FUNC_DECL float | unpackSnorm1x8 (uint8 const &p) |
| GLM_FUNC_DECL vec2 | unpackSnorm2x8 (uint16 const &p) |
| GLM_FUNC_DECL vec4 | unpackSnorm3x10_1x2 (uint32 const &p) |
| GLM_FUNC_DECL vec4 | unpackSnorm4x16 (uint64 const &p) |
| GLM_FUNC_DECL uvec4 | unpackU3x10_1x2 (uint32 const &p) |
| GLM_FUNC_DECL float | unpackUnorm1x16 (uint16 const &p) |
| GLM_FUNC_DECL float | unpackUnorm1x8 (uint8 const &p) |
| GLM_FUNC_DECL vec2 | unpackUnorm2x8 (uint16 const &p) |
| GLM_FUNC_DECL vec4 | unpackUnorm3x10_1x2 (uint32 const &p) |
| GLM_FUNC_DECL vec4 | unpackUnorm4x16 (uint64 const &p) |
This extension provides a set of function to convert vertors to packed formats.
<glm/gtc/packing.hpp> need to be included to use these features.
| GLM_FUNC_DECL uint32 glm::packF2x11_1x10 | ( | vec3 const & | v | ) |
First, converts the first two components of the normalized floating-point value v into 11-bit signless floating-point values.
Then, converts the third component of the normalized floating-point value v into a 10-bit signless floating-point value. Then, the results are packed into the returned 32-bit unsigned integer.
The first vector component specifies the 11 least-significant bits of the result; the last component specifies the 10 most-significant bits.
| GLM_FUNC_DECL uint16 glm::packHalf1x16 | ( | float const & | v | ) |
Returns an unsigned integer obtained by converting the components of a floating-point scalar to the 16-bit floating-point representation found in the OpenGL Specification, and then packing this 16-bit value into a 16-bit unsigned integer.
| GLM_FUNC_DECL uint64 glm::packHalf4x16 | ( | vec4 const & | v | ) |
Returns an unsigned integer obtained by converting the components of a four-component floating-point vector to the 16-bit floating-point representation found in the OpenGL Specification, and then packing these four 16-bit values into a 64-bit unsigned integer.
The first vector component specifies the 16 least-significant bits of the result; the forth component specifies the 16 most-significant bits.
| GLM_FUNC_DECL uint32 glm::packI3x10_1x2 | ( | ivec4 const & | v | ) |
Returns an unsigned integer obtained by converting the components of a four-component signed integer vector to the 10-10-10-2-bit signed integer representation found in the OpenGL Specification, and then packing these four values into a 32-bit unsigned integer.
The first vector component specifies the 10 least-significant bits of the result; the forth component specifies the 2 most-significant bits.
| GLM_FUNC_DECL uint16 glm::packSnorm1x16 | ( | float const & | v | ) |
First, converts the normalized floating-point value v into 16-bit integer value.
Then, the results are packed into the returned 16-bit unsigned integer.
The conversion to fixed point is done as follows: packSnorm1x8: round(clamp(s, -1, +1) * 32767.0)
| GLM_FUNC_DECL uint8 glm::packSnorm1x8 | ( | float const & | s | ) |
First, converts the normalized floating-point value v into 8-bit integer value.
Then, the results are packed into the returned 8-bit unsigned integer.
The conversion to fixed point is done as follows: packSnorm1x8: round(clamp(s, -1, +1) * 127.0)
| GLM_FUNC_DECL uint16 glm::packSnorm2x8 | ( | vec2 const & | v | ) |
First, converts each component of the normalized floating-point value v into 8-bit integer values.
Then, the results are packed into the returned 16-bit unsigned integer.
The conversion for component c of v to fixed point is done as follows: packSnorm2x8: round(clamp(c, -1, +1) * 127.0)
The first component of the vector will be written to the least significant bits of the output; the last component will be written to the most significant bits.
| GLM_FUNC_DECL uint32 glm::packSnorm3x10_1x2 | ( | vec4 const & | v | ) |
First, converts the first three components of the normalized floating-point value v into 10-bit signed integer values.
Then, converts the forth component of the normalized floating-point value v into 2-bit signed integer values. Then, the results are packed into the returned 32-bit unsigned integer.
The conversion for component c of v to fixed point is done as follows: packSnorm3x10_1x2(xyz): round(clamp(c, -1, +1) * 511.0) packSnorm3x10_1x2(w): round(clamp(c, -1, +1) * 1.0)
The first vector component specifies the 10 least-significant bits of the result; the forth component specifies the 2 most-significant bits.
| GLM_FUNC_DECL uint64 glm::packSnorm4x16 | ( | vec4 const & | v | ) |
First, converts each component of the normalized floating-point value v into 16-bit integer values.
Then, the results are packed into the returned 64-bit unsigned integer.
The conversion for component c of v to fixed point is done as follows: packSnorm2x8: round(clamp(c, -1, +1) * 32767.0)
The first component of the vector will be written to the least significant bits of the output; the last component will be written to the most significant bits.
| GLM_FUNC_DECL uint32 glm::packU3x10_1x2 | ( | uvec4 const & | v | ) |
Returns an unsigned integer obtained by converting the components of a four-component unsigned integer vector to the 10-10-10-2-bit unsigned integer representation found in the OpenGL Specification, and then packing these four values into a 32-bit unsigned integer.
The first vector component specifies the 10 least-significant bits of the result; the forth component specifies the 2 most-significant bits.
| GLM_FUNC_DECL uint16 glm::packUnorm1x16 | ( | float const & | v | ) |
First, converts the normalized floating-point value v into a 16-bit integer value.
Then, the results are packed into the returned 16-bit unsigned integer.
The conversion for component c of v to fixed point is done as follows: packUnorm1x16: round(clamp(c, 0, +1) * 65535.0)
| GLM_FUNC_DECL uint8 glm::packUnorm1x8 | ( | float const & | v | ) |
First, converts the normalized floating-point value v into a 8-bit integer value.
Then, the results are packed into the returned 8-bit unsigned integer.
The conversion for component c of v to fixed point is done as follows: packUnorm1x8: round(clamp(c, 0, +1) * 255.0)
| GLM_FUNC_DECL uint16 glm::packUnorm2x8 | ( | vec2 const & | v | ) |
First, converts each component of the normalized floating-point value v into 8-bit integer values.
Then, the results are packed into the returned 16-bit unsigned integer.
The conversion for component c of v to fixed point is done as follows: packUnorm2x8: round(clamp(c, 0, +1) * 255.0)
The first component of the vector will be written to the least significant bits of the output; the last component will be written to the most significant bits.
| GLM_FUNC_DECL uint32 glm::packUnorm3x10_1x2 | ( | vec4 const & | v | ) |
First, converts the first three components of the normalized floating-point value v into 10-bit unsigned integer values.
Then, converts the forth component of the normalized floating-point value v into 2-bit signed uninteger values. Then, the results are packed into the returned 32-bit unsigned integer.
The conversion for component c of v to fixed point is done as follows: packUnorm3x10_1x2(xyz): round(clamp(c, 0, +1) * 1023.0) packUnorm3x10_1x2(w): round(clamp(c, 0, +1) * 3.0)
The first vector component specifies the 10 least-significant bits of the result; the forth component specifies the 2 most-significant bits.
| GLM_FUNC_DECL uint64 glm::packUnorm4x16 | ( | vec4 const & | v | ) |
First, converts each component of the normalized floating-point value v into 16-bit integer values.
Then, the results are packed into the returned 64-bit unsigned integer.
The conversion for component c of v to fixed point is done as follows: packUnorm4x16: round(clamp(c, 0, +1) * 65535.0)
The first component of the vector will be written to the least significant bits of the output; the last component will be written to the most significant bits.
| GLM_FUNC_DECL vec3 glm::unpackF2x11_1x10 | ( | uint32 const & | p | ) |
First, unpacks a single 32-bit unsigned integer p into two 11-bit signless floating-point values and one 10-bit signless floating-point value .
Then, each component is converted to a normalized floating-point value to generate the returned three-component vector.
The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.
| GLM_FUNC_DECL float glm::unpackHalf1x16 | ( | uint16 const & | v | ) |
Returns a floating-point scalar with components obtained by unpacking a 16-bit unsigned integer into a 16-bit value, interpreted as a 16-bit floating-point number according to the OpenGL Specification, and converting it to 32-bit floating-point values.
| GLM_FUNC_DECL vec4 glm::unpackHalf4x16 | ( | uint64 const & | p | ) |
Returns a four-component floating-point vector with components obtained by unpacking a 64-bit unsigned integer into four 16-bit values, interpreting those values as 16-bit floating-point numbers according to the OpenGL Specification, and converting them to 32-bit floating-point values.
The first component of the vector is obtained from the 16 least-significant bits of v; the forth component is obtained from the 16 most-significant bits of v.
| GLM_FUNC_DECL ivec4 glm::unpackI3x10_1x2 | ( | uint32 const & | p | ) |
Unpacks a single 32-bit unsigned integer p into three 10-bit and one 2-bit signed integers.
The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.
| GLM_FUNC_DECL float glm::unpackSnorm1x16 | ( | uint16 const & | p | ) |
First, unpacks a single 16-bit unsigned integer p into a single 16-bit signed integers.
Then, each component is converted to a normalized floating-point value to generate the returned scalar.
The conversion for unpacked fixed-point value f to floating point is done as follows: unpackSnorm1x16: clamp(f / 32767.0, -1, +1)
| GLM_FUNC_DECL float glm::unpackSnorm1x8 | ( | uint8 const & | p | ) |
First, unpacks a single 8-bit unsigned integer p into a single 8-bit signed integers.
Then, the value is converted to a normalized floating-point value to generate the returned scalar.
The conversion for unpacked fixed-point value f to floating point is done as follows: unpackSnorm1x8: clamp(f / 127.0, -1, +1)
| GLM_FUNC_DECL vec2 glm::unpackSnorm2x8 | ( | uint16 const & | p | ) |
First, unpacks a single 16-bit unsigned integer p into a pair of 8-bit signed integers.
Then, each component is converted to a normalized floating-point value to generate the returned two-component vector.
The conversion for unpacked fixed-point value f to floating point is done as follows: unpackSnorm2x8: clamp(f / 127.0, -1, +1)
The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.
| GLM_FUNC_DECL vec4 glm::unpackSnorm3x10_1x2 | ( | uint32 const & | p | ) |
First, unpacks a single 32-bit unsigned integer p into four 16-bit signed integers.
Then, each component is converted to a normalized floating-point value to generate the returned four-component vector.
The conversion for unpacked fixed-point value f to floating point is done as follows: unpackSnorm3x10_1x2(xyz): clamp(f / 511.0, -1, +1) unpackSnorm3x10_1x2(w): clamp(f / 511.0, -1, +1)
The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.
| GLM_FUNC_DECL vec4 glm::unpackSnorm4x16 | ( | uint64 const & | p | ) |
First, unpacks a single 64-bit unsigned integer p into four 16-bit signed integers.
Then, each component is converted to a normalized floating-point value to generate the returned four-component vector.
The conversion for unpacked fixed-point value f to floating point is done as follows: unpackSnorm4x16: clamp(f / 32767.0, -1, +1)
The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.
| GLM_FUNC_DECL uvec4 glm::unpackU3x10_1x2 | ( | uint32 const & | p | ) |
Unpacks a single 32-bit unsigned integer p into three 10-bit and one 2-bit unsigned integers.
The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.
| GLM_FUNC_DECL float glm::unpackUnorm1x16 | ( | uint16 const & | p | ) |
First, unpacks a single 16-bit unsigned integer p into a of 16-bit unsigned integers.
Then, the value is converted to a normalized floating-point value to generate the returned scalar.
The conversion for unpacked fixed-point value f to floating point is done as follows: unpackUnorm1x16: f / 65535.0
| GLM_FUNC_DECL float glm::unpackUnorm1x8 | ( | uint8 const & | p | ) |
Convert a single 8-bit integer to a normalized floating-point value.
The conversion for unpacked fixed-point value f to floating point is done as follows: unpackUnorm4x8: f / 255.0
| GLM_FUNC_DECL vec2 glm::unpackUnorm2x8 | ( | uint16 const & | p | ) |
First, unpacks a single 16-bit unsigned integer p into a pair of 8-bit unsigned integers.
Then, each component is converted to a normalized floating-point value to generate the returned two-component vector.
The conversion for unpacked fixed-point value f to floating point is done as follows: unpackUnorm4x8: f / 255.0
The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.
| GLM_FUNC_DECL vec4 glm::unpackUnorm3x10_1x2 | ( | uint32 const & | p | ) |
First, unpacks a single 32-bit unsigned integer p into four 16-bit signed integers.
Then, each component is converted to a normalized floating-point value to generate the returned four-component vector.
The conversion for unpacked fixed-point value f to floating point is done as follows: unpackSnorm3x10_1x2(xyz): clamp(f / 1023.0, 0, +1) unpackSnorm3x10_1x2(w): clamp(f / 3.0, 0, +1)
The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.
| GLM_FUNC_DECL vec4 glm::unpackUnorm4x16 | ( | uint64 const & | p | ) |
First, unpacks a single 64-bit unsigned integer p into four 16-bit unsigned integers.
Then, each component is converted to a normalized floating-point value to generate the returned four-component vector.
The conversion for unpacked fixed-point value f to floating point is done as follows: unpackUnormx4x16: f / 65535.0
The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.


Wayne Rooney in, but who goes out? - Decide who Derby County keep or sell
Latest Derby County transfer news | You decide which players the Rams should keep or sell during the January transfer window
by Mark WakefieldDerby County, like the rest of the teams in the Sky Bet Championship, are enjoying a two-week hiatus from action due to the international break.
The Rams are in 16th place in the league table following their latest outing which saw them lose 1-0 to arch rivals Nottingham Forest last weekend.
Phillip Cocu and his players will rest up before preparing to take on Preston North End on Saturday, November 23 when their league campaign resumes.
The summer transfer window saw Derby recruit six new players, with four of those coming in on loan deals for the campaign.
Derby will also welcome Wayne Rooney to Pride Park in January in a player-coach role, after he recently left DC United.
And in terms of outgoings, Cocu allowed nine players to depart while a number of others leave the club after the expiration of their contracts or, in the case of Luke Thomas to Barnsley, via a sale.
So what will the upcoming January transfer window have in store for the Rams?
Cocu could look to bring in fresh faces in the New Year in order to galvanise his squad which could enhance the club’s chances of success this season.
But what about the players that are already at Pride Park?
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This is your round up of all things Derby; the one-stop shop that will keep you updated on the latest goings on at Pride Park and beyond.
Transfers, injuries, match-days and managers, we’ve got you covered.
We’ll be bringing the very latest on the Rams throughout the week and around the clock.
Make sure you keep yourself updated with our handy daily catch up...
Must reads...
- The reason Lawrence is out of Wales squad
- Phillip Cocu's January priorities
- The area Derby County must improve to turn their season around
- 'Punished' - Derby County fans must look at bigger picture when it comes to Jayden Bogle after mistake
- 'I was surprised' - Leeds United star makes this Derby County admission
- Who is Luciano Acosta? Profile on D.C. United star invited to Derby County by Wayne Rooney
For all your latest Rams news, opinion, analysis and transfer gossip, click here
You can follow our Rams reporter Steve Nicholson on Twitter, and his email address is<EMAIL_ADDRESS>Which players should the Rams keep beyond January, and which should they let go, either on a permanent basis, loan deal, or even end their current loan prematurely?
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