nips nips2002 nips2002-24 nips2002-24-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Yves Grandvalet, Stéphane Canu
Abstract: This paper introduces an algorithm for the automatic relevance determination of input variables in kernelized Support Vector Machines. Relevance is measured by scale factors deﬁning the input space metric, and feature selection is performed by assigning zero weights to irrelevant variables. The metric is automatically tuned by the minimization of the standard SVM empirical risk, where scale factors are added to the usual set of parameters deﬁning the classiﬁer. Feature selection is achieved by constraints encouraging the sparsity of scale factors. The resulting algorithm compares favorably to state-of-the-art feature selection procedures and demonstrates its effectiveness on a demanding facial expression recognition problem.
[1] P. S. Bradley and O. L. Mangasarian. Feature selection via concave minimization and support vector machines. In Proc. 15th International Conf. on Machine Learning, pages 82–90. Morgan Kaufmann, San Francisco, CA, 1998.
[2] L. Breiman. Heuristics of instability and stabilization in model selection. The Annals of Statistics, 24(6):2350–2383, 1996.
[3] O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee. Choosing multiple parameters for support vector machines. Machine Learning, 46(1):131–159, 2002.
[4] N. Cristianini, C. Campbell, and J. Shawe-Taylor. Dynamically adapting kernels in support vector machines. In M. S. Kearns, S. A. Solla, and D. A. Cohn, editors, Advances in Neural Information Processing Systems 11. MIT Press, 1999.
[5] T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: data mining , inference, and prediction. Springer series in statistics. Springer, 2001.
[6] T. Jebara and T. Jaakkola. Feature selection and dualities in maximum entropy discrimination. In Uncertainity In Artiﬁcial Intellegence, 2000.
[7] R. M. Neal. Bayesian Learning for Neural Networks, volume 118 of Lecture Notes in Statistics. Springer, 1996.
[8] V. N. Vapnik. The Nature of Statistical Learning Theory. Springer Series in Statistics. Springer, 1995.
[9] J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, and V. Vapnik. Feature selection for SVMs. In Advances in Neural Information Processing Systems 13. MIT Press, 2000.


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Hummingbirds of Ocean Beach
Tessa Felice and I put together an exhibit at the
Lazy Hummingbird Cafe in Ocean Beach, San Diego. The Lazy Hummingbird Cafe was where I came everyday when I first moved to OB in August, 2018 from the East Coast. I'd sit outside and listen to the chimes, looking online for places to live. As I walked around OB, I began to hear and see hummingbirds and they filled me with a sense of magic, awe, and a sense of home.
I met Tessa working at
High Tech High. She and a teacher named John Santos taught me how to build frames out of wood and then together, Tessa and I took photographs and built the frames for this exhibit.
We hope to sell some of the photographs and frames we made. All proceeds generated from the exhibit will go to
Saving Species, a nonprofit organization which helps to save habitats and hummingbirds who are threatened by climate change.
Here is where we're documenting our project digitally. Use the arrows to navigate and see our project and our process.
Molly Josephs—
Hummingbirds enchant me. I listen for their gurgly songs wherever I go in San Diego. Throughout my life on the East Coast, I had seen a hummingbird only once. But here, I've fallen in love with them. Seeing them as I walk to the store or bike home from work, is one of the things that makes living in OB so special to me.
Tessa Felice —
As a young girl, playing outside always filled me with joy. I used to run around and make purple potions with our mexican bush sage, or salvia leucantha. I’ve always been aware of another creature that enjoyed this plant as I did, hummingbirds! Hummingbirds always had a mesmerizing effect on me when they’d fly close to my face. It was as if they weren’t actually moving, but floating. And that buzzing always had an aligning, healing property to it, as a singing bowl would. I remember pulling each sage bulb, packed with a fine drop of nectar. I sometimes pretended to be one of my little hummingbird companions and would taste this succulent ambrosia.
I think of my mother when I see them because of her love for hummingbirds and hawks. She told me they were her spirit guides, and shared them with me graciously. I’d love to share that feeling with everyone.
The Drip — Tessa Felice
We know patience is a virtue
We know practice makes perfect
But your subject’s a foot away
Right as you begin your day,
That shot’s yours
You rightfully deserve it
Etiwanda
— Molly Josephs
I woke up early on my first day in OB eager to photograph hummingbirds frequenting this red hibiscus bush on Etiwanda Street. When I spotted this immature allen's or rufous male flying to and from the bush, I was thrilled! He was the first humminbird I ever photographed.
I’m not sure why hummingbirds are attracted to hibiscus bushes. but many of the flowers from which hummingbirds eat nectar are red.
Muir — Molly Josephs
One of the things I love about going around the neighborhood looking for hummingbirds, is discovering the "secret" spots that seem to be particularly popular.
There are these two dead bushes on Muir Street whose bare limbs serve as perches and watchtowers. The upper branches stretch above a fence, surrounding a small cottage community. They're tall enough that they seem to give the birds a good, 360° view of the neighborhood.

Ligar — Tessa Felice
Thank you to the man that opened the gate to his community patio for us. He allowed us to watch and photograph hummingbirds at his feeder for as long as we liked. Similar to the allen’s hummingbird that opens her wings to stay balanced in life, as this mature female nurtures herself, she knows that he is a nice man (: This girl kept flying back and forth from the power lines thirty feet away just to flirt with us. Tee-Hee
Branching out — Tessa Felice
I love figs, and I love birds that visit the trees, even with absolutely no crop. We learn from them to sit peacefully with our friends, and look at the trees around us. I wonder what it was that both of these (species) were staring at. Sitting together, content with the breeze, and the grey sky.
Up Close — Molly Josephs
These are portraits of a bird (I believe a male Anna's) who spent the morning perched on one of the dead bushes where I go to find hummingbirds. From one perspective, he looks sleek and fierce with his sword of a bill. From a different perspective he appears fluffy and round. This dynamism touches on the contrasting habits and qualities —small but fierce— of hummingbirds.
Paradise — Tessa Felice
Flight! Landing! If the flower is a bird of paradise, converting it to our scale, we can imagine our hesitation to land on the human of paradise. Imagine landing on a flower bigger than you are. Or flee it, if this is what this female Anna’s hummingbird is doing. It can be quite the game, but at times we must! Leap in! Each of us will have someone that gives us paradise, it’s only the leap that we lack. This hummingbird above a strelitzia, named after the duchy of Mecklenburg-Strelitz, are engaging one another. This image has balance, and love. Perch upon paradise!
Sluuuurrrrrrrpppppp — Tessa Felice
I know what it feels like, after a day of thirst.
Downy — Molly Josephs
This male Anna’s is the same one as in Up Close. I loved getting near enough to him to glimpse the downy feathers close to his body. I would not associate those white feathers with hummingbirds. They’re so sleek and magical. They seem to transcend the laws of physics and biology, even though they too need to stay warm.
Symmetry — Molly Josephs
I spotted this immature rufous male hummingbird singing on a very tall bird of paradise flower on Muir street. Despite the fact that the bird was small, and the plant, incredibly tall, I was struck by the symmetry and resonance between the sharp bill of the hummingbird and the pointed beak of the flower.
The Process —
- Move to OB and falling in love with the Lazy Hummingbird Cafe and with Hummingbirds
- Paying attention and learning where hummingbirds are likely to be found in my neighborhood.
- Borrowing a Sony RX10 camera from a friend and learning how to use it to photograph hummingbirds
- Talk to cafe
- Meeting Tessa and photograph hummingbirds together
- Select photos for the exhibit
- Digitally edit photos so that photo sizes match frames and are at 600 DPI
- Building frames from scratch — rout wood, cut wood with 45° angles, make sure it fits around glass, glue wood, staple nails, sand, woody putty, sand, stain, make backs, add attachments so can hang, put in photo!
- Write descriptions for photos
- Get photos printed in correct sizes at Costco
- Put photos in frames and take to cafe
- Figure out arrangement of photo display on the floor
- Hang photos in wall display and tape descriptions to the wall!
Acknowledgements
Thank you to John Santos for his incredible generosity and patience in teaching us. He gave us so much support and all of the materials for the frames.
Thank you, to Brian Delgado, Andrew Lerario, and Blue Dot Education for lending us their Sony RX10 camera, and giving us photography advice. Thank you to Pat Holder for driving us to the cafe in the rain! Of course, thanks to High Tech High for providing the opportunity to work on projects like this!
Lastly, thank you to all of the hummingbirds, flowers, and The Lazy Hummingbird Cafe, which make OB so special!


The full code and documentation is available on Github.
If you were offered $1 today or $1 a year from now, which would you take? A smart businessman would take the dollar today. Why? Because the present value of the future $1, is actually only around 90¢ after adjusting for inflation and other economic factors. The idea that money in the future is worth less in the present is often referred to as the time value of money, and it is the fundamental principle behind discounted cash flow analysis.
The share price of a company is equal to its present value divided by its total number of outstanding shares. If it were possible to know both how much a company will be worth in future and how much that future value is worth in the present, it would be possible to determine what the share price of the company should be right now (fair value). In reality, there are only two ways of knowing either of these for certain: you are God or you are cheating. The purpose of discounted cash flow analysis is to estimate the fair value of a company by approximating its future free cash flows and discounting that amount by some reasonable rate to compensate for inflation, risk, and the passage of time. If this estimated fair value of the company is less than its current share price, the stock is likely overvalued and represents a sell opportunity and if the fair value is greater than the current share price, the stock is likely undervalued and represents a buy opportunity.
It turns out that free cash flow is a very well defined concept in financial circles. Free cash flow measures how much cash a business has after subtracting out expenditures, taxes, and investments. Therefore, determining future free cash flow simply requires forecasting future revenues and subtracting out future expenditures, taxes, and investments. I developed an automated forecasting engine that utilizes a series of neural networks to predict the future free cash flows of any publicly traded company.
Neural networks are useless without training data. Luckily, the Securities and Exchange Commission (SEC) publishes massive datasets containing every financial document from every publicly traded company. I developed a SQL script that parses these datasets, extracts out useful information, and stores it inside a relational database from which I can efficiently store and effectively query the data. Once I had scraped and stored the data, I utilized the Hibernate ORM framework to map my Java models to database tables and to gain access to the powerful Hibernate Criteria API.
My SEC parser ended up working much, much better than I expected. I ended up with 18,105,547 data points for 9,303 unique companies spread across 450 industries. In order to deal with the massive volume of data, I developed an algorithm to filter out the less important data points. This algorithm first categorizes companies by industry (using their sic codes) and then determines which data points appear most often in financial statements for companies within a particular industrial sector (e.g., “Oil and Gas Revenues” appears very often in financial statements for Crude Petroleum & Natural Gas companies, so it is considered to be an “important” data point for companies in that sector).
The automated forecasting engine is composed of a collection of neural networks, each of which outputs the predicted quarter-over-quarter growth rate of a different component of the free cash flow formula using the “most important” data points as inputs. This quarter-over-quarter growth rate is then multiplied by current actuals to predict future values. These networks were then trained via backpropagation on historical SEC financial data.
The forecasting engine worked surprisingly well! I trained the engine on sic code 1311 (Crude Petroleum & Natural Gas) and then used the engine to forecast the future free cash flow of Apco Oil and Gas International, Inc. By applying some of the same optimization techniques that I used when computing revenues and operating costs, I believe I can substantially reduce the errors in some of the other measurements.
| Term | Predicted (MUSD) | Actual (MUSD) | Error |
|---|---|---|---|
| Revenues | 45.39 | 46.34 | -2.05% |
| Operating Costs | 35.85 | 35.05 | 2.28% |
| Taxes | 4.14 | 4.48 | -7.59% |
| Investments | 4.76 | 5.81 | 18.07% |
| Assets | 87.43 | 76.25 | 14.66% |
| Liabilities | 47.25 | 38.69 | 22.12% |
Work in progress! Check back for updates.
Cover photograph from Nepse Forum.


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Autograd mechanics¶
This note will present an overview of how autograd works and records the operations. It’s not strictly necessary to understand all this, but we recommend getting familiar with it, as it will help you write more efficient, cleaner programs, and can aid you in debugging.
Excluding subgraphs from backward¶
Every Tensor has a flag: requires_grad
that allows for fine grained
exclusion of subgraphs from gradient computation and can increase efficiency.
requires_grad
¶
If there’s a single input to an operation that requires gradient, its output will also require gradient. Conversely, only if all inputs don’t require gradient, the output also won’t require it. Backward computation is never performed in the subgraphs, where all Tensors didn’t require gradients.
>>> x = torch.randn(5, 5) # requires_grad=False by default
>>> y = torch.randn(5, 5) # requires_grad=False by default
>>> z = torch.randn((5, 5), requires_grad=True)
>>> a = x + y
>>> a.requires_grad
False
>>> b = a + z
>>> b.requires_grad
True
This is especially useful when you want to freeze part of your model, or you
know in advance that you’re not going to use gradients w.r.t. some parameters.
For example if you want to finetune a pretrained CNN, it’s enough to switch the
requires_grad
flags in the frozen base, and no intermediate buffers will
be saved, until the computation gets to the last layer, where the affine
transform will use weights that require gradient, and the output of the network
will also require them.
model = torchvision.models.resnet18(pretrained=True)
for param in model.parameters():
param.requires_grad = False
# Replace the last fully-connected layer
# Parameters of newly constructed modules have requires_grad=True by default
model.fc = nn.Linear(512, 100)
# Optimize only the classifier
optimizer = optim.SGD(model.fc.parameters(), lr=1e-2, momentum=0.9)
How autograd encodes the history¶
Autograd is reverse automatic differentiation system. Conceptually, autograd records a graph recording all of the operations that created the data as you execute operations, giving you a directed acyclic graph whose leaves are the input tensors and roots are the output tensors. By tracing this graph from roots to leaves, you can automatically compute the gradients using the chain rule.
Internally, autograd represents this graph as a graph of
Function
objects (really expressions), which can be
apply()
ed to compute the result of
evaluating the graph. When computing the forwards pass, autograd
simultaneously performs the requested computations and builds up a graph
representing the function that computes the gradient (the .grad_fn
attribute of each torch.Tensor
is an entry point into this graph).
When the forwards pass is completed, we evaluate this graph in the
backwards pass to compute the gradients.
An important thing to note is that the graph is recreated from scratch at every iteration, and this is exactly what allows for using arbitrary Python control flow statements, that can change the overall shape and size of the graph at every iteration.
In-place operations with autograd¶
Supporting in-place operations in autograd is a hard matter, and we discourage their use in most cases. Autograd’s aggressive buffer freeing and reuse makes it very efficient and there are very few occasions when in-place operations actually lower memory usage by any significant amount. Unless you’re operating under heavy memory pressure, you might never need to use them.
There are two main reasons that limit the applicability of in-place operations:
In-place operations can potentially overwrite values required to compute gradients.
Every in-place operation actually requires the implementation to rewrite the computational graph. Out-of-place versions simply allocate new objects and keep references to the old graph, while in-place operations, require changing the creator of all inputs to the
Function
representing this operation. This can be tricky, especially if there are many Tensors that reference the same storage (e.g. created by indexing or transposing), and in-place functions will actually raise an error if the storage of modified inputs is referenced by any otherTensor
.
In-place correctness checks¶
Every tensor keeps a version counter, that is incremented every time it is
marked dirty in any operation. When a Function saves any tensors for backward,
a version counter of their containing Tensor is saved as well. Once you access
self.saved_tensors
it is checked, and if it is greater than the saved value
an error is raised. This ensures that if you’re using in-place
functions and not seeing any errors, you can be sure that the computed
gradients are correct.


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7 Multiple Regression
Now that we are equipped with data visualization skills from Chapter 3, data wrangling skills from Chapter 5, and an understanding of the “tidy” data format from Chapter 4, we now proceed with data modeling. The fundamental premise of data modeling is to model the relationship between:
- An outcome variable \(y\), also called a dependent variable and
- An explanatory/predictor variable \(x\), also called an independent variable or covariate.
Another way to state this is using mathematical terminology: we will model the outcome variable \(y\) as a function of the explanatory/predictor variable \(x\). Why do we have two different labels, explanatory and predictor, for the variable \(x\)? That’s because roughly speaking data modeling can be used for two purposes:
- Modeling for prediction: You want to predict an outcome variable \(y\) based on the information contained in a set of predictor variables. You don’t care so much about understanding how all the variables relate and interact, but so long as you can make good predictions about \(y\), you’re fine. For example, if we know many individuals’ risk factors for lung cancer, such as smoking habits and age, can we predict whether or not they will develop lung cancer? Here we wouldn’t care so much about distinguishing the degree to which the different risk factors contribute to lung cancer, but instead only on whether or not they could be put together to make reliable predictions.
- Modeling for explanation: You want to explicitly describe the relationship between an outcome variable \(y\) and a set of explanatory variables, determine the significance of any found relationships, and have measures summarizing these. Continuing our example from above, we would now be interested in describing the individual effects of the different risk factors and quantifying the magnitude of these effects. One reason could be to design an intervention to reduce lung cancer cases in a population, such as targeting smokers of a specific age group with advertisement for smoking cessation programs. In this book, we’ll focus more on this latter purpose.
Data modeling is used in a wide variety of fields, including statistical inference, causal inference, artificial intelligence, and machine learning. There are many techniques for data modeling, such as tree-based models, neural networks/deep learning, and more. However, we’ll focus on one particular technique: linear regression, one of the most commonly-used and easy to understand approaches to modeling. Recall our discussion in Chapter 2.4.3 on numerical and categorical variables. Linear regression involves:
- An outcome variable \(y\) that is numerical
- Explanatory variables \(\vec{x}\) that are either numerical or categorical
Whereas there is always only one numerical outcome variable \(y\), we have choices on both the number and the type of explanatory variables \(\vec{x}\) to use. We’re going to the following regression scenarios, where we have
- A single numerical explanatory variable \(x\) in Chapter @(model1). This scenario is known as simple linear regression.
- A single categorical explanatory variable \(x\) in Chapter @(model2).
- Two numerical explanatory variables \(x_1\) and \(x_2\) in Chapter @(model3). This is the first of two multiple regression scenarios we’ll cover where there are now more than one explanatory variable.
- One numerical and one categorical explanatory variable in Chapter @(model3). This is the second multiple regression scenario we’ll cover. We’ll also introduce interaction models here.
We’ll study all 4 of these regression scenarios using real data, all easily accessible via R packages!
Needed packages
In this chapter we introduce a new package, moderndive
, that is an accompaniment package to the ModernDive book that includes useful functions for linear regression and other functions and data used in later in the book. However because this is a relatively new package, we’re have to install this package in a different way than was covered in Section 2.3.
First, install the remotes
package like you would normally install a package, then run the following line to install the moderndive
package:
library(remotes)
# remotes::install_github("moderndive/moderndive")
Let’s now load all the packages needed for this chapter. If needed, read Section 2.3 for information on how to install and load R packages.
library(ggplot2)
library(dplyr)
library(moderndive)
7.1 Two numerical x
Much like our teacher evaluation data in Section 6.1, let’s now attempt to identify factors that are associated with how much credit card debt an individual will hold. The textbook An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani is an intermediate-level textbook on statistical and machine learning freely available here. It has an accompanying R packaged called ISLR
with datasets that the authors use to demonstrate various machine learning methods. One dataset that is frequently used by the authors is the Credit
dataset where predictions are made on the balance held by various credit card holders based on information like income, credit limit, and education level. Let’s consider the following variables:
- Outcome variable \(y\): Credit card balance in dollars
- Explanatory variables \(x\):
- income in $1000’s
- credit card limit in dollars
- credit rating
- card holder’s age
- years of education
Let’s load the data and select()
only the 6 variables listed above:
library(ISLR)
Credit <- Credit %>%
select(Balance, Limit, Income, Rating, Age, Education)
Unfortunately, no information on how this data was sampled was provided, so we’re not able to generalize any such results to a greater population, but we will still use this data to demonstrate multiple regression. Multiple regression is a form of linear regression where there are now more than one explanatory variables and thus the interpretation of the associated effect of any one explanatory variable must be made in conjunction with the other explanatory variable. We’ll perform multiple regression with:
- A numerical outcome variable \(y\), in this case teaching credit card
Balance
- Two explanatory variables:
- A first numerical explanatory variable \(x_1\), in this case credit
Limit
- A second numerical explanatory variable \(x_2\), in this case
Income
- A first numerical explanatory variable \(x_1\), in this case credit
7.1.1 Exploratory data analysis
Let’s look at the raw data values both by bringing up RStudio’s spreadsheet viewer, although in Table 7.1 we only show 5 randomly selected credit card holders out of 400, and using the glimpse()
function:
View(Credit)
| Balance | Limit | Income | Rating | Age | Education | |
|---|---|---|---|---|---|---|
| 270 | 772 | 5565 | 39.1 | 410 | 48 | 18 |
| 212 | 799 | 5309 | 29.6 | 397 | 25 | 15 |
| 119 | 0 | 2161 | 27.0 | 173 | 40 | 17 |
| 250 | 98 | 1551 | 22.6 | 134 | 43 | 13 |
| 138 | 187 | 3613 | 36.4 | 278 | 35 | 9 |
glimpse(Credit)
Observations: 400
Variables: 6
$ Balance <int> 333, 903, 580, 964, 331, 1151, 203, 872, 279, 1350, 1407,...
$ Limit <int> 3606, 6645, 7075, 9504, 4897, 8047, 3388, 7114, 3300, 681...
$ Income <dbl> 14.9, 106.0, 104.6, 148.9, 55.9, 80.2, 21.0, 71.4, 15.1, ...
$ Rating <int> 283, 483, 514, 681, 357, 569, 259, 512, 266, 491, 589, 13...
$ Age <int> 34, 82, 71, 36, 68, 77, 37, 87, 66, 41, 30, 64, 57, 49, 7...
$ Education <int> 11, 15, 11, 11, 16, 10, 12, 9, 13, 19, 14, 16, 7, 9, 13, ...
Let’s look at some summary statistics:
summary(Credit)
Balance Limit Income Rating Age
Min. : 0 Min. : 855 Min. : 10.4 Min. : 93 Min. :23.0
1st Qu.: 69 1st Qu.: 3088 1st Qu.: 21.0 1st Qu.:247 1st Qu.:41.8
Median : 460 Median : 4622 Median : 33.1 Median :344 Median :56.0
Mean : 520 Mean : 4736 Mean : 45.2 Mean :355 Mean :55.7
3rd Qu.: 863 3rd Qu.: 5873 3rd Qu.: 57.5 3rd Qu.:437 3rd Qu.:70.0
Max. :1999 Max. :13913 Max. :186.6 Max. :982 Max. :98.0
Education
Min. : 5.0
1st Qu.:11.0
Median :14.0
Mean :13.4
3rd Qu.:16.0
Max. :20.0
We observe for example
- The typical credit card balance is around $500
- The typical credit card limit is around $5000
- The typical income is around $40,000.
Since our outcome variable Balance
and the explanatory variables Limit
and Rating
are numerical, we can compute the correlation coefficient between pairs of these variables. However, instead running the cor()
command twice, once for each explanatory variable:
cor(Credit$Balance, Credit$Limit)
cor(Credit$Balance, Credit$Income)
We can simultaneously compute them by returning a correlation matrix in Table 7.2. We read off the correlation coefficient for any pair of variables by looking them up in the appropriate row/column combination.
Credit %>%
select(Balance, Limit, Income) %>%
cor()
| Balance | Limit | Income | |
|---|---|---|---|
| Balance | 1.000 | 0.862 | 0.464 |
| Limit | 0.862 | 1.000 | 0.792 |
| Income | 0.464 | 0.792 | 1.000 |
For example, the correlation coefficients of:
Balance
with itself is 1Balance
withLimit
is 0.862, indicating a strong positive linear relationship, which makes sense as only individuals with large credit limits and accrue large credit card balances.Balance
withIncome
is 0.464, suggestive of another positive linear relationship, although not as strong as the relationship betweenBalance
andLimit
.- As an added bonus, we can read off the correlation coefficient of the two explanatory variables,
Limit
andIncome
of 0.792. In this case, we say there is a high degree of collinearity between these two explanatory variables.
Let’s visualize the relationship of the outcome variable with each of the two explanatory variables in two separate plots
ggplot(Credit, aes(x = Limit, y = Balance)) +
geom_point() +
labs(x = "Credit limit (in $)", y = "Credit card balance (in $)",
title = "Relationship between balance and credit limit") +
geom_smooth(method = "lm", se = FALSE)
ggplot(Credit, aes(x = Income, y = Balance)) +
geom_point() +
labs(x = "Income (in $1000)", y = "Credit card balance (in $)",
title = "Relationship between balance and income") +
geom_smooth(method = "lm", se = FALSE)
In both cases, there seems to be a positive relationship. However the two plots in Figure 7.1 only focus on the relationship of the outcome variable with each of the explanatory variables independently. To get a sense of the joint relationship of all three variables simultaneously through a visualization, let’s display the data in a 3-dimensional (3D) scatterplot, where
- The numerical outcome variable \(y\)
Balance
is on the z-axis (vertical one) - The two numerical explanatory variables form the x and y axes (on the floor). In this case
- The first numerical explanatory variable \(x_1\)
Income
is on the x-axis - The second numerical explanatory variable \(x_2\)
Limit
is on the y-axis
- The first numerical explanatory variable \(x_1\)
Click on the following image to open an interactive 3D scatterplot in your browser:
Previously in Figure 6.5, we plotted a “best-fitting” regression line through a set of points where the numerical outcome variable \(y\) was teaching score
and a single numerical explanatory variable \(x\) bty_avg
. What is the analogous concept when we have two numerical predictor variables? Instead of a best-fitting line, we now have a best-fitting plane, which is 3D generalization of lines which exist in 2D. Click on the following image to open an interactive plot of the regression plane in your browser.
7.1.2 Multiple regression
Just as we did when we had a single numerical explanatory variable \(x\) in Section 6.1.2 and when we had a single categorical explanatory variable \(x\) in Section 6.2.2, we fit a regression model and obtain the regression table in our two numerical explanatory variable scenario. To fit a regression model and obtain a table using get_regression_table()
, we now use a +
to consider multiple explanatory variables. In this case since we want to preform a regression of Limit
and Income
simultaneously, we input Balance ~ Limit + Income
Balance_model <- lm(Balance ~ Limit + Income, data = Credit)
get_regression_table(Balance_model)
| term | estimate | std_error | statistic | p_value | conf_low | conf_high |
|---|---|---|---|---|---|---|
| intercept | -385.179 | 19.465 | -19.8 | 0 | -423.446 | -346.912 |
| Limit | 0.264 | 0.006 | 45.0 | 0 | 0.253 | 0.276 |
| Income | -7.663 | 0.385 | -19.9 | 0 | -8.420 | -6.906 |
How do we interpret these three values that define the regression plane?
- Intercept: -$385.18. The intercept in our case represents the credit card balance for an individual who has both a credit
Limit
of $0 andIncome
of $0. In our data however, the intercept has limited practical interpretation as no individuals hadLimit
orIncome
values of $0 and furthermore the smallest credit card balance was $0. Rather, it is used to situate the regression plane in 3D space. - Limit: $0.26. Now that we have multiple variables to consider, we have to add a caveat to our interpretation: all other things being equal, for every increase of one unit in credit
Limit
(dollars), there is an associated increase of on average $0.26 in credit card balance. Note:- Just as we did in Section 6.1.2, we are not making any causal statements, only statements relating to the association between credit limit and balance
- The all other things being equal is making a statement about all other explanatory variables, in this case only one:
Income
. This is equivalent to saying “holdingIncome
constant, we observed an associated increase of $0.26 in credit card balance for every dollar increase in credit limit”
- Income: -$7.66. Similarly, all other things being equal, for every increase of one unit in
Income
(in other words, $1000 in income), there is an associated decrease of on average $7.66 in credit card balance.
However, recall in Figure 7.1 that when considered separately, both Limit
and Income
had positive relationships with the outcome variable Balance
: as card holders’ credit limits increased their credit card balances tended to increase as well, and a similar relationship held for incomes and balances. In the above multiple regression, however, the slope for Income
is now -7.66, suggesting a negative relationship between income and credit card balance. What explains these contradictory results? This is a phenomenon known as Simpson’s Paradox, a phenomenon in which a trend appears in several different groups of data but disappears or reverses when these groups are combined; we expand on this in Section 7.3.1.
7.1.3 Observed/fitted values and residuals
As we did previously, in Table 7.4 let’s unpack the output of the get_regression_points()
function for our model for credit card balance
- for all 400 card holders in the dataset, in other words
- for all 400 rows in the
Credit
data frame, in other words - for all 400 points
regression_points <- get_regression_points(Balance_model)
regression_points
| ID | Balance | Limit | Income | Balance_hat | residual |
|---|---|---|---|---|---|
| 1 | 333 | 3606 | 14.9 | 454 | -120.8 |
| 2 | 903 | 6645 | 106.0 | 559 | 344.3 |
| 3 | 580 | 7075 | 104.6 | 683 | -103.4 |
| 4 | 964 | 9504 | 148.9 | 986 | -21.7 |
| 5 | 331 | 4897 | 55.9 | 481 | -150.0 |
Recall the format of the output:
balance
corresponds to \(y\) the observed valuebalance_hat
corresponds to \(\widehat{y}\) the fitted valuereisdual
corresponds to the residual \(y - \widehat{y}\)
As we did in Section 6.1.3 let’s prove to ourselves that the results make sense by manually replicate the internal computation that get_regression_points()
performs using the mutate()
verb from Chapter 5
- First we compute a duplicate of the fitted values \(\widehat{y} = b_0 + b_1 \times x_1 + b_2 \times x_2\) where the intercept \(b_0\) = -385.179, the slope \(b_1\) for
Limit
of 0.264, and the slope \(b_2\) forIncome
of -7.663 and store these inbalance_hat_2
- Then we compute the a duplicated the residuals \(y - \widehat{y}\) and store these in
residual_2
regression_points <- regression_points %>%
mutate(Balance_hat_2 = -385.179 + 0.264 * Limit - 7.663 * Income) %>%
mutate(residual_2 = Balance - Balance_hat_2)
regression_points
| ID | Balance | Limit | Income | Balance_hat | residual | Balance_hat_2 | residual_2 |
|---|---|---|---|---|---|---|---|
| 1 | 333 | 3606 | 14.9 | 454 | -120.8 | 453 | -119.7 |
| 2 | 903 | 6645 | 106.0 | 559 | 344.3 | 557 | 346.4 |
| 3 | 580 | 7075 | 104.6 | 683 | -103.4 | 681 | -101.1 |
| 4 | 964 | 9504 | 148.9 | 986 | -21.7 | 983 | -18.7 |
| 5 | 331 | 4897 | 55.9 | 481 | -150.0 | 479 | -148.4 |
We see that our manually computed variables balance_hat_2
and residual_2
equal the original results (up to rounding error).
7.1.4 Residual analysis
Recall in Section 6.1.4, our first residual analysis plot investigated the presense of any systematic pattern in the residuals when we had a single numerical predictor: bty_age
. For the Credit
card dataset, since we have two numerical predictors, Limit
and Income
, we must perform this twice:
ggplot(regression_points, aes(x = Limit, y = residual)) +
geom_point() +
labs(x = "Credit limit (in $)", y = "Residual", title = "Residuals vs credit limit")
ggplot(regression_points, aes(x = Income, y = residual)) +
geom_point() +
labs(x = "Income (in $1000)", y = "Residual", title = "Residuals vs income")
In this case, there does appear to be a systematic pattern to the residuals, as the scatter of the residuals around the line \(y=0\) is definitely not consistent. This behavior of the residuals is further evidenced by the histogram of residuals in Figure @ref(fig:model3_residuals_hist). We observe that the residuals have a slight right-skew (a long tail towards the right). Ideally, these residuals should be bell-shaped around a residual value of 0.
ggplot(regression_points, aes(x = residual)) +
geom_histogram(color = "white") +
labs(x = "Residual")
Another way to intepret this histogram is that since the residual is computed as \(y - \widehat{y}\) = balance
- balance_hat
, we have some values where the fitted value \(\widehat{y}\) is very much lower than the observed value \(y\). In other words, we are underfitting certain credit card holder’s balance by a very large amount.
7.1.5 Your turn
Repeat this same analysis but using Age
and Rating
as the two numerical explanatory variables. Recall the four steps we’ve been following:
- Perform an exploratory data analysis (EDA)
- Fit a linear regression model and get information about the model
- Get information on all \(n\) data points considered in this analysis.
- Perform a residual analysis and look for any systematic patterns in the residuals.
7.2 One numerical & one categorical x
Let’s revisit the instructor evaluation data introduced in Section 6.1, where we studied the relationship between
- the numerical outcome variable \(y\): instructor evaluation score
score
- the numerical explanatory variable \(x\): beauty score
bty_avg
This analysis suggested that there is a positive relationship between bty_avg
and score
, in other words as instructors had higher beauty scores, they also tended to have higher teaching evaluation scores. Now let’s say instead of bty_avg
we are interested in the numerical explanatory variable \(x_1\) age
and furthermore we want to use a second \(x_2\), the (binary) categorical variable gender
. Our modeling scenario now becomes
- the numerical outcome variable \(y\): instructor evaluation score
score
- Two explanatory variables:
- A first numerical explanatory variable \(x_1\):
age
- A second numerical explanatory variable \(x_2\), in this case
gender
- A first numerical explanatory variable \(x_1\):
7.2.1 Exploratory data analysis
load(url("http://www.openintro.org/stat/data/evals.RData"))
evals <- evals %>%
select(score, bty_avg, age, gender)
We’ve already performed some elements of our exploratory data analysis (EDA) in Section 7.2.1, in particular of the outcome variable score
, so let’s now only consider our two new explanatory variables
evals %>%
select(age, gender) %>%
summary()
age gender
Min. :29.0 female:195
1st Qu.:42.0 male :268
Median :48.0
Mean :48.4
3rd Qu.:57.0
Max. :73.0
cor(evals$score, evals$age)
[1] -0.107
In Figure ??, we plot a scatterplot of score
over age
, but given that gender
is a binary categorical variable
- We can assign a color to points from each of the two levels of
gender
: female and male - Furthermore, the
geom_smooth(method = "lm", se = FALSE)
layer automatically fits a different regression line for each
ggplot(evals, aes(x = age, y = score, col = gender)) +
geom_jitter() +
labs(x = "Age", y = "Teaching Score", color = "Gender") +
geom_smooth(method = "lm", se = FALSE)
We notice some interesting trends:
- There are almost no women faculty over the age of 60.
- Fitting separate regression lines for men and women, we see they have different slopes. We see that the associated effect of increasing age seems to be much harsher for women than men. In other words, they have different slopes.
7.2.2 Multiple regression
Let’s now compute the regression table
score_model_2 <- lm(score ~ age + gender, data = evals)
get_regression_table(score_model_2)
| term | estimate | std_error | statistic | p_value | conf_low | conf_high |
|---|---|---|---|---|---|---|
| intercept | 4.484 | 0.125 | 35.79 | 0.000 | 4.238 | 4.730 |
| age | -0.009 | 0.003 | -3.28 | 0.001 | -0.014 | -0.003 |
| gendermale | 0.191 | 0.052 | 3.63 | 0.000 | 0.087 | 0.294 |
The modeling equation for this is:
\[ \begin{align} \widehat{y} &= b_0 + b_1 x_1 + b_2 x_2 \\ \widehat{score} &= b_0 + b_{age} age + b_{male} \mathbb{1}[\mbox{is male}] \\ \end{align} \]
What this looks like is in Figure 7.4 below.
We see that:
- Females are treated as the baseline for comparison for no other reason than “female” is alphabetically earlier than “male”. The \(b_{male} = 0.1906\) is the vertical “bump” that men get in their teaching evaluation scores. Or more precisely, it is the average difference in teaching score that men get relative to the baseline of women
- Accordingly, the intercepts are (which in this case make no sense since no instructor can have age 0):
- for women: \(b_0\) = 4.484
- for men: \(b_0 + b_{male}\) = 4.484 + 0.191 = 4.675
- Both men and women have the same slope. In other words, in this model the associated effect of age is the same for men and women: all other things being equal, for every increase in 1 in age, there is on average an associated decrease of \(b_{age}\) = -0.0086 in teaching score
Hold up: Figure 7.4 is different than Figure 7.3! What is going on? What we have in the original plot is an interaction effect between age and gender!
7.2.3 Mutiple regression with interaction effects
7.2.4 Observed/fitted values and residuals
We say a model has an interaction effect if the associated effect of one variable depends on the value of another variable.
Let’s now compute the regression table
score_model_2 <- lm(score ~ age * gender, data = evals)
get_regression_table(score_model_2)
| term | estimate | std_error | statistic | p_value | conf_low | conf_high |
|---|---|---|---|---|---|---|
| intercept | 4.484 | 0.125 | 35.79 | 0.000 | 4.238 | 4.730 |
| age | -0.009 | 0.003 | -3.28 | 0.001 | -0.014 | -0.003 |
| gendermale | 0.191 | 0.052 | 3.63 | 0.000 | 0.087 | 0.294 |
The model formula is
\[ \begin{align} \widehat{y} &= b_0 + b_1 x_1 + b_2 x_2 + b_3 x_1x_2\\ \widehat{score} &= b_0 + b_{age} age + b_{male} \mathbb{1}[\mbox{is male}] + b_{age,male}age\mathbb{1}[\mbox{is male}] \\ \end{align} \]
7.2.5 Residual analysis
7.3 Other topics
7.3.1 Simpson’s Paradox
Recall in Section 7.1, we saw the two following seemingly contraditory results when studying the relationship between credit card balance, credit limit, and income. On the one hand, the right hand plot of Figure 7.1 suggested that credit card balance and income were positively related:
On the other hand, the multiple regression in Table 7.3, suggested that when modeling credit card balance as a function of both credit limit and income at the same time, credit limit has a negative relationship with balance, as evidenced by the slope of -7.66. How can this be?
First, let’s dive a little deeper into the explanatory variable Limit
. Figure @ref(fig:credit_limit_quartiles) shows a histogram of all 400 values of Limit
, along with vertical red lines that cut up the data into quartiles, meaning:
- 25% of credit limits were between $0 and $3088. Let’s call this the “low” credit limit bracket.
- 25% of credit limits were between $3088 and $4622. Let’s call this the “medium-low” credit limit bracket.
- 25% of credit limits were between $4622 and $5873. Let’s call this the “medium-high” credit limit bracket.
- 25% of credit limits were over $5873. Let’s call this the “high” credit limit bracket.
Let’s now display
- The scatterplot showing the relationship between credit card balance and limit (the right-hand plot of Figure 7.1).
- The scatterplot showing the relationship between credit card balance and limit now with a color aesthetic added corresponding to the credit limit bracket.
The left-hand plot focuses of the relationship between balance and income in aggregate, but the right-hand plot focuses on the relationship between balance and income broken down by credit limit bracket. Whereas in aggregate there is an overall positive relationship, but when broken down We now see that for the
- low
- medium-low
- medium-high
income bracket groups, the strong positive relationship between credit card balance and income disappears! Only for the high bracket does the relationship stay somewhat positive. In this example credit limit is a confounding variable for credit card balance and income.
7.3.2 Script of R code
An R script file of all R code used in this chapter is available here.


This package implements the abstract CurrencyDateCalculator interfaces; it is not released alone but included in the datecalc-jdk, datecalc-jdk8 or datecalc-joda JARs.


Camera Move
...
Translation
avatar walking/flying speed limit (ie: the value eased-into)
Rotation
look up/down (pitch) and turn left/right (yaw) speed limit


Abstract
The WhatToMake Base Ontology contains all of the main classes and properties. The individuals contained in this ontology are individuals related to some of the classes and do not include the individual recipes or ingredients. Also included in this ontology are the additional classes made to extend the FoodOn Ontology.


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Inheritance diagram for ComponentEffect::ComponentDestroyListener:
Definition at line 25 of file ComponentEffect.h.
Public Member Functions | |
| void | addReference () |
| Increases a reference count by one. | |
| void * | getModel () |
| Returns the application (or game) defined data. | |
| SkinData * | getSkinData () |
| Returns the skin specific settings and other skin data. | |
| virtual void | onAnyKey (EventTarget *target, bool option1, bool option2) |
| Called when any key is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target. | |
| virtual void | onBackSpace (EventTarget *target) |
| Called when the BACKSPACE key (backward delete) is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target. | |
| virtual void | onBackTab (EventTarget *target, bool secondary) |
| Called when the back TAB key is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target. | |
| virtual void | onCharacter (EventTarget *target, char character, bool option1, bool option2) |
| Called when a regular character has been entered on the event target, the target has the inner-most focus, and the event not handled otherwise by the target. | |
| virtual void | onClick (EventTarget *target, Vector position, bool option1, bool option2) |
| Called when the target is clicked (usually using the left mouse button). | |
| virtual void | onContextClick (EventTarget *target, Vector position, bool option1, bool option2) |
| Called when the target is context-clicked (usually using the right mouse button). | |
| virtual void | onCopy (EventTarget *target) |
| Called when the event target should perform a clipboard copy. | |
| virtual void | onCut (EventTarget *target) |
| Called when the event target should perform a clipboard cut. | |
| virtual void | onDelete (EventTarget *target) |
| Called when the DELETE key (forward delete) is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target. | |
| virtual void | onDoubleClick (EventTarget *target, Vector position, bool option1, bool option2) |
| Called when the target is double-clicked (usually using the left mouse button). | |
| virtual void | onDropped (EventTarget *draggedObject, EventTarget *dropTarget, Vector relativePosition, bool option1, bool option2) |
| Called when a dragging operation successfully ended. | |
| virtual void | onEnter (EventTarget *target, bool option1, bool option2) |
| Called when the ENTER key is pressed on an event target, the target has the inner-most focus, and the event not handled otherwise by the target. | |
| virtual void | onGotFocus (Component *component) |
| Called when the component has just newly got the focus. | |
| virtual void | onHidden (Component *component) |
| Called when the component has been hidden. | |
| virtual void | onHotKey (EventTarget *target, char character, bool option1, bool option2) |
| Called when a hot key has been pressed on an event target. | |
| virtual void | onKeyStroke (EventTarget *target, int keyCode, bool option1, bool option2) |
| Called when a planned key stroke has been pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target. | |
| virtual void | onLostFocus (Component *component) |
| Called when the component has just lost the focus. | |
| virtual void | onMoved (Component *component, Vector oldPosition) |
| Called when the component has been moved. | |
| virtual void | onMoveDown (EventTarget *target, bool option1, bool option2) |
| Called when the DOWN key is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target. | |
| virtual void | onMoveLeft (EventTarget *target, bool option1, bool option2) |
| Called when the LEFT key is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target. | |
| virtual void | onMoveRight (EventTarget *target, bool option1, bool option2) |
| Called when the RIGHT key is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target. | |
| virtual void | onMoveToEnd (EventTarget *target, bool option1, bool option2) |
| Called when the END key is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target. | |
| virtual void | onMoveToStart (EventTarget *target, bool option1, bool option2) |
| Called when the START key is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target. | |
| virtual void | onMoveUp (EventTarget *target, bool option1, bool option2) |
| Called when the UP key is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target. | |
| virtual void | onPageDown (EventTarget *target, bool option1, bool option2) |
| Called when the PAGE DOWN key is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target. | |
| virtual void | onPageUp (EventTarget *target, bool option1, bool option2) |
| Called when the PAGE UP key is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target. | |
| virtual void | onPaste (EventTarget *target) |
| Called when the event target should perform a clipboard paste. | |
| virtual void | onPress (EventTarget *target, Vector position, bool option1, bool option2) |
| Called when the target is pressed down (usually using the left mouse button). | |
| virtual void | onRelease (EventTarget *target, bool option1, bool option2) |
| Called when the ointer target press is released. | |
| virtual void | onResized (Component *component, Vector oldSize) |
| Called when the component has been resized. | |
| virtual void | onSelectAll (EventTarget *target) |
| Called when the user requested to select all (e.g. | |
| virtual void | onShown (Component *component) |
| Called when the component has been shown. | |
| virtual void | onTab (EventTarget *target, bool secondary) |
| Called when the TAB key is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target. | |
| virtual void | onWheel (EventTarget *target, int delta) |
| Called when a pointer wheel (mouse, joystick, etc.) is changed while the mouse is over this event target. | |
| void | release () |
| Decreases a reference count by one. | |
| void | setModel (void *model, void(*modelDestructor)(void *model)=NULL) |
| Sets the application (or game) defined data. | |
| virtual void | setSkinData (SkinData *skinData) |
| Sets the skin specific settings and other skin data. | |
Static Public Member Functions | |
| void | GlazieryObject::destructor (void *model) |
| Destructor function for the setModel() method, when setting a model that is a glaziery object itself. | |
Private Member Functions | |
| ComponentDestroyListener (Effect *effect) | |
| Creates a new component destroy listener. | |
| virtual void | onDestroying (Component *component) |
| Called when the component is about to be destroyed (its memory is released). | |
Private Attributes | |
| friend | ComponentEffect |
| ComponentEffect * | effect |
| The effect this listener observes. |
|
|
Creates a new component destroy listener.
|
|
|
Increases a reference count by one.
|
|
|
Returns the application (or game) defined data. Typically, there is an object that contains application data that corresponds to this graphical element. This object (or raw data) is stored here. Glaziery will not perform any operation on it, but you can use it to identify application objects for given event targets (such as components).
|
|
|
Returns the skin specific settings and other skin data. The skin data is managed by the skin. If you want to implement a skin independent application (or game), do not use this object outside the skin. However, if you know which skin is in use and you require to read or write special property, you may cast this object to the respective type. This object may be NULL, if the skin does not need to store any data.
|
|
|
Destructor function for the setModel() method, when setting a model that is a glaziery object itself.
|
|
|
Called when any key is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target. This method is called in addition to another key method, such as onCharacter or onMoveDown. The onAnyKey method is called first.
|
|
|
Called when the BACKSPACE key (backward delete) is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target.
|
|
|
Called when the back TAB key is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target. On Windows systems, the back TAB key is SHIFT+TAB. Back tabs step the focus from field to field in backward order.
|
|
|
Called when a regular character has been entered on the event target, the target has the inner-most focus, and the event not handled otherwise by the target. This is used for text input fields to enter text.
|
|
|
Called when the target is clicked (usually using the left mouse button). A click is raised after the mouse has been pressed and released, and no dragging operation has started.
|
|
|
Called when the target is context-clicked (usually using the right mouse button).
|
|
|
Called when the event target should perform a clipboard copy.
|
|
|
Called when the event target should perform a clipboard cut.
|
|
|
Called when the DELETE key (forward delete) is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target.
|
|
|
Called when the component is about to be destroyed (its memory is released). This method is called before the component is deleted, but its parent container references may have been released already.
Reimplemented from Component::Listener. |
|
|
Called when the target is double-clicked (usually using the left mouse button). A double click is raised when an onClick() event has been raised within a short time. onDoubleClick() is called after both onClick() method calls.
|
|
|
Called when a dragging operation successfully ended. This is when a dragging operation is in progress and the primary pointer button has been released.
|
|
|
Called when the ENTER key is pressed on an event target, the target has the inner-most focus, and the event not handled otherwise by the target.
|
|
|
Called when the component has just newly got the focus. The component with focus e.g. receives the key events.
|
|
|
Called when the component has been hidden.
|
|
|
Called when a hot key has been pressed on an event target. Hot keys represent special characters e.g. to easily access fields (indicated by underlined characters).
|
|
|
Called when a planned key stroke has been pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target. Key strokes are usually received by key stroke fields. When the desktop is in key stroke mode, the next key pressed should be sent to this method. This is useful e.g. to define keyboard layouts for games.
|
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|
Called when the component has just lost the focus. The component with focus e.g. receives the key events.
|
|
|
Called when the component has been moved.
|
|
|
Called when the DOWN key is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target.
|
|
|
Called when the LEFT key is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target.
|
|
|
Called when the RIGHT key is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target.
|
|
|
Called when the END key is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target.
|
|
|
Called when the START key is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target.
|
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Called when the UP key is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target.
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Called when the PAGE DOWN key is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target.
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Called when the PAGE UP key is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target.
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Called when the event target should perform a clipboard paste.
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Called when the target is pressed down (usually using the left mouse button).
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Called when the ointer target press is released.
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Called when the component has been resized.
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Called when the user requested to select all (e.g. by pressing Ctrl+A on Windows systems), when the target has the inner-most focus, and the event not handled otherwise by the target.
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Called when the component has been shown.
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Called when the TAB key is pressed on the event target, the target has the inner-most focus, and the event not handled otherwise by the target. Tabs step the focus from field to field.
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Called when a pointer wheel (mouse, joystick, etc.) is changed while the mouse is over this event target.
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Decreases a reference count by one. If no references remain, the object is automatically destroying itself. |
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Sets the application (or game) defined data. Typically, there is an object that contains application data that corresponds to this graphical element. This object (or raw data) is stored here. Glaziery will not perform any operation on it, but you can use it to identify application objects for given event targets (such as components).
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Sets the skin specific settings and other skin data. The skin data is managed by the skin. If you want to implement a skin independent application (or game), do not use this object outside the skin. However, if you know which skin is in use and you require to read or write special property, you may cast this object to the respective type. This object may be NULL, if the skin does not need to store any data.
Reimplemented in Component. |
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Definition at line 27 of file ComponentEffect.h. |
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The effect this listener observes.
Definition at line 34 of file ComponentEffect.h. |


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Previous: CHANGING THE DEFAULTS
One can divide the checks ftnchek does into two categories, local and global. Local checking is restricted to within a single routine, and catches things like uninitialized variables, unintended loss of precision in arithmetic expressions, etc. This sort of checking can be done on each subprogram independently. Furthermore, local checking of a subprogram does not need to be repeated when some other subprogram is changed. Global checking catches things like calling a subroutine with the wrong argument types, or disagreeing in common block declarations. It requires looking at the whole set of subprograms interacting with each other.
The purpose of project files is to allow the local checking and global checking steps to be separated. Assuming that each subprogram is in its own source file, you can run ftnchek once on each one to do local checking while suppressing global checking. Then ftnchek can be run once on all the project files together to do the global checking. The sample makefile below shows how to automate this task. The ``.f.prj'' target updates a project file for a particular file any time the source file changes. The information needed for global checking is saved in the project file. The ``check'' target does the combined global checking. Typically ``make check'' would repeat the ``ftnchek -project'' step only on changed source files, then do the global check. This is obviously a big advantage for large programs, when many subprograms seldom if ever change.
It is best when using project files to place each subprogram in a separate source file. If each source file may contain more than one subprogram, it complicates the definition of ``local'' and ``global'' checking because there is some inter-module checking that is contained within a file. ftnchek tries to do the right thing in this case, but there are some complications (described below) due to the trade-off between avoiding re-doing cross-checks and preserving information about the program's structure.
Ordinarily, to do the least amount of re-checking, project files should be created with the -library flag in effect and trimming turned on. In this mode, the information saved in the project file consists of all subprogram declarations, all subprogram invocations not resolved by declarations in the same file, and one instance of each COMMON block declaration. This is the minimum amount of information needed to check agreement between files.
If the source file contains more than one routine, there are some possible problems that can arise from creating the project file in library mode, because the calling hierarchy among routines defined within the file is lost. Also, if the routines in the file make use of COMMON blocks that are shared with routines in other files, there will not be enough information saved for the correct checking of set and used status of COMMON blocks and COMMON variables according to the -usage setting. Therefore if you plan to use project files when -usage checking is turned on (which is the default situation), and if multiple routines in one project file share COMMON blocks with routines in other files, the project files should be created with the -library flag turned off. In this mode, ftnchek saves, besides the information listed above, one invocation of each subprogram by any other subprogram in the same file, and all COMMON block declarations. This means that the project file will be larger than necessary, and that when it is read in, ftnchek may repeat some inter-module checks that it already did when the project file was created. If each project file contains only one module, there is no loss of information in creating the project files in library mode.
Because of the possible loss of information entailed by creating a project file with the -library flag in effect, whenever that project file is read in later, it will be treated as a library file regardless of the current setting of the -library flag. On the other hand, a project file created with library mode turned off can be read in later in either mode.
The foregoing discussion assumes that the trimming options of the -project setting are turned on when the project file is created. This is the normal situation. The no-trim options of the -project setting are provided in case one wants to use the project files for purposes other than checking the program with ftnchek. For instance, one could write a Perl script to analyze the project files for information about how the different subprograms are called. You should not use the no-trim options to deal with the issues of information loss discussed above, since they cause more information than necessary to be stored. This makes the project files bigger and causes ftnchek to do more work later when it reads them to check your complete program. Ordinarily, you should use the -library option to control how much information to store for later use by ftnchek in checking your program.
Here is an example of how to use the UNIX make utility to automatically create a new project file each time the corresponding source file is altered, and to check the set of files for consistency. Add these lines to your makefile. The example assumes that a macro OBJS has been defined which lists all the names of object files to be linked together to form the complete executable program. (In this makefile, the indented lines should each begin with a tab, not blanks.) If any source file contains multiple routines that share common blocks among themselves, then the no-com-\* option should be removed from NOGLOBAL, and/or drop the -library flag.
# tell make what a project file suffix is .SUFFIXES: .prj # these options suppress global checks. NOGLOBAL=-usage=no-ext-undefined,no-com-\* # tell make how to create a .prj file from a .f file .f.prj: ftnchek -project $(NOGLOBAL) -library $< # set up macro PRJS containing project filenames PRJS= $(OBJS:.o=.prj) # "make check" will check everything that has been changed. check: $(PRJS) ftnchek $(PRJS)
When a program uses many routines defined in a large number of different source files in different directories, it can be cumbersome to specify all the different project files needed to check the program properly. To deal with such cases, ftnchek allows project files to be concatenated into a single large file. This single file can then be given to ftnchek to provide the information for checking the use of any or all of the routines defined in the combined project files. When using such a ``library'' project file, you may want ftnchek's error reports to document precisely the name of the file where the specific function is defined. If the various source files are in several directories, an error report that gives only the file name may be ambiguous, and rather should include the path to the file. The solution is to create each of the individual project files by giving the complete path to the source file.  For example, suppose that all of the library subprogram source files are in subdirectories of a directory named /util/lib. Then the individual project files could first be created by a command such as
find /util/lib -name '*.f' -exec ftnchek -project '{}' ';'(Possibly other options would be provided to ftnchek as discussed above. Also, this step could be handled instead by a revised makefile rule that would provide the complete source file path instead of just the local name when invoking ftnchek.) Next, concatenate all of these project files manually.
find /util/lib -name '*.prj' -exec cat '{}' ';' > ourlib.prjThen a program source file can be checked by using the command
ftnchek prog.f ... -lib ourlib.prjand an error message related to any library routine will include the full path to the routine's source file.
At present, there is no archive utility like ar to manage the contents of a concatenated project file like the one in the illustration above. If changes are made to one of the library routines, the only way to update the combined project file is to concatenate all the individual project files once again. Such a utility would be quite easy to write. Someone should do so and contribute it to the ftnchek effort.
Next: AN EXAMPLE


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o---o---o---B / ---o---1---o---o---o---A
git-merge-base(1) Manual Page
NAME
git-merge-base - Find as good common ancestors as possible for a merge
SYNOPSIS
git merge-base [-a|--all] <commit> <commit>... git merge-base [-a|--all] --octopus <commit>... git merge-base --is-ancestor <commit> <commit> git merge-base --independent <commit>...
DESCRIPTION
git merge-base finds best common ancestor(s) between two commits to use in a three-way merge. One common ancestor is better than another common ancestor if the latter is an ancestor of the former. A common ancestor that does not have any better common ancestor is a best common ancestor, i.e. a merge base. Note that there can be more than one merge base for a pair of commits.
OPERATION MODE
As the most common special case, specifying only two commits on the command line means computing the merge base between the given two commits.
More generally, among the two commits to compute the merge base from, one is specified by the first commit argument on the command line; the other commit is a (possibly hypothetical) commit that is a merge across all the remaining commits on the command line.
As a consequence, the merge base is not necessarily contained in each of the commit arguments if more than two commits are specified. This is different from git-show-branch(1) when used with the --merge-base option.
- --octopus
Compute the best common ancestors of all supplied commits, in preparation for an n-way merge. This mimics the behavior of git show-branch --merge-base.
- --independent
Instead of printing merge bases, print a minimal subset of the supplied commits with the same ancestors. In other words, among the commits given, list those which cannot be reached from any other. This mimics the behavior of git show-branch --independent.
- --is-ancestor
Check if the first <commit> is an ancestor of the second <commit>, and exit with status 0 if true, or with status 1 if not. Errors are signaled by a non-zero status that is not 1.
OPTIONS
- -a
- --all
Output all merge bases for the commits, instead of just one.
DISCUSSION
Given two commits A and B, git merge-base A B will output a commit which is reachable from both A and B through the parent relationship.
For example, with this topology:
the merge base between A and B is 1.
Given three commits A, B and C, git merge-base A B C will compute the merge base between A and a hypothetical commit M, which is a merge between B and C. For example, with this topology:
o---o---o---o---C / / o---o---o---B / / ---2---1---o---o---o---A
the result of git merge-base A B C is 1. This is because the equivalent topology with a merge commit M between B and C is:
o---o---o---o---o / \ / o---o---o---o---M / / ---2---1---o---o---o---A
and the result of git merge-base A M is 1. Commit 2 is also a common ancestor between A and M, but 1 is a better common ancestor, because 2 is an ancestor of 1. Hence, 2 is not a merge base.
The result of git merge-base --octopus A B C is 2, because 2 is the best common ancestor of all commits.
When the history involves criss-cross merges, there can be more than one best common ancestor for two commits. For example, with this topology:
---1---o---A \ / X / \ ---2---o---o---B
both 1 and 2 are merge-bases of A and B. Neither one is better than the other (both are best merge bases). When the --all option is not given, it is unspecified which best one is output.
A common idiom to check "fast-forward-ness" between two commits A and B is (or at least used to be) to compute the merge base between A and B, and check if it is the same as A, in which case, A is an ancestor of B. You will see this idiom used often in older scripts.
A=$(git rev-parse --verify A) if test "$A" = "$(git merge-base A B)" then ... A is an ancestor of B ... fi
In modern git, you can say this in a more direct way:
if git merge-base --is-ancestor A B then ... A is an ancestor of B ... fi
instead.
See also
GIT
Part of the git(1) suite


Full Stack Web Application Developer
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Justin Davenport
Underneath the bridge
Underneath the bridge
tarp has sprung a leak
and the animals I've trapped
have all become my pets
and I'm living off of grass
and the drippings from my ceiling
it's ok to eat fish
because they don't have any feelings


Jaclyn Hill Will Be Launching A Full Cosmetics Line After Viral Lipstick Snafu
by Ricki MathersJust months after her lipstick launch went terribly awry, Jaclyn Hill filed for a trademark for a full cosmetics line. The Beautuber recently took to social media to tell fans that she has a lot coming.
The Jaclyn Hill Cosmetics and Jaclyn Hill trademark were filed in July. The line will include lipstick, blush, skin bronzer, eye shadow, false eyelashes ,and make-up brushes.
Her Instagram account shared that a collection of highlighters will be available for sale on November 26.
This comes after the viral disaster for her So Rich Lipstick collection that turned dangerous after customers found everything from hair to glass shards in their products.
The beauty guru apologized to fans in a video where she explained what happened. She also took responsibility and refunded all purchases.
Since then, Jaclyn has been working on getting it right.
‘At Jaclyn Cosmetics, we’re dedicated to giving you the best products & customer experience. It is clear that our first launch did not meet the standards we’ve set as a brand, and for that we are truly sorry. We will do better because you deserve better from us and from our products. As we take a moment to rebuild, we just want you to know how grateful we are for the love and support we’ve received. Your satisfaction is our top priority. The future is bright, and we’re so excited for what’s to come,’ the makeup account reads.
Last month Hill was slammed for going as herself for Halloween.
She showed up to her party proudly flaunting a ‘canceled’ title as fans took to Twitter with their grievances. Some felt that she was mocking the fact that people spent their hard-earned money on defective products.
Jaclyn insisted that it was not meant to be an insult to her fan base but was merely mocking cancel culture.
Will you purchase something from her line?


allright, Ive wanted to type up a report on this site for ages, I just havent gotten around to it, so here goes nothing. Last summer, A friend of mine calls me up and told me about this stuff called 2C-I, I was interested but didnt want it, after a week of avid research I came to the coclusion that I would be allright, I bought 2 pills, one for me and one for a friend. I dont know how much was it in, but ive never read a report like mine so I assume that I took way to much. Despite the worry I took to much and the eventual crash, and let me tell you its a crash, I had the most wonderful, intriguing experience in my life.
setting - On vacation, camping in maine, up all night driving, no sleep.
8AM - popped our dose, went boating, after about 30 minutes we returned to the dock to fish cuz we had no anchor and the winds were bad
9AM or so - we decided there was no fish and walked back, I felt odd but didnt think it was anything besides lack of sleep so we ( a friend and I ) were walking back to my trailer when I noticed my fishing lure that was bouncing with each step had a very vivid, almost highlighter bright trails that kept chasing it around with every step. I was in awe, at that moment the trip or rolled, it was kinda both, increases at an exponential rate.
I lost sense of time but Im guessing it was like 9:30 or so, as we returned I started to lose my sense of reality, but not in a mushroom kinda way, just an extreme elevation in mood, not particually like MDMA, more of a 'Im an infant and the world is new to me, lets explore and love every minute of it' it was like that. So anyway, I walk into my trailer and couldnt believe what I was seeing. The roof of the trailer has a window, and from that window was color of every kind pouring into the window and spilling onto the walls and spreading down to the floor, first red, then yellow, then orange then all the colors faded into shadows and faded.
from that point on the curtains lost all texture and started ot move like they were on an invisible assembly line, it was as if they were moving from the left to the right, but abruptly ended when it reached all the way to the right, its hard to explain but the motion was constant. that never went away, I just kinda got used to it, at that moment the wood grain on the walls crept up the walls like water flowing and added to the curtains. after that happened the colors started flowing again and then the morphing started. things were growing, shrinking, changing size and then going back to normal. I was in such awe to all this, I was making trails with my hands thats were bright almost neon green and was drinking tea, I was talking to my friend who was not tripping as hard as I, he said he felt like he was rolling hard.
We were joking and laughing and went for many walks, everything was shimmering and glowing, as long as I was in motion nothing moved or morphed, in motion visually it was like a mix of mushrooms and MDMA.
after a few hours the visuals werent as pronounced and the other half of the trip/roll kicked in. I had the most pleasurable body sensations I have ever experienced. It started in the base of my back and rolled up to the tip of my neck, it was a warm feeling of complete sensual ecstacy. It was almost as if I had an orgasm that rolled up and down my body and lingered in the base of my neck untill I shook it off. that happened several times, after a while it wasnt as powerful as an orgasm but i could still feel this invisible pleasure move around inside of me and it was so wonderful.
After I stood in front of a fan and felt this pleasure roll over me I had to take a shower to bath in my senses. we smoked a bowl and I walked over to the showers, the tile design was changing colors and growing then shrinking and moving about, I put the 50 cents into the machine and turned on the shower. stream of rainbow colored water came out and the mist sprayed me, I could feel every bit of mist, and it felt so good, not nearly as good as the water itself though.
After 3 showers, lol yes 3, I started to come down and experienced the other half of what this drug has to offer, unimaginable pain. oh man I had the worst headache I could remeber in a long long time. It almost wasnt worth it, but I think it was cuz I didnt sleep the night before because Ive taken 2C-I again and didnt feel bad at the end of the trip at all, but to be fair I reduced the amount I ingested probably by half, at lower doses its much like MDMA. Anyway, that is my 2C-I experience in Maine. This drug would be wonderful to share with a lover, its soaks my body in sensual sensations and creates a wonderful uplifting in mood.
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.


Task
Given a corpus (a long sequence of words, e.g. the text of Wikipedia) to learn from, answer analogy questions such as ?:queen::man:woman.
Previous work, observed phenomena
The usual approach is to learn “word vectors.”
Define a context of a word \(w\) in the corpus to be the two words (say) on either side of \(w\). (Thus, for a context \(\chi=(w_{-2},w_{-1},w_1,w_2)\), \(\Pj(\chi|w)\) means the probability of observing the words of \(\chi\) in a window of length 2, given that the middle word is \(w\).) (Mikolov)
Pennington et al. and Levy and Goldberg posit the following approach.
- Model: If \(a:b::c:d\) is an analogy, then \[ \fc{\Pj(\chi|a)}{\Pj(\chi|b)} \approx \fc{\Pj(\chi|a)}{\Pj(\chi|b)}.\]
- Thus, the solution to the analogy \(?:b::c:d\)$ is \[ \amin_w \sum_\chi \pa{ \ln \pf{\Pj(\chi|w)}{\Pj(\chi|b)} - \ln \pf{\Pj(\chi|c)}{\Pj(\chi|d)} }^2.\]
- Define probability-mutual-information \[ PMI(w,\chi) = \ln \pf{\Pj(w,\chi)}{\Pj(w)\Pj(\chi)} = \ln \pf{\Pj(\chi|w)}{\Pj(\chi)}.\] Let \(C\) be the set of possible contexts. For a word \(w\), let \(v_w\) be the vector indexed by contexts \(\chi \in C\), \[ v_w(\chi)= {PMI}(w,\chi) = \ln \pf{ \Pj(w,\chi)}{\Pj(w) \Pj(\chi)} = \ln \pf{ \Pj(\chi|w)}{\Pj(\chi)}.\] Under this embedding, the summand equals \(v_a-v_b-v_c+v_d\). To find \(a\), solve \[ \amin_a \ve{v_a-v_b-v_c+v_d}^2.\]
- Algorithm: GloVe (global vector) method (Pennington) Let \(X_{w,w'}\) be the co-occurrence for words \(w,w'\). Find low-dimensional \(\wt v_w, \wt v_{w'}, \wt b_w, \wt b_{w'}\) to minimize \[ \sum_{w,w'} f(X_{w,w'}) (\an{\wt v_w, \wt v_{w'}} - \wt b_w- \wt b_{w'} - \ln X_{w,w'})^2\] for some function \(f\). They choose \(f(x) = \min\bc{\pf{x}{x_{\max}}^{.75}, 1}, x_{\max}=100\) from experiments.
(How to optimize this?)
Mysteries
- There is a disconnect between the definition of \(v_w\) and the estimate \(\wt v_w\). Namely, the \(v_w\) are vectors giving the PMI with all contexts while \(\wt v_w\) are the vectors such that \(\an{\wt v_w,\wt v_{w'}}\) give the word-word co-occurences. Why do the learned \(\wt v_w\) help in solving analogies? Why is the optimization problem in the algorithm a good proxy?
- Why is this method stable to noise?
Model
Let the dimension of the underlying space be \(d\).
- There are \(n\) words \(w\) in the dictionary \(W\), and they are associated with vectors \(v_w\in \R^d\).
- The vectors \(v_w\) are iid generated by \(v=s\cdot \wh v\) where
- \(\wh v\sim N(0,I_d)\),
- \(s\) is a random scalar with \(\si^2\le d\) and \(|s|\le \ka \sqrt d\) for some constant \(\ka\).
- At each time \(t\in \N\), there is a context vector \(c_t\in \R^d\).
- The vectors \(c_t\) follow a random walk, satisfying the following:
- (Uniform stationary distribution) The stationary distribution is \([-\rc{\sqrt d},\rc{\sqrt d}]^d\).
- (Small drift) The drift in the context vector is \(\ve{c_{t+1}-c_t}_1\le \rc{\ln n}\). (There are more complicated general conditions under which the theorems work; take this for simplicity.)
- At each time \(t\), word \(w\) is emitted with probability
\[\begin{align}
\Pj[w|c_t] &= \rc{Z_{c_t}} \exp(\an{v_w,c_t})\\
\text{where} Z_{c}:&= \sum_w \exp(\an{v_w,c}).
\end{align}\]
Explanation
Let \(\Pj(w,w')\) be the probability that \(w,w'\) appear consecutively at time \(t,t+1\) when \(c_t\sim U_{[-\rc{\sqrt d},\rc{\sqrt d}]^d}\) is drawn from the stationary distribution. Then the following hold.
Theorem 1: With high probability over choice of
\(v_w\)’s,
\[\begin{align}
\forall &w,w', &
\ln \Pj (w,w')&\approx \rc{2d}|v_w+v_w'|^2 - 2\lg Z - o(1)\\
\forall & w,&
\lg \Pj(w) &\approx \rc{2d} |v_w|^2 - \lg Z -o(1)\\
\therefore && \lg \fc{\Pj[w,w']}{\Pj[w]\Pj[w']} &\approx \rc d \an{v_w,v_w'}\pm o(1).
\end{align}\]
This is exactly the PMI, so the theorem “explains” Mystery 1.
Proof idea:
Let \(c\) be the context vector at time \(t\) and \(c'\) be the vector at time \(t+1\).
- The main difficulty is that \(Z_c\) is intractable to compute. However, because the \(v_w\) are random (or isotropic), so \(Z_c\) concentrates around its mean, and we can approximate it by a constant \(Z\) (Theorem 2 in the paper).
- Because drift is small, we can make the approximation \(c'\approx c\). Then
\[\begin{align}
\Pj(w,w') &= \int_{c,c'} \Pj(w|c)\Pj(w'|c_{t+1})\Pj(c,c')\,dc\,dc'\\
&\sim \rc{Z^2} \EE_{c} \exp(\an{v_w+v_w',c})\\
&\sim \rc{Z^2}\exp\pf{|v_w+v_{w'}|^2}{2}
\end{align}\]
using some calculus in the last step (exercise).
The calculation for \(\Pj(w)\) is even simpler.
Algorithm
What algorithm does the theory suggest to estimate the \(v_w\)’s?
It suggests minimizing an objective as in GloVe. The weights \(f_{w,w'}\) re selected to compensate noise in \(X_{w,w'}\); when \(X_{w,w'}\) is on the average larger, it has lower variance, and the weight \(f_{w,w'}\) is larger.
(What improvement does it suggest?)
Followup
- Polysemy
- Weighted SVD (Yuanzhi Li)


Certain games really are timeless. I may have spent only 3 months as a 90's kid, but even I recognize the intellectual genius of the game that is Galaga. So why not recreate it on an Atmega 1284 using atmel studio? Who knows, maybe I could get some school credit for it as well...
Let's start off with our components. I began this planning to use a cheap 7$ color LED display from amazon, a spare SNES controller for game inputs, and of course an Atmega 1284 microcontroller. Unluckily for me, the 5110 wasn't going to arrive in time for the project I had originally planned this for, so I had to settle with doing the entire thing with a generic 16x2 LCD display :c
Oh well. Time to tackle the SNES.
The controller's communication protocol revolves principly around three pins: Clock, Latch, and Data. The SNES controller recieves a 60 Hz latch signal on pin3 which prompts the controller to latch its button states. The falling edge of each latch signal is followed by 16 clock pulses on pin2. Once during each clock cycle, pin4 is sampled. The controller shifts through the latched button states until all 16 states are transmitted serially.
Once the protocol was deciphered, communicating with it was a piece of cake. I had quite a few problems with game design and the LCD display being way too small to recreate anything that remotely looks like Galaga on it. Another problem was displaying high scores, since powering off the microcontroller erased anything on volatile memory. This was remedied by learning how to write data straight to the uC's EEPROM, where data could be stored safely in between power loss.
It works!
Game design definitely took the majority of my time, but I had fun doing it and learned quite a bit. A few weeks after finishing I was still bummed I had to use the lame 2 lane 16x2 display, so I ended up later redesigning the game to fit a leftover Nokia 5110 display using SPI that a friend had after he had finished his project.


UNIDO is a website created to contact Rapid Reporters and other Immigration Services when Immigration and Customs Enforcement (ICE) are nearby. Our objective is to provide resources to undocumented families who lack support due to the endangering immigration laws in the U.S .


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See:
Description
| Interface Summary | |
| ObjectStreamContext | Provides a collection of context information objects that are
associated with an ObjectOutputStream or ObjectInputStream instance that implements this interface.
|
| Class Summary | |
| MarshalInputStream | An extension of ObjectInputStream that implements the
dynamic class loading semantics of Java(TM) Remote Method
Invocation (Java RMI) argument and result
unmarshalling (using RMIClassLoader ). |
| MarshalledInstance | A MarshalledInstance contains an object in serialized
form. |
| MarshalOutputStream | An extension of ObjectOutputStream that implements the
dynamic class loading semantics of Java(TM) Remote Method Invocation
(Java RMI) argument and result
marshalling (using RMIClassLoader ). |
| Exception Summary | |
| UnsupportedConstraintException | Typically used as the nested exception of a
ConnectIOException if the constraints for a remote call
cannot be satisfied. |
Provides I/O interfaces and utility classes.
ObjectStreamContext
The interface ObjectStreamContext
may be
implemented by an object serialization stream class (a subclass of
ObjectOutputStream
or ObjectInputStream
) to provide each object being serialized or
deserialized with access to context information relevant to the
overall serialization or deserialization operation. The class of an
object that is being serialized or deserialized can test (in its
private writeObject(ObjectOutputStream)
or
readObject(ObjectInputStream)
method) if the object
stream being used is an instance of ObjectStreamContext
.
If the stream class does implement that interface, the class can then
retrieve the context information (as a Collection
of context objects) by invoking the stream's
getObjectStreamContext
method. The context information
available from a given element of the collection is determined by that
element's type. Examples of types that a context object might
implement include ClientHost
and ClientSubject
.
MarshalOutputStream
and MarshalInputStream
The classes MarshalOutputStream
and MarshalInputStream
extend the object serialization stream
classes ObjectOutputStream
and ObjectInputStream
to implement the dynamic class loading
semantics of RMI argument and result marshalling and unmarshalling.
MarshalOutputStream
and MarshalInputStream
use RMIClassLoader
to get class annotations
for marshalling and to resolve codebase annotations to Class
objects for unmarshalling.
MarshalInputStream
supports optional verification that
the codebase annotation URLs used to resolve classes provide content
integrity (see Security.verifyCodebaseIntegrity
). MarshalOutputStream
and MarshalInputStream
may be extended (in concert) to
communicate class annotations in a way other than in the streams' own
data. Both MarshalOutputStream
and
MarshalInputStream
implement
ObjectStreamContext
.
MarshalledInstance
A MarshalledInstance
contains an object in
serialized form. A MarshalledInstance
can be
unmarshalled on demand, allowing an object to be sent in such a way
that the receiver can control when and in what context the object is
unmarshalled. MarshalledInstance
provides functionality
similar to MarshalledObject
, but it additionally
supports use of object stream contexts and optional verification that
the codebase annotation URLs used to resolve classes provide content
integrity. A convenience constructor and method are provided for
converting between MarshalledObject
and
MarshalledInstance
instances.
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It's like they want to do something to him - Jowie's mother worried after he could not be found at Kamiti
by Miriam MwendeWhere is my son? - Jowie Irungu's mother
Anastacia, mother to murder suspect Joseph Irungu alias Jowie, is a worried woman after his son was moved from Kamiti Maximum Security Prison.
Speaking to reporters, Anastacia revealed that she went to Kamiti to visit him on Monday and was informed that he had been moved to Manyani Maximum Security Prison in Taita Taveta.
She further raised concerns that she could not ascertain if he was unharmed since she was not allowed to see him before he was moved.
"It's like they want to do something to him. The prison officer says it's a normal transfer but our lawyer says he has never heard of something like that," she stated.
Anastacia told reporters that she could not be certain that Jowie had been transported to Manyani, saying that she would make the trip to find her son.

Justice James Wakiaga postponed the ruling on Jowie's application to February 2020, leaving Jowie to spend another Christmas behind bars.
Jowie is the prime suspect in the murder of business woman Monica kimani with his co-accused being former Citizen TV anchor Jackie Maribe.


File Formats
Here is a list of file formats Inferno has, and specification for them
Inferno Font
The Inferno Font File Format is an easy way of storing all information about a font including its texture in one compressed file (GZIPPED).
Specification (V1.2)
The entire file is generated THEN gzipped. This version yields a 25% smaller filesize.
Order of data:
- Header - String - "INFERNOFONT"
- Version - String - "VERSION_1.2"
- Width - Int - Width of font texture
- Height - Int - Height of font texture
- SpaceSize - Int - The number of pixels in a space
- LineHeight - Int - The number of pixels between lines
- ImageBytesCount - Int - Number of bytes in the ImageBytes array
- ImageBytes - Byte[] - Array of UInts (Array of packed color values) converted into a byte array
- CharMapCount - Int - Number of bytes in the CharMap array
- CharMap - Byte[] - Array of Floats (Array of vector components) converted into a byte array
Specification (V1.1)
The entire file is generated THEN gzipped. This format is outdated.
Order of data:
- Header - String - "INFERNOFONT"
- Version - String - "VERSION_1.1"
- Width - Int - Width of Font Bitmap
- Height - Int - Height of Font Bitmap
- SpaceSize - Int - Amount of pixels a space is
- LineHeight - Int - How many pixels down to move when a new line is met.
- Image - String - CSV of color values in packed formats (Base64 Encoded)
- CharData - String - CSV of floats (Length must be a factor of 4, Base64 Encoded), this is turned into a Vector4 array, with one Vector4 for each character, The X and Y components are the X and Y coordinates of the character on the image. The Z and W components are the width and height of the texture.
Specification (V1)
The information for this specification is not available, as it was never publicly released. It lacked the ability to store the SpaceSize and LineHeight of the font and was not gzipped.


[gecode-users] edge-finder propagator
Filip Konvička
filip.konvicka at logis.cz
Fri Aug 22 08:38:11 CEST 2008
Hi Jan,
> thank you for the reply. I forgot to mention that we are implementing
> disjunctive edge-finder.
at
http://kti.mff.cuni.cz/~vilim/
you'll find some interesting papers, one of which is
"O(n log n) Filtering Algorithms for Unary Resource Constraint"
http://kti.mff.cuni.cz/~vilim/nlogn.pdf
That's pretty easy to implement. You can also derive a branching for
first/lasts quite easily from that.
Hope this helps,
Filip
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