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Blackhat TRAILER 1 (2015) - Chris Hemsworth Action Movie HD
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| call_standardise {rlang} | R Documentation |
This is essentially equivalent to base::match.call()
, but with
experimental handling of primitive functions.
call_standardise(call, env = caller_env())
call |
Can be a call or a quosure that wraps a call. |
env |
The environment where to find the definition of the
function quoted in |
A quosure if call
is a quosure, a raw call otherwise.
In rlang 0.2.0, lang_standardise()
was deprecated and renamed to
call_standardise()
. See lifecycle section in call2()
for more
about this change.


David De GeaMANCHESTER UNITED (SPAIN) | Age: 26It's hard to believe now that he was once considered a flop. The willowy youngster endured a nightmare debut at Wembley in the Community Shield in 2011 and the slip-ups just kept coming. One reporter described him as "a kid who'd won a competition to play in goal for United." Now, of course, he's one of the greatest goalkeepers on the planet, blessed with supernatural reflexes and a reach like Stretch Armstrong. On balance, his critics may have been a little hasty. |
||
Manuel NeuerBAYERN MUNICH (GERMANY) | Age: 31The year 2017 hasn't been kind to Germany's No. 1. Neuer broke his foot in the Champions League quarterfinal defeat by Real Madrid in April and suffered a recurrence of the same injury just three games into this season. Nevertheless, his enduring excellence and role-redefining "keepero" style has remained hugely influential well beyond the confines of the Allianz Arena. His broad frame and ice-cool demeanour mark him out as football's most daunting goalkeeper in an age where more acrobatic versions are coming to the fore. |
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Thibaut CourtoisREAL MADRID (BELGIUM) | Age: 25It was never going to be easy to replace Petr Cech at Chelsea but Thibaut Courtois made it look as though it was. The enormous Belgium international had been the Blues' worst-kept secret: a young goalkeeper so good that he was on loan at arguably a more successful club. Atletico Madrid wanted to keep him, but Chelsea were ready to act given Courtois' record: He's one of only four goalkeepers in Europe's top five leagues with more than 90 clean sheets since 2011. Out went club legend Cech, in came Courtois. Oddly enough, they won the title that season. |
||
Alisson BeckerLIVERPOOL (BRAZIL) | Age: 24On 19 July 2018, Liverpool officially confirmed the signing of Alisson for a fee of £66.8 million (€72.5 million), making him the most expensive goalkeeper of all time, surpassing the transfers of Ederson Moraes (most expensive in pound sterling) and Gianluigi Buffon (most expensive in Euros). However, the fee was broken just four weeks later when Chelsea signed Kepa Arrizabalaga for a reported £71 million (€78.8 million) from Athletic Bilbao. On 10 August, Liverpool confirmed via their official website that he will wear the vacant number 13 shirt in the 2018–19 season. |
||
Jan OblakATLETICO MADRID (SLOVENIA) | Age: 24Few neutrals or Atletico Madrid fans knew very much about Oblak when he joined from Benfica for €16 million in 2014. Coach Diego Simeone also took some convincing, but not anymore. In his first 125 games for the club, Oblak conceded fewer goals per game (0.64) and kept more clean sheets (71) than predecessor Thibaut Courtois, now at Chelsea. The 24-year-old Slovenia international has also saved six of the 10 penalties he's faced in open play during his time at Atletico. Had he saved at least one spot-kick in 2016's Champions League final shootout against Real Madrid, his standing would be even higher. |
||
Marc-Andre ter StegenBARCELONA (GERMANY) | Age: 25Ter Stegen has always been nifty with his feet, but this season he's showing that he's not bad with his hands, either, which is what's important for a goalkeeper. Now Barcelona's undisputed No. 1 after spending his first two years at the club battling Claudio Bravo for the job, he's not only more mature but more confident, too: since taking over that starting role he's got the highest save percentage across Europe's top five leagues. There's no reason he can't stick around at Camp Nou for the next decade while also turning his attention to wrestling Manuel Neuer out of the Germany job. |
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Gianluigi BuffonPSG (ITALY) | Age: 39Upon receiving the award for top goalkeeper at FIFA's "The Best" ceremony in London last month, Buffon more than once referred to the joy of achieving such recognition "at my age." It was a humble statement, acknowledging the fortune that has brought him this far over a two-decade career, but it could just as easily have been wonderment at his own continued excellence. Buffon is that rare example of a veteran footballer who seems to get better with age, and it will be a huge loss to the game if he retires after the World Cup. Buffon, a wonderful player and ambassador for the sport, is one of football's genuine treasures. |
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Hugo LlorisTOTTENHAM (FRANCE) | Age: 30It was a good thing that Tottenham stepped up and consolidated their status as regular title challengers, because for a long time it seemed that Hugo Lloris was far too good for them. A goalkeeper of unimpeachable credentials, combining world class shot-stopping with the intelligence and speed to play off his line, he must go down as one of the club's best signings in recent years. Had he wanted to cause trouble, he could have forced a move to anywhere in Europe. Perhaps his loyalty will soon be rewarded with silverware. |
||
EdersonMANCHESTER CITY (BRAZIL) | Age:24An agile, courageous, energetic, and physically imposing keeper, Ederson possesses physical strength, excellent reflexes and shot-stopping abilities between the posts, and developed a reputation as a penalty saving specialist during his time with Benfica. However, he is mostly highly regarded for his outstanding distribution and skill with the ball at his feet: his control and confidence on the ball enables him to retain possession and quickly play the ball out from the back on the ground with his hands or either foot – even when put under pressure – or launch an attack with long kicks. |


LASSO Regression¶
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Lasso
import matplotlib.pyplot as plt
import seaborn as sns
from dmba import regressionSummary
%matplotlib inline
Overview¶
The Lasso is a shrinkage and selection method for linear regression. It minimizes the usual sum of squared errors, with a bound on the sum of the absolute values of the coefficients.
It includes a “penalty” that is based on aggregation of coefficient values. Standardizing the variable is important in this method, as this will prepare your data for wrangling and will allow you to draw clearer conclusions.
In this chapter we will provide an example of how to implement LASSO. This is a feature selection method that shrinks our coefficient estimates (\(\hat{\beta}_i\)s) towards zero, even setting some to be 0. This helps us create an easier-to-interpret model and reduces the risk of overfitting. (Recall, overfitting my model means I’ve made a model that fits too closely to the training data but isn’t generalizable to other data - therefore, it won’t make good predictions.)
We’ll implement LASSO using our beloved Toyota Corolla dataset. In the MLR notebook, we learned how to use MLR to make predictions about the price a used Toyota Corolla would sell for based on car features. In this notebook, we’ll learn how to use LASSO to make those predictions.
Toyota Corollas Data¶
This data represents the sale of used Toyota Corollas in the Netherlands, and our mission is to predict the price of a car based on its options and features.
Below, we show descriptions of some of the variables in our dataset.
Variable |
Description |
|---|---|
Price |
Offer price in Euros |
Age |
Age in months as of August 2004 |
Kilometers |
Accumulated Kilometers on odometer |
Fuel type |
Fuel type (Petrol, Diesel, CNG) |
HP |
Horsepower |
Metallic |
Metallic color (Yes = 1, No= 0) |
Automatic |
Automatic (Yes = 1, No = 0) |
CC |
Cylinder volume in cubic centimeters |
Doors |
Number of doors |
QuartTax |
Quarterly road tax in Euros |
Weight |
Weight in Kilograms |
Data Processing¶
Read the data using the read_csv
function from pandas package and save it as the dataframe car_df
. The data is called 'ToyotaCorolla.csv'
. Use the .head()
method to make sure the dataframe has been created correctly.
car_df = pd.read_csv('../jb/data/ToyotaCorolla.csv')
car_df.head()
| Id | Model | Price | Age_08_04 | Mfg_Month | Mfg_Year | KM | Fuel_Type | HP | Met_Color | ... | Powered_Windows | Power_Steering | Radio | Mistlamps | Sport_Model | Backseat_Divider | Metallic_Rim | Radio_cassette | Parking_Assistant | Tow_Bar | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | TOYOTA Corolla 2.0 D4D HATCHB TERRA 2/3-Doors | 13500 | 23 | 10 | 2002 | 46986 | Diesel | 90 | 1 | ... | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 1 | 2 | TOYOTA Corolla 2.0 D4D HATCHB TERRA 2/3-Doors | 13750 | 23 | 10 | 2002 | 72937 | Diesel | 90 | 1 | ... | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 2 | 3 | TOYOTA Corolla 2.0 D4D HATCHB TERRA 2/3-Doors | 13950 | 24 | 9 | 2002 | 41711 | Diesel | 90 | 1 | ... | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 3 | 4 | TOYOTA Corolla 2.0 D4D HATCHB TERRA 2/3-Doors | 14950 | 26 | 7 | 2002 | 48000 | Diesel | 90 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 4 | 5 | TOYOTA Corolla 2.0 D4D HATCHB SOL 2/3-Doors | 13750 | 30 | 3 | 2002 | 38500 | Diesel | 90 | 0 | ... | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
5 rows × 39 columns
We use the map function to iterate through a list of values (in this case, the column names, and apply a function to each item in the list. In this case, we want make each item lowercase.)
car_df.columns = map(str.lower, car_df.columns)
car_df.columns
Index(['id', 'model', 'price', 'age_08_04', 'mfg_month', 'mfg_year', 'km',
'fuel_type', 'hp', 'met_color', 'color', 'automatic', 'cc', 'doors',
'cylinders', 'gears', 'quarterly_tax', 'weight', 'mfr_guarantee',
'bovag_guarantee', 'guarantee_period', 'abs', 'airbag_1', 'airbag_2',
'airco', 'automatic_airco', 'boardcomputer', 'cd_player',
'central_lock', 'powered_windows', 'power_steering', 'radio',
'mistlamps', 'sport_model', 'backseat_divider', 'metallic_rim',
'radio_cassette', 'parking_assistant', 'tow_bar'],
dtype='object')
We can see that id
and model
aren’t useful to include, as id
provides a unique but meaningless number for each row, and model is not important, as these are all Toyota Corollas. Let’s drop those.
car_df=car_df.drop(columns =['model','id'])
car_df.columns
Index(['price', 'age_08_04', 'mfg_month', 'mfg_year', 'km', 'fuel_type', 'hp',
'met_color', 'color', 'automatic', 'cc', 'doors', 'cylinders', 'gears',
'quarterly_tax', 'weight', 'mfr_guarantee', 'bovag_guarantee',
'guarantee_period', 'abs', 'airbag_1', 'airbag_2', 'airco',
'automatic_airco', 'boardcomputer', 'cd_player', 'central_lock',
'powered_windows', 'power_steering', 'radio', 'mistlamps',
'sport_model', 'backseat_divider', 'metallic_rim', 'radio_cassette',
'parking_assistant', 'tow_bar'],
dtype='object')
Recall that we have to convert categorical variables to numbers. We only have nominal (unordered) categorical variables, so they should be converted to a set of dummy variables.
We’ve already dropped model
, so our two remaining categorical variables are color
and fuel_type
.
We use pd.get_dummies
to dummy-code all the variables. Each categorical variable will generate two or more dummy variables. Dropping the first dummy variable (using the argument drop_first
) ensures we won’t have problems with perfect multicollinearity.
car_df = pd.get_dummies(car_df, drop_first=True)
car_df.columns
Index(['price', 'age_08_04', 'mfg_month', 'mfg_year', 'km', 'hp', 'met_color',
'automatic', 'cc', 'doors', 'cylinders', 'gears', 'quarterly_tax',
'weight', 'mfr_guarantee', 'bovag_guarantee', 'guarantee_period', 'abs',
'airbag_1', 'airbag_2', 'airco', 'automatic_airco', 'boardcomputer',
'cd_player', 'central_lock', 'powered_windows', 'power_steering',
'radio', 'mistlamps', 'sport_model', 'backseat_divider', 'metallic_rim',
'radio_cassette', 'parking_assistant', 'tow_bar', 'fuel_type_Diesel',
'fuel_type_Petrol', 'color_Black', 'color_Blue', 'color_Green',
'color_Grey', 'color_Red', 'color_Silver', 'color_Violet',
'color_White', 'color_Yellow'],
dtype='object')
Let’s use bracket notation with the new column names to again select the first three rows. What do we see? The first car (row index equal to 0) had a blue car that used diesel. We see below that fuel_type_Diesel = 1, color_Blue = 1, and all the other dummy variables are equal to 0.
For a car with fuel type CNG, what would fuel_type_Diesel and fuel_type_Petrol equal?
How would I know from these new dummy variables that a car is Beige?
car_df[['fuel_type_Diesel', 'fuel_type_Petrol', 'color_Black', 'color_Blue', 'color_Green',
'color_Grey', 'color_Red', 'color_Silver', 'color_Violet',
'color_White', 'color_Yellow']].head(3)
| fuel_type_Diesel | fuel_type_Petrol | color_Black | color_Blue | color_Green | color_Grey | color_Red | color_Silver | color_Violet | color_White | color_Yellow | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 2 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Model Building¶
Now, we can finally move on to what we’re really interested in - using a regularization technique called LASSO to help us decide which predictors we should include in our model.
Let’s define our predictors and outcome variable. Lasso will help me determine which, if any, predictor variables I should drop.
We’ll then create the training and test sets, using our X
and y
objects.
outcome = 'price'
predictors = ['age_08_04', 'km', 'fuel_type_Petrol', 'fuel_type_Diesel', 'hp', 'met_color', 'automatic', 'cc', 'doors', 'quarterly_tax', 'weight']
X = car_df[predictors]
y = car_df[outcome]
train_X, valid_X, train_y, valid_y = train_test_split(X, y, test_size=0.4, random_state=1)
train_X
| age_08_04 | km | fuel_type_Petrol | fuel_type_Diesel | hp | met_color | automatic | cc | doors | quarterly_tax | weight | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1238 | 75 | 82256 | 1 | 0 | 110 | 1 | 0 | 1600 | 3 | 69 | 1050 |
| 1085 | 79 | 131500 | 0 | 1 | 72 | 0 | 0 | 2000 | 5 | 185 | 1140 |
| 680 | 61 | 102106 | 1 | 0 | 110 | 0 | 0 | 1600 | 3 | 69 | 1050 |
| 593 | 50 | 22648 | 1 | 0 | 97 | 1 | 0 | 1400 | 5 | 85 | 1060 |
| 647 | 68 | 117000 | 0 | 1 | 72 | 0 | 0 | 2000 | 3 | 185 | 1115 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 715 | 62 | 90000 | 1 | 0 | 110 | 0 | 0 | 1600 | 3 | 69 | 1050 |
| 905 | 65 | 59000 | 1 | 0 | 110 | 0 | 0 | 1600 | 3 | 69 | 1050 |
| 1096 | 75 | 125400 | 1 | 0 | 110 | 0 | 0 | 1600 | 3 | 69 | 1050 |
| 235 | 38 | 60829 | 1 | 0 | 110 | 1 | 0 | 1600 | 5 | 85 | 1075 |
| 1061 | 78 | 161775 | 1 | 0 | 86 | 1 | 0 | 1300 | 3 | 69 | 1015 |
861 rows × 11 columns
Remember that in a LASSO model, we penalize large coefficient estimates. The magnitude of a coefficient estimate can vary depending on the units of the variable. We address this by standardizing our data.
#Standardization
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
train_X=scaler.fit_transform(train_X)
valid_X=scaler.transform(valid_X)
Fitting a LASSO model is very similar to fitting an MLR model, except we’ll need to specify the penalty parameter, \(\lambda\). Somewhat confusingly, we specify \(\lambda\) in the alpha
argument in the LASSO function. (LASSO is an algorithm in the scikit learn package.)
We need to take the following steps.
Choose a value for \(\lambda\) (the penalty parameter). Below, we set it to equal 1.
Initialize the LASSO algorithm, setting the \(\lambda\) value (alpha argument)
Fit the model using the training data (this finds the best coefficients AKA the \(\hat{\beta}_i\)s)
Use the results (that is, use the coefficient estimates) from the fitted model to predict the outcome (price) for the validation data
# Step 1: Set lambda
lambda_val = 1
# Step 2: load the lasso regression algorithm into an object called "car_lasso"
car_lasso = Lasso(alpha = lambda_val)
# Step 3: fit the lasso model using the training data
car_lasso.fit(train_X, train_y)
# Step 4 use the fitted model to predict the outcome for the validation data
car_lasso_predict = car_lasso.predict(valid_X)
regressionSummary(valid_y, car_lasso_predict)
Regression statistics
Mean Error (ME) : 187.2769
Root Mean Squared Error (RMSE) : 3244.3391
Mean Absolute Error (MAE) : 1090.5446
Mean Percentage Error (MPE) : 0.2298
Mean Absolute Percentage Error (MAPE) : 10.3188
# Estimated coefficients
car_lasso.coef_
array([-2351.6150999 , -604.89497276,<PHONE_NUMBER>9, 1336.31090827,
1088.08162747, 21.35621241,<PHONE_NUMBER>5, -919.41872088,
52.85072699,<PHONE_NUMBER>6,<PHONE_NUMBER>9])
In the previous step, we used a \(\lambda\) value of 1 when fitting the LASSO regression model. However, how do we know whether 1 is a good choice for \(\lambda\)? We don’t!
The beauty of Python is that we can quickly try lots of different \(\lambda\) values and find the one that does best, where doing best means minimizing the error. To do this, we’ll put the above steps into a for loop, where we fit a LASSO regression model for many different values of \(\lambda\). We can then choose the \(\lambda\) value that results in the best prediction (lowest error).
Plotting 𝜆 values¶
Now, let’s use the create a for loop to fit the LASSO regression model for many different \(\lambda\) values. Let’s break this down into several steps.
First, decide what you want to use as your \(\lambda\) values. We put this \(\lambda\) values into a list. I’ve created a few lists of \(\lambda\) values below. I’ve commented out all but one - you can uncomment to try to different lists or even make your own.
For whatever list I create, I save the length of the list (that is, the number of \(\lambda\) in my list) as l_num
.
# Define lambda to be used in LASS0
# lambdas = [0.1,1,5,10]
# lambdas = (0.1,1,5,10,100,500,1000)
lambdas = list(range(1, 40, 2))
# Length of lambda
l_num = len(lambdas)
Next, create an empty array (sort of a fancy list) to hold the hold the prediction accuracy measure for each fitted model. Remember, we’ll have the same number of models as we have \(\lambda\)s in our list above, so we create an array that is the same length. We put in zeros as placeholders.
# initilize a vector to hold values of RMSE
valid_RMSE = np.zeros(l_num)
Now we're ready for our for loop! For every single $\lambda$ in our list above, we want to fit a LASSO regression model, using that $\lambda$ value. So, we need to iterate through our list of $\lambda$s. As I iterate through the list, I get two pieces of information about the $\lambda$: the index (it's order in the list) and the actual $\lambda$ value.
Okay, finally ready to put the for loop code together with the LASSO code! Let’s do it.
# enumerate through lambdas with index and val
for ind, val in enumerate(lambdas):
# Initialize the lasso regression by setting the penalty parameter equal to lambda[ind]
car_lasso = Lasso(alpha = val)
# Fit the lasso model using training data
car_lasso.fit(train_X, train_y)
# Calculate the error using the RMSE function and using the validation set data
# Save this RMSE into the appropriate position in the RMSE array
valid_RMSE[ind] = rmse(valid_y,car_lasso.predict(valid_X))
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-14-2fe1df20e0b9> in <module>
7 # Calculate the error using the RMSE function and using the validation set data
8 # Save this RMSE into the appropriate position in the RMSE array
----> 9 valid_RMSE[ind] = rmse(valid_y,car_lasso.predict(valid_X))
NameError: name 'rmse' is not defined
We’ve now fit {l_num} models and for each one we’ve saved the RMSE. Let’s look at how they do using a plot!
When you run the code, you’ll see that for a smaller lambda value, the RMSE is higher (overfitting). As lambda increases, my RMSE starts to drop; the sweet spot is at around lambda = 5. As my \(\lambda\) continutes to increase, my RMSE shoots up, my lambda starts to increase again. For these larger lambdas, I’m penalizing the magnitude of the coefficients to the point where I’m shrinking lots of the coefficients to zero, which means I’m (implicitly) dropping a lot of predictors out of my model. In this case, I’m underfitting.
plt.figure(figsize=(18, 8))
plt.plot(lambdas,valid_RMSE, 'bo-', label=r'$RMSE$ Test set', color="darkred", alpha=0.6, linewidth=3)
plt.xlabel('Lambda value'); plt.ylabel(r'$RMSE$')
#plt.xlim(0, 6)
plt.title(r'Evaluate lasso regression with different lambdas')
plt.legend(loc='best')
plt.grid()
car_lasso = Lasso(alpha = 13)
car_lasso.fit(train_X, train_y)
car_lasso.coef_
for ind, val in enumerate(car_lasso.coef_):
predvar = predictors[ind]
print("The estimated coefficient for",predvar,"is",round(val,2))
Let’s look at the “paths” of the coefficients as \(\lambda\) increases. To do this, we take the following steps.
Set the list of \(\lambda\)s. Use a wider range to see when the coefficients are pushed down to zero.
Run the LASSO model for each of the \(\lambda\) in the list. Save the coefficients generated from each fitted model
Create a plot of each of the coefficient estimates as a function of \(\lambda\).
Notice how as \(\lambda\) increases, the magnitude of more and more of the coefficients are pushed towards zero? By the time \(\lambda\) is 100, only age_08_04
, hp
, quarterly_tax
, weight
, and km
have non-zero coefficients.
# Set lambdas
lambdas = [0.1,15,50,100,500,1000, 3000]
# Fit lasso regression for each lambda, save coefficients
coefs = []
for val in lambdas:
car_lasso = Lasso(alpha = val)
car_lasso.fit(train_X, train_y)
coefs.append(car_lasso.coef_)
# Generate a plot
# Use log scale so that we can see it a little more easily
fig, ax = plt.subplots(figsize=(30, 10))
ax.plot(lambdas, coefs, marker="o")
ax.set_xscale('log')
plt.xlabel('lambda')
plt.ylabel('Coefficients (beta hats)')
plt.title('Lasso coefficients as a function of choice of lambda')
plt.axis('tight')
plt.legend(predictors,prop={'size': 15})
plt.show()


{% extends 'layout.html' %} {% block pageTitle %} Start page template - NHS.UK prototype kit {% endblock %} {% block outerContent %} {{ backLink({ "href": "/rapid-test", "text": "Go back", "classes": "nhsuk-u-margin-top-4 nhsuk-u-margin-bottom-0" }) }} {% endblock %} {% block content %}
No filters selected
Test site for people with no symptoms - a booking is required
Holywell Lane Day Center, WF10 3HH
Normal opening times today
08:45am - 07:00pm
Status
Closed
Pick up site - for at home test kits No booking required
Centenary Square, BD1 1HY
Normal opening times today
03:00pm - 08:00pm
Status
Closed
Pick up site - for at home test kits No booking required
Waterfront Car Park, WF15J
Normal opening times today
03:00pm - 08:00pm
Status
Closed


Hint
Think about the mathematical relationship between days, hours, minutes, and seconds
How do you convert days to hours, hours to minutes, and minutes to seconds?
Also, remember the right way to access an element from the list. (e.g. time[0] is the first element in the list)


To load in content from a separate HTML document, place the following CSS rule on
an IFRAME element: -ms-flow-into: content;
This will load the HTML document
that is specified in the IFRAME's src
attribute.
To declare a Grid, place the following CSS rules on a DIV element:
display: -ms-grid;
-ms-grid-columns: 1fr 1fr 1fr;
-ms-grid-rows: 1fr 1fr;
This will create a Grid with three columns and two rows.
To declare CSS Regions to accept content, place the following CSS rule on any DIV
element: -ms-flow-from: content;
This will flow the HTML content loaded
by the IFRAME element and distribute it over the CSS Region elements.
To position the CSS Regions within the Grid, place the following CSS rules on the
CSS Region element:
-ms-grid-column: <column number>;
.
-ms-grid-row: <row number>;


EDIT
This blogpost is in need of being revisioned after a discussion on reddit. This post attempts to do a benchmark and fails at doing a meaningful one.
I've been dabbling at Julia for a while in some of my spare time and I like some of the things that I am seeing. It's a language that is mainly popular in academics, particularily statistics and operations research. It has a community with some impressive members that sets high goals; trying to create a language with many characteristics from other dynamically typed languages while trying to keep speed as a main feature.
I've never gotten the impression that Julia was out to replace other languages but some people have been asking me when (not if) they should transition from R/Python to Julia. This seems like a very strange question. For some reason some people feel as if Julia will cause a change that is iminent instead of perhaps possible.
In this blogpost I'd like to shed some light on why I feel that Julia won't be replacing R/python any time soon (probably ever). On this premise, I would like to emphesize that I enjoy watching what the Julia project is doing. I merely want to warn people who focus on the benchmarks.
A comperison
One of the main features of Julia, is it's speed without having to vectorize code. So for the most popular science languages (Julia, R and python). I'll repeat a very similar exercize. I'll loop over three very long loops to add a number; an exercize that Julia should excel in. For each primary language I'll show how I've benchmarked my code.
Julia
import Base.time
function f(n)
a = 0
for i = 1:n
for j = 1:n
for k = 1:n
a += i
a += j
a += k
end
end
end
end
function time(f, n)
tic = Base.time()
f(n)
Base.time() - tic
end
Base Python
import time
def f(n):
a = 0
for i in range(n):
for j in range(n):
for k in range(n):
a += i
a += j
a += k
return a
%%time
You might be temped to wonder how this might work in numpy because common sense would suggest that anything numeric in python should probably be done in numpy. Turns out not to be that true.
Using numpy caused a memory overflow (and a very hot macbook). Translating the above into something that is vectorized will result in something like below:
def f(n):
return np.sum(np.sum(np.full((n, n, n), np.arange(n)), axis = 0)[0:n,n-1])
def timit(f, n):
tic = time.time()
f(n)
return time.time() - tic
Note that if $n$ is getting larger that we need to keep an object (np.full((n, n, n), np.arange(n))
) in memory that is getting $n^3$ larger. Numpy doesn't seem like much help when $n >> 100$.
Base R
f <- function(n){
a <- 0
for(i in 1:n){
for(j in 1:n){
for(k in 1:n){
a <- a + i
a <- a + j
a <- a + k
}
}
}
a
}
timit <- function(f, n){
tic <- Sys.time()
f(n)
as.numeric(difftime(Sys.time(), tic, units = c("secs")))
}
First Comperisons
For loops in R are notiriously slow. Even when $n=100$ it already takes 593 ms. Python does better with 2.5 seconds for $n=5000$ but Julia manages to win here with 0.007 seconds for $n=5000$.
Julia now manages a speedup of about 350x, which is impressive considering that I am not using a type system or any form of parallism. Still, you should not be fully convinced just quite yet.
Skill to the rescue
Writing code in python or R is meant to make you productive, not the code that is running. But the communities behind these languages have been aware of this and have added tools to make code run performant when it needs to. In python you can use numba as a just-in-time compiler to compile python to native machine code. In R, you can define functions in C++ which can then directly be used in the language. Code wise, it's not a huge investment.
Numba
from numba import jit
@jit
def f(n):
a = 0
for i in range(n):
for j in range(n):
for k in range(n):
a += i
a += j
a += k
return a
def timit(f, n):
tic = time.time()
f(n)
return time.time() - tic
In case you didn't know yet; numba is crazy fast.
Rcpp
library(Rcpp)
cppFunction('int f(int n){
int a = 0;
for(int i = 0; i < n; ++i) {
for(int j = 0; j < n; ++j) {
for(int k = 0; k < n; ++k) {
a += i;
a += j;
a += k;
}
}
}
return a;
}')
New statistics
count numba rcpp julia
1 1000 1.00e-06 3.60e-05 0.000307798
2 5000 1.29e-06 3.72e-05 0.007336140
3 10000 1.00e-05 3.90e-05 0.029693100
4 50000 1.00e-05 4.00e-05 0.729448000
5 100000 1.00e-05 4.10e-05 2.910130000
Suddenly, numba turns out to be the clear winner and even Rcpp seems to outperform Julia on this one. That is not to say that Julia is not doing something impressive. Julia is performing very well as a general language here. It only happens to get beaten by specialist packages from other popular languages.
Conclusion
People want to use R or Python to make something quickly and not necessarily something that immediately runs fast. As a result these two langauges have common patterns to combat their comprimised speed:
-
for common tasks that do put pressure on a language they use packages/tools that are optimized and written in a language that performs better (like pandas/dplyr or spark)
-
for something less common these languages allow ways to rewrite slow parts in another more performant and tightly intergrated language (Rcpp, cython, numba)
This combination works very well for most people, but not all. And for those people Julia is very interesting.
What Julia offers is a language that is inheritly a whole lot quicker if you write your own custom functions while keeping the syntax relatively clean. It might feel like Matlab, but is free and more open. If you are an academic, this is good news and it is no suprise that a lot of the community comes from the academic field switching over from Matlab. A lot of cool researchers use Julia. Keeping an eye on Julia also allows you to keep an eye on new algorithms.
I'd be hard pressed to apply it in production anytime soon for a client though. R and Python are proven tools with gigantic communities and with proven methods to give great computational performance (which can compete with Julia's speed). Another downside of Julia is that it is very young and thus you may expect stackoverflow answers to be more sparse and packages to have more bugs.
I like to think that just like R and python have learned from eachother, Julia might also offer lessons. Already, it's fun and easy to dabble with it (although the plotting functionality is painfully slow at the moment). Only time will tell if and how Julia might go beyond the academic field though. It would certainly not be a bad language, just not one that is automagically a better alternative to python or R.


super tall image super wide image large than viewport? (also modal, no click on underlay) large than viewport, long title
wide image, multiline title square wide image Broken link
Rich Text Title square 4:3 image tall
Rich Text Title square 4:3 image tall
remove one from group3, then show group | remove group3
lorem ipsum dolor amit
lorem ipsum dolor amit
lorem ipsum dolor amit
lorem ipsum dolor amit
lorem ipsum dolor amit
lorem ipsum dolor amit


The Viewer consists of three widgets, one MultiPanel widget, and two HTML widgets. The MultiPanel allows the user to select only those items she is interested in, and then bring more detailed information into the HTML widgets. The upper HTML widget contains toplevel information about the particular class at all times, while the lower HTML widget contains the specific information requested by the user. The MultiPanel also allows a quick scan of the items in it by passing the mouse pointer over relevant text.
The relative sizes of the constituent widgets of the IDoc are flexible. When passing the mouse pointer over the interior borders of the widgets, it changes into an arrow indicating which way the border may be dragged. Users may also specify a preference for a particular size by using the Tycho PDF as well as PostScript® formats.
In brief, the IDoc system consists of the Class model, which encapsulates relevant class information across programming language boundaries; the documentation generator, currently available in Itcl and Java flavors, which generates the Class models; and the IDoc Viewer, which understands how to display models according to their languages.


We are a team of professionals consisting of Corporate Lawyers, Litigating Solicitors, Trademark Attorneys, Patent Agents, Finance Professionals, Company Secretaries, GST Practitioners and Chartered Accountants. The partners of the firm carry experience of more than 10 years in Legal and Secretarial arena. The firm works passionately and provides ethical services to the clients by combining the knowledge of Company Secretaries and Lawyers. Furthermore, the experts associated with the firm have strategic experience and are brilliant minds from the top universities of the world.
We provide a comprehensive range of services and solutions in the field of Accountancy, Companies Compliance, Corporate Advisory, Intellectual Property Rights, Secretarial and Taxation. We are a team comprising of prominent Management and Legal Consultants who provide aid and advice on the matters pertaining to the Law, Administration and Finance inclusive of Litigation, Statutory Records, Book-keeping, and Consultation.
The firm has affiliations and tie-ups on an international level which blends the firm to work together successfully and address the issues according to different territorial jurisdictions. Our strength lies in the efforts of our teammates, who are truly the asset of the organization. We as a team are committed to serve you and we assure you to provide help at the time of duress.


{% block byline %}
{% ifequal event.object.in_reply_to.type_id "audio" %}
{% blocktrans with event.actor.display_name as name and event.actor.get_absolute_url as author_url and entry.get_absolute_url as url and event.object.in_reply_to.type_label as type and event.object.in_reply_to.author.display_name as author and entry.published|date_microformat as published and entry.published|pithy_timesince:"on" as timestamp %}
{{ name }} commented on an {{ type }} post by {{ author }} {{ timestamp }}.
{% endblocktrans %}
{% else %}
{% blocktrans with event.actor.display_name as name and event.actor.get_absolute_url as author_url and entry.get_absolute_url as url and event.object.in_reply_to.type_label as type and event.object.in_reply_to.author.display_name as author and entry.published|date_microformat as published and entry.published|pithy_timesince:"on" as timestamp %}
{{ name }} commented on a {{ type }} by {{ author }} {{ timestamp }}.
{% endblocktrans %}
{% endifequal %}
{% endblock %}


GitHub
# x-zangief
He is Zangief.
## Install
Using npm:
```bash
$ npm install x-zangief
```
## Usage
Import and register scripts.
```html
```
Put ``!
```html
```
If `type` attribute is omitted, He appears randomly.
## License
[MIT](https://1000ch.mit-license.org) © [Shogo Sensui](https://github.com/1000ch)


All Classes
Action
DateTimeAction
DefaultAction
ExitAction
HelloAction
HelpAction
Main
MensaAction
WeatherAction


| Up | TOC | Index | |||||
| << 7 Included Tools | < 8.6 I/O | Up: 8 API Reference | 8.8 Pipe Input and Output > | 9 Release Notes >> |
8.7 Operating System Helpers
<IP_ADDRESS> CMD_SWITCHES
include std/os.e namespace os public constant CMD_SWITCHES
8.7.1 Operating System Constants
<IP_ADDRESS> WIN32
include std/os.e namespace os public enum WIN32
<IP_ADDRESS> WINDOWS
include std/os.e namespace os public enum WINDOWS
<IP_ADDRESS> LINUX
include std/os.e namespace os public enum LINUX
<IP_ADDRESS> OSX
include std/os.e namespace os public enum OSX
<IP_ADDRESS> OPENBSD
include std/os.e namespace os public enum OPENBSD
<IP_ADDRESS> NETBSD
include std/os.e namespace os public enum NETBSD
<IP_ADDRESS> FREEBSD
include std/os.e namespace os public enum FREEBSD
These constants are returned by the platform function.
- WIN32 -- Host operating system is Windows
- LINUX -- Host operating system is Linux
- FREEBSD -- Host operating system is FreeBSD
- OSX -- Host operating system is Mac OS X
- OPENBSD -- Host operating system is OpenBSD
- NETBSD -- Host operating system is NetBSD
Note:
In most situations you are better off to test the host platform by using the ifdef statement. It is faster.
8.7.2 Environment
<IP_ADDRESS> instance
include std/os.e namespace os public function instance()
returns hInstance on Windows and Process ID (pid) on Unix.
Comments:
On Windows the hInstance can be passed around to various Windows routines.
<IP_ADDRESS> get_pid
include std/os.e namespace os public function get_pid()
returns the ID of the current Process (pid).
Returns:
An atom: The current id for a process.
Example 1:
mypid = get_pid()
<IP_ADDRESS> uname
include std/os.e namespace os public function uname()
retrieves the name of the host OS.
Returns:
A sequence, starting with the OS name. If identification fails, returns an OS name of UNKNOWN. Extra information depends on the OS.
On Unix returns the same information as the uname syscall in the same order as the struct utsname. This information is:
OS Name/Kernel Name Local Hostname Kernel Version/Kernel Release Kernel Specific Version information (This is usually the date that the kernel was compiled on and the name of the host that performed the compiling.) Architecture Name (Usually a string of i386 vs x86_64 vs ARM vs etc)
On Windows returns the following in order:
Windows Platform (out of WinCE, Win9x, WinNT, Win32s, or Unknown Windows) Name of Windows OS (Windows 3.1, Win95, WinXP, etc) Platform Number Build Number Minor OS version number Major OS version number
On UNKNOWN returns an OS name of "UNKNOWN". No other information is returned.
Returns an empty string of "" if an internal error has occured.
Comments:
On Unix M_UNAME is defined as a machine_func and this is passed to the C backend. If the M_UNAME call fails, the raw machine_func returns -1. On non-Unix platforms, calling the machine_func directly returns 0.
<IP_ADDRESS> is_win_nt
include std/os.e namespace os public function is_win_nt()
reports whether the host system is a newer Windows version (NT/2K/XP/Vista).
Returns:
An integer, 1 if host system is a newer Windows (NT/2K/XP/Vista), else 0.
<IP_ADDRESS> getenv
<built-in> function getenv(sequence var_name)
returns the value of an environment variable.
Parameters:
- var_name : a string, the name of the variable being queried.
Returns:
An object, -1 if the variable does not exist, else a sequence holding its value.
Comments:
Both the argument and the return value, may, or may not be, case sensitive. You might need to test this on your own system.
Example 1:
e = getenv("EUDIR") -- e will be "C:\EUPHORIA" -- or perhaps D:, E: etc.
See Also:
<IP_ADDRESS> setenv
include std/os.e namespace os public function setenv(sequence name, sequence val, integer overwrite = 1)
sets an environment variable.
Parameters:
- name : a string, the environment variable name
- val : a string, the value to set to
- overwrite : an integer, nonzero to overwrite an existing variable, 0 to disallow this.
Example 1:
? setenv("NAME", "John Doe") ? setenv("NAME", "Jane Doe") ? setenv("NAME", "Jim Doe", 0)
See Also:
<IP_ADDRESS> unsetenv
include std/os.e namespace os public function unsetenv(sequence env)
unsets an environment variable.
Parameters:
- name : name of environment variable to unset
Example 1:
? unsetenv("NAME")
See Also:
<IP_ADDRESS> platform
<built-in> function platform()
indicates the platform that the program is being executed on.
Returns:
An integer,
public constant WIN32 = WINDOWS, LINUX, FREEBSD, OSX, OPENBSD, NETBSD, FREEBSD
Comments:
The ifdef statement is much more versatile and in most cases supersedes platform.
platform used to be the way to execute different code depending on which platform the program is running on. Additional platforms will be added as Euphoria is ported to new machines and operating environments.
Example 1:
ifdef WINDOWS then -- call system Beep routine err = c_func(Beep, {0,0}) elsedef -- do nothing (Linux/FreeBSD) end if
See Also:
Platform-Specific Issues, ifdef statement
8.7.3 Interacting with the OS
<IP_ADDRESS> system
<built-in> procedure system(sequence command, integer mode=0)
passes a command string to the operating system command interpreter.
Parameters:
- command : a string to be passed to the shell
- mode : an integer, indicating the manner in which to return from the call.
Errors:
command should not exceed 1_024 characters.
Comments:
Allowable values for mode are:
- 0: the previous graphics mode is restored and the screen is cleared.
- 1: a beep sound will be made and the program will wait for the user to press a key before the previous graphics mode is restored.
- 2: the graphics mode is not restored and the screen is not cleared.
mode = 2 should only be used when it is known that the command executed by system will not change the graphics mode.
You can use Euphoria as a sophisticated "batch" (.bat) language by making calls to system and system_exec.
system will start a new command shell.
system allows you to use command-line redirection of standard input and output in command.
Example 1:
system("copy temp.txt a:\\temp.bak", 2) -- note use of double backslash in literal string to get -- single backslash
Example 2:
system("eui \\test\\myprog.ex < indata > outdata", 2) -- executes myprog by redirecting standard input and -- standard output
See Also:
system_exec, command_line, current_dir, getenv
<IP_ADDRESS> system_exec
<built-in> function system_exec(sequence command, integer mode=0)
tries to run the a shell executable command.
Parameters:
- command : a string to be passed to the shell, representing an executable command
- mode : an integer, indicating the manner in which to return from the call.
Returns:
An integer, basically the exit or return code from the called process.
Errors:
command should not exceed 1_024 characters.
Comments:
Allowable values for mode are:
- 0 -- the previous graphics mode is restored and the screen is cleared.
- 1 -- a beep sound will be made and the program will wait for the user to press a key before the previous graphics mode is restored.
- 2 -- the graphics mode is not restored and the screen is not cleared.
If it is not possible to run the program, system_exec will return -1.
On Windows system_exec will only run .exe and .com programs. To run .bat files, or built-in shell commands, you need system. Some commands, such as DEL, are not programs, they are actually built-in to the command interpreter.
On Windows system_exec does not allow the use of command-line redirection in command. Nor does it allow you to quote strings that contain blanks, such as file names.
exit codes from Windows programs are normally in the range 0 to 255, with 0 indicating "success".
You can run a Euphoria program using system_exec. A Euphoria program can return an exit code using abort.
system_exec does not start a new command shell.
Example 1:
integer exit_code exit_code = system_exec("xcopy temp1.dat temp2.dat", 2) if exit_code = -1 then puts(2, "\n couldn't run xcopy.exe\n") elsif exit_code = 0 then puts(2, "\n xcopy succeeded\n") else printf(2, "\n xcopy failed with code %d\n", exit_code) end if
Example 2:
-- executes myprog with two file names as arguments if system_exec("eui \\test\\myprog.ex indata outdata", 2) then puts(2, "failure!\n") end if
See Also:
8.7.4 Miscellaneous
<IP_ADDRESS> sleep
include std/os.e namespace os public procedure sleep(atom t)
suspend thread execution for t seconds.
Parameters:
- t : an atom, the number of seconds for which to sleep.
Comments:
The operating system will suspend your process and schedule other processes.
With multiple tasks, the whole program sleeps, not just the current task. To make just the current task sleep, you can call task_schedule(task_self(), {i, i}) and then execute task_yield. Another option is to call task_delay.
Example 1:
puts(1, "Waiting 15 seconds and a quarter...\n") sleep(15.25) puts(1, "Done.\n")
See Also:
task_schedule, task_yield, task_delay


Vim documentation: pi_tar
main help file*pi_tar.txt* For Vim version 7.2. Last change: 2008 Aug 09 +====================+ | Tar File Interface | +====================+ Author: Charles E. Campbell, Jr<EMAIL_ADDRESS>(remove NOSPAM from Campbell's email first) Copyright 2005-2008: The GPL (gnu public license) applies to *tar-copyright* tar.vim, tarPlugin.vim, and pi_tar.txt. No warranty, express or implied. Use At-Your-Own-Risk.
1. Contents *tar* *tar-contents* 1. Contents..................................................|tar-contentsYXXY 2. Usage.....................................................|tar-usageYXXY 3. Options...................................................|tar-optionsYXXY 4. History...................................................|tar-historyYXXY
2. Usage *tar-usage* *tar-manual* When one edits a *.tar file, this plugin will handle displaying a contents page. Select a file to edit by moving the cursor atop the desired file, then hit the <return> key. After editing, one may also write to the file. Currently, one may not make a new file in tar archives via the plugin.
3. Options *tar-options* These options are variables that one may change, typically in one's <.vimrc> file. Default Variable Value Explanation *g:tar_browseoptions* "Ptf" used to get a list of contents *g:tar_readoptions* "OPxf" used to extract a file from a tarball *g:tar_cmd* "tar" the name of the tar program *g:tar_nomax* 0 if true, file window will not be maximized *g:tar_secure* undef if exists: "--" will be used to prevent unwanted option expansion in tar commands. Please be sure that your tar command accepts "--"; Posix compliant tar utilities do accept it. if not exists: The tar plugin will reject any member files that begin with "-" Not all tar's support the "--" which is why it isn't default. *g:tar_writeoptions* "uf" used to update/replace a file
4. History *tar-history* v22 Aug 08, 2008 * security fixes v16 Jun 06, 2008 * tarfile:: used instead of tarfile: when editing files inside tarballs. Fixes a problem with tarballs called things like c:\abc.tar. (tnx to Bill McCarthy) v14 May 09, 2008 * arno caught a security bug May 28, 2008 * various security improvements. Now requires patch 299 which provides the fnameescape() function May 30, 2008 * allows one to view *.gz and *.bz2 files that are in *.tar files. v12 Sep 07, 2007 * &shq now used if not the empty string for g:tar_shq v10 May 02, 2006 * now using "redraw then echo" to show messages, instead of "echo and prompt user" v9 May 02, 2006 * improved detection of masquerading as tar file v8 May 02, 2006 * allows editing of files that merely masquerade as tar files v7 Mar 22, 2006 * work on making tar plugin work across network Mar 27, 2006 * g:tar_cmd now available for users to change the name of the tar program to be used. By default, of course, it's "tar". v6 Dec 21, 2005 * writing to files not in directories caused problems - fixed (pointed out by Christian Robinson) v5 Nov 22, 2005 * report option workaround installed v3 Sep 16, 2005 * handles writing files in an archive back to the archive Oct 18, 2005 * <amatch> used instead of <afile> in autocmds Oct 18, 2005 * handles writing to compressed archives Nov 03, 2005 * handles writing tarfiles across a network using netrw#NetWrite() v2 * converted to use Vim7's new autoload feature by Bram Moolenaar v1 original * Michael Toren see http://michael.toren.net/code/
top - main help file


This example shows how to use CSS animations to make H1 elements move across the page.
H1
In addition, we output some text each time an animation event fires, so you can see them in action.


Spectangular
Member Summary
| Public Members | ||
| public set |
baseUrl: * |
|
| public get |
baseUrl: * |
|
| public get |
library: * |
|
| public set |
library: * |
Method Summary
| Public Methods | ||
| public |
button(config: *): * |
|
| public |
checkbox(config: *): * |
|
| public |
component(config: *): * |
|
| public |
container(config: *): * |
|
| public |
datefield(config: *): * |
|
| public |
datepicker(config: *): * |
|
| public |
dialog(config: *): * |
|
| public |
drawer(config: *): * |
|
| public |
form(config: *): * |
|
| public |
getImplementationFor(clsName: *): * Returns the specific implementation class for the current library. |
|
| public |
loadPage(url: *, waitForElementSelector,: *) Page loading of angular pages. |
|
| public |
loadPageAndWait(url,: *, waitForElementSelector,: *) Page loading of non-angular pages. |
|
| public |
loadPageRefresh(url: *, waitForElementSelector: *) Refreshes the page and loads again the page. |
|
| public |
menu(config: *): * |
|
| public |
menuButton(config: *): * |
|
| public |
pagination(config: *): * |
|
| public |
searchfield(config: *): * |
|
| public |
select(config: *): * |
|
| public |
selectfield(config: *): * |
|
| public |
tab(config: *): * |
|
| public |
table(config: *): * |
|
| public |
textarea(config: *): * |
|
| public |
textfield(config: *): * |
Public Members
public set baseUrl: * source
public get baseUrl: * source
public get library: * source
public set library: * source
Public Methods
public getImplementationFor(clsName: *): * source
Returns the specific implementation class for the current library.
Params:
| Name | Type | Attribute | Description |
| clsName | * | class name |
Return:
| * | or an exception if the clsName does not exits |
public loadPage(url: *, waitForElementSelector,: *) source
Page loading of angular pages. This method is called before each tests in the beforeEach method.
The page loads the url. Protractor waits until the page is loaded and expect that the web element is loaded. This method is used for application which do NOT use AMD module pattern and load the Angular application with a ng-app directive.
Example: Spectangular.loadAngularPage('http://www.my-angular-app.nl','.toolbar');
Params:
| Name | Type | Attribute | Description |
| url | * | of the page |
|
| waitForElementSelector, | * | css selector for the element |
public loadPageAndWait(url,: *, waitForElementSelector,: *) source
Page loading of non-angular pages. This method is called before each tests in the beforeEach method.
The page loads the url and waits until the web element is loaded. This method is used for Angular applications which use AMD module pattern to load asynchronyously modules. See https://github.com/angular/protractor/issues/66 for discussion.
Example: Spectangular.loadPage('http://www.google.com','input#search');
Params:
| Name | Type | Attribute | Description |
| url, | * | location of the page |
|
| waitForElementSelector, | * | css selector for the element |
public loadPageRefresh(url: *, waitForElementSelector: *) source
Refreshes the page and loads again the page. This is used in combination with closePage. This method reloads the Angular context before continue with the loading the page. This method is useful if tests use the same fixture and the test need a fully reload of the fixture and Angular context.
Params:
| Name | Type | Attribute | Description |
| url | * | ||
| waitForElementSelector | * |


--- layout: post date: 2006-04-28 time: "09:00:00" authors: ["Greg Wilson"] title: "Corrections Done" tags: ["Curriculum"] ---
All the outstanding minor corrections to the Software Carpentry notes have now been made; there are still 21 diagrams outstanding, but they should be in by Sunday. My thanks to everyone who provided feedback—especially Adam Goucher and his very sharp eyes.


The
com.jme.app.state
package provides {@link com.jme.app.state.AppState app states}, an abstract way of handling application logic.


An introduction to the most important discoveries and intellectual paradigms in computer science, designed for students with little or no previous background. Explores problem-solving using high and low-level programming languages; presents an integrated view of computer systems, from switching circuits up through compilers and GUI design. Examines theoretical and practical limitations related to unsolvable and intractable computational problems, and the social and ethical dilemmas presented by
Widely applicable mathematical tools for computer science, including topics from logic, set theory, combinatorics, number theory, probability theory, and graph theory. Practice in reasoning formally and proving theorems.
Introduction to the intellectual enterprises of computer science and the art of programming. This course teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Languages include C, PHP, and JavaScript plus SQL, CSS, and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. Des
Abstraction and design in computation. Topics include functional and object-oriented styles of programming, software engineering in the small, and models of computation. Our main goal is to understand how to design large programs to make them readable, maintainable, elegant, and efficient.
Fundamentals of computer systems programming, machine organization, and performance tuning. This course provides a solid background in systems programming and a deep understanding of low-level machine organization and design. Topics include C and assembly language programming, program optimization, memory hierarchy and caching, virtual memory and dynamic memory management, concurrency, threads, and synchronization.
In this course, we study cyberspace governance. Cyberspace is a unique combination of physical and virtual properties. In this course, we view cyberspace as a structure consisting of three layers: the physical layer represented by the global communication infrastructure and two virtual layers, composed by the Internet layer and the social and economic layer. Our focus concentrates on the top two layers. The principles, norms, rules, and procedures that shape the cyberspace are
The digital environment offers greatly expanded opportunities for intelligence gathering. This seminar will explore some of the most difficult problems arising in managing the collection and use of intelligence for national security, including compliance with the law; avoidance of, and remedies for, over collection; and protection of privacy and other fundamental values.We will discuss how an intelligence community's activities can be meaningfully communicated to the public while respecting
Supervised individual study of advanced topics in computer science. A student wishing to enroll in Computer Science 91r must be accepted by a faculty member who will supervise the course work. A form available from the Student Affairs Office, Pierce Hall 110, must be filled out and signed by the student and faculty supervisor. Students writing theses may enroll in this course while conducting thesis research and writing.
Supervised individual study of advanced topics in computer science. A student wishing to enroll in Computer Science 91r must be accepted by a faculty member who will supervise the course work. A form available from the Student Affairs Office, Pierce Hall 110, must be filled out and signed by the student and faculty supervisor. Students writing theses may enroll in this course while conducting thesis research and writing.
Cooperative design, development, and testing of a sizable and realistic computer system. Students work as a group with a client on a real-world open-ended problem, and gain experience in problem definition, software development, and system lifecycle issues, and in the area of application. Students work in groups; both student participation in the classroom and effective group cooperation outside the classroom are stressed.
What is privacy, and how is it affected by recent developments in technology? This course critically examines popular concepts of privacy and uses a rigorous analysis of technologies to understand the policy and ethical issues at play. Case studies: database anonymity, research ethics, wiretapping, surveillance, and others. Course relies on some technical material, but is open and accessible to all students, especially those with interest in economics, engineering, political science, computer sc
For centuries, people have imagined smart machines in fictional stories. Computer systems now communicate in speech and text, learn, negotiate, and work in teams (with people and other systems). These intelligent-systems capabilities raise questions about the impact of such systems on people and societies. This course introduces the basic techniques of AI in the context of (science) fiction imaginings and ethical challenges. It examines the roles of design and of policy in redu
Data Science 1 is the first half of a one-year introduction to data science. The course will focus on the analysis of messy, real life data to perform predictions using statistical and machine learning methods. Material covered will integrate the five key facets of an investigation using data: (1) data collection - data wrangling, cleaning, and sampling to get a suitable data set; (2) data management - accessing data quickly and reliably; (3) exploratory data analysis – generating hy
Data Science 2 is the second half of a one-year introduction to data science. Building upon the material in Data Science 1, the course introduces advanced methods for data wrangling, data visualization, and statistical modeling and prediction. Topics include big data and database management, interactive visualizations, nonlinear statistical models, and deep learning.
General introduction to the theory of computation, teaching how to reason precisely about computation and prove mathematical theorems about its capabilities and limitations. Finite automata, Turing machines, formal languages, computability, uncomputability, computational complexity, and the P vs. NP question.
Design and analysis of efficient algorithms and data structures. Algorithm design methods, graph algorithms, approximation algorithms, and randomized algorithms are covered.
An accelerated introduction to theoretical computer science for students with strong mathematical preparation, to be taken in place of both Computer Science 121 and 124. Algorithm design methods, including graph algorithms, approximation algorithms, and randomized algorithms. Models of computation, computability theory, and computational complexity, including the P vs. NP question.
Cryptography is as old as human communication itself, but has undergone a revolution in the last few decades. It is now about much more than "secret writing" and includes seemingly paradoxical notions such as communicating securely without a shared secret, and computing on encrypted data. In this challenging but rewarding course we will start from the basics of private and public key cryptography and go all the way up to advanced notions such as fully homomorphic encryption and softwar
Networks—of social relationships, economic interdependencies, and digital interactions—are critical in shaping our lives. This course introduces models and algorithms that help us understand networks. Fundamental concepts from applied mathematics, microeconomics, and computer science will be presented through the lens of network science, in order to equip students to usefully analyze the “big data” generated by online networks. Applications discussed include the viral spr
The interplay between economic thinking and computational thinking as it relates to electronic commerce, social networks, collective intelligence and networked systems. Topics covered include: game theory, peer production, reputation and recommender systems, prediction markets, crowd sourcing, network influence and dynamics, auctions and mechanisms, privacy and security, matching and allocation problems, computational social choice and behavioral game theory. Emphasis will be given to core metho
Introduction to the design, structure, and operation of digital computers; logic circuits and digital electronics; computer arithmetic; computer architecture; and machine language programming. Consideration of the design interactions between hardware and software systems.
Networking has enabled the emergence of mobile and cloud computing, creating the most important technological paradigm shift in computing of the past decade. Further advancements in networking are expected to similarly transform the technological landscape over the next decade through the emergence of the Internet of Things and gigabit wireless networks. In order to play a role in this era of new network-powered advancements, students must have a thorough understanding of emerging networking top
Networking has evolved to a new era of supporting large-scale distributed computing for big data. These new networks are software defined in the sense that they can be dynamically programmed to meet various communications needs in computing. In CS 144r/244r, students will obtain the knowledge and experience necessary not only to understand this momentous technological shift in networking, but also to be able to play a part themselves. To this end, the course will not only teach the fundamental c
Review of the fundamental structures in modern processor design. Topics include computer organization, memory system design, pipelining, and other techniques to exploit parallelism. Emphasis on a quantitative evaluation of design alternatives and an understanding of timing issues.
Presentation of concepts and techniques for the design and fabrication of VLSI systems and digital MOS integrated circuits. Topics include: basic semiconductor theory; MOS transistors and digital MOS circuits design; synchronous machines, clocking, and timing issues; high-level description and modeling of VLSI systems; synthesis and place and route design flows; and testing of VLSI circuits and systems. Various CAD tools for design, simulation, and verification are extensively used.
Comprehensive introduction to the principal features and overall design of both traditional and modern programming languages, including syntax, formal semantics, abstraction mechanisms, modularity, type systems, naming, polymorphism, closures, continuations, and concurrency. Provides the intellectual tools needed to design, evaluate, choose, and use programming languages.
Implementation of efficient interpreters and compilers for programming languages. Associated algorithms and pragmatic issues. Emphasizes practical applications including those outside of programming languages proper. Also shows relationships to programming-language theory and design. Participants build a working compiler including lexical analysis, parsing, type checking, code generation, and register allocation. Exposure to run-time issues and optimization.
This course focuses on the design and implementation of modern operating systems. The course discusses threads, processes, virtual memory, schedulers, and the other fundamental primitives that an OS uses to represent active computations. An exploration of the system call interface explains how applications interact with hardware and other programs which are concurrently executing. Case studies of popular file systems reveal how an OS makes IO efficient and robust in the midst of crashes and unex
Introduction to principles of software engineering and best practices, including code reviews, source control, and unit tests. Topics include Ajax, database schemas, event handling, HTTP, MVC, object-oriented design, and user experience. Projects include web apps with front-end UIs (mobile and desktop) and back-end APIs. Languages include JavaScript and PHP.
We are in the big data era and data systems sit in the critical path of everything we do. We are going through major transformations in businesses, sciences, as well as everyday life - collecting and analyzing data changes everything and data systems provide the means to store and analyze a massive amount of data. This course is a comprehensive introduction to modern data systems. The primary focus of the course is on the modern trends that are shaping the data management industry right now: col
An introduction to key design principles and techniques for visualizing data. Covers design practices, data and image models, visual perception, interaction principles, visualization tools, and applications. Introduces programming of web-based interactive visualizations.
This course covers the fundamentals of 3D computer graphics using a modern shader-based version of OpenGL. Main topics include: geometric coordinate systems and transformations, keyframe animation and interpolation, camera simulation, triangle rasterization, material simulation, texture mapping, image sampling and color theory. The course also touches on ray tracing, geometric modeling and simulation-based animation.
The course covers skills and techniques necessary to design innovative interactive products that are useful, usable and that address important needs of people other than yourself. You will learn how to uncover needs that your customers cannot even articulate. You will also learn a range of design principles, effective creativity-related practices, and techniques for rapidly creating and evaluating product prototypes. You will also have several opportunities to formally communicate your
Introduction to machine learning, providing a probabilistic view on artificial intelligence and reasoning under uncertainty. Topics include: supervised learning, ensemble methods and boosting, neural networks, support vector machines, kernel methods, clustering and unsupervised learning, maximum likelihood, graphical models, hidden Markov models, inference methods, and computational learning theory. Students should feel comfortable with multivariate calculus, linear algebra, probability theory,
Introduction to AI focused on problems in reasoning about action and rational decision making, covering search, knowledge representation and planning. Search: heuristics, informed search and optimization; constraint satisfaction; game playing. Knowledge representation: logics, efficient logical inference, reasoning about categories. Planning: action representations and planning algorithms, hierarchical task networks, sequential decision making. Applications to multi-agent systems, robotics and n
Watson is the world Jeopardy champion. Siri responds accurately to "Should I bring an umbrella tomorrow?". How do they work? This course provides an introduction to the field of computational linguistics, the study of human language using the tools and techniques of computer science, with applications to a variety of natural-language-processing problems such as those deployed in Watson and Siri, and covers pertinent ideas from linguistics, logic programming, and statistical modeling. The course
Building autonomous robotic systems requires understanding how to make robots that observe, reason, and act. Each component uses many engineering principles: how to fuse, multiple, noisy sensors; how to balance short-term versus long-term goals; how to control one’s actions and how to coordinate with others. This year theme will be "Robots Roam the Halls", where we will focus on kinect-based robots that move in the SEAS buildings, to do applications like navigating, map bui
An applications course highlighting the use of computers in solving scientific problems. Students will be exposed to fundamental computer science concepts such as computer architectures, data structures, algorithms, and parallel computing. Fundamentals of scientific computing including abstract thinking, algorithmic development, and assessment of computational approaches. Students will learn to use open source tools and libraries and apply them to data analysis, modeling, and visualization of re
This is a project-based course emphasizing designing, building, testing, maintaining and modifying software for scientific computing. Students will work in groups on a number of projects, ranging from small data-transformation utilities to large-scale systems. Students will learn to use a variety of tools and languages, as well as various techniques for organizing teams. Most important, students will learn to fit tools and approaches to the problem being solved.
Students will work in teams in a semester-long project to analyze complex data for a local client. Through this process, they will learn practical data science techniques as well as refine their communication skills.
A quantitative theory of the resources needed for computing and the impediments to efficient computation. The models of computation considered include ones that are finite or infinite, deterministic, randomized, quantum or nondeterministic, discrete or algebraic, sequential or parallel.
Covers topics related to algorithms for big data, especially related to networks. Themes include compression, cryptography, coding, and information retrieval related to the World Wide Web. Requires a major final project.
Probabilistic techniques and tools for the design and analysis of algorithms. Designed for all first-year graduate students in all areas.
Advanced algorithm design, including but not limited to amortization, randomization, online algorithms, graph algorithms, approximation algorithms, linear programming, and data structures.
Efficiently generating objects that ``look random'' despite being constructed using little or no randomness. Connections and applications to computational complexity, cryptography, and combinatorics. Pseudorandom generators, randomness extractors, expander graphs, error-correcting codes, hash functions.
Topics in cryptography and data privacy drawn from the theoretical computer science research literature. Focus for 2016-2017: Differential Privacy -- a mathematical framework for privacy-preserving analysis of datasets, which enables aggregate computations while preventing the leakage of individual-level information -- and statistical validity in adaptive (exploratory) data analysis.
Possibilities of and limitations to performing learning by computational agents. Topics include computational models, polynomial time learnability, learning from examples and learning from queries to oracles. Applications to Boolean functions, automata and geometric functions.
In this graduate seminar we will cover recent research on the use of mathematical programming for problems arising from optimization, machine learning, computational complexity and more, with a particular focus on the Parrilo-Lasserre "Sum of Squares" semidefinite programming hierarchy. We will discuss both lower and upper bounds, as well as how such mathematical programs give rise to a general theory of computational difficulty, computation vs. sample size tradeoffs, and computational
Introduces essential elements the theory of error-correcting codes. Focuses on the basic results in the area, taught from first principles. Special focus will be given on results of asymptotic or algorithmic significance. Principal topics include - Construction and existence results for error-correcting codes; - Limitations on the combinatorial performance of error-correcting codes; - Decoding algorithms - Applications to ot
Topics on the design and analysis of algorithms, processes, and systems related to crowds and social networks. Readings in AI, theoretical CS, machine learning, social science theory, economic theory, and operations research.


Insert your monthly recurring revenue (MRR) and costs (MRC)
Set the expected Churrn Rate and Grouwth Efficency Index (GEI) of your customer cohort
Set the budget allocation for acquiring monthly contract value (MCV)


This document is a User Guide to the command line version of the Daisy Pipeline. If you want to run Daisy Pipeline using a graphical user interface (GUI), read the GUI user guide instead.
This guide refers to the "Daisy Pipeline installation directory" several times. If you installed the DAISY Pipeline command-line-only distribution, this directory is the place where you unzipped the Pipeline files.
If you installed the Pipeline GUI application, this directory refers to $PIPELINE_GUI_INSTALLDIR/plugins/org.daisy.pipeline_1.X.X/
The DAISY Pipeline is a framework for XML- and DTB-related transformations. As such, it can be employed in various contexts, relating to both content creation and distribution.
The actual functionality within the DAISY Pipeline is provided by several collaborating organizations. Most contributions are open source, and reside at the DAISY Pipeline Sourceforge project.
The DAISY Pipeline fully depends on the spirit of sharing within the DAISY community. Remember: you can contribute to its future in many ways: by developing new functionality, by writing or reviewing documentation, by supplying feature requests, and by reporting bugs.
If you want to contribute, contact any of the project participants, or send an email to markus.gylling[at-sign]tpb.se.
See also the Making a feature request and Reporting a bug sections below.
Before running the Pipeline, open pipeline.user.properties
located in the pipeline installation directory and edit the following properties:
To run Daisy Pipeline, open a command shell and run the pipeline.bat
file in the Daisy Pipeline installation directory. Since a few command line paramaters need to be supplied, it is not possible to double click on the .bat
file from Windows Explorer.
If your Java 5 (or later) isn't found on your system path, you need to edit the pipeline.bat
file and supply a full path to the java.exe on the line that starts with set JAVA=
.
To run Daisy Pipeline, execute the pipeline.sh
shell script. The executable bit must be set on the file in order to be executable, run chmod u+x pipeline.sh
to achieve that.
If you get complaints about missing or incorrect version of Java when you run pipeline.sh
, edit the line in the beginning of the file that starts with JAVA=
and supply a full path to a Java 5 (or later) executable.
The basic command line parameters pattern is:
[-i] scriptfile --name=value...
scripts
subdirectory of the Daisy Pipeline installation."--name=value"
.To make a feature request, please sign on to SourceForge, and use the DAISY Pipeline Feature Request tracker.
If you want to discuss a feature before making a request for it, you may subscribe to the DAISY Pipeline interest group emailing list. To do this, please contact markus.gylling[at-sign]tpb.se.
To report a bug, please sign on to SourceForge, and use the DAISY Pipeline Bug tracker.
If you need help or have some other type of question, you should primarily use the DAISY Consortium Training and Technical Support emailing list. Information on how to subscribe to this list is available on http://www.daisy.org/get_involved/join-lists.asp.


Pure Direction:
Data that consist only of a direction (azimuth). Such data do not have defined lengths.


Preparation:
After much research, my brother and I decided we would like to try Blue Lotus, so we purchased 1oz of dried plant material from an internet site. We had tried an extract in tea before but felt subtle, if any effects. So this time we steeped the whole ounce (save enough for a joint) in a sweet red wine with an alcohol content of 12%. We had to drink a glass or so of wine before we filled the bottle with Lotus in order to compensate for displacement. We let the wine/lotus mixture steep for 7 days, shaking it occasionally.
After the 7 days, we re-uncorked the bottle and filtered the drink through a coffee filter, which we placed inside a strainer and allowed the liquid to seep slowly through the filter into a bowl with a pour spout. The liquid was draining extremely slowly through the filter, so I took this time to roll a joint out of the plant material while my brother watched the “pot boil,” so to speak. Eventually, we both got impatient and poked holes in the filter with a knife, and consequently some plant material got through—but not much. I also broke the wine bottle with a hammer in order to get the plant material out of the bottle, because the dry material soaks up a good bit of liquid. We squeezed the liquid from it, as I think it is the most potent stuff.
Experience:
We drank the mixture as fast as we possibly could, because we had heard that it is really bitter, but, much to our delight, it wasn’t bad at all. Immediately, I sparked a joint, and we sat back at the kitchen and enjoyed the quietness of the morning and the steady hum of the refrigerator. The joint ran pretty bad; probably because the plant material is so fluffy and light, but it brought on a subtle feeling of inebriation, nonetheless.
Within 10 minutes the effect of the mixture was already hitting. My brother and I were talking about religion and spirituality in an almost seamless conversation, that is, where I would leave off he would pick up. At one point, we both stopped talking to comment on the effect of the “drug.” We both agreed that there was no mistaking the “high,” which seemed to us very similar to the affects of a good absinthe. We continued to talk for some time, say, an hour, but time—although there was no time-loss like there is on LSD—hardly seemed to matter. The peak came after about 30 minutes. At this point, my vision became “clearer” or “wider,” but this only made it more difficult to focus on any one thing. The body high, for me, was limited to my face. This is not to say I could not function on Blue Lotus. I could function splendidly, but I wouldn’t try driving.
After about an hour and a half we were near baseline and becoming quite sleepy. We chatted for a little longer before we went to our separate rooms and took a nap. I woke up two hours later with a heavy headache, which I sometimes get from too much sweet wine, and my brother woke up shortly after me with no side-affects to speak of. Overall, we had a pleasant mindful inebriation and will certainly try Blue Lotus again.
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.


We have developed a new platform-independent web-based molecular viewer using JavaScript and WebGL. The molecular viewer, Molmil, has been integrated into several services offered by Protein Data Bank Japan and can be easily extended with new functionality by third party developers. Furthermore, the viewer can be used to load files in various formats from the user's local hard drive without uploading the data to a server.
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§235-104 Penalties. Penalties and interest shall be added to and become part of the tax, when and as provided by sections 231-39 and 235-97. The penalties and interest provided by section 231-39 shall apply to employers as well as taxpayers. [L Sp 1957, c 1, pt of §2; Supp, §121-38; HRS §235-104]
Case Notes
When penalty applied. 33 H. 766 (1936).