- Experiment
- “We define an experiment as a process that generates well-defined outcomes” (ASWCC P171)
- Sample Space
- “The sample space for an experiment is the set of all experimental outcomes” (ASWCC P172)
- Experimental outcomes
- “An experimental outcome is also called a sample point to identify it as an element of the sample space.” (ASWCC P172)
- Events
- “An Event is a collection of Sample Points” (AWSCC P181), could be just one sample point (one experimental outcome)
- Probability
- “Probability is a numerical measure of the likelihood that an event will occur.” (AWSCC P171)
4.1.1 Sample Space and Probabilities
Sample Space and Experimental Outcomes
We can use the letter \(S\) or \(\Omega\) to denote sample space. Supose we have a set of \(n\) experimental outcomes:
\[S = \left\{ E_1, E_2, ..., E_{n-1}, E_n\right\}\]
We can call \(S\) a sample space if:
- \(E_i\) are mutually exclusive:
- \(E_i\) for all \(i\) are separate outcomes that do not overlap.
- Suppose there are multiple people running for the Presidency, some of these candidates are women, and some of these women are from Texas. If one of the \(E_i\) is that a woman becomes the President, and another \(E_i\) is that someone from Texas becomes the President, \(S\) would not be a sample space, because we could have a woman who is also from Texas win the Presidency.
- \(E_i\) are jointly exhaustive:
- For the experiment with well-defined outcomes, the \(E_i\) in \(S\) need to cover all possible outcomes.
- If you are throwing a six sided dice, there are six possible experimental outcomes, if \(S\) only has five of them, it would not be a sample space.
Thinking about the world in terms of sample space is pretty amazing.
Assigning Probability to Experimental Outcomes
We can assign probabilities to events of a sample space. Since experimental outcomes are themselves events as well, we can assign probabilities to each experimental outcome.
For the mutually exclusive and jointly exhaustive experimental outcomes of the sample space, there are two equirements for assigning probabilities: - Each element of the sample space can not have negative probability of happening, and also can not have more than \(1\) probability of happening, with \(P\) denotes probability, we have: \[0 \le P(E_i) \le 1\] - The probabilities of all the mutually exclusive and jointly exhaustive experimental outcomes in the sample space sum up to \(1\). For an experimental with \(n\) experimental outcomes: \[\Sigma_{i=1}^{n} P(E_i) = 1\]
# Load Library
library(tidyverse)
# Define a List of Experimental Outcomes
experimental.outcomes.list <- c('Heavy Rain', 'Light Rain', 'No Rain')
# Probabilities on experimental outcomes
experimental.outcome.prob <- c(0.1, 0.2, 0.7)
# Show these in a Tibble
kable(tibble(tomorrow.experimental.outcomes = experimental.outcomes.list,
experimental.outcome.prob = experimental.outcome.prob)) %>% kable_styling_fc()
| tomorrow.experimental.outcomes | experimental.outcome.prob |
|---|---|
| Heavy Rain | 0.1 |
| Light Rain | 0.2 |
| No Rain | 0.7 |
# What could happen tomorrow?
# We live in a probabilistic world, from today's perspective, tomorrow is uncertain
# If we draw tomorrow from a hat, given our possible outcomes
# and the probabilities associated with the outcomes
# what are the possible tomorrows?
number.of.tomorrow.to.draw = 20
tomorrow.weather.draws <- sample(experimental.outcomes.list,
size = number.of.tomorrow.to.draw,
replace = TRUE,
prob = experimental.outcome.prob)
# A little tibble to show results
# There are only three unique tomorrows, each of three weather outcomes
# could happen, but the chance of each happening differs by the probabilities
# we set in experimental.outcome.prob
kable(tibble(which.tomorrow = paste0('tomorrow:', 1:number.of.tomorrow.to.draw),
tomorrow.weather = tomorrow.weather.draws)) %>% kable_styling_fc()
| which.tomorrow | tomorrow.weather |
|---|---|
| tomorrow:1 | No Rain |
| tomorrow:2 | No Rain |
| tomorrow:3 | No Rain |
| tomorrow:4 | No Rain |
| tomorrow:5 | Light Rain |
| tomorrow:6 | No Rain |
| tomorrow:7 | No Rain |
| tomorrow:8 | No Rain |
| tomorrow:9 | No Rain |
| tomorrow:10 | No Rain |
| tomorrow:11 | Light Rain |
| tomorrow:12 | Heavy Rain |
| tomorrow:13 | No Rain |
| tomorrow:14 | No Rain |
| tomorrow:15 | Light Rain |
| tomorrow:16 | No Rain |
| tomorrow:17 | Heavy Rain |
| tomorrow:18 | Light Rain |
| tomorrow:19 | No Rain |
| tomorrow:20 | No Rain |
4.1.2 Union and Intersection and Complements
Definitions:
- Complement of Event \(A\):
- “Given an event A, the complement of A is defined to be the event consisting of all sample points that are not in A. The complement of A is denoted by \(A^c\).” (AWSCC P185)
- The Union of Events \(A\) and \(B\):
- “The union of A and B is the event containing all sample points belonging to \(A\) or \(B\) or both. The union is denoted by \(A \cup B\).” (AWSCC P186)
- The Intersection of Events \(A\) and \(B\):
- “Given two events \(A\) and \(B\), the intersection of \(A\) and \(B\) is the event containing the sample points belonging to both \(A\) and \(B\). The intersection is denoted by \(A \cap B\).” (AWSCC P187)
Probabilities for Complements and Union
The Probabilities of Complements add up to 1: \[P(A) + P(A^c) = 1\]
The Addition Law: \[P (A \cup B) = P(A) + P(B) - P (A \cap B)\]
If two events \(A\) and \(B\) are mutually exclusive, which means they do not share any experimental outcomes (sample points), then: \(P (A \cap B) = 0\), and \(P (A \cup B) = P(A) + P(B)\).
The Multiplication Law for Indepedent Events: \[P (A \cap B) = P(A) \cdot P(B)\]
If the probability of event \(A\) happening does not change the probability of event \(B\) happening, and vice-versa. The two events are independent. Below we arrive this formulation from conditional probability.
4.1.3 Conditional Probability
We use a straight line \(\mid\) to denote conditional probability. Given \(A\) happens, what is the probability of \(B\) happening?
\[P (A \mid B) = \frac{P(A \cap B)}{P(B)}\]
This says the probability of \(A\) happening given that \(B\) happens is equal to the ratio of the probability that both \(A\) and \(B\) happen divided by the probability of \(B\) happening.
The formula also means that the probability that both \(A\) and \(B\) happens is equal to the probability that \(B\) happens times the probability that \(A\) happens conditional on \(B\) happening: \[ P(A \cap B) = P (A \mid B)\cdot P(B)\]
If \(A\) and \(B\) are independent, that means the probability of \(A\) happening does not change whether \(B\) happens or not, then, \(P (A \mid B) = P(A)\), and: \[ \text{If A and B are independent: } P(A \cap B) = P(A) \cdot P(B)\] This is what we wrote down earlier as well.
4.2 Sample Space and Probability Examples
Go back to fan’s REconTools Package, R Code Examples Repository (bookdown site), or Intro Stats with R Repository (bookdown site).
In Sample Space, Experimental Outcomes, Events, Probabilities, we discussed various definitions. Here are some examples.
- Experiment: Throwing a Quarter
- Experimental outcomes: lands on Heads or lands on Tails
- Sample Space: \(S=\left\{H, T\right\}\)
- Event: There are only two experimental outcomes.
- Probability:
- \(1 \ge P(H) \ge 0\)
- \(1 \ge P(T) \ge 0\)
- \(P(H) + P(T) = 1\)
4.2.1 Presidential Election
After various primaries there are four major candidates who could become president. Voting takes place in half a year. Over this year, many events could happen that would increase or decrease the support for candidates. After voting, the outcome is certain. But from today’s perspective, when voting is one year from now, outcomes are uncertain.
- Experiment: Presidential Election in One Year
- Experimental outcomes: candidates DT, HC, JS, GJ winning.
- Sample Space: {DT, HC, JS, GJ}
- Event:
- A woman wins is an event: \(\text{W} = \left\{HC, JS\right\}\)
- A man wins: \(\text{M} = \left\{DT, GJ\right\}\)
- Someone from a small party wins: \(\text{S} = \left\{JS, GJ\right\}\)
- Probability:
- The probability that a woman does not win: \[P(\text{W}^c) = 1 - P(\text{W}) = P(\text{M})\]
- There are no experimental outcomes, given the four that we have, where both woman and man wins, the two events are mutually exclusive: \[P(\text{W} \cap \text{M}) = 0\]
- The probability that a woman wins conditional on a man winnning is zero: \[P(\text{W} \mid \text{M}) = \frac{P(\text{W} \cap \text{M})}{P(\text{M})} = \frac{0}{P(DT) + P(GJ)} = 0\]
- The probability that a woman wins and someone from a small party wins: \[P(\text{W} \cap \text{S}) = P(JS)\]
- The probability that a woman wins conditional on someone from a small party winning: \[P(\text{W} \mid \text{S}) = \frac{P(\text{W} \cap \text{S})}{P(\text{S})} = \frac{P(JS)}{P(JS) + P(GJ)} \]
- Either a women wins or someone from a small party wins: \[P(\text{W} \cup \text{S}) = P(\text{W}) + P(\text{S}) - P(\text{W} \cap \text{S}) = P(HC) + P(JS) + P(JS) + P(GJ) - P(JS) = P(HC) + P(JS) + P(GJ)\]
4.2.2 Throwing a Dice
- Experiment: Throwing a Dice
- Experimental outcomes: lands on one of the six sides
- Sample Space: \(S=\left\{1,2,3,4,5,6\right\}\)
- Event: One could define many possible events. The experimental outcome is less than 3 is an event, greater than 2 is an event, been odd is an event, etc.
In Throw an Unfair Four Sided Dice, we show what happens when we throw an unfair four sided dice many times. In the example below, we will throw an unfair dice, where the probability of landing on the side with 1 is 50 percent, and the chance of landing on each successive side is half of the chance of landing on the previous side. This is a dice weighted towards the smaller numbers.
See the table below for the true probabilities of the unfiar dice.
# Load Library
library(tidyverse)
# Define a List of Experimental Outcomes
experimental.outcomes.list <- c(1,2,3,4,5,6)
# Probabilities on experimental outcomes
# Suppose dice is weighted towards 1
fracbase <- 0.50
experimental.outcome.prob <- c((1-fracbase)^0*fracbase,
(1-fracbase)^1*fracbase,
(1-fracbase)^2*fracbase,
(1-fracbase)^3*fracbase,
(1-fracbase)^4*fracbase,
(1-fracbase)^5)
# Show these in a Tibble
dice.true.prob <- tibble(dice.outcomes.list = experimental.outcomes.list,
dice.true.prob = experimental.outcome.prob)
kable(dice.true.prob) %>% kable_styling_fc()
| dice.outcomes.list | dice.true.prob |
|---|---|
| 1 | 0.50000 |
| 2 | 0.25000 |
| 3 | 0.12500 |
| 4 | 0.06250 |
| 5 | 0.03125 |
| 6 | 0.03125 |
<IP_ADDRESS> Throw the Dice 10 Times
We throw the dice 10 times, each time, get one of the six possible experimental outcomes, the chance of getting these outcomes are determined by the true probabilities of the unfair dice. If we throw 10 times, that does not mean we will get exact side 1 five times. Side 1 having proability of 50 percent means for each throw there is a 50 percent chance of getting side 1. But each throw is independent of other throws, so we could just by chance end up with no side 1 after ten throws, although the chance of that happening is low. How many times did we get each side in the ten random tosses below?
# What could happen tomorrow?
# We live in a probabilistic world, drawing future from a hat
# If we draw 20 times, what happens in the future?
# It's pretty amazing, we get to see the future!
number.of.futures.to.draw = 10
future.dice.draws <- sample(experimental.outcomes.list,
size = number.of.futures.to.draw,
replace = TRUE,
prob = experimental.outcome.prob)
# A little tibble to show results
kable(tibble(which.future.dice = paste0('dice draws:', 1:number.of.futures.to.draw),
dice.draws = future.dice.draws)) %>% kable_styling_fc()
| which.future.dice | dice.draws |
|---|---|
| dice draws:1 | 1 |
| dice draws:2 | 1 |
| dice draws:3 | 1 |
| dice draws:4 | 2 |
| dice draws:5 | 6 |
| dice draws:6 | 3 |
| dice draws:7 | 1 |
| dice draws:8 | 1 |
| dice draws:9 | 2 |
| dice draws:10 | 1 |
<IP_ADDRESS> Redefining Sample Space for Throwing a Dice
Following our definition for sample space above, we can actually define an alternative sample spaces, for example:
- Experiment: Throwing a Dice
- Experimental outcomes: lands on greater or equal to 4, lands on less or equal to 3
- Sample Space: \(S=\left\{\text{'less equal to 3'}, \text{'greater equal to 4'}\right\}\)
This is also a sample space with two sample points (or experimental outcomes). Following our definitions earlier, these two experimental outcomes are mutually exclusive and jointly exhaustive. We could also call these two experimental outcomes here two different events of the sample space with the six sample points.
4.2.3 Two Basketball Games
- Experiment: One team plays two games
- Experimental outcomes:
- \(E_1\): win first, win second
- \(E_2\): win first, lose second
- \(E_3\): lose first, win second
- \(E_4\): lose first, lose second
- Sample Space: \(S=\left\{E_1, E_2, E_3, E_4\right\}\)
- Events:
- no wins: \(\text{W0} = \left\{E_4\right\}\)
- win twice: \(\text{W2} = \left\{E_1\right\}\)
- win first game: \(\text{WF} = \left\{E_1, E_2\right\}\)
- win second game: \(\text{WS} = \left\{E_1, E_3\right\}\)
- etc.
- Probability:
- What is the probability of winning the second game and the first game? Using the conditional probability formula, it is the probability of winning the first game times the probability of winning the second game conditional on winning the first game:
\[P(\text{WS} \cap \text{WF}) = P(\text{WS} \mid \text{WF}) \cdot P(\text{WF}) = P(E_1)\]
- If winning the first game does not change the probability of winning the second game, then we have: \[P(E_1) = P(\text{WS} \mid \text{WF}) \cdot P(\text{WF}) = P(\text{WS}) \cdot P(\text{WF}) \]
- If the chance of winning the first and second game are the same, \(P(\text{WS})=P(\text{WF})=p\), then we have: \[P(E_1) = P(\text{WS}) \cdot P(\text{WF}) = p \cdot p = p^2 \]
- The probability of winning at least one game: \[P(\text{WS} \cup \text{WF}) = P(\text{WS}) + P(\text{WF}) - P(\text{WS} \cap \text{WF}) = P(E_1) + P(E_2) + P(E_3) \]
- What is the probability of winning the second game and the first game? Using the conditional probability formula, it is the probability of winning the first game times the probability of winning the second game conditional on winning the first game:
\[P(\text{WS} \cap \text{WF}) = P(\text{WS} \mid \text{WF}) \cdot P(\text{WF}) = P(E_1)\]
<IP_ADDRESS> Redefining Sample Space for two Basketball Game
Following our definition for sample space above, we can actually define an alternative sample spaces using these events:
- Experiment: One team plays two games
- Experimental outcomes: Win 0, 1, or 2 times
- Sample Space: \(S=\left\{\text{'0 Win'}, \text{'1 Win'}, \text{'2 Wins'}\right\}\)
This is also a sample space with three sample points (or experimental outcomes). Following our definitions earlier, these three experimental outcomes are mutually exclusive and jointly exhaustive. We could also call these three experimental outcomes here three different events of the sample space with the four paths of game play experimental outcomes (sample points).
4.3 Law of Large Number
Go back to fan’s REconTools Package, R Code Examples Repository (bookdown site), or Intro Stats with R Repository (bookdown site).
In Sample Space, Experimental Outcomes, Events, Probabilities, we discussed various definitions. We went over various examples in Examples of Sample Space and Probabilities. Here we look at what happens if we throw a four sided dice many times.
4.3.1 An Unfair Dice
Throwing a Dice:
- Experiment: Throwing a Four Sided Dice
- Experimental outcomes: lands on one of the four sides
- Sample Space: \(S=\left\{1,2,3,4\right\}\)
In the example below, we will throw an unfair dice, where the probability of landing on the side with 1 is 60 percent, and the chance of landing on each successive side is 60 percent of the chance of landing on the previous side. This is a dice weighted towards the smaller numbers.
See the table below for the true probabilities of the unfair dice.
# Load Library
library(tidyverse)
# Define a List of Experimental Outcomes
experimental.outcomes.list <- c(1,2,3,4)
# Probabilities on experimental outcomes
# Suppose dice is weighted towards 1
fracbase <- 0.6
experimental.outcome.prob <- c((1-fracbase)^0*fracbase,
(1-fracbase)^1*fracbase,
(1-fracbase)^2*fracbase,
(1-fracbase)^3)
# Show these in a Tibble
dice.true.prob <- tibble(dice.outcomes.list = experimental.outcomes.list,
dice.true.prob = experimental.outcome.prob)
kable(dice.true.prob) %>% kable_styling_fc()
| dice.outcomes.list | dice.true.prob |
|---|---|
| 1 | 0.600 |
| 2 | 0.240 |
| 3 | 0.096 |
| 4 | 0.064 |
<IP_ADDRESS> Throw the Dice 5 Times
We throw the dice 5 times, each time, get one of the four possible experimental outcomes, the chance of getting these outcomes are determined by the true probabilities of the unfair dice.
# What could happen tomorrow?
# We live in a probabilistic world, drawing future from a hat
# If we draw 5 times, what happens in the future?
# It's pretty amazing, we get to see the future!
number.of.futures.to.draw = 5
future.dice.draws <- sample(experimental.outcomes.list,
size = number.of.futures.to.draw,
replace = TRUE,
prob = experimental.outcome.prob)
# A little tibble to show results
kable(tibble(which.future.dice = paste0('dice draws:', 1:number.of.futures.to.draw),
dice.draws = future.dice.draws)) %>% kable_styling_fc()
| which.future.dice | dice.draws |
|---|---|
| dice draws:1 | 2 |
| dice draws:2 | 1 |
| dice draws:3 | 1 |
| dice draws:4 | 1 |
| dice draws:5 | 2 |
<IP_ADDRESS> Throw the dice 50, 5000 and 500,000 times
If we throw the dice 50 times, 5000 times, 500,000 times, what will happen?
For each group of experiments, we can aggregate the empirical distribution of the four sides. The more times we throw the dice, the closer our empirical distribution gets to the true distribution. We can see the result from the table below.
To do this, we first write a function, then we lapply to invoke the function multiple times.
# Function to Make Many Draws
future.draws <- function(number.of.futures.to.draw, select.dice.draws=FALSE) {
# Number.of.futures.to.draw = 500
# Future Draws
dice.draws <- sample(experimental.outcomes.list,
size = number.of.futures.to.draw,
replace = TRUE,
prob = experimental.outcome.prob)
# Empirical Distribution Name
sample.frac.var <- paste0('sample.frac.n', sprintf("%d", number.of.futures.to.draw))
# Group Futures
group.fracs <- tibble(dice.draw = dice.draws) %>%
group_by(dice.draw) %>%
summarise(freq = n()) %>%
mutate(!!sample.frac.var :=
as.numeric(sprintf("%0.5f", freq / sum(freq)))) %>%
arrange(dice.draw) %>%
select(dice.draw, !!sample.frac.var)
# Whether to includ dice.draws categorical
if (select.dice.draws){
# group.fracs <- group.fracs
} else {
group.fracs <- group.fracs %>% select(!!sample.frac.var)
}
# Return
return(group.fracs)
}
# Draw future 10, 100, 1000, 10000, 100000 times
# How many times we get 1,2,3,4?
number.of.futures.to.draw.list = c(1000, 5000, 500000)
# Apply function
kable(bind_cols(dice.true.prob,
lapply(number.of.futures.to.draw.list, future.draws))) %>%
kable_styling_fc()
| dice.outcomes.list | dice.true.prob | sample.frac.n1000 | sample.frac.n5000 | sample.frac.n500000 |
|---|---|---|---|---|
| 1 | 0.600 | 0.568 | 0.6068 | 0.59939 |
| 2 | 0.240 | 0.276 | 0.2404 | 0.24024 |
| 3 | 0.096 | 0.091 | 0.0926 | 0.09622 |
| 4 | 0.064 | 0.065 | 0.0602 | 0.06415 |
<IP_ADDRESS> Throw Four Sided Dice Different Number of Times, and Melt Data
Using the function we created above, we can draw a graph that shows what happens to the empirical distribution of four dice sides as we increase the number of draws.
# Generate Data
# Log Space Draws of Outcomes
number.future.logspace <- floor(exp(log(10)*seq(log10(100),log10(1000000), length.out=500)))
# lapply, generating a list of dataframes, then merge together
draw.outcomes <- lapply(number.future.logspace,
future.draws, select.dice.draws=TRUE) %>%
reduce(full_join, by = 'dice.draw') %>%
mutate_all(funs(replace_na(.,0)))
# Melt Data
draw.outcomes.long <- draw.outcomes %>%
gather(variable, value, -dice.draw) %>%
dplyr::mutate(draw.count =
as.numeric(str_extract(variable, "[^.n]+$"))) %>%
select(dice.draw, draw.count, value)
# 1 to 6 are categorical factors
draw.outcomes.long$dice.draw <- paste0('dice side = ', draw.outcomes.long$dice.draw)
draw.outcomes.long$dice.draw <- factor(draw.outcomes.long$dice.draw)
# Show Melt Table
kable(head(draw.outcomes.long, n=10)) %>% kable_styling_fc()
| dice.draw | draw.count | value |
|---|---|---|
| dice side = 1 | 100 | 0.59000 |
| dice side = 2 | 100 | 0.32000 |
| dice side = 3 | 100 | 0.06000 |
| dice side = 4 | 100 | 0.03000 |
| dice side = 1 | 101 | 0.59406 |
| dice side = 2 | 101 | 0.24752 |
| dice side = 3 | 101 | 0.07921 |
| dice side = 4 | 101 | 0.07921 |
| dice side = 1 | 103 | 0.59223 |
| dice side = 2 | 103 | 0.27184 |
| dice.draw | draw.count | value |
|---|---|---|
| dice side = 3 | 963757 | 0.09585 |
| dice side = 4 | 963757 | 0.06368 |
| dice side = 1 | 981711 | 0.60043 |
| dice side = 2 | 981711 | 0.23932 |
| dice side = 3 | 981711 | 0.09628 |
| dice side = 4 | 981711 | 0.06397 |
| dice side = 1 | 1000000 | 0.60107 |
| dice side = 2 | 1000000 | 0.23974 |
| dice side = 3 | 1000000 | 0.09537 |
| dice side = 4 | 1000000 | 0.06382 |
Graphically, What happens when the number of dice throws increases? A crucial thing to understand about probability is that we are not saying if you throw 10 dice, there will be exactly 6 dice out of the 10 that will land on side=1 (given 60 percent probability of landing on side 1).
What we are saying is that, given that each dice throw is independent, if we throw the dice many many times, the empirical distribution of dice throws will approximate the actual true probability of landing on each of the four sides of the dice.
The graph between demonstrates this. The x-axis is in log-scale. We start with 10 throws, and end with 1 million throws. The Y-axis is the empirical probability, with 0.1=10 percent. We have four colors for each of the four sides.
We can see that the empirical probability based on actual dice throws converges to the true probability as we increase the number of dice throws.
# Graph
# Control Graph Size
options(repr.plot.width = 6, repr.plot.height = 4)
# x-labels
x.labels <- c('n=100', 'n=1000', 'n=10k', 'n=100k', 'n=1mil')
x.breaks <- c(100, 1000, 10000, 100000, 1000000)
# title line 2
title_line2 <- sprintf("P(S=1)=%0.3f, P(S=2)=%0.3f, P(S=3)=%0.3f, P(S=4)=%0.3f",
experimental.outcome.prob[1], experimental.outcome.prob[2],
experimental.outcome.prob[3], experimental.outcome.prob[4])
# Graph Results--Draw
line.plot <- draw.outcomes.long %>%
ggplot(aes(x=draw.count, y=value,
group=dice.draw,
colour=dice.draw)) +
geom_line(size=0.75) +
labs(title = paste0('Law of Large Number, Unfair Four Sided Dice\n', title_line2),
x = 'Number of Times Throwing the Four-sided Dice',
y = 'Empirical Probability Based on Dice Throws',
caption = 'As n increases, approximates true probabilities') +
scale_x_continuous(trans='log10', labels = x.labels, breaks = x.breaks) +
theme_bw()
print(line.plot)
4.4 Multiple-Step Experiment
Go back to fan’s REconTools Package, R Code Examples Repository (bookdown site), or Intro Stats with R Repository (bookdown site).
4.4.1 Playing the Lottery Three times
In Sample Space, Experimental Outcomes, Events, Probabilities, we discussed various definitions. We studied various examples in Examples of Sample Space and Probabilities.
In the example below, we have a multiple-step experiment: “If an experiment can be describe as a sequence of \(n\) steps with \(m\) possible outcomes on each step, then the total number of experimental outcomes is \(m^n\).” (ASWCC P172) You can use letters other than \(n\) and \(m\) as well.
Key questions:
- What are the possible experimental outcomes?
- What are the probabilities associated with each of the experimental outcomes?
We are thinking about the problem from day zero perspective. We could play the lottery three times, on day one, day two and day three.
Suppose you can either win or lose when you play the lottery. Each lottery ticket costs the same. You will buy 1 lottery ticket on each day for three days straight. The chance of winning is the same for each lottery ticket (perhaps the number of people buying lottery is the same every day). You see at the end of each day whether you won or lost.
The question is, what are the possible outcomes after three days? You are standing from the perspective of today, looking forward to what could happen tomorrow, two days from now, three days from now.
<IP_ADDRESS> Assumptions and Symbols
Assumptions
Based on the descriptions above, we can write down the following assumptions we are making, which are also the assumptions for the binomial distribution:
- There are \(n\) numbers of identical lottery plays
- The outcomes of each lottery are always win or lose, just these two possible outcomes.
- The chance of winning each time, \(p_{\text{win}}\), is the same during each play. The chance of losing is the same as well, \(1-p_{\text{win}}\).
- Whether you won or not before does not impact whether you will win in the future.
Symbols we will use
- \(n\): denotes the number of lottery plays (number of trials)
- \(x\): denotes the number of times you win, at most \(x=n\), at least \(x=0\), the number of losses is \(n-x\)
- \(p\): the probability of winning, the probability of losing is therefore \(1-p\).
The Tree Structure with Probability Written on it, Notations:
- W = Win, L = Lose
- D0 = Day 0, D1 = Day 1, D2 = Day 2
<IP_ADDRESS> Play the Lottery Just once
From day zero perspective, by the end of day one, there are two possible experimental outcomes, with different probabilities associated with each outcome.
- D0:
- D1 \(W\): \(P(E_1) = P(W) = p\)
- D1 \(L\): \(P(E_2) = P(L) = 1-p\)
There are two experimental outcomes in the sample space: \[\Sigma_{i=1}^2 \left(P\left(E_i\right)\right) = p + \left(1-p\right) = 1\]
<IP_ADDRESS> Play the Lottery Twice
From day zero perspective, by the end of day two, there are four possible experimental outcomes, with different probabilities associated with each outcome.
We are assuming that each lottery play is independent, we learned before that:
\[ P(A \cap B) = P (A \mid B)\cdot P(B)\]
and when \(A\) and \(B\) are independent:
\[ P(A \cap B) = P (A)\cdot P(B)\]
- D0:
- D1 W:
- D2 \(W\): \(P(E_1) = P(\text{First Game Win} \cap \text{Second Game Win}) = P(WW) = P(W) \cdot P(W) = p \cdot p\)
- D2 \(L\): \(P(E_2) = P(\text{First Game Win} \cap \text{Second Game Lose}) = P(WL) = P(W) \cdot \left(1-P(W)\right) = p \cdot (1-p)\)
- D1 L:
- D2 \(W\): \(P(E_3) = P(LW)=(1-p) \cdot p\)
- D2 \(L\): \(P(E_4) = P(LL)=(1-p) \cdot (1-p)\)
- D1 W:
There are four experimental outcomes in the sample space:
\[\begin{align} \Sigma_{i=1}^4 \left(P\left(E_i\right)\right) =& p \cdot p + p \cdot (1-p) + (1-p) \cdot p + (1-p) \cdot (1-p)\\ =& p \cdot (p + 1 - p) + (1-p) \cdot (p + 1 - p) \\ =& p + (1-p) \\ =& 1 \\ \end{align}\]
<IP_ADDRESS> Play the Lottery Three Times
From day zero perspective, by the end of day three, there are eight possible experimental outcomes, with different probabilities associated with each outcome.
- D0:
- D1 \(W\):
- D2 \(W\)
- D3 \(W\): \(P(E_1)=P(WWW)=p^{3} \cdot (1-p)^{0}\)
- D3 \(L\): \(P(E_2)=P(WWL)=p^{2} \cdot (1-p)^{1}\)
- D2 \(L\):
- D3 \(W\): \(P(E_3)=P(WLW)=p^{2} \cdot (1-p)^{1}\)
- D3 \(L\): \(P(E_4)=P(WLL)=p^{1} \cdot (1-p)^{2}\)
- D2 \(W\)
- D1 \(L\):
- D2 \(W\)
- D3 \(W\): \(P(E_5)=P(LWW)=p^{2} \cdot (1-p)^{1}\)
- D3 \(L\): \(P(E_6)=P(LWL)=p^{1} \cdot (1-p)^{2}\)
- D2 \(L\):
- D3 \(W\): \(P(E_7)=P(LLW)=p^{1} \cdot (1-p)^{2}\)
- D3 \(L\): \(P(E_8)=P(LLL)=p^{0} \cdot (1-p)^{3}\)
- D2 \(W\)
- D1 \(W\):
There are eight experimental outcomes in the sample space, and they sum up to one as before:
\[\begin{align} \Sigma_{i=1}^8 \left(P\left(E_i\right)\right) = 1 \\ \end{align}\]
<IP_ADDRESS> Probabilities of Events
Generally, after \(n\) trials/lotteries, if a particular experimental outcome had \(x\) number of wins/successes (which means \(n-x\) number of losses/failures), the probability that this particular experimental outcome happen is: \[ p^x \cdot p^{n-x} \]
The probabilities we wrote in the previous examples all follow this rule. Remember that if someone won every time, \(n=x\), and we will have \(p^x \cdot p^{n-x}=p^x \cdot p^0 = p^x \cdot 1 = p^x\).
So far we have discussed different experimental outcomes with unique sequences of wins and losses. In the 3 lottery example above, we have 8 experimental outcomes. If we are mainly interested in the total number of wins and losses, we can group these 8 experimental outcomes into 4 events.
- Event win three times: W3 = \(\left\{E_1\right\}\) \[P(W3)=p^{3} \cdot (1-p)^{0}\]
- Event win two times: W2 = \(\left\{E_2, E_3, E_5\right\}\)
- There are three experimental outcomes out of the eight where you win two times. The probability of each of these experimental outcomes happening is: \(p^{2} \cdot (1-p)^{1}\), hence: \[P(W2) = 3 \cdot p^{2} \cdot (1-p)^{1}\]
- Event win one time: W1 = \(\left\{E_4, E_6, E_7\right\}\)
- There are three experimental outcomes out of the eight where you win one time. The probability of each of these experimental outcomes happening is: \(p^{1} \cdot (1-p)^{2}\), hence: \[P(W1) = 3 \cdot p^{1} \cdot (1-p)^{2}\]
- Event win zero time: W0 = \(\left\{E_8\right\}\) \[P(W0)=p^{0} \cdot (1-p)^{3}\]
These four events are mutually exclusive and jointly exhaustive, we can define a new sample space that includes four experimental outcomes, win zero times, win once, win twice, win three times.


Please note that the uploaded image will be treated as background for all blends Remember to choose a new background before changing the mode if you would like to use a different mode on a new image
WARNING: Filters are applied automatically.
Though the course was never completed, the app itself is an extension of some assignments from Duke University's Java Team's course on the foundations of web development with JavaScript, CSS, and html, and uses their SimpleImage Library extensively.
The following links direct to pages whose information was used in the development process:
(* For codepen users: control/command-click on the links below)


Once you're ready to start your study session, click on the button below!
This website will help you power through!


---
title: Politics Rewired
description: Coop building political tech.
---
Politics Rewired is a worker-owned cooperative that builds and supports digital organizing
platforms, freeing organizers to build power efficiently, democratically, and with 8 hours of sleep a night. If you're looking to get in touch, email<EMAIL_ADDRESS>Politics Rewired
We are not a politically neutral tech vendor. We want our tools to be used to grow the Left into a force that can challenge political and economic elites and build a society that works for the many, not the few. In practice, that means being selective about the candidates and organizations with whom we work.
For political campaigns, we have a simple rule of thumb: Work with the leftmost viable candidate in any given race. In a partisan general election, this virtually always means the Democrat running against a Republican. In a Democratic or nonpartisan primary election or top-two runoff election, this often means an insurgent progressive or socialist candidate running against a more establishment centrist or conservative candidate.
Our process is straightforward: Every time we receive a new client request from a candidate’s campaign, we research the race and ask ourselves three questions:
Based on the answers, our team decides whether to approve the candidate as a client or not.
For political organizations, we evaluate their mission, their membership, and their methods. We’re excited to support movement organizations building bottom-up power for popular demands, and encourage the use of our tool for meaningful connections and asks, rather than spam. There are countless different kinds of organizations, and just as we do with candidates, we research each one with care.
There’s no magic formula for making these decisions, many of which are quite complex. Our commitment is to consider anyone interested in working with us and to follow a principled process that ensures that we lead with our politics and stay true to our goal of building a more just, free, and equal world.


TutorNow is an on demand peer-to-peer campus tutoring application aims to impact the education industry by connecting college students who need help with college students who can give help. Following our contextual inquiry, task analysis, and initial sketches, TutorNow has evolved from sketches to a high-fidelity prototype.


U.S. senators call for review of Canadian protections of endangered right whales
Two U.S. senators from Massachusetts, including presidential candidate Elizabeth Warren, are calling for an investigation of Canada’s regulatory measures to protect the North Atlantic right whale.
Democrat senators Edward Markey and Warren sent a letter this week to the National Oceanic and Atmospheric Administration arguing Canadian rules for protecting the endangered whales are less strict than those in the United States.
Their request notes New England lobster fishermen are currently responding to a federal plan that reduces the amount of rope in the water and requires lines that break more easily.
A necropsy of a male right whale discovered off Long Island, N.Y., on Sept. 16 concluded it likely died due to entanglement in trap lines.
The whale was last spotted in late August in the Gulf of St. Lawrence and was entangled in lines at that time.
As of Nov. 3, eight right whales were known to have died in Canadian waters this year out of an endangered population numbering about 400 animals.
The senators say if Canada’s rules are shown to be less stringent than those in the United States, then the U.S. government should consider restricting imports of Canadian fishery products.
Our Morning Update and Evening Update newsletters are written by Globe editors, giving you a concise summary of the day’s most important headlines. Sign up today .


|
Script: C:\OracleATS\OFT\tttt
Date Time: 10/26/2017 16:21:01 下午 CST (UTC +8:00)
OpenScript Version: <IP_ADDRESS>.95
|
|
Iterations: 1
Total Steps: 2
Total User-Defined Tests: 0
Total Script Actions: 7
Total Passes: 7 (100.00%)
Total Failures: 0 (0.00%)
Total Warnings: 0 (0.00%)
Overall Result: Passed
|
Passed: 0
Passed: 7
|
Failed: 0
Failed: 0
|
Warning: 0
Warning: 0
|
| - Script Summary | Toggle Screen Shot Expand All Collapse All |
|
| - Test Results Summary |
|


Ocean [ay72], ocean, in Crystal Sea ------------------------------------------------------------------------ Routes leaving Ocean: North, to Ocean [ax72], 6 days East, to Ocean [ay73], 6 days South, to Ocean [az72], 6 days West, to Ocean [ay71], 6 days


Airbnb Predictor
Use historical booking data to predict the optimal price for an AirBnB in Berlin.
Fill this form and find the price!
Use historical booking data to predict the optimal price for an AirBnB in Berlin.
Fill this form and find the price!


Type your instructions here:
What is the correct terminal response?
Type in the folder structure you'd like to use for this challenge (optional):
What gets printed out on a successful input?
:>


skip to main content
Test case: Events-validNoMouseRequired.html
Events Tests
Test - Events with Javascript
Legend text: Automobiles
Fieldset text: Events while inside this textarea!
List of Automobiles
Ford, GM, Chrysler
Ford, GM, Chrysler
Ford, GM, Chrysler
Ford, GM, Chrysler
Ford, GM, Chrysler
Submit


I implemented a decision tree algorithm in order to predict which pasengers on the Titanic survived. I used different traits of each passenger such as their age, sex, and their number of siblings to determine whether or not they survived.
{% comment %} Check it out {% endcomment %}We uploaded our dataset to Lobe so that it could train a model for us to use. We then exported that model as a tensorflow model and eventually mounted the model onto a web page that utilized our laptop's webcam to get input.
{% comment %} Check it out {% endcomment %}


Image Viewer Test
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Adding traces
From the command window, the most common way to start plt is with a command such as:
plt(x,y)
where x and y contain the data that you want to plot. Of course this is the same as you
would have done before being exposed to plt, except that you would have spelled it "plot".
This command opens a new axis in a new figure window containing just the single trace defined
by the x,y parameter pair. There are many ways to call plt so that multiple traces are defined,
and most of these methods are also shared with the native Matlab plot
command. These methods are reviewed in the
Calling sequence section.
Where plt and plot diverge more noticeably
is how you add traces to a figure that has already been created. With plot
this is done using the hold on and hold off
commands that I think you will recognize as cumbersome after learning how it is done with
plt. The more traces you add, the more you will recognize the advantage of
the active legend automatically provided by plt, both for identifying the traces and controlling
which traces are visible. (Soon you may begin to wonder how you managed without plt.)
Assuming a single trace was defined using the plt command mentioned above, the command:
pltt(x1,y1,'TraceName')
will cause a new trace defined by the x1,y1 pair to appear on the plot. Also a second entry will
appear in the TraceID box with the trace name specified in the call to pltt. If you are feeling
lazy, you could omit the trace name in the argument list, in which case a default (incrementing)
trace name will be used. The more traces you add, the more you will realize that you should have
specified the trace names all along.
When you type the plt command in the command window, space will be reserved in the TraceID box
for eight additional traces (beyond those defined in the plt command itself) to be added using
the pltt command. This is usually enough, but if you go beyond this limit, pltt will overwrite
the trace data and traceID of the previously added trace to make room for the new trace being added.
If you anticipate needing to add more than eight traces, you can allocate more space in the TraceID
box using the + parameter in the plt argument list.
(The + is the last parameter described in the
Trace properties section.)
Usually you will probably find it most convenient to add just one trace at a time, however pltt
also provides a few ways to add more than one trace at a time. (This is especially useful inside
script files). For example, both of these commands:
pltt(x,{y1 y2 y3},{'new1' 'new2' 'new3'});
pltt(x,[y1;y2;y3],{'new1' 'new2' 'new3'});
are equivalent to the three separate commands:
pltt(x,y1,'new1');
pltt(x,y2,'new2');
pltt(x,y3,'new3');
Note that in the 2nd form above, it was assumed that y1,y2,y3 were row vectors. (If they were column vectors
they would need to be delimited with spaces or commas in place of the semicolons.)
And one last form to consider for when you are adding traces with differing x data. The command:
pltt({x1 x2 x3},{y1 y2 y3},{'new1' 'new2' 'new3'});
is equivalent to these three commands:
pltt(x1,y1,'new1');
pltt(x2,y2,'new2');
pltt(x3,y3,'new3');


org.ivar.leveltools.LevelTools
- type
- class
Static Methods
- Parameters:
tilemap
- the tilemap.
idx
- index to be clamped.
- Returns:
- clamped value.
- Parameters:
tilemap
- the tilemap.
value
- value to be clamped.
- Returns:
- clamped value.
- Parameters:
tilemap
- the tilemap.
value
- value to be clamped.
- Returns:
- clamped value.
- Parameters:
tilemap
- the tilemap.
f
- function that is called on each tile.
- Returns:
- the tilemap passed as the first argument.
- Parameters:
tilemap
- the tilemap.
x1
- x of the top left point of the rectangle.
y1
- y of the top left point of the rectangle.
x2
- x of the bottom right point of the rectangle.
y2
- y of the bottom right point of the rectangle.
f
- function that is called on each tile.
- Returns:
- the tilemap passed as the first argument.
- Parameters:
tilemap
- the tilemap.
tileId
- tile id to filter on.
f
- function that is called on each tile.
- Returns:
- the tilemap passed as the first argument.
- Parameters:
tilemap
- the tilemap.
- Returns:
- A tilemap iterator.
- Parameters:
tilemap
- the tilemap.
x1
- x of the top left point of the rectangle.
y1
- y of the top left point of the rectangle.
x2
- x of the bottom right point of the rectangle.
y2
- y of the bottom right point of the rectangle.
- Returns:
- A tilemap iterator.
- Parameters:
tilemap
- the tilemap.
oldId
- the tile ID to replace.
oldId
- the tile ID to replace it with.
- Returns:
- the tilemap passed as the first argument.
- Parameters:
tilemap
- the tilemap.
x
- The x coordinate.
y
- The y coordinate.
- Returns:
- index of the tile.


‘Iranian Hulk’ Sajad Gharibi joins Bare Knuckle Fighting Championship
by Kate Sheehy(NEW YORK POST) — The “Iranian Hulk” is set to unleash his fury in the Bare Knuckle Fighting Championship.
Internet sensation Sajad Gharibi — an extreme bodybuilder from Iran with an outrageously puffed-up physique — has joined the boxing fight club, according to MMA Fighting on Sunday.
Gharibi, 27, also known as the Persian Hercules, is set to debut next year, BKFC President David Feldman said.
“We’re going to do a show entitled ‘USA vs. Iran: World War III,’ ” Feldman said. “[Gharibi] is literally our biggest signing we’ve ever had.”
Gharibi gained fame in 2016 not only for his bizarre behemoth build but because he vowed to join the fight against ISIS.
More International News Stories
-
US: The kinky secrets found inside Pablo Escobar’s private chambers November 12, 2019
-
US: Slain college student Alexis Crawford was close friends with ‘killer’ roommate, family says November 12, 2019
-
Professor dismembered student-turned-lover, planned to kill himself dressed as Napoleon November 11, 2019
-
Texas man allegedly killed mom’s dog because he couldn’t have sex in the house November 11, 2019
-
Doctor allegedly performed hysterectomies without consent November 11, 2019
-
‘Get down!’: Mother’s last words to her kids before Mexican cartel slayings November 11, 2019
-
8-year-old boy steers car to safety after mom has seizure behind wheel November 11, 2019
-
Internet pours out support for autistic man who asked ‘Would someone like me?’ November 11, 2019
-
Long Island woman busted for allegedly harassing ‘harlequin’ tot with skin condition November 11, 2019


{% extends 'index.html' %} {% block content %}
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“There lay exposed a strange little brown image, a root of the potato species distorted into human shape, with grotesquely human features, nose, lips, the indication of eyes, and hairy filaments falling from the sides of the head and forming a kind of beard upon the shrivelled jaw and chin. The creature appeared a distinct miniature effigy of a man. The shape of the limbs was clearly traceable, and two little brown tentacles of arms with rudimentary hands lay, one by the side and the other half over the breast. Bits of the earth from which it had been torn still clung in the indentations of the shape, and on the top of the head, mingling with the tufts of hair, were the shrivelled remains of a stalk which had been removed or had mouldered away. Marillier examined the thing with intense curiosity, at the same time revolted by its quasi-human appearance.” — Mrs Campbell Praed, The Insane Root


Stuart Saves His Family (1995)
Starring Al Franken, Laura San Giacomo, Vincent D'Onofrio, and Harris Yulin.
Review by Laurence<EMAIL_ADDRESS>A Fully Loaded Entertainment Review - Website Coming in July!
***1/2 out of ****
I didn't really expect very much when I rented Stuart Saves His Family. The movie bombed at the box office, and I had never really liked the Saturday Night Live sketch on which the movie was based. My real concern, though, was the general reputation of SNL-related movies translating to the big screen with less-than-stellar results(Half-Baked, anyone?)
I was surprised to discover a truly entertaining, often hilarious yet also touching little film. It is an Al Franken production through-and-through, who not only created the title character but also wrote and starred in the picture. For those of you who don't know, Al Franken plays a guy named Stuart Smalley, who has a really annoying show on a public-access station about increasing your self-esteem, etc. Although not a licensed therapist, Stuart Smalley is, as the program proudly boasts, "a graduate of several 12-step programs."
Stuart is doing well enough with his tv show, but he keeps getting distracted by family troubles. Stuart's relatives are a mess: his dad and brother(Harris Yulin and Vincent D'Onofrio, respectively)are alcoholics, his sister is overweight and getting over another divorce, and his mom is in constant self-denial and solves all perceived problems by baking. Then, disaster strikes.
Stuart accidentally insults the manager of the station and gets his show revoked. He is forced to move back in with his family after he can no longer support himself, and there the comedy truly picks up.
Franken wrote all of the characters not just as cliches or stereotypes, but as sharply-drawn, real people. Although Smalley's dad has always imbibed too much, Stuart can still recall times when his father seemed like an okay guy. Also, after an accident towards the end of the movie, both Stuart and the audience learn that there is more to his mother than previously thought.
Although filled with small, very funny scenes, Stuart Saves His Family also has its share of dramatic moments, especially towards the conclusion. For a lightweight comedy, it handles these scenes especially well, often better than many so-called "serious dramas" of today. All the more amazing is that such a movie comes from a Saturday Night Live sketch that is essentially a one-joke bit.
If you feel like renting a comedy, and you haven't seen this film, I definitely recommend you try this one out. You'll laugh...you'll cry....it's all there.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


25 January 2002
Disease Outbreak Reported
As of 24 January 2002, the Institute of Public Health (IPH), Pristina, has reported 496 suspected cases of tularemia since the outbreak began on 1 November 2001. One hundred and nine of these cases have been laboratory confirmed. There have been no deaths to date (see previous report).
Measures for case management, environmental control and health education are continuing. WHO Collaborating Centre, Istituto Superiore di Sanità, Italy and the German component of Kosovo Force (K-FOR) are assisting the IPH by providing reagents for diagnostic tests.
Tularemia, which is endemic in many parts of the world, including north America, eastern Europe, China, Japan and Scandinavia, is a bacterial disease normally transmitted from animal hosts and has a variety of clinical manifestations. Symptoms include high fever and body aches, swollen glands and difficulty with swallowing, which continue over a period of a couple of weeks.


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Progress components are built with two HTML elements, some CSS to set the width, and a
few attributes. We don't use
the HTML5 <progress>
element
, ensuring you can stack progress bars, animate them, and place text labels over them.
Add labels to your progress bars by placing text within the
<c-progress-bar>
.
We only set a height
[value] on the <c-progress>
, so if
you change that [value] the inner <c-progress-bar>
will automatically
resize accordingly.
Use color
prop to change the appearance of individual progress bars.
Include multiple progress bars in a progress component if you need.
Add variant="striped"
to any <c-progress-bar>
to
apply a stripe via CSS gradient over the progress bar's background color.
The striped gradient can also be animated. Add [animated]="true"
property to
<c-progress-bar>
to animate the stripes right to left via CSS3
animations.


--- notes: Used to upload a new avatar. The component is generally useful as an image uploader. Could be used with a solid background instead of an existing image when such isn't present. ---


Customer id ID is required
Customer id should be 9 digits
Customer id should only be digits
Account type is required
Balance is required
Balance cannot be negative
Balance is invalid


Timothy Hayes
Full Stack Web Developer
Timothy is a Full Stack Web Developer, specializing in backend development and database management, with a background in management and customer service. He is able to build websites from the front to the back end. He has the aptitude to learn quickly and enjoys problem solving.
He is experienced in JavaScript, Node.js, Express.js, Handlebars.js, React.js, jQuery, GIT, Github, MongoDB, MySQL, HTML5, APIs, NPMs, JSON, AJAX, CSS3, PHP, Nightmare, Jasmine, computer science fundamentals.


ASStackLayoutElement Protocol Reference
| Conforms to | NSObject |
|---|---|
| Declared in | ASStackLayoutElement.h |
Overview
Layout options that can be defined for an ASLayoutElement being added to a ASStackLayoutSpec.
spacingBefore
required method
Additional space to place before this object in the stacking direction. Used when attached to a stack layout.
@property (nonatomic, readwrite) CGFloat spacingBefore
Declared In
ASStackLayoutElement.h
spacingAfter
required method
Additional space to place after this object in the stacking direction. Used when attached to a stack layout.
@property (nonatomic, readwrite) CGFloat spacingAfter
Declared In
ASStackLayoutElement.h
flexGrow
required method
If the sum of children's stack dimensions is less than the minimum size, how much should this component grow? This value represents the “flex grow factor” and determines how much this component should grow in relation to any other flexible children.
@property (nonatomic, readwrite) CGFloat flexGrow
Declared In
ASStackLayoutElement.h
flexShrink
required method
If the sum of children's stack dimensions is greater than the maximum size, how much should this component shrink? This value represents the “flex shrink factor” and determines how much this component should shink in relation to other flexible children.
@property (nonatomic, readwrite) CGFloat flexShrink
Declared In
ASStackLayoutElement.h
flexBasis
required method
Specifies the initial size in the stack dimension for this object. Default to ASDimensionAuto Used when attached to a stack layout.
@property (nonatomic, readwrite) ASDimension flexBasis
Declared In
ASStackLayoutElement.h
alignSelf
required method
Orientation of the object along cross axis, overriding alignItems Used when attached to a stack layout.
@property (nonatomic, readwrite) ASStackLayoutAlignSelf alignSelf
Declared In
ASStackLayoutElement.h
ascender
required method
Used for baseline alignment. The distance from the top of the object to its baseline.
@property (nonatomic, readwrite) CGFloat ascender
Declared In
ASStackLayoutElement.h
descender
required method
Used for baseline alignment. The distance from the baseline of the object to its bottom.
@property (nonatomic, readwrite) CGFloat descender
Declared In
ASStackLayoutElement.h


LegalProseReference(uri: String)
The annotated Contract implements the legal prose identified by the given URI.


Minecraft Earth Early Access now available in the UK
by AshThe Early Access stage of Minecraft Earth is currently building its way across the globe and, as of today, is officially available in the UK.
Along with the UK, Minecraft Earth Early Access is also available in Canada, South Korea, the Philippines, Sweden, Mexico, Australia, New Zealand, and Iceland.
You can pick up Minecraft Earth on iOS here (if you’re in one of the above countries but can’t see it yet – don’t panic! Just check back later today) and on Android here.
In the current Early Access build, players can throw down a buildplate and create an interactive Minecraft diorama.
They’ll also be able to find tappables and unlock resources and unique mobs, craft and smelt resources, collaborate with other Early Access builders, and fight hostile mobs in adventures.
Here are just some of the features of Minecraft Earth:
- Collecting: Minecraft Earth features many of the mobs that Minecraft players know and love (and potentially hate), along with new ones. The more you play, the higher the chance of discovering unique variants and using them to populate your builds.
- Exploring: You can explore the world around you to gather resources, take on challenges, and experience a whole new blocky world.
- Collaborating: You can team up with other Minecraft Earth players to craft together! You know what they say – it takes a village to craft a whole bunch of Minecraft creations, or something like that.
- Surviving: You can battle Minecraft mobs in real life! Just make sure you don’t stand too close to the Creepers.
With its launch in the UK, the total number of countries with Early Access to Minecraft is nine. Who will be the 10th lucky country? We’ll just have to wait and see.
If you want to be one of the first to know which countries will get Early Access next, you can follow the official Minecraft Earth Twitter account.
Minecraft Earth should be out globally by the end of 2019. The game will support all the same languages as regular vanilla Minecraft. To play Minecraft Earth, you’ll need a mobile device that runs at least Android 7 or iOS 10. You’ll also need a Microsoft or Xbox Live login account.


If you would like try get started or try beekeeping or if you are
looking for an original and exciting present, our courses can teach
you everything you need to know to make you a safe and successful
beekeeper.
Our comprehensive courses are aimed at beginner medium level. We
offer one day courses and family / group discounts. All you need for
the course can be provided by us and we can even provide you with
your own equipment to take home with you.


-Removed
-As its not updated anymore and i don't feel i can do it any better then other addons out there. ( i feel my time can be spent better somewhere else on the project)
-You can download this as a separate Download if you wish.
- Some Back-End Work for Legion/7.0.x
- New tooltips on the options config to help make it easier to understand what some buttons to do and to clean up some of the long line texts.
- Changed Help to Commands - as it shows more of the command Line stuff.
- If a Module is not loaded on all characters then it no longer Lua-Error and tells the client what Module will need to be loaded.
- Updated LibActionButton
- Updated AceGUI-3.0-SharedMediaWidgets
- Updated settings Frame for config. Resize and close button.
- Added two new ways to add team members to the team with the command line and GUI.
---@target will add your current target to the team list
---@mouseOver Will add your current MouseOver to the team list.
- cleaned up some loot code. and some offline code.
- Loot can olny be set to Group/FFA/Personal. ( Olny works in a Instances )
-Renamed from Jamba Quest Watcher to Jamba Objective Tracker(To fit in line with blizzards own naming)
- Now Shows Scenario Objective on the watcher.
- Shows Bouns Scenario Objective.
- Fixed with Broken Shore QuestLines a % bar needed coding in.
- Only Show stage if there is more the one stage (so does not show in dungeons.)
- Added support to change the font And text size
- Now automatically sets the frame to the Right Hand Side of the UI.
- A big thanks to Schilm from dual-boxing.com to the new changes. little edited by me
- Added buttons to blizzard Quest Rightclick Dropdown Menu to Track and un-track and to abandon all quests.
-New Bar Now part of the QuestMap Frame. with new buttons
--Abandon All
-Works just like it used to. It loops through all of your toons and abandons all the quests on each toon.
-Now ignores quests that cannot be abandoned, such as the Account Wide quests Luxlunae mentioned.
--Share All
-Used to just loop through the current toons quests and just shared those, NOW loops through all toons and attempts to share all Quests from all toons.
--Track All
-Back from the abyss!!!
-Used to just loop through the current toons quests and just tracked those, NOW loops through all toons and attempts to track all Quests from all toons.
--Untrack All
-Back from the abyss!!!
-Used to just loop through the current toons quests and just untracked those, NOW loops through all toons and attempts to un-tracks all Quests from all toons.
- Values are now Abbreviated to smaller Numbers.
- No longer Shows max Value when showing Percentage
- Added support to change the font And text size
- Added a NewTooltip when you hover over Follow bar to show
* Level of minion If not level capped.
* ItemLevel of minion.
* Durability of the minion.
* Bag Space of the minion.
* Gold of the minion.
* Mail if the minion has mail and who it is from (Same as minimap)
- Tooltip should no longer show in combat.
- Removed the old Display Bag Space Bar added to tooltips
- Renamed "Combo PointBar" to "Class Power Bar"
- Added Support for Other class Power
* Warlocks - Soul Shards
* Paladin - Holy Power
* DeathKight - Runes
* Mage - ARCANE CHARGES
* Monk - CHI
- Removed the Old Reputation bar.
- Changes to the XP bar.
* Your can show up for four XP Bars For the 4 XP type each bar is on the same XP frame and makes each bar a little smaller.
** Xp, Players XP.
** Artifact XP, Show the players weapon Artifact XP.
** Show Honor XP, Show a bar for the new honor xp bar.
** Show Reputation, show a bar for Reputation
- Added new checks to fix The FollowDance when spamming follow key.
- You can now specify the number of characters per row up to 10.
- Removed Jamba-Currency From the toon module
- Added Team Release Prompts when you die to Release the spirits of all minions. And release back to the minions bodies.
- All Currency Information
- There is a now a DropDown Box to show the Currency's you like to show
- You can Have a max of 6 Currencies.
- Now shows Currency Icon Then The Three Letters of text.
- Now shows the Currency Gold Icons when showing gold/Sliver/Copper
- No Longer Shows Currency for Offline minions.
- Should Now Order minions That are Offline/Online correctly.
- Added support to change the font And text size.
- Fixed a Frame Bug with The new flight maps in broken island (Legion Zones)
- Fixed a text bug that was not working with trade goods. (cloth)
- Personal-Loot OverRights The Advanced Loot list.
- You can now Use spells on the item bar without errors.
- Now Automatically sets to the Bottom Right then the center of the UI.
- Made some backend checks to make sure follow has been really been broken. (spamming follow key should not spam "follow Borken")
- NoLonger warns if the player is on a taxi
- Added an option to turn of the Messages in chat when updating FTL...
- Moved in options to Chat from Advanced


pip uninstall package_name
pip uninstall --requirement path/to/requirements.txt
pip uninstall --yes package_name


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Maintaining your car battery: Turn off stereo, grease the terminals
No matter how new they are, car batteries need looking after, particularly in cold weather. Proper maintenance will increase the life of the battery and help reduce fuel consumption.
Cold weather can hit your car battery hard, so it needs some special care during the winter months. To start, make sure it's not overloaded with unnecessary demands before you turn on the ignition.
Experts at Germany's testing organisation Tuev Sued say this is important because the fuel pump, injection nozzles and engine management systems alone require about 400 watts of power to work.
Ventilation fans and air conditioning, for example, use up a further 100 to 500 watts, and lighting, radio and heating guzzle up to 1,000 watts more. Window mechanisms and heaters add further loads that result in more electricity being consumed than the alternator can produce. That's bad news for the battery, which won't get properly charged.
Increased power requirements also take a toll at the filling station. Experts estimate that for every 100 watts of extra power the car's alternator has to supply, there is an additional consumption of 0.1 litres of petrol per 100 kilometres.
In order to avoid corrosion and leakage, drivers should keep the housing and poles of the battery as clean as possible.
Build-ups of oxides can be removed from the terminals with fine sandpaper, and should then be wiped with a dry cloth. Follow this up by applying special grease to the poles and the connection terminals, making sure to always follow the manufacturer's instructions.
The battery's housing can simply be wiped down with a damp cloth.
Read also
Read More
AAB hosts Hybrid Familiarisation Program for corporate customers
Abdullah Abdulghani & Bros. Co. (AAB) sole agents for Toyota vehicles in Qatar organised a Hybrid Familiarisation Program for their top corporate customers. The event held at The Westin Doha Hotel was attended by was attended by senior officials from various companies including Rent a Car, Limousine Companies etc.
Read More
Enthusiasts in Qatar experience all new Ferrari F8 Tributo for the first time
The world class circuit hosted 29 Prancing Horse automobiles with the all new Ferrari F8 Tributo in the spotlight as it features on the track for the first time in Qatar.


| Class | Description |
|---|---|
| StreamingXXHash32 |
Streaming interface for
XXHash32 . |
| StreamingXXHash64 |
Streaming interface for
XXHash64 . |
| XXHash32 |
A 32-bits hash.
|
| XXHash64 |
A 64-bits hash.
|
| XXHashFactory |
Entry point to get
XXHash32 and StreamingXXHash32 instances. |
xxhash hashing. This package supports both block hashing via
XXHash32
and streaming hashing via
StreamingXXHash32
. Have a look at
XXHashFactory
to know how to get instances of these
interfaces.
Streaming hashing is a little slower but doesn't require to load the whole stream into memory.
Sample block usage:
XXHashFactory factory = XXHashFactory.fastestInstance(); byte[] data = "12345345234572".getBytes("UTF-8"); XXHash32 hash32 = factory.hash32(); int seed = 0x9747b28c; // used to initialize the hash value, use whatever // value you want, but always the same int hash = hash32.hash(data, 0, data.length, seed);
Sample streaming usage:
XXHashFactory factory = XXHashFactory.fastestInstance(); byte[] data = "12345345234572".getBytes("UTF-8"); ByteArrayInputStream in = new ByteArrayInputStream(data); int seed = 0x9747b28c; // used to initialize the hash value, use whatever // value you want, but always the same StreamingXXHash32 hash32 = factory.newStreamingHash32(seed); byte[] buf = new byte[8]; // for real-world usage, use a larger buffer, like 8192 bytes for (;;) { int read = in.read(buf); if (read == -1) { break; } hash32.update(buf, 0, read); } int hash = hash32.getValue();


[gecode-users] printing of time in script.cpp
Jan Kelbel
kelbelj at fel.cvut.cz
Thu Oct 13 14:13:13 CEST 2011
Hi,
I found some non-correct behavior in function
void stop(Support::Timer& timer, std::ostream& os)
lines 57 to 67 in release 3.7.1 in file gecode\driver\script.cpp
When for example (o_hour > 0) and (o_min == 0), then the result of the
output is o_hour:o_sec.o_msec, and the minute part is skipped.
Possibly, the lines 59 and 60 can be replaced by:
if (o_hour) {
os << o_hour << ":";
if (0 == o_min) {
os << "00:";
os.width(2); os.fill('0');
}
}
Cheers,
Jan
More information about the users
mailing list


Report: Liverpool sweating on fitness of Andrew Robertson and Mo Salah but Joe Gomez should be injury-free
All three players pulled out of international duty.
by Jay WilliamsAndrew Robertson and Mo Salah could miss the next game with ankle injuries, The Liverpool Echo reports.
Liverpool manager Jurgen Klopp has two fresh injury concerns for this weekend. Both Robertson and Salah aggravated pre-existing ankle knocks during the 3-1 win over Manchester City prior to the international break.
Salah’s injury flared up after a sliding challenge from Fernandinho, while Robertson was hurt by a tackle from Rodri. Both players had to drop out of international duty because of this.
Egypt star Salah did not play in the AFCON qualifiers after undergoing a scan in Cairo. Meanwhile, Robertson dropped out of the Scotland squad because he did not want to make his ankle worse.
Joe Gomez was also a concern for the upcoming game against Crystal Palace. However, the 22-year-old, who dropped out of the England squad with a knee injury, is expected to be fit for the weekend.
TBR’s view
Losing Robertson and Salah presents Klopp with two major selection concerns. Firstly, he has to find someone to fill in at left-back to replace Robertson. This will probably be James Milner, who is reliable but not as impressive as Robertson.
Salah’s absence would break up the front three with Roberto Firmino and Sadio Mane. Divock Origi will eye a starting spot while Alex Oxlade-Chamberlain and Adam Lallana are also in contention.
Have something to tell us about this article?
Let us know


$CDD2/52690: Open channel Jump from $CDBD, $CDF2: CDD2: A6 D3 LDX $D3 CDD4: E6 D3 INC $D3 CDD6: BD 85 02 LDA $0285,X ; buffer number CDD9: A8 TAY CDDA: 88 DEY CDDB: 88 DEY CDDC: C0 0C CPY #$0C ; buffer number smaller than 14? CDDE: 90 05 BCC $CDE5 ; yes Jump from $CDEA: CDE0: A9 70 LDA #$70 CDE2: 4C C8 C1 JMP $C1C8 ; 70, 'no channel' Jump from $CDDE: CDE5: 85 83 STA $83 ; secondary address CDE7: 20 EB D0 JSR $D0EB ; open channel CDEA: B0 F4 BCS $CDE0 ; already allocated, 'no channel' CDEC: 20 93 DF JSR $DF93 ; buffer number CDEF: 85 F9 STA $F9 ; set CDF1: 60 RTS Previous Page / Next Page


Use this coupon code before the next release to claim the reward.Hurry up & don't loose your chance.
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{% include "cannlytics_website/components/placeholders/placeholder.html" with
url="#"
image="cannlytics_website/images/illustrations/chemist.svg"
image_height="120px"
title="No activity"
message="Changes made to this lab's data will be listed here."
only
%}


| Opcode | Instruction | Op/En | 64-bit Mode | Compat/Leg Mode | Description |
|---|---|---|---|---|---|
| 0F BB /r | BTC r/m16, r16 | MR | Valid | Valid | Store selected bit in CF flag and complement. |
| 0F BB /r | BTC r/m32, r32 | MR | Valid | Valid | Store selected bit in CF flag and complement. |
| REX.W + 0F BB /r | BTC r/m64, r64 | MR | Valid | N.E. | Store selected bit in CF flag and complement. |
| 0F BA /7 ib | BTC r/m16, imm8 | MI | Valid | Valid | Store selected bit in CF flag and complement. |
| 0F BA /7 ib | BTC r/m32, imm8 | MI | Valid | Valid | Store selected bit in CF flag and complement. |
| REX.W + 0F BA /7 ib | BTC r/m64, imm8 | MI | Valid | N.E. | Store selected bit in CF flag and complement. |
| Op/En | Operand 1 | Operand 2 | Operand 3 | Operand 4 |
| MR | ModRM:r/m (r, w) | ModRM:reg (r) | NA | NA |
| MI | ModRM:r/m (r, w) | imm8 | NA | NA |
Selects the bit in a bit string (specified with the first operand, called the bit base) at the bit-position designated by the bit offset operand (second operand), stores the value of the bit in the CF flag, and complements the selected bit in the bit string. The bit base operand can be a register or a memory location; the bit offset operand can be a register or an immediate value:
See also: Bit(BitBase, BitOffset) on page 3-11.
Some assemblers support immediate bit offsets larger than 31 by using the immediate bit offset field in combina-tion with the displacement field of the memory operand. See “BT—Bit Test” in this chapter for more information on this addressing mechanism.
This instruction can be used with a LOCK prefix to allow the instruction to be executed atomically.
In 64-bit mode, the instruction’s default operation size is 32 bits. Using a REX prefix in the form of REX.R permits access to additional registers (R8-R15). Using a REX prefix in the form of REX.W promotes operation to 64 bits. See the summary chart at the beginning of this section for encoding data and limits.
CF ← Bit(BitBase, BitOffset); Bit(BitBase, BitOffset) ← NOT Bit(BitBase, BitOffset);
The CF flag contains the value of the selected bit before it is complemented. The ZF flag is unaffected. The OF, SF, AF, and PF flags are undefined.
| #GP(0) |
If the destination operand points to a non-writable segment. If a memory operand effective address is outside the CS, DS, ES, FS, or GS segment limit. If the DS, ES, FS, or GS register contains a NULL segment selector. |
| #SS(0) | If a memory operand effective address is outside the SS segment limit. |
| #PF(fault-code) | If a page fault occurs. |
| #AC(0) | If alignment checking is enabled and an unaligned memory reference is made while the current privilege level is 3. |
| #UD | If the LOCK prefix is used but the destination is not a memory operand. |
| #GP | If a memory operand effective address is outside the CS, DS, ES, FS, or GS segment limit. |
| #SS | If a memory operand effective address is outside the SS segment limit. |
| #UD | If the LOCK prefix is used but the destination is not a memory operand. |
| #GP(0) | If a memory operand effective address is outside the CS, DS, ES, FS, or GS segment limit. |
| #SS(0) | If a memory operand effective address is outside the SS segment limit. |
| #PF(fault-code) | If a page fault occurs. |
| #AC(0) | If alignment checking is enabled and an unaligned memory reference is made. |
| #UD | If the LOCK prefix is used but the destination is not a memory operand. |
Same exceptions as in protected mode.
| #SS(0) | If a memory address referencing the SS segment is in a non-canonical form. |
| #GP(0) | If the memory address is in a non-canonical form. |
| #PF(fault-code) | If a page fault occurs. |
| #AC(0) | If alignment checking is enabled and an unaligned memory reference is made while the current privilege level is 3. |
| #UD | If the LOCK prefix is used but the destination is not a memory operand. |


| qqPlot {car} | R Documentation |
Plots empirical quantiles of a variable, or of studentized residuals from
a linear model, against theoretical quantiles of a comparison distribution. Includes
options not available in the qqnorm
function.
qqPlot(x, ...) qqp(...) ## Default S3 method: qqPlot(x, distribution="norm", groups, layout, ylim=range(x, na.rm=TRUE), ylab=deparse(substitute(x)), xlab=paste(distribution, "quantiles"), glab=deparse(substitute(groups)), main=NULL, las=par("las"), envelope=.95, col=carPalette()[1], col.lines=carPalette()[2], lwd=2, pch=1, cex=par("cex"), line=c("quartiles", "robust", "none"), id=TRUE, grid=TRUE, ...) ## S3 method for class 'formula' qqPlot(formula, data, subset, id=TRUE, ylab, glab, ...) ## S3 method for class 'lm' qqPlot(x, xlab=paste(distribution, "Quantiles"), ylab=paste("Studentized Residuals(", deparse(substitute(x)), ")", sep=""), main=NULL, distribution=c("t", "norm"), line=c("robust", "quartiles", "none"), las=par("las"), simulate=TRUE, envelope=.95, reps=100, col=carPalette()[1], col.lines=carPalette()[2], lwd=2, pch=1, cex=par("cex"), id=TRUE, grid=TRUE, ...)
x |
vector of numeric values or |
distribution |
root name of comparison distribution – e.g., |
groups |
an optional factor; if specified, a QQ plot will be drawn for |
layout |
a 2-vector with the number of rows and columns for plotting by
groups – for example |
formula |
one-sided formula specifying a single variable to be plotted or a two-sided formula of
the form |
data |
optional data frame within which to evaluage the formula. |
subset |
optional subset expression to select cases to plot. |
ylim |
limits for vertical axis; defaults to the range of |
ylab |
label for vertical (empirical quantiles) axis. |
xlab |
label for horizontal (comparison quantiles) axis. |
glab |
label for the grouping variable. |
main |
label for plot. |
envelope |
confidence level for point-wise confidence envelope, or
|
las |
if |
col |
color for points; the default is the first entry
in the current car palette (see |
col.lines |
color for lines; the default is the second entry in the current car palette. |
pch |
plotting character for points; default is |
cex |
factor for expanding the size of plotted symbols; the default is
|
id |
controls point identification; if |
lwd |
line width; default is |
line |
|
simulate |
if |
reps |
integer; number of bootstrap replications for confidence envelope. |
... |
arguments such as |
grid |
If TRUE, the default, a light-gray background grid is put on the graph |
Draws theoretical quantile-comparison plots for variables and for studentized residuals from a linear model. A comparison line is drawn on the plot either through the quartiles of the two distributions, or by robust regression.
Any distribution for which quantile and
density functions exist in R (with prefixes q
and d
, respectively) may be used.
When plotting a vector, the confidence envelope is based on the SEs of the order statistics
of an independent random sample from the comparison distribution (see Fox, 2016).
Studentized residuals from linear models are plotted against the appropriate t-distribution with a point-wise
confidence envelope computed by default by a parametric bootstrap,
as described by Atkinson (1985).
The function qqp
is an abbreviation for qqPlot
.
These functions return the labels of identified points, unless a grouping factor is employed,
in which case NULL
is returned invisibly.
John Fox<EMAIL_ADDRESS>Fox, J. (2016) Applied Regression Analysis and Generalized Linear Models, Third Edition. Sage.
Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.
Atkinson, A. C. (1985) Plots, Transformations, and Regression. Oxford.
qqplot
, qqnorm
,
qqline
, showLabels
x<-rchisq(100, df=2) qqPlot(x) qqPlot(x, dist="chisq", df=2) qqPlot(~ income, data=Prestige, subset = type == "prof") qqPlot(income ~ type, data=Prestige, layout=c(1, 3)) qqPlot(lm(prestige ~ income + education + type, data=Duncan), envelope=.99)


|
Bitstream Interpretation Library (BIL)
0.1
|
Writes a Virtex-5 bitstream into XML. More...
#include <bitstream/V5BitstreamXMLWriter.hpp>
Public Member Functions | |
| V5BitstreamXMLWriter (XMLWriter &xmlWriter) | |
| Creates a new V5BitstreamXMLWriter instance. | |
| virtual void | visit (const BuswidthPattern &buswidthPattern) |
| Visits a BuswidthPattern packet. | |
| virtual void | visit (const DummyWord &dummyWord) |
| Visits a DummyWord packet. | |
| virtual void | visit (const SyncWord &syncWord) |
| Visits a SyncWord packet. | |
| virtual void | visit (const Type1Packet &type1Packet) |
| Visits a Type1Packet packet. | |
| virtual void | visit (const Type2Packet &type2Packet) |
| Visits a Type2Packet packet. | |
| Public Member Functions inherited from bil::V5BitstreamSyntaxChecker | |
| V5BitstreamSyntaxChecker () | |
| bool | behindSyncWord () const |
| Tells, if sync word was passed. | |
| bool | lastPacketWasType1 () const |
| Tells, if the last packet processed was a type 1 packet. | |
| RegisterAddress::address_t | lastType1Address () const |
| Gets the register address of the last processed type 1 packet. | |
| virtual void | reset () |
| Resets the internal state. | |
| Public Member Functions inherited from bil::V5PacketVisitor | |
| virtual void | visit (const Packet &packet) |
| Irrelevant visit() method for Virtex-5 bitstreams. | |
| Public Member Functions inherited from bil::PacketVisitor | |
| virtual | ~PacketVisitor () |
Protected Member Functions | |
| void | writePacketTag (const BuswidthPattern &buswidthPattern) |
| Writes a BuswidthPattern packet into XML. | |
| void | writePacketTag (const DummyWord &dummyWord) |
| Writes a DummyWord packet into XML. | |
| void | writePacketTag (const SyncWord &syncWord) |
| Writes a SyncWord packet into XML. | |
| void | writePacketTag (const std::string &tagName, const Type2Packet &type2Packet) |
| Writes a Type2Packet packet (and its descendants) into XML. | |
| void | writeData (const boost::uint32_t *words, size_t wordCount) |
| Writes packet payload data into XML. |
Writes a Virtex-5 bitstream into XML.
Being based on the V5BitstreamSyntaxChecker, V5BitstreamXMLWriter syntax checks the packet sequence and writes every visited packet out to XML using a given XMLWriter.
Definition at line 26 of file V5BitstreamXMLWriter.hpp.
|
explicit |
Creates a new V5BitstreamXMLWriter instance.
| xmlWriter | The XMLWriter instance to be used for writing. |
Definition at line 30 of file V5BitstreamXMLWriter.cpp.
|
virtual |
Visits a BuswidthPattern packet.
| buswidthPattern | BuswidthPattern instance to be visited. |
| . |
Reimplemented from bil::V5BitstreamSyntaxChecker.
Definition at line 37 of file V5BitstreamXMLWriter.cpp.
|
virtual |
Visits a DummyWord packet.
| dummyWord | DummyWord instance to be visited. |
| . |
Reimplemented from bil::V5BitstreamSyntaxChecker.
Definition at line 44 of file V5BitstreamXMLWriter.cpp.
|
virtual |
Visits a SyncWord packet.
| syncWord | SyncWord instance to be visited. |
| . |
Reimplemented from bil::V5BitstreamSyntaxChecker.
Definition at line 51 of file V5BitstreamXMLWriter.cpp.
|
virtual |
Visits a Type1Packet packet.
| type1Packet | Type1Packet instance to be visited. |
| . |
Reimplemented from bil::V5BitstreamSyntaxChecker.
Definition at line 58 of file V5BitstreamXMLWriter.cpp.
|
virtual |
Visits a Type2Packet packet.
| type2Packet | Type2Packet instance to be visited. |
| . |
Reimplemented from bil::V5BitstreamSyntaxChecker.
Definition at line 65 of file V5BitstreamXMLWriter.cpp.
|
protected |
Writes a BuswidthPattern packet into XML.
| buswidthPattern | BuswidthPattern instance to be written. |
| . |
Definition at line 72 of file V5BitstreamXMLWriter.cpp.
|
protected |
Writes a DummyWord packet into XML.
| dummyWord | DummyWord instance to be written. |
| . |
Definition at line 79 of file V5BitstreamXMLWriter.cpp.
|
protected |
Writes a SyncWord packet into XML.
| syncWord | SyncWord instance to be written. |
| . |
Definition at line 87 of file V5BitstreamXMLWriter.cpp.
|
protected |
Writes a Type2Packet packet (and its descendants) into XML.
| tagName | XML tag to use. |
| type2Packet | Type2Packet instance to be written. |
| . |
Definition at line 94 of file V5BitstreamXMLWriter.cpp.
|
protected |
Writes packet payload data into XML.
| words | Pointer to data words to write. |
| wordCount | Number of data words to write. |
| . |
Definition at line 127 of file V5BitstreamXMLWriter.cpp.


Basic knowledge of Flutter Willing to learn React
Confident in Python, intermediate in Java and C, basic knowledge of Dart, Haskell, Erlang and Scheme
Most frequent used tools: Visual Studio Code, Vim, Jupyter Notebook


Project 3: original data analysis¶
As discussed in class, project #3 will be a free-form version of work along the lines of what you did in project #2. The goal of the project will be to develop a complete analysis of a dataset that you find online, ask some interesting questions from it, and share your complete set of results in a well documented manner, using the practices we've been developing during the course.
Team setup and logistics¶
Due date: the last commit should be no later than Wednesday December 13th, at 11pm local (Pacific) Time.
Teams: the project is to be completed with the same team of three that you used for hw 3, the dataset selection.
Team names: use the same team name as for hw3: your repo should be named p3-XX-YY-ZZ
, where XX, YY and ZZ are the first two letters of each member's last name.
Project deliverables¶
Data¶
Depending on the size of your data, you may choose to include it in the repository or not (use your judgment - up to a few dozen megabytes it's not a problem, something in the Gigabyte range is too big to put into a repo). If the data isn't included locally, your code should access it remotely and cache it adequately for subsequent executions. If you don't include the data in the repository, consider the implications of doing so for long-term reproducibility: is the source you are using a reliable one? Will it be there in 10 years?
Functional structure of your code and testing¶
You should identify steps amenable to be put into standalone functions and write a proper function for those, with a complete docstring describing their inputs, as I've shown in some of our examples (the Numpy project offers detailed examples of high-quality docstrings).
You may choose to put a few analysis functions into a pure python file that you import separately, make the choice that you find cleanest/most fluid for your workflow.
Whether you do it in a notebook or python scripts, make sure to write at least a few test for utility functions that you write.
Note: this is a requirement. You must structure your code to include at least two functions, that you test and document properly (feel free to use more as your analysis dictates).
Analysis notebooks and supporting code¶
Break down your analysis into as many notebooks as is reasonable for convenient reading and execution. There is no hard and fast rule for this, just as there isn't for how many paragraphs or figures a good scientific paper should have. You should consider readability, execution time, total notebook length, etc, as criteria in your decisions. This essay by Stephen Wolfram, the creator of Mathematica (part of the inspiration for the Jupyter Notebook) has some good thoughts on what makes a well written computational narrative.
Consider how to "chain" your analyses with intermediate results being stored so they don't need to be recomputed from scratch each time. Also, be mindful of saving key figures you may want to reuse in your report to disk, so the main narrative notebook can reuse them for display and discussion. Look back at the instructions for project 2 if you need a refresher.
Main narrative notebook¶
Write a main.ipynb
notebook that summarizes and discusses your results. It can contain figures referenced from disk or simple summary information (for example if you want to display part of a dataframe) from variables read from disk, but no significant computations at all. Think of this as your "paper".
Discuss the assumptions you can make about the data to justify your analysis. You have no control over the original data acquisition and measurement, and for many of you this project will likely fall under the broad purview of Exploratory Data Analysis. If you propose any statistical hypothesis/model in your analysis, discuss your justifications, considering how the data was acquired.
Your final report, when completed, may well diverge your initial plan as submitted in hw3. Discuss any changes you made from this plan and why you chose to pursue new questions or modify your original ones.
Reproducibility support¶
- Environment: like in Projects #1 and #2, provide an
environment.yml
file that creates an environment with all necessary dependencies to run your analysis. Makefile
: you will also create aMakefile
withenv
andall
targets, similar to that of Project #1. Theenv
target should make the environment with all necessary libraries,all
should run all notebooks.- Don't forget to control your random seeds for anything that has a stochastic component.
Good repository practices¶
README.md
: see instructions therein.LICENSE
: you should explicitly state which licensing conditions apply to your work (see Github's help). I strongly suggest looking at Victoria Stodden's ENABLING REPRODUCIBLE RESEARCH: LICENSING SCIENTIFIC INNOVATION. For a short summary, her slides can be useful. She suggests a "Reproducible research standard" that licenses code, data and text/media materials in comparable terms that maximize resharing potential while providing due credit guarantees for the original authors..gitignore
: provide a git ignore file that prevents the automatic inclusion of unwanted files. This also helps you avoid noisygit status
output that lists things you know you won't actually want to put under git's control.


Load External Content
It is possible to extract parts or your page into external HTML pages and load these pages into
your current page. Just use source: 'someURL'
.
You can register a callback on sourceLoaded
that will be triggered after the content is loaded.
This can be useful for performing custom post-processing.
<div data-bind="page: { id: 'load_external_content', sourceLoaded: externalContentLoaded, source: 'load_external_content.html .content'}"> ... </div>
var externalContentLoaded = function(page) { prettyPrint(); };Find out how to lazy load external content!


This report is made to ensure the continuity of business processes, preserve and share internal organization expertise within the Group, as well as to prove the feasibility and validity of the developed model.
To achieve the goals, the Report provides insights into some essential methodological approaches to modeling.
{{ model_aim }}
The report has the following structure:
Key model identifiers are given in Table 1.
| Parameter | Value |
|---|---|
| Model name / ID | {{ model_name }} |
| Report version | {{ report_version }} |
| Model customer | {{ zakazchik }} |
| Name of the Group member and high level department | {{ high_level_department }} |
| Model developer (if an external developer is involved, it is necessary to indicate Company's name) | {{ ds_name }} |
Description and specification of datasets that are used to develop and test the model are presented in Table 2.
| Parameter | Training set | Test set | |||
| Target event description | {{ target_descr }} | ||||
| Non-target event description | {{ non_target_descr }} | ||||
| Observations count in sets | {{ count_train }} | {{ count_test }} | |||
| Target events count | {{ train_target_cnt }} | {{ test_target_cnt }} | |||
| Non-target events count | {{ train_nontarget_cnt }} | {{ test_nontarget_cnt }} | |||
| Mean of target in sets | {{ train_target_perc }}% | {{ test_target_perc }}% |
Predictions of the model trained on the training set are shown in Table 3.
| Parameter | Training set | Test set |
| AUC | {{ train_auc_full }}% | {{ test_auc_full }}% |
| Gini | {{ train_gini_full }}% | {{ test_gini_full }}% |
Coefficients of the regression model are shown in Table 4.
| Feature name | Regression coefficient |
| {{ pair[0] }} | {{ pair[1] }} |
Statistics related to the number of missing values between training and test sets are shown in Table 5.
| Feature name | Number of valid values in the training set | Number of valid values in the test set | Number of missing values in the training set | Number of missing values in the test set | Percentage of valid values in the training set | Percentage of valid values in the test set | Difference in percentage of valid values in sets |
| {{ val[0] }} | {{ val[1] }} | {{ val[2] }} | {{ val[3] }} | {{ val[4] }} | {{ val[5] }} | {{ val[6] }} | {{ val[7] }} |
| Feature name | P-value |
| {{ val[0] }} | {{ val[1] }} |
| Feature name | P-value |
| {{ val[0] }} | {{ val[1] }} |
Required to train model with a parameter regularized_refit=False
{% endif %}Gini for the training set
Gini for the test set
| Feature name | VIF value |
| {{ val[0] }} | {{ val[1] }} |
Two or more features required for VIF calculation
{% endif %}Total PSI
| Feature name | PSI value |
| {{ val[0] }} | {{ val[1] }} |
PSI for non-target events
| Feature name | PSI value |
| {{ val[0] }} | {{ val[1] }} |
PSI for target events
| Feature name | PSI value |
| {{ val[0] }} | {{ val[1] }} |
Grouping by predictions on the training set (total)
Grouping by predictions of target and non-target events on the training set
Grouping by predictions on the test set (total)
Grouping by predictions of target and non-target events on the test set
PSI by grouped predictions of model
| Total PSI | {{ psi_binned_total }} |
| PSI for non-target events | {{ psi_binned_zeros }} |
| PSI for target events | {{ psi_binned_ones }} |
To calculate PSI, you must first call fit() and predict_proba()
{% endif %}| ScoreBin | count | mean | std | min | 25% | 50% | 75% | max |
| {{ val[0] }} | {{ val[1] }} | {{ val[2] }} | {{ val[3] }} | {{ val[4] }} | {{ val[5] }} | {{ val[6] }} | {{ val[7] }} | {{ val[8] }} |
| ScoreBin | count | mean | std | min | 25% | 50% | 75% | max |
| {{ val[0] }} | {{ val[1] }} | {{ val[2] }} | {{ val[3] }} | {{ val[4] }} | {{ val[5] }} | {{ val[6] }} | {{ val[7] }} | {{ val[8] }} |
| Variable | Value | WOE | COEF | POINTS |
| {{ val[0] }} | {{ val[1] }} | {{ val[2] }} | {{ val[3] }} | {{ val[4] }} |
| Feature | |
| {{ val[0] }} | {{ val[1] }} |
| Feature | Contribution to ROC AUC |
| {{ val[0] }} | {{ val[1] }} |
Evaluation requires at least 2 features in the final model
{% endif %} {% else %}Required to train the model with a parameter regularized_refit=False
{% endif %}


Could it be related to this? https://askubuntu.com/questions/1404049/ssh-without-password-does-not-work-after-upgrading-from-18-04-to-22-04
assigned
by Max Kadel at 2023-03-21 15:08:40.labeled
by Kim Leaman at 2023-03-21 14:31:49.labeled
by Kim Leaman at 2023-03-21 14:42:37.labeled
by Kim Leaman at 2023-03-21 14:42:53.labeled
by Kim Leaman at 2023-03-21 14:43:15.labeled
by Kim Leaman at 2023-03-21 14:43:27.labeled
by Kevin Reiss at 2023-03-21 13:17:00.closed
by Kevin Reiss at 2023-03-21 15:05:12.Catalogers will suppress missing items / holdings in Alma.
closed
by Max Kadel at 2023-03-21 19:42:11.head_ref_deleted
by Max Kadel at 2023-03-21 19:42:34.referenced
by Kevin Reiss at 2023-03-21 20:00:58.merged
by Kevin Reiss at 2023-03-21 20:00:58.closed
by Kevin Reiss at 2023-03-21 20:00:58.head_ref_deleted
by Kevin Reiss at 2023-03-21 20:01:00.SC is going to suppress items in the statuses that are truly non-requestable rather than having us change the current codebase.
closed
by Kevin Reiss at 2023-03-21 21:48:45.referenced
by Max Kadel at 2023-03-21 20:26:38.merged
by Max Kadel at 2023-03-21 20:26:38.closed
by Max Kadel at 2023-03-21 20:26:38.head_ref_deleted
by Max Kadel at 2023-03-21 20:26:39.closed
by James R. Griffin III at 2023-03-21 16:16:07.assigned
by James R. Griffin III at 2023-03-21 17:59:41.Manually triggering a rebuild of the index was needed in order to ensure that these records were also not listed for end-users.
assigned
by James R. Griffin III at 2023-03-21 17:51:29.labeled
by James R. Griffin III at 2023-03-21 18:20:49.assigned
by None at 2023-03-21 13:41:44.deployed
by None at 2023-03-21 13:20:11.deployed
by None at 2023-03-21 13:25:58.review_dismissed
by None at 2023-03-21 13:35:58.referenced
by None at 2023-03-21 13:47:01.merged
by None at 2023-03-21 13:47:01.closed
by None at 2023-03-21 13:47:02.head_ref_deleted
by None at 2023-03-21 13:47:03.closed
by None at 2023-03-21 13:47:03.labeled
by Trey Pendragon at 2023-03-21 17:36:39.labeled
by Trey Pendragon at 2023-03-21 17:36:39.renamed
by None at 2023-03-21 17:36:55.assigned
by Thanya Begum at 2023-03-21 18:18:45.deployed
by None at 2023-03-21 21:30:53.referenced
by Robert-Anthony Lee-Faison at 2023-03-21 14:31:20.merged
by Robert-Anthony Lee-Faison at 2023-03-21 14:31:20.closed
by Robert-Anthony Lee-Faison at 2023-03-21 14:31:20.head_ref_deleted
by Robert-Anthony Lee-Faison at 2023-03-21 14:31:22.Looks like rack is up-to-date now, so this is no longer needed.
closed
by None at 2023-03-21 14:40:36.head_ref_deleted
by None at 2023-03-21 14:40:38.referenced
by Bess Sadler at 2023-03-21 19:40:54.merged
by Bess Sadler at 2023-03-21 19:40:54.closed
by Bess Sadler at 2023-03-21 19:40:54.head_ref_deleted
by Bess Sadler at 2023-03-21 19:40:55.referenced
by Vickie Karasic at 2023-03-21 14:59:53.merged
by Vickie Karasic at 2023-03-21 14:59:53.closed
by Vickie Karasic at 2023-03-21 14:59:53.labeled
by Kim Leaman at 2023-03-21 14:41:23.labeled
by Kim Leaman at 2023-03-21 14:41:40.labeled
by Kim Leaman at 2023-03-21 14:42:07.labeled
by Kim Leaman at 2023-03-21 14:43:03.labeled
by Kim Leaman at 2023-03-21 14:44:02.This looks like it has already been done (see screenshot). Closing issue, @kevinreiss and @eliotjordan
mentioned
by Kevin Reiss at 2023-03-21 14:59:11.subscribed
by Kevin Reiss at 2023-03-21 14:59:11.mentioned
by Eliot Jordan at 2023-03-21 14:59:11.subscribed
by Eliot Jordan at 2023-03-21 14:59:12.closed
by Kim Leaman at 2023-03-21 14:59:13.SC is going to suppress missing items that it does not want requested rather than have the codebase updated to deal with this case.
closed
by Kevin Reiss at 2023-03-21 21:49:23.In response to a request from OIT, I have provided the IdP metadata for https://thesis-central.princeton.edu to OIT over SN@P.
As detailed within the SN@P ticket, the X.509 certificate specified in the SP Shibboleth metadata was cached by the OIT servers, and this needed to be updated. Following this update, users could then successfully authenticate.
closed
by James R. Griffin III at 2023-03-21 16:15:51.Successfully ran on load balancer ✅
review_requested
by Robert-Anthony Lee-Faison at 2023-03-21 20:24:55.referenced
by Francis Kayiwa at 2023-03-21 20:51:33.merged
by Francis Kayiwa at 2023-03-21 20:51:33.closed
by Francis Kayiwa at 2023-03-21 20:51:33.head_ref_deleted
by Francis Kayiwa at 2023-03-21 20:51:34.upstream mariadb uses mariadb 10.6
convert_to_draft
by Francis Kayiwa at 2023-03-21 21:22:52.referenced
by Bess Sadler at 2023-03-21 18:28:32.merged
by Bess Sadler at 2023-03-21 18:28:32.closed
by Bess Sadler at 2023-03-21 18:28:32.head_ref_deleted
by Bess Sadler at 2023-03-21 18:28:34.referenced
by Kevin Reiss at 2023-03-21 13:02:54.merged
by Kevin Reiss at 2023-03-21 13:02:54.closed
by Kevin Reiss at 2023-03-21 13:02:54.head_ref_deleted
by Kevin Reiss at 2023-03-21 13:02:55.Deployed to production.
closed
by Kevin Reiss at 2023-03-21 13:35:53.referenced
by Max Kadel at 2023-03-21 15:53:53.merged
by Max Kadel at 2023-03-21 15:53:53.closed
by Max Kadel at 2023-03-21 15:53:54.head_ref_deleted
by Max Kadel at 2023-03-21 15:53:55.deployed
by None at 2023-03-21 23:01:01.assigned
by Kevin Reiss at 2023-03-21 23:21:44.assigned
by Kevin Reiss at 2023-03-21 22:31:23.deployed
by None at 2023-03-21 21:46:02.referenced
by None at 2023-03-21 12:11:54.merged
by None at 2023-03-21 12:11:54.closed
by None at 2023-03-21 12:11:54.head_ref_deleted
by None at 2023-03-21 12:11:55.head_ref_force_pushed
by None at 2023-03-21 12:14:28.@jrgriffiniii This PR has conflicts with main. Did you want to fix it up or close it?
mentioned
by James R. Griffin III at 2023-03-21 12:30:46.subscribed
by James R. Griffin III at 2023-03-21 12:30:46.referenced
by James R. Griffin III at 2023-03-21 19:27:02.merged
by James R. Griffin III at 2023-03-21 19:27:02.closed
by James R. Griffin III at 2023-03-21 19:27:02.head_ref_deleted
by James R. Griffin III at 2023-03-21 19:27:03.assigned
by None at 2023-03-21 19:27:15.@carolyncole it looks like we need to go through the dictionary to add tags.
> dictionary.add :entry < :definition "third time is a charm" :term "term03" >
> dictionary.entries.list
:term "term01"
:term "term02"
:term "term03"
> asset.tag.add :id 1023 :tag < :name "term03" >
> asset.get :id 1023
...
:tag -id "1"
:name "term01"
:creator -id "15"
:domain "system"
:user "manager"
:ctime -tz "Etc/UTC" -gmt-offset "0.0" -dst "false" -millisec "1679431179102" "21-Mar-2023 20:39:39"
:tag -id "2"
:name "term03"
:creator -id "15"
:domain "system"
:user "manager"
:ctime -tz "Etc/UTC" -gmt-offset "0.0" -dst "false" -millisec "1679431415355" "21-Mar-2023 20:43:35"
...
mentioned
by None at 2023-03-21 20:45:27.subscribed
by None at 2023-03-21 20:45:27.