|
Applied Machine Learning with R Basel R Bootcamp |
adapted from xkcd.com
In this practical you’ll practice the basics of fitting and exploring regression models in R.
By the end of this practical you will know how to:
| File | Rows | Columns |
|---|---|---|
| college_train.csv | 1000 | 21 |
college_train
data are taken from the College
dataset in the ISLR
package. To see column descriptions, run the following:library(ISLR) # Load ISLR package
?College # Look at help menu for College
| Function | Package | Description |
|---|---|---|
trainControl() |
caret |
Define modelling control parameters |
train() |
caret |
Train a model |
predict(object, newdata) |
base |
Predict the criterion values of newdata based on object |
postResample() |
caret |
Calculate aggregate model performance in regression tasks |
confusionMatrix() |
caret |
Calculate aggregate model performance in classification tasks |
| Package | Installation |
|---|---|
tidyverse |
install.packages("tidyverse") |
caret |
install.packages("caret") |
# Fitting and evaluating a regression model ------------------------------------
# Step 0: Load packages-----------
library(tidyverse) # Load tidyverse for dplyr and tidyr
library(caret) # For ML mastery
# Step 1: Load and Clean, and Explore Training data ----------------------
# I'll use the mpg dataset from the dplyr package in this example
# no need to load an external dataset
data_train <- read_csv("1_Data/mpg_train.csv")
# Convert all characters to factor
# Some ML models require factors
data_train <- data_train %>%
mutate_if(is.character, factor)
# Explore training data
data_train # Print the dataset
View(data_train) # Open in a new spreadsheet-like window
dim(data_train) # Print dimensions
names(data_train) # Print the names
# Step 2: Define training control parameters -------------
# In this case, I will set method = "none" to fit to
# the entire dataset without any fancy methods
# such as cross-validation
train_control <- trainControl(method = "none")
# Step 3: Train model: -----------------------------
# Criterion: hwy
# Features: year, cyl, displ, trans
# Regression
hwy_glm <- train(form = hwy ~ year + cyl + displ + trans,
data = data_train,
method = "glm",
trControl = train_control)
# Look at summary information
summary(hwy_glm)
# Step 4: Access fit ------------------------------
# Save fitted values
glm_fit <- predict(hwy_glm)
# Define data_train$hwy as the true criterion
criterion <- data_train$hwy
# Regression Fitting Accuracy
postResample(pred = glm_fit,
obs = criterion)
# RMSE Rsquared MAE
# 3.246182 0.678465 2.501346
# On average, the model fits are 2.8 away from the true
# criterion values
# Step 5: Visualise Accuracy -------------------------
# Tidy competition results
accuracy <- tibble(criterion = criterion,
Regression = glm_fit) %>%
gather(model, prediction, -criterion) %>%
# Add error measures
mutate(se = prediction - criterion,
ae = abs(prediction - criterion))
# Calculate summaries
accuracy_agg <- accuracy %>%
group_by(model) %>%
summarise(mae = mean(ae)) # Calculate MAE (mean absolute error)
# Plot A) Scatterplot of criterion versus predictions
ggplot(data = accuracy,
aes(x = criterion, y = prediction, col = model)) +
geom_point(alpha = .2) +
geom_abline(slope = 1, intercept = 0) +
labs(title = "Predicting mpg$hwy",
subtitle = "Black line indicates perfect performance")
# Plot B) Violin plot of absolute errors
ggplot(data = accuracy,
aes(x = model, y = ae, fill = model)) +
geom_violin() +
geom_jitter(width = .05, alpha = .2) +
labs(title = "Distributions of Fitting Absolute Errors",
subtitle = "Numbers indicate means",
x = "Model",
y = "Absolute Error") +
guides(fill = FALSE) +
annotate(geom = "label",
x = accuracy_agg$model,
y = accuracy_agg$mae,
label = round(accuracy_agg$mae, 2))
Open your BaselRBootcamp
R project. It should already have the folders 1_Data
and 2_Code
. Make sure that the data file(s) listed in the Datasets
section above are in your 1_Data
folder
Fitting_practical.R
in the 2_Code
folder.Using library()
load the set of packages for this practical listed in the packages section above.
## NAME
## DATE
## Fitting Practical
library(tidyverse)
library(caret)
college_train.csv
. Using the following template, load the dataset into R as college_train
:# Load in college_train.csv data as college_train
college_train <- read_csv(file = "1_Data/college_train.csv")
college_train
# A tibble: 500 x 18
Private Apps Accept Enroll Top10perc Top25perc F.Undergrad P.Undergrad
<chr> <int> <int> <int> <int> <int> <int> <int>
1 Yes 1202 1054 326 18 44 1410 299
2 Yes 1415 714 338 18 52 1345 44
3 Yes 4778 2767 678 50 89 2587 120
4 Yes 1220 974 481 28 67 1964 623
5 Yes 1981 1541 514 18 36 1927 1084
6 Yes 1217 1088 496 36 69 1773 884
7 No 8579 5561 3681 25 50 17880 1673
8 No 833 669 279 3 13 1224 345
9 No 10706 7219 2397 12 37 14826 1979
10 Yes 938 864 511 29 62 1715 103
# ... with 490 more rows, and 10 more variables: Outstate <int>,
# Room.Board <int>, Books <int>, Personal <int>, PhD <int>,
# Terminal <int>, S.F.Ratio <dbl>, perc.alumni <int>, Expend <int>,
# Grad.Rate <int>
dim()
function# Print number of rows and columns of college_train
dim(XXX)
# Print number of rows and columns of college_train
dim(college_train)
[1] 500 18
names()
functionnames(XXX)
names(college_train)
[1] "Private" "Apps" "Accept" "Enroll" "Top10perc"
[6] "Top25perc" "F.Undergrad" "P.Undergrad" "Outstate" "Room.Board"
[11] "Books" "Personal" "PhD" "Terminal" "S.F.Ratio"
[16] "perc.alumni" "Expend" "Grad.Rate"
View()
. How does it look?View(XXX)
# Convert character to factor
college_train <- college_train %>%
mutate_if(is.character, factor)
In caret
, we always need to define computational nuances of training using the trainControl()
function. Because we’re learning the basics of fitting, we’ll set method = "none"
(Note that you would almost never do this for a real prediction task, you’ll see why later!)
# Set training resampling method to "none" to keep everything super simple
# for demonstration purposes. Note that you would almost never
# do this for a real prediction task!
ctrl_none <- trainControl(method = "none")
Grad.Rate
) as a function of one feature PhD
, the percent of faculty with PhDs). Save the result as an object Grad.Rate_glm
form
argument to Grad.Rate ~ PhD
data
argument to your training data college_train
method
argument to "glm"
for regressiontrControl
argument to ctrl_none
, the object you created previously# Grad.Rate_glm: Regression Model
# Criterion: Grad.Rate
# Features: PhD
Grad.Rate_glm <- train(form = XX ~ XX,
data = XX,
method = "XX",
trControl = XX)
# Grad.Rate_glm: Regression Model
# Criterion: Grad.Rate
# Features: PhD
Grad.Rate_glm <- train(form = Grad.Rate ~ PhD,
data = college_train,
method = "glm",
trControl = ctrl_none)
summary()
function.Grad.Rate_glm
# Show summary information from the regression model
summary(XXX)
# Show summary information from the regression model
summary(Grad.Rate_glm)
Call:
NULL
Deviance Residuals:
Min 1Q Median 3Q Max
-43.83 -10.44 0.49 10.93 41.47
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 41.372 3.382 12.23 < 2e-16 ***
PhD 0.330 0.045 7.33 9.1e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 257)
Null deviance: 141641 on 499 degrees of freedom
Residual deviance: 127832 on 498 degrees of freedom
AIC: 4197
Number of Fisher Scoring iterations: 2
# For every increase of one in PhD (the percent of faculty with a PhD), the expected graduation rate increases by 0.33
glm_fit
Grad.Rate_glm
# Get fitted values from the Grad.Rate_glm model and save as glm_fit
glm_fit <- predict(XXX)
# Get fitted values from the model and save as glm_fit
glm_fit <- predict(Grad.Rate_glm)
glm_fit
object - what are these values? Do they look reasonable? That is, are they in the range of what you expect the criterion to be?#  Define criterion as Grad.Rate
criterion <- college_train$Grad.Rate
postResample()
function, with the fitted values as the prediction, and the criterion as the observed values:pred
argument to glm_fit
(your fitted values)obs
argument to criterion
(a vector of the criterion values)# Regression Fitting Accuracy
postResample(pred = XXX, # Fitted values
obs = XXX) # criterion values
# Regression Fitting Accuracy
postResample(pred = glm_fit, # Fitted values
obs = criterion) # criterion values
RMSE Rsquared MAE
15.9895 0.0975 12.8633
# On average, the model fits are 12.8633 away from the true values.
# Whether this is 'good' or not depends on you :)
accuracy
: Raw absolute errorsaccuracy_agg
Aggregate (i.e.; mean) absolute errors# accuracy: a dataframe of raw absolute errors
accuracy <- tibble(criterion = criterion,
Regression = glm_fit) %>%
gather(model, prediction, -criterion) %>%
# Add error measures
mutate(ae = abs(prediction - criterion))
# accuracy_agg: Dataframe of aggregate errors
accuracy_agg <- accuracy %>%
group_by(model) %>%
summarise(mae = mean(ae)) # Calculate MAE (mean absolute error)
accuracy
and accuracy_agg
objects to see how they look.accuracy
accuracy_agg
accuracy %>% head() # Just printing the first few rows
# A tibble: 6 x 4
criterion model prediction ae
<int> <chr> <dbl> <dbl>
1 65 Regression 67.1 2.12
2 69 Regression 58.5 10.5
3 83 Regression 66.8 16.2
4 49 Regression 61.5 12.5
5 80 Regression 65.5 14.5
6 67 Regression 52.9 14.1
accuracy_agg
# A tibble: 1 x 2
model mae
<chr> <dbl>
1 Regression 12.9
# Plot A) Scatterplot of criterion versus predictions
ggplot(data = accuracy,
aes(x = criterion, y = prediction)) +
geom_point(alpha = .2) +
geom_abline(slope = 1, intercept = 0) +
labs(title = "Regression: One Feature",
subtitle = "Line indicates perfect performance",
x = "True Graduation Rates",
y = "Fitted Graduation Rates") +
xlim(0, 120) +
ylim(0, 120)
# No the model is not great, values do not fall very closely to the black diagonal line.
# Plot B) Violin plot of absolute errors
ggplot(data = accuracy,
aes(x = model, y = ae, fill = model)) +
geom_violin() +
geom_jitter(width = .05, alpha = .2) +
labs(title = "Distributions of Fitting Absolute Errors",
subtitle = "Numbers indicate means",
x = "Model",
y = "Absolute Error") +
guides(fill = FALSE) +
annotate(geom = "label",
x = accuracy_agg$model,
y = accuracy_agg$mae,
label = round(accuracy_agg$mae, 2))
# On average, the model fits are 12.86 away from the true criterion values.
# However, there is also quite a bit of variability
So far we have only used one feature (PhD
), to predict Grad.Rate
. Let’s try again, but now we’ll use a total of four features:
PhD
: The percent of faculty with a PhDRoom.Board
: Room and board costsTerminal
: Percent of faculty with a terminal degreeS.F.Ratio
: Student to faculty ratioGrad.Rate_glm
which predicts Grad.Rate
using all 4 features (you can also call it something else if you want to save your original model!).form
argument to Grad.Rate ~ PhD + Room.Board + Terminal + S.F.Ratio
data
argument to your training data college_train
method
argument to "glm"
for regressiontrControl
argument to ctrl_none
# Grad.Rate_glm: Regression Model
# Criterion: Grad.Rate
# Features: PhD, Room.Board, Terminal, S.F.Ratio
Grad.Rate_glm <- train(form = XXX ~ XXX + XXX + XXX + XXX,
data = XXX,
method = "XXX",
trControl = XXX)
# Grad.Rate_glm: Regression Model
# Criterion: Grad.Rate
# Features: PhD, Room.Board, Terminal, S.F.Ratio
Grad.Rate_glm <- train(form = Grad.Rate ~ PhD + Room.Board + Terminal + S.F.Ratio,
data = college_train,
method = "glm",
trControl = ctrl_none)
summary()
, which features seem to be important?Grad.Rate_glm
summary(XXX)
summary(Grad.Rate_glm)
Call:
NULL
Deviance Residuals:
Min 1Q Median 3Q Max
-45.10 -9.63 0.40 10.07 48.55
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 38.635042 5.288467 7.31 1.1e-12 ***
PhD 0.217725 0.080744 2.70 0.0072 **
Room.Board 0.004674 0.000676 6.91 1.5e-11 ***
Terminal -0.021957 0.088196 -0.25 0.8035
S.F.Ratio -0.524664 0.176980 -2.96 0.0032 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 224)
Null deviance: 141641 on 499 degrees of freedom
Residual deviance: 110813 on 495 degrees of freedom
AIC: 4131
Number of Fisher Scoring iterations: 2
glm_fit
predict()
to your Grad.Rate_glm
model# Save new model fits
glm_fit <- predict(XXX)
# Save new model fits
glm_fit <- predict(Grad.Rate_glm)
postResample()
calculate the Mean Absolute Error (MAE) of your new model that uses 4 features. How does this compare to your previous model that only used 1 feature?pred
argument to glm_fit
, your model fitsobs
argument to criterion
, a vector of the true criterion values# New model fitting accuracy
postResample(pred = XXX, # Fitted values
obs = XXX) # criterion values
# Save new model fits
glm_fit <- predict(Grad.Rate_glm)
# New model fitting accuracy
postResample(pred = glm_fit, # Fitted values
obs = criterion) # criterion values
RMSE Rsquared MAE
14.887 0.218 11.779
# The new MAE value is 11.779, it's better (smaller) than the previous model, but still not great (in my opinion)
# accuracy: a dataframe of raw absolute errors
accuracy <- tibble(criterion = criterion,
Regression = glm_fit) %>%
gather(model, prediction, -criterion) %>%
# Add error measures
mutate(ae = abs(prediction - criterion))
# accuracy_agg: Dataframe of aggregate errors
accuracy_agg <- accuracy %>%
group_by(model) %>%
summarise(mae = mean(ae)) # Calculate MAE (mean absolute error)
# Plot A) Scatterplot of criterion versus predictions
ggplot(data = accuracy,
aes(x = criterion, y = prediction)) +
geom_point(alpha = .2) +
geom_abline(slope = 1, intercept = 0) +
labs(title = "Regression: Four Features",
subtitle = "Line indicates perfect performance",
x = "True Graduation Rates",
y = "Fitted Graduation Rates") +
xlim(0, 120) +
ylim(0, 120)
# Plot B) Violin plot of absolute errors
ggplot(data = accuracy,
aes(x = model, y = ae, fill = model)) +
geom_violin() +
geom_jitter(width = .05, alpha = .2) +
labs(title = "Distributions of Fitting Absolute Errors",
subtitle = "Numbers indicate means",
x = "Model",
y = "Absolute Error") +
guides(fill = FALSE) +
annotate(geom = "label",
x = accuracy_agg$model,
y = accuracy_agg$mae,
label = round(accuracy_agg$mae, 2))
Alright now it’s time to use all features available!
glm_fit
which predicts Grad.Rate
using all features in the dataset.form
argument to Grad.Rate ~ .
data
argument to the training data college_train
method
argument to "glm"
for regressiontrControl
argument to ctrl_none
Grad.Rate_glm <- train(form = XXX ~ .,
data = XXX,
method = "glm",
trControl = XXX)
Grad.Rate_glm <- train(form = Grad.Rate ~ .,
data = college_train,
method = "glm",
trControl = ctrl_none)
summary()
, which features seem to be important?summary(XXX)
summary(Grad.Rate_glm)
Call:
NULL
Deviance Residuals:
Min 1Q Median 3Q Max
-38.10 -7.24 -0.58 7.51 47.10
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 31.010972 5.911481 5.25 2.3e-07 ***
PrivateYes 1.701840 2.114677 0.80 0.42135
Apps 0.001926 0.000572 3.37 0.00082 ***
Accept -0.001754 0.001046 -1.68 0.09417 .
Enroll 0.005550 0.002872 1.93 0.05387 .
Top10perc -0.049727 0.086281 -0.58 0.56466
Top25perc 0.206252 0.066972 3.08 0.00219 **
F.Undergrad -0.001069 0.000461 -2.32 0.02068 *
P.Undergrad -0.001294 0.000444 -2.92 0.00369 **
Outstate 0.001782 0.000297 6.01 3.7e-09 ***
Room.Board 0.000871 0.000721 1.21 0.22790
Books -0.000932 0.004089 -0.23 0.81988
Personal -0.001457 0.000998 -1.46 0.14494
PhD 0.104743 0.071027 1.47 0.14095
Terminal -0.101789 0.076321 -1.33 0.18293
S.F.Ratio 0.275943 0.191423 1.44 0.15008
perc.alumni 0.219944 0.061576 3.57 0.00039 ***
Expend -0.000683 0.000202 -3.39 0.00077 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 155)
Null deviance: 141641 on 499 degrees of freedom
Residual deviance: 74595 on 482 degrees of freedom
AIC: 3960
Number of Fisher Scoring iterations: 2
glm_fit
predict()
to your Grad.Rate_glm
model# Save new model fits
glm_fit <- predict(XXX)
# Save new model fits
glm_fit <- predict(Grad.Rate_glm)
What is the Mean Absolute Error (MAE) of your new model that uses 17 features? How does this compare to your previous model that only used 1 feature?
(Optional). Create a scatter plot showing the relationship between your new model fits and the true values. How does this plot compare to your previous one?
(Optional). Create a violin plot showing the distribution of absolute errors. How does this compare to your previous one?
Now it’s time to do a classification task! Recall that in a classification task, we are predicting a category, not a continuous number. In this task, we’ll predict whether or not a college is Private or Public, this is stored as the variable college_train$Private
caret
, all you need to do is make sure that the criterion is coded as a factor. To test whether is coded as a factor, you can look at its class as follows:# Look at the class of the variable Private, should be a factor!
class(college_train$Private)
[1] "factor"
criterion
# Define criterion as college_train$Private
criterion <- college_train$Private
train()
, create Private_glm
, a regression model predicting the variable Private
form
argument to Private ~ .
data
argument to the training data college_train
method
argument to "glm"
trControl
argument to ctrl_none
# Fit regression model predicting Private
Private_glm <- train(form = XXX ~ .,
data = XXX,
method = "XXX",
trControl = XXX)
# Fit regression model predicting private
Private_glm <- train(form = Private ~ .,
data = college_train,
method = "glm",
trControl = ctrl_none)
Private_glm
object using the summary()
function.Private_glm
# Explore the Private_glm object
summary(XXX)
# Show summary information from the regression model
summary(Private_glm)
Call:
NULL
Deviance Residuals:
Min 1Q Median 3Q Max
-2.9426 -0.0453 0.0272 0.1179 2.5261
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.25e+00 2.28e+00 0.55 0.5839
Apps -2.79e-04 2.71e-04 -1.03 0.3028
Accept -1.21e-03 5.48e-04 -2.20 0.0276 *
Enroll 3.90e-03 1.40e-03 2.80 0.0052 **
Top10perc -1.67e-02 3.82e-02 -0.44 0.6619
Top25perc 3.09e-02 2.76e-02 1.12 0.2640
F.Undergrad -4.14e-04 1.68e-04 -2.46 0.0140 *
P.Undergrad -1.76e-04 2.05e-04 -0.86 0.3899
Outstate 8.48e-04 1.55e-04 5.47 4.5e-08 ***
Room.Board 7.35e-04 3.60e-04 2.04 0.0410 *
Books 3.42e-03 1.83e-03 1.87 0.0619 .
Personal -6.20e-04 3.88e-04 -1.60 0.1097
PhD -5.63e-02 3.73e-02 -1.51 0.1315
Terminal -6.57e-02 3.68e-02 -1.79 0.0739 .
S.F.Ratio -1.91e-01 7.46e-02 -2.56 0.0104 *
perc.alumni 4.77e-02 2.79e-02 1.71 0.0876 .
Expend 2.81e-05 1.53e-04 0.18 0.8542
Grad.Rate 7.30e-03 1.48e-02 0.49 0.6220
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 609.16 on 499 degrees of freedom
Residual deviance: 144.29 on 482 degrees of freedom
AIC: 180.3
Number of Fisher Scoring iterations: 8
# Looking at the Z statistics, Outstate, Enroll and S.F.Ratio (...) have quite large z-statistics
glm_fit
object
argument to your Private_glm
object# Get fitted values from the Private_glm object
glm_fit <- predict(XXX)
# Get fitted values from the Private_glm object
glm_fit <- predict(Private_glm)
glm_fit
object - what are these values? Do they look reasonable?# Print glm_fit
glm_fit
[1] Yes No Yes Yes Yes Yes No Yes No Yes Yes No Yes Yes No Yes Yes
[18] Yes Yes Yes Yes No No No No Yes Yes No No No Yes Yes Yes Yes
[35] Yes No Yes No Yes Yes Yes Yes No No Yes Yes No Yes Yes No Yes
[52] Yes Yes Yes No Yes Yes No Yes Yes Yes Yes No No Yes Yes No Yes
[69] Yes Yes Yes Yes Yes No Yes Yes Yes Yes No Yes Yes No Yes Yes Yes
[86] Yes Yes Yes No Yes No Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes
[103] No Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes No Yes No No Yes
[120] Yes Yes Yes Yes Yes Yes Yes No No Yes Yes Yes Yes Yes No Yes Yes
[137] Yes No Yes No Yes Yes Yes Yes Yes Yes Yes No Yes No No Yes Yes
[154] Yes No Yes Yes Yes Yes Yes Yes Yes No Yes Yes Yes Yes No Yes Yes
[171] Yes Yes Yes Yes Yes Yes No Yes No No Yes Yes Yes Yes Yes No Yes
[188] No Yes Yes No Yes No No No Yes No Yes Yes Yes Yes Yes Yes Yes
[205] Yes Yes No Yes Yes No No Yes Yes Yes Yes No Yes Yes Yes No Yes
[222] Yes Yes Yes Yes No Yes No No Yes Yes No Yes Yes Yes Yes No Yes
[239] No No Yes No No No Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes
[256] Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No No Yes Yes No No Yes
[273] No Yes Yes Yes Yes No Yes Yes Yes No Yes Yes No Yes No No No
[290] Yes Yes No Yes Yes Yes Yes Yes No No No Yes Yes No Yes No Yes
[307] No Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes No Yes Yes Yes Yes
[324] Yes No Yes No Yes Yes Yes Yes No No No Yes No No No Yes No
[341] No Yes Yes Yes No Yes Yes Yes Yes No Yes Yes Yes Yes Yes No No
[358] No No Yes Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes No
[375] Yes Yes No Yes Yes Yes No Yes Yes No Yes Yes Yes No Yes Yes Yes
[392] Yes Yes No No No Yes Yes Yes No No Yes Yes Yes No Yes Yes Yes
[409] Yes Yes No Yes No No No No No Yes No Yes Yes No No Yes Yes
[426] Yes Yes Yes No Yes Yes No No Yes Yes Yes No Yes No No No Yes
[443] Yes Yes Yes Yes Yes Yes No No No Yes Yes Yes Yes No Yes Yes Yes
[460] Yes Yes Yes No Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes No Yes
[477] Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No Yes
[494] Yes Yes Yes Yes Yes No No
Levels: No Yes
confusionMatrix()
. This function compares model predictions to a ‘reference’ (in our case, the criterion, and returns several summary statistics). In the code below, we’ll use glm_fit
as the model predictions, and our already defined criterion
vector as the reference (aka, truth)data
argument to your glm_fit
valuesreference
argument to the criterion
values# Show accuracy of glm_fit versus the true criterion values
confusionMatrix(data = XXX, # This is the prediction!
reference = XXX) # This is the truth!
# Show accuracy of glm_fit versus the true values
confusionMatrix(data = glm_fit, # This is the prediction!
reference = criterion) # This is the truth!
Confusion Matrix and Statistics
Reference
Prediction No Yes
No 133 13
Yes 16 338
Accuracy : 0.942
95% CI : (0.918, 0.961)
No Information Rate : 0.702
P-Value [Acc > NIR] : <2e-16
Kappa : 0.861
Mcnemar's Test P-Value : 0.71
Sensitivity : 0.893
Specificity : 0.963
Pos Pred Value : 0.911
Neg Pred Value : 0.955
Prevalence : 0.298
Detection Rate : 0.266
Detection Prevalence : 0.292
Balanced Accuracy : 0.928
'Positive' Class : No
# The overall accuracy is 0.942. Across all cases, the model fits the true class values 94.2% of the time
# The sensitivity is 0.893. Of those collges that truly are private, the model fits are correct 89.3% of the time
# The sensitivity is 0.963. Of those collges that truly are not private, the model fits are correct 96.3% of the time
# Get overall accuracy from regression model
glm_accuracy <- confusionMatrix(data = glm_fit,
reference = criterion)$overall[1]
# Combine results into one table
accuracy <- tibble(Regression = glm_accuracy) %>%
gather(model, accuracy)
# Plot the results!
ggplot(accuracy, aes(x = model, y = accuracy, fill = model)) +
geom_bar(stat = "identity") +
labs(title = "Is a college private or public?",
subtitle = "Fitting classification accuracy",
y = "Overall Accuracy") +
ylim(c(0, 1)) +
annotate(geom = "label",
x = accuracy$model,
y = accuracy$accuracy,
label = round(accuracy$accuracy, 2))
Conduct a regression analysis predicting the percent of alumni who donate to the college (perc_alumni
). How good can your regression model fit this criterion? Which variables seem to be important in predicting it?
Conduct a classification analysis predicting whether or not a school is ‘hot’ – where a ‘hot’ school is one that receives at least 10,000 applications (Hint: use the code below to create the hot
variable).
# Add a new factor criterion 'hot' which indicates whether or not a schol receives at least 10,000 applications
college_train <- college_train %>%
mutate(hot = factor(Apps >= 10000))
Did you notice anything strange in your model when doing the previous task? If you used all available predictors you will have gotten a warning that your model did not converge. That can happen if the maximum number of iterations (glm uses an iterative procedure when fitting the model) is reached. The default is a maximum of 25 iterations, see ?glm.control
. To fix it just add the following code in your train()
function control = list(maxit = 75)
, and run it again.
Now the model should have converged, but there is still another warning occurring: glm.fit: fitted probabilities numerically 0 or 1 occurred
. This can happen if very strong predictors occur in the dataset (see Venables & Ripley, 2002, p. 197). If you added all predictors (except again the college names), then this problem occurs because the Apps
variable, used to create the criterion, was also part of the predictors (plus some other variables that highly correlate with Apps
). Check the variable correlations (the code below will give you a matrix of bivariate correlations).
# get correlation matrix of numeric variables
cor(college_train[,sapply(college_train, is.numeric)])


Reversible Jump MCMC (RJ-MCMC)sampling for approximating the posterior distribution of a time varying regulatory network, under the Auto Regressive TIme VArying (ARTIVA) model (for a detailed description of the algorithm, see Lebre et al. BMC Systems Biology, 2010). Starting from time-course gene expression measurements for a gene of interest (referred to as "target gene") and a set of genes (referred to as "parent genes") which may explain the expression of the target gene, the ARTIVA procedure identifies temporal segments for which a set of interactions occur between the "parent genes" and the "target gene". The time points that delimit the different temporal segments are referred to as changepoints (CP).
| Version: | 1.2.3 |
| Depends: | MASS, igraph, gplots |
| Published: | 2015-05-20 |
| Author: | S. Lebre and G. Lelandais. |
| Maintainer: | S. Lebre <sophie.lebre at icube.unistra.fr> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | ARTIVA results |
| Reference manual: | ARTIVA.pdf |
| Package source: | ARTIVA_1.2.3.tar.gz |
| Windows binaries: | r-devel: ARTIVA_1.2.3.zip, r-release: ARTIVA_1.2.3.zip, r-oldrel: ARTIVA_1.2.3.zip |
| OS X binaries: | r-release: ARTIVA_1.2.3.tgz, r-oldrel: ARTIVA_1.2.3.tgz |
| Old sources: | ARTIVA archive |
Please use the canonical form https://CRAN.R-project.org/package=ARTIVA to link to this page.


Team Lead
Helped get project started/organized and oversee production from the team
UI Developer
Worked with a team of 3 UI Developers to build a multi-page marketing website.
UI Developer
Contributed to the design and UI
UI Developer
Contributed to the UI Development team by creating a clean UI
Front End Architect
Front end architect , implementing CRUD functionality.
Front End Architect
Built out main operations alongside Carlos and implemented json-server.
Backend Architect
Contributed to the design and UI


This is the index for jquery socialize.
Before using it, be sure to include the files in a virtual directory, that is to say, don't use it accesssing directly from file system (URLs of type "file:///"), it won't work because of the restrictions of the social network's code.
You can start looking at it here.


Secret Message is a safer way to share passwords and secrets with trusted receivers over insecure channels.
It is NOT a replacement for good security practices or password managers.
Write your secret and choose a pass phrase or password that is easy to communicate.


CreativeSpace
Home
(current)
About
Search
My profile
Log out
Edit My Profile
View My Profile
Archives
URL of creation you would like to add your profile
(NOT featured)
Save Changes
Title of creation you would like to add to your profile
(NOT featured)
Save Changes
Description of creation you would like to add to your profile
(NOT featured)
Save Changes
Creator Details
Display Name you would like on your profile
Save Changes
URL of your chosen avatar (will be displayed within 150px by 150px)
Save Changes
Bio you would like displayed on your profile
Save Changes
Account Details
Username
Save Changes
E-mail address
Save Changes
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Location
Save Changes


|
.NET Framework | |||||||||
| PREV NAMESPACE NEXT NAMESPACE | FRAMES NO FRAMES |
| Interface Summary | |
|---|---|
| Iterator |
| Class Summary | |
|---|---|
| SimplePool | Simple object pool. Based on ThreadPool and few other classes * The pool will ignore overflow and return null if empty. * |
| StringUtils | This class provides some methods for dynamically invoking methods in objects, and some string manipulation methods used by torque. The string methods will soon be moved into the turbine string utilities class. * |
|
.NET Framework | |||||||||
| PREV NAMESPACE NEXT NAMESPACE | FRAMES NO FRAMES |


---
layout: post
title: Me Myself & I
category:
- B&W
- Street Photos
tags: []
status: publish
type: post
published: true
meta:
_edit_last: '1'
---
The last couple of weeks were pretty busy, so I didn't have enough time to develop my last rolls of film. Nevertheless, I uploaded the last roll I shot with my Yashica ME-1! It's a zone-focusing camera, which is nice for street photography, with a decently sharp f2.8 yashinon lens. Besides that, it's really nice to hold and handle, and it has a very sleek look.
Mrs. Shadow
assembling
Ribeira State of Mind
Old News
Bouncer
Bolhão, can't get enough
Happiness seen from above
More (real) rolls to come!A. Capelo


3 Part 3
3.1 Determine ground heights underneath sample volumes
Now that you have a basegrid made, we will overlay it with a Digital Elevation Model (DEM) to determine the mean ground height underneath each sample volume. Determining ground heights under sample volumes is important for estimaitng beam heights and topographic blockage later.
## Grab DEM data using elevater package
dem <- get_elev_raster(my_spatial_polys_rp, z = 10)
dem<-rast(dem) #convert it to a SpatRaster
## Plot it
plot(dem)
## Perform Zonal stats
base_dem<-st_transform(my_spatial_polys_rp,crs(dem))
base_sf1<-rasterize(base_dem,dem,field="id")
demz<- data.frame(zonal(dem,base_sf1,fun='mean'))
demz$groundht <- round(demz[,2], -1) ##round DEM to nearest 10-meters
demz<-demz[,c(1,3)]
my_spatial_polys_rp <- merge(my_spatial_polys_rp, demz, by.x='id',all.x=T)
my_spatial_polys_rp$groundht[my_spatial_polys_rp$groundht<0]<- 0
my_spatial_polys_rp$groundht[my_spatial_polys_rp$groundht=="NaN"]<- 0
my_spatial_polys_rp$groundht<-na_locf(na_locf(my_spatial_polys_rp$groundht))
## Save as a shapefile
st_write(my_spatial_polys_rp, paste0(OUT,"/",site,"_",maxrange/1000,"km_super_grid.shp"), append=FALSE)
## Deleting layer `KLWX_5km_super_grid' using driver `ESRI Shapefile'
## Writing layer `KLWX_5km_super_grid' to data source
## `C:/Users/jbule/Dropbox/RAW_2022/KLWX_5km_super_grid.shp' using driver `ESRI Shapefile'
## Writing 9360 features with 4 fields and geometry type Polygon.
3.2 Load basgrid polygon into QGIS - easy way
Via Browser Panel:
1. When you open QGIS, there will be two windows on the left side: Browser Panel and Layers Panel
2. Navigate in the Browser Panel to the file you'd like to add to QGIS.
3. Click on the basegrid shapefile you just created (probably called "KLWX_5km_super_grid.shp") and drag it into the Layers Panel or double-click it.
3.3 Changing the layer properties to display data
1. In the Layers Panel, right-click on the basegrid shapefile and click Properties
2. A new window will open. Click Symbology in the left-side menu.
3. At the top of the window, click the down arrow and click on Graduated
4. Under Column, find the column name "groundht" to display.
5. Under Symbol, click on the bar to Change and click Simple fill. Click the down arrow on the Stroke Color bar and click Transparent Stroke.
6. Pick a color ramp you like or create one using the Edit button next to the Color ramp
7. Under Classes, click Classify. You can adjust the Mode of classification (e.g., Equal Interval) and the number of Classes (e.g., 10).
8. Click OK when you are happy with your selections.


Across: 1. Brave chore I completed (6) 5. Non-military US gaol holds 1,651 (8) 9. Even Tina may be Italian! (8) 10. One may be more insensitive (6) 11. Louse bit curs and caused disease! (12) 13. Fight with Pole (4) 14. Rent horn from Cumbria, possibly (8) 17. In two hours excitedly await a brave hero (8) 18. LA comeback given to singer (4) 20. Making important discovery of DNA during a Forces' mutiny (12) 23. Small traveller takes in a Spanish city (6) 24. Raining around East African (8) 25. Quiet one involved in fuel riot with Penny (8) 26. Inferior eastern tzars (6) Down: 2. Looked at the old journalist outside (4) 3. Remove public argument about hospital (9) 4. Comes up with 151 doctors (6) 5. Argumentative fool on train can't change! (15) 6. Mobile security man is in the lead (8) 7. Time spent waiting for Lego? (5) 8. Improve meal arrangements before I speak (10) 12. Permits gin distribution in April (10) 15. Hags riled by school goody-goodies! (4 5) 16. Mr Laurel, remove this impasse! (5-3) 19. Stinging sensation can affect leg (6) 21. Old music maker gets on after Tooting (5) 22. Reportedly forced into agreement (4)


By Jaiden DeChon
"Willing to achieve Is not willing for success But perserverance." - Harshitha Gn
Often times when looking for ways to make money, people will go to Google and search, "how to make money fast" or "best way to make money quick." Nobody seems to understand that money is an asset that will almost always come becuase of effort and skill. Those who are willing to put the time and effort it takes into learning a valuable skill will acquire currency, but the vast majority of those who do not will have to settle with mediocre lives, and a burning question will always be torturing them:
"What if I had Googled something useful that day?"
When you see a child selling lemonade for dirt-cheap prices on the side of the road, what is your first thought?  Maybe you thing both, or maybe you think neither. Maybe you go up to him and buy a glass, or maybe you cruise past, wondering if he will be succesfull at all in his venture.
Whatever you do, that child will learn from the experience. If you buy a glass from him, he might see that his idea is working-- and soon he'll have enough for that cool bike he's saving for. Or if you cruise past, staring, he'll wonder why you didn't stop... And perhaps will brainstorm ways of getting you to stop at his homemade kiosk.
Let's say the next day you return to location, and the child has set up shop once again- except this time, he has returned with a new business idea: Sell cookies with the lemonade! Suddenly people are stopping regularly, and that kid has to go home to make more lemonade and cookies.
That little dude learned a valuable lesson, even if it was an easy one to learn: Without perseverance, he wouldn't have a shoebox full of cash and change to count. And because of that perserverance, he's $11.75 closer to that shiny new bike.
That kid is no different than you and I. As adults, we need more money than what we can likely make at a single lemonade stand at the side of the road, so we are forced to persue more elaborate ways of doing it. However, the concept is the same: Do something worthy of being paid. Sure, hit Google and looks for ways to make money. But try this: instead of searching "Best way to make money fast," instead, search "Highest paying job of [insert current year." Pick an interesting line of work from what you found, and start searching, "How to get started as a(n) [insert job here.]"


David Rajchenbach-Teller, @ImYoric or<EMAIL_ADDRESS>Contributing to Firefox has helped me impact, interact and collaborate with more people (from different countries, expertise and time zones :P) in 6 months than my entire stay in University till nowSaurabh Anand, Indian Institute Of Technology, Delhi.
"I have gained a great deal of confidence in my abilities and I have never felt more sure about the career path I've chosen for myself."Christopher De Cairos, graduate from Mozilla courses, Seneca University, Canada, now Researcher in Center for Development of Open Technology.


Binded ng-grid to the Backand REST API
This directive provides out-of-the-box CRUD functioality, advanced sorting and filtering
All settings can be provided as attributes in the bknd-ng-grid
or through the query string parameters.
view-name
(Defaults: null) :
A name of a table or view from the database
filter-options
(Defaults: null) :
uses BackAnd REST API filter options: {fieldName, operator, value}
input-style
(Defaults: null) :
css style
gridConfigCompleted
Emit when the configuration of the grid finishes loading from the Backand REST API and before the configuration of the ng-grid
gridDataCompleted
Emit when the data of the grid finishes loading from the Backand REST API and before the loading of the rows into ng-grid
setCellTemplate
Emit when setting the cellTemplate option of ng-grid

 Static Grid Example <bknd-ng-grid view-name="'Employees'" input-style="{'height': 400}"></bknd-ng-grid>

 Dynamic Grid Example <bknd-ng-grid view-name="viewName" input-style="inputStyle"></bknd-ng-grid>

Binded Form to BackAnd REST API
This directive provide you a quick Form connected to your database
All settings can be provided as attributes in the bknd-ng-grid
or through the query string parameters.
view-name
(Defaults: null) :
A name of a table or view from the database
row-id
(Defaults: null) :
The row primary key, if provided then the form is in EDIT mode otherwise it is in a CREATE mode.
if the primary key of the row has multiple column values then they should be comma delimited in the primary key columns' order
default-fields-values
(Defaults: null) :
Default value for each field: {fieldName, value}

 Static Form Example

 Dynamic Form Example

Binded dashboard to BackAnd REST API
This directive provide you a quick dashboard display based on your settings in the console
All settings can be provided as attributes in the bknd-ng-grid
or through the query string parameters.
dashboard-id
(Defaults: null) :
An id of a dashboard from BackAnd REST API configuration

 Dashboard Example <bknd-dashboard dashboard-id="1"></bknd-dashboard>


 WE CANNOT WARRANT THAT ACCESS AND/OR USE OF THIS SITE
AND ITS SERVICES WILL BE UNINTERRUPTED AND/OR ERROR FREE.
USING THIS WEBSITE AND ANY INFORMATION PERTAINING TO IT IS DONE AT YOUR OWN RISK.
 FT-NIR CONSULTING INC RESERVES ALL RIGHTS TO MAKE ANY CORRECTIONS AND/OR CHANGES TO ITS INFORMATION AND MAKES NO REPRESENTATIONS
OR WARRANTIES TO THE ACCURACY OF THE INFORMATION PROVIDED.  FT-NIR CONSULTING INC RESERVES THE RIGHT TO TAKE THIS WEBSITE DOWN WITHOUT NOTICE.
2.   NO LICENSE TO OR RIGHT TO WHAT THE COMPANY HAS DISPLAYED,
STATED, AND OWNS IS GRANTED OR TRANSFERRED TO YOU.  ANY BREACH OF THIS SITE TERMS WILL HOLD YOU LIABLE FOR THE
INJURY AND DAMAGES MADE TO THE COMPANY, AND AS SUCH, WILL AGREE THAT FT-NIR CONSULTING INC WILL BE ENTITLED TO RELIEF AS IT SEES FIT.
2.


1 this directive doesnt use template so evething between tags is being displayed
and it inherits scope from MainConroller as : {{mainCtrl.valueFromMainController}}
2 this directive doesnt use template so evething between tags is being displayed
scope:true and it inherits scope from MainConroller as : {{mainCtrl.valueFromMainController}}
this is being replaced by template
this is being replaced by template
this is being replaced by template
this is being replaced by template 6
7 this is isolated scope with connecting atributes connectingAttribute 7 name controller is.....[ {{ directiveController7.directiveValue }} ]...... ONE WAY DATA BINDING is directiveValue2 name controller 7.....[ {{ directiveController7.directiveValue2 }}......]
this is from main Controller cause scope is not defined or set scope:true so in directive inheritence {{ mainCtrl.valueFromMainController }}


|
System Requirements In order to run Virtual Frap successfully, your computer must meet the following minimum system requirements.Windows 1GHz Intel/AMD CPU Microsoft Windows 2000/X/7/8 1GB RAM 10GB Free Disk Space Monitor resolution 1024 by 768 pixels Macintosh 1GHz Intel CPU Mac OS X 10.x 1GB RAM 10GB Free Disk Space Monitor resolution 1024 by 768 pixels Since VFrap performs computations locally, having additional CPU and memory resources will result in noticeable improvement in performance, particularly for large geometries. |


Reactive Maintance Solver
Easy self diagnosis for various technology
Basic Information
Who is having the issue?
What device is malfunctioning?
Choose
PC
Phone
Printer
What operating system do you use?
Choose
Windows
Mac
Linux
Other
Enter the operating system name
What type of phone do you have?
Choose
iOS (Apple)
Android
Windows Phone
Other
Enter your phone's model name
What brand of printer are you using?
Choose
Brother
HP
Canon
Epson
Other
Enter the name of your printer
What type of problem are you having?
Choose
Startup
Internet
Freeze/Crash
Speed
Graphics
Couruption
Battery
Peripherals (Keyboard, Mouse, etc)
Touchscreen
Camera
Other
Choose
Startup
Internet
Freeze/Crash
Speed
Graphics
Battery
Touchscreen
Couruption
Camera
Other
Choose
Printing
Scanning
Startup
Internet
Freeze/Crash
Speed
Touchscreen
Other
Briefly describe your issue
Freeze/Crash
Which of these best describes your issue?
Choose
Sudden shutdown
Screen turned blue with error text
A program suddenly closed
Entire device became unresponsive and froze
A program froze and isn't responding
Other
Choose
Sudden shutdown
A program suddenly closed
Entire device became unresponsive and froze
A program froze and isn't responding
Other
Briefly describe the problem you are having
How often does this happen?
Choose
This is the first time
It's happened before, but it's uncommon
It happens frequently
What were you doing when the crash occured?
Choose
Using my device normally
Installing Windows updates
Other
Briefly describe what you were doing before the crash
Did you have any unsaved work?
Choose
Yes, and I need to recover it
Yes, but it's alright if it's lost
No
Internet
How do you access the internet?
Choose
Ethernet
WiFi
Other
Choose
Celluar
WiFi
Other
Briefly describe your way of accessing the internet
Which of these best describes your issue?
Choose
No Connection
Slow Internet
Only some websites load
Other
Choose
No Connection
Cannot print wirelessly
Other
Briefly describe the problem you are having
When did this first become a problem?
What is your current internet speed in MB/s? Use
Speedtest.net
to test
What speed do you expect/pay for in MB/s?
Choose a site you are unable to access and use
isthissitedown.net
to check if it is down
Choose
The site is down
The site is online
Can you access the site using incognito mode or a different browser?
Choose
Yes
No
What information do visitors need to connect to your network?
Choose
Nothing
Password
Username and Password
Certificate
Other
Briefly explain what is used to gain access
Can you browse the internet on a different network? Try a mobile hotspot
Choose
Yes
No
Do you have the same issue when using a different device on the same network?
Choose
Yes
No
Do you have access to your router?
Choose
Yes
No
Potential Solutions
We have some potential fixes for you. Try these from top to bottom and tick any that solve the issue.
View a different website - the one you are trying to access is currently unavalable
Follow the instructions to
refresh your cache
and try to visit the site again
Use a different
Speedtest
server to test your internet speed
Restart the network setting on your device
Wait for your PC to automatically restart, then continue using it normally
Force quit
the program and reopen it later when needed
Force quit
the program and reopen it later when needed
Force quit
the program and reopen it later when needed
Restart your device
Restart your router
Use a different router with the same network cable
Contact your network administrator and inform them of the issue
Replace your ethernet cable


As an employee, you have access to this employee benefit. This benefit allows you to expand your skills and learning ability by attending educational events that an employer pays for through a pre-determined amount.
The events you are able to attend to receive your Reimbursement are:


{% if wrong %}
wrong username or passsword !
{% endif%}
Log In
Username
Password
Login
Remember me
Cancel
Forgot
password?


Shadowman Arena |
|
|---|---|
|
Irena
Consultant
Loading...
|
|
Instructions
* As host answer "yes" to first question, then choose a name for the session, e.g. "abc" |


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Core Products International Inc was founded by Phil Mattison who himself suffered from stiff neck and pain and couldn’t find a comforting product till he invented it himself! Core Products International Inc is designed with you in mind. Find massagers, hot and cold packs, rolls and cushions and so much more. There is a product for all your aches and pains. There is a product that will make your life easy, visit Core Products International Inc and find it today.
Core Products International Inc has loads of useful and helpful products. Do you feel your backpack weighs more than you? You can get the ergonomic air pack from Core Products International Inc; it protects your back from injury by reducing the effective weight off the load by 50%. Visit Core Products International Inc and navigate through all the wonderful and effective products there are that can enrich your life and decrease your suffering. The website is easy to use and you can find everything you need. Use our discount coupons for saving money on all Core Products International Inc products.


Copyright © ( C ) 2021, agmadi
Behaviours: gen_server.
Authors: agmadi.
clientcert() = {{certfile, string()}, {keyfile, string()}} | undefined
option() = {autoredirect, true | false} | {clientcert, clientcert()}
optionlist() = [option()]
returnheader() = {Code::string(), Meta::string()}
returnresult() = {returnheader(), Result::string()}
| gather_links/1 | Gather all links present in the result. |
| generate_outline/1 | Generate an outline using heading markers in the result. |
| getopt/1 | Get an option. |
| getopts/0 | Get all options. |
| make_cert/2 | Create a certificate tuple to be used by setopt. |
| request/1 | Send a request with default timeout of 30 seconds. |
| request/2 | Send a request with specific timeout. |
| setopt/2 | Set an option. |
| start_link/0 | Starts the server. |
gather_links(Result::returnresult()) -> [string()]
returns: A list of strings containing any links from the result.
Gather all links present in the result
generate_outline(Result::returnresult()) -> [string()]
returns: A list of strings containing any headers from the result.
Generate an outline using heading markers in the result
getopt(Opt::term()) -> option() | error
Opt: The key for the option to get
returns: The value of the option or the atom 'error' if not found
Get an option
getopts() -> optionlist()
returns: The complete options list with all settings
Get all options
make_cert(CertFile::string(), KeyFile::string()) -> clientcert()
CertFile: The file containing the certificate.
KeyFile: The file containin the private key.
returns: A tuple that can be used by setopt to set the clientcert option.
Create a certificate tuple to be used by setopt.
request(URI::string()) -> returnresult() | {error, Reason::term()}
URI: The full URI requested, including the protocol.
returns: A tuple of { Header, Body } or { error, Reason }
Send a request with default timeout of 30 seconds.
request(URI::string(), Timeout::integer() | infinity) -> returnresult() | {error, Reason::term()}
URI: The full URI requested, including the protocol.
Timeout: The timeout in milliseconds or 'infinity'
returns: A tuple of { Header, Body } or { error, Reason }
Send a request with specific timeout.
setopt(Opt::term(), Val::term()) -> optionlist()
Opt: The key for the option to set
Val: The value for the option to set
returns: The complete options list with all settings
Set an option
start_link() -> {ok, Pid} | ignore | {error, Error}
Starts the server
Generated by EDoc


In a sworn deposition in 2008, Russian-born Felix Sater testifies he would pitch business ideas to Trump “just me and him” and his team “on a constant basis.”


This plugin brings Google Maps native view into your PhoneGap App! As of v1.2.0, you can put any HTMLs on the map.
Thus, you can create your app more flexisible!


Tomek Wielgocki
How to evaluate this formula:
2*height*(width + length)
for given values of height, width and length?
Kernel#eval
formula = '2*height*(width + length)'
variables = { 'height' => 3, 'width' => 4, 'length' => 5 }
variables.each do |key, value|
formula.gsub!(key, value)
end
eval(formula)
Improved Kernel#eval
VALID_EXPRESSION = /\A[0-9\.\+\-\*\/\(\)]*\z/
formula = '2*height*(width + length)'
variables = { 'height' => 3, 'width' => 4, 'length' => 5 }
variables.each do |key, value|
formula.gsub!(key, value)
end
if VALID_EXPRESSION =~ formula
eval(formula)
else
raise 'Invalid formula'
end
Abstract Syntax Tree (AST)
Do we have to implement it?
No! We have Dentaku!
formula = '2*height*(width + length)'
variables = { 'height' => 3, 'width' => 4, 'length' => 5 }
calculator = Dentaku::Calculator.new
calculator.evaluate!(formula, variables)
Support for case sensitivity:
formula = '2*HEIGHT*(width + length)'
variables = { 'height' => 3, 'width' => 4, 'length' => 5 }
calculator = Dentaku::Calculator.new(case_sensitive: true)
calculator.evaluate!(formula, variables) # ERROR!
Support for functions:
formula = 'roundup(2*radius*PI)'
variables = { 'radius' => 4.75, 'PI' => 3.14 }
calculator = Dentaku::Calculator.new
calculator.evaluate!(formula, variables)
Demo
https://github.com/tiwi/mathematical-formulas
<EMAIL_ADDRESS><EMAIL_ADDRESS>Questions?


Capital market disruptor Capitolis raises $40m in Series B
by Ruby HinchliffeNew York-based capital market fintech Capitolis has raised $40 million in a Series B funding round led by Spark Capital and Silicon Valley Bank (SVB) Capital.
The start-up’s product connects financial institutions (FIs) across the global market, allowing them to collaborate, more easily detect attractive capital sources and avoid over-utilisation of capacity which can increase costs. Investors see the fintech in a position “to transform a whole industry”.
Existing backers in the investment round included technology capital funds Index Ventures and Sequoia Capital, as well as Tel Aviv-based S Capital, bringing Capitolis’ total funding raised to date to $70 million.
“We now have the funding, the management team, and the core customer adoption to grow Capitolis into a major industry infrastructure and force,” says the start-up’s executive chairman Tom Glocer.
Spark Capital, which has funded the likes of Tumblr, Twitter and Slack, will see its partner Jeremy Philips join Capitolis’ board to help grow the investment.
Read more: BNP Paribas picks up stake in AssetMetrix
With offices in London and Tel Aviv alongside its US base, the 2016-founded company was started by ex-Thomson Reuters CEO Glocer, alongside Traiana’s founder and former head of technology Gil Mandelzis and Igor Teleshevsky.
Investors see the team “on a multi-trillion dollar mission to reduce risk in the global financial system while improving returns”.
“By connecting all the parties in the market more efficiently, Capitolis removes bottlenecks that are limiting the flow of capital,” says Index Ventures’ partner Jan Hammer.
“The response from leading banks and other financial players has shown a growing need for such a platform, and we’re excited to continue to support the team as they scale.”
Read next: Silicon Valley Bank expands to Denmark, announces new MD Sebastian Penn


Can you think of an analogy to describe HTML, CSS and the DOM? How would you describe it to your non-technical friend?
The best way to explain HTML is to think about car ,the car has defined shape,it has windows ,doors and position for everything.Now css is the internal decoration of car ,leather seats ,space between seats,color of carpet etc.While DOM is the visual representation of car which we can change virtually to see how it will look like .without changing real one.
What is meant by boxifying design?
Boxifying design means represting design by dividing into boxes.The outer box can be the title and next box inside it has top as header and bottom as footer and internal box as heading,text etc.All this by side by side defining the magin of every box and space it will take.
What is the box model?
The Box Model is basically a box that wraps around every single HTML element, and is made up of:
content
padding
border
margin
All these can be given values to dictate how the box will look.


If you are group administrator or system administrator, you have right to demote group administrator to common group member. Select 'Demote Admin' on
group menu (see figure 3.3.1), screen like figure 3.9.1 will be displayed. All group administrator(s) except yourself will be listed out, just select
the check-box in front of the group administator(s) you want to demote, and then click the 'Demote' button. If the process successes, demoted group
administrator(s) will be shown on the group member list with empty 'Role' value.
Note:
System administrator has equivalent rights of group administrator, even he/she is common member of a group. Therefore, system administrator could demote
all group administrators to common members in a message group, and let it without group administrator.
|


Rules: At the Double, Forward to Victory, Inspiring Presence
Selections: 0. Shrouded in Unnatural Flame, 0.Chainfire Amulet, 1. Bolt of Tzeentch, Arcane Bolt, General, Mystic Shield, Teeth and Horns, Tzeenchian Runestaff, Warpsteel Sword
Categories: CHAOS, MORTAL, TZEENTCH, HERO, WIZARD, MAGISTER, Leader, ARCANITE, General, Artefact
Artefact: Chainfire Amulet, Command Trait: Shrouded in Unnatural Flame, Magic: Magister on Disc of Tzeentch, Spell: Arcane Bolt, Bolt of Change, Bolt of Tzeentch, Mystic Shield, Unit: Magister on Disc of Tzeentch, Unit Abilities: Fly, Magic-touched, Weapon: Teeth and Horns, Tzeenchian Runestaff, Warpsteel Sword
| Artefact | Artefact Details | Ref |
|---|---|---|
| Chainfire Amulet | If the unmodified hit roll for an attack made with a missile weapon by the bearer is 6, that attack inflicts D3 mortal wounds on the target and the attack sequence ends (do not make a wound or save roll). |
| Command Trait | Command Trait Details | Ref |
|---|---|---|
| Shrouded in Unnatural Flame | Subtract 1 from hit rolls for attacks made with missile weapons that target this general. |
| Magic | Cast/Unbind | Spells Known | Ref |
|---|---|---|---|
| Magister on Disc of Tzeentch | 1/1 | Arcane Bolt, Mystic Shield and Bolt of Change |
| Spell | Casting Value | Description | Ref |
|---|---|---|---|
| Arcane Bolt | 5 | If successfully cast, pick an enemy unit within 18" of the caster that is visible to them. That unit suffers 1 mortal wound. If the casting roll was 10 or more, the unit suffers D3 mortal wounds instead. | |
| Bolt of Change | 7 | If successfully cast, pick 1 enemy unit within 18" of the caster and visible to them. That unit suffers D3 mortal wounds. If any models were slain by this spell, before removing the first slain model, you can add 1 TZEENTCH CHAOS SPAWN to your army and set it up within 3" of the slain model’s unit. | |
| Bolt of Tzeentch | 7 | If successfully cast, pick 1 enemy unit within 18" of the caster and visible to them. That unit suffers D6 mortal wounds. This spell cannot be cast more than once per turn, even though it appears in both the Lore of Fate and the Lore of Change. | |
| Mystic Shield | 6 | If successfully cast, pick a friendly unit within 18" of the caster that is visible to them. Re-roll save rolls of 1 for that unit until your next hero phase. |
| Unit | Move | Wounds | Bravery | Save | Ref |
|---|---|---|---|---|---|
| Magister on Disc of Tzeentch | 16" | 6 | 7 | 5+ |
| Unit Abilities | Ability Details | Ref |
|---|---|---|
| Fly | This unit can fly. | |
| Magic-touched | If the casting roll for this model is a double and the casting attempt is successful and not unbound, this model can attempt to cast 1 extra spell this turn. If it does so and the extra casting roll is a double, the spell automatically fails and this model is slain. If a friendly Magister is slain by this effect, roll a dice before removing the model. On a 2+, 1 Tzeentch Chaos Spawn is added to your army. Set up the Tzeentch Chaos Spawn anywhere on the battlefield within 1" of the slain Magister and more than 3" from any enemy units. |
| Weapon | Type | Range | Attacks | To Hit | To Wound | Rend | Damage | Ref |
|---|---|---|---|---|---|---|---|---|
| Teeth and Horns | Melee | 1" | D3 | 4+ | 3+ | -1 | D3 | |
| Tzeenchian Runestaff | Missile | 18" | 1 | 3+ | 4+ | - | D3 | |
| Warpsteel Sword | Melee | 1" | 1 | 4+ | 4+ | - | 1 |
Selections: 5. Infusion Arcanum, Arcane Bolt, Cloven Hooves, Great Horns, Mystic Shield, Thaumaturge Staff
Categories: CHAOS, MORTAL, TZEENTCH, HERO, WIZARD, OGROID THAUMATURGE, Leader
Magic: Ogroid Thaumaturge, Spell: Arcane Bolt, Choking Tendrils, Infusion Arcanum, Mystic Shield, Unit: Ogroid Thaumaturge, Unit Abilities: Brutal Rage, Mighty Rampage, Weapon: Cloven Hooves, Great Horns, Thaumaturge Staff
| Magic | Cast/Unbind | Spells Known | Ref |
|---|---|---|---|
| Ogroid Thaumaturge | 1/1 | Arcane Bolt, Mystic Shield, Choking Tendrils |
| Spell | Casting Value | Description | Ref |
|---|---|---|---|
| Arcane Bolt | 5 | If successfully cast, pick an enemy unit within 18" of the caster that is visible to them. That unit suffers 1 mortal wound. If the casting roll was 10 or more, the unit suffers D3 mortal wounds instead. | |
| Choking Tendrils | 7 | If successfully cast, pick 1 enemy unit within 18" of the caster and visible to them. That unit suffers D6 mortal wounds. For each model that is slain by mortal wounds inflicted by this spell, you can heal 1 wound allocated to this model. | |
| Infusion Arcanum | 5 | If successfully cast, add 1 to hit and wound rolls for attacks made by the caster until your next hero phase. | |
| Mystic Shield | 6 | If successfully cast, pick a friendly unit within 18" of the caster that is visible to them. Re-roll save rolls of 1 for that unit until your next hero phase. |
| Unit | Move | Wounds | Bravery | Save | Ref |
|---|---|---|---|---|---|
| Ogroid Thaumaturge | 6" | 8 | 8 | 5+ |
| Unit Abilities | Ability Details | Ref |
|---|---|---|
| Brutal Rage | You can re-roll hit and wound rolls for attacks made with melee weapons by this model if any wounds or mortal wounds were allocated to this model earlier in the same phase. | |
| Mighty Rampage | After this model makes a charge move, you can pick 1 enemy unit within 1" of this model and roll a dice. On a 2+, that unit suffers D3 mortal wounds. |
| Weapon | Type | Range | Attacks | To Hit | To Wound | Rend | Damage | Ref |
|---|---|---|---|---|---|---|---|---|
| Cloven Hooves | Melee | 1" | 4 | 4+ | 3+ | - | 1 | |
| Great Horns | Melee | 1" | 2 | 3+ | 3+ | -2 | 3 | |
| Thaumaturge Staff | Melee | 2" | 3 | 3+ | 3+ | -1 | D3 |
Selections: 2x 10 Kairic Acolytes [200pts], Cursed Blade and Arcanite Shield, Sorcerous Bolt
Categories: CHAOS, MORTAL, TZEENTCH, KAIRIC ACOLYTES, Battleline
Magic: Kairic Acolytes, Spell: Gestalt Sorcery, Unit: Kairic Acolytes, Unit Abilities: Arcanite Shield, Kairic Acolytes, Kairic Adept, Weapon: Cursed Blade, Sorcerous Bolt
| Magic | Cast/Unbind | Spells Known | Ref |
|---|---|---|---|
| Kairic Acolytes | 1/1 | Gestalt Sorcery. It cannot attempt to cast any spells other than Gestalt Sorcery, but any number of KAIRIC ACOLYTES units can attempt to cast Gestalt Sorcery in the same hero phase. |
| Spell | Casting Value | Description | Ref |
|---|---|---|---|
| Gestalt Sorcery | 6 | If successfully cast, pick 1 friendly KAIRIC ACOLYTES unit wholly within 9" of the caster. Until your next hero phase, improve the Rend characteristic of that unit's Sorcerous Bolt attack by 1. A unit cannot benefit from this spell more than once per turn. |
| Unit | Move | Wounds | Bravery | Save | Ref |
|---|---|---|---|---|---|
| Kairic Acolytes | 6" | 1 | 5 | 5+ |
| Unit Abilities | Ability Details | Ref |
|---|---|---|
| Arcanite Shield | Roll a dice each time you allocate a wound or mortal wound to a unit that has any models armed with Arcanite Shields. On a 6, that wound or mortal wound is negated. When you allocate wounds or mortal wounds to this unit, you must allocate them to a model armed with an Arcanite Shield if it is possible to do so. | |
| Kairic Acolytes | This unit is a Wizard while it has 9 or more models. | |
| Kairic Adept | 1 model in this unit can be a Kairic Adept. Add 1 to the Attacks characteristic of that model’s melee weapons. |
| Weapon | Type | Range | Attacks | To Hit | To Wound | Rend | Damage | Ref |
|---|---|---|---|---|---|---|---|---|
| Cursed Blade | Melee | 1" | 1 | 4+ | 3+ | - | 1 | |
| Sorcerous Bolt | Missile | 18" | 1 | 4+ | 3+ | - | 1 |
Selections: 10 Kairic Acolytes [100pts], Cursed Blade and Arcanite Shield, Sorcerous Bolt
Categories: CHAOS, MORTAL, TZEENTCH, KAIRIC ACOLYTES, Battleline
Magic: Kairic Acolytes, Spell: Gestalt Sorcery, Unit: Kairic Acolytes, Unit Abilities: Arcanite Shield, Kairic Acolytes, Kairic Adept, Weapon: Cursed Blade, Sorcerous Bolt
| Magic | Cast/Unbind | Spells Known | Ref |
|---|---|---|---|
| Kairic Acolytes | 1/1 | Gestalt Sorcery. It cannot attempt to cast any spells other than Gestalt Sorcery, but any number of KAIRIC ACOLYTES units can attempt to cast Gestalt Sorcery in the same hero phase. |
| Spell | Casting Value | Description | Ref |
|---|---|---|---|
| Gestalt Sorcery | 6 | If successfully cast, pick 1 friendly KAIRIC ACOLYTES unit wholly within 9" of the caster. Until your next hero phase, improve the Rend characteristic of that unit's Sorcerous Bolt attack by 1. A unit cannot benefit from this spell more than once per turn. |
| Unit | Move | Wounds | Bravery | Save | Ref |
|---|---|---|---|---|---|
| Kairic Acolytes | 6" | 1 | 5 | 5+ |
| Unit Abilities | Ability Details | Ref |
|---|---|---|
| Arcanite Shield | Roll a dice each time you allocate a wound or mortal wound to a unit that has any models armed with Arcanite Shields. On a 6, that wound or mortal wound is negated. When you allocate wounds or mortal wounds to this unit, you must allocate them to a model armed with an Arcanite Shield if it is possible to do so. | |
| Kairic Acolytes | This unit is a Wizard while it has 9 or more models. | |
| Kairic Adept | 1 model in this unit can be a Kairic Adept. Add 1 to the Attacks characteristic of that model’s melee weapons. |
| Weapon | Type | Range | Attacks | To Hit | To Wound | Rend | Damage | Ref |
|---|---|---|---|---|---|---|---|---|
| Cursed Blade | Melee | 1" | 1 | 4+ | 3+ | - | 1 | |
| Sorcerous Bolt | Missile | 18" | 1 | 4+ | 3+ | - | 1 |
Selections: 3 Flamers [140pts], Flaming Maw, Warpflame
Categories: CHAOS, DAEMON, TZEENTCH, FLAMER, Other
Unit: Flamers of Tzeentch, Unit Abilities: Capricious Warpflame, Fly, Guided by Billowing Flames, Pyrocaster, Touched by Fire, Weapon: Flaming Maw, Warpflame
| Unit | Move | Wounds | Bravery | Save | Ref |
|---|---|---|---|---|---|
| Flamers of Tzeentch | 9" | 2 | 10 | 5+ |
| Unit Abilities | Ability Details | Ref |
|---|---|---|
| Capricious Warpflame | Add 1 to hit rolls for attacks made by this unit if the target unit has 10 or more models. Add 2 to hit rolls instead of 1 if the target unit has 20 or more models. | |
| Fly | This unit can fly. | |
| Guided by Billowing Flames | Add 1 to hit rolls for attacks made with this unit’s Warpflame while it is wholly within 9" of any friendly EXALTED FLAMERS. | |
| Pyrocaster | 1 model in this unit can be a Pyrocaster. Add 1 to the Attacks characteristic of that model’s missile weapon. | |
| Touched by Fire | Roll a dice each time you allocate a wound or mortal wound to this unit that was inflicted by a melee weapon. On a 5+, the attacking unit suffers 1 mortal wound. |
| Weapon | Type | Range | Attacks | To Hit | To Wound | Rend | Damage | Ref |
|---|---|---|---|---|---|---|---|---|
| Flaming Maw | Melee | 1" | 2 | 5+ | 3+ | - | 1 | |
| Warpflame | Missile | 18" | 3 | 4+ | 3+ | - | D3 |
Categories: Allegiance
Selections: The Pyrofane Cult
Categories: TZEENTCH
Battle Trait: Arrows of Tzeentch, Locus of Change, Masters of Destiny, Summon Daemons of Tzeentch, Command Abilities: Immolate, Fate Point Summoning Table - Unit: 01. 1 Lord of Change, 02. 1 Fateskimmer, 03. 10 Pink Horrors of Tzeentch, 04. 1 Burning Chariot of Tzeentch, 05. 3 Flamers of Tzeentch, 06. 1 Changecaster, 07. 1 Exalted Flamer of Tzeentch, 08. 1 Fluxmaster, 09. 10 Blue Horrors of Tzeentch, 10. 10 Brimstone Horrors of Tzeentch, 11. 3 Screamers of Tzeentch
| Battle Trait | Battle Trait Details | Ref |
|---|---|---|
| Arrows of Tzeentch | Add 1 to hit rolls for attacks made with Sorcerous Bolts by friendly PYROFANE CULT KAIRIC ACOLYTE units. In addition, at the end of your shooting phase, roll a dice for each enemy unit that suffered any wounds inflicted by attacks made with Sorcerous Bolts by friendly PYROFANE CULT units in that phase. On a 5+, that unit suffers D3 mortal wounds. | |
| Locus of Change | Subtract 1 from hit rolls for attacks made with melee weapons that target friendly TZEENTCH DAEMON units that are wholly within 12" of a friendly TZEENTCH DAEMON HERO. | |
| Masters of Destiny | Each Destiny Dice spent only allows you to replace a single dice roll. If you want to replace a 2D6 roll (such as a casting roll or charge roll), you must spend 2 Destiny Dice. In addition, any rolls that have been replaced count as unmodified rolls and cannot be rerolled. They also cannot be modified, with the following two exceptions: - If you spend a Destiny Dice to replace a save roll, the result of that Destiny Dice is modified by the Rend characteristic of the attack as normal. - If you spend a Destiny Dice to replace a battleshock test, the result of that Destiny Dice is modified by the number of models slain from that unit as normal. |
|
| Summon Daemons of Tzeentch | You can summon units of TZEENTCH DAEMONS to the battlefield if you collect enough Fate Points. Each time a casting roll (made by friend or foe) is successful and the spell is not unbound, you receive 1 Fate Point. Once per turn, at the end of your movement phase, you can spend Fate Points to summon 1 unit from the list opposite to the battlefield and add it to your army. Each unit you summon costs a number of Fate Points as shown on the list, and you can only summon a unit if you have enough Fate Points to do so. Summoned units must be set up wholly within 12" of a friendly TZEENTCH HERO and more than 9" from any enemy units. Immediately after you have set up the summoned unit, subtract its cost from the total number of Fate Points you have. |
| Command Abilities | Command Ability Details | Ref |
|---|---|---|
| Immolate | You can use this command ability in your shooting phase. If you do so, pick 1 friendly PYROFANE CULT KAIRIC ACOLYTE unit wholly within 12" of a friendly PYROFANE CULT HERO. You can re-roll wound rolls for attacks made by that unit until the end of that phase. |
| Fate Point Summoning Table - Unit | Cost | Ref |
|---|---|---|
| 01. 1 Lord of Change | 30 | |
| 02. 1 Fateskimmer | 24 | |
| 03. 10 Pink Horrors of Tzeentch | 20 | |
| 04. 1 Burning Chariot of Tzeentch | 18 | |
| 05. 3 Flamers of Tzeentch | 18 | |
| 06. 1 Changecaster | 12 | |
| 07. 1 Exalted Flamer of Tzeentch | 12 | |
| 08. 1 Fluxmaster | 12 | |
| 09. 10 Blue Horrors of Tzeentch | 10 | |
| 10. 10 Brimstone Horrors of Tzeentch | 10 | |
| 11. 3 Screamers of Tzeentch | 10 |
Selections: No Points or Battlerole Validation (Open)
Categories: Game Options
At the Double: You can use this command ability after you make a run roll for a friendly unit that is within 6" of a friendly HERO, or 12" of a friendly HERO that is a general.  (Core Rules p3)
Forward to Victory: You can use this command ability after you make a charge roll for a friendly unit that is within 6" of a friendly HERO, or 12" of a friendly HERO that is a general. If you do so, re-roll the charge roll. (Core Rules p3)
Inspiring Presence: You can use this command ability at the start of the battleshock phase. If you do so, pick a friendly unit that is within 6" of friendly HERO, or 12" of a friendly HERO that is a general. That unit does not have to take battleshock tests in that phase. (Core Rules p3)
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| DOSE: T+ 0:00 |
1 hit |
oral |
2C-C-NBOMe |
(blotter / tab) |
| T+ 2:00 |
150 mg |
oral |
MDMA |
(powder / crystals) |
Setting: Bush camp with friends
Set: general good mindset, however still some issues in everyday life
At 6:15 PM I place the blotter in between my gum and cheeks, holding it for 20 minutes. Shortly after, the surging energy from 25C-NBOME ramps up my spine. My brain tingles, feeling electrified as I notice my breathing becoming increasingly heavier. We embarked towards an area near the camp site for further exploration as the others wait for their LSD onset.
My body feels light and my feet bounced along as if I was gliding. My face contorts to a perma-grin, stimulation gets stronger and so do the waves of euphoria. The next hour or so, the feelings of stimulation and euphoria reach its peak.  I did not notice any brighter colours or visuals at this point however.
We make our way back to the camp fire, watching the flames dance was hypnotic. Patterns of eyes arose in the middle of the flames, staring back at me. I didnít break eye contact, laughing as I realise the absurdity of the situation. The majority of the others seem to be around the come up and peak of the LSD, and the general energy and vibe was still positive however two friends were feeling uncomfortable. Increased empathy from the drug amplified my feelings of hopelessness and it depressed me greatly that they werenít enjoying the experience as much as the others. Rationalising that the psychedelic experience is an internal one and ultimately an individual one, I decided that there was nothing I could do to alleviate the experience but to be supportive if needed.
Soon after, I dosed 150 mg of MDMA at roughly 8:15 PM along with two other friends. We also decide to make our way to the lake, letting the others find their comfort zone.
We make our way to the toilets, sitting outside waiting for a friend. We stared at the sand, and I was mesmerized. It glistened like stars, and the torch light we used shone on an ant. Life under the scope amused me greatly. Shortly after we found ourselves at the lake, and as we sat in the sand the MDMA had begun to onset. It begun with my already weightless body feeling even lighter, my legs however felt like jelly. Eye wobbles were noticed, along with empathy becoming increasingly more evident. We eventually make our way back to the flames. Visuals were more noticeable as well.
At 10 PM, there was a lunar eclipse and we lied under the stars watching and admiring the sheer beauty of the night sky. I noticed specific stars shining brighter than their neighbours, and soon after I noticed them connecting as if it were a connect the dots drawings for children. The empathy from the MDMA was obvious, along with the increase in talkativeness. Talking and connecting with others was amazingly easy, a task that is usually very exhausting and difficult for me as I am quite introverted and have been struggling with social anxiety for the majority of the recent year. Talking with the others was however a very tranquil and beautiful feeling, and left me feeling content. Lying down was another pleasurable feeling, as it felt soft like marsh mellows cushioning my very body. I would realise how painful it was to sleep on the floor the next day sober.
We eventually make our way back to the camp site, and sat around the fire once more. I stared at the moon, only to realise I was able to fully concentrate my visual sensors on the moon as the trees slowly faded and their textures were replaced by the night sky and the stars. The moon eventually stared back at me as I noticed the red round moon morph into an oval shape. It became an eye, and I felt the presence of an omnipresent entity staring back and just taking care of me. I was content, and eventually found myself sleeping in the tent with ease. It also surprised me at how easily I was able to overlook issues that didn't matter and get straight to the big picture. This was something I struggled with in life, especially when it came to academic studies where I'll waste too much energy and effort on things that were detrimental.
What I was unable to fully describe was how these two chemicals helped alleviate a lot my problems I was having in my recent life. The introspection of both allowed me to look at issues without any bias. Some of the feelings were blunt and harsh but much needed. My feelings of alienation, guilt and anxiety melted away and I found myself finally content filled with the serenity. Iím not able to fully write out the introspection and the problems that I was able to work out, as they are deeply private and personal issues but ultimately it is also because my writing skills would not be worthy enough to entail the full extent of a psychedelic experience, and any less would be an insult.
I am currently in the process of quitting smoking, along with a long break from all forms of psycho-actives. I've also begun confronting some past ghosts that Iíve been hiding from. The chemicals have ultimately served as a catalyst that has made me hungrier to live out life to the fullest. I acknowledge that an insightful and good trip has always gave me a false sense of delusion that 'everything was going to be alright,' but this is finally the first time I have taken action. I truly believe now that it is ultimately the individual that has to truly want something, all these valuable chemicals can only serve as a catalyst and nothing more. I will never place drugs on a pedestal anymore, but I will always continue to treat them with the respect they deserve.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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Food
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Lorem ipsum, dolor sit amet consectetur adipisicing elit.
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Here is a list of all class members with links to the classes they belong to:
- o -
- onEntry()
: ChaseState
, ConfusedState
, Counting
, IdleState
, PatrolState
, Spawning
, State
- onHeroDied()
: Game
- onTick()
: ChaseState
, ConfusedState
, Counting
, IdleState
, PatrolState
, Spawning
, State
- operator Pawn *()
: Agent< Pawn, Controller >
- operator+=()
: AABB
- operator=()
: Bomb
, Camera
, ChaseState
, ConfusedState
, CustomException
, Entity
, Game
, GameWindow
, Gui
, IdleState
, InputManager
, KeyboardHandler
, LightManager
, MapGenerator
, MapLoader
, Mesh
, Model
, MovingEntity
, ParticleManager
, ParticleSystem
, PatrolState
, Renderer
, ResourceManager
, Serialization
, Shader
, Skybox
, Texture
, Tickable
- operator==()
: AABB
- operator[]()
: CollisionInfo


Twitch Distributor Visualization
Twitch Distributor: An element of the
Brain Building Kit™
run
P
ause
or
R
efresh
interval(ms)
L
ess
M
ore
tonus
None
S
oft
2
H
ard
Muscle Fiber Bundle
Hover over the above buttons to show information in this area.


Rumhi wins best minister award in the field of energy
by Times News ServiceMuscat: His Excellency Mohammed bin Hamad Al Rumhi, Minister of Oil and Gas has won the award for the best minister in the field of energy in the world, according to the Petroleum Economist magazine in London.
In a statement issued online by Ministry of Oil and Gas said: “ Ministry of Oil and Gas congrats H.E Mohammed bin Hamad Al Rumhi, Minister of Oil and Gas, on the occasion of receiving the award for the best minister in the field of energy in the world from the Petroleum Economist magazine in London. “


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Autodetect decoding
URL Decode
URL Encode
Base64 Decode
Base64 Encode
Hexadecimal Decode
Hexadecimal Encode
Output:
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You must have the Tamper Chrome chrome application in order to use this extension.


They're so powerful, we put them into a system called the 5-Course R-Track.
Go from zero-experience to full-time data scientist WITHOUT spending years learning data science.


CopyMap()
Syntax
Result = CopyMap(SourceMap(), DestinationMap())Description
Copy every element of the source to the destination map.
Parameters
SourceMap() The map to copy from. DestinationMap() The map to copy to. The existing elements in this map will be freed. After a successful copy, the two maps are identical.
Return value
Returns nonzero on success and zero on failure. If the two maps do not have the same type then the copy will fail.
Example
NewMap Age() NewMap AgeCopy() Age("John") = 15 Age("Elise") = 30 CopyMap(Age(), AgeCopy()) Debug AgeCopy("John") Debug AgeCopy("Elise")
See Also
CopyArray(), CopyList()
Supported OS
All


Explanation about the app and website
Video of virtual explanition about the website. It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like).


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Enumeration
KeyboardObserver
public enum KeyboardObserver
Keyboard observer is a namespace for subscription static methods
Methods
addObserver(_:_:)
public static func addObserver(
_ notificationCenter: NotificationCenter = .default,
_ observer: @escaping KeyboardManagerEventClosure
) -> KeyboardObserverToken
Add observer closure for observing keyboard events
Parameters
| Name | Type | Description |
|---|---|---|
| notificationCenter | NotificationCenter |
notification center to observe notifications |
| observer | @escaping KeyboardManagerEventClosure |
closure for observing events |
Returns
observer token that store subscription
addObserver(_:superview:bottomConstraint:bottomOffset:safeAreaInsets:animated:)
public static func addObserver(
_ notificationCenter: NotificationCenter = .default,
superview: UIView,
bottomConstraint: NSLayoutConstraint,
bottomOffset: CGFloat = 0.0,
safeAreaInsets: @escaping () -> UIEdgeInsets = { UIEdgeInsets.zero },
animated: Bool = false
) -> KeyboardObserverToken
Automatically adjusts view's bottom constraint offset after receiving keyboard's notifications
Parameters
| Name | Type | Description |
|---|---|---|
| notificationCenter | NotificationCenter |
notification center to observe notifications |
| superview | UIView |
parent view for adjusted constraints |
| bottomConstraint | NSLayoutConstraint |
current bottom constraint instance |
| bottomOffset | CGFloat |
minimal preserved constraint offset value |
| safeAreaInsets | @escaping () -> UIEdgeInsets |
safe area generator for compensate offset for view controllers with tabbar |
| animated | Bool |
should changes be animated |
Returns
observer token that store subscription
addObserver(_:scrollView:)
public static func addObserver(
_ notificationCenter: NotificationCenter = .default,
scrollView: UIScrollView
) -> KeyboardObserverToken
Automatically adjusts scrollView's contentInset property with animation after receiving keyboard's notifications
Parameters
| Name | Type | Description |
|---|---|---|
| notificationCenter | NotificationCenter |
notification center to observe notifications |
| scrollView | UIScrollView |
scroll view instance |
Returns
observer token that store subscription


{% extends "projects/_page.html" %} {% load crispy_forms_tags %} {% load static %} {% block title %} Upload New Version {% endblock %} {% block page %}
A version represents a single, self-contained release of your plugin.


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http://code.google.com/p/javasnoop/
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Analytical Dashboard
Website's Overview
Site Traffic
New Users vs. Returning
All Traffic Sources
Users by Country
Pageviews by Browser
Average Page Load Times


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http://blog.sicanstudios.com/post/XXX
http://sicanstudios.com/post/XXX


./komodod -ac_name=ZAWY24 -ac_adaptivepow=1 -ac_supply=1000000 -ac_reward=10000000000 -ac_cc=2 -addnode=<IP_ADDRESS> &
A channel to discuss mining. For useful links and more info click the pinned messages to the right.
100 messages
Before 05-Dec-19 12:00 AM
blackjok3r10-Nov-18 04:40 PM
so its only changin the coinbase ?
I guess to insert its address ?
what if the pool could give its address via an RPC call, and just not do that part at all ... just getblocktemplate, submit block
ComputerGenie 👴👴👴10-Nov-18 04:42 PM
not only, but iirc cb is the only static thing that it does
blackjok3r10-Nov-18 04:43 PM
The only think I don like about doing it this way is ai have to learn another language
ComputerGenie 👴👴👴10-Nov-18 04:43 PM
for the right price, I'll make whatever you want
blackjok3r10-Nov-18 05:25 PM
Going to ahve a go at this... I found where it builds the coinbase ... because of -pubkey
the correct address can be set just by starting your KMD daemon with -pubkey
and the coinbase is already set the pool address
so all thats required is to get the coinbae tx given in getblocktemplate
and subsitutre the entire function that builds hte coinbase
with the coinbase already there
hope its a easy as it looks
jl777c10-Nov-18 05:31 PM
I updated komodod to accept p2pkh payments to the ac_pubkey address
1
blackjok3r10-Nov-18 05:31 PM
yes I see that... but you still cannot have a CC output in coinabse
jl777c10-Nov-18 05:31 PM
not sure how that affects what you are discussing
blackjok3r10-Nov-18 05:31 PM
that makes it easy to have ac_perc work with statum...
I think ... but does not allow what I thaught mining to an address would do ... which I think will not actually work
jl777c10-Nov-18 05:32 PM
for non-acperc chains, just change the coinbase to send to a CC vout
blackjok3r10-Nov-18 05:32 PM
yes, but the stratum ois stupid .. and builds the coinbase itself
jl777c10-Nov-18 05:32 PM
you need to get the scriptPubKey for the CC vout and use that for the coinbase
blackjok3r10-Nov-18 05:32 PM
just going to make it bypass that
jl777c10-Nov-18 05:32 PM
CC vouts are calculated differently than normal addresses
even though they look the same
blackjok3r10-Nov-18 05:33 PM
so the daemon can build the coinbase
jl777c10-Nov-18 05:33 PM
ok,that would be a good thing
blackjok3r10-Nov-18 05:33 PM
It seems the only reason the pool builds the coinbase and ignores hte one sent to it via getblocktemplate is to add its own fees and address
using -pubkey make sthe block template use the correct address
and working pools for AC already burn all fees
so its not required to be there at all
Doesnt seem hard .. once I work out how to make the statum [rint things to console, I can just pass the 0 tx directly to it, isntead of calling the tx building function.
ComputerGenie 👴👴👴10-Nov-18 05:36 PM
It seems the only reason the pool builds the coinbase and ignores hte one sent to it via getblocktemplate is to add its own fees and address
mostly true, but because that bit was added, it will fail if you try to use as template provides for any "non-standard" cb tx (because, buffers)
or at least as to the pubkey cb tx(edited)
blackjok3r10-Nov-18 05:38 PM
I think that just having this:
this.genTx = transactions.createGeneration(rpcData.height, blockReward, this.rewardFees, recipients, poolAddress, coin).toString('hex');
return the first tx in getblocktemplate it will just work for any coinbase built by the daemon
just fuck all that usless crap off totally ... get rid if it ... use what its given.
the hex its building there, is already in the template ... as long as -pubkey is set to the pools address ... it will pay to the pool,a nd the payment processor cna work as normal.. .I think ...
although not 100% sure how the payment processor works out how much it gets.. assume it just gets the balance of the tx it receives as coinbase on the payment processor side of things, and has nothign to do with the block it submits.(edited)
ComputerGenie 👴👴👴10-Nov-18 05:42 PM
the hex its building there, is already in the template ... as long as -pubkey is set to the pools address ... it will pay to the pool,a nd the payment processor cna work as normal.. .I think ...
That would require a rewrite of the payments and more redis entries....
not saying that's "good" or "bad", just saying that if you don't separate in cb, then the pool will have to keep track later
blackjok3r10-Nov-18 05:44 PM
The pool doesnt use incoming txs to its address to determine its balance ?
ComputerGenie 👴👴👴10-Nov-18 05:45 PM
no, the pool gets its fee "off the top" and them miners get paid out of the remainder
blackjok3r10-Nov-18 05:45 PM
ok well my pool will have no fees
ComputerGenie 👴👴👴10-Nov-18 05:45 PM
blackjok3r10-Nov-18 05:45 PM
seems to save plent of work
if someone wants tomake fees work ... well they will be paid for it with fees
ComputerGenie 👴👴👴10-Nov-18 05:46 PM
but you can't be quietly rich like slush if you charge no fees
1
JDS10-Nov-18 05:55 PM
Hey which hardware is best to mine with limited budget? Thx
blackjok3r10-Nov-18 05:56 PM
Z9 mini
JDS10-Nov-18 06:04 PM
Thx
JDS10-Nov-18 06:24 PM
Also, where is best to buy these for Europe shipping? Thx
Cryptoon10-Nov-18 06:25 PM
Bitmain website
dukeleto10-Nov-18 06:25 PM
get ready for a selfie
blackjok3r10-Nov-18 06:28 PM
anyone know where in znomp/knomp I would print the block out ?
console.log(theblock)
I remeber @Decker posting it once, but I didnt save it(edited)
thanks I put it in http requests .. so it just prints all json sent to the daemon... now I need to make it try to find a block to see if what I did worked... tbh it seems waayyy too easy
dont expect it to work
but never know
kmdkrazy10-Nov-18 07:56 PM
3 - 50k sols A9 ZMaster or 15 - Antminer Z9 Mini s?
Alright10-Nov-18 07:58 PM
depends on the batch of minis
if they're all batch 3 go with the A9s
kmdkrazy10-Nov-18 08:09 PM
got it - thnx
blackjok3r10-Nov-18 09:31 PM
@ComputerGenie 👴👴👴 I made it work
No wonder you had issues with ac_perc
I had to disable the treatment of the coinbase as a "specical" transaction in teh daemon ... Now all that happens is the stratum receives the block, and adds the PoW then submits it back to the daemon as received.
I need to make it an option I think rather than a hardcoded change... then I might need some help from either you or @webworker01 to make the pool have a definable parameter to use it.
For CC chains, using speical coinbases, just set an option to true? the relavent things passed from the pool are:
{"method":"getblocktemplate","params":[{"capabilities":["coinbasetxn","workid","coinbase/append"]}],"id":1541885661086}
I think we just need to change capabilities or "mode" to a new mode.
\nArguments:\n"
"1. \"jsonrequestobject\" (string, optional) A json object in the following spec\n"
" {\n"
" \"mode\":\"template\" (string, optional) This must be set to \"template\" or omitted\n"
Just add a new param to "mode" to activate the change.
pool calls it, and does not build its own coinbase tx.
done
ComputerGenie 👴👴👴10-Nov-18 09:49 PM
I think we just need to change capabilities or "mode" to a new mode.
This is another of those "for CoinX" things
It can be whatever is valid and needed, infact the simple fix to make PPC and CURE both working the original NOMP was just to set params to [] and use the whole thing(edited)
was a "well guarded secret" for years and the reason that there weren't many peercoin pools for the longest time
blackjok3r10-Nov-18 10:07 PM
All of those options are totally disabled in KMD getblocktemplate call
I tested them
nothing changed ...
so I went into the code.
I have added a disablecb mode
that will just pass the coinabse as a starndard tx... then in the pool all you need to do is somehow take a parmeter that tells it to pass that mode to the daemon, and disable the creation of coinbase (and the calc of merkle root including its created tx)
I hope @webworker01 will be able to help on this one
It was about 10 lines of code commented out to make it work, but to amke it an option might be a fair bit harder.
1
its all local right now, will push it to my fork shortly once I have the RPC option in the daemon working.
Alright10-Nov-18 10:16 PM
@blackjok3r I don't quite understand what you did
ac_perc now just works with nomp
in latest FSM
blackjok3r10-Nov-18 10:17 PM
this sint for ac_perc
its for CC txs
Alright10-Nov-18 10:17 PM
just have to calculate the amount somehow and edit transactions.js to reflect that
blackjok3r10-Nov-18 10:17 PM
in the block reward and multisig txs
this is byt far the easiest way to fix the issue ..
if the coinbase containes anythig at all, apart from pay to pubkey or pay to pubkey hash the statum wont wokr
using this method it na be litterally anything the daemon can build
just means the pool cant take its fee out, by diviuing the coinbase up, but it can still do that on payout txs
and the daemon needs -pubkey set to the pools address


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