diabetes_example.Rmd
The performance of Corels on test data is compared to Rebecca Barter’s tidymodels walkthrough applying a random forest model.
The accuarcy of Corels on test data of 75% is the same as Rebbeca’s random forest example and similar to that achieved by popular Kaggle notebooks. Though Corels has the advantage of readable rules.
Corels are ‘Certifiably Optimal RulE ListS’. They are short and simple human interpretable rule lists created on categorical data.
This analysis compares the performance of a simple Corels rule set to a randome forest on the Pima Indians Diabetes datase.
In this example, we re-use the exact recipes
data preparation steps from the excellent tidymodels walkthrough by Rebecca Barter
library(tidymodels) library(corels) library(tidycorels) library(kableExtra) library(easyalluvial) library(parcats) library(formattable) kable_table <- function(table, title) { kableExtra::kable(table, caption = title) %>% kableExtra::kable_styling( latex_options = "hold_position", full_width = F, bootstrap_options = c("striped", "condensed"), position = "left" ) } # load the Pima Indians dataset from the mlbench dataset library(mlbench) data(PimaIndiansDiabetes) diabetes_orig <- PimaIndiansDiabetes diabetes_clean <- diabetes_orig %>% dplyr::mutate_at( vars(triceps, glucose, pressure, insulin, mass), function(.var) { if_else(condition = (.var == 0), # if true (i.e. the entry is 0) true = as.numeric(NA), # replace the value with NA false = .var # otherwise leave it as it is ) } ) kable_table(head(diabetes_clean, 5), title = "Pima Indians Dataset")
| pregnant | glucose | pressure | triceps | insulin | mass | pedigree | age | diabetes |
|---|---|---|---|---|---|---|---|---|
| 6 | 148 | 72 | 35 | NA | 33.6 | 0.627 | 50 | pos |
| 1 | 85 | 66 | 29 | NA | 26.6 | 0.351 | 31 | neg |
| 8 | 183 | 64 | NA | NA | 23.3 | 0.672 | 32 | pos |
| 1 | 89 | 66 | 23 | 94 | 28.1 | 0.167 | 21 | neg |
| 0 | 137 | 40 | 35 | 168 | 43.1 | 2.288 | 33 | pos |
The Pima Indians diabetes data set is split into train and test datasets.
set.seed(234589) diabetes_split <- rsample::initial_split(diabetes_clean,prop = 3 / 4) diabetes_train <- rsample::training(diabetes_split) diabetes_test <- rsample::testing(diabetes_split) diabetes_cv <- vfold_cv(diabetes_train)
We now apply the same recipe
steps as Rebecca, but with additional step to discretise the continuous variables, then each categorsied value is given its own 0/1 binary column using recipes::step_dummy()
. This is sometimes called one-hot encoding.
Finally, Corels requires the label column (diabetes
) to be split into two columns representing each class. First we ensure the values in the label are 0 and 1 instead of words (using recipes::step_integer()
), then use recipes::step_dummy()
to create the two label columns.
diabetes_corels_recipe <- recipes::recipe(diabetes ~ pregnant + glucose + pressure + triceps + insulin + mass + pedigree + age, data = diabetes_train) %>% recipes::step_normalize(all_numeric()) %>% recipes::step_knnimpute(all_predictors()) %>% # 1 discretise continous variables into bins recipes::step_discretize(-recipes::all_outcomes(), min_unique = 1) %>% # 2 convert each value of each predictor into its own 0/1 binary column recipes::step_mutate_at(recipes::all_predictors(), fn = list(~ as.factor(.))) %>% recipes::step_dummy(recipes::all_predictors(), one_hot = TRUE) %>% # 3 convert each value of the outcome column into its own 0/1 binary column recipes::step_integer(recipes::all_outcomes(), zero_based = TRUE) %>% # ensure outcome is 0/1 rather than words recipes::step_mutate_at(recipes::all_outcomes(), fn = list(~ as.factor(.))) %>% recipes::step_dummy(recipes::all_outcomes(), one_hot = TRUE) # Train the data recipe on the training data diabetes_trained_rec <- recipes::prep(diabetes_corels_recipe, training = diabetes_train, retain = TRUE) # Extract the train data with recipe applied (juice), and the same recipe applied to the test data (bake) diabetes_train_preprocessed <- recipes::juice(diabetes_trained_rec) diabetes_test_preprocessed <- recipes::bake(diabetes_trained_rec, new_data = diabetes_test) # View the proccessed trained data kable_table(head(diabetes_train_preprocessed, 5), title = "diabetes training data preprocessed")
| pregnant_bin_missing | pregnant_bin1 | pregnant_bin2 | pregnant_bin3 | pregnant_bin4 | glucose_bin_missing | glucose_bin1 | glucose_bin2 | glucose_bin3 | glucose_bin4 | pressure_bin_missing | pressure_bin1 | pressure_bin2 | pressure_bin3 | pressure_bin4 | triceps_bin_missing | triceps_bin1 | triceps_bin2 | triceps_bin3 | triceps_bin4 | insulin_bin_missing | insulin_bin1 | insulin_bin2 | insulin_bin3 | insulin_bin4 | mass_bin_missing | mass_bin1 | mass_bin2 | mass_bin3 | mass_bin4 | pedigree_bin_missing | pedigree_bin1 | pedigree_bin2 | pedigree_bin3 | pedigree_bin4 | age_bin_missing | age_bin1 | age_bin2 | age_bin3 | age_bin4 | diabetes_X0 | diabetes_X1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
We can now run tidycorels::tidy_corels()
function on the prepared diabetes training data.
diabetes_train_model <- tidycorels::tidy_corels( df = diabetes_train_preprocessed, label_cols = c("diabetes_X0", "diabetes_X1"), value_delim = "_", run_bfs = TRUE, calculate_size = TRUE, run_curiosity = TRUE, regularization = 0.01, curiosity_policy = 3, map_type = 1 )
Here are the Corels rules for the diabetes data.
diabetes_train_model$corels_console_output[4:10] #> [1] "OPTIMAL RULE LIST" #> [2] "if ({age:bin1}) then ({diabetes:X0})" #> [3] "else if ({glucose:bin4}) then ({diabetes:X1})" #> [4] "else if ({insulin:bin1}) then ({diabetes:X0})" #> [5] "else if ({pedigree:bin1}) then ({diabetes:X0})" #> [6] "else if ({triceps:bin4}) then ({diabetes:X1})" #> [7] "else ({diabetes:X0})"
And here are those rules converted to data.table code.
diabetes_train_model$corels_rules_DT #> [1] "DT[,corels_label := fifelse( `age_bin1` == 1, 0,fifelse( `glucose_bin4` == 1, 1,fifelse( `insulin_bin1` == 1, 0,fifelse( `pedigree_bin1` == 1, 0,fifelse( `triceps_bin4` == 1, 1,0)))))]"
A dataframe of just the true label, the columns used in the Corels rules, and the Corels predictions is also available. The columns have been ordered for you to work well in an alluvial plot.
diabetes_train_model$alluvial %>% easyalluvial::alluvial_wide(stratum_width = 0.2) + ggplot2::theme_minimal() + ggplot2::labs( title = "Corels if-then-else logic", subtitle = " From truth (far left column) to Corels classification (far right column)" )
Next we use the function tidycorels::corels_predict()
to apply the Corels rules created on the training data to the test data.
diabetes_test_predict <- tidycorels::predict_corels( model = diabetes_train_model, new_df = diabetes_test_preprocessed )
We can now use the test data that has been labelled using the Corels rules to compare to the true labe with a confusion matrix and performance statistics.
conf_matrix <- diabetes_test_predict$new_df_labelled %>% yardstick::conf_mat( truth = "diabetes_X1", estimate = "corels_label" ) ggplot2::autoplot(conf_matrix, "heatmap")
# https://github.com/tidymodels/yardstick/issues/160 conf_matrix <- withr::with_options(c(yardstick.event_first = FALSE),summary(conf_matrix)) %>% dplyr:::mutate(.estimate = round(.estimate, digits = 3)) %>% dplyr::select(.metric, .estimate) %>% dplyr::filter(.metric %in% c("accuracy","bal_accuracy","mcc","precision", "recall", "f_meas")) conf_matrix %>% dplyr::mutate(.estimate = formattable::color_tile("white", "orange")(.estimate)) %>% kableExtra::kable(escape = F) %>% kableExtra::kable_styling("hover", full_width = F)
| .metric | .estimate |
|---|---|
| accuracy | 0.745 |
| mcc | 0.439 |
| bal_accuracy | 0.714 |
| precision | 0.667 |
| recall | 0.600 |
| f_meas | 0.632 |
The accuracy achieved by Corels on the unseen test data is 0.745. This is the same accuracy the random forest reached in Rebbeca’s example. In contrast, Corels rules are easy to read and visualise with an alluvial plot. The screenshot below is one path through the Corels rules (highlighted in darker green) where diabetes was correctly labelled. We can see they are:
Further, below we visualise the distribution of the raw values of each categorised bin used in each rule.
diabetes_recipe_non_dummy <- recipes::recipe(diabetes ~ pregnant + glucose + pressure + triceps + insulin + mass + pedigree + age, data = diabetes_clean) %>% recipes::step_normalize(all_numeric()) %>% recipes::step_knnimpute(all_predictors()) %>% # discretise numeric variables into bins recipes::step_discretize(-recipes::all_outcomes(), min_unique = 1) %>% recipes::step_mutate_at(recipes::all_predictors(), fn = list(~ as.factor(.))) %>% recipes::step_mutate_at(recipes::all_outcomes(), fn = list(~ as.factor(.))) diabetes_train_preprocessed <- diabetes_recipe_non_dummy %>% recipes::prep(diabetes_train) %>% recipes::juice() combined <- diabetes_train_preprocessed %>% dplyr::rename_with(toupper) %>% dplyr::bind_cols(diabetes_train) plot_fun <- function(X, Y, bin) { X <- rlang::ensym(X) Y <- rlang::ensym(Y) bin <- rlang::ensym(bin) combined %>% ggplot2::ggplot(aes( x = !!X, y = !!Y )) + ggplot2::geom_violin(scale = "count") + gghighlight::gghighlight(!!X == bin) + ggplot2::theme_minimal() } p1 <- plot_fun(X = AGE, Y = age, bin = bin1) p2 <- plot_fun(X = GLUCOSE, Y = glucose, bin = bin4) p3 <- plot_fun(X = INSULIN, Y = insulin, bin = bin1) p4 <- plot_fun(X = PEDIGREE, Y = pedigree, bin = bin1) p5 <- plot_fun(X = TRICEPS, Y = triceps, bin = bin4) cowplot::plot_grid(p1, p2, p3, p4, p5)
The plots from left to right highlight which bin is used in each Corels rule in the sequential order of the rules. The plot is created by combining the discretised and one-hot encoded data with the raw column values.


Albums
KIND OF BLUE
Kind of Blue is a studio album by American jazz trumpeter Miles Davis. It is regarded by many critics as jazz's greatest record, Davis's masterpiece, and one of the best albums of all time. Its influence on music, including jazz, rock, and classical genres,
has led writers to also deem it one of the most influential albums ever recorded.
1/2
Freddie Freeloader
9:34
2/2
Flamenco Sketches
9:26


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- abortMatch(int) - Method in class database.Database
-
Deletes game(s) that has been created by the player_id given as a parameter.
- actualSurrender() - Method in class gameplay.GameMain
-
This method is called upon by surrender() method, and this method
uses the method surrenderGame() from the Database Class to send information about
which player won, and increment games won for that player.
- addObstacles() - Method in class gameplay.GameMain
-
Player 2 adds obstacles when he joins the game, this method calls upon the
method (GameLogic) game.createObstacles() which creates obstacles and give them a random placement
by using a Random number generator.Player 1 continuously wait and polls to check if obstacles have been added
before resuming the game flow of the program.
- addUserToDatabase(String, String, byte[], byte[]) - Method in class database.Database
-
Adds user to database.
- addUserToStatistics(int) - Method in class database.Database
-
Adds a given user to the statistics table.
- alertField - Variable in class menus.controller.LoginController
-
- alertField - Variable in class menus.controller.SignUpController
-
- Archer - Class in gameplay.units.unitTypes
-
- Archer(String, int, double, int, double, int, int, int, int, String, String, Image, AudioClip) - Constructor for class gameplay.units.unitTypes.Archer
-
Constructor that fills inn all the class variables
- archerDescription - Static variable in class gameplay.units.UnitGenerator
-
- archerDescriptionTag - Static variable in class gameplay.units.UnitGenerator
-
- archerImage - Static variable in class gameplay.units.UnitGenerator
-
- archerSound - Static variable in class gameplay.units.UnitGenerator
-
- archerUnitType - Static variable in class gameplay.units.UnitGenerator
-
- arialFont - Variable in class gameplay.GameMain
-
- arrow - Static variable in class gameplay.GameMain
-
- arrowColor - Variable in class gameplay.GameMain
-
- arrowHeadSize - Variable in class gameplay.GameMain
-
- arrowWidth - Variable in class gameplay.GameMain
-
- attack(MouseEvent) - Method in class gameplay.GameMain
-
Checks to see if in movementphase and if it's players turn, if it is then this method uses
getPosXFromEvent and getPosYFromEvent to get the MouseEvent.MouseClick transformed into
grid coordinates and uses the method getAttackableTiles() to get all pieces on tiles within attack
movement and uses checkForLegalAttack to see whether the piece player wants to attack is in the array of
attackable Tiles.
- Attack - Class in gameplay.logic
-
- Attack(int, int, int, int, int, int) - Constructor for class gameplay.logic.Attack
-
- attack - Variable in class gameplay.units.ProtoUnitType
-
- attack - Variable in class gameplay.units.Unit
-
- attack - Variable in class gameplay.units.unitTypes.Archer
-
- attack - Variable in class gameplay.units.unitTypes.Catapult
-
- attack - Variable in class gameplay.units.unitTypes.Juggernaut
-
- attack - Variable in class gameplay.units.unitTypes.Swordsman
-
- attackbackgroundImagePane - Variable in class menus.controller.GameInfoController
-
- attackerPieceID - Variable in class gameplay.logic.Attack
-
- attackHighlightColor - Variable in class gameplay.GameMain
-
- attackingPlayerId - Variable in class gameplay.logic.Attack
-
- attackList - Variable in class gameplay.GameMain
-
- attackText - Variable in class menus.controller.GameInfoController
-
- audio - Variable in class gameplay.units.ProtoUnitType
-
- audio - Variable in class gameplay.units.unitTypes.Archer
-
- audio - Variable in class gameplay.units.unitTypes.Catapult
-
- audio - Variable in class gameplay.units.unitTypes.Juggernaut
-
- audio - Variable in class gameplay.units.unitTypes.Swordsman
-


{% extends "base.html" %} {% block title %}Solution Architect Database{% endblock %} {% block header %}{% endblock %} {% block content %}
A place for keeping track of things you're told to keep track of


| runFuzzyModel {FSDM} | R Documentation |
runFuzzyModel
runs a fuzzy model given scenario inputs for starting
values and starting changes.
runFuzzyModel(M, S, Type)
M |
a fuzzy model created using the create FuzzyModel function. |
S |
a scenario created using the loadScenario function. |
Type |
a string that may have either the value 'Linear' or 'Exponential' which identifies whether independent concepts should change linearly or exponentially. |
This function runs a FCM model that was created using the createFuzzyModel function with a scenarios that was created using the createFuzzyScenario function. The function uses a nested loop to calculate final values. The outer loop applies the starting changes in small increments so that the changes appropriate for applying 'point elasticities'. The inner loop iterates the model until a stable equilibrium result is achieved for each increment.
A list containing 3 components: Summary - a matrix containing the final relative concept values (e.g. 0 - 100) for each concept and increment; ScaleSummary - a matrix containing the final concept values in the nominal measurement units for each concept and increment; Full - the relative concept values (e.g. 0 - 100) for each increment and each iteration. Message - a string containing a final completion message identifying whether the model run completed successfully and some information about the run. Success - a logical vector identifying whether the solution converged on each increment. NumIter - a numeric vector identifying the number of iterations to model convergence for each increment.


2 men arrested after S$60,000 stolen from Marsiling Lane shop
SINGAPORE: Two men have been arrested for allegedly stealing S$60,000 from a shop at Marsiling Lane, the police said in a news release on Friday (Nov 22).
The police said they received a report on Nov 16 at about 9.30pm after a room on the second floor of a shop at Block 18 Marsiling Lane was broken into.
A total of S$60,000 in cash was missing.
READ: Man stole S$12,000 worth of valuables from father-in-law, gets divorced and fined
Following ground enquiries and investigations, officers from the Woodlands Police Division identified the two men.
A 50-year-old man was arrested on the same day while a 62-year-old man was arrested on Friday.
The 62-year-old was charged on Nov 18 with housebreaking and theft by night with common intention, while the 50-year-old will be charged on Saturday with the same offences.
If found guilty, each man faces a jail term of between two and 14 years, as well as a fine.
The police advised property owners to take crime prevention measures such as securing all doors, roller shutters, windows and other openings with grilles and close-shackled padlocks.
READ: Neighbourhood thief found with 122 pairs of stolen footwear gets jail
Property owners should also refrain from keeping large sums of cash at commercial premises.
Burglar alarms, motion sensor lights and closed circuit surveillance cameras should be installed and tested periodically, the police added.


{% if not include %}
Hmm... so reverse psychology works on you?
Then don't hire me, and don't use the form on the right!


As always, we continue to make improvements to Aurelia's core as well as its most popular plugins. This set of releases includes not only bug fixes, but substantial improvements to the Aurelia Webpack Plugin, Aurelia UX, Aurelia CLI and our UI Virtualization Library.
In addition to the improvements we're announcing today, in the next week we'll be making announcements around new Webpack skeletons and our new dialog plugin.
Minor Releases
Minor updates include new features, updates to bugs and performance
aurelia-router 1.3.0
Features
- viewport: add viewport name to life cycle arguments ( 34ef2c4 )
aurelia-templating-resources 1.4.0
Features
- templating: support popular CSS transpiler file extensions in the require element ( 901cfd8 )
aurelia-webpack-plugin 2.0.0-rc.2
Features
- flag to opt-out of IE support
- support module.loaders
Bug Fixes
- errors might crash SubFolderPlugin
- compat with IE < 11
- support more CommonJS import styles
aurelia-cli 0.28.0
Bug Fixes
- all: improved error reporting ( 4060148 )
- build: enforce strict mode ( 1694290 )
- bundle: ensure that dependencies are added in order ( 51a2cce )
- project-template: paths should be relatively from src ( b23c8dd )
- sourcemap: use src as the sourceroot for sourcemaps ( 87ca276 )
- test: resolve join of undefined error ( 0207c78 )
Features
- index.html: add viewport meta element ( 96ce8a9 )
aurelia-ux 0.1.18
- chore(all): lint ( 8d3a53a )
- chore(form): prepare for pr ( f4ac0f7 )
- chore(lint): remove uses of Function type ( b02205d )
- chore(vscode): turn off format on save ( 25f7473 )
- feat(form): add ux form and form-row utility ( 8ed6f83 )
- feat(form): add ux-field component ( d20e78b )
- feat(form): submit ( 0bc8799 )
- feat(form): submit-on-enter attribute ( 6d44f7e )
- feat(formField): add label by attribute ( 7aaae37 )
- feat(input-info): find & set target automatically ( 0143c93 )
- feat(input): add input types ( 056ff7c )
- style(button): remove left and right margin ( 7b4254d )
- style(input): change input textarea margins ( 88b3d54 )
- style(input): further refactor style controls ( 4ea3248 )
- Fix #30 (#42) ( c9166dd ), closes #30 #42
- refactor(chip): code cleanup ( 971c1f2 )
- refactor(chip): code cleanup ( 6767f1b )
- refactor(chips): refactor chips ( 62e6391 )
- fix(chip-input): null check ( b026e70 )
- fix(chip): more value checking ( b981456 )
- feat(chip): add closeable chip and final size ( 3b73225 )
- feat(chip): add tag closing and finish styling ( 085273d )
- feat(chips): add chip input ( 32c5792 )
- feat(tag-input): add implimentation for tag components ( 38c945e )
aurelia-ui-virtualization 1.0.0-beta.3.1.0
Bug Fixes
- virtual-repeat: handle array changes correctly ( b861aa6 )
aurelia-i18n 1.5.0
Bug Fixes
- rt: default interpolation as fallback ( 9407c58 )
Features
- relative-time: relative time bindingbehavior ( fdddded )
Patch Releases
Patch updates include only fixes to bugs and performance improvements.


A waterfall chart can be used to show the cumulative effect of each feature. Starting with
the baseline probability for credit defaults (at the bottom of the chart), we can see how
each additional feature shifts the probability. Green arrows indicate that the feature decreased the predicted credit default
risk for the individual credit application. While red arrows indicate that the
feature increased the
predicted credit default risk for the individual credit application. After all
features have been considered, we reach the final predicted credit default risk (at the top
of the chart).


Oak Park Township values the mobility needs of residents of all ages, therefore we offer transportation services to our residents 60 years and older and to persons with disabilities of any age. Service is provided curb-to-curb within Oak Park and River Forest, and all buses are equipped with lifts for full accessibility. Safe, reliable service can be arranged for any purpose, including:
Our Transportation schedule has been expanded to help bring seniors to additional places throughout Oak Park and River Forest. Please click here to see the current monthly schedule of trips.
Phone Hours: Monday through Friday 8:30am - 1:30pm
Appointments: 24 hours to 1 week in advance
Ride Hours: Monday through Friday 7:45am – 4:00pm
Rate: Suggested donation of $1.00 per ride
We provide curb-to-curb service within the communities of Oak Park and River Forest. Special group trips outside of the service area are available throughout the year. Advance registration is recommended.
The Park District of Oak Park and the Township Transportation entered into an Intergovernmental Agreement to provide an additional van for senior citizens and people with disabilities. For the official press release, please click here.
To arrange transportation, please call:
Transportation Office
708-383-4806
RTA Transit Permits
Residents seeking reduced transit cards through the RTA may process their applications at the Township Office located at 105 S. Oak Park Avenue.


Teen dies 'while having sex with man in car after leaving party'
Police are investigating whether a drug overdose or "rough sex" was the possible cause of the 19-year-old's death, it is reported
by Chris Kitching, https://www.mirror.co.uk/authors/chris-kitching/A young woman died while having sex with a man in a car after they both left a party at a nearby flat, it is claimed.
Police are investigating whether a drug overdose or "rough sex" was the possible cause of the 19-year-old's death, according to reports.
The woman's body was found in the vehicle, which was in a car park in the US city of Hackensack, New Jersey, just after 2am local time on Saturday.
Bergen County Prosecutor Mark Musella told local media the sudden death was considered "suspicious" and a post-mortem was taking place to determine the cause.
Emergency responders told the Daily Voice that the woman died while having sex with a man.
A post-mortem was being carried out to determine whether it was a possible drug overdose or the result of "rough sex", the news website reported, citing an unnamed law enforcement official.
"We'll see what it shows," the official added.
Investigators have questioned the man, aged in his 20s, who was with the woman in the car.
He told them that both he and the teen had taken prescription medication and were having sex when she lost consciousness, the Daily Voice reported.
The woman was pronounced dead at the scene, NJ.com reported.
She and the man had attended a party at a block of flats next to the car park, local media said.
The car was later towed from the scene as police conducted a forensic investigation.


Attn: My name is Mr. Kelvin Taylor, I am the first son of Liberia's former President Charles Taylor, Before he left office, he instruct me to look for a capable hand who can help us to invest our money in foreign country for a period of five years, the trustee of the money will have 30% of it and the intrest will be share 50/50% each, the contract can be renewed after five years and we share proceed in long lasting relationship until my father problem is over. If you are ready to help us to invest the money. You can view the website to see more regarding my fataher’s arrest (http://news.bbc.co.uk/1/hi/world/africa/2961390.stm) If yon are intrested to invest the money for us, contact me immediately for more details. Regards, Taylor. __________________________________________________ Do You Yahoo!? Tired of spam? Yahoo! Mail has the best spam protection around http://mail.yahoo.com


A TICKLE IN THE HEART (director/writer: Stefan Schwietert; screenwriter: Thomas Kufus; cinematographer: Robert Richman; editor: Arpad Bondy; cast: Julius J. Epstein, Max Epstein, Willie Epstein; Runtime: 90; 1996)
Reviewed by Dennis Schwartz
A charming black-and-white documentary about three elderly musician brothers, the Epsteins. Max is a clarinet player and the oldest at 83, Willie is the middle one, who plays the trumpet, and Max the youngest in his seventies, who is the drummer. They moved to a south Florida senior citizens community (which they comically say is God's waiting room) from Brooklyn to retire but continued to professionally play the Yiddish music they love, Kletzmer. The Jewish music had a rebirth and became popular again due to the young people clammoring for this music not to be forgotten. Max had the good fortune to play with the old Jewish Kletzmer masters who came to this country in the beginning of the 1900s, but since they all died out, he is the only one left who was trained by them to carry out the tradition as it was handed down from the old country. Kletzmer is a Yiddish term for an itinerant musician, a type of folk music that was popular for the Jews who lived in Eastern Europe before World War 11, but in America it was considered a derogatory term, indicating a musician who doesn't have steady work. But that has all changed now with its revival. The Epsteins, who always earned a good living playing Jewish music and were very popular with the Hasidics, especially from the 1950s through the early '70s, playing at their festivals, bar mitzvahs, and weddings.
They are shown living in their very ordinary Florida residence, travelling to concerts in Berlin and Poland, and on a nostalgia tour of their old haunts in the once Jewish enclave of Brownsville and the still Hasidic neighborhood of Williamsburg in Brooklyn, and they will also visit the Lower East Side in Manhattan, where they were born.
The Epsteins are wonderful musicians and storytellers, openly explaining their music and talking about themselves with no pretensions or false modesty. They have no inhibitions about talking, in fact, it is hard to get them to keep quiet. The unevenness from the film comes from the lacklustre direction, as Schwietert, the thirtysomething, gentile German director, who lived in Florida with the Epsteins for three months, is unsure of how to pace a film and how to go with the flow of the story. But the richness of the lively music and the very natural and easy way the brothers play and communicate, makes this a heartfelt film, just like the music being played.  But when they played in their Florida neighborhood to an elderly Jewish audience, you couldn't help but notice how much more lively and even more appreciative their audience was.
The documentary spends a lot of time showing them booking concerts in synagogues, setting club dates, bickering over concert fees, making potato latkes and eating a Nathan's hot dog in Coney Island. It also goes on a dull side trip to Pinsk, a place that borders Russia-Poland, where their parents are from, but someplace they are unfamiliar with and because of that there was nothing worthwhile to see there. But, in all fairness to the director, aside from a few lapses in the film, he did capture the essence of the Kletzmer music and what makes the Epsteins so special.
The brothers take pride in their craft and it shows in their musical pieces presented, as they bring out a heartfelt joy in their music. It was a fun film to watch, and it was informative to watch them practice and discuss the music, never taking themselves too seriously, but always serious about doing a good job. They seem to be very aware that they are one of the last links with the past, a tradition that might have shamefully died out if not for them becoming spokesmen for kletzmer music.
The title of the film comes from a song Max wrote for their grandfather, who went to hear an opera at the Met and liked it but said that he preferred Yiddish music, because it tickles his heart.
This film should be seen for the remakable music, the film itself is not that remarkable.
REVIEWED ON 7/26/2000 GRADE: B
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
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


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