|
My project
v1.0
|
Go to the source code of this file.
Functions | |
| void | subtreesize (int cur, vector< int > *graph) |
| void | hld_impl (int cur, vector< int > *graph) |
| void | HLD (int root, int num_nodes, vector< int > graph[]) |
Variables | |
| int | chainNo =0 |
| int * | subtree |
| int * | visit |
| int * | chainHead |
| int * | chainPos |
| int * | chainInd |
| int * | chainSize |
| void HLD | ( | int | root, |
| int | num_nodes, | ||
| vector< int > | graph[] | ||
| ) |
| void hld_impl | ( | int | cur, |
| vector< int > * | graph | ||
| ) |
| void subtreesize | ( | int | cur, |
| vector< int > * | graph | ||
| ) |
| int * chainHead |
| int * chainInd |
| int chainNo =0 |
| int * chainPos |
| int * chainSize |
| int* subtree |
| int * visit |


Does Homeopathy Work
What do you think?
Yes, it is amazing and totally safe
No, it's practiced by charlatans and quacks


Learn how to work with the CV Cloud Share platform
We offer various support-, resource- and documentation channels. ownCloud thrives on community support and mutual assistance. We are very much looking forward to the sharing of ideas!
Download and install ownCloud on your machine in a few easy steps.
Install CLI


Previous teaching experiences
2021/2022: I teach the bachelor students of University of Southern Britanny.
- Algorithms and programming (more)
I also teach the engineering students of Télécom SudParis.
- Applied Statistics (more)
2020/2021: I teach the engineering students of Télécom SudParis.
- Statistical Methods for Image Segmentation (more)
- Bayesian Inference in Markov Models (more)
- Applied Statistics (more)
- Statistics and Data Analysis (more)
2019/2020: I teach the engineering students of Télécom Physique Strasbourg.
- Signal processing: here and here and here.
- Foundamental of image processing: here.
- Machine Learning and Pattern Recognition: here.
2018/2019: I teach the engineering students of Télécom Physique Strasbourg.


Potato Venezuela
"Sweet, Nutritious and Delicious"
The key to happiness is hidden is the Potato
Thanks for select one of our product
Potato
Pro
Price: 400 Bs.F
2015 © Potato Venezuela
Made with love in Venezuela


This are people from West-Africa,an ethnic group
specifically from Ghana that frequently name
their children after the day of the week they were
born and order in which they were born.
In West Africa, newborns are given special names
depending on the day they are born.The naming
system of the Akan people is distinct as they use
the Kwa language group’s system of timekeeping,
which is based
on a six-day week (‘nnanson‘), with
the seventh day inclusive.


This is a test extension.
In a normal extension, this area would show the extension's user interface.


Task 1 60% Complete
Task 2 28% Complete
Task 3 60% Complete
Task 4 85% Complete
Lorem Ipsum has been the industry's standard dummy text ever since the 1500s...
Lorem Ipsum has been the industry's standard dummy text ever since an kwilnw...
Lorem Ipsum has been the industry's standard dummy text ever since the...


In order to apply the model, we need to first pull history data to build it. The project can be briefly divided into four parts: the historical data request, model training, prediction and execution.
The first function takes two arguments: symbol and number of daily data points requested. This function requests historical QuoteBars and builds it into a pandas DataFrame. For more information about pandas DataFrame, please refer to the help documentation DataFrame. The calculate_return function takes a DataFrame as an argument to calculate the mean and standard deviation of the log prices, and create new columns for the DataFrame (return, reversal factor and momentum) - it prepares the DataFrame for multiple linear regression.
def get_history(self,symbol, num): data = {} dates = [] history = self.History([symbol], num, Resolution.Daily).loc[symbol]['close'] #request the historical data for a single symbol for time in history.index: t = time.to_pydatetime().date() dates.append(t) dates = pd.to_datetime(dates) df = pd.DataFrame(history) df.reset_index(drop=True) df.index = dates df.columns = ['price'] return df def calculate_return(self,df): #calculate the mean for further use mean = np.mean(df.price) # cauculate the standard deviation sd = np.std(df.price) # pandas method to take the last datapoint of each month. df = df.resample('BM',how = lambda x: x[-1]) # the following three lines are for further experiment purpose # df['j1'] = df.price.shift(1) - df.price.shift(2) # df['j2'] = df.price.shift(2) - df.price.shift(3) # df['j3'] = df.price.shift(3) - df.price.shift(4) # take the return as depend variable df['log_return'] = df.price - df.price.shift(1) # calculate the reversal factor df['reversal'] = (df.price.shift(1) - mean)/sd # calculate the momentum factor df['mom'] = df.price.shift(1) - df.price.shift(4) df = df.dropna() #remove nan value return (df,mean,sd)
The concat function requests history and joins the results into a single DataFrame. As \(\mu \) varies by country so we assign the mean and standard deviation to the symbol for each currency for future use. The OLS function takes the resulting DataFrame to conduct an OLS regression. We write it into a function because it's easier to change the formula here if we need.
def concat(self): # we requested as many daily tradebars as we can his = self.get_history(self.quoted[0].Value,20*365) # get the clean DataFrame for linear regression his = self.calculate_return(his) # add property to the symbol object for further use. self.quoted[0].mean = his[1] self.quoted[0].sd = his[2] df = his[0] # repeat the above procedure for each symbols, and concat the dataframes for i in range(1,len(self.quoted)): his = self.get_history(self.quoted[i].Value,20*365) his = self.calculate_return(his) self.quoted[i].mean = his[1] self.quoted[i].sd = his[2] df = pd.concat([df,his[0]]) df = df.sort_index() # remove outliers that outside the 99.9% confidence interval df = df[df.apply(lambda x: np.abs(x - x.mean()) / x.std() < 3).all(axis=1)] return df def OLS(self,df): res = sm.ols(formula = 'return ~ reversal + mom',data = df).fit() return res
The predict function uses the history for the last 3 months, merges it into a DataFrame and then calculates the updated factors. Using these updated factors (together with the model we built) we calculate the expected return.
def predict(self,symbol): # get current month in string month = str(self.Time).split(' ')[0][5:7] # request the data in the last three months res = self.get_history(symbol.Value,33*3) # pandas method to take the last datapoint of each month res = res.resample('BM',how = lambda x: x[-1]) # remove the data points in the current month res = res[res.index.month != int(month)] # calculate the variables res = self.calculate_input(res,symbol.mean,symbol.sd) res = res.ix[0] # take the coefficient. The first one will not be used for sum-product because it's the intercept params = self.formula.params[1:] # calculate the expected return re = sum([a*b for a,b in zip(res[1:],params)]) + self.formula.params[0] return re def calculate_input(self, df, mean, sd): df['reversal'] = (df.price - mean)/sd df['mom'] = df.price - df.price.shift(3) df = df.dropna() return df
There are a few points of note:
In the Initialize function we prepare the data and conduct a linear regression. The class property 'self.formula' is the result of the OLS regression. We will use this object each time we rebalance the portfolio.
def Initialize(self): self.SetStartDate(2013,6,1) self.SetEndDate(2016,6,1) self.SetCash(10000) self.syls = ['EURUSD','GBPUSD','USDCAD','USDJPY'] self.quoted = [] for i in range(len(self.syls)): self.quoted.append(self.AddForex(self.syls[i],Resolution.Daily,Market.Oanda).Symbol) df = self.concat() self.Log(str(df)) self.formula = self.OLS(df) self.Log(str(self.formula.summary())) self.Log(str(df)) self.Log(str(df.describe())) for i in self.quoted: self.Log(str(i.mean) + ' ' + str(i.sd)) self.Schedule.On(self.DateRules.MonthStart(), self.TimeRules.At(9,31), Action(self.action))
Every month we rebalance the portfolio using the Schedule Event helper method. The predicted returns are added to the rank array and then sorted by return. The first element in the list is the best return paired with the associated symbol. When all the expected returns in the rank array are positive we only go long the pair with the highest expected return. When all returns are negative, we only go short the pair with the lowest expected return.
def action(self): rank = [] long_short = [] for i in self.quoted: rank.append((i,self.predict(i))) # rank the symbols by their expected return rank.sort(key = lambda x: x[1],reverse = True) # the first element in long_short is the one with the highest expected return, which we are going to long, and the second one is going to be shorted. long_short.append(rank[0]) long_short.append(rank[-1]) self.Liquidate() # the product < 0 means the expected return of the first one is positive and that of the second one is negative--we are going to long and short. if long_short[0][1]*long_short[1][1] < 0: self.SetHoldings(long_short[0][0],1) self.SetHoldings(long_short[1][0],-1) # this means we long only because all of the expected return is positive elif long_short[0][1] > 0 and long_short[1][1] > 0: self.SetHoldings(long_short[0][0],1) # short only else: self.SetHoldings(long_short[1][0],-1)


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java.lang.Object | +--iterator.TupleUtils
some useful method when processing Tuple
| Constructor Summary | |
TupleUtils()
|
| Method Summary | |
static int |
CompareTupleWithTuple(AttrType fldType,
Tuple t1,
int t1_fld_no,
Tuple t2,
int t2_fld_no)
This function compares a tuple with another tuple in respective field, and returns: 0 if the two are equal, 1 if the tuple is greater, -1 if the tuple is smaller, |
static int |
CompareTupleWithValue(AttrType fldType,
Tuple t1,
int t1_fld_no,
Tuple value)
This function compares tuple1 with another tuple2 whose field number is same as the tuple1 |
static boolean |
Equal(Tuple t1,
Tuple t2,
AttrType[] types,
int len)
This function Compares two Tuple inn all fields |
static short[] |
setup_op_tuple(Tuple Jtuple,
AttrType[] res_attrs,
AttrType[] in1,
int len_in1,
AttrType[] in2,
int len_in2,
short[] t1_str_sizes,
short[] t2_str_sizes,
FldSpec[] proj_list,
int nOutFlds)
set up the Jtuple's attrtype, string size,field number for using join |
static short[] |
setup_op_tuple(Tuple Jtuple,
AttrType[] res_attrs,
AttrType[] in1,
int len_in1,
short[] t1_str_sizes,
FldSpec[] proj_list,
int nOutFlds)
set up the Jtuple's attrtype, string size,field number for using project |
static void |
SetValue(Tuple value,
Tuple tuple,
int fld_no,
AttrType fldType)
set up a tuple in specified field from a tuple |
static java.lang.String |
Value(Tuple tuple,
int fldno)
get the string specified by the field number |
| Methods inherited from class java.lang.Object |
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
public TupleUtils()
| Method Detail |
public static int CompareTupleWithTuple(AttrType fldType, Tuple t1, int t1_fld_no, Tuple t2, int t2_fld_no) throws java.io.IOException, UnknowAttrType, TupleUtilsException
fldType
- the type of the field being compared.t1
- one tuple.t2
- another tuple.t1_fld_no
- the field numbers in the tuples to be compared.t2_fld_no
- the field numbers in the tuples to be compared.UnknowAttrType
- don't know the attribute typejava.io.IOException
- some I/O faultTupleUtilsException
- exception from this classpublic static int CompareTupleWithValue(AttrType fldType, Tuple t1, int t1_fld_no, Tuple value) throws java.io.IOException, UnknowAttrType, TupleUtilsException
fldType
- the type of the field being compared.t1
- one tuplevalue
- another tuple.t1_fld_no
- the field numbers in the tuples to be compared.UnknowAttrType
- don't know the attribute typejava.io.IOException
- some I/O faultTupleUtilsException
- exception from this classpublic static boolean Equal(Tuple t1, Tuple t2, AttrType[] types, int len) throws java.io.IOException, UnknowAttrType, TupleUtilsException
t1
- the first tuplet2
- the secocnd tupletype[]
- the field typeslen
- the field numbersUnknowAttrType
- don't know the attribute typejava.io.IOException
- some I/O faultTupleUtilsException
- exception from this classpublic static java.lang.String Value(Tuple tuple, int fldno) throws java.io.IOException, TupleUtilsException
tuple
- the tuplefidno
- the field numberjava.io.IOException
- some I/O faultTupleUtilsException
- exception from this classpublic static void SetValue(Tuple value, Tuple tuple, int fld_no, AttrType fldType) throws java.io.IOException, UnknowAttrType, TupleUtilsException
value
- the tuple to be settuple
- the given tuplefld_no
- the field numberfldType
- the tuple attr typeUnknowAttrType
- don't know the attribute typejava.io.IOException
- some I/O faultTupleUtilsException
- exception from this classpublic static short[] setup_op_tuple(Tuple Jtuple, AttrType[] res_attrs, AttrType[] in1, int len_in1, AttrType[] in2, int len_in2, short[] t1_str_sizes, short[] t2_str_sizes, FldSpec[] proj_list, int nOutFlds) throws java.io.IOException, TupleUtilsException
Jtuple
- reference to an actual tuple - no memory has been mallocedres_attrs
- attributes type of result tuplein1
- array of the attributes of the tuple (ok)len_in1
- num of attributes of in1in2
- array of the attributes of the tuple (ok)len_in2
- num of attributes of in2t1_str_sizes
- shows the length of the string fields in St2_str_sizes
- shows the length of the string fields in Rproj_list
- shows what input fields go where in the output tuplenOutFlds
- number of outer relation filedsjava.io.IOException
- some I/O faultTupleUtilsException
- exception from this classpublic static short[] setup_op_tuple(Tuple Jtuple, AttrType[] res_attrs, AttrType[] in1, int len_in1, short[] t1_str_sizes, FldSpec[] proj_list, int nOutFlds) throws java.io.IOException, TupleUtilsException, InvalidRelation
Jtuple
- reference to an actual tuple - no memory has been mallocedres_attrs
- attributes type of result tuplein1
- array of the attributes of the tuple (ok)len_in1
- num of attributes of in1t1_str_sizes
- shows the length of the string fields in Sproj_list
- shows what input fields go where in the output tuplenOutFlds
- number of outer relation filedsjava.io.IOException
- some I/O faultTupleUtilsException
- exception from this classInvalidRelation
- invalid relation
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UI.calendar is a complete AngularJS directive for the Arshaw FullCalendar.
FullCalendar is a drag-n-drop jQuery plugin for displaying events on a full-sized calendar.
A JavaScript event calendar. Customizable and open source.
Messages:


Soft, delicate cookies originally from Argentina. Smooth and satiny texture, filled with dulce de leche that holds both cookies together.
Combine all the ingredients in the mixer, with the kneading hook. Once it has a consistency that looks easy to knead, remove it from the mixer and stretch it with a rolling pin. Cut the dough with a round cookie cutter. Bake at 300 degrees Fahrenheit for about 8 minutes. Cookies should look white and not golden. When cold, fill them with dulce de leche and sprinkle powdered sugar.


University of Stuttgart
Keplerstraße 7
70174 Stuttgart
Germany
Represented by:
The University of Stuttgart is a public corporation.
It is legally represented by rector Prof. Dr.-Ing. Wolfram Ressel (rektor@uni-stuttgart.de).
Phone: +49 (0) 711/685-0
Fax: +49 (0) 711/685-82113
E-Mail<EMAIL_ADDRESS>Internet: http://www.uni-stuttgart.de/
Tax identification number according to §27 a of Value Added Tax Act:
DE 1477 94 196


My Team
Ahmed
Manager
ID: 1
Email:
<EMAIL_ADDRESS>Office Number: 1
John
Engineer
ID: 2
Email:
<EMAIL_ADDRESS>Github:
John-Smith
Carlos
Intern
ID: 3
Email:
<EMAIL_ADDRESS>School: Carlos University


Welcome @{{ user.username }}!
Before you start using PROJECT NAME we need you to do one last thing. Please visit the following link and enter this code
{{ token }}


<ul> <li> Slide #1 </li> <li> Slide #2 </li> </ul>
You can use our exisiting css and change the sizes or just make your own but make sure that its based upon
ul { margin: 0; padding: 0; list-style-type: none; position: absolute; -webkit-transform-style: preserve-3d; -ms-transform-style: preserve-3d; transform-style: preserve-3d; } li { float: left; }
$("ul").itemslide();
- duration - duration of swipe animation {default: 350ms}
- swipe_sensitivity - swiping sensitivity {default: 150}
- disable_slide - disable swiping and panning {default: false}
- disable_clicktoslide - disable click to slide {default: false}
- disable_autowidth - disable calculation of width {default: false} (if you want to do it manually)
- disable_scroll - disable scrolling {default: false}
- start - index of slide that appears when initializing {default: 0}
- pan_threshold - can be also considered as panning sensitivity {default: 0.3}(precentage of slide width)
- one_item - set this to true if the navigation is full screen or one slide every time. {default: false}
(Full Screen Example) - parent_width - set this to true if you want the width of the slides to be the width of the parent of ul. {default: false}
- swipe_out - set this to true to enable the swipe out feature. {default: false} (Homepage has it enabled)
Example implementation
$("ul").itemslide( { disable_slide: true, duration: 1500 } );
Get
- getActiveIndex() - get current active slide index
- getCurrentPos() - get current position of carousel (pixels)
console.log($("ul").getActiveIndex());
Set
- next() - goes to next slide
- previous() - goes to previous slide
- gotoSlide(i) - goes to a specific slide by index
- reload() - recalculates width and center object (recommended to call when resize occures)
- addSlide(data) - adds in the end of the carousel a new item.
- removeSlide(index) - removes a specific slide by index.
- NOTE: addSlide automatically adds li tags.
$("ul").next(); $("ul").addSlide("New Slide");
Events
- changePos - triggered when the position of the carousel is changed
- pan - triggered when user pans
- changeActiveIndex - triggered when the current active item has changed
- swipeout - triggered when user swipes out a slide (if swipe_out is enabled)
- event.slide - get swiped out slide
$("ul").on('changePos', function(e) { console.log("new pos: "+ $("ul").getCurrentPos()); }); $("ul").on('swipeout', function(event) { console.log("swiped out slide - "+ event.slide); });
Classes
The current active slide gets the 'itemslide-active' class.


2.2 Apply
Consider the women
dataset, holding height and weight of a population sample of 15 women:
To calculate the average height and the average weight of this sample, one could of course simply do
## [1] 65
## [1] 136.7333
However, when your dataset has (a lot) more columns, repeating this will be quite tedious…unless you use a for
loop
## [1] 65
## [1] 136.7333
Enter apply()
, a very nice function to do this in a handy one-liner
## height weight
## 65.0000 136.7333
The arguments I supplied to apply
have the following purpose:
X = women
specifies the data to be processedMARGIN = 2
specifies wether columns or rows shoud be processed; 1 = rows and 2 = columnsFUN = mean
speciefies the function to be applied to the given dataframe
Not only gives apply the the exact same result (of course, duh), but this approach has several advantages:
apply
returns a named vector where the elements are named the same as the corresponding columns of the original dataframeapply
is computationally more efficient than the other approaches- it requires less code; a good programmer types as little as possible - except for Java programmers of course :-)
If you really have strongh feelings about typing no more than strictly required, you can of course also omit the method parameters:
## height weight
## 65.0000 136.7333
But if you are just starting out with R, I suggest you invest those few character strokes for readability later on.
The above example dealt with columns. For instance, if you want to calculate the BMI of these women, you’ll need to target the rows. The BMI formula is \[weight/height^2*703\]
where weight is in pounds and height is in inches.
This formula is implemented in the following function.
## [1] 24.95858
You can also apply the formula to the women
dataset:
women$bmi1 <- apply(
X = women,
MARGIN = 1,
FUN = function(x){(x[2] / x[1]^2) * 703})
head(women, n = 4)
## height weight bmi1
## 1 58 115 24.03240
## 2 59 117 23.62856
## 3 60 120 23.43333
## 4 61 123 23.23811
if you like to use your own formula (it’s always a good idea to write logic only once and reuse it in different places), you’ll still need to wrap it inside an anonymous function call:
## height weight bmi1 bmi2
## 1 58 115 24.03240 24.03240
## 2 59 117 23.62856 23.62856
## 3 60 120 23.43333 23.43333
## 4 61 123 23.23811 23.23811


News story
Schools and organisations are invited to apply to be recognised as a leader in character education through the 2015 DfE Character Awards.
From today (7 January 2015) schools and organisations will be able to submit an online application to be recognised as a leader in character education through the 2015 DfE Character Awards.
Applicants should be able to prove their programme develops character traits, attributes and behaviours that underpin success in school and work, including:
The government wants to celebrate the excellence and diversity in this field, recognising that character is already being encouraged, nurtured and developed alongside academic rigour through a variety of programmes in and outside schools across the county.
Character education can be found within a school’s ethos, in the classroom and on the playground, as much as it can be found on the sports field and outside of school in the local community. Research has shown that both universal and targeted programmes can be effective, with excellent approaches including:
Through these approaches in character education pupils can emerge from school more fully rounded and better equipped to meet the challenges of employment and future life. That’s why applicants should offer evidence that proves their programmes have had an impact on engagement, attainment or employment outcomes.
The Character Awards were announced on 8 December as part of the £5 million Character Innovation Fund to support the development of character in schools. The government will shortly be opening applications for a grants programme to fund innovative projects and build capacity in this field, as outlined in the announcement on 16 December: ‘ England to become a global leader of teaching character .’
Commenting on the announcement of the Character Awards, Education Secretary Nicky Morgan said:
Delivering the best schools and skills is a key part of our long-term economic plan that is turning Britain around.
As well as high academic standards, this means providing opportunities for all young people to develop the character and resilience they need to succeed in modern Britain.
The new Character Awards schools will help give schools and organisations the tools and support they need to ensure they develop well-rounded pupils ready to go on to an apprenticeship, university or the world of work.
Please note that applicants are required to declare that there is no current or historical knowledge of financial misconduct, maladministration or safeguarding at the school or organisation.
Applicants should demonstrate outstanding practice of character education in the 2013 to 2014 academic year. The programme:
The core achievements should have taken place during this main period.
Organisations that work across multiple regions should only apply for 1 region, and this must be the region where their programme (or part of the programme) is based.
Applicants from all 9 regions will be looked at in order to seek out the most effective practice. Applicants will be judged by experts in the field and system leaders from across the national teaching schools network, supported by the Teaching Schools Council ( TSC ). Shortlisting will be supported by the Cabinet Office. The judging criteria is outlined below.
Up to 3 winners will be selected from all applicants within each region (North East, North West, Yorkshire and the Humber, West Midlands, East Midlands, East of England, London, South East, South West) and awarded £15,000.
From amongst the regional winners, 1 national prize winner will be chosen and awarded an additional prize of £20,000.
All regional winners will be invited to a national awards ceremony in March, where they will receive their prize and the national award will be announced.
Alongside headteachers and pupils from the winning schools, business representatives and experts in the field will be invited to attend - providing a platform for leaders in character and resilience to come together to share best practice.
| Criteria | Assessment method - online application |
|---|---|
| Character building forms a core part of the ethos, culture and day-to-day running of the school or organisation and is actively encouraged among pupils and staff | Question 1 |
| Clarity and focus of aims, and the extent to which they develop and improve character traits, attributes and behaviours of an appropriate number of pupils, and balance developing the skills alongside securing academic excellence | Questions 2 and 3 |
| Programme is innovative and relevant to other contexts | Questions 2 and 3 |
| Programme engages with other key organisations/stakeholders (such as parents, teachers, schools, employers and/or the local community) | Question 4 |
|
Evidence that the programme has impacted on aspects of pupils’ character in a way which is improving their engagement and achievement
Programmes with secondary-age children could provide evidence of improvement of preparation for further education/higher education and/or work Evidence could include (but does not have to be limited to): • measures of behaviour • attendance • parental feedback • Ofsted judgments or equivalent • achievement • progress • destination measures |
Question 5 |
| Evidences monitoring, evaluation, captures lessons learned and incorporates these into project/programme development | Question 5 |
| Evidence that the school/organisation will use the award to support further character building activities | Question 6 |
Organisations have until Friday 30 January 2015 to submit an application for an award through the online application form . Once applicants have submitted their applications they will receive a confirmation email.
| Application window closes | 30 January 2015 |
| Winners announced | end of February/beginning March 2015 |
| Awards ceremony | March 2015 |
Published: 7 January 2015
Updated: 13 January 2015
From: Department for Education The Rt Hon Nicky Morgan MP
Part of: Young people


Link properties
To work with external links to data objects outside current object without operating surogate keys, XData represent special property type - Link<TVal,TSrc>, where TVal - type of property for preview, TSrc - type of external object. For type Link is defined...
properties:
- TVal Value - this property to get preview of linked object
- TSrc Source - this property can be used to set linked object
- (read-only) bool Assigned - check link value for NULL value
operations:
- += - "syntax sugar", _data.SomeLink += _someDictionaryObject identical with _data.SomeLink.Source = _someDictionaryObject.
newInvoice.DocState += dataScope.GetDictionaryValue<DocState>(x => x.Code == "CREATED");
To make link properties work correct, at mapping level need to be defined pairs of properties source and current objects, that values will be copied from source object to current when link is applied. Link description is deferent for static and dynamic methods of mapping.


Edward Newgate
Founder Circle
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Ricciardo hoped to reduce the gap to the top three teams but doesn't regret switch
by Joseph TyrrellIt is fair to say Daniel Ricciardo's first season with Renault hasn't quite gone to plan but the Australian has admitted he doesn't regret his decision to move, despite not closing the gap to the top three.
Ricciardo moved to Renault for the 2019 season after failing to agree on a new deal with his former team Red Bull. The aim of the move was to help Renault close the gap to the top three teams but they have failed to do so, slipping behind McLaren in the midfield battle.
Despite not achieving everything they had set out to, Ricciardo does not regret his move to Renault.
"Coming from Red Bull, I hoped we could reduce the gap to the top three teams, which we did not succeed in. It takes us at least another year to do that, I want to be better, that's clear, but it does is not that I regret the change," Ricciardo said as quoted by Motorsport-Magazin.
"I was pretty frustrated last year, I had some technical problems and needed a change I always leave the racetrack happy and fulfilled when I have achieved what was possible for me It is enough for yourself that you know you have done everything there have already been podiums with me was not satisfied."


Username is mandatory Field!!!
Password should be atleast 5 char long!!!
Password is mandatory Field!!!
Password should have Exclamation Mark!!!


Rescue
Rescue is a colorful game that relaxes you from daily stress. It plays a beautiful stream of songs along with satisfying sound effects. It was originated from an idea to create a mind relaxing game that I can play without stress, but also something I wouldn't want quit so easily. As the "Ghost" rescues other souls, it absorbs their colors. Rescuing dark souls will eventually turn the Ghost to pitch black. Brighter ones, make it brighter.
Physical Controller
Makey Makey is an electronic invention kit that can basically connect any conductive material to the keyboard. Objects such as play-doh, bananas, even pencial traces and send electric signals to the board and to the machine. Leveraging Makey Makey, I can hook up play-doh to the game and use them as physical controllers.


This is a portrait I created using images that were gathered from the internet. I used a number of layers and masking techniques to create the portrait of Skrillex (all the reference images you see here were used to create the illustration). From there, I placed the portrait unto a mockup template of a record album cover. The composition is inspired by one of the art pieces in the collection of gathered images. Can you guess which one?


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Computing Bounds for Clamped Means¶
Sometimes, the settings for private algorithms can inadvertently reveal private information.
Consider computing average energy use for a rural zip code. The largest energy consumer here may not consume very much energy, and the noise needed for differential privacy would not be very high, say \(\pm1,000\)kWh.
Imagine, then, that a wealthy environmentalist moves into the neighborhood. This person’s house, while proclaimed an efficiency engineering masterpiece, happens to consume much more electricity than expected, much more than neighboring houses (perhaps our environmentalist couldn’t resist running a bitcoin rig in the basement).
Now, because of the increased range of the dataset, obscuring this zip code’s total energy use requires more noise; instead of \(\pm1,000\) kWh it would be \(\pm10,000\) kWh. If we use the exact upper and lower bound of the dataset to scale DP noise, the confidence interval published alongside it would reveal our environmentalist’s embarassingly high energy usage almost exactly. Oops!
The solution to this problem is to privately compute the required scale of noise before computing the private mean. In this notebook, we employ a method that focuses on ease of implementation and high accuracy that does not minimize the privacy budget consumed.
By the end, we’ll have made sure not to have inadvertently publicly shamed our high-profile environmentalist.
[13]:
# Preamble: imports and figure settings
from eeprivacy import (
PrivateClampedMean,
PrivateQuantile,
laplace_mechanism,
)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import matplotlib as mpl
from scipy import stats
np.random.seed(1234) # Fix seed for deterministic documentation
mpl.style.use("seaborn-white")
MD = 28
LG = 36
plt.rcParams.update({
"figure.figsize": [25, 7],
"legend.fontsize": MD,
"axes.labelsize": LG,
"axes.titlesize": LG,
"xtick.labelsize": LG,
"ytick.labelsize": LG,
})
Dataset¶
For this example, we consider a dataset of one year of hourly energy consumption readings from 20,000 homes. In total, there are 20,000 * 365 * 24 = 175,200,000 data points, with each home contributing 8,760 each.
From experience, we know this sort of dataset follows a power law distribution.
[2]:
N = 20000
hourly_consumption_kwh = np.random.pareto(4, N*365*24)
fig, ax = plt.subplots()
ax.hist(hourly_consumption_kwh, bins=50)
ax.set_yscale("log")
plt.title(f"Synthetic distribution of energy consumption data \n One year of hourly readings for {N:,} homes")
plt.xlabel("Hourly Energy Consumption (kWh)")
plt.ylabel("Count (log scale)")
plt.show()
Background Theory¶
To compute a differentially private mean, noise is drawn from the Laplace distribution at \(b=\Delta/\epsilon\) where \(b\) is the scale of the Laplace distribution, \(\Delta\) is the sensitivity, and \(\epsilon\) is the privacy parameter.
The unknown parameter in this case is the sensitivity \(\Delta\). For private means, the sensitivity is:
Where \(B\) is largest value in the dataset, \(A\) is the smallest, and \(N\) is the number of elements in the dataset. Intuitively, the sensitivity is the maximum amount that any single element in the dataset can affect the computed mean.
For datasets where the range of data is known up front, the values of \(A\) and \(B\) are known exactly.
However, for datasets where the range of data can vary considerably, the values of \(A\) and \(B\) must be computed with a private query to avoid leaking information about the largest and smallest values in the dataset.
Approach to Privately Finding Bounds¶
First, we define a criteria for a good choice of upper bound. The eventual use for building science requires private means to be accurate within +/-0.1 kWh. Since the error term for the clamping procedure is difficult to derive exactly, we will target error from the clamping procedure as small enough to be neglible compared to the required accuracy. Therefore, we target +/-0.01 kWh.
To find the upper bound for this level of accuracy, we simulate clamping random samples from our synthetic dataset. We find that a clamping cutoff that includes 99% of all consumption values reaches this target error:
[3]:
def simulate_clamping_error(number_of_sites, cutoff):
NS = number_of_sites
HY = 8760 # hours per year
trials = []
for t in range(100):
sample = np.random.choice(hourly_consumption_kwh, NS * HY)
true_mean = np.mean(sample)
clipped_mean = np.mean(np.clip(sample, 0, cutoff))
trials.append(true_mean - clipped_mean)
plt.hist(trials)
plt.title("Error from clamping procedure")
plt.xlabel("Error (Average kWh)")
plt.ylabel("Count")
plt.show()
cutoff = 2.2
simulate_clamping_error(number_of_sites=200, cutoff=cutoff)
percent_included = np.sum(hourly_consumption_kwh <= cutoff) / len(hourly_consumption_kwh)
print(f"The cutoff includes {percent_included:.2%} percent of consumption readings")
The cutoff includes 99.05% percent of consumption readings
If we were able to query for the distribution of data exactly, we would ask for the 99th percentile directly.
[4]:
cutoff = np.quantile(hourly_consumption_kwh, 0.99)
print(f"The 99% cutoff is {cutoff:.1f} kWh")
plt.hist(hourly_consumption_kwh, bins=200)
plt.axvline(cutoff, color="r", linewidth=5)
plt.title("Cutoff to include 99% of data points")
plt.ylabel("Count")
plt.show()
fig, ax = plt.subplots()
ax.hist(hourly_consumption_kwh, bins=50)
ax.set_yscale("log")
plt.axvline(cutoff, color="r", linewidth=5)
plt.xlabel("Hourly Energy Consumption (kWh)")
plt.ylabel("Count (log scale)")
plt.show()
The 99% cutoff is 2.2 kWh
However, this query must be done privately to avoid leaking information. Fortunately, there is a mechanism that can be used to answer this question directly: eeprivacy.private_quantile
.
Private Quantiles¶
Under the hood, eeprivacy.PrivateQuantile
uses a “private noisy max” mechanism to score a set of possible outputs. In this case, the outputs are choices for quantiles.
The following code finds the 99th percentile:
[11]:
result = PrivateQuantile(
options=np.linspace(0, 5, num=200),
max_individual_contribution=8760
).execute(
values=hourly_consumption_kwh,
quantile=0.99,
epsilon=0.1,
)
print(f"Private 99th percentile is {result:.2f} kWh")
Private 99th percentile is 2.24 kWh
Private Quantile Accuracy¶
An appropriate choice of \(\epsilon\) is required for private_quantile
. Too high, and too much privacy budget is spent. Too low, and a poor clipping bound risks introducing error to all further analyses.
For this demonstration, we show that a choice of \(\epsilon\)=0.1 provides a distribution of results that are within acceptable boundaries based on the earlier accuracy simulation.
[14]:
# Unroll the implementation of `private_quantile` for performance reasons:
# computing `scores` is quite heavy and only needs to be done once.
values = hourly_consumption_kwh
options = np.linspace(0, 5, num=200)
quantile = 0.99
epsilon = 0.1
sensitivity=8760
N = len(values)
def score(values, option):
count = np.sum(values < option)
return -np.abs(count - N * quantile)
scores = [score(values, option) for option in options]
trials = []
for t in range(100):
noisy_scores = laplace_mechanism(
values=scores, epsilon=epsilon, sensitivity=sensitivity
)
max_idx = np.argmax(noisy_scores)
result = options[max_idx]
trials.append(result)
plt.hist(trials)
plt.title(f"Distribution of private 99th percentile at ε={epsilon}")
plt.xlabel("99th percentile result")
plt.ylabel("Count")
plt.show()
This choice of privacy parameter is not optimal; it could be further reduced and still meet accuracy needs. In the work that this demo was based on, there was enough privacy budget allocated that \(\epsilon\)=0.1 did not hamper further analysis.
Results¶
In our example, the final computed clamping bound was 2.16 kWh. There was some error introduced in choosing the clamping boundaries with the private quantile mechanism.
To get a feel for this error, we use our original accuracy simulation for the upper and lower bounds of results from the private quantile function, comparing it to an exact mean computation:
[15]:
def simulate_clamping_error(number_of_sites, cutoff):
NS = number_of_sites
HY = 8760 # hours per year
trials = []
for t in range(100):
sample = np.random.choice(hourly_consumption_kwh, NS * HY)
true_mean = np.mean(sample)
clipped_mean = np.mean(np.clip(sample, 0, cutoff))
trials.append(true_mean - clipped_mean)
plt.hist(trials)
plt.title(f"Error from clamping procedure (cutoff={cutoff})")
plt.xlabel("Error (Average kWh)")
plt.ylabel("Count")
plt.show()
simulate_clamping_error(number_of_sites=200, cutoff=2.05)
simulate_clamping_error(number_of_sites=200, cutoff=2.30)
The resulting error is within \(\pm0.01\) kWh in all cases. This is well-suited for the statistics that will be prepared with these data, which can tolerate at least +/- 0.1 kWh of error, an order of magnitude more.


Syncs in realtime across clients
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Maximizes popup.
max()
Description
The popup must exists. The method maximizes the popup and triggers onMax event
If popup was already maximized, no action will be taken. It will also change maximized
property of popup options to true.
To maximize popup:


Data Fields | |
| ADLDisplayID | displayID |
| The DisplayID structure. | |
| int | iDisplayControllerIndex |
| char | strDisplayName [256] |
| The display's EDID name. | |
| char | strDisplayManufacturerName [256] |
| The display's manufacturer name. | |
| int | iDisplayType |
| The Display type. For example: CRT, TV, CV, DFP. | |
| int | iDisplayOutputType |
| The display output type. For example: HDMI, SVIDEO, COMPONMNET VIDEO. | |
| int | iDisplayConnector |
| The connector type for the device. | |
| int | iDisplayInfoMask |
| The bit mask identifies the number of bits ADLDisplayInfo is currently using. It will be the sum all the bit definitions in ADL_DISPLAY_DISPLAYINFO_xxx. | |
| int | iDisplayInfoValue |
| The bit mask identifies the display status. Display Info Mask Values. |
This structure is used to store various information about the display device. This information can be returned to the user, or used to access various driver calls to set or fetch various display-device-related settings upon the user's request
ColorCaps/main.cpp, DDCBlockAccess/DDCBlockAccessDlg.cpp, Sample-Managed/ADL.cs, and Sample-Managed/program.cs.
| char ADLDisplayInfo::strDisplayName[256] |
The display's EDID name.
The display's manufacturer name.
The Display type. For example: CRT, TV, CV, DFP.
The display output type. For example: HDMI, SVIDEO, COMPONMNET VIDEO.
The connector type for the device.
The bit mask identifies the number of bits ADLDisplayInfo is currently using.
It will be the sum all the bit definitions in ADL_DISPLAY_DISPLAYINFO_xxx.
The bit mask identifies the display status. Display Info Mask Values.


K-Maps
Table of contents
- Introduction
- Enumeration and Gray Codes
- Simple Groupings
- Two Dimension Groupings
- Disjoint Groupings
- Overlapping Groupings
- Minimizing Group Count
Introduction
Karnaugh Maps are a way to visually display a boolean expression onto a 2D grid. We take the variables and bind them to an axis, and then enumerate through the possible combinations of input values that could occur for all those variables bounded to an axis (either horizontally or vertically).
For example, we can display the following 2 variable Karnaugh Map:
We have bounded to the vertical axis, the variable A
, and we enumerate through the possible values for A
(being {0, 1}
). Similarily, we perform a similar operation for the B
variable. Since we are using a 2 variable expression, we can bound one variable to each axis and the visualization works fine in a 2x2
matrix.
Let’s instead look at a more involved example with 4 variables:
We have now bounded the A
and B
variables to the vertical axis, while we bounded the C
and D
variables to the horizontal axis. We now enumarate through different combinations of the bounded variables for each axis in reflected binary code order (more on this in the following section). Lastly, we indicate on the matrix each true value by augmenting a 1
value.
Enumeration and Gray Codes
When enumerating through the variable input combinations for the binded axis, we take advantage of reflected binary code order, otherwise known as gray codes. If we observe carefully, we can notice that from one combination to another, we only vary by one bit.  This provides us the core for how Karnaugh Maps work.
Simple Groupings
The main idea for how Karnaugh Maps can be used to simplify expressions is to group pairs of 1
values that are adjacent, and exploit the fact that each one has only a bit difference from another.
For the purpose of this example, let F(ABCD) = CELL
. We start with the expression F(0000) = 1
and F(0001) = 1
. However, notice that regardless of the value of the last bit, we still get 1
. Hence, let’s take a look at the SOP expressions:
F(ABCD) = A'B'C'D' + A'B'C'D
F(0000) = 1
F(0001) = 1
Since the last bit is the same, we can ignore the D value, thus:
F(ABCD) = A'B'C'
We can confirm by simplifying algebraically:
F(ABCD) = A'B'C'D' + A'B'C'D
= A'B'C'(D' + D)
= A'B'C'
Therefore, the simplification is true.
We can then extend this rule to work for rectangles and more!
Two Dimension Groupings
Extending the idea of isolating changing bits that retain a consistent value, we can then generalize this to work in a higher dimension. Consider the following example:
Letting F(ABCD) = CELL
:
F(0000) = 1
F(0001) = 1
F(0100) = 1
F(0101) = 1
Observe that the bits do not change by one for all pairs of numbers, for example {0000, 0101}
differ by two bits. However, we can take advantage of the fact that for any bit change horizontally or vertically, it’s irrelvant what that bit is. More concretely, take a look at the following example.
0000 0001
0100 0101
=> A'B'C'D' + A'B'C'D + A'BC'D' + A'BC'D
Regardless of the B variable, we still get true for all products in the SOP expression.
This is bounded vertically:
=> A'C'D' + A'C'D + A'C'D' + A'C'D
Regardless of the D variable, we still get true for all products in the SOP expression.
This is bounded horizontally:
=> A'C' + A'C' + A'C' + A'C'
=> A'C' (1 + 1 + 1 + 1)
=> A'C' (1)
=> A'C'
Since the differences in bits needs to generalize throughout a binding of an axis, you can only have a binding of size 2^n
for a given axis. For example, 1x1, 1x2, 1x4, 2x2, 2x4, 4x4
.
Disjoint Groupings
Consider the following example:
The algorithm follows precisely as it did before, except that now the two groups are joined in the SOP expression. Letting F(ABCD) = CELL
:
F(0000) = 1
F(0001) = 1
F(1111) = 1
F(1011) = 1
This yields the following:
A'B'C'D' + A'B'C'D + ABCD + AB'CD
Breaking down the expression:
(A'B'C'D' + A'B'C'D) + (ABCD + AB'CD)
=> (A'B'C'(D + D')) + (ACD(B + B'))
=> (A'B'C') + (ACD)
=> A'B'C' + ACD
Clearly this is the exact same process as before, but iterated throughout all the disjoint sets.
Overlapping Groupings
Overlapping groupings become more complex, because there exist ambigious cases and sometimes what may appear to be a locally optimal solutuion is not a globally optimal solution.
The general technique for evaluating for overlapping groups follows a greedy algorithm. Define an unvisited cell as a cell that has a value of 1
however it is currently not matched with a grouping yet.
Iterate through all the cells, and once you find a cell with 1
, if it is unvisited then find the largest possible square or rectangle such that each side length is a power of 2, where all the cells are 1
in its enclosed area. If there is a tie for size (ie, 1x4
vs 2x2
), assign the one that is a square (this is by convention).
Repeat this process for all remaining unvisited cells. Note: You can overlap the groupings with already visited nodes, but you never instantiate a new grouping unless the current node is unvisited.
In this example, at F(0000)
, we can create a grouping of size 2 (because 2 is the largest possible grouping, 3 is not a power of 2). We then iterate through to F(0001)
, however F(0001)
was already resolved to a grouping. For the latest active cell, F(0011)
is not resolved to a grouping thus it’s unvisited. The largest possible grouping is also of size 2, thus we create another group.
To resolve the groupings into an SOP expression, we iterate through the groups and identify changing bits:
Group #1 => F(ABCD) = [0000, 0001]
Group #2 => F(ABCD) = [0001, 0011]
For Group #1:
0000 0001
^ ^
F(ABCD) = A'B'C'D' + A'B'C'D
=> A'B'C'(D + D')
=> A'B'C'
For Group #2:
0001 0011
^ ^
F(ABCD) = A'B'C'D + A'B'CD
=> A'B'D(C' + C)
=> A'B'D
Now we add the two results:
F(ABCD) = A'B'C' + A'B'D
=> F(ABCD) = A'B'D + A'B'C' (by commutative property)
Minimizing Group Count
The following example will ilustrate how the greedy approach may occasionally produce too many groups. Consider the following example:
This grouping state is optimal. However, consider adding a 1
to F(1111)
.
Following the previous algorithm, iterating top-bottom and left-right, when getting to F(0110)
, the algorithm can choose to make the largest grouping. However, there are two possible groupings:
Candidate #1:
F(ABCD) = [0011, 0010, 0111, 0110]
Candidate #2:
F(ABCD) = [0111, 0110, 1111, 1110]
Both groupings have the same size, and are the same dimension. However, upon reaching F(1110)
, another grouping needs to be instantiated, in which case if the first candidate grouping was created then we made a group that was not neccessary increasing the size of our SOP expression.
This illustrates the idea that this is a greedy algorithm, and does not always return the most simplified SOP expression. In later sections, algorithms illustrating a globally optimal algorithm will be discussed.


Module edu.citadel.cprl
Package edu.citadel.cprl.ast
-
Interface Summary Interface Description NamedDecl Interface for a named declaration, which can be either a "single" variable declaration or a parameter declaration. -
Class Summary Class Description AddingExpr The abstract syntax tree node for an adding expression.ArrayTypeDecl The abstract syntax tree node for an array type declaration.AssignmentStmt The abstract syntax tree node for an assignment statement.AST Base class for all abstract syntax tree classes.BinaryExpr Base class for all binary expressions.ConstDecl The abstract syntax tree node for a constant declaration.ConstValue The abstract syntax tree node for a constant value expression, which is either a literal or a declared constant identifier representing a literal.Declaration Base class for all CPRL declarations (constants, variables, procedures, etc.).DeclarativePart The abstract syntax tree node for the declarative part of a program.ElsifPart The AST for an elsif part of an if statement.ExitStmt The abstract syntax tree node for an exit statement.Expression Base class for all CPRL expressions.FunctionCall The abstract syntax tree node for a function call expression.FunctionDecl The abstract syntax tree node for a function declaration.IfStmt The abstract syntax tree node for an if statement.InitialDecl Base class for all initial declarations.LogicalExpr The abstract syntax tree node for a logical expression.LoopStmt The abstract syntax tree node for a loop statement.MultiplyingExpr The abstract syntax tree node for a multiplying expression.NamedValue The abstract syntax tree node for a named value.NegationExpr The abstract syntax tree node for a negation expression.NotExpr The abstract syntax tree node for a not expression.OutputStmt Base class with common code for write and writeln statements.ParameterDecl The abstract syntax tree node for a parameter declaration.ProcedureCallStmt The abstract syntax tree node for a procedure call statement.ProcedureDecl The abstract syntax tree node for a procedure declaration.Program The abstract syntax tree node for a CPRL program.ReadStmt The abstract syntax tree node for a read statement.RelationalExpr The abstract syntax tree node for a relational expression.ReturnStmt The abstract syntax tree node for a return statement.SingleVarDecl The abstract syntax tree node for a single variable declaration.Statement Base class for all CPRL statements.StatementPart The abstract syntax tree node for the statement part of a program.SubprogramDecl Base class for CPRL procedures and functions.UnaryExpr Base class for all unary expressions.VarDecl The abstract syntax tree node for a variable declaration.Variable The abstract syntax tree node for a variable, which is any named variable that can appear on the left hand side of an assignment statement.WritelnStmt The abstract syntax tree node for a writeln statement.WriteStmt The abstract syntax tree node for a write statement.


Summary
The first week felt busy, but it also seemed like I didn't make much progress on some important tasks.
I still need to find the right balance for how much time I spend on each class, as well as my personal development.
One major mistake I made this week was not working on Cow-duction (for GMAP 378).
Since it's a project that is carrying over from last term, we should have made the necessary preparations to hit the ground running.
Now that I am the Technical Director, I should be handling more responsibility and guiding the rest of the team on the technical side of development.
It's still early in the term, but the sooner I can get into a quick and efficient process for experimentation and iteration, the better.
Class Recap
Monday
- (JAPN 101) Japanese I - Seems like a fun class so far. Mostly focused on speaking, starting out by learning some verbs and katakana.
Tuesday
- (CI 492) Senior Project II - Had a brief meeting to go over deliverables for the term.
- (CS 432) Interactive Computer Graphics - Covered a rough history of computer graphics. Got a head start on the assignments.
- (GMAP 378) Game Development: Workshop II - Broke down key areas to focus on moving forward. Multiplayer is important to get done as early as possible. Also discussed ways of reducing the time between iterations (solving the wrong problem analogy).
- (CS 435) Computational Photography - Getting started with MATLAB, a bit confusing but I've been experimenting with some of the example code shown in the lecture slides.
Wednesday
- (JAPN 101) Japanese I - Practiced the ten core conversations to be memorized by Jan 22.
Thursday
- (CS 432) Interactive Computer Graphics - Worked on the second assignment while the lecture went over material for the first assignment.


elevator scroll_floor
Scrolls the floor texture of a sector. Player moves with the floor texture by default, but see the FLAGS variable.
Stop values are distances in pixels ( x by 8 to get distances in level geometry units).


All Classes
Activity
Affect
Behaviors
Bio
CognitiveAspects
Context
ContextAwareRecommender
DateOfBirth
Demographics
Email
Empathy
Food
FromActivity
FromFile
FromText
Gender
GraphRecommender
GraphSettings
Heart
HORBot
HORCommands
HORLogger
HORMessageCallbackHandler
HORMessageHandler
HORMessages
Image
Industry
Interest
Item
Language
Location
Location
LoggerInterface
Login
Name
Ontology
Personality
PhysicalStates
PriorsFunction
Question
Recommender
RecommendUtils
RequestHTTP
Sleep
SocialRelation
Survey
SurveyContext
TelegramBot
UserContext
UserPreferences
Utils


"TIMNAH "a portion. (1.) A town of Judah (Joshua 15:10). The Philistines took possession of it in the days of Ahaz (2 Chronicles 28:18). It was about 20 miles west of Jerusalem. It has been identified with Timnatha of Dan (Joshua 19:43), and also with Timnath (Judges 14:1,5). (2.) A city in the mountains of Judah (Joshua 15:57)= Tibna near Jeba’. (3.) A “duke” or sheik of Edom (Genesis 36:40).
"TIMNAH "a portion. (1.) A town of Judah (Joshua 15:10). The Philistines took possession of it in the days of Ahaz (2 Chronicles 28:18). It was about 20 miles west of Jerusalem. It has been identified with Timnatha of Dan (Joshua 19:43), and also with Timnath (Judges 14:1,5).
(2.) A city in the mountains of Judah (Joshua 15:57)= Tibna near Jeba’.
(3.) A “duke” or sheik of Edom (Genesis 36:40).


Two US Soldiers Killed in Helicopter Crash
The Taliban claimed to have shot down a helicopter in Logar province.
by TOLOnews.comAt least two US soldiers were killed in a helicopter crash on Wednesday, US Forces--Afghanistan said in a statement.
The cause of the crash is under investigation, however, preliminary reports do not indicate it was caused by enemy fire, the statement said.
In accordance with US Department of Defense policy, the names of the service members killed in action are being withheld until 24 hours after notification of next of kin is complete, it added.
The Taliban in a statement also on Wednesday claimed that they shot down a US chinook helicopter in Chark district of Logar province, adding that a number of soldiers have been killed.
The US currently have about 12,000 troops in Afghanistan engaged in training, advising and assisting Afghan security forces and performing counter-terrorism operations.


|
JavaTM 2 Platform Standard Ed. 5.0 |
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
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java.lang.Object java.lang.ref.Reference<T> java.lang.ref.SoftReference<T>
public class SoftReference<T>
Soft reference objects, which are cleared at the discretion of the garbage collector in response to memory demand. Soft references are most often used to implement memory-sensitive caches.
Suppose that the garbage collector determines at a certain point in time that an object is softly reachable. At that time it may choose to clear atomically all soft references to that object and all soft references to any other softly-reachable objects from which that object is reachable through a chain of strong references. At the same time or at some later time it will enqueue those newly-cleared soft references that are registered with reference queues.
All soft references to softly-reachable objects are guaranteed to have
been cleared before the virtual machine throws an
OutOfMemoryError
. Otherwise no constraints are placed upon the
time at which a soft reference will be cleared or the order in which a set
of such references to different objects will be cleared. Virtual machine
implementations are, however, encouraged to bias against clearing
recently-created or recently-used soft references.
Direct instances of this class may be used to implement simple caches; this class or derived subclasses may also be used in larger data structures to implement more sophisticated caches. As long as the referent of a soft reference is strongly reachable, that is, is actually in use, the soft reference will not be cleared. Thus a sophisticated cache can, for example, prevent its most recently used entries from being discarded by keeping strong referents to those entries, leaving the remaining entries to be discarded at the discretion of the garbage collector.
| Constructor Summary | |
|---|---|
SoftReference(T referent)
Creates a new soft reference that refers to the given object. |
|
SoftReference(T referent,
ReferenceQueue<? super T> q)
Creates a new soft reference that refers to the given object and is registered with the given queue. |
| Method Summary | |
|---|---|
T |
get()
Returns this reference object's referent. |
| Methods inherited from class java.lang.ref.Reference |
|---|
clear, enqueue, isEnqueued |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public SoftReference(T referent)
referent
- object the new soft reference will refer topublic SoftReference(T referent, ReferenceQueue<? super T> q)
referent
- object the new soft reference will refer toq
- the queue with which the reference is to be registered,
or null if registration is not required| Method Detail |
|---|
public T get()
null
.
get
in class Reference<T>
null
if this reference object has been cleared
|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
Copyright © 2004, 2010 Oracle and/or its affiliates. All rights reserved. Use is subject to license terms. Also see the documentation redistribution policy.


public interface StorageEngine
| Modifier and Type | Method and Description |
|---|---|
void |
addBooking(TourBooking tourBooking)
this method will add a booking made by a customer in the DB
|
void |
addCustomer(Customer customer)
This method will add a new customer to the DB
|
Customer |
getCustomerDetails(java.lang.String identifier)
This method will serve to get details about a specific customer
|
java.lang.String |
getFAQResponse(java.lang.String quesion)
This method will be used to fetch the answers to a FAQ from the database
|
GeneralTour |
getGeneralTourDetails(java.lang.String name)
this method will serve to get details about a tour (not according to the date)
|
java.util.ArrayList<java.lang.String> |
getNotPaidCustomers(java.util.Date date)
this method will serve to get a list of the customers who have not paid for tours on
a specific date
|
int |
getNumberBookedTours(Tour tour)
this method will return the number of bookings made for a tour
|
java.util.ArrayList<java.util.Date> |
getTourDates(java.lang.String identifier)
this method will serve to get the list of dates for a tour specified by its id
|
Tour |
getTourDetails(java.lang.String id,
java.util.Date date)
This method will serve to fetch the details of a specific tour that is stored in the database
|
void |
logQuestion(java.lang.String question)
This method will serve to log the questions of the users in the DB
|
java.lang.String getFAQResponse(java.lang.String quesion) throws java.lang.Exception
quesion
- the question we want to askjava.lang.Exception
- if the question is not present in the databaseTour getTourDetails(java.lang.String id, java.util.Date date) throws java.lang.Exception
id
- the id of the tourdate
- the date at which the tour will take placejava.lang.Exception
- if the id/date of the tour do not correspond to any entries in the databaseCustomer getCustomerDetails(java.lang.String identifier) throws java.lang.Exception
identifier
- the String that identifies the customerjava.lang.Exception
- if the identifier is not present in the databaseGeneralTour getGeneralTourDetails(java.lang.String name) throws java.lang.Exception
name
- the name of the tourjava.lang.Exception
- in case name of the tour is not in the databasejava.util.ArrayList<java.util.Date> getTourDates(java.lang.String identifier) throws java.lang.Exception
identifier
- the id of the tourjava.lang.Exception
- if the tour is not in the DBjava.util.ArrayList<java.lang.String> getNotPaidCustomers(java.util.Date date) throws java.lang.Exception
date
- the datejava.lang.Exception
- if there is no tour for the specified datevoid logQuestion(java.lang.String question)
question
- the question the user askedvoid addBooking(TourBooking tourBooking) throws java.net.URISyntaxException
tourBooking
- the bookingjava.net.URISyntaxException
- if the fields are in an incorrect formatvoid addCustomer(Customer customer) throws java.net.URISyntaxException
customer
- the customer we want to addjava.net.URISyntaxException
- if the fields are in an incorrect formatint getNumberBookedTours(Tour tour)
tour
- the tour we want to know the number of bookings for


This is only a small project for me to learn more about the text editor Atom. I am also simultaneously learning some PowerShell commands as well as re-familiarizing myelf with Git.


compdef heroku
# Heroku Autocomplete plugin for Oh-My-Zsh # Requires: The Heroku client gem (github.com/heroku/heroku) # Author: Ali B. (awhitebox.com)
local -a _1st_arguments _1st_arguments=(
"account\:confirm_billing":"Confirm that your account can be billed at the end of the month" "addons":"list installed addons" "addons\:list":"list all available addons" "addons\:add":"install an addon" "addons\:upgrade":"upgrade an existing addon" "addons\:downgrade":"downgrade an existing addon" "addons\:remove":"uninstall an addon" "addons\:open":"open an addon's dashboard in your browser" "apps":"list your apps" "apps\:info":"show detailed app information" "apps\:create":"create a new app" "apps\:rename":"rename the app" "apps\:open":"open the app in a web browser" "apps\:destroy":"permanently destroy an app" "auth\:login":"log in with your heroku credentials" "auth\:logout":"clear local authentication credentials" "config":"display the config vars for an app" "config\:add":"add one or more config vars" "config\:remove":"remove a config var" "db\:push":"push local data up to your app" "db\:pull":"pull heroku data down into your local database" "domains":"list custom domains for an app" "domains\:add":"add a custom domain to an app" "domains\:remove":"remove a custom domain from an app" "domains\:clear":"remove all custom domains from an app" "help":"list available commands or display help for a specific command" "keys":"display keys for the current user" "keys\:add":"add a key for the current user" "keys\:remove":"remove a key from the current user" "keys\:clear":"remove all authentication keys from the current user" "logs":"display recent log output" "logs\:cron":"DEPRECATED: display cron logs from legacy logging" "logs\:drains":"manage syslog drains" "maintenance\:on":"put the app into maintenance mode" "maintenance\:off":"take the app out of maintenance mode" "pg\:info":"display database information" "pg\:ingress":"allow direct connections to the database from this IP for one minute" "pg\:promote":"sets DATABASE as your DATABASE_URL" "pg\:psql":"open a psql shell to the database" "pg\:reset":"delete all data in DATABASE" "pg\:unfollow":"stop a replica from following and make it a read/write database" "pg\:wait":"monitor database creation, exit when complete" "pgbackups":"list captured backups" "pgbackups\:url":"get a temporary URL for a backup" "pgbackups\:capture":"capture a backup from a database id" "pgbackups\:restore":"restore a backup to a database" "pgbackups\:destroy":"destroys a backup" "plugins":"list installed plugins" "plugins\:install":"install a plugin" "plugins\:uninstall":"uninstall a plugin" "ps\:dynos":"scale to QTY web processes" "ps\:workers":"scale to QTY background processes" "ps":"list processes for an app" "ps\:restart":"restart an app process" "ps\:scale":"scale processes by the given amount" "releases":"list releases" "releases\:info":"view detailed information for a release" "rollback":"roll back to an older release" "run":"run an attached process" "run\:rake":"remotely execute a rake command" "run\:console":"open a remote console session" "sharing":"list collaborators on an app" "sharing\:add":"add a collaborator to an app" "sharing\:remove":"remove a collaborator from an app" "sharing\:transfer":"transfer an app to a new owner" "ssl":"list certificates for an app" "ssl\:add":"add an ssl certificate to an app" "ssl\:remove":"remove an ssl certificate from an app" "ssl\:clear":"remove all ssl certificates from an app" "stack":"show the list of available stacks" "stack\:migrate":"prepare migration of this app to a new stack" "version":"show heroku client version"
)
:->command’
if (( CURRENT == 1 )); then
_describe -t commands "heroku command" _1st_arguments return
fi
local -a _command_args case “$words” in
apps:info) _command_args=( '(-r|--raw)'{-r,--raw}'[output info as raw key/value pairs]' \ ) ;; apps:create) _command_args=( '(-a|--addons)'{-a,--addons}'[a list of addons to install]' \ '(-r|--remote)'{-r,--remote}'[the git remote to create, default "heroku"]' \ '(-s|--stack)'{-s,--stack}'[the stack on which to create the app]' \ ) ;; config) _command_args=( '(-s|--shell)'{-s,--shell}'[output config vars in shell format]' \ ) ;; db:push) _command_args=( '(-c|--chunksize)'{-c,--chunksize}'[specify the number of rows to send in each batch]' \ '(-d|--debug)'{-d,--debug}'[enable debugging output]' \ '(-e|--exclude)'{-e,--exclude}'[exclude the specified tables from the push]' \ '(-f|--filter)'{-f,--filter}'[only push certain tables]' \ '(-r|--resume)'{-r,--resume}'[resume transfer described by a .dat file]' \ '(-t|--tables)'{-t,--tables}'[only push the specified tables]' \ ) ;; db:pull) _command_args=( '(-c|--chunksize)'{-c,--chunksize}'[specify the number of rows to send in each batch]' \ '(-d|--debug)'{-d,--debug}'[enable debugging output]' \ '(-e|--exclude)'{-e,--exclude}'[exclude the specified tables from the pull]' \ '(-f|--filter)'{-f,--filter}'[only pull certain tables]' \ '(-r|--resume)'{-r,--resume}'[resume transfer described by a .dat file]' \ '(-t|--tables)'{-t,--tables}'[only pull the specified tables]' \ ) ;; keys) _command_args=( '(-l|--long)'{-l,--long}'[display extended information for each key]' \ ) ;; logs) _command_args=( '(-n|--num)'{-n,--num}'[the number of lines to display]' \ '(-p|--ps)'{-p,--ps}'[only display logs from the given process]' \ '(-s|--source)'{-s,--source}'[only display logs from the given source]' \ '(-t|--tail)'{-t,--tail}'[continually stream logs]' \ ) ;; pgbackups:capture) _command_args=( '(-e|--expire)'{-e,--expire}'[if no slots are available to capture, delete the oldest backup to make room]' \ ) ;; stack) _command_args=( '(-a|--all)'{-a,--all}'[include deprecated stacks]' \ ) ;; esac
_arguments \
$_command_args \ '(--app)--app[the app name]' \ && return 0


Ricardo Mestre's Web Thingie
v3.0 © 2010-2012 Info!
Greetings! I'm Ricardo Mestre, yada yada yada... from Lisbon, Portugal.
Get in touch<EMAIL_ADDRESS>What's with that "about blank" stuff?
Don't look at me, man!
We're ghosts!
We're... what?!
I'm floating!
Me too!
Actually, I don't like exclamation marks!!
This website was designed and developed by me.
It uses HTML 4.01 Strict for markup and CSS 3 techniques for presentational elements.
Eye-candy tricks are provided by the open-source jQuery Javascript library and a couple of plugins (Background-Position, ColorBox, jQuery Easing & Preload Css Images).
This website won't be half as fun on Internet Explorer as it would be on any decent browser, e.g., Firefox, Google Chrome & Safari. Actually, it says goodbye to IE6.
Hat tip to Leo Xavier and Luís Abreu for the Javascript and jQuery expertness!
Follow me


Infantino planning Iran visit for game with female fans
Lausanne - FIFA president Gianni Infantino plans to attend a league game in Iran where female fans will be allowed to attend.
Infantino recently wrote to the Iranian football federation expressing his intention to travel to Iran and watch a game with women present, according to a source.
For the first time in 40 years, several thousand women were able to watch Iran's 2022 World Cup qualifier against Cambodia on October 10 at the Azadi Stadium in Tehran.
Since the Islamic revolution in 1979, the country has barred female spectators from entering football and other sports stadiums, with clerics arguing women must be shielded from the masculine atmosphere and sight of semi-clad men.
FIFA, which sent a delegation to that game, said it was looking "more than ever towards a future when ALL girls and women wishing to attend football matches in... Iran will be free to do so, and in a safe environment".
Another source said that world football's governing body hopes the process of allowing women into Iranian stadiums will be "prolonged, not stopped and not reversed".
A FIFA delegation will have to go to Tehran in advance to prepare for Infantino's visit.
The match on October 10 came only a month after the death of Sahar Khodayari, who set herself ablaze in September outside a court in fear of being jailed for trying to attend a game.
Dubbed "Blue Girl" because of the colours of the club she supported -- Esteghlal FC -- she had reportedly been detained last year when trying to enter a stadium dressed as a boy.
Her death sparked an outcry, with many calling for Iran to be banned and matches boycotted.


int SomeMethod () { // This matches when the hashtable key is a pointer. templateclass hash_munger { public: static size_t MungedHash(size_t hash) { // TODO(csilvers): consider rotating instead: // static const int shift = (sizeof(void *) == 4) ? 2 : 3; // return (hash << (sizeof(hash) * 8) - shift)) | (hash >> shift); // This matters if we ever change sparse/dense_hash_* to compare // hashes before comparing actual values. It's speedy on x86. return hash / sizeof(void*); // get rid of known-0 bits } }; }
/* Note: On most machines and ABIs, pointers are generally word-aligned. So dividing by the sizeof pointers is essentially ignoring the least bits (e.g. 3 lowest bits of bytes addresses are 0 on most 64 bits processors since a 64 bits word has 8 bytes, each of 8 bits).*/


This countermeasure primarily prevents Cross-Site Request Forgery attacks but can also mitigate several other client side attacks.
The primary goal of the software defense is to cause the http requests to become unique to the user. This is usually done through a request authentication token separate from cookies and platform authentication headers.
The example below shows an HTTP request protected against CSRF. It is one of the requests from this site.
GET /api/teams HTTP/1.1 Host: localhost:8081 User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.12; rv:53.0) Gecko/20100101 Firefox/53.0 Accept: application/json, text/plain, */* Accept-Language: en-US,en;q=0.5 Accept-Encoding: gzip, deflate Referer: http://localhost:8081/main xsrfToken: EITPDAj48qzt2z5J6wE4uPn6iC3ggw8imBdIeeBwknWybC-SnPCmlTdmDIPM1XRJjgXv9Gfd1RSV1RbDkaRU1w Cookie: connect.sid=s%3AieghQmy09PUrMqYqMBlfKSW5cA4RRsLE.90RDCtrG6aJoFC74R4OfuJYXKPiZEVLtHpfP6dMNQXs Connection: keep-alive
The xsrfToken will be different for each user and an attacker will not be able to store the request in an external site because they won't know it. Even if they knew the token they could only target a single user and the request would expire with the session limiting the ammount of time for the attack.
The countermeasure works for any other attacks where the attacker would have to manipulate a request and e-mail it to a victim or store a request in an external site.
A few CSRF token best practices:


Our mission is to solve the labor problem facing farmers by making robots like Wall-E a reality. To do this, we are starting first with a collaborative robotic platform called Burro, that helps people work more productively and builds a base for the future where farming is fully autonomous.
Our team includes robotic startup veterans with deep experience in computer vision, robotics, and deep learning, as well as entrepreneurs with direct experience selling and marketing ag machinery with some of the worl'ds largest companies. We get up every day excited to build the future, where robots do all tedious tasks outdoors.
Charlie grew up on a 191 acre working fruit, vegetable, and livestock farm. This experience lead him to believe that all tedious farm labor should be automated, and simultaneously to understand the many challenges that robots face in agriculture. He founded Augean Robotics after gaining experience with Case New Holland, John Deere's largest competitor, where he reported to the head of the North American operating unit and worked on special projects across sales, marketing, manufacturing, and distribution. He received a BA from Amherst College, and an MBA from Harvard Business School.
Deniz has over a decade of experience in Robotics, specialized in embedded sensorimotor system design, power electronics, robotic platform development, system integration, and autonomous behaviors. He received his PhD from University of Pennsylvania's GRASP lab where his research focused on task encoding and behavior development for autonomous robots in outdoor settings. He grew up in a fruit & vegetable farming town in Turkey, and has spent years building commercial robots in several startups including COSY, where he was the lead roboticist.
Terry leads the development of a unified technical vision, architecture, and autonomy roadmap with Augean Robotics. Prior to Augean, Terry worked as a robotic software developer with COSY, and as a design engineer/project manager with a large manufacturer supplying African railroads. Terry received his PhD from University of Oxford's Robotic Institute where his research focused on computer-vision-based autonomous navigation and mapping, and a BSc in Electro-Mechanical Engineering from University of Capetown. He grew up in South Africa where he gained a love for machinery and robotics.
Vibhor builds Burros and his software controls them. He develops many of the computer-vision approaches to localization and autonomy with Augean Robotics. Prior to Augean, Vibhor worked as a researcher at Lehigh's Vader Labs where he specialized in accurate infrastructure free outdoor localization, and as a software engineer at Samsung. He received a Masters in Electrical/Electronics Engineering from Lehigh University, and a bachelors degree in Electrical, Electronics, and Communications Engineering from Manav Rachna International University in Faridabad, India.
Anthony designs and builds the mechanical skeletons of Burro, and also supports the development of computer vision based localization. Prior to working with Augean, Anthony developed firmware and user software for 3D printers at a bioprinting startup. Additionally, Anthony's experience includes researching autonomous unmanned aerial systems (UASs) at NASA Ames Research Center and working on research and development for SpaceX's Starlink satellite project. He received his bachelor's degree in Mechanical and Aerospace Engineering at Princeton University and is a current Venture for America fellow.
Kirk Haney is the Co-Founder & Managing Partner at Radicle Growth. Under Kirkâ€™s leadership, Radicle Growth has become a market leader in early-stage ag and foodtech investing and company building and has secured top-tier financial and corporate investors. A serial entrepreneur with three exits, Kirk has raised over $200M in venture capital, private equity and corporate capital and has had three successful exits. Heâ€™s worked in technology, venture capital and ag & food his entire career and has a clear understanding of what early-stage companies need to be successful. Kirk is a proud father and is passionate about food, health and sustainability.
Adam Plotkin is a Partner at ff Venture Capital. In this capacity he provides targeted strategic counsel to a range of ffVC investments, with a focus on technological innovation in logistics and manufacturing, insurance, e-commerce, financial services and media. Adam has experience serving on the boards of CardFlight, Drop Loyalty, Four Mine and Sure, to name a few. Adam holds a Master of Business Administration degree from the Harvard Business School. He also holds Bachelor of Arts, Bachelor of Applied Sciences, and Bachelor of Science in Economics degrees, cum laude with distinction from the College of Arts and Sciences, the School of Engineering and Applied Sciences, and the Wharton School, at the University of Pennsylvania, as a Benjamin Franklin Scholar and a Joseph Wharton Scholar.
CJ is a professor at University of Pennsylvania's GRASP lab, which he joined in 1997. His research interests focus on computer vision and robotics. His current research include work on self-localizing embedded smart camera systems and their applications to problems such as automated surveying systems, ad-hoc surveillance systems, three-dimensional reconstruction and mobile robot localization. He received his A.B. degree in Electrical Computer and Systems Engineering from Harvard College in 1988 and his M.S. and Ph.D. degrees from Yale University in 1990 and 1994 respectively.
Simo has a passion for all things technical, and has spent time at McKinsey, and Oracle. He is currently a partner at the Synetro Group where he invests in companies and provides operational support to help them succeed. He received an MBA from Harvard and has two degrees in EE/Computer Science from MIT.
John is an Associate Professor of Computer Science and Engineering at Lehigh University where he heads up the VADER Laboratory. His research focus is field and service robotics in unstructured, outdoor environments. John received his PhD from the GRASP Laboratory at UPenn, and has 20 years of experience in the robotics field. He has also been involved in local robotics startups, including serving as CTO of Love Park Robotics which was acquired by industrial sensor company IFM earlier this year.


One la-la-la morning, Tiny Enid was limping along the cliffs at the edge of the Big Frightening Sea when, across the sky, zooming directly at her, came a gigantic colourful bewinged flying insect of unknown provenance. Most infants would have screeched in terror and cried for Ma or Pa to rescue them, but of course the plucky fascist tot was not most infants. She raised her arms, as if in terror, to pull the wool over the huge airborne being's eyes, if it actually had any, and screech she did, but it was a screech not of fright but of blood-curdling menace. It was so terrific a screech that the enormous brightly-coloured mothish type of thing was immediately deafened, playing havoc with its complicated navigational head integuments, and it plummeted forthwith into the broiling waters where, unable to swim, it drowned.
Tiny Enid let fall her arms and patted the pocket wherein she carried her tiny toy Mannlicher-Carcano sniper's rifle which, in spite of being tiny, and a toy, was a devastatingly effective firearm.
“I am glad I did not have to use my trusty sniper's rifle to slaughter that curious wing-flapping monster,” she thought, “For it remains fully loaded and I can put the bullets to better use, should I happen later this morning to come upon malefactors and ne'er-do-wells roaming the cliffs.”
As indeed she did, but that is another story for another, more gruesome, time.
Illustration by Vladimir Konashevich, 1923


Training ML-Agents
ML-Agents conducts training using an external Python training process. During training, this external process communicates with the Academy object in the Unity scene to generate a block of agent experiences. These experiences become the training set for a neural network used to optimize the agent's policy (which is essentially a mathematical function mapping observations to actions). In reinforcement learning, the neural network optimizes the policy by maximizing the expected rewards. In imitation learning, the neural network optimizes the policy to achieve the smallest difference between the actions chosen by the agent trainee and the actions chosen by the expert in the same situation.
The output of the training process is a model file containing the optimized policy. This model file is a TensorFlow data graph containing the mathematical operations and the optimized weights selected during the training process. You can use the generated model file with the Internal Brain type in your Unity project to decide the best course of action for an agent.
Use the Python program, learn.py
to train your agents. This program can be found in the python
directory of the ML-Agents SDK. The configuration file, trainer_config.yaml
specifies the hyperparameters used during training. You can edit this file with a text editor to add a specific configuration for each brain.
For a broader overview of reinforcement learning, imitation learning and the ML-Agents training process, see ML-Agents Overview.
Training with Learn.py
Use the Python Learn.py
program to train agents. Learn.py
supports training with reinforcement learning, curriculum learning, and behavioral cloning imitation learning.
Run Learn.py
from the command line to launch the training process. Use the command line patterns and the trainer_config.yaml
file to control training options.
The basic command for training is:
python3 learn.py <env_file_path> --run-id=<run-identifier> --train
where <env_file_path>
is the path to your Unity executable containing the agents to be trained and <run-identifier>
is an optional identifier you can use to identify the results of individual training runs.
For example, suppose you have a project in Unity named "CatsOnBicyclesCatsOnBicycles" which contains agents ready to train. To perform the training:
- Build the project, making sure that you only include the training scene.
- Open a terminal or console window.
- Navigate to the ml-agents
python
folder. Run the following to launch the training process using the path to the Unity environment you built in step 1:
python3 learn.py ../../projects/Cats/CatsOnBicycles.app --run-id=cob_1 --train
During a training session, the training program prints out and saves updates at regular intervals (specified by the summary_freq
option). The saved statistics are grouped by the run-id
value so you should assign a unique id to each training run if you plan to view the statistics. You can view these statistics using TensorBoard during or after training by running the following command (from the ML-Agents python directory):
tensorboard --logdir=summaries
And then opening the URL: localhost:6006.
When training is finished, you can find the saved model in the python/models
folder under the assigned run-id — in the cats example, the path to the model would be python/models/cob_1/CatsOnBicycles_cob_1.bytes
.
While this example used the default training hyperparameters, you can edit the training_config.yaml file with a text editor to set different values.
Command line training options
In addition to passing the path of the Unity executable containing your training environment, you can set the following command line options when invoking learn.py
:
--curriculum=<file>
– Specify a curriculum JSON file for defining the lessons for curriculum training. See Curriculum Training for more information.--keep-checkpoints=<n>
– Specify the maximum number of model checkpoints to keep. Checkpoints are saved after the number of steps specified by thesave-freq
option. Once the maximum number of checkpoints has been reached, the oldest checkpoint is deleted when saving a new checkpoint. Defaults to 5.--lesson=<n>
– Specify which lesson to start with when performing curriculum training. Defaults to 0.--load
– If set, the training code loads an already trained model to initialize the neural network before training. The learning code looks for the model inpython/models/<run-id>/
(which is also where it saves models at the end of training). When not set (the default), the neural network weights are randomly initialized and an existing model is not loaded.--run-id=<path>
– Specifies an identifier for each training run. This identifier is used to name the subdirectories in which the trained model and summary statistics are saved as well as the saved model itself. The default id is "ppo". If you use TensorBoard to view the training statistics, always set a unique run-id for each training run. (The statistics for all runs with the same id are combined as if they were produced by a the same session.)--save-freq=<n>
Specifies how often (in steps) to save the model during training. Defaults to 50000.--seed=<n>
– Specifies a number to use as a seed for the random number generator used by the training code.--slow
– Specify this option to run the Unity environment at normal, game speed. The--slow
mode uses the Time Scale and Target Frame Rate specified in the Academy's Inference Configuration. By default, training runs using the speeds specified in your Academy's Training Configuration. See Academy Properties.--train
– Specifies whether to train model or only run in inference mode. When training, always use the--train
option.--worker-id=<n>
– When you are running more than one training environment at the same time, assign each a unique worker-id number. The worker-id is added to the communication port opened between the current instance of learn.py and the ExternalCommunicator object in the Unity environment. Defaults to 0.--docker-target-name=<dt>
– The Docker Volume on which to store curriculum, executable and model files. See Using Docker.
Training config file
The training config file, trainer_config.yaml
specifies the training method, the hyperparameters, and a few additional values to use during training. The file is divided into sections. The default section defines the default values for all the available settings. You can also add new sections to override these defaults to train specific Brains. Name each of these override sections after the GameObject containing the Brain component that should use these settings. (This GameObject will be a child of the Academy in your scene.) Sections for the example environments are included in the provided config file. Learn.py
finds the config file by name and looks for it in the same directory as itself.
| ** Setting ** | Description | Applies To Trainer |
|---|---|---|
| batch_size | The number of experiences in each iteration of gradient descent. | PPO, BC |
| batches_per_epoch | In imitation learning, the number of batches of training examples to collect before training the model. | BC |
| beta | The strength of entropy regularization. | PPO, BC |
| brain_to_imitate | For imitation learning, the name of the GameObject containing the Brain component to imitate. | BC |
| buffer_size | The number of experiences to collect before updating the policy model. | PPO, BC |
| epsilon | Influences how rapidly the policy can evolve during training. | PPO, BC |
| gamma | The reward discount rate for the Generalized Advantage Estimator (GAE). | PPO |
| hidden_units | The number of units in the hidden layers of the neural network. | PPO, BC |
| lambd | The regularization parameter. | PPO |
| learning_rate | The initial learning rate for gradient descent. | PPO, BC |
| max_steps | The maximum number of simulation steps to run during a training session. | PPO, BC |
| memory_size | The size of the memory an agent must keep. Used for training with a recurrent neural network. See Using Recurrent Neural Networks. | PPO, BC |
| normalize | Whether to automatically normalize observations. | PPO, BC |
| num_epoch | The number of passes to make through the experience buffer when performing gradient descent optimization. | PPO, BC |
| num_layers | The number of hidden layers in the neural network. | PPO, BC |
| sequence_length | Defines how long the sequences of experiences must be while training. Only used for training with a recurrent neural network. See Using Recurrent Neural Networks. | PPO, BC |
| summary_freq | How often, in steps, to save training statistics. This determines the number of data points shown by TensorBoard. | PPO, BC |
| time_horizon | How many steps of experience to collect per-agent before adding it to the experience buffer. | PPO, BC |
| trainer | The type of training to perform: "ppo" or "imitation". | PPO, BC |
| use_recurrent | Train using a recurrent neural network. See Using Recurrent Neural Networks. | PPO, BC |
| PPO = Proximal Policy Optimization, BC = Behavioral Cloning (Imitation)) |
For specific advice on setting hyperparameters based on the type of training you are conducting, see:
- Training with PPO
- Using Recurrent Neural Networks
- Training with Curriculum Learning
- Training with Imitation Learning
You can also compare the example environments to the corresponding sections of the trainer-config.yaml
file for each example to see how the hyperparameters and other configuration variables have been changed from the defaults.


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This document was generated by GDB Administrator on March, 27 2008 using texi2html


The number of the currently running behaviour
The number of the currently running example (a global counter)
Override this method if you wish to output extra HTML for a failed spec. For example, you could output links to images or other files produced during the specs.
[Validate]


Visit the architecture page for suggestions on secure layout.
Since the engine is interacted with via a directory-like syntax, you can easily manage access privileges using the Apache "Location" directive. For example you may want disallow any external access to the engine:
Perhaps, internally you have two networks, one more priveleged than the other, for example an application server network and a monitoring network. You want to allow the application network update and status rights, but only status rights to the monitoring network:<Location /ath> Order deny,allow Deny from all Allow from INTERNAL_NETWORK </Location>
<Location /ath/update> Order deny, allow Deny from all Allow from APP_NETWORK </Location>
Security for this tool is all about who can access what from where. Think carefully and consider who can reach these features, and then control them tightly using the Location directives:


- getCount() - Method in class com.oprahs_voice.android.movies.models.main.MovieAdapter
-
Get the number of movies saved in the local cache
- getEntries() - Method in class com.oprahs_voice.android.movies.interfaces.FDBInterface
-
Get a list of all movies in the favorite movies database.
- getFavoriteMovies() - Method in class com.oprahs_voice.android.movies.models.favorites.FavoritesModel
-
Get a list of all the user's favorite movies.
- getFavoritesModel() - Static method in class com.oprahs_voice.android.movies.activities.MainActivity
-
Method to return the favoritesModel member to be displayed.
- getItem(int) - Method in class com.oprahs_voice.android.movies.models.main.MovieAdapter
-
- getItemId(int) - Method in class com.oprahs_voice.android.movies.models.main.MovieAdapter
-
- getMovieAdapter() - Static method in class com.oprahs_voice.android.movies.activities.MainActivity
-
Method to return the movieAdapter member to be acted upon by FetchMovieData and FavoritesModel.
- getMovieByID(int)
- Method in class com.oprahs_voice.android.movies.interfaces.ServerInterface
-
- getMovieByID(int) - Method in class com.oprahs_voice.android.movies.models.main.MovieAdapter
-
Get a movie and its information by providing the movie ID
- getMovieID() - Method in class com.oprahs_voice.android.movies.utilities.Movie
-
- getMovies() - Method in class com.oprahs_voice.android.movies.models.main.MovieAdapter
-
- getMovies()
- Method in class com.oprahs_voice.android.movies.models.main.MovieDataParser
-
- getPage()
- Method in class com.oprahs_voice.android.movies.models.main.MovieDataParser
-
- getPages() - Method in class com.oprahs_voice.android.movies.settings.Settings
-
Gets the pages that is stored in the SharedPreferences file.
- getPicUrl() - Method in class com.oprahs_voice.android.movies.utilities.Movie
-
- getPlotSynopsis() - Method in class com.oprahs_voice.android.movies.utilities.Movie
-
- getReleaseDate() - Method in class com.oprahs_voice.android.movies.utilities.Movie
-
- getSort() - Method in class com.oprahs_voice.android.movies.settings.Settings
-
Gets the sort state that is stored in SharedPreferences file.
- getSortedMovies(int, String) - Method in class com.oprahs_voice.android.movies.interfaces.ServerInterface
-
Method to fetch the movie data from the TMDB server.
- getTitle() - Method in class com.oprahs_voice.android.movies.utilities.Movie
-
- getUserRating() - Method in class com.oprahs_voice.android.movies.utilities.Movie
-
- getView(int, View, ViewGroup) - Method in class com.oprahs_voice.android.movies.data.FavoritesAdapter
-
Processess the information for each movie in the movie list.
- getView(int, View, ViewGroup) - Method in class com.oprahs_voice.android.movies.models.main.MovieAdapter
-


We can have multiple sets of selections. This requires the use of the "Selection Manager". It can be accessed either from the tab inside the "Layer Manager", or from the 2nd toolbar button, with a price tag icon. Selections are organized just like layers. The "Linked" property is currently disabled. However, selections can be created, renamed, deleted, cleared, selected, and reordered, just like layers.
When a selection has it's visible property on, it will show up on the "selected type" layer, wtih a default name of "Selection". The color of these large, unfilled, circles is the same as the color of the selection itself. Selections can be saved and restored. It is possible to have the Jmol show only the rRNA from one selection. To do this, make the desired selection the selected one, and click "Apply Selection 3D", which is located in the "Select" part of the Main Menu.


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