|
Linear and Monotonic Correlation Measures
|
|
| The standard measure of
linear correlation in a time series is the autocorrelation given as a
function of the delay parameter. The autocorrelation for each delay t is
actually the Pearson correlation coefficient for the variable pair
(x(i), x(i+t)). In this sense, the autocorrelation at each delay
t can be
estimated by other estimates of the pair-wise correlation and here the
Kendall and Spearman correlation coefficients are also implemented
giving the Kendall and Spearman autocorrelation, respectively. Note that the autocorrelation (Pearson, Kendall,
Spearman) at different delays are considered as different measures, so that for a range of delay values the
same number of measures are generated. We consider in this group also
the partial autocorrelation that measures the correlation of the
variable pair (x(i), x(i+t))
accounting for the intermediate variables x(i+1),...,x(i+t-1)
(partial autocorrelation is the same as Pearson autocorrelation for t=1). In addition to the autocorrelation functions, the cumulative autocorrelations (Pearson, Kendall, Spearman) are implemented as new measures that sums up the autocorrelation magnitudes for the delays up to a given delay. There are two special values of the autocorrelation function and the respective delays are used in time series analysis: the decorrelation time, i.e. the delay for which the autocorrelation falls for the first time to the level 1/e, and the delay of zero autocorrelation, for which the autocorrelation crosses zero for the first time. These specific delays are computed for all three types of autocorrelation. The definition of the Pearson, Kendall and Spearman correlation coefficients can be found in any standard textbook of statistics. The Pearson autocorrelation is a basic tool of linear analysis of time series and statistical description for this (estimation, inference) can be found in any standard book on time series analysis, e.g. see Box, G.E.P., Jenkins, G.M., and Reinsel, G.C. (1994), Time Series Analysis: Forecasting and Control, Prentice-Hall, Third Edition, New Jersey. The partial autocorrelation for each lag t is the last coefficient of the autoregressive (AR) model of order t and we implement the AR fitting to derive the partial autocorrelation measure for different given lags. The reader may refer to the same book for further details. The implementation of Kendall and Spearman correlation coefficient on the estimation of the autocorrelation is not standard but may be useful in some cases, as when the marginal distribution of a time series deviates substantially from Gaussian. Note that the Kendall and Spearman autocorrelation estimate correlations in the ranks of the samples in the time series and therefore cannot be considered as linear measures. We call them monotonic measures, since their values do not change under monotonic transforms of the time series. |
|
| Pearson Autocorrelation (PearsAutoc) |
|
| Pearson
Autocorrelation is the
standard autocorrelation for the given range of delays. The
following parameter can be specified: - delay (t): any valid matlab format denoting an array of positive integers or a single positive integer. The default is '1:10' meaning delays from 1 to 10. In addition the Cumulative Pearson Autocorrelation is computed for the same range of delays. Also, if the autocorrelation for delays up to the maximum given delay crosses the 1/e or zero level the delay of decorrelation or zero-autocorrelation, respectively, is assigned a value. The cumulative Pearson autocorrelation and the delay of decorrelation and zero autocorrelation can then be simply assigned to the respective measures if these are selected with the same set of delay values. Note that when the delay parameter is changed, the change is passed to the same parameter in the measures of Cumulative Pearson Autocorrelation, Pearson Decorrelation Time and Zero Pearson Autocorrelation Time. Example: If the user selects this measure by activating the check box in the beginning of the measure line and sets for delay (t) '1:5 10 20', then Pearson Autocorrelation is computed for these delays and in the measure list the following measure names will appear PearsAutoct1 |
|
|
Cumulative Pearson Autocorrelation (PearsCAuto) |
|
| Cumulative
Pearson Autocorrelation is the
cumulative function of the Pearson autocorrelation for the given range of delays. The
delay parameter is determined as for the Pearson autocorrelation. The
Cumulative Pearson Autocorrelation
for each delay t is the sum of the absolute
values of the Pearson autocorrelation up to the delay
t. Example: If the user selects this measure by activating the check box in the beginning of the measure line and sets for delay (t) '10 20', then Cumulative Pearson Autocorrelation is computed for these delays and in the measure list the following measure names will appear PearsCAutot10 |
|
| Pearson Decorrelation Time (PearsDecoT) |
|
| The Pearson
Decorrelation Time is the delay for which the Pearson autocorrelation
falls to the level 1/e. So, if the autocorrelation, computed for delays up to the
maximum of the given delays (if more than one delay value is given),
crosses 1/e for a delay te, then the Pearson Decorrelation Time is
assigned te otherwise it has a NaN value (NaN=not a number). Example: If the user selects this measure by activating the check box in the beginning of the measure line and sets for delay (t) '10 20', then Pearson Decorrelation Time is computed and in the measure list the following measure name will appear (for the maximum t=20) PearsDecoTt20 |
|
| Zero Pearson Autocorrelation Time (PearsZeroT) |
|
| The Zero
Pearson Autocorrelation Time is the delay for which the Pearson
autocorrelation falls to the zero level. So, if the autocorrelation,
computed for
delays up to the maximum of the given delays (if more than one delay
value is given), crosses zero for a delay t0, then the Zero Pearson
Autocorrelation Time is assigned to t0 otherwise it has a NaN value (NaN=not
a number). Example: If the user selects this measure by activating the check box in the beginning of the measure line and sets for delay (t) '10 20', then Zero Pearson Autocorrelation Time is computed and in the measure list the following measure name will appear (for the maximum t=20) PearsZeroTt20 |
|
| Kendall Autocorrelation (KendaAutoc) |
|
| Kendall
Autocorrelation is the estimate of autocorrelation for the given range of delays
making use of the Kendall correlation coefficient. For each delay t, the
Kendall Autocorrelation is actually the Kendall (or so-called tau) correlation
coefficient for the variable pair (x(i), x(i+t)). The
following parameter can be specified: - delay (t): any valid matlab format denoting an array of positive integers or a single positive integer. The default is '1:10' meaning delays from 1 to 10. In addition, the Cumulative Kendall Autocorrelation is computed for the same range of delays. Also, if the Kendall autocorrelation for delays up to the maximum given delay crosses the 1/e or zero level the delay of Kendall decorrelation or zero Kendall autocorrelation, respectively, is assigned a value. The cumulative Kendall autocorrelation and the delay of Kendall decorrelation and zero Kendall autocorrelation can then be simply assigned to the respective measures, if these are selected with the same set of delay values. Note that when the delay parameter is changed, the change is passed to the same parameter in the measures of Cumulative Kendall Autocorrelation, Kendall Decorrelation Time and Zero Kendall Autocorrelation Time. Example: If the user selects this measure by activating the check box in the beginning of the measure line and sets for delay (t) '1:5 10 20', then Kendall Autocorrelation is computed for these delays and in the measure list the following measure names will appear KendaAutoct1 |
|
|
Cumulative Kendall Autocorrelation (KendaCAuto) |
|
| Cumulative
Kendall Autocorrelation is the
cumulative function of the Kendall autocorrelation for the given range of delays. The
delay parameter is determined as for the Kendall autocorrelation. The
Cumulative Kendall Autocorrelation
for each delay t is the sum of the absolute
values of the Kendall autocorrelation up to the delay
t. Example: If the user selects this measure by activating the check box in the beginning of the measure line and sets for delay (t) '10 20', then Cumulative Kendall Autocorrelation is computed for these delays and in the measure list the following measure names will appear KendaCAutot10 |
|
| Kendall Decorrelation Time (KendaDecoT) |
|
| The Kendall
Decorrelation Time is the delay for which the Kendall autocorrelation
falls to the level 1/e. So, if the autocorrelation, computed for delays up to the
maximum of the given delays (if more than one delay value is given),
crosses 1/e for a delay te, then the Kendall Decorrelation Time is
assigned te otherwise it has a NaN value (NaN = not a number). Example: If the user selects this measure by activating the check box in the beginning of the measure line and sets for delay (t) '10 20', then Kendall Decorrelation Time is computed and in the measure list the following measure name will appear (for the maximum t=20) KendaDecoTt20 |
|
| Zero Kendall Autocorrelation Time (KendaZeroT) |
|
| The Zero
Kendall Autocorrelation Time is the delay for which the Kendall
autocorrelation falls to the zero level. So, if the autocorrelation,
computed for
delays up to the maximum of the given delays (if more than one delay
value is given), crosses zero for a delay t0, then the Zero Kendall
Autocorrelation Time is assigned to t0 otherwise it has a NaN value (NaN
= not
a number). Example: If the user selects this measure by activating the check box in the beginning of the measure line and sets for delay (t) '10 20', then Zero Kendall Autocorrelation Time is computed and in the measure list the following measure name will appear (for the maximum t=20) KendaZeroTt20 |
|
| Spearman Autocorrelation (SpearAutoc) |
|
| Spearman
Autocorrelation is the estimate of autocorrelation for the given range of delays
making use of the Spearman correlation coefficient. For each delay t,
the Spearman Autocorrelation is actually the Spearman
correlation coefficient for the variable pair (x(i), x(i+t)). The
following parameter can be specified: - delay (t): any valid matlab format denoting an array of positive integers or a single positive integer. The default is '1:10' meaning delays from 1 to 10. In addition the Cumulative Spearman Autocorrelation is computed for the same range of delays. Also, if the Spearman autocorrelation, computed for delays up to the maximum given delay, crosses the 1/e or zero level the delay of Spearman decorrelation or zero Spearman autocorrelation, respectively, is assigned a value. The cumulative Spearman autocorrelation and the delay of Spearman decorrelation and zero Spearman autocorrelation can then be simply assigned to the respective measures, if these are selected with the same set of delay values. Note that when the delay parameter is changed, the change is passed to the same parameter in the measures of Cumulative Spearman Autocorrelation, Spearman Decorrelation Time and Zero Spearman Autocorrelation Time. Example: If the user selects this measure by activating the check box in the beginning of the measure line and sets for delay (t) '1:5 10 20', then Spearman Autocorrelation is computed for these delays and in the measure list the following measure names will appear SpearAutoct1 |
|
|
Cumulative Spearman Autocorrelation (SpearCAuto) |
|
| Cumulative
Spearman Autocorrelation is the
cumulative function of the Spearman autocorrelation for the given range of delays. The
delay parameter is determined as for the Spearman autocorrelation.
The Cumulative Spearman
Autocorrelation
for each delay t is the sum of the absolute
values of the Spearman autocorrelation up to the delay
t. Example: If the user selects this measure by activating the check box in the beginning of the measure line and sets for delay (t) '10 20', then Cumulative Spearman Autocorrelation is computed for these delays and in the measure list the following measure names will appear SpearCAutot10 |
|
| Spearman Decorrelation Time (SpearDecoT) |
|
| The
Spearman
Decorrelation Time is the delay for which the Spearman autocorrelation
falls to the level 1/e. So, if the autocorrelation, computed for delays up to the
maximum of the given delays (if more than one delay value is given),
crosses 1/e for a delay te, then the Spearman Decorrelation Time is
assigned te otherwise it has a NaN value (NaN = not a number). Example: If the user selects this measure by activating the check box in the beginning of the measure line and sets for delay (t) '10 20', then Spearman Decorrelation Time is computed and in the measure list the following measure name will appear (for the maximum t=20) SpearDecoTt20 |
|
| Zero Spearman Autocorrelation Time (SpearZeroT) |
|
| The Zero
Spearman Autocorrelation Time is the delay for which the Spearman
autocorrelation falls to the zero level. So, if the autocorrelation,
computed for
delays up to the maximum of the given delays (if more than one delay
value is given), crosses zero for a delay t0, then the Zero Spearman
Autocorrelation Time is assigned to t0 otherwise it has a NaN value (NaN
= not
a number). Example: If the user selects this measure by activating the check box in the beginning of the measure line and sets for delay (t) '10 20', then Zero Spearman Autocorrelation Time is computed and in the measure list the following measure name will appear (for the maximum t=20) SpearZeroTt20 |
|
| Partial Autocorrelation (PartialAut) |
|
| Partial
Autocorrelation for a lag t is the correlation of the variable
pair (x(i), x(i+t)) accounting for
the intermediate variables x(i+1),...,x(i+t-1).
Here it can be computed for the given range of delays. The
following parameter can be specified: - delay (t): any valid matlab format denoting an array of positive integers or a single positive integer. The default is '1:10' meaning delays from 1 to 10. Example: If the user selects this measure by activating the check box in the beginning of the measure line and sets for delay (t) '1:5 10 20', then Partial Autocorrelation is computed for these delays and in the measure list the following measure names will appear PartialAutt1 |
|
| OK |
|
| By pressing this button the window of "Linear and Monotonic Correlation Measures" will disappear and the user will be moved to the "Select / run measures" window. Any changes in the measures and parameter values will be stored. | |
| Cancel |
|
| Quit without doing anything and return to the "Select / run measures" window. Any changes in the measures and parameter values will be ignored. | |
| Help |
|
| This file will be shown |


Financial support for the Center comes from Stuart Subotnick ('62), Vice Chairman and Chief Executive Officer of Metromedia International Group, Inc., and Bert W. ('54) and Sandra ('55) Wasserman.
A number of organizations also provided very generous support that enabled the SFSC to be the leading facility that it is. These included Compaq Computer, Nortel Networks, Merrill Lynch, Gruntal & Co., Neuberger and Berman, and the New York Stock Exchange.
As our sponsors recognize, a revolution is occurring in financial services. Changes in markets and products, the computerization of equity, security, money and derivative trading, and the globalization of finance and risk management have altered the way in which market operate. The internet provides a new channel for distribution of financial services. With overwhelming volumes of data and increasing complexity, tomorrow's financial market participants require more educational exposure to the tools and systems used to analyze markets and assess financial decisions.
The Subotnick Financial Services Center provides Baruch students with access to professional market resources. Our students are benefiting from access to FactSet, CapitalIQ and Bloomberg market data systems, FirstCall investment research, brokerage platforms from TradeStation, Thinkorswim, Interactive Brokers and CQG, and analytic software from KX, OneTick and other vendors. Baruch students apply economic and finance theory, risk management processes, financial engineering and financial IT to gain exposure to the rapidly changing trading and investment environment.
Using the SFSC's cutting-edge computing and network resources, Baruch faculty and staff have developed experiential learning modules in market analysis, financial modeling, trading, and information systems design and development in use by thousands of students each year.


 She said Monero.
[dscrd-prt] <ComputerGenie > Stacy's mom has got it goin' on
[dscrd-prt] <Tek> will 8k break tonight?
[dscrd-prt] <ComputerGenie > who cares? and why?
[dscrd-prt] <Tek> cause if it doesn't the alt market will tank
[dscrd-prt] <Tek> together with bitcoin
[dscrd-prt] <ComputerGenie > bitcoin is an alt
[dscrd-prt] <Tek> ooooh 8k hit
[dscrd-prt] <Tek> but will it pump through?
[dscrd-prt] <Tek> it would confirm the bullmarket
[dscrd-prt] <ComputerGenie > 10% gain = "bull"?
so glad you don't handle my investments
(edited)
[dscrd-prt] <Tek> there she goes
[dscrd-prt] <Tek> nigga 8k was a major resistance
[dscrd-prt] <Tek> now its probably gonna pump to 9k
[dscrd-prt] <Tek> and it confirms the bullmarket
[dscrd-prt] <Tek> also, everyone is gonna dump pirates when Bitcoin and other Big alts soak up all the new money
[dscrd-prt] <Tek> and i'll buy pirates at 1 cent
[dscrd-prt] <Tek> HEHEHEH
[dscrd-prt] <Tek> Also, after this bullrun I'll finally have more KMD than you, geenlet
[dscrd-prt] <Tek> I will have completed the spitening of the geenlet
[dscrd-prt] <Tek> after years of waiting
[dscrd-prt] <Tek> God bless
[dscrd-prt] <ComputerGenie > I'll finally have more KMD than you
well, that might impress the ugly chick at the Burger Center, but no one else
[dscrd-prt] <Tek> can't get any worse than your wife am i rite
[dscrd-prt] <Tek> i wonder if BCH will keep 0.03


class DogeEntity
- DogeEntity
- Reference
- Object
Direct Known Subclasses
Defined in:
dogehouse_cr/entity.crInstance Method Summary
-
#join_room(roomId : String)
Join room will join the room associated with a roomId
` client.join_room "roomid"
`
-
#send_message(message : String)
Sends message to whatever room currently in
` client.send_message "msg"
`
Instance Method Detail
def join_room(roomId : String)
#
Join room will join the room associated with a roomId
client.join_room "roomid"
def send_message(message : String)
#
Sends message to whatever room currently in
client.send_message "msg"


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syntax: str = ngx.req.raw_header(no_request_line?)
context: set_by_lua*, rewrite_by_lua*, access_by_lua*, content_by_lua*, header_filter_by_lua*
Returns the original raw HTTP protocol header received by the Nginx server.
By default, the request line and trailing CR LF
terminator will also be included. For example,
```lua
ngx.print(ngx.req.raw_header())
```
gives something like this:
GET /t HTTP/1.1
Host: localhost
Connection: close
Foo: bar
You can specify the optional no_request_line
argument as a true
value to exclude the request line from the result. For example,
```lua
ngx.print(ngx.req.raw_header(true)) ```
outputs something like this:
Host: localhost
Connection: close
Foo: bar
This method was first introduced in the v0.7.17
release.
This method does not work in HTTP/2 requests yet.


Beto O’Rourke ends 2020 US presidential bid
Beto O’Rourke, the former Texas congressman, has announced he is ending his Democratic US presidential campaign.
In an online post, Mr O’Rourke said: “My service to the country will not be as a candidate or as the nominee.”
He was scheduled to address supporters later on Friday at an event in Iowa.
Mr O’Rourke was urged to run for president by many Democrats who were energised by his narrow Senate loss last year in Texas, a reliably Republican state.
He raised an astonishing amount of money from small donors across the country, visited every county in Texas and used social media and live-streaming video to engage directly with voters. He ultimately lost to incumbent Republican Senator Ted Cruz by three percentage points.
But Mr O’Rourke struggled to replicate that model in the presidential primary and both his polling and his fundraising dwindled significantly in recent months.
“Though it is difficult to accept, it is clear to me now that this campaign does not have the means to move forward successfully,” he wrote.
“Acknowledging this now is in the best interests of those in the campaign; it is in the best interests of this party as we seek to unify around a nominee; and it is in the best interests of the country.”


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Semi serious attempt to understand the core concepts of Machine Learning
What is a PERCEPTRON
A perceptron is an artificial neuron, it was developed between the 50’s and 60’s by the scientist
Frank Rosenblatt. But before we start to describe how an artificial neuron works it is worth to understand how our
biological neurons operate.
“The biological neuron is a nerve cell that provides the fundamental functional unit for the nervous system of all animals”
[1]
That said how does it really work?
Before getting into the functionalities let’s see how a neuron is made. Every neuron is composed by a cell body called
soma. The soma itself is attached to many dendrites but to only one axon. (Fig. 1) The Axon is a very long fiber that eventually
branches to connect to other neurons. The connection between the axon and the neuron is composed by two parts: the synapse
and the dendrite. The synapse process the impulse from the axon and transmits it to the dendrite that will deliver the
impulse to the soma or cell body. The soma reads an interprets the information carried by the impulse and sends a new impulse
to the axon. This procedure goes on and on endlessly. Neurons basically communicate each other via electro-chemical impulses.
The impulse to proceed from one neuron to the other must
“be strong enough to activate the the release of chemicals across a synaptic cleft.“
[2]
The impulse must surpass a the threshold of the synapses otherwise the chemicals will not be released.
How does an artificial neuron work?
As already said at the end of the 50’s Frank Rosenblatt developed the first artificial neuron modeled onto the biological
one: the perceptron. The perceptron is a linear model for binary classification. It takes some inputs, those inputs are
than processed with dot product between themselves and the weights. The result is than fed to the step function that returns
0 or 1. The nature of the output gives the perceptron the possibility to learn to classify between two distinct states.
This means that it is a linear classifier, it can classify things that in a graph can be separated by a line.(???) Before
we deepen into the mathematical and algorithmic concepts regarding the perceptron it is worth to describe what the weights
are. The weights were introduced by Rosenblatt in his perceptron model. Those numbers express
“the importance of the respective inputs to the output.”
[3]
Therefore the weights define which input is more important in order to get a desired output.
The whole purpose of machine learning is to tune the weights of artificial neurons, so that given a set of inputs the neuron
will output a correct answer.
It is important to make clear how important it is to understand the role of the weights. If we look again at the fig. 2 we
see a perceptron. Let’s assume this perceptron can classify whether an animal is a dog or a cat given two inputs value
like size and sound/pitch (assuming that is possible to encode sound in a unique number). The value of the weights determines
the output. In the case of the dog the weights favor the size as a cat has always lower size while cats weights favor the
pitch as cats have an higher pitch than dogs. Of course chiwawas might be confused as dogs. But this because we would need
much more inputs to really classify between the two species.
How does the weights tuning happen?
One of the methodologies for tuning the weights is with the method of
supervised learning. Here there is an great explanation by
Daniel Shiffman
With this method[supervised learning], the network is provided with inputs for which there is a known answer. This way the
network can find out if it has made a correct guess.
If it’s incorrect, the network can learn from its mistake and adjust its weights. The process is as follows:
Provide the perceptron with inputs for which there is a known answer.
Ask the perceptron to guess an answer.
Compute the error. (Did it get the answer right or wrong?)
The the result of the dot product is the sum of the product of each input with its own weight. There is an important thing
to add: the bias. As you can see above the we are dealing with a sum of products. Therefore if we have an input value equal
to 0 it will always return 0. To avoid that we insert a third input called the bias that we set to 1.
On the the other hand the step function will look like this
$$f(x)=\begin{cases}\phantom{}0 & x > 0 \\ 1 & x \leq 0\end{cases}$$
And here the
JavaScript implementation
Therefore if the result of the dot product is greater or equal than 0 the result will be 1 otherwise it will be 0.
So far so good, now we need the last piece, a way to train our perceptron. To do this we will teach the the artificial
neuron to learn from his errors with the following formula:
$$\Delta W = error \times x_{i}$$
And here the
JavaScript implementation
As already mentioned the perceptron is good in classifying linearly separable problems. Than lets consider a very simple
situation where we have a two-dimensional space separated by a wall and we want our perceptron to be able to classify if
the a random point placed in the space is either on one side or on the other.
First we need to build our perceptron.
The
Trainer class will help us in classifying data and labeling it
The first thing to do will be to create a linear function like this one
this will separate the canvas into two parts. Than we create our data-set with labels with the following code
And here is our perceptron classifying if a dot is on one side or the other of the line
If you visualize the perceptron you can see that after enough training the weights will stabilize. When this point is reached
the perceptron is trained and will always recognize if a point is above or below the line.
If you made it until here than tap on your shoulder, you understood how a perceptron works!
Neural Networks
We already mentioned that a a single perceptron can ony solve
linearly separable problems. But what does it mean?
Let's look at the image below:
The image shows two data-sets, the one on the left is linearly separable, similar to our experiment above. The data-set
on the right shows data that is not linearly separable.
Linearly separable data-sets are ones where it is possible to separate the data with a straight line, or a plane in
a three dimensional dataset. However if the data is distributed like in the example on the right side than it is not linearly
separable anymore, and therefore a perceptron will not be able to solve such a problem. A famous non linearly separable
problem is the XOR truth table.
The XOR logical operator returns true if one of the two elements is false, and returns false if they are both either true
or false. It is impossible to draw a single line that separates the true from the false, therefore one perceptron can't
solve such a problem. To do so we need
multi-layered perceptron, a network of many perceptrons.
Gibson, Adam, and Josh Patterson. Deep Learning: A Practitioner’s Approach. Sebastopol CA: O’Reilly, 2017.


Protected methods can be converted to private.
final class User { protected function name() {} // <-- Method can be private }


% include ./partials/head %>
Node Starter App with Mongo
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Adaptation logic in Bitmovin Player
HTML5 Adaptive Streaming Player for MPEG-DASH & HLS
Your videos play everywhere with low startup delay, no buffering and in highest quality.
Max Bitrate (Mbps)
For more information about the bitmovin player, please have a look at our online
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Thomas Michael Wallace
DevicePilot
The first thing I ever designed was a bridge (it’s still standing). These days I can be found leading development for DevicePilot, a startup that helps companies measure the success of their IoT offerings.
Over the last year and a half we’ve delivered a platform entirely based on serverless that lets our customers do ad hoc analysis across nearly 50k devices; and we’re just about to sign-up 200k more. After a year and a half of serverless, I’m always happy to share my mistakes with others.
Talk: We built our own serverless database, and you should too. Probably.
At two in the morning I was rapidly firing up EC2 instances in a desperate attempt to keep our platform alive. It wasn’t working. We were on track to losing our first big customer. We did. But six weeks later, we closed the deal.
This is a story about building a time-series database in lambda. About the agility being able to spin up 3000 machines in an instant can give you. About how you can use that to learn about the product you need to build. And about a team of three developers who no longer hide under their desks every time one of our customers treble in size. It’s also about that sinking feeling you get when you notice bits of your architecture randomly failing- and you realise there’s some important small print in the lambda definition of “scalable”. If you’ve ever wondered what it’s like to build and run your product on map-reduce in AWS lambda. This is the talk for you.


My name is Declan Behan. I am a 2nd year Game Design & Development student attending Rochester Institute of Technology. I am pasionate about front end design, UI/UX, designing games, writing Dungeons' & Dragons adventures, and producing music. I started my gaming journey in my small hometown of Jim Thorpe, Pennsylvania. I was launched into the virtual world of games with my trusty Nintendo DS and a twinkle in my eye. Now I am working towards my own D&D module, working on my second original soundtrack, and a 2D platformer about a boy and an umbrella.
I pride myself in designing experiences that put the user first
With each project I find new and creative ways to implement my ideas
I like to create eye-catching visuals to truely immerse the user
I create sounds and music to show how I beat to my own drum
Want to get in touch with me? That's great! Give me a call, send me an email, or find me in cyberspace. I will get back to you as soon as possible!
© Declan E. Behan 2017. All Rights Reserved. Shout out to: Bootstrap


I got the mix from a fairly reliable website that offered a sampler of different herbal smokes. I managed to get an extra joint containing the fly agaric because my father taught me a lot about the ritualistic power of mushrooms (thanks, dad!) and I wanted to smoke this one with him while I was in town visiting. When I approached him with the joint, he said I should save it with my friends, and that I'd have more fun being on drugs with them. I was a little disappointed, but decided that he was right, and I planned to do it at a small party one of my friends was having the following week.
The day of the party, I started into heavy meditation after a light breakfast. I let my mind wander and run in loops until I felt clear of any distracting thoughts. While going through various breathing exercises, I felt troubled by certain responsibilities I hadn't been keeping up and it stopped me from reaching a comfortable level in meditation. I stayed in a lotus position for hours until I had to get dressed and leave.
When most of my friends got there, we went off to smoke. While they kicked back and smoked pot, I took out the amanita joint and lit it. The first thing I noticed when taking a hit was that the smoke was flavorless, despite containing the raw sewage-tasting Calea, and that I could take as large hits as I want, breathing completely in, and even taking a French Draw (letting the smoke waft out of the mouth and having it be sucked into your lungs through the nose). Smoking itself was so enjoyable that when I was finished with the first joint, I lit up the other and continued smoking.
After I had finished the second joint, I sat back and got involved in conversation. I didn't feel anything right away, but as I talked, I felt conversation flowing more easily and the subjects got more intense. After a half-hour or so, someone suggested taking a walk to the store. As I stood up, I felt all the blood rush to my head, and it stayed there as I walked. From that point on, I stopped talking, and kept on a long-running internal dialogue. I ran through everything that I could think of in my life and told myself that I accept it. I felt myself elevating above all of my problems, petty differences, and trivialities. It was if I were emotionally invincible and that nothing short of death could squelch my peace. This feeling lasted two hours. After that, my fatigue was getting to me and my mind swarmed with thoughts as I slowly crept down from the mental invulnerability.
As a final treat, the Calea that was in the joint gave me amazingly vivid dreams that (while completely irrelevant) finished off the experience with a sense of wonder.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
#data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
input_real = tf.placeholder(tf.float32, shape=(None, image_width, image_height, image_channels), name="input_real")
input_z = tf.placeholder(tf.float32, shape=(None, z_dim), name="input_z")
learning_rate = tf.placeholder(tf.float32, name="learning_rate")
return input_real, input_z, learning_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param image: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
alpha = 0.2
with tf.variable_scope('discriminator', reuse=reuse):
# input layer is 28x28x3 => 14x14x32
conv1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
conv1 = tf.maximum(alpha * conv1, conv1)
# conv2 => 7x7x128
conv2 = tf.layers.conv2d(conv1, 128, 5, strides=2, padding='same')
conv2 = tf.layers.batch_normalization(conv2, training=True)
conv2 = tf.maximum(alpha * conv2, conv2)
# conv3 => 4x4x256
conv3 = tf.layers.conv2d(conv2, 256, 5, strides=2, padding='same')
conv3 = tf.layers.batch_normalization(conv3, training=True)
conv3 = tf.maximum(alpha * conv3, conv3)
# flatten
flatten = tf.reshape(conv3, (-1, 4*4*256))
# logits
logits = tf.layers.dense(flatten, 1)
output = tf.sigmoid(logits)
return output, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
alpha = 0.2
with tf.variable_scope('generator', reuse=not is_train):
fc1 = tf.layers.dense(z, 2*2*512)
fc1 = tf.reshape(fc1, (-1, 2, 2, 512))
fc1 = tf.layers.batch_normalization(fc1, training=is_train)
fc1 = tf.maximum(alpha * fc1, fc1)
conv2 = tf.layers.conv2d_transpose(fc1, 256, 5, strides=2, padding='valid')
conv2 = tf.layers.batch_normalization(conv2, training=is_train)
conv2 = tf.maximum(alpha * conv2, conv2)
conv3 = tf.layers.conv2d_transpose(conv2, 128, 5, strides=2, padding='same')
conv3 = tf.layers.batch_normalization(conv3, training=is_train)
conv3 = tf.maximum(alpha * conv3, conv3)
logits = tf.layers.conv2d_transpose(conv3, out_channel_dim, 5, strides=2, padding='same')
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
gmodel = generator(input_z, out_channel_dim)
dmodel_real, dlogits_real = discriminator(input_real)
dmodel_fake, dlogits_fake = discriminator(gmodel, reuse=True)
dloss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=dlogits_real, labels=tf.ones_like(dmodel_real)))
dloss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=dlogits_fake, labels=tf.zeros_like(dmodel_fake)))
gloss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=dlogits_fake, labels=tf.ones_like(dmodel_fake)))
dloss = dloss_real + dloss_fake
return dloss, gloss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
t_vars = tf.trainable_variables()
dvars = [ var for var in t_vars if var.name.startswith('discrim')]
gvars = [ var for var in t_vars if var.name.startswith('generat')]
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
dtrain_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=dvars)
gtrain_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=gvars)
return dtrain_opt, gtrain_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
steps = 0
show_every = 100
print_every = 10
n_samples, width, height, channels = data_shape
input_real, input_z, lrate = model_inputs(width, height, channels, z_dim)
dloss, gloss = model_loss(input_real, input_z, channels)
dopt, gopt = model_opt(dloss, gloss, lrate, beta1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
batch_images *= 2
steps += 1
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
_ = sess.run(dopt, feed_dict = {input_real: batch_images, input_z: batch_z, lrate: learning_rate})
_ = sess.run(gopt, feed_dict = {input_real: batch_images, input_z: batch_z, lrate: learning_rate})
if steps % show_every == 0:
n_images= 25
show_generator_output(sess, n_images, input_z, channels, data_image_mode)
if steps % print_every == 0:
dloss_train = dloss.eval({input_z: batch_z, input_real: batch_images})
gloss_train = gloss.eval({input_z: batch_z})
print("Epoch/Steps {}-{}/{} ... ".format((epoch_i+1), epoch_count, steps),
"Discriminator Loss: {:.4f} ... ".format(dloss_train),
"Generator Loss: {:.4f} ... ".format(gloss_train))
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 64
z_dim = 100
learning_rate = 0.001
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 64
z_dim = 100
learning_rate = 0.001
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.
print ("Hello")


Biman Bangladesh orders two more Boeing 787 Dreamliner jets
by bdnews24.comBiman Bangladesh Airlines is set to buy two 787-9 Dreamliner jets valued at $585 million at list prices.
Biman and Boeing announced the deal the 2019 Dubai Airshow on Sunday, according to a statement.
Prime Minister Sheikh Hasina, who had inaugurated the four 787-8 Dreamliner airplanes of Biman in Dhaka, is currently in Dubai to watch the airshow.
The new purchase was recorded in October as an unidentified order on Boeing's website.
It complements Biman's fleet of 787-8 jets with the larger and longer-range 787-9 variant.
The national flag carrier of Bangladesh says the addition of the 787-9 will help modernise its fleet and expand its international network, according to the statement.
"One of our key priorities is to have a modern fleet with technologically-advanced airplanes that will enable us to expand our international reach," Biman Chairman Muhammad Enamul Bari said in the statement.
"While we have a good domestic network, we plan to extend our international network to include more destinations in Europe, Asia and the Middle East. The 787 with its technological superiority, excellent operational performance and passenger experience will enable us to achieve that goal," he added.
The 787-9 is part of a three-member family that offers long range and unmatched fuel efficiency in the 200 to 350 seat market.
For Biman Bangladesh, the 787-9 can carry 298 passengers in a standard three-class configuration and fly up to 7,530 nautical miles (13,950km) while reducing fuel use and emissions by up to 25 percent compared to older airplanes.
Stan Deal, president and chief executive officer, Boeing Commercial Airplanes, said Biman was showing the planemaker “the powerful potential of the Dreamliner family”.
“Just last month, the airline launched a new non-stop flight from its hub in Dhaka to Medina, Saudi Arabia. It's a great example of the 787-8 serving as a 'market opener.' And now, Biman adds the 787-9 which brings more seats, more range and more cargo-carry capability for the routes that need it. The two will form a profitable network solution for Biman."
The statement said Boeing also provides services that help Biman operate more efficiently.


Counter: [[counter]]
Date: [[clock]]
Variables `clock` and `counter` should get passed into foo and baz components that can render them. This is controlled by `passDownwards` property on uiview
Links:


HANDCRAFTT
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Websites in a day
Lansing and Grand Rapids
Lansing, Michigan
(269) 301-0060
<EMAIL_ADDRESS>Lansing, Michigan
(269) 301-0060
<EMAIL_ADDRESS>Each package brings you a complete website and web presence in one day. The personal communication and collaborative process of our work is what sets us apart. What we offer is more than a presence on the web, it is also a lesson in making the most of the tools you'll be using.
We sit down one-on-one at a coffee shop and crank out a website and full web presence for your business, the coffee is on us. Here's what this package includes:
We come to your place of business, and set up your web presence as you go about your normal workday. If you do not have a physical location, other arrangements will be made. This is the complete package, and it includes:
Take a look at our full list of services, or shoot us an email at<EMAIL_ADDRESS>InterKnights was created to help those who are unable to pay the premium rates that are so often charged by web development and marketing firms. We realized that in many cases, the cost of a website alone may set a small business back so far that it ends up hurting, rather than helping. The odds are already stacked against the small business owner, so we have made it our mission to tip the scales. The Interknights believe that the web can be a balancing force, and our mission is to empower all people to grow and flourish using the greatest tool equal opportunity has ever had, the internet.


IP-based phone service providers to use technology to tackle caller ID spoofing
by The Canadian PressOTTAWA -- Canadian providers of Internet-based phone systems should be able to adopt new technology next year that goes after fake phone calls.
The Canadian Radio-television and Telecommunications Commission says telecom companies should be able to implement systems by Sept. 30, 2020 that will help consumers using IP-based phone systems verify whether incoming calls are from legitimate people, businesses or government agencies.
The regulator says the new technology is already used in parts of the United States.
The impact for many Canadians will be limited because the technology isn't available for traditional land-lines or mobile phones.
The regulator says about 40 per cent of the complaints it receives each year are about caller ID spoofing, which allows callers to hide their real number by displaying a fake one on the receiving phone.
The Canadian Anti Fraud Centre estimates Canadians have lost millions of dollars to scams artists who use computer programs to spoof legitimate telephone numbers, including numbers used by the Canada Revenue Agency, Service Canada and even local police.
This report by The Canadian Press was first published Dec. 9, 2019.


eol — Convert/filter end-of-line sequences
eol [eol string]
eol is a filter that reads lines from the input stream and writes them to the output stream with converted end-of-line sequences. The eol command takes a single string for its command-line argument which specifies the exact pattern to use.
The command string can be an arbitrary-length string of the following:
| c | the character 'c' |
| \a | alert (or bell) |
| \b | backspace |
| \f | formfeed |
| \n | newline (or line feed) |
| \r | carriage return (cursor return to left margin) |
| \t | horizontal tab |
| \v | vertical tab |
| \0 | null (byte zero) |
| \ooo | octal number ooo |
| \xhh | hexadecimal number hh |
| \\ | back-slash |
Note that eol interprets input end-of-lines as a sequence of zero or one of \r, \n, and \0. For example, \r\n\n\r\0\n would be interpreted as three EOLs as follows \r\n, \n\r\0, \n.
To convert file1 to file2 for use on Unix systems, run the following command:
eol \n <file1 >file2
To convert file1 to file2 for use on MSDOS systems, do this:
eol \r\n <file1 >file2
Suppose that you're monitoring output from a command that spits out lines every once in a while and you want each new line to beep when it comes out:
tail -f <syslog | eol \a\n
If you want to double-space a text file:
eol \r\n\r\n <file1 >file2
eol doesn't seem to flush correctly in MSDOS. For example, if the eol string is "\a", the first output line will not beep until the second line is processed.


Dropdown
Action
Another Action
Something else here
Separated link
Dropdown
Action
Another Action
Something else here
Separated link


Branching Strategy
Release Managers
A release manager is responsible for:
- Creating stable branches
- Creating and gathering feedback on release candidates
- Cutting "pre" releases
These responsibilities are described below.
Release Cadence
At least one release begins every week, based on a weekly release manager rotation maintained in a shared calendar (to volunteer to be added to the calendar, see How to Contribute).
The release manager decides whether to cut "stable" or "pre" releases (or both!) based on the following criteria:
- If it is an "even" week, decide whether to cut a new stable release branch:
- To determine whether to create a new stable branch (and release candidate), the release manager should inspect changes that have landed in master since the previous stable branch was cut, and decide whether they justify a new branch (this is intentionally left a bit open-ended). If a new branch is justified, it will be either a Major or Minor stable branch (described below).
- If (for any reason) a new stable branch is not created, either because it is an "odd" week, or because the release manager decided there was insufficient change in master to justify the stable vetting process, the release manager must cut a pre release from master.
- In addition to any pre or newly-created stable branches, the release manager should determine whether any existing stable branches need new release candidates by inspecting the Pants Backport Proposals sheet. If there are requests "sufficient" to justify a new patch release for existing stable branches, the release manager should cut release candidates for those branches.
In other words: either a pre release or a new stable release branch is created each week, and additionally, patch releases for existing stable branches might be created.
Release Types
Pre releases
Pre releases are releases that occur directly from master, without the additional vetting that is applied to stable releases. They help to ensure a steady release cadence from master by filling in the gaps between the (generally more time consuming) stable releases.
Stable releases
Stable release candidates generally happen every two weeks, provided that there are enough user facing changes to warrant a new stable release. Of those two weeks, five business days are allocated to bugfixing and testing by pants contributors on a release candidate announcement thread (described below).
Major and Minor Stable releases
The decision to do a major or a minor release is based on the impact of the changes.
Major releases signify large or breaking changes. Minor releases however should be compatible
with the last two minor releases. In other words if a feature is deprecated in version 1.2.x
you
should be able to continue using that feature at least through version 1.4.0
.
Patch Stable Releases
In order to allow us to react quickly to bugs, patch fixes are released as needed and should
always consist of fixes backported from master. These releases update the patch version number,
(ie, from 1.0.x
to 1.0.y
) and should only include commits from the Pants
Backport Proposals that are deemed to be
backwards compatible.
Naming conventions
Stable naming
Leading up to a stable release, development work should be done on a branch in the following format:
n.n.x
where n.n are the major/minor version numbers and "x
" is a literal character placeholder for
the patch version. Example: "the 1.1.x
stable branch".
Pre naming
Pre releases occur between stable releases, and are differentiated by a -preN
suffix. The pattern
to follow is N.N.0-preN
, where N.N
are the next major/minor stable branch that will be created
and N is the next sequential number starting from 0
. Example: "the 1.1.0-pre0
pre release".
Workflow Examples
-
Leading up to the release of
1.0.0
the release manager would create a stable branch with the literal name "1.0.x
". When finalizing the1.0.0
release in that1.0.x
branch, they would tag the commit with the release version ofv1.0.0
. -
If a release manager had a bugfix from master that they needed to backport to the
1.0.x
minor stable branch, they would cherry-pick the commit to the1.0.x
branch, run a series of release candidates, and finally tag the commit with a new patch version (iev1.0.1
) when it was ready for release. -
If pre releases were required after having cut the
1.0.x
stable branch, but before having cut the1.1.x
stable branch, then they would start with1.1.0-pre0
, and continue weekly to1.1.0-preN
until the1.1.x
stable branch had been cut.
Stable Release Candidates
In order to make a stable release, the release manager needs to create a release candidate for other parties to test. Once the release candidate has been created and announced, the release manager should allow five business days for contributors to raise concerns on the release candidate announcement thread. During those five days, the release manager should expect to perform multiple release candidates until no more blockers are raised against a particular release candidate... at which point, the final version of that release can be cut.


Faceting
For a good overview of what facets are see the original docs on the subject.
Specify facets during search.
In its simplest form you can add a facet to your query like this:
var queryResults = this.ConnectedClient.Search<ElasticSearchProject>(s=>s
.From(0)
.Size(10)
.MatchAll()
.FacetTerm(t => t
.OnField(f => f.Country)
.Size(20)
);
);
Adding more then one facet is also really easy:
var queryResults = this.ConnectedClient.Search<ElasticSearchProject>(s=>s
.From(0)
.Size(10)
.MatchAll()
.FacetTerm(t => t
.OnField(f => f.Country)
.Size(20)
);
.FacetTerm(t => t
.OnField(f => f.Author)
.Size(20)
);
);
Nest supports all the additional properties you can set on facets
var queryResults = this.ConnectedClient.Search<ElasticSearchProject>(s=>s
.From(0)
.Size(10)
.MatchAll()
.FacetTerm(t => t
.OnField(f => f.Country)
.Size(20)
.Order(TermsOrder.reverse_count)
.Exclude("term1", "term2")
.AllTerms()
.Regex(@"\s+", RegexFlags.DOTALL)
.Script("term + 'aaa'")
.ScriptField("_source.my_field")
);
.FacetDateHistogram(h => h
.OnField(f => f.StartedOn)
.Interval(DateInterval.Day, DateRounding.Half_Floor)
.TimeZones(Pre: "-3", Post: "-4")
)
);
Allowing you to take advantage of all the cool facets stuff built in to elasticsearch.
Getting to your facet
If you are interested in the facet meta data (such as missing, total) you can use the following methods:
var facet = queryResults.Facet<TermFacet>(p=>p.Followers.Select(f=>f.LastName));
this will return a TermFacet
object which has an .Items
property holding all the facets.
queryResult
also holds a .Facets
dictionary one can use to itterate over the facets returned from the query.
Shortcut to facet items
To get the facet items for followers.lastName
the prettiest way to get them is.
var facets = queryResults.FacetItems<FacetItem>(p=>p.Followers.Select(f=>f.LastName));
NEST will infer the right key from the specified lambda. You can also opt for specifying the name directly.
var facets = queryResults.FacetItems<FacetItem>("followers.lastName");
NOTE more types then just term facets are supported see the 'Corresponding Types' section
Corresponding Types
The following lists the elasticsearch facet type and their corresponding NEST strongly typed class
terms_stats => TermStatsFacet
statistical => StatisticalFacet
terms => TermFacet
histogram => HistogramFacet
date_histogram => DateHistogramFacet
range => DateRangeFacet, RangeFacet
geo_distance => GeoDistanceFacet
query => QueryFacet
filter => FilterFacet
See also each individual facet's documentation
Spotted a mistake? Something to add? Fork and edit this file on github without leaving the browser


The impact of the front door on its frame shook the house; the noise made the panes of glass sing in the rickety window at the top of the stairs, and return to Rhys like the echo of a cry. Another day of wandering round, trying to be left alone, in vain. Every few steps Rhys would draw a bead from one stone to another, trying to lose himself in calculations, only to find either the line or his own body jostled by some gabbling pakamak. Rhys had been gloomily glad when the rain had started to fall on him, present as he was with no jacket of his own, as it gave him the excuse to quit, and not prolong his disappointment.
He made to go upstairs, then paused, as a thought began to form. He turned back and locked the door. The idea began to rush upon him that the day might not be wasted yet; that fired his muscles and raised the hairs on the back of his neck. Though he was still stood at the bottom of the stairs his mind was already up in his room, and his body had to catch up: quick. Hammering his feet on each step he could hear the blood pound just as hard in his ears, and felt his stomach drop away from him. He tilted forwards towards the top step and flapped his hands on the carpet to propel himself faster, flailing up in a four-limbed tangle; his mind was above, his lights down below, and his arms and legs all over the place.
At the top of the stairs, Rhys grabbed the handle, opened his bedroom door, left it to bang shut behind him, lifted up his duvet, pulled out the trunk, clicked its locks free, lifted the lid and grabbed the large scroll and purse within. He slowed then, arranging the objects with reverence, as if they were more than priceless: magical or masonic, full of invisible energy. He unfurled the scroll across his bed, weighing down each corner with four pebbles from the purse.
The museums could afford their intricately carved models, but this paper map, wide as two arm-lengths and tall as one, was as useful to Rhys as any of them might have been. More so, for his purposes, for his design emphasized what theirs did not; indeed, could not, because the museum curators were uninterested to the point of blindness in what Rhys had discovered.
Although the plan of the stones themselves still existed in thin pencil lines, occasionally redrawn and darkened where they had become too faint, such a detail was no longer the main purpose of the diagram. These mere details of geography were covered with lines in several different colours: green, red and blue for astronomical directions; but also two shades of purple and three of orange-to-yellow, each of which denoted a different flavour to a survey that Rhys had taken across the site, with shades of meaning that Rhys understood but could not put into words. Sometimes they overlapped with ley lines, or the paths between the nearby villages, or both; sometimes they had been made on a particular day, at a particular time, and produced an effect pleasing to Rhys. The trilithons, the Heel Stone, and the Slaughter Stone were all marked with deep-blue crosses, whilst the Aubrey holes, a circle of 56 post holes some 80 metres in diameter, were coloured in a particularly vivid red.
Rhys had guessed some time ago that each successive rebuilding of the stones had been a refinement on some principle of the previous construction. And what he had seen a few weeks ago—or, more precisely, not been able to see—had finally confirmed to Rhys what that principle was. As he knelt down by the chart on his bed, he tried to picture where he had been that time, and where he had been looking; with that in mind, even the scant and incomplete observations he had been able to make today might have some value.
Deep in thought, he marked places from time to time on the map with flattened glass marbles. Often the juxtaposition of two or three or even four such markers led him to put another down in their centre, or on a particular axis of conjunction. Occasionally, with a triumphant flourish, he would take a ruler and pencil from his desk and affirm the arrangement with a permanent line.
In this way Rhys spent the next few hours playing what appeared to be some ancient boardgame against himself, thrilling at his own victories, excited even further by his failures. The day darkened and became evening; his parents came home and called up to him; the television went on, and once or twice the telephone rang; but nothing penetrated Rhys' concentration, as he wondered again and again about the next move he might make.


Press n or j to go to the next uncovered block, b, p or k for the previous block.
| File | Statements | Branches | Functions | Lines | |||||
|---|---|---|---|---|---|---|---|---|---|
| cart.js |
|
41.94% | 26/62 | 0% | 0/10 | 33.33% | 2/6 | 41.94% | 26/62 |
| checkout.js |
|
38.1% | 16/42 | 12.5% | 1/8 | 20% | 1/5 | 38.1% | 16/42 |
| clothing.js |
|
80% | 4/5 | 100% | 0/0 | 0% | 0/1 | 80% | 4/5 |
| deals.js |
|
94.12% | 16/17 | 50% | 1/2 | 100% | 3/3 | 94.12% | 16/17 |
| grid-page.js |
|
80% | 4/5 | 100% | 0/0 | 0% | 0/1 | 80% | 4/5 |
| help.js |
|
100% | 5/5 | 100% | 0/0 | 100% | 1/1 | 100% | 5/5 |
| index.js |
|
36.27% | 37/102 | 11.43% | 4/35 | 41.18% | 7/17 | 36.63% | 37/101 |
| list-page.js |
|
80% | 4/5 | 100% | 0/0 | 0% | 0/1 | 80% | 4/5 |
| login.js |
|
100% | 5/5 | 100% | 0/0 | 100% | 1/1 | 100% | 5/5 |
| orders.js |
|
80% | 4/5 | 100% | 0/0 | 0% | 0/1 | 80% | 4/5 |
| product.js |
|
70.59% | 12/17 | 0% | 0/2 | 33.33% | 1/3 | 70.59% | 12/17 |
| profile.js |
|
50% | 18/36 | 10% | 1/10 | 33.33% | 2/6 | 50% | 18/36 |
| register.js |
|
100% | 5/5 | 100% | 0/0 | 100% | 1/1 | 100% | 5/5 |
| users.js |
|
80% | 4/5 | 100% | 0/0 | 0% | 0/1 | 80% | 4/5 |
| wishlist.js |
|
100% | 5/5 | 100% | 0/0 | 100% | 1/1 | 100% | 5/5 |


todos
Mark all as complete
This is a paragraph, yo.
And this here
is inside jaja jaja a paragraph
, about paragliders.


Due by 23:59:59 pm on Monday, April 24th, 2017
Running Ocaml
Use ocaml
on ieng6 to edit, run and submit the code.
Overview
The objective of this assignment is for you to gain some hands-on experience with OCaml. All the problems require relatively little code ranging from 2 to 15 lines. If any function requires more than that, you can be sure that you need to rethink your solution.
The assignment is in the single file
that you need to download, if you wish, by using wget
:
$ wget https://ucsd-cse130.github.io/web/static/raw/hw1.ml
The file contains:
- skeleton functions with missing bodies that you will fill in,
- sample tests, and,
- testing code that you will use to check your assignments before submitting.
You should only need to modify the first part of the file, which has expressions of the form:
failwith "TBD: ..."
Your task is to replace those expressions with the appropriate Ocaml implementations.
Note: All the solutions can be done using the purely functional fragment of OCaml, using constructs covered in class, and most require the use of recursion. Solutions using imperative features such as references, while loops or library functions (other than those explicitly allowed) will receive no credit. It is a good idea to start this assignment early; ML programming, while quite simple, when you know how, may seem somewhat foreign at first, particularly when it comes to recursion and list manipulation.
Assignment Testing and Evaluation
Your functions/programs must compile and run with ocaml
on ieng6.ucsd.edu
.
Most of the points, will be awarded automatically, by evaluating your functions against a given test suite. hw1.ml contains a very small suite of tests which gives you a flavor of of these tests.
At any point, on the bash shell, enter:
$ ocaml hw1.ml
or if you are in the Ocaml REPL enter:
# #use "hw1.ml" ;;
to get a report on how your code stacks up against the simple tests.
The last line of the shell must look like:
130>>Compiled
- : int * int = (SCORE, TOTAL)
where SCORE
and TOTAL
are a pair of integers, reflecting your score and the max possible score on the sample tests.
If instead an error message appears, your code will receive a zero.
If for some problem, you cannot get the code to compile, leave it as is with the failwith ...
with your partial solution enclosed below as a comment.
The other lines will give you a readout for each test. You are encouraged to try to understand the testing code, but you will not be graded on this.
Submission Instructions
To submit your homework (on ieng6
), enter:
$ turnin -c cs130s -p 01 hw1.ml
turnin
will provide you with a confirmation of the submission process; make sure that the size of the file indicated by turnin
matches the size of your file. See the ACS Web page on turnin for more information on the operation of the program.
Problem 1: Digital Roots and Additive Persistence
(a) 10 points
Write an OCaml function
val sumList : int list -> int
that such that sumList xs
returns the sum of the integer elements of xs
. Once you have implemented the function, you should get the following behavior at the OCaml prompt:
# sumList [1; 2; 3; 4];;
- : int = 10
# sumList [1; -2; 3; 5];;
- : int = 7
# sumList [1; 3; 5; 7; 9; 11];;
- : int = 36
(b) 10 points
Write an OCaml function
val digitsOfInt : int -> int list
such that digitsOfInt n
returns []
if n
is not positive, and returns the list of digits of n
in the order in which they appear in n
. Once you have implemented the function, you should get the following behavior at the OCaml prompt:
# digitsOfInt 3124;;
- : int list = [3; 1; 2; 4]
# digitsOfInt 352663;;
- : int list = [3; 5; 2; 6; 6; 3]
(c) 10+10 points
Consider the process of taking a number, adding its digits, then adding the digits of the number derived from it, etc., until the remaining number has only one digit. The number of additions required to obtain a single digit from a number n
is called the additive persistence of n
, and the digit obtained is called the digital root of n
.
For example, the sequence obtained from the starting number 9876
is 9876
, 30
, 3
, so 9876
has an additive persistence of 2
and a digital root of 3
.
Write two OCaml functions
val additivePersistence : int -> int
val digitalRoot : int -> int
that take positive integer arguments n
and return respectively the additive persistence and the digital root of n
. Once you have implemented the functions, you should get the following behavior at the OCaml prompt:
# additivePersistence 9876;;
- : int = 2
# digitalRoot 9876;;
- : int = 3
Problem 2: Palindromes
(a) 15 points
Without using any built-in OCaml functions, write an OCaml function:
val listReverse : 'a list -> 'a list
such that listReverse xs
returns the list of elements of xs
in the reversed order (in which the appear in xs
.) Once you have implemented the function, you should get the following behavior at the OCaml prompt:
# listReverse [1; 2; 3; 4];;
- : int list = [4; 3; 2; 1]
# listReverse ["a"; "b"; "c"; "d"];;
- : string list = ["d"; "c"; "b"; "a"]
(b) 10 points
A palindrome is a word that reads the same from left-to-right and right-to-left. Write an OCaml function
val palindrome : string -> bool
such that palindrome w
returns true
if the string is a palindrome and false
otherwise. You may use the given helper function explode
. Once you have implemented the function, you should get the following behavior at the OCaml prompt:
# palindrome "malayalam";;
- : bool = true
# palindrome "myxomatosis";;
- : bool = false