Saturday's Bets of the Day: Selections for racing, football, golf and F1
by Sporting LifeSaturday's Bets of the Day include horses at Cheltenham, Lingfield and Wetherby, a dual forecast in the golf and an EFL tip from Tom Carnduff.
- Click on tipster name for full preview
- Click on selection to populate betslip
Racing
Matt Brocklebank
Bad ground won’t hold any fears for COUNT MERIBEL in the BetVictor Gold Cup and he’s taken to give trainer Nigel Twiston-Davies another victory in the prestigious handicap chase.
Always at his peak around this time of the year, the seven-year-old showed heart and guts to win last year’s Steel Plate And Sections Novices’ Chase despite stumbling and almost coming down two fences from home.
He made a pleasing start to the current campaign when chasing home potential superstar Lostintranslation at Carlisle and that race has been a nice pointer for this over the year.
From a rating of 146 he looks ahead of his mark back in the handicap ranks at a course he seems to love.
Hugo Palmer’s GIFTED MASTER has plenty going for him in Saturday’s Betway Golden Rose Stakes.
He won this Listed sprint in 2017 and was an unfortunate fifth last year (trouble in running) so we know he’s suited to the track, while stall five this time looks to give him the perfect opportunity to attack from the front.
The six-year-old – a Group Three winner at his peak and Stewards’ Cup hero in 2018 – was out of form on turf during this year but he returned from a break with a fine second behind War Glory over seven furlongs here last month.
That should put him right for another crack at this race, in which he comes out best at the adjusted weights.
David Ord
The handicapper was kind on FINGERONTHESWITCH by dropping him a pound for finishing third at Cheltenham last time and he can take advantage by landing the William Hill Best Odds Guaranteed Conditional Jockeys' Handicap Chase at Wetherby.
The nine-year-old only cried enough after the last behind Petite Power and his revised mark is just a pound above the one from which he won at Worcester in June.
He’s a course-and-distance winner here who handles soft ground and is fancied to get the better of his three rivals, the most dangerous of which is the reappearing Bigirononhiship for the resurgent Rose Dobbin team.
You won’t get rich backing Philip Hobbs’ THYME HILL but he’s a very exciting prospect who should take the opening Ballymore Novices' Hurdle en route to even better things.
He was one of last season’s leading bumper performers, finishing third behind Envoi Allen here at the Festival, and there was an awful lot to like about his hurdling debut at Chepstow.
Jumping fluently in midfield he showed bravery to make his challenge between horses and then powered clear in the closing stages to beat Fiddlerontheroof by two lengths.
He’s the sort to continue progressing and two miles five furlongs on soft ground at this track should prove ideal. A five-pound penalty doesn’t make life easy but he’s fancied to defy it with L’Air Du Vent, third here at the Showcase Meeting, the main threat.
John Gosden could have a tremendous day at Lingfield on Saturday and the rapidly-improving ALRAJAA forms part of the strong team for the Clarehaven handler.
Three from four since being gelded, including his last two in handicaps off marks of 82 and 93, the son of Dubawi is on a steep upward curve and those last two wins were on the all-weather as well.
He did them for speed last time at Kempton off a slow early gallop, but that bodes well for a Lingfield handicap and a 7lb rise may well not be enough to stop him rattling in the four-timer.
If Cheltenham gets the green light conditions are going to be tough and they could be a great leveller in the mallardjewellers.com Novices’ Chase at 1.15.
Wholestone sets the standard but jockey Daryl Jacob has concerns about the combination of the trip and the ground and it could well bring the others into it.
Step forward POKER PLAY for David Pipe, a horse that loves heavy ground (won on such conditions twice) and who looked in great nick when winning on his seasonal reappearance, on just his second start over fences, in the Ffos Las mud last time out.
Pipe and the owners had options for this race and the fact they go with Poker Play could be a tip in itself.
Football
SAM SMITH will see this as a real opportunity to add to the seven goals he has already scored this season. The 21-year-old is on-loan from Championship side Reading and has established himself as a key part of this Cambridge side.
He's also seeing plenty of opportunities on goal and should perhaps have a higher tally than he does at this point. Regardless, the current amount of goals will still be pleasing to all parties concerned.
His 22% goal conversion rate is a big positive when compared to other strikers in the league. A general price of above 2/1 is available on Smith striking in 90 minutes.
Formula One
CHARLES LECLERC will be taking a 10-place grid drop in Brazil as a result of an engine change, so his hopes of a podium finish have diminished. But the gap between the top three teams and their midfield counterparts is such that a top six finish is a formality as long as Leclerc stays out of trouble.
If all went to plan he'd be adrift of the Mercedes, Red Bulls and the other Ferrari but so far clear of seventh place that a late pit-stop for fresh tyres wouldn't lose him a place as the laps ticked down.
Leclerc has taken fastest lap honours four times this season, including at the last two events; 3/1 for the hat-trick can't be argued with.
Golf
Ben Coley
Zander Lombard is a big talent with a bright future, but converting a halfway lead in the Nedbank Challenge is probably beyond him at this early stage in his career.
The 24-year-old is yet to win on the European Tour, so to break through in a big-money, Rolex Series event, one known as 'Africa's major', will require both skill and heart and he might just be found wanting for now.
That ought to leave the way clear for pre-tournament tip Louis Oosthuizen, but he's short enough now at 13/8 and the best value going is the 7/1 quoted about him and Tommy Fleetwood filling the first two places.
This is a leaderboard lacking in experience at a course which tends to require it, so after a solid first 36 holes we should expect Fleetwood to hang around for the final 36, advancing his score towards 10-under.
That may yet be a competitive total, even if Lombard enters the weekend on 11-under, and it could well secure second place for the Englishman, who remains the chief threat to Oosthuizen.
There are slight concerns over fatigue - it's been a long year for the world number 18 - but being just off the pace will help on Saturday and he's a lively contender to make a significant move.
Hopefully, Oosthuizen does the same and goes on to dominate, which he threatened to do with an opening 63. Certainly, I'd expect him to bounce back from a low-key Friday and if anyone does beat him, they'll likely win the thing.


Movies always leave outstanding impressions on me. From big blockbusters to indie films, each movie
brings a unique and enjoyable experience. I am interested in movie related datasets because there are a lot
of relationships and patterns between movies and their corresponding information and data. There
are countless questions that can be hinted at from such datasets. Some of them question the popularity of
movies against certain review scores. I wonder if certain MPAA ratings like G, R, or NC-17 can affect the
score of films and or its gross in the box ofice. How have movies and rating groups done well in terms of
gross and scores over the years?
Note: The selection from this dataset consists of movies with the country entry of 'USA', non-null
MPAA content ratings, and year entries from 1916 to 2016. The original IMDb Dataset selection, which
consists of around 5000 records can be found
here. The massaged dataset with
the previous conditions applied can be seen here, and the original dataset
can be seen here. This selection and dataset does not represent
all movies and is just a small sample set. All findings will not hold the same if the sample size
included all movies produced.
Above is the distribution of movies in my dataset grouped by year of release. There is a minimal amount of movies before the 1970s and 1980s, where blockbusters started dominating the cinema landscape. Since the original dataset was collected in late 2016, the amount of films from that year in retrospect to previous years is significantly less.
Similar to the previous visualization, this is the distribution of movies grouped by its IMDb score as voted by IMDb users. Unlike the year distribution, the IMDb score distribution fits to a nice curve maxing out at a 6.7 rating. What is important is that as we get closer to the ends of the distribution, the number of films for each rating decreases. There is one film each at the two highest ratings: The Shawshank Redemption at 9.3 and The Godfather at 9.2. (Side Note: The Shawshank Redemption is currently at 9.2 and tied with The Godfather as of 2017.)
This pie chart provides the distribution of movies grouped by MPAA Rating. The majority of films have a content rating of either PG, PG-13, or R. This is can explained by the notion that movies released in theaters today are mostly either G, PG, PG-13, or R rated, with the majority of them being the last two. Movies that are rated NC-17 or Unrated usually do not get released in theaters and are usually shown at festivals or are released straight to DVD or streaming services.
Movie budget to box office gross in the United States is compared in the above scatterplot. There are many films that are made with a budget less than $100,000,000, and a good majority of them make their money back in gross with a good amount making double or triple the budget in gross. While there are some outliers for massive blockbusters, some errors in the data can be seen where the gross or budget of a film is $0. As noted from the dataset scraper author, the dataset could not retrieve all values in some of the fields for movies, so they are filled with the value $0.
The heatmap shows the summed gross of movies grouped by IMDb score and MPAA rating. The summed gross value range is represented by the color yellow to blue, where yellow is the lowest and blue is the highest. Since it is the summed gross, the highest values mostly occur in groupings that contain the most amount of movies. The ratings PG, PG-13, and R contain the highest summed gross for films and this can be attributed to the earlier explanation where these films are the most popular films released in theaters, which generates income thus allowing opportunities to become blockbuster hits.
In the series chart above, each line represents a specific MPAA rating's average IMDb score across each year. One thing to notice is that from this visualization, some of the ratings in the past were used and discontinued, such as Approved and Passed. It appears that the overall trend of average IMDb scores decrease over the years due to a number of reasons, but is not truly represented for all data. I believe that for this dataset, there are outliers for earlier dated films, in which some of them may have been well received and or are popular films. Another reason could be due to the fact that IMDb is a website that existed since the start of the internet. The demographic of users on IMDb are most likely just as old as the internet so many of these users may rate only films made after 1990 or similar. This in turn causes a slight decrease in average rating.
Above is a line chart with two interesting lines for average domestic box office gross and for average
number of IMDb votes for movies grouped by IMDb rating. One thing I wanted to look at is the popularity of a
movie defined by the amount of people who paid to see the movie as shown by the gross line, and the amount of
people who have rated it on IMDb which should be sometime after they have seen a movie. There seems to be a
trend of the average gross increasing as each IMDb score goes higher. This can be explained with people wanting
to spend their money on watching good movies. The exceptions are the drastic outliers at both sides of the
chart. The lowest score on the graph is a 1.6 for the infamous hit movie Justin Bieber: Never Say Never, while
at the other side is the critically acclaimed but non-blockbuster The Shawshank Redemption. These outliers are
caused solely to the fact that so few movies are near these ratings. A reason as to why there is a sudden
increase in IMDb votes beyond IMDb scores of 8.0 is due to the fact that these movies start to hit the IMDb Top
250 Movie list.
From the visualizations overall, I have found that even though this dataset selection is somewhat flawed with
some errors and even though it does not represent all movies ever made, this dataset selection does provide
some nice information about movies released in theaters, their introduction to popularity and revenue numbers,
and its relation to scores and ratings by the MPAA and by users on IMDb.


Hackage download ranking.
Hackage has {{nPackages}} packages as of {{lastUpdate | date:mediumDate }}.


Modes
- Active/active
- Active/passive
Active/active
- Users and client application send requests to both datacenters
- After write on first DC, data need to be immediately visible for reading on second DC
- Default settings
- Worse performance
Active/passive
- Users and client application send requests to one datacenter
- Second DC used just as a backup in case of failure of first DC
- Better performance
Communication details
- 5 infinispan clusters with 2 datacenters setup
- Keycloak nodes cluster in site1
- JDG nodes cluster in site1
- Cluster between JDG nodes on site1 and on site2 (JGroups RELAY2 protocol and backup caches)
- JDG nodes cluster in site2
- Keycloak nodes cluster in site2
- Keycloak sends message to JDG server on same site
- JDG server sends it to JDG server in the other site through RELAY2
- JDG server on second site propagates it to Keycloak servers on second site through HotRod protocol (Client listeners)
- Keycloak servers listen to events through client listeners and do appropriate actions (Invalidate cache, update session caches)
- Communication between JDG servers from different sites through RELAY2 protocol.
- RELAY2 protocol - configured in JGroups subsystem on JDG server side
- Infinispan caches on JDG side - "backup" element
- Keycloak nodes and JDG nodes in same DC communicate through HotRod protocol
- Keycloak infinispan caches are configured with "remote-store" in standalone-ha.xml
- JDG server needs to have caches with same names configured on it's side
Basic setup
- Documentation: http://www.keycloak.org/docs/latest/server_installation/index.html#setup
- Recommended to try this to understand cross-dc better
Cross-DC deployment administration
Recommended startup order
- Replicated databases in both datacenters
- JDG servers in both datacenters
- Keycloak servers in both datacenters
Requirements
- Keycloak requires database in its DC to be running
- Keycloak requires (at least one) JDG server in its DC to be running
- Keycloak doesn't strictly need JDG server on the other DC to be running, but it's recommended
- If JDG server on other DC is not running, it means that second datacenter is "offline" from first datacenter PoV
Taking site offline
- Datacenter "site2" is considered offline from the "site1" PoV if:
- There are no running JDG servers in site2
- Network between site2 and site1 is broken
- "Take site offline" = make sure that site1 consider site2 as offline
- When site1 knows that site2 is offline, it will ignore it.
Manually taking site offline
- It's possible on JDG server side through JMX (jconsole) or through CLI
- Refer to JDG documentation for more details
- Site needs to be taken offline separately for every cache or at CacheManager level
Automatically taking site offline
- Done through the configuration on JDG side
- Element "take-offline" inside "backup" on caches
- With our default configuration, site is taken offline after it's unreachable for 60 seconds since first failed request
- On Keycloak side, user requests will be blocked for few minutes
- Exceptions in JDG server logs about failed backups
- Possible to decrease timeout by switch backup policy from FAIL to WARN
Taking site online
- Can be done once the network between sites is fixed and/or JDG servers on site2 started
- Needs to be done manually by admin
- JMX or CLI
- Other possible needed actions
- State transfer
- Clear Keycloak caches
State transfer
- Needs to be done manually
- Again through JMX or CLI on JDG server side
- Admin needs to decide if bidirectionally or unidirectionaly
- Some data may be lost/overwritten
Clearing caches on Keycloak side
- Needed if some KC entities were updated, but caches not invalidated during outage
- Can be done on single KC server on any site -- should propagate it to all others
Backup policy
- Configurable on JDG server side
- FAIL (default) or WARN
- FAIL will propagate backup failures to the caller (Keycloak server)
- Keycloak can then retry the operation
FAIL policy - advantages
- Consistency of data between sites
- Correct behaviour if there is concurrent update of the entity
- One of the update operations will fail and will be retried
- No lost update (write skew)
- Correct behaviour if there is shorter outage between sites (EG. few seconds)
- Because operations will be retried, there won't be lost update
WARN policy - advantages
- In case of real longer outage (split-brain), the caller won't be blocked for long time
- EG. With site outage, the user logins will be blocked just for 10-30 seconds. With FAIL, 1-3 minutes.
Conclusion
WARN is better if you don't need 100% consistency AND you expect often split-brains (offline sites)
SYNC or ASYNC backups?
- ASYNC is sufficient for Active/Passive mode for all caches
- ASYNC won't notify if backup to the second site failed
ASYNC for actionTokens cache?
- ASYNC useful if it's not strictly needed single-use ticket
- ASYNC doesn't guarantee the single-use of OAuth2 code, which is REQUIRED per specs
- ASYNC - better performance, but worse security
ASYNC for session caches?
- Sufficient if all user and client requests end on same DC
- Case if all frontend clients use javascript adapter
- Case if loadbalancer forwards requests based on location and apps are available on both sites too


{#
Copyright (c) 2015 The Pycroft Authors. See the AUTHORS file.
This file is part of the Pycroft project and licensed under the terms of
the Apache License, Version 2.0. See the LICENSE file for details.
#}
{% extends "layout.html" %}
{% set page_title = "Nutzer anzeigen" %}
{% import "macros/forms.html" as forms %}
{% import "macros/resources.html" as resources %}
{% block content %}
{# Stammdaten #}


Once all team members have scanned in at the location you will receive additional information to proceed.
Log in successful
Welcome to the simulation lab. As part of your training to join a special team of rainforest ecologists to save the rain forest, we've prepared a number of exercises to test your knowledge, problem solving-skills and ability to work in a team-based environment.
Your first task is in the domain of evolution. You will examine the Borneo area between 200 million years ago (mya) and present day. You will work in teams, with each member specializing in different species of the rainforest.
You will be collectively creating a cladogram and determine how the species of Borneo came to be in their current form.
Welcome to the simulation lab
For this exercise, you will be working with people from various specialties as a team. Your team should have at least one expert from each of the specialty groups.
Team assignment:
Team members:
Instructions: There are four different versions of Southeast Asia set at different times in this room: .
If you have been preassigned as a guide for any of these four time periods, please select the "Guide" button. Otherwise, please select the "Participant" button.
Instructions: Once the activity begins, a "Next" button will appear on this screen
When you click "Next" you will be assigned an organism. Your task is to determine whether your organism is present at the time periods and if not, whether its ancestral forms are present
When you are done, another organism will be assigned to you until all of the organisms are complete.
Instructions: Please select the time period for which you have been assigned as a guide
Instructions: You've been selected as a guide for:
Please go to the station and help your peers to determine whether their organism is present at your time period. If not, help them find the organism's ancestral form.
Once the activity begins, a "Next" button will appear. This gives you access to a specialty field guide that has been created to assist you with this task.
Time:
Organism:
Is the organism present here?
Time:
Organism:
Present: No
Instructions: Which of the following is most likely the organism’s ancestor? Click on each organism to read more about it. Select your choice from the drop down menu.
 When everyone is done, you will review this information in your teams.
Find your team members and meet up at:
Team members:
If you are the first in your team to finish, take this time to review the cladogram.
Instructions: As a team, you will compare the environment between:
There are three questions to complete. We have also provided you with additional information about these time periods.
Please assign specific roles amongst yourselves for this task. For example, each person in the team could serve as a scribe for one of the questions and another could look up information.
Instructions: As a team, you will compare the environment between:
Discuss the following with your group members and record your answer below. Please note that only one person per group should submit an answer, otherwise your answer will be lost
Instructions: As a team, you will compare the environment between:
Your assignment at this location is to look up information about these time periods. Select a time period below to begin.
Time:
Instructions: Click an outlined component in the image to read more about it.
All questions submitted!
Instructions: Go up to the cladogram and take a look at the organisms in your specialty (e.g., birds, primates, other mammals, plants and insects). An organism will be assigned to you and your task is to follow its evolution from 200 mya to present day.
Your teacher will take you "back in time" so that you can collect evidence related to your organism's evolution, e.g. by writing a note, taking a photo, interviewing colleagues.
Organism:
Introduction: While you're waiting for your teacher to take you "back in time" go to the cladogram and survey important time periods for your organism.
Think about:
1. When was the most interesting point in time for your species and
2. What adaptation and evolutionary forces do you think are at play at the different time periods? Consider interactions with other organisms.
Organism: some organism
Instructions: For your organism, what evolutionary forces do you think are at play between


GZEXE(1) GZEXE(1) NAME gzexe - compress executable files in place SYNOPSIS gzexe name ... DESCRIPTION The gzexe utility allows you to compress executables in place and have them automatically uncompress and execute when you run them (at a penalty in performance). For example if you execute ``gzexe /usr/bin/gdb'' it will create the following two files: -rwxr-xr-x 1 root root 1026675 Jun 7 13:53 /usr/bin/gdb -rwxr-xr-x 1 root root 2304524 May 30 13:02 /usr/bin/gdb~ /usr/bin/gdb~ is the original file and /usr/bin/gdb is the self-uncom‐ pressing executable file. You can remove /usr/bin/gdb~ once you are sure that /usr/bin/gdb works properly. This utility is most useful on systems with very small disks. OPTIONS -d Decompress the given executables instead of compressing them. SEE ALSO gzip(1), znew(1), zmore(1), zcmp(1), zforce(1) CAVEATS The compressed executable is a shell script. This may create some secu‐ rity holes. In particular, the compressed executable relies on the PATH environment variable to find gzip and some standard utilities (basename, chmod, ln, mkdir, mktemp, rm, sleep, and tail). BUGS gzexe attempts to retain the original file attributes on the compressed executable, but you may have to fix them manually in some cases, using chmod or chown. GZEXE(1)


Your insurance is currently being processed as follows
PAYMENTS
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FOR 1996 TOYOTA CARINA VALUED AT KES 450,000 FROM JUNE 14 2016
12,900
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Policy Holders Compensation Fund (PHCF) @ 0.25%
Stamp Duty
26
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KES 12,999
Your car insurance sticker will be processed in the next 2 hours.
Jacinta will call you on your phone number +254 722 362490 with insurance car sticker
collection details.
Change contact telephone
Your purchase receipt has been sent to your email address<EMAIL_ADDRESS>A notification will also be sent to this email address as soon your car insurance is ready.
For any queries, call us on 0722 555 555 and quote Sale ID Number 264364


\n
) and carriage return (\r
) character escape sequences used in string literals,
character literals or text blocks. These characters are commonly used as line separators, and portability may suffer if they are hardcoded.
Example:
String count = "first\nsecond\rthird";


Hello Let Me introduce sample Template.Create with Bootstrap Currently v3.3.7 and Css and jQuery
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Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s,
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s,
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Algorithm
Principal Component Analysis, or PCA, is a dimensionality-reduction method that is often used to reduce the dimensionality of large data sets.
Reducing the number of variables of a data set naturally comes at the expense of accuracy, but the trick in dimensionality reduction is to trade a little accuracy for simplicity. Because smaller data sets are easier to explore and visualize, it becomes easier and faster to analyze your data.
The idea of PCA is simple — reduce the number of variables of a data set, while preserving as much information as possible.
The pca
library contains various functionalities to carefully examine the data, which can help for better understanding and removal of redundant information.
Standardization
Feature scaling through standardization (or Z-score normalization) is an important preprocessing step for many machine learning algorithms. Standardization involves rescaling the features such that they have the properties of a standard normal distribution with a mean of zero and a standard deviation of one. The standardization step depends on the type of variables, the distribution of the data, and also your aim. In general, the standardizing step is to range the continuous initial variables so that each one of them contributes equally to the analysis.
It is utterly important to carefully standardize your data because PCA works under the assumption that the data is normal distributed, and is very sensitive to the variance of the variables. Or in other words, large differences between the ranges of variables will dominate over those with small ranges. Let me explain this by example; a variable that ranges between 0 and 100 will dominate over a variable that ranges between 0 and 1. Transforming the data to comparable scales can prevent this issue.
The most straightforward manner for standardization is by computing the Z-scores or Standardized scores. Once the standardization is done, all the variables will be transformed to the same scale.
Scaling your data can easily being done with the sklearn library. In the following example we will import the wine dataset and scale the variables. Think carefully whether you want to standardize column-wise or row-wise. In general, you want to standardize row-wise. This means that the Z-score is computer per row.
# Import the StandardScaler
from sklearn.preprocessing import StandardScaler
from sklearn import datasets
# Load dataset
data = datasets.load_wine()
X = data.data
y = data.target
col_labels = data.feature_names
#In general it is a good idea to scale the data
scaler = StandardScaler(with_mean=True, with_std=True)
X = scaler.fit_transform(X)
The normalization step is also incorporated in pca
that can be set by the parameter normalize=True
.
# Load library
from pca import pca
# Initialize pca with default parameters
model = pca(normalize=True)
An example of the differences of feature reduction using PCA with and without standardization.
Explained Variance
Before jumping into the explained variance, we first need to understand what principal components are.
Principal components are new (latent) variables that are constructed as linear combinations or mixtures of the initial variables. These combinations are in such a way that the new variables (i.e., principal components) are uncorrelated and most of the information within the initial variables is squeezed or compressed into the first components. Explained variance refers to the variance explained by each of the principal components (eigenvectors). By organizing information Principal
Let’s compute the explained variance for the wine dataset (this is a follow up from the previous standardization part).
# Load library
from pca import pca
# Initialize pca with default parameters
model = pca(normalize=True)
# Fit transform
results = model.fit_transform(X)
# Plot the explained variance
model.plot()
In this example we have 13 variables in the wine dataset, and thus 13 dimensions. PCA will optimize to store maximum variance in the first PC, then in the second and so on, until having something like shown in the plot below. This plot provides insights in the amount of information or explained variance in the data. We can clearly see that the 1st PC contains almost 36% of explained variance in total. With the top 10 PCs we cover 97.9% of all variance.
There are as many principal components as there are variables in the data. The explained variance plot can therefore never have more then 13 PCs in this case. Principal components are constructed in such a manner that the first principal component accounts for the largest possible variance in the data set.
Loadings
It is important to realize that principal components are less interpretable and don’t have any real meaning since they are constructed as linear combinations of the initial variables. But we can analyze the loadings which describe the importance of the independent variables. The first principal component (Y1) is given by a linear combination of the variables X1, X2, …, Xp, and is calculated such that it accounts for the greatest possible variance in the data.
Of course, one could make the variance of Y1 as large as possible by choosing large values for the weights a11, a12, … a1p. To prevent this, the sum of squares of the weights is constrained to be 1.
For example, let’s assume that the scatter plot of our data set is as shown below. Can we guess the first principal component? Yes, it’s approximately the line that matches the purple marks because it goes through the origin and it’s the line in which the projection of the points (red dots) is the most spread out. Or mathematically speaking, it’s the line that maximizes the variance which is the average of the squared distances from the projected points (red dots) to the origin.
The second principal component is calculated in the same way, with the conditions that it is uncorrelated with (i.e., perpendicular to) the first principal component and that it accounts for the next highest variance.
This continues until a total of p principal components have been calculated, that is, the number of principal components is the same as the original number of variables. At this point, the total variance on all of the principal components will equal the total variance among all of the variables. In this way, all of the information contained in the original data is preserved; no information is lost: PCA is just a rotation of the data.
The elements of an eigenvector, that is, the values within a particular row of matrix, are the weights aij. These values are called the loadings, and they describe how much each variable contributes to a particular principal component.
Large loadings (+ or -) indicate that a particular variable has a strong relationship to a particular principal component.
The sign of a loading indicates whether a variable and a principal component are positively or negatively correlated.
Let’s go back to our wine example and plot the loadings of the PCs.
# Load dataset
data = datasets.load_wine()
X = data.data
y = data.target
col_labels = data.feature_names
# Load library
from pca import pca
# Initialize pca with default parameters
model = pca(normalize=True)
# Fit transform and include the column labels and row labels
results = model.fit_transform(X, col_labels=col_labels, row_labels=y)
# Scatter plot with loadings
model.biplot()
First of all, we see a nice seperation of the 3 wine classes (red, orange and gray samples). In the middle of the plot we see various arrows. Each of the arrows describes its story in the Principal Components. The angle of the arrow describes the contribution of the variable that is seen in the particular PC. The length describes the strength of the loading.
Examination of the loadings
Let’s examine the loadings (arrows) a bit more to understand what is going on in the distribution of the samples given the variables. The variable flavanoids has a positive loading and explaines mostly the variance in the first PC1 (it is almost a horizontal line). If we would color the samples in the scatter plot based on flavanoids values, we expect to see a distinction between samples that are respectively left and right side of the scatter plot.
# Grap the values for flavanoids
X_feat = X[:, np.array(col_labels)=='flavanoids']
# Color based on mean
color_label = (X_feat>=np.mean(X_feat)).flatten()
# Scatter based on discrete color
model.scatter(labels=color_label, title='Color on flavanoids (Gray colored samples are > mean)')
# 3d scatter plot
model.scatter3d(labels=color_label, title='Color on flavanoids (Gray colored samples are > mean)')
Let’s take another variable for demonstration purposes. The variable alcohol has a strong negative loading (almost vertical), and should therefoe explains mostly the 2nd PC but the angle is not exactly vertical, thus there is also some variance seen in the 1st PC. Let’s color the samples based on alcohol.
# Grap the values for alcohol
X_feat = X[:, np.array(col_labels)=='alcohol']
# Color based on mean
color_label = (X_feat>=np.mean(X_feat)).flatten()
# Scatter based on discrete color
model.scatter(labels=color_label, title='Color on alcohol (Gray colored samples are < mean)')
# 3d scatter plot
model.scatter3d(labels=color_label, title='Color on alcohol (Gray colored samples are < mean)')
Best Performing Features
Extracting the best performing features is based on the loadings of the Principal Components, which are readily computed. The information is stored in the object itself. We can extract it as following:
# Print the top features.
print(model.results['topfeat'])
# PC feature loading type
# PC1 flavanoids 0.422934 best
# PC2 color_intensity -0.529996 best
# PC3 ash 0.626224 best
# PC4 malic_acid 0.536890 best
# PC5 magnesium 0.727049 best
# PC6 malic_acid -0.536814 best
# PC7 nonflavanoid_phenols 0.595447 best
# PC8 hue -0.436624 best
# PC9 proline -0.575786 best
# PC10 od280/od315_of_diluted_wines 0.523706 best
# PC9 alcohol 0.508619 weak
# PC3 alcalinity_of_ash 0.612080 weak
# PC8 total_phenols 0.405934 weak
# PC6 proanthocyanins 0.533795 weak
We see that most of the variance for the 1st PC is derived from the variable flavanoids. For the 2nd component, it is by color_intensity, etc.
References
[1] https://builtin.com/data-science/step-step-explanation-principal-component-analysis
[2] http://strata.uga.edu/8370/lecturenotes/principalComponents.html


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Over 5 years professional experince
"Alexa, Turn on candles"
Arduino, Raspberry Pi, Esp2866
This one speaks for itself
I am an enthusiastic and innovative developer with four years of professional development behind me working on enterprise level web applications. I have experience creating and maintaining front and back end applications utilizing c# and Javascript. I have worked on projects that range from small quality of life improvements to large new features such as creating the base for auctions to be added to Rezi. Outside of work I have put my mechatronic engineering skills to practice with many projects completed in my spare time. My projects have ranged from IOT such as home automation and wifi connected sensors to an autonomous robot and an alexa controlled robot arm. I have a passion for finding out how devices operate and coming up with innovative new ideas for improvement. I'm always involving myself in new projects and enjoy being on the cutting edge of technology
Technical Architect
Providing technical expertise to set the technical direction and manage issues, architecture, technical integration and technical service levels for a group of technologies.
Software Developer
Working on the new product Rezi using Typescript and HTML in an agile environment. Rezi is a multi platform web application for estate agents to manage their business.
Ethos Building, Kings Road, Swansea
SA1 8AS
HTML developer and Deputy department manager


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Queue is also an abstract data type or a linear data structure, in which the first element is inserted from one end called REAR(also called tail), and the deletion of exisiting element takes place from the other end called as FRONT(also called head). This makes queue as FIFO data structure, which means that element inserted first will also be removed first.
The process to add an element into queue is called Enqueue and the process of removal of an element from queue is called Dequeue.
Queue, as the name suggests is used whenever we need to have any group of objects in an order in which the first one coming in, also gets out first while the others wait for there turn, like in the following scenarios :
Queue can be implemented using an Array, Stack or Linked List. The easiest way of implementing a queue is by using an Array. Initially the head(FRONT) and the tail(REAR) of the queue points at the first index of the array (starting the index of array from 0). As we add elements to the queue, the tail keeps on moving ahead, always pointing to the position where the next element will be inserted, while the head remains at the first index.
When we remove element from Queue, we can follow two possible approaches (mentioned [A] and [B] in above diagram). In [A] approach, we remove the element at head position, and then one by one move all the other elements on position forward. In approach [B] we remove the element from head position and then move head to the next position.
In approach [A] there is an overhead of shifting the elements one position forward every time we remove the first element. In approach [B] there is no such overhead, but whener we move head one position ahead, after removal of first element, the size on Queue is reduced by one space each time.
/*Below program is written in C++ language*/ #define SIZE 100 class Queue { int a[100]; int rear; //same as tail int front; //same as head public: Queue() { rear = front = -1; } void enqueue(int x); int dequeue(); void display(); } void Queue :: enqueue(int x) { if( rear = SIZE-1) { cout << "Queue is full"; } else { a[++rear] = x; } } int queue :: dequeue() { return a[++front]; } void queue :: display() { int i; for( i = front; i <= rear; i++) { cout << a[i]; } }
To implement approach [A], you simply need to change the dequeue method, and include a for loop which will shift all the remaining elements one position.
return a[0]; //returning first element for (i = 0; i < tail-1; i++) { a[i]= a[i+1]; tail--; }


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https://docs.python.org/3/library/exceptions.html
https://docs.python.org/3/reference/executionmodel.html#exceptions
https://nedbatchelder.com/text/exceptions-vs-status.html
https://nedbatchelder.com/text/exceptions-in-the-rainforest.html
Exceptions are a means of breaking out of the normal flow of control of a code block in order to handle errors or other exceptional conditions. An exception is 'raised' at the point where the error is detected; it may be 'handled' by the surrounding code block or by any code block that directly or indirectly invoked the code block where the error occurred.
When an exception is not handled at all, the interpreter terminates execution of the program, or returns to its interactive main loop. In either case, it prints a stack traceback, except when the exception is 'SystemExit'.
Broadly speaking, there are two ways to handle errors as they pass from layer to layer in software: throwing exceptions and returning status codes.
Why are we using exceptions [instead of error codes]?
(a) To raport errors or warnings [both methods].
(b) Exceptions are propagated by default up the call stack
(error codes do not). The intermediate layers of code may have
no knowledge of them (clean code).
(c) An exception class hierarchy can be used for related results.
(d) Exceptions cannot be ignored (unless we want this), return codes can.
(e) Exceptions can carry richer information than error codes.
(f) Exceptions leave the primary channel (function returns)
available for the primary work.
Status returns can’t even be used with some functions [C++].
+----------------------------------------+---------------------+ | Statements | Meaning | +----------------------------------------+---------------------+ | raise MyException("message") | raising exceptions | | try/except/finally | handling exceptions | | class MyException(Exception): pass | creating exceptions | | assert expression | assertions | | with ContextManager() as context: pass | context managers | +----------------------------------------+---------------------+
The built-in exceptions can be generated by the interpreter or built-in functions.
print(23 / 0) # ZeroDivisionError L = [] # an empty list print(L[5]) # IndexError D = {} # an empty dictionary print(D["key"]) # KeyError
>>> import exceptions # Py2.6+ >>> help(exceptions)
>>> import builtins # Py3, built-in functions, exceptions, and other objects >>> help(builtins)
BaseException +-- SystemExit +-- KeyboardInterrupt +-- GeneratorExit +-- Exception # all other current built-in exceptions +-- StopIteration +-- StandardError | +-- ArithmeticError | +-- AssertionError | +-- AttributeError | +-- NameError | +-- SyntaxError | +-- IndentationError | +-- TypeError | +-- ValueError | +-- ImportError | +-- MemoryError | +-- RuntimeError | +-- NotImplementedError | +-- EnvironmentError | +-- IOError | +-- OSError | +-- LookupError | +-- IndexError | +-- KeyError | +-- ... +-- Warning +-- DeprecationWarning +-- UserWarning +-- SyntaxWarning +-- RuntimeWarning +-- ...


19 November 2020 | S2-Tech
During your career as a Web Developer you are going to notice that you have a few favourite or go-to Font layouts for texts.
You may want to use the same Text Layout throughout your website. But you dont want to keep copying and pasting those same CSS rule-sets for every element you choose.
You can easily create one Rule-set that links to a class of your choice.
To start, go and find your favourite rule-set or you can create one from scratch in CSS. Choose all the declorations you want.
Next, choose an appropriate name of your choice which will become the new Class name.
Finally within HTML add that Class name to any elements that you want to use that rule-set on. Also another cool thing with Classes is you can mix and match different rule-sets.
Let's say you have a webpage set up, with quite a bit of text or images etc. It then leads the user to scroll down the page. To save time for the user scrolling back up, you can create a 'Back to top' button, using an ID.
To start, you want to create an element at the bottom of the page to be an anchor. This can be text or you can create a button. Input the text as 'Back To Top'
Next you want to create an ID. The great thing with an ID is you can attach it to any element on your page to be used as a reference point. In this case you want the reference point to be the top of the page. In my case I am going to add an ID to my Nav element, and call the ID "Top".
You finally want to edit your 'Back To Top' element using the 'href' within the anchor. Inside the 'href', you first input a hash '#' followed by the ID 'Top'.


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)
"""
# Real input images placeholder with rank 4 using `image_width`, `image_height`, and `image_channels`.
real_input = tf.placeholder(tf.float32,(None,image_width, image_height, image_channels),name='real_input')
# Z input placeholder with rank 2 using `z_dim`.
z = tf.placeholder(tf.float32,(None,z_dim),name='z')
# Learning rate placeholder with rank 0.
learning_rate = tf.placeholder(tf.float32,name='learning_rate')
return 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)
"""
with tf.variable_scope('discriminator', reuse=reuse):
#ReLU param
alpha=0.2
#hidden layer channel
n=64
#Input layer shape: 28x28x3
x1 = tf.layers.conv2d(images, n, 5, strides=2, padding='same')
#no need to batch_normalization in first layer
x1 = tf.maximum(alpha*x1, x1)
#14x14xn
x2 = tf.layers.conv2d(x1, 2*n, 5, strides=2, padding='same')
x2 = tf.layers.batch_normalization(x2, training=True)
x2 = tf.maximum(alpha*x2, x2)
#7x7x2n
x3 = tf.layers.conv2d(x2, 4*n, 5, strides=2, padding='same')
x3 = tf.layers.batch_normalization(x3, training=True)
x3 = tf.maximum(alpha*x3, x3)
#4x4x4n
x4 = tf.layers.conv2d(x3, 8*n, 5, strides=2, padding='same')
x4 = tf.layers.batch_normalization(x4, training=True)
x4 = tf.maximum(alpha*x4, x4)
#2x2x8n
#flatten layer to shape 2x2x512
flatten = tf.reshape(x3, (-1, 2*2*8*n))
logits = tf.layers.dense(flatten, 1)
out = tf.sigmoid(logits)
return out, 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
"""
with tf.variable_scope('generator', reuse=not is_train):
#ReLU param
alpha=0.2
#hidden layer channel
n=64
#dense layer on z
d = tf.layers.dense(z, 2*2*8*n)
#reshaped dense layer
dr = tf.reshape(d, (-1, 2, 2, 8*n))
drn = tf.layers.batch_normalization(dr, training=is_train)
drnr = tf.maximum(alpha*drn, drn)
#7x7x4n
c1 = tf.layers.conv2d_transpose(drnr, 4*n, 5, strides=2, padding='same')
c1n = tf.layers.batch_normalization(c1, training=is_train)
c1nr = tf.maximum(alpha*c1n, c1n)
#14x14x2n
c2 = tf.layers.conv2d_transpose(c1nr, 2*n, 4, strides=1, padding='valid')
c2n = tf.layers.batch_normalization(c2, training=is_train)
c2nr = tf.maximum(alpha*c2n, c2n)
#28x28xn
c3 = tf.layers.conv2d_transpose(c2nr, n, 3, strides=2, padding='same')
c3n = tf.layers.batch_normalization(c3, training=is_train)
c3nr = tf.maximum(alpha*c3n, c3n)
#28x28xout_channel_dim
c4 = tf.layers.conv2d_transpose(c3nr, out_channel_dim, 5, strides=2, padding='same')
# final output
out = tf.tanh(c4)
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)
"""
#discriminator outputs for real images
d_out_real, d_logits_real = discriminator(input_real)
#fake images from z
images_fake = generator(input_z, out_channel_dim)
# discriminator outputs for fake images
d_out_fake, d_logits_fake = discriminator(images_fake, reuse=True)
#real image discriminator loss
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_out_real) * 0.9))
#fake image discriminator loss
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_out_fake)))
#sum of discriminator loss
d_loss = d_loss_real + d_loss_fake
#generator loss
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_out_fake)))
return d_loss, g_loss
"""
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)
"""
#list of all tf trainable variables
t_vars = tf.trainable_variables()
#discriminator variables
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
#generator variables
g_vars = [var for var in t_vars if var.name.startswith('generator')]
#update generator
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
gen_updates = [op for op in update_ops if op.name.startswith('generator')]
#apply optimizers
with tf.control_dependencies(gen_updates):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_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")
"""
#build model
_, image_width, image_height, image_channels = data_shape
input_real, input_z, lr = model_inputs(image_width, image_height, image_channels, z_dim)
d_loss, g_loss = model_loss(input_real, input_z, image_channels)
d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
steps = 0
dloss_sum = 0
gloss_sum = 0
batch_count = 0
for batch_real in get_batches(batch_size):
steps += 1
#rescale image batch to [-1,1]
batch_real *= 2
#get batch of noise
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
#run discriminator training optimizer
_ = sess.run(d_opt, feed_dict={input_real: batch_real, input_z: batch_z, lr: learning_rate})
#run generator training optimizer
_ = sess.run(g_opt, feed_dict={input_z: batch_z, lr: learning_rate})
dloss_sum += d_loss.eval({input_z: batch_z, input_real: batch_real})
gloss_sum += g_loss.eval({input_z: batch_z})
batch_count += 1
if steps % 100 == 0:
show_generator_output(sess, 16, input_z, image_channels, data_image_mode)
print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
"Avg. Discriminator Loss: {:.3f}".format(dloss_sum / batch_count),
"Avg. Generator Loss: {:.3f}".format(gloss_sum / batch_count))
dloss_sum = 0
gloss_sum = 0
batch_count = 0
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 = 7e-4
beta1 = 0.1
"""
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 = 7e-4
beta1 = 0.1
"""
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.


Given a prior knowledge to estimate the model.
Bayesian Learning: \[P(\theta|\mathcal{D}) = \frac{P(\mathcal{D}|\theta)P(\theta)}{P(\mathcal{D})}\] or equivalently \[P(\theta|\mathcal{D}) \propto P(\mathcal{D}|\theta)P(\theta)\] Likelihood measures the fitness between data and parameters, Prior is the knowledge how possible the parameters to be.
Prior information encoded as a distribution over possible values of parameter.
Using the Bayes rule to get an updated posterior distribution over parameters.
Represents expert knowledge (philosophical approach)
Simple posterior form (engineer’s approach)
Simple distribution.
Closed-form representation of posterior
prior and posterior have the same algebraic form as a function of parameters
Bernoulli Example: (Binomial’s conjugate prior is Beta distribution)
Likelihood in Bernoulli model: \(P(D|\theta)=\theta^{\alpha_1}(1-\theta)^{\alpha_2}\)
Prior is Beta distribution: \(P(\theta)=\frac{\theta^{\beta_1-1}(1-\theta)^{\beta_2-1}}{B(\beta_1,\beta_2)} \sim Beta(\beta_1,\beta_2)\)
Posterior is also Beta distribution: \(P(\theta|D)\sim Beta(\beta_1+\alpha_1,\beta_2+\alpha_2)\)
Multinomial example: (Multinomial’s conjugate prior is Dirichelet distribution)
Likelihood is Multinomial(\(\theta=\{\theta_1,\dots,\theta_k\}\)), \(P(D|\theta)=\prod_{i=1}^{k}\theta_i^{\alpha_i}\), \(\alpha_i\in\{0,1\}\) is the data \(D\), \(\sum_{i=1}^{k}\theta_i =1\).
Prior is Dirichlet distribution: \(P(\theta)=\frac{\prod_{i=1}^{k}\theta_i^{\beta_i-1}}{B(\beta_1,\dots,\beta_k)} \sim Dirichlet(\beta_1,\dots,\beta_k)\)
Posterior is also dirichlet distribution: \(P(\theta|D) \sim Dirichlet(\beta_1+\alpha_1,\dots,\beta_k+\alpha_k)\)
As we get more samples, effect of prior is “washed out”
Choose \(\theta\) that maximizes a posterior probability: \(\hat{\theta}_{MAP}=\arg\max_\theta{P(\theta|D)}\)
\[\begin{array}{rcl} \hat{\theta}_{MAP} & = & \arg\max_\theta{P(\theta|D)} \\ & = & \arg\max_\theta{P(D|\theta)P(\theta)} \end{array}\]
Bernoulli example:
\[\begin{array}{rcl} P(\theta|D) & \sim & Beta(\beta_1+\alpha_1,\beta_2+\beta_2) \\ \hat{\theta}_{MAP} & = & \frac{\alpha_1+\beta_1-1}{\alpha_1+\beta_1+\alpha_2+\beta_2-2} \end{array}\]
MLE: Choose value that maximizes the probability of observed data
MAP: Choose value that is mot probable given observed data and prior belief
When prior is a uniform distribution, MLE=MAP.
Conjugate priors
Gaussian prior: \[P(\mu|\eta,\lambda)=\frac{1}{\lambda\sqrt{2\pi}}\exp(-\frac{(\mu-\eta)^2}{2\lambda^2})=\mathcal{N}(\eta,\lambda)\]
Variance: Wishart Distribution1
MAP for Gasussian Mean:
\(\hat{\mu}_{MLE}=\frac{1}{n}\sum_{i=1}^{n}x_i\)
\(\hat{\mu}_{MAP}=\frac{\frac{1}{\sigma^2}\sum_{i=1}^{n}x_i+\frac{\eta}{\lambda^2}}{\frac{n}{\sigma^2}+\frac{1}{\lambda^2}}\)
MLE: \[\max_\theta\log P(D|\theta)\]
Constrained MLE: \[\max _\theta\log P(D|\theta)~~s.t.~\mathcal{R}(\theta)\leq C\]
When \(\mathcal{R}\) is convex, constrained MLE is equivalent to regularized MLE (lagrange multiplier2): \[\max_\theta\{\log P(D|\theta)+\lambda\mathcal{R}(\theta)\}\]
The MAP estimator can be seen to be a special case by simply setting: \[\lambda\mathcal{R}(\theta)=\log P(\theta)\]


Are you an engineer that want to do some volunteer work? Register here and be notified about non-profit projects that needs your help!
A 2013 Summer of Hack project by Michal and Ellora.


[[Another passage]] [[A third passage]]
[[A fourth passage]] [[A third passage]]
[[Start]]
[[A fifth passage]]
Double-click this passage to edit it.


Dwaine is in Harry's Room
“After you went outside to puke, the rest were asking me to tell them more about my line of cowboy boots I’m putting out”
I did not go outside to puke…………
“these boots are going to be the next big thing in men’s fashion. I am getting a lot of investor interest in it, I think Alex was ready to put a lot of money behind it, would you like to see my catalogue?”
“Not right now, umm, maybe later, for now let’s focus on more pressing matters. Give me your recollection of events last night after dinner.”
“Yeah, of course. Like I was saying, you went outside to puke, and the girls started arguing about the Bahamas again. I don’t think Wilma ever forgave Jane about what happened there. I wasn’t paying them too much attention, I think they should just bury the hatchet……” probably poor choice of words under the circumstances “Alex was puffing on a large one and we were discussing the investment opportunities. Fred wasn’t looking too good, so he begged off early and headed upstairs, not too long after that Wilma left too. Jane and Alex started to get that cozy look in their eyes and I started to feel like a third wheel, a lot has changed since school hasn’t it, so I left and went upstairs to bed.”
“What time was that?”
“It was around 10:00, I heard that grandfather clock in the study chiming when I closed the door to Wilma’s room.”
“When you closed the door to Wilma’s room? I thought she had gone to bed by then.”
“Yeah, I thought that was strange too? When I passed, the door was open and a draft was coming in from an open window, so I closed the door and went to bed, that was it for me until this morning.”
I thanked Harry and left.


|
User-space NVME Driver
|
NVMe implementation. More...
#include <sys/mman.h>
#include <string.h>
#include <errno.h>
#include "rdtsc.h"
#include "unvme_log.h"
#include "unvme_nvme.h"
Functions | |
| static int | nvme_ctlr_wait_ready (nvme_device_t *dev, int ready) |
| static int | nvme_ctlr_disable (nvme_device_t *dev) |
| static int | nvme_ctlr_enable (nvme_device_t *dev, nvme_controller_config_t cc) |
| static int | nvme_submit_cmd (nvme_queue_t *q) |
| int | nvme_check_completion (nvme_queue_t *q, int *stat, u32 *cqe_cs) |
| int | nvme_wait_completion (nvme_queue_t *q, int cid, int timeout) |
| int | nvme_acmd_identify (nvme_device_t *dev, int nsid, u64 prp1, u64 prp2) |
| int | nvme_acmd_get_log_page (nvme_device_t *dev, int nsid, int lid, int numd, u64 prp1, u64 prp2) |
| int | nvme_acmd_get_features (nvme_device_t *dev, int nsid, int fid, u64 prp1, u64 prp2, u32 *res) |
| int | nvme_acmd_set_features (nvme_device_t *dev, int nsid, int fid, u64 prp1, u64 prp2, u32 *res) |
| int | nvme_acmd_create_cq (nvme_queue_t *ioq, u64 prp) |
| int | nvme_acmd_create_sq (nvme_queue_t *ioq, u64 prp) |
| static int | nvme_acmd_delete_ioq (nvme_queue_t *ioq, int opc) |
| int | nvme_acmd_delete_sq (nvme_queue_t *ioq) |
| int | nvme_acmd_delete_cq (nvme_queue_t *ioq) |
| int | nvme_acmd_vs (nvme_device_t *dev, int nsid, u64 prp1, u64 prp2, int opc, u32 cdw10_15[6]) |
| int | nvme_cmd_vs (nvme_queue_t *ioq, u16 cid, int nsid, u64 prp1, u64 prp2, int opc, u32 cdw10_15[6]) |
| int | nvme_cmd_rw (nvme_queue_t *ioq, int opc, u16 cid, int nsid, u64 slba, int nlb, u64 prp1, u64 prp2) |
| int | nvme_cmd_rw_extended (nvme_queue_t *ioq, int opc, u16 cid, int nsid, u64 slba, int nlb, u64 prp1, u64 prp2, int rsvd12) |
| int | nvme_cmd_read (nvme_queue_t *ioq, u16 cid, int nsid, u64 slba, int nlb, u64 prp1, u64 prp2) |
| int | nvme_cmd_write (nvme_queue_t *ioq, u16 cid, int nsid, u64 slba, int nlb, u64 prp1, u64 prp2) |
| nvme_queue_t * | nvme_create_ioq (nvme_device_t *dev, nvme_queue_t *ioq, int id, int qsize, void *sqbuf, u64 sqpa, void *cqbuf, u64 cqpa) |
| int | nvme_delete_ioq (nvme_queue_t *ioq) |
| nvme_queue_t * | nvme_setup_adminq (nvme_device_t *dev, int qsize, void *sqbuf, u64 sqpa, void *cqbuf, u64 cqpa) |
| nvme_device_t * | nvme_create (nvme_device_t *dev, int mapfd) |
| void | nvme_delete (nvme_device_t *dev) |
Variables | |
| static u64 | nvme_rdtsec |
| rdtsc per second | |
NVMe implementation.
Copyright (c) 2015-2016, Micron Technology, Inc. All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
| int nvme_acmd_create_cq | ( | nvme_queue_t * | ioq, |
| u64 | prp | ||
| ) |
NVMe create I/O completion queue command. Submit the command and wait for completion.
| ioq | io queue |
| prp | PRP1 address |
| int nvme_acmd_create_sq | ( | nvme_queue_t * | ioq, |
| u64 | prp | ||
| ) |
NVMe create I/O submission queue command. Submit the command and wait for completion.
| ioq | io queue |
| prp | PRP1 address |
| int nvme_acmd_delete_cq | ( | nvme_queue_t * | ioq | ) |
NVMe delete I/O completion queue command. Submit the command and wait for completion.
| ioq | io queue |
|
inlinestatic |
NVMe delete I/O queue command. Submit the command and wait for completion.
| ioq | io queue |
| opc | op code |
| int nvme_acmd_delete_sq | ( | nvme_queue_t * | ioq | ) |
NVMe delete I/O submission queue command. Submit the command and wait for completion.
| ioq | io queue |
| int nvme_acmd_get_features | ( | nvme_device_t * | dev, |
| int | nsid, | ||
| int | fid, | ||
| u64 | prp1, | ||
| u64 | prp2, | ||
| u32 * | res | ||
| ) |
NVMe get features command. Submit the command and wait for completion.
| dev | device context |
| nsid | namespace id |
| fid | feature id |
| prp1 | PRP1 address |
| prp2 | PRP2 address |
| res | dword 0 value returned |
| int nvme_acmd_get_log_page | ( | nvme_device_t * | dev, |
| int | nsid, | ||
| int | lid, | ||
| int | numd, | ||
| u64 | prp1, | ||
| u64 | prp2 | ||
| ) |
NVMe get log page command. Submit the command and wait for completion.
| dev | device context |
| nsid | namespace id |
| lid | log page id |
| numd | number of dwords |
| prp1 | PRP1 address |
| prp2 | PRP2 address |
| int nvme_acmd_identify | ( | nvme_device_t * | dev, |
| int | nsid, | ||
| u64 | prp1, | ||
| u64 | prp2 | ||
| ) |
NVMe identify command. Submit the command and wait for completion.
| dev | device context |
| nsid | namespace id (< 0 implies cns) |
| prp1 | PRP1 address |
| prp2 | PRP2 address |
| int nvme_acmd_set_features | ( | nvme_device_t * | dev, |
| int | nsid, | ||
| int | fid, | ||
| u64 | prp1, | ||
| u64 | prp2, | ||
| u32 * | res | ||
| ) |
NVMe set features command. Submit the command and wait for completion.
| dev | device context |
| nsid | namespace id |
| fid | feature id |
| prp1 | PRP1 address |
| prp2 | PRP2 address |
| res | dword 0 value returned |
| int nvme_acmd_vs | ( | nvme_device_t * | dev, |
| int | nsid, | ||
| u64 | prp1, | ||
| u64 | prp2, | ||
| int | opc, | ||
| u32 | cdw10_15[6] | ||
| ) |
NVMe submit a vendor specific command.
| dev | device context |
| nsid | namespace |
| prp1 | PRP1 address |
| prp2 | PRP2 address |
| opc | vendor specific op code |
| cdw10_15 | command dwords 10-15 |
| int nvme_check_completion | ( | nvme_queue_t * | q, |
| int * | stat, | ||
| u32 * | cqe_cs | ||
| ) |
Check a completion queue and return the completed command id and status.
| q | queue |
| stat | completion status returned |
| cqe_cs | CQE command specific DW0 returned |
| int nvme_cmd_read | ( | nvme_queue_t * | ioq, |
| u16 | cid, | ||
| int | nsid, | ||
| u64 | slba, | ||
| int | nlb, | ||
| u64 | prp1, | ||
| u64 | prp2 | ||
| ) |
NVMe submit a read command.
| ioq | io queue |
| cid | command id |
| nsid | namespace |
| slba | startling logical block address |
| nlb | number of logical blocks |
| prp1 | PRP1 address |
| prp2 | PRP2 address |
| int nvme_cmd_rw | ( | nvme_queue_t * | ioq, |
| int | opc, | ||
| u16 | cid, | ||
| int | nsid, | ||
| u64 | slba, | ||
| int | nlb, | ||
| u64 | prp1, | ||
| u64 | prp2 | ||
| ) |
NVMe submit a read write command.
| ioq | io queue |
| opc | op code |
| cid | command id |
| nsid | namespace |
| slba | startling logical block address |
| nlb | number of logical blocks |
| prp1 | PRP1 address |
| prp2 | PRP2 address |
| int nvme_cmd_vs | ( | nvme_queue_t * | ioq, |
| u16 | cid, | ||
| int | nsid, | ||
| u64 | prp1, | ||
| u64 | prp2, | ||
| int | opc, | ||
| u32 | cdw10_15[6] | ||
| ) |
NVMe submit a vendor specific command.
| ioq | io queue |
| cid | command id |
| nsid | namespace |
| prp1 | PRP1 address |
| prp2 | PRP2 address |
| opc | vendor specific op code |
| cdw10_15 | command dwords 10-15 |
| int nvme_cmd_write | ( | nvme_queue_t * | ioq, |
| u16 | cid, | ||
| int | nsid, | ||
| u64 | slba, | ||
| int | nlb, | ||
| u64 | prp1, | ||
| u64 | prp2 | ||
| ) |
NVMe submit a write command.
| ioq | io queue |
| cid | command id |
| nsid | namespace |
| slba | startling logical block address |
| nlb | number of blocks |
| prp1 | PRP1 address |
| prp2 | PRP2 address |
| nvme_device_t* nvme_create | ( | nvme_device_t * | dev, |
| int | mapfd | ||
| ) |
Create an NVMe device context and map the controller register.
| dev | if NULL then allocate context |
| mapfd | file descriptor for register mapping |
| nvme_queue_t* nvme_create_ioq | ( | nvme_device_t * | dev, |
| nvme_queue_t * | ioq, | ||
| int | id, | ||
| int | qsize, | ||
| void * | sqbuf, | ||
| u64 | sqpa, | ||
| void * | cqbuf, | ||
| u64 | cqpa | ||
| ) |
Create an IO submission-completion queue pair.
| dev | device context |
| ioq | if NULL then allocate queue |
| id | queue id |
| qsize | queue size |
| sqbuf | submission queue buffer |
| sqpa | submission queue IO physical address |
| cqbuf | completion queue buffer |
| cqpa | admin completion IO physical address |
|
static |
Disable controller.
| dev | device context |
|
static |
Enable controller.
| dev | device context |
| cc | controller configuration settings |
|
static |
Wait for controller enabled/disabled state.
| dev | device context |
| ready | ready state (enabled/disabled) |
| void nvme_delete | ( | nvme_device_t * | dev | ) |
Delete an NVMe device context
| dev | device context |
| int nvme_delete_ioq | ( | nvme_queue_t * | ioq | ) |
Delete an IO submission-completion queue pair.
| ioq | io queue to setup |
| nvme_queue_t* nvme_setup_adminq | ( | nvme_device_t * | dev, |
| int | qsize, | ||
| void * | sqbuf, | ||
| u64 | sqpa, | ||
| void * | cqbuf, | ||
| u64 | cqpa | ||
| ) |
NVMe setup admin submission-completion queue pair.
| dev | device context |
| qsize | queue size |
| sqbuf | submission queue buffer |
| sqpa | submission queue IO physical address |
| cqbuf | completion queue buffer |
| cqpa | admin completion physical address |
|
static |
Submit an entry at submission queue tail.
| q | queue |
| int nvme_wait_completion | ( | nvme_queue_t * | q, |
| int | cid, | ||
| int | timeout | ||
| ) |
Wait for a given command completion until timeout.
| q | queue |
| cid | cid |
| timeout | timeout in seconds |


workflow {{ workflow_detail.pk}} details
session: {{ workflow_detail.session}}
index: {{ workflow_detail.index}}
mapper: {{ workflow_detail.mapper}}
assembler: {{ workflow_detail.assembler}}
analysis: {{ workflow_detail.analysis}}
status: {{ workflow_detail.status}}
Update
Delete
{% endblock %}


Types and expressions
A coq script extracted from the chapter 2 of the book
Exercises
- Exercise 2.5, page 30 and 2.6, page 32 Simple expressions
- Exercise 2.7, page 36 Polynomials
Errata
- Page 24: section <IP_ADDRESS>:
In the sentence "In the second example, the function Zplus expects an integer..." the word Zplus should be replaced by Zmult
Going home
Pierre Castéran


The French Horn is an often overlook instrument because of its silly appearance.  Ah the life of the mighty majestic beast that is the dog.
Clean code makes the world a much finer place to live in. And hula skirts of course.


Beta This is a test version of the layout of this page. Take the survey to help us improve it
Guidance
Steps to follow to become an academy sponsor.
Sponsors are responsible for:
Sponsors work closely with the governing body to run the academy.
Regional schools commissioners ( RSCs ) aim to match sponsors with schools in the sponsor’s local area.
An academy sponsor is an organisation or person who has received approval from the Department for Education ( DfE ) to support an underperforming academy or group of academies.
Sponsors work with the academies they support through the academy trust. An academy trust is the charitable company that runs an academy or a group of academies.
Any of the following can apply to become an academy sponsor:
Academy sponsors are responsible for:
If you don’t comply with your responsibilities as sponsor, the Secretary of State for Education can remove your sponsor status.
If you’re interested in becoming an academy sponsor, you should first contact your RSC office to discuss your ideas.
If your RSC agrees that you should apply, DfE will nominate a ‘sponsor lead’ to work with you. Your sponsor lead will ask what sort of academy you wish to sponsor so they can advise on the sponsor opportunities in your region. They’ll also ask if you are already working with a particular school that may need a sponsor.
If you’re an academy, you must ask your academy trust to pass a resolution to show that they agree with your plans to become a sponsor.
If you’re a state-funded school that isn’t an academy, you’ll need to do the following before you can apply to become a sponsor:
If your school has a religious character, you’ll also need to get approval from your relevant religious body (for example, the local diocese for a church school) before you can apply.
Other organisations or individuals don’t need to show that they have consent.
We also recommend that you read ‘Multi-academy trusts: good practice guidance’ before you begin the application process.
This guide gives more detail on what RSCs will look for when assessing your application.
As part of the sponsor application process, you’ll need to show:
If you’re a school or academy, we recommend that you inform parents and the local community that you intend to become an academy sponsor. You should consider their views before applying.
Other organisations applying to become a sponsor need to prove that they have the skills, expertise and ability to help schools improve. This includes universities.
If you’re an overseas sponsor, you’ll also need to show that you have good knowledge of the British education system.
If you wish to become a sponsor in an individual capacity, you’ll need to demonstrate at least one of the following:
You’ll also need to show:
You should fill in the academy sponsor application form and submit it to us.
You should work with your sponsor lead to complete your application.

If your application is successful, your RSC will select an appropriate school that needs a sponsor. If this school isn’t an academy, it will have to convert to academy status before you can become its sponsor.
Your RSC will ask you to explain what specific support you would offer the school and to provide evidence that you can give this support.
Your RSC will also discuss the plans for the school with the local headteacher board ( HTB ) . If the RSC agrees that you’re the right sponsor for the academy, they will approve the match.
If the school you’ve been matched with is a ‘school causing concern’, your RSC will name you as the school’s sponsor as soon as they’ve approved the match. Schools causing concern are schools that are ‘coasting’, have been judged inadequate by Ofsted, or have failed to comply with a warning notice from their RSC . Read more about ‘schools causing concern’ .
You’ll officially become the school’s sponsor once the school has signed its funding agreement and has become an academy.
Converter academies can also join your trust, if the RSC agrees.
Once the school you’re going to sponsor has been granted an academy order (ie confirmation that it can become an academy), you’ll receive a grant to cover the new academy’s pre-opening costs.
Read guidance on sponsored academy funding to find out what type of start-up grant you’ll receive.
There are several places you can go to for support and guidance on developing your multi-academy trust .
Published: 25 March 2014
Updated: 9 December 2016
From: Department for Education


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Moments
| Moments | |||
|---|---|---|---|
| N | 135 | Sum Weights | 135 |
| Mean | 966022.481 | Sum Observations | 130413035 |
| Std Deviation | 1568838.29 | Variance | 2.46125E12 |
| Skewness | 3.23070233 | Kurtosis | 14.5979115 |
| Uncorrected SS | 4.5579E14 | Corrected SS | 3.29808E14 |
| Coeff Variation | 162.40184 | Std Error Mean | 135024.101 |


{% extends "base.html" %} {% block content %}
Number of mathces
Number of wins
Number of draws
Number of losses
Goal for
Goal against
Shots
Shots on Target
Fouls
Corners
Yellow cards
Red Cards
Team's statisctic


NEW
PUBLICATIONS
Family Research in Monroe County, Ohio
-Volume 5 A continuation of the series featuring newspaper
columns published by Catharine Fedorchak. This is a continuation of the series
featuring newspaper columns published by Catharine Fedorchak. This book s
highlights include: Roots in Rumbach, Monroe County genealogical hints, Monroe
County wills, Ohio land grants, death records, cemetery inscriptions, Civil War
fatalities, Hope Ridge early settlers, O. R. & W. Railroad, obituaries,
Zion Evangelical church records, deeds, naturalizations, early area histories
and more!
Plus articles on the families of Dougherty,
Denbow, Burkhart, Powell, Dye, Bryson, Adamson, Roos/Rose, Mozena, Smith,
Schooley, Miller, Kittel, Becker, Henthorne, Rucker, Pfalzgraph, Winland,
Latshaw, Trotha, Cuntz, Steed.
There s also an all-name index that includes over
4,000 individuals in Monroe County. See our Publications for Sale page for
information on ordering.
Monroe County, Ohio
Cemetery Inscriptions Benton & Washington Townships
Our fifth volume of
cemetery inscriptions includes all 26 known cemeteries of these two townships.
We ve also included Carson Cemetery in Grandview Township, Washington County,
Ohio, as it was the burial site of many residents of the village of
Cochransville, Monroe County, Ohio. Cemetery records are extremely important
because they are often the only record of the birth and death of an individual
who lived before 1867.
Indexed, soft cover,
154 pages. Price is $16.00 plus $2.00 postage. OHIO residents add $1.04 tax.
Order your copy from the Monroe Chapter OGS, Box 641, Woodsfield, OH 43793.
Green Township Cemetery Inscriptions And Gleanings All of the known cemeteries in Green Township have been copied and included in this volume. Also included are Laings Presbyterian early church records, Jones Bible Records, an account of Peter King (the only man executed in Monroe County), and Green Township history. Every known name has been included in the index -- that is, the index is not just a surname index.
Monroe County, Ohio Newspaper Abstracts 8
March 1844 25 August 1852 compiled
by Rita Bone Kopp. This book helps recover some of the information lost in the
courthouse fire of 1867. Abstracted from issues of the Spirit of
Democracy newspaper, the volume contains valuable genealogical
information that can t be found anywhere else!
Some families included are:
Massie. Lunsford, Baker, Burkhart, Brubach, Yoho, Miles, Bidenharen, Mann,
Ball, Walter, Clingan, Donner, Luckey, Dougherty, Bottenfield, Orange, Calvin, Cox,
Lang, Lincicome, Dunn, Noffsinger, Nieb, Gates, Buskirk, Mallory, Ringer,
Jones, Largent, Okey, Tisher, Von Trotha, Neuparz, Billman, Hamilton, Howell,
Lang, Devoe, Green, Gates. Also War of
1812 company, Graves of Civil War soldiers, tracing land records, Zion
Evangelical Church records, Hugeunots, German-Swiss Research, Research in
National Archives, Center Township Minute Book, French Ancestors.
$20.00 plus $2.00 postage Ohio Residents add $1.30 tax
Family Research in Monroe County, Ohio Volume 7 by Catharine Fedorchak -- Catharine's Volume 7 is now published. A description of this book will be available soon on this site. $20.00 plus $2.00 postage Ohio Residents add $1.30 tax
Family Research in Monroe County, Ohio Volume 8 by Catharine Fedorchak. This
volume includes the newspaper articles written by Mrs. Fedorchak between 1986
and 1988. Records from the Civil War, death records, court of common pleas,
cemeteries and newspapers are incorporated with features on these families:
Henthorn, Weisend, Yontz, Haren, Enochs, Burkhart, Baldwin, Allen, Guthrie, ,
Brown, Morris, Bare, Ruch, Krebs, Trembly, Okey, Brister, Dailey, Fisher,
Cooley, Sprout/Sproat, Hofer, Diehl, Fisher, Gauding, Yockey, Weaver,
Pfalzgraph, Martin, Christman, Ullman and Fankahuser. Index includes hundreds
of other individuals in Monroe County. Order from the Monroe County
Genealogical Chapter, Box 641, Woodsfield, OH 43793. $20.00. Ohio residents add
$1.30. Postage & Handling $2.00.
Now
Available: Wayne Township, Monroe County,
Ohio, Cemetery Inscriptions and Gleanings. Lists the tombstone inscriptions
from the cemeteries of Barber Ridge, Christy-Sloan, Conner Ridge Methodist, New
Conner Ridge, Hartshorn Ridge and Winland-Pryor. Also includes additional
information such as obituaries and notes on selected burials. Indexed,
soft cover, 72 pp, $9.00. Add $2.00 postage and Ohio residents add 59
cents sales tax.
Family Research in Monroe County, Ohio Volume 9 by Catharine Fedorchak. Order
from the Monroe County Genealogical Chapter, Box 641, Woodsfield, OH 43793. Families mentioned in this
vol. (9) include Farley, Hamilton, Miller, Tisher, Carpenter Keevert,
Pennington, Clegg, Dearth, Wise and Burkhart. In addition it contains
information on cemeteries, land, death, Court of Common Pleas Records.
The book contains 335 pages. The book
is a steal at just $20.00. Ohio residents add $1.50 for sales tax. Postage &
Handling $2.00 for the first book; $0.75 for each additional book.
Family Research in Monroe County, Ohio, Volume 10 by
Catharine Fedorchak. This is the tenth and last
volume in this series comprised of genealogical columns written by Mrs.
Fedorchak for the Spirit of Democracy newspaper from 1967 until her death in
1994. Cemetery, court of common pleas, marriage, land patents, death, wills,
Civil War Discharges, church, items from newspapers are some of the records
included in this volume. Families featured in this book include Haugh, Curtis,
Davis, Dement, Schwallie-Ulrich, Edington-Edrington, Stille, Dye, Hall, Craig-Piatt,
Yunkas, Cisne/Cessna, Kochert, Dicks, and more. Every name index lists
thousands of other individuals. Paper. 330 pp. $20.00.Postage is $2.00 for the
first item and $.75 for each additional item. Ohio residents add $1.50 sales
tax.
Send orders to: Monroe County Genealogical Society, P.O. Box 641, Woodsfield, OH
43793-0641
Click here for the Short-Cut Table of Contents


3.4. Story Time: Galton’s Pendulum¶
In the 1880s, scientific progress had opened up a novel entertainment option for Her Majesty Queen Victoria’s subjects: have their psychometric traits measured in Francis Galton’s Science Galleries, at the South Kensington Museum. Visitors paid “3 pence” (presumably some sort of currency) and then partake in a series of encounters with novel and strange machinery. Thousands of participants volunteered, establishing one of the first large-sample psychometric databases in history. Amongst the things measured was how long it took the curious museum goer to press a button in response to a simple light stimulus.
Galton measured response times using an ingenious design called the Pendulum chronograph. Simplified, the position of a swinging pendulum at the time point of a key-press could be compared to a reference table. This Galton used as an estimate of response time, which allowed a state of the art resolution of 1/100th of a second. Never before had mental process been measured with higher precision.
Other experimenters around this time were stopping response latencies with manual stopwatches. Galton himself conducted human geographic surveys by walking around London, mentally rating the women he saw for their attractiveness, and covertly noting the ratings with a self made device: a paper cross, into which he, in his pocket, punched a hole indicating if the woman was of below average, average, or above average “beauty”.
It is not advised to mention such practices in a contemporary presentation of scientific results. At the very least, the practices would be seen as grossly inaccurate, and for many, highly open to experimenter bias. In particular, they lack precision. Also, to be frank – they sound like a lot of work! While some might decry a lack of entrepreneurial spirit, most of us will readily admit the advantage of computerized methods.
Progress has made the work of the researcher much simpler. (Remember this sentence whenever you feel frustrated attempting to program an experiment! At least you are not Galton, keeping a pendulum chronograph in working condition, or walking across London to mentally file people into rather crude and subjective boxes.) Today, computers can show various visual input, play all kinds of sounds, and accurately measure … well, a narrow kind of behavioral responses made by experimental participants. Our computer’s graphics and sound cards, and the keyboard and mouse drivers, are relatively arcane. There is as of yet no convenient way to get a computer to show words or pictures on a screen for the purposes of psychological experiments from our favourite programming language, R. There are many commercial, closed-source solutions, which we will all ignore in favour of the powerful and open Python options.
Why? Perhaps the most important benefit computerized experiments have is that they are much more reproducible. Using an Open Source program and making experimental stimuli and scripts available online allows other researchers to 1. exactly retrace what happened in the original experiment, 2. repeat it ad libitum. To actually exploit this reproducibility potential, we must use software that is open. The biggest open source experimental presentation software is Psychopy (citation missing).


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Handling Errors
Many functions in Libbson can result in errors. To simplify this, Libbson contains a structure called bson_error_t. This structure enapsulates an error from the underlying system and can be passed back up the stack. Think of it as an exception as an out parameter.
The structure contains domain and code fields that can be used to check for specific error conditions. The value of these fields is API specific. If you simply want to print an error message, use the message field of bson_error_t.
Functions that can result in error specify the return value that indicates the error out parameter was initialized. Typically this is 0 or NULL, but may sometimes be -1 depending on the API.
This structure should be stack-allocated and requires no explicit freeing.


Data Attributes
Hovering over each film poster will reveal the non-visible data attribute which is the film's rating out of 10.
The data has then been used within a script to calculate the average rating of the 4 films


Purpose
Know the why behind what you do and build
{% include "../src/partials/head.html" with { 'relative_path_to_root': relative_path_to_root, 'title': 'Zalando Tech – Europe’s Most Fashionable Tech Department', 'description': 'This is the home of Zalando Tech. We dress code! Check out our job page for available positions, or catch up on what is happening in the world of fashion technology in our blog.', 'keywords': 'Zalando Tech, careers, jobs, blog, company culture, working' } %}
{% include "../src/partials/header.html" with { 'relative_path_to_root': relative_path_to_root, 'active': 'home' } %}Zalandos are passionate about collaboration and giving back to the community. Check out our open source projects to learn more about the technologies we are contributing to and building.
Zalando is Europe’s leading online fashion platform, with more than 16 million customers in 15 countries. Our department has built most of our platform in-house, using open source and cutting-edge technologies such as Scala, Cassandra, Clojure, and React.js. We work in small, agile, autonomous teams and follow principles that enable us to produce the most delightful shopping experiences possible.
Purpose
Know the why behind what you do and build
Autonomy
The freedom to solve hard problems in original ways
Mastery
Gain knowledge to build your career at Zalando and beyond


Question 1
Answer to question 1
Question 2
Answer to question 2
Question 3
Answer to question 3
Question 4
Answer to question 4


About
Sarah Kidd Hubbard - Developer
I love writing code, but I'm not a fan of writing paragraphs. Here's a little about me:
Coffee or tea? Coffee
Dress or pants? Dress
Winter or summer? SUMMER
Early bird or night owl? Early bird
Introvert or extrovert? Introvert
Rural or urban? Urban
Crushed ice or cubed? No ice, please
Roller coaster or ferris wheel? Coaster
Dancing or singing? Dancing
Cake or ice cream? Ice cream


I am a Full-stack Web Developer. I'm the founder and CTO of the Thenx Ghana. I love a mixer of coffee & tea.
I started learning how to manuplate images with photoshop at the age of 14 years old becasue it was one of the basic things my senior brother showed me. I began to have an intrest in how code also works at after senior high school.
Video gaming is one of the most interesting things I found at a tender age which has been a hobby I use to burn time when am having a hard time thinking through a project.