2021-01-01
No Rules Rules was book number eleven for our book club at work. We had previously read Powerful by Patty McCord, so many concepts weren’t new, but it was an interesting new perspective.
Overall, how I felt about the book was a bit of an emotional roller coaster.
The whole book is laid out as a sequence of quite specific steps a company can take to become like Netflix, by
Each dimension then is iterated over three times. The first step, and a precondition for all the rest, is to have high “talent density”, i.e., only have really high performers working at the company. For Netflix, this kind of happened when they had to lay off a third of their workforce in 2001: executives realized that everything was suddenly better because all the “just adequately” performing employees were gone.
Well, what if your company happens to not go through an existential crisis? How do you identify and get rid of all the non-stellar employees in a way that doesn’t cause (justified) outrage? Is this even possible for an already established company? I couldn’t find an answer to this, and seeing that all other steps require this high talent density, it felt like the rest of the book might be difficult to apply.
I did like the reference to the “bad apple” study (Felps, Mitchell, and Byington 2006), though, seeing that it was also mentioned in The Culture Code: a single underperforming (or actively disruptive) team member pulls down the performance of the whole team significantly. In The Culture Code, the focus was on how there can be “good apples” to counteract this; in No Rules Rules, the emphasis is on the negative effects and eliminating them.
Another great reference introduced me to the origins of the mythical “10x engineer”: a paper from the 60s describes how programmers were given standardized tasks and how vast the performance differences between individuals were (Sackman, Erikson, and Grant 1968). I’m not convinced that this should be taken at face value for engineers 50 years later, though.
On a related note, the authors point out multiple times that the model they describe isn’t applicable everywhere. It’s great for creative endeavours where failure isn’t critical, but probably shouldn’t be used when you want to produce “a million doses of penicillin” or “ten thousand identical cars”.
Now, software engineers fancy their craft as somewhat creative at times—can the model be applied here? My guess is that it still depends: are you writing software to control a nuclear submarine, or to stream cartoons? What about an app for personal banking?
I don’t recall the book using any examples involving Netflix engineers, so it’s not obvious if the same principles are applied also for engineers, or if engineering is somehow treated differently within Netflix as “not all that creative”.
There are surveys comparing employee happiness among FAANG (and MSFT) workers such as this one conducted by AngelList and Blind in October 2019:
Or a similar survey from November 2020, this time by Blind alone, where Netflix tops every single category. There are of course valid questions such as “how was this measured?”, “this is all self-reported, survivor bias much?”, or “what about significant differences between engineering and the rest of a company?”, but it’s at least some evidence that Netflix seems to be doing alright as an employer.
While overall I felt the time spent reading the book was a good investment, I didn’t exactly nod in agreement with every single section.
Some of the examples talk about people for whom the culture wasn’t a good fit, like “Michelle” and her problematic “business” trips to Las Vegas with suspiciously high food and drink expenses (which got her fired). To me, these examples felt very contrived and eroded my trust in the authenticity of the examples for things working out fine—but to be fair, many of the latter use full names of high profile Netflix employees. There also didn’t seem to be much in terms of helping people adapt to such a culture; apparently, you’re either a fit or you are not.
Reading about the Keeper Test 1, I couldn’t help but being slightly horrified at the thought of having this kind of pressure looming over me. The examples of people having trouble to adjust to it didn’t exactly help, either: what happened to Marta, who moved from Spain to the Netherlands, and had trouble breathing in the elevator every single morning from all the stress? I need closure! Since her full name was given, I kind of expected to read about a marvellous transformation, but for all I know, she still suffers from anxiety every day. (But she still works at Netflix, I checked!)
Chapter 10 talks about scaling Netflix culture globally. There are a few neat ideas, like a culture map of the company and anticipating regional challenges for strong deviations in certain dimensions: “communication in Japan is a lot more high-context than at Netflix, we should probably take that into consideration for the local workforce there.”
The rest of the chapter, however, read a bit like people realizing for the first time in their lives that the world consists of more than just the United States, and then applying that knowledge by means of a cultural clichée hammer: “the Dutch do X, the Japanese do Y”—I feel like there’s more nuance to it.
My overall impression oscillated between “this all makes so much sense” and “of course they think it’s great, it’s their company, but it sounds like a cult”.
I concluded that it’s unlikely for any company I work at to ever undergo the complete “Netflixification”, but there are a few key points I’m taking away as useful on their own.
A policy of not just unlimited “personal time off” days, but unlimited holidays. Multiple references indicate that people take about the same amount of time off as they would with limited holidays, but it would be more flexible, and signal trust in employees. (As long as leaders model actually taking time off.)
No expense policy. At Netflix, the entire policy is
Act in Netflix’s best interest
(and if you abuse it, you’re fired). This might be too extreme for many companies, but there probably is some middle ground between “every expensed pencil goes through a five-tier approval process” and “there is no approval process”.
Defaulting to public visibility. With very few exceptions for things that are personal, everything (meeting notes, strategy docs, roadmaps and so on) should be accessible by everybody. I don’t see any benefit to selectively withholding information, and having the information provides context.
Actually giving and encouraging feedback. I’m pretty sure every company ever says that personal growth and feedback are very important to them; actually living that is another story. I want to point out the “Live 360s” from the book (public, real-time feedback frenzies) as going way too far for my taste, though—but “Only say about someone what you will say to their face” sounds like a solid principle. And the “4A” guidelines for providers and recipients of feedback (aim to assist, actionable, appreciate, accept or discard) feel useful as well.
Leading with context. Understanding why you’re doing something is always going to get better results than being an implementation robot.
Overall, I wouldn’t say that No Rules Rules rules indiscriminately, but there are certainly a few interesting ideas in there, and it’s an entertaining read.


Random Satellite Map Classifier
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This demos the ways you can react to appearing DOM elements.
They can appear:
1. by adding or removing some class like "hidden" triggered by a click handler
2. by loading data via XHR and insering it into the DOM (Like jQuery.load() for example)
Triggered by adding/removing a classes:
The trick here is to look for the HTML that changes and choose some part of it as the "content" selector in Form-O-Fill.
Then activate "automatic rematch" (in the popup or in settings) and the rule will show up once the HTML is present in the DOM.
The rule will look something like:
var rules = [{
name: "Element visibility 1 : classes",
content: /<select class=""/, // Activate rule if the "hidden" class on the select is gone
fields: [{
selector: "select",
value: "1"
}]
}
];
Triggered by some XHR
This is even easier.
The XHR will change the HTML or will change the values of some element so the same applies as above.
Here two new <option>s will be added after 2 seconds and then the rule will trigger because some
known option is present. Requires "automatic rematch" to be active.
After waiting two seconds the extension icon will show one matching rule.


[1]
more complex units tend to direct 38 re-direct.
[2]
more complex units tend to default out after the path it should call back of POIs no Text-to-Speech.


< Home
Enter your username and password to login.
Log In
Solution and thoughts on how I got it to work can be found here


{{ content }}
Date
Saturday, July 8th @ 6PM
Rehearsal Dinner Location
Cliff Dwellers Club
200 S. Michigan Avenue, 22nd Floor
Chicago IL, 60604-2487
Enter the building. Tell the security guard you are here for a private party at Cliff Dwellers and they will direct you to the correct elevator to take (not all of the elevators reach the 22nd floor).
Brunch
Stop in, grab coffee and a bagel, and say goodbye before your flight.
Date
Monday, July 10th @ 8AM-11AM
Brunch Location
Palmer House Hilton (we have a private conference room reserved).
Mezzanine.
Enter the building and take the elevator to the Mezzanine (the floor marked 'm').


Susan Granger's review of "I STILL KNOW WHAT YOU DID LAST SUMMER" (Columbia Pictures)
After its predecessor, "I Know What You Did Last Summer," did screamingly well at the box-office, a sequel was inevitable. Jennifer Love Hewitt and Freddie Prinze Jr. are back but, this time, they're spending a Fourth of July weekend on a remote Bahamian island which seems a perfect locale for the stabbings, shootings, and stranglings that occur in the predictable script by Trey Callaway, directed by Danny Cannon. The premise set up in the original film is that four teenagers are being hunted by a hook-handed fisherman, the victim of a supposedly fatal hit-and-run automobile accident that took place in North Carolina. Two of the original four are back, along with the vengeful Muse Watson, joined now by the pop singer Brandy and her boy-friend, Mekhi Phifer, along with clean-cut Matthew Settle. They have won this trip, courtesy of a radio station contest, and check into a seedy hotel where the desk clerk murmurs, "I see four spoiled city kids who would not know a hurricane if it blew up their ass." Which is somewhat relevant since a hurricane's approaching.  What made the original somewhat interesting was trying to figure out the identity of the killer, plus the lingering suspicion that one of the perky, paranoid four friends was at fault. Both those elements are missing from this cheesy, teens-in-jeopardy chiller. On the Granger Movie Gauge of 1 to 10, "I Still Know What You Did Last Summer" is a slasher 3. It's just another formulaic fright flick.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Are you tired of looking up how many calories your food has? Do you want to find calories on a mobile application?
Click here to download a mobile application that allows you to find how much calories are in foods by taking a picture.
[Instructions] Press the camera icon on the top right. Take a picture of the food item. Press the checkmark button. The results should appear similar to the photos above.
[Additional Steps] Input data into the Track your Calories tab and keep adding meals to find the total amount of calories and fats you've eaten a day.


001 (ns kulu-backend.invoices.search
002 (:require [kulu-backend.config :as cfg]
003 [kulu-backend.invoices.model :as inv]
004 [kulu-backend.search.common :as search]
005 [kulu-backend.search.schema :refer [invoice-mapping-type]]))
006
007 (defn- filter-attrs
008 [i]
009 (select-keys i [:organization-id :name :status :expense-type :date
010 :amount :currency :status :conflict :created-at :user-name :category-name]))
011
012 (defn create-or-update
013 "transforms an invoice into a ES document format and upserts it."
014 [{id :id :as invoice}]
015 (->> invoice
016 filter-attrs
017 (search/put invoice-mapping-type id)))
018
019 (defn migrate-indexes
020 "indexes all processed invoices"
021 []
022 (doseq [i (inv/all-for-search)]
023 (create-or-update i)))
024
025 (defn lookup [id]
026 (search/lookup invoice-mapping-type id))
027
028 (defn search
029 ([q filters sort size-params]
030 (:hits (search/search q filters sort size-params))))


Jane Doe
Super Power User
Elements
Basic Elements
Text Elements
FlexboxGrid
Buttons
Header & Footer
Listviews
Forms
Tables
Dialog & Popup
Panels
Autocomplete
Collapsible & Accordions
Extended Elements
Tabs
Cards
Search
Icons
Charts
Toasts
Bottom Sheets
Layouts
Real-World-Examples
Profile
Dashboard
Gallery
Chat
E-Mail Inbox
Team
Products & Services
Blog
Blogpost
Information
Nice to Know
Colors & Styles
Credits & License
Clipboard
Hot Stuff
Cloud
Fillcolor
Page Title
(home)


Max said, “Don't you ever feel inspired to paint the Finnish countryside in summer?”
“It's all so damned green,” she answered.
Then she told us about the squirrel, the one squirrel which has appeared on the island; and it slept under her neck and tried to collect food there. As the relationship between artist and squirrel developed, the squirrel came to expect a game at four o' clock in the morning. Tove Jansson had to get out of bed and pretend to be a tree. The squirrel would run up and down her frozen limbs.
Oswell Blakeston, Sun At Midnight (1958)


Beta This is a test version of the layout of this page. Take the survey to help us improve it
Transparency data
A list of allocated high needs place numbers to help collaboration between local authorities and institutions.
These publications present the allocated high needs place numbers by institution for the academic years:
A notes page is included within each of the documents.


0 items
Products > Dog harness > Coastal harness
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Equation Parser, let users define size of charts (make sure dragging
only goes to the edge of this bc the plugin makes dragging beyond that
look bad). Embed link. See ball moving according to the graph. Get raw
array of values. Add points by clicking at a spot. Negative t.
Available presets for simple kinematics like having one for const
acceleration. Make weird resolutions not look terrible. Add slope 0
acceleration option.
Guide to future me for when I have time to implement this:
I figured out what is wrong with the lambdas. I am trying to
model the math for how they should work with traditional kinematics
scenarios. The problem is that the fundamental way these graphs work
does not support those scenarios. For example, if you have x slope for
position which suddenly changes the velocity graph should be a
constant then instantly become a different constant. Any changes take
at least the distance between two points to occur. So, the easiest way
to fix this would be having two points at each spot where there should
be one point so it can instantly change. Maybe three points bc in the
acceleration graph it would show the acceleration going up to x for an
instant then immediately back down. Anyways, the structure will have
to be re-done to support this so it will take a little while.


A health check of NBFCs points to prolonged trouble
Two of the four gauges compiled by Bloomberg show weakness remains
by BloombergThe health of India’s shadow banks remained weak last month as a credit crisis continued to sting.
Among four indicators compiled by Bloomberg News covering areas including liquidity and share performance, three were stuck in the same position as the previous month, with two at levels indicating weakness.
Another gauge showed total outstanding debt increased at 50 financial firms and other companies impacted by the crisis, as banking-system liquidity remained buoyant given the central banks monetary easing.
To be sure, the absolute amount of outstanding debt wasn’t at alarming levels, and access to credit markets for healthier financiers could help get money flowing more efficiently again -- just what the flagging economy needs. But if more troubled borrowers continue to add rather than pare borrowings, that could prolong the crisis.
Authorities have taken more steps recently to help the shadow bank sector, which plays a vital role in getting money to everyone from small merchants to property tycoons. Some observers see an extended battle, as the nations slowing economy complicates those efforts.
Moody’s Investors Service recently warned that a prolonged credit squeeze among India’s financiers might worsen, just as S&P Global Ratings said risks of contagion are rising in the financial sector. The premium that investors demand to hold shadow lender bonds over sovereign notes is persistently high, and a custom gauge of shares of 20 financial firms and other companies impacted by the crisis remained sluggish.
To help creditors recover their money quickly from troubled shadow lenders, India last week unveiled insolvency rules. The more than 15-month-old credit crisis has choked economic growth to its slowest pace in six years, and company defaults on rupee bonds are at a record high. Dewan Housing Finance Corp. and Altico Capital India Ltd. are among lenders that defaulted this year, adding to the risk-off sentiment in local markets.
The scores attached to each of the indicators have been calculated by Bloomberg by normalizing the deviation of the latest value of the indicator from its yearly average and have been assigned on a scale of 1 to 7, with one implying weakness and 7 showing strength.
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PyLadies
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BeHappy's localisation system is very simple. The localisation information is contained in the BeHappy.loc file for the BeHappy application, and in the different *.loc files for the add-ons. If you want to add a localisation system to your add-on, you'll have to create a localisation file. The file name must be the same than the add-on, with a .loc at the end.
First a line beginning with a '#' will be ignored. You can use this to put somme commentaries. Then come the languages list. The add-on's "native" language should come first, and be between a '[' and a ']'. Each language should be put on a different line. As an example:
[English]
Français
Deutch
Then you can put the translations. First put the string to translate, bewteen a '[' and a ']', and then put the translated strings. Each string should be put on a different line. The languages used must be in the same order than the one used previously. As an example:
[Spawn new window]
Créer une nouvelle fenêtre
Neues Fenster öffnen


Quick overview¶
Here are some quick examples of what you can do with GeoWombat
objects.
To begin, import GeoWombat and Xarray:
In [1]: import geowombat as gw
In [2]: import numpy as np
In [3]: import xarray as xr
Open a DataArray with a GeoWombat accessor¶
Any DataArray or Dataset will have the GeoWombat accessors appended:
In [4]: data = xr.DataArray(np.random.randn(2, 3),
...: dims=('x', 'y'),
...: coords={'x': [10, 20]})
...:
In [5]: print(data)
<xarray.DataArray (x: 2, y: 3)>
array([[ 0.88781501, -0.35932686, 0.53659426],
[-1.19902484, -1.54678157, -1.31214245]])
Coordinates:
* x (x) int64 10 20
Dimensions without coordinates: y
In [6]: print(data.gw)
<geowombat.core.geoxarray.GeoWombatAccessor object at 0x7fdc58252518>
Open a file¶
GeoWombat
has its own file opening API (Raster I/O):
In [7]: from geowombat.data import rgbn
In [8]: with gw.open(rgbn) as src:
...: print(src)
...: print(src.gw)
...:
<xarray.DataArray (band: 4, y: 403, x: 515)>
dask.array<open_rasterio-3362294af964b9662dc51d74accec470<this-array>, shape=(4, 403, 515), dtype=uint8, chunksize=(1, 64, 64), chunktype=numpy.ndarray>
Coordinates:
* band (band) int64 1 2 3 4
* y (y) float64 2.05e+06 2.05e+06 2.05e+06 ... 2.048e+06 2.048e+06
* x (x) float64 7.93e+05 7.93e+05 7.93e+05 ... 7.956e+05 7.956e+05
Attributes:
transform: (5.0, 0.0, 792988.0, 0.0, -5.0, 2050382.0)
crs: +init=epsg:32618
res: (5.0, 5.0)
is_tiled: 1
nodatavals: (nan, nan, nan, nan)
scales: (1.0, 1.0, 1.0, 1.0)
offsets: (0.0, 0.0, 0.0, 0.0)
AREA_OR_POINT: Area
DataType: Generic
filename: /home/jcgr/Documents/scripts/Python/git_repos/geowombat/e...
resampling: nearest
<geowombat.core.geoxarray.GeoWombatAccessor object at 0x7fdc58248080>
Write a raster¶
import geowombat as gw
with gw.open(l8_224077_20200518_B4, chunks=1024) as src:
# Xarray drops attributes
attrs = src.attrs.copy()
# Apply operations on the DataArray
src = src * 10.0
src.attrs = attrs
# Write the data to a GeoTiff
src.gw.to_raster('output.tif',
verbose=1,
n_workers=4, # number of process workers sent to ``concurrent.futures``
n_threads=2, # number of thread workers sent to ``dask.compute``
n_chunks=200) # number of window chunks to send as concurrent futures


ID: ${manager.id}
Email: ${manager.email}
Office Number: ${manager.officeNumber}
ID: ${engineer.id}
Email: ${engineer.email}
Github: ${engineer.github}
ID: ${intern.id}
Email:${intern.email}
School: ${intern.school}


Responsive Video
Aspect Ratio 4:3
Responsive video
Aspect ratio 16:9
Bootstrap Jumbotron
Bootstrap Tutorial
Bootstrap is the most popular HTML, CSS and JavaScript framework for developing responsive and mobile first projects..
This is some text without bootstrap jumbotron..
Bootstrap basic well
Bootstrap well size
Small Well
Normal well
Large well
Bootstrap Alert links
Closing Alerts
Animated alerts
Bootstarp button styles
Bootstrap button with different tags
Link Button
Bootstrap button size
Block level button
Active / Disabled button
Bootstrap button groups
Applying size to group of button
Vertical group button
Justified button group
Using "a" tag
Using "Button" tag
Nesting button groups and dropdown menus.
Split button and dropdown
Bootstrap Glyphicons
Envelope icon :
Envelope icon as a link :
Search icon on a button :
Print icon on a styled link button :
print
Bootstrap badges
News5
Comments10
Updates6
Bootstrap Labels
Example New
Example New
Example New
Example New
Example New
Example New
Bootstrap basic progress bar
Progress bar with label
Colored progress bar
Striped Progress bar
Animated progress bar
Stacked progress bar
Bootastrap pagination
Pagination - Active State
Pagination - Disabled state
Pagination Sizing
Bootstrap Breadcrumb
Bootstrap Pager
Align buttons
Bootstrap List Group
- First Item
- Second Item
- Third Item
List group with badges
- First Item3
- Second Item6
- Third Item8
List Group with linked items
List group - Active state
List group - Disabled state
Contextual classes - Colored list group
List group - Custom content
Bootstrap Panel
Panel Heading
A Basic panel (panel Content)
Panel Group
Panel Heading
A Basic panel (panel Content)
Panel Heading
A Basic panel (panel Content)
Panel Heading
A Basic panel (panel Content)
Panel Group with contextual classes
Panel Heading
A Basic panel (panel Content)
Panel Heading
A Basic panel (panel Content)
Panel Heading
A Basic panel (panel Content)
Panel Heading
A Basic panel (panel Content)
Panel Heading
A Basic panel (panel Content)
Panel Heading
A Basic panel (panel Content)
Bootstrap Dropdown
Dropdown position, Active and Disabled
Dropdown left
DropUp
Dropdown Accessibility
Bootstrap Simple Collapse
Bootstrap collapsibles are useful when you want show and hide large amount of content..
Collapse
Collasibles are useful when you want to hide and show large amount of content..
Collapsible panel
Collapsible Accordion
Collasibles are useful when you want to hide and show large amount of content..
Collasibles are useful when you want to hide and show large amount of content..
Collasibles are useful when you want to hide and show large amount of content..
Bootstrap tabs and pills
Bootstrap Menu (Horizontal menu / Inline list)
Tabs
Tabs with dropdown menu
Pills
Vertical pills
Pills in a row
Collasibles are useful when you want to hide and show large amount of content..
Collasibles are useful when you want to hide and show large amount of content..
Collasibles are useful when you want to hide and show large amount of content..
Pills with dropdown menu
Centered tabs and pills
Toggleable / Dynamic tabs
Toggleable / Dynamic pills
menu 1
Some content in menu 1
menu 2
Some content in menu 2


{% extends "emails/base.html" %} {% block content %}
You have requested to reset your password. Please follow this link to do so:
{{host}}/user/resetpassword?key={{key}}&token={{token}}
If you did not request a password reset then you can safely ignore this email. The reset link will automatically expire in one hour.
{% endblock %}


""" impyute.imputation.cs.knn """
import numpy as np
from impyute.util import find_null
from impyute.util import checks
from impyute.util import preprocess
from impyute.imputation.cs import mean
from scipy.spatial import KDTree
# pylint: disable=invalid-name
# pylint:disable=unused-argument
[docs]@preprocess
@checks
def fast_knn(data, k=3, eps=0, p=2, distance_upper_bound=np.inf, leafsize=10, **kwargs):
""" Impute using a variant of the nearest neighbours approach
Basic idea: Impute array with a basic mean impute and then use the resulting complete
array to construct a KDTree. Use this KDTree to compute nearest neighbours.
After finding `k` nearest neighbours, take the weighted average of them. Basically,
find the nearest row in terms of distance
This approach is much, much faster than the other implementation (fit+transform
for each subset) which is almost prohibitively expensive.
Parameters
----------
data: numpy.ndarray
2D matrix to impute.
k: int, optional
Parameter used for method querying the KDTree class object. Number of
neighbours used in the KNN query. Refer to the docs for
[`scipy.spatial.KDTree.query`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.KDTree.query.html).
eps: nonnegative float, optional
Parameter used for method querying the KDTree class object. From the
SciPy docs: "Return approximate nearest neighbors; the kth returned
value is guaranteed to be no further than (1+eps) times the distance to
the real kth nearest neighbor". Refer to the docs for
[`scipy.spatial.KDTree.query`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.KDTree.query.html).
p : float, 1<=p<=infinity, optional
Parameter used for method querying the KDTree class object. Straight from the
SciPy docs: "Which Minkowski p-norm to use. 1 is the
sum-of-absolute-values Manhattan distance 2 is the usual Euclidean
distance infinity is the maximum-coordinate-difference distance". Refer to
the docs for
[`scipy.spatial.KDTree.query`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.KDTree.query.html).
distance_upper_bound : nonnegative float, optional
Parameter used for method querying the KDTree class object. Straight
from the SciPy docs: "Return only neighbors within this distance. This
is used to prune tree searches, so if you are doing a series of
nearest-neighbor queries, it may help to supply the distance to the
nearest neighbor of the most recent point." Refer to the docs for
[`scipy.spatial.KDTree.query`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.KDTree.query.html).
leafsize: int, optional
Parameter used for construction of the `KDTree` class object. Straight from
the SciPy docs: "The number of points at which the algorithm switches
over to brute-force. Has to be positive". Refer to the docs for
[`scipy.spatial.KDTree`](https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.spatial.KDTree.html)
for more information.
Returns
-------
numpy.ndarray
Imputed data.
Examples
--------
>>> data = np.arange(25).reshape((5, 5)).astype(np.float)
>>> data[0][2] = np.nan
>>> data
array([[ 0., 1., nan, 3., 4.],
[ 5., 6., 7., 8., 9.],
[10., 11., 12., 13., 14.],
[15., 16., 17., 18., 19.],
[20., 21., 22., 23., 24.]])
>> fast_knn(data, k=1) # Weighted average (by distance) of nearest 1 neighbour
array([[ 0., 1., 7., 3., 4.],
[ 5., 6., 7., 8., 9.],
[10., 11., 12., 13., 14.],
[15., 16., 17., 18., 19.],
[20., 21., 22., 23., 24.]])
>> fast_knn(data, k=2) # Weighted average of nearest 2 neighbours
array([[ 0. , 1. , 10.08608891, 3. , 4. ],
[ 5. , 6. , 7. , 8. , 9. ],
[10. , 11. , 12. , 13. , 14. ],
[15. , 16. , 17. , 18. , 19. ],
[20. , 21. , 22. , 23. , 24. ]])
>> fast_knn(data, k=3)
array([[ 0. , 1. , 13.40249283, 3. , 4. ],
[ 5. , 6. , 7. , 8. , 9. ],
[10. , 11. , 12. , 13. , 14. ],
[15. , 16. , 17. , 18. , 19. ],
[20. , 21. , 22. , 23. , 24. ]])
>> fast_knn(data, k=5) # There are at most only 4 neighbours. Raises error
...
IndexError: index 5 is out of bounds for axis 0 with size 5
"""
null_xy = find_null(data)
data_c = mean(data)
kdtree = KDTree(data_c, leafsize=leafsize)
for x_i, y_i in null_xy:
distances, indices = kdtree.query(data_c[x_i], k=k+1, eps=eps,
p=p, distance_upper_bound=distance_upper_bound)
# Will always return itself in the first index. Delete it.
distances, indices = distances[1:], indices[1:]
weights = distances/np.sum(distances)
# Assign missing value the weighted average of `k` nearest neighbours
data[x_i][y_i] = np.dot(weights, [data_c[ind][y_i] for ind in indices])
return data


Raspberry Pi
Status:
Time on Pi:
Serial Number:
Speed:
CPU:
Cores:
Click a pin to toggle its level
Non GPIO
●
Pin High
●
Pin Low
●
Function
Type
BCM
PHY
PHY
BCM
Type
Function
●
●
input
output


Paul Sorey
9021 12th Avenue Southwest
Seattle, Washington 98106
<EMAIL_ADDRESS>(206) 767-4117
Work Experience as a Landscape Architect:
Consulting Services for Jones & Jones, Seattle, Washington. 1999 to present.
Consulting Services for Pomegranate Center, Issaquah, Washington. 2001 to present.
Commissioned Artist for Salmon Waves at the Ballard Locks, through Seattle Arts Commission and King County Arts Commission. 2000 2001.
Landscape Architect for Jones & Jones, Seattle, Washington. 1988 through 1999.
Landscape Architect for Civitas, Denver, Colorado. 1983 through 1988.
Education:
University of Washington, School of Art, Seattle, Washington, Master of Fine Arts in Sculpture 1993.
Utah State University, Landscape Architecture and Environmental Planning, Bachelor of Landscape Architecture, 1984.
Bethany College, Lindsborg, Kansas, Bachelor of Arts in Fine Art, 1980.
Landscape Architectural and Planning Project Experience:
Singapore Botanic Gardens, Singapore. Project landscape architect with Jones & Jones, Seattle. Master planning of botanic gardens, and design and construction documents for new Orchid Gardens and Orchid Centre research facility, Symphony Island and outdoor amphitheatre, Rainforest Waterfall, Palm Valley palm tree arboretum. 1993-1994.
Waterworks Park, Renton, Washington. Project landscape architect with Jones & Jones. Worked with artist Lorna Jordan from concept to construction documents on design and implementation of 12-acre site artwork and surface water treatment facility. 1994 - 1995.
The Peace Fountain, Cathedral of Saint John the Divine, New York City, New York. Project landscape architect with Civitas. Worked with the artist to create a landscape setting for a 30 tall cast bronze sculpture and fountain. 1985.
Detroit Zoo Polar Bear Arctic Exhibit, Detroit, Michigan. Exhibit and site design for worlds largest polar bear exhibit with summer and winter arctic experience, underwater viewing of polar bears and seals, Inuit summer and winter dwellings, and nineteenth century arctic explorer exhibits. Landscape architect with Jones & Jones, Seattle. 1997 1999.
Disney Wild Animal Park, Orlando, Florida. Master plan and exhibit development for Disneys zoo experience in Florida. Landscape architect with Jones & Jones. 1997 1999.
San Diego Zoo, Heart of the Zoo, San Diego, California. Exhibit & site design for new tropical primates exhibits, with Jones & Jones. 1988 1990.
Woodland Park Zoo, Seattle, Washington. Exhibit and site design for African Savanna Exhibits and Penguin Exhibit, with Jones and Jones. 1992 1994.
US 93, Missoula, Montana. With Jones & Jones, Seattle. 50 miles of new divided highway through Flathead Reservation lands in western Montana. 1999 to present.
Paris Lexington Highway, Lexington, Kentucky. With Jones & Jones, Seattle. Six miles of new divided highway through scenic and historically rich Kentucky Bluegrass country. Jones & Jones provided the needed design and community negotiation skills to complete a design that had been stalled for twelve years because of unresolved issues. 1994 to 1999.
Speer Boulevard Master Plan, Denver, Colorado. With Civitas, Inc. Traffic and land use analysis, planning and conceptual design to generate alignment alternatives for a downtown riverfront arterial relocation. 1987.
Denver International Airport Boulevard Master Plan, Denver, Colorado, with Civitas, Inc. Part of a team to generate alternatives for alignment, landscape treatement and interface with planned urban development along a 16-mile corridor. 1987-1988.
Larimer Square Historic District Streetscape Improvements, pedestrian areas, street furnishings, street trees, master plan through construction, Denver, Colorado, with Civitas, Inc., 1987-1988.
Denver 16th Steet Transit Mall, street furnishings including design of porcelain enameled transit map at bus stops.
Auraria Campus Pedestrian Mall, master plan through construction, Denver, Colorado, with Civitas, Inc., 1984-1988.
Capitol Lake Park Master Plan, urban park and streetscape master plan, Olympia, Washington, with Jones & Jones, Inc., 1990.
Downtown Pueblo Streetscape, master plan through construction drawings, Pueblo, Colorado, with Civitas, Inc., 1985-1986.
Fred Hutchinson Cancer Research Center, site and planting design, Seattle, Washington, with Jones & Jones, 1992-1993.
Awards:
Artist Trust GAP Grant 2001: $1,400 grant for Oaxacan Tin Works project, a collaboration with tin workers in Mexico to make sculptural forms using 3-D design software I developed.
American Society of Landscape Architects Merit Award: US 93 Design Discussions, Flathead Indian Reservations in Western Montana. 2001.
American Society of Landscape Architects Merit Award: The Evolving San Diego Zoo. 1991.


--- layout: page title: About header-img: "img/about-bg.png" ---
Hi! I'm Ashley, I went to Georgia State University for Ceramics and graduated in Fall 2020. Now I'm taking a course at Flatiron for Software Engineering!


Mexican embassy awards Cuban artists
by Redacción DigitalThe Mexican Embassy in Havana granted the Cultural Merit award to Cuban prima ballerina Viengsay Valdes, choreographer Alberto Mendez and Cultural Director of Habana Vieja municipality, Reinaldo Mendez.
Valdes, deputy artistic director of the National Ballet of Cuba (BNC), has shined with her dance in all five continents and has been the guest artist of several galas, companies and festivals in many countries, a Mexican diplomat noted at a gala in Havana’s Cathedral Square.
Cuban Deputy Culture Minister Fernando Rojas and Russian Ambassador Andrei Guskov were present at the ceremony, at which the Mariana de Gonitch Singing Academy, the Okantomi group and the BNC performed.
During the event, Valdes was also presented with the Gitana Tropical distinction, which is granted every year by Havana’s Provincial Culture Directorate to those who contribute to the development of Cuban culture.
‘I would like to express my deepest gratitude to those who appreciate art and all the organizations involved in such a beautiful event. It is a great honor for me to receive this award and above all to dedicate it to all those Cuban women who are also heroines of the dance we have today in Cuba,’ Valdes said. (Taken from Prensa Latina)


Using Docker-compose you have your setup working in no time.
color_lens
Docker
Select the components you need from the tabs above, optionally configure them, and get your
personal Docker-compose file for personal development or trialling solutions.
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Fast development
Because you don't need to install and integrate all these tools separately, but the come together
in one package. Integrate your own Dockerized test-bed solutions and share them with us.
build
Completely Open Source
The test-bed reference implementation is completely open source, and can be found on
GitHub. So start hacking away immediately.
and share your improvements with us.
Trialing crisis management solutions has never been easier
Kick-start your own test-bed today
Get Started
Company info
The DRIVER+ test-bed is developed by many organizations, working together to make the world a safer place.
For more information, please go to the project website.
Contact us
Program manager: Peter Petiet
phone+31 6 xxx<EMAIL_ADDRESS>This project has received funding from the European Union’s Seventh Framework Programme
for research, technological development and demonstration under grant agreement n° 607798.
The information and views set out in this presentation are those of the author(s) and do
not necessarily reflect the official opinion of the European Union.


{{ pageTitle }}
{% include "gs-app/views/pages/home.html" %}
{% include "gs-app/views/pages/users.html" %}
{% include "gs-app/views/pages/user-info.html" %}
{% include "gs-app/views/pages/contact.html" %}
{% include "gs-app/views/pages/settings.html" %}


Nice utility class that extends Ruby’s core String class with some useful-ish extras.
Or something
Methods
- N
- S
- U
Instance Public methods
normalize()
Link
Returns a normalised version of the string, where all UTF-8/accented/weird characters are converted to their nearest ASCII equivalents. This requires the Babosa gem to be installed and available.
Source: show
# File lib/core_ext/string.rb, line 9 def normalize to_slug.transliterate.to_s end
numeric?()
Link
Returns whether or not the string appears to be numeric (ie, is a sequence of numbers possibly starting ‘-’)
Source: show
# File lib/core_ext/string.rb, line 19 def numeric? (self =~ (/\A[+-]?\d+\Z/)) != nil end


@*
* Copyright 2021 HM Revenue & Customs
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*@
@import helpers._
@import models.registration._
@import uk.gov.hmrc.play.views.html.helpers.form
@import models.registration.FieldData
@import config.ApplicationConfig
@(model: Form[IndividualUserAccountDetails], individualFieldData: FieldData)(implicit request: Request[_], messages: Messages, config: ApplicationConfig)
@main_template(title = Messages("enrolment.title"), topNavigation = false) {


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This is the current pack amperage value for the battery pack. This will report a constant 0 if no current sensor is selected.


I am a passionate developer which has worked on different fields of the computer science. Also, I enjoy doing a lot of things.
Despite I like coding anything, I prefer doing front-end!
You can contact me if you have any concern regarding my profile. This is my LinkedIn Profile.


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ACLU accuses Boston of ‘racially biased policing’
Top police officials said the study is based on outdated data, and does not reflect changes undertaken since 2011.
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You can now read 10 articles a month for free. Read as much as you want anywhere and anytime for just 99¢.
Top police officials said the study is based on outdated data, and does not reflect changes undertaken since 2011.
The vote could set up a showdown with Mayor Walsh, who said he would reject an earlier proposal.
Azamat Tazhayakov could tell jurors about “comments’’ Dzhokhar Tsarnaev made during their friendship, Tazhayakov’s lawyer said.

Magazine | The Medical Issue
Can a single week away from computers, smartphones, and other digital devices really make a difference to kids’ brains?
opinion | Stephen Kinzer
These dysfunctional relationships have persisted far longer than rational self-interest would dictate.
Brainiac
On other planets, louder sounds are theoretically possible.
Liberian Thomas Eric Duncan’s diagnosis exposed gaps in the nation’s defenses against the disease.
James Carroll
The full arrival of gay people has occurred, and it raises hopes for an even larger transformation.
Beckett, who is facing hip surgery, pitched on World Series winners for the Red Sox and Marlins.
The bail hearings for the other two men who fled from Cape Cod last month to seek asylum were continued to a later date.
The fate of Kobani has emerged as a key test of whether coalition air power can roll back the extremist group.
Democrat Martha Coakley and Republican Charlie Baker tangled in a heated and contentious forum.
The age group known as millennials has become a special focus for financial institutions -- and a big worry.
Touring behind his acclaimed new album, Perfume Genius’ Mike Hadreas broke hearts and taboos at Brighton Music Hall.
Located on a residential side street, Phu-Ket has solid Thai specialties, friendly service, and charming decor.
design new england
As an apprentice to Frank Lloyd Wright, Grattan Gill designed a house for his parents’ retirement. Now he’s retired there himself.
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A new generation of self-made experts is tracking extremists through their online activity — and rewriting the rules of intelligence in the process.
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William Lloyd-Lavery, from Richmond Avenue in Lisburn, was principal of Lurgan Technical College from 1992-97.
The 71-year-old faced 13 sex offence charges at Craigavon Crown Court, but was found not guilty today.
The jury of four men and eight women deliberated for just less than an hour before returning unanimous not guilty verdicts.
When all the verdicts had been read he looked at the jury and mouthed the words: "Thank you."
Thanking the jury for their service, trial Judge Patrick Lynch QC told Mr Lloyd-Lavery he was free to go and leaving the dock, he thanked the judge.
The retired headmaster has always denied the offences which were seven counts of indecent assault, four of committing an act of gross indecency with or towards a child, one of inciting a child to engage in an act of gross indecency and one of taking an indecent photograph of a child, all alleged to have been committed on dates unknown between 29 December 1980 and 1 February 1988.
He gave evidence on his own behalf to deny that he had ever done anything against the complainant, labelling the allegations as a “pack of lies” which “did not happen and could not have happened.”
During his evidence in chief, the specifics of the allegations were put to Lloyd-Lavery and he told defence QC Kieron Mallon: “I certainly did nothing of the kind - I’m quite outraged by it.”
Mr Lloyd-Lavery told Mr Mallon the account given by the complainant, who said he was aged between six and 13 at the time, “is complete lies and nonsense [with] no truth in it whatsoever.”
“There’s no truth in it - it didn’t happen hundreds of times, it didn’t happen once,” he declared.
Outside the courthouse as he was leaving, Mr Lloyd-Lavery declined to comment.


Travelling to destinations not travelled before
Trying out foods not eaten before
Puzzle Solving
Learning new things
Here is a link to order KFC incase you get hungry


Created as a fun way to discover interesting color combinations. Palettes are hand-selected from the Twitter bot @colorschemez. The feed randomly generates color combinations as well as their descriptions, with each color being matched with an adjective from a list of over 20,000 words.
Created by Adam Fuhrer.


July, 26 2015
Posts
July, 26 2015
July, 20 2015
An introduction to Raspberry Pi 2
July, 20 2015
Resolving fights at school
July, 13 2015
Affirmation and Stereotype Threat
Combatting Stereotype Threat at Dev Bootcamp.
July, 13 2015
Ruby Hashes vs. JavaScript Objects: Concepts Compared
Ruby and JavaScript work with key:value pairs (or name:value pairs in JavaScript) in similar but not identical ways.
July, 5 2015
Inheritance indicates the features that one class gets from another. If one class inherits from another then it inherits the accessible methods and variables of that class. Composition is a more explicit way to obtain the allow access to the same information in a different class as you would if the class was inheriting from another class. With composition, you call the original class instead of implicitly allowing the second class to inherit information.
July, 5 2015
Stereotype Threat: My Experience as a Physics Major
Physics can be a frustrating major. It starts off very concrete but it becomes abstract and very mathematical quite quickly after the first few semesters. There is inherently an expanse of space for one to question ones abilities and capabilities in such a context.
June, 22 2015
June, 22 2015
Feedback: The Pair Programming Experience
Pair programming is the process of two developers working together to solve a programming challenge, each alternating between writing code ("driving") and providing direction ("navigating"). Particularly useful to get past a challenging roadblock, or when learning new techniques, pair programming pushes developers to communicate about why and what...
June, 14 2015
Tech Diversity: Whos problems are we solving?
There has never been a better time in the history of our country to be an individual with a good idea. Startups all over the world are challenging the status quo; giving a voice to users long taken for granted by big business. This innovation is not only changing the way we solve problems as a society, but it’s changing the way we generate problem solving ideas. Amidst the distruption and transformation of the tech industry resides incredible inequity.
June, 14 2015
Enumerables: Use Cases of the "cycle" Method
Enumerables are a collection of classes with methods related to searching, sorting and traversing elements of an array or hash. In each class, successive elements of a collection are made available through the implementation of the each method. The method cycle is an example of one such method from the enumerable mixin.
June, 7 2015
Hacking Learning: A Look at Gregorc Thinking Styles
In preparation for the start of Phase 0, we were asked to explore our Gregorc Thinking Style and take a personal thinking style quiz. Gregorc describes four thinking styles: Concrete Sequential, Abstract Sequential, Abstract Random, and Concrete Random.
June, 7 2015
Arrays and Hashes: What are they and when do you use them?
In the first week of DBC we focused on the command line; in the second week, HTML and CSS were introduced; this week we started Ruby. Amongst the excitement: arrays and hashes. What are they are when do you use them?
May, 30 2015
Margins define how much space exists around an element. The CSS format for defining the margin looks something like this: "margin: top right bottom left".
May, 24 2015
Version control is used so that if there are mistakes made or breaking changes are pushed to production, code can be rolled back to a previous version and fixed before being deployed. It’s a safeguard mechanism that keeps track of what the app looked like at different points in time.
May, 24 2015
I'm asked to refect on my take on the DBC experience, my general impression of DBC, how I see myself engaging with this type of culture, how(if) my expectations of DBC have changed, and if I'm excited to participate in this kind of learning environment or possibly nervous.


Laura Klamot
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Contact Me
Feel free to reach out!
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You're probably here because you're interested in Neural Nets and Deep Learning, but nothing you've read so far has provided a comprehensive understanding of both how and why they work. You may have even written some lines of code using libraries like TensorFlow or Keras that caused some quite amazing things to happen that you nevertheless didn't fully understand. If you're here, that means that you're the kind of person where that bothered you - you wanted to understand both what was actually happening and why it was working (two separate but individually very important questions).
This series of blog posts is for you. It is intended to be careful, thorough explanation of how neural nets work from first principles. I will use math in this blog post and others: understanding of the basics of the concept of derivatives from calculus is necessary, and I intend to write a supplemental "refresher" on derivatives down the line. The goal will be that, after understanding these basic first principles, you will be able to understand much more complex concepts in Deep Learning, since in most cases they are simply the basic principles combined together in different ways.
Let's begin.
--
There are two things you need to know to understand neural nets: you need to know what they are doing and why what they are doing works.
Typically, tutorials on neural nets start by defining them in terms of drawings like this:
This is, in my view, a great way to make neural nets look like something complicated and impressive but a terrible way to introduce people to them. This drawing provides insight neither into how neural nets work - e.g. the actual computations performed during "learning" - nor into why they work.
These kinds of drawings will be important much later, when we describe neural nets with many "layers" (another term that should not be introduced too early).
How should we think of neural nets then?
Simple: recall that in linear regression, or other basic predictive problems, the outputs are modeled as a simple function of the inputs, and some parameters. In the language of neural nets, these parameters are called weights, so here I'll call then weights.
For example, we will write a model $M$ as
$$M(x_1, ..., x_n, \beta_1, ... , \beta_n) = x_1 * \beta_1 + ... + x_n * \beta_n$$and then, for given values of $\beta_1, ... , \beta_n$, resulting in an output $M$, compute the loss L as a function of this prediction $M$ simply by taking the difference between $M$ and the actual value of the prediction $y$:
$$L = \frac{1}{2}(M - y)^2$$Finally, we can do the standard computation involved in gradient descent, computing:
$$\frac{\partial L}{\partial \beta_1} , ... , \frac{\partial L}{\partial \beta_n}$$to figure out whether we need to increase or decrease each $\beta$ to reduce the loss. (In the future, I will write a separate blog post on that; stay tuned)
How are neural nets different than this? We know that, at a high level, they are more complicated versions of simple regression. But how, specifically, are they more complicated?
Simple: rather than just computing a function of the output in terms of the input and some parameters, $F(X,B)$, neural nets are nested functions, so that instead we would compute $G(F(X,B))$ for example.
From this simple definition, you can begin to understand:
So, now we get to it: let's make this linear regression example one "step" more complicated and walk through and example in detail:
Let's consider a neural net that in fact just consists of two nested functions: the first calculates a linear combination of the inputs with some weights, and the second applies a sigmoid to this.
Again, for simplicity's sake, we'll assume that we start with three inputs.
A verbal description of the net would be:
"We are going to take in three features,
which we will call $ X $, take a linear combination of those features with weights $ W $,
then take the sigmoid of those features, and that will be our prediction $ P $."
Note: recall that the sigmoid function is:
$$ \sigma(x) = \frac{1}{1 + e^{-x}} $$A mathematical description of this is:
$$ A = X_1 * W_1 + X_2 * W_2 + X_3 * W_3 $$
$$ P = \sigma(A) $$
We can think of this neural net in terms of function composition:
How would we code this? Well, we can make $ X $ a 1 x 3 matrix - everything could be transposed, but ultimately we'll want to "stack" observations on top of each other as rows, so we'll make $ X $ a 1 x 3.
import numpy as np X = np.array([ [0,0,1] ])
$ y $ will, for obvious reasons, be a 1 x 1 matrix
y = np.array([ [1] ])
I will completely sidestep the very important topic of how to initialize our weights, and just say here that we’ll initialize them to be mean 0 and standard deviation 1.
W = np.random.randn(3, 1)
Now, we’ll compute $ A $, the linear combination of those weights with the inputs $ X $. We’ll do this using the syntax of matrix multiplication.
A = np.dot(X,W)
This is now just a number. Now, we’ll take that $ A $ and apply a sigmoid function to it, getting $ P $.
P = \sigma(A)
Finally, we’ll compute the squared loss, $ L $, which will itself be a function of $ P $.
L = 0.5 * ( y - P ) ** 2
Now, here’s the question we have to answer - answering this question will determine how well neural nets will work.
~~How can we update the weight vector W to reduce this loss?~~
We know that the steps, at a high level are:
The question then becomes: in this slightly more complicated context that linear regression, how do we compute $ \frac{\partial L}{\partial W} $.
The answer is that we have to use the chain rule from calculus. To see how this will work - and this is the beauty of describing neural nets as nested functions - note that as we've defined things above,
$$ L(W) = L(P(B(A(W)))) $$Therefore, $ \frac{\partial L}{\partial W} $ is the product of three quantities:
This all may look complicated, considered together. Nevertheless, each of these quantities can be computed rather simply.
Indeed, the first of these is:
$$ \frac{\partial L}{\partial P} = -1 * (y - P) $$We can code this as:
dLdP = -1.0 * (y - P)
The second of these is:
$$ \frac{\partial P}{\partial A} = \sigma(A) * (1 - \sigma(A)) $$We can code this as:
dHdA = sigmoid(A) * (1.0 - sigmoid(A))
Finally, using the chain rule, we can write:
$$ \frac{\partial L}{\partial A} = \frac{\partial L}{\partial P} * \frac{\partial P}{\partial A} $$And in code:
dLdA = dLdH * dHdA
The third is slightly trickier, but not by much. Recall that when we say $ \frac{\partial L}{\partial W} $ we are really separately computing $ \frac{\partial L}{\partial W_1} $, $ \frac{\partial L}{\partial W_2} $, and $ \frac{\partial L}{\partial W_3} $. And since
$$ A = X_1 * W_1 + X_2 * W_2 + X_3 * W_3 $$we can compute:
$$ \frac{\partial A}{\partial W_1} = X_1 $$ $$ \frac{\partial A}{\partial W_2} = X_2 $$ $$ \frac{\partial A}{\partial W_3} = X_3 $$Now for a cool trick. Since
$$ W=\begin{bmatrix} W_1 \\ W_2 \\ W_3\\ \end{bmatrix} $$and
$$ X=\begin{bmatrix} X_1 & X_2 & X_3 \end{bmatrix} $$We can write, as shorthand for the above three equations:
$$ \frac{\partial A}{\partial W} = X^T $$And in code, we can write this as:
dAdW = X.T
So, putting these three pieces together:
$$ \frac{\partial L}{\partial W_1} = \frac{\partial L}{\partial P} * \frac{\partial P}{\partial A} * \frac{\partial A}{\partial W_1} $$ $$ \frac{\partial L}{\partial W_2} = \frac{\partial L}{\partial P} * \frac{\partial P}{\partial A} * \frac{\partial A}{\partial W_2} $$ $$ \frac{\partial L}{\partial W_3} = \frac{\partial L}{\partial P} * \frac{\partial P}{\partial A} * \frac{\partial A}{\partial W_3} $$Or, more generally:
$$ \frac{\partial L}{\partial W_i} = \frac{\partial L}{\partial P} * \frac{\partial P}{\partial A} * \frac{\partial A}{\partial W_i} $$Since we have computed these first two pieces, we can write:
$$ \frac{\partial L}{\partial W_i} = -1.0 * (y - P) * \sigma(A) * (1.0 - \sigma(A)) * X_i $$In code, taking advantage of the two quantities we have already computed and using the matrix multiplication shorthand (which will come in handy later), we can write:
dLdW = np.dot(dAdW, dLdA)
To understand why this matrix multiplication "makes sense" remember that $ X $ is a 1 x 3 matrix, so $ X^T $ is a 3 x 1 matrix - the same dimensions as $ W $, and $ \frac{\partial A}{\partial W} $, the product of $ \frac{\partial L}{\partial P} $ and $ \frac{\partial P}{\partial A} $, is a 1 x 1 matrix.
Finally, we actually make an update. We’ll choose an arbitrary learning rate of 0.5
W -= 0.5 * dLdW
And there you have it. You have properly coded - and understood at each step why you were coding - all the steps of a backpropogation algorithm.
Putting this all together:X = np.array([ [0,0,1] ]) y = np.array([ [1] ]).T W = np.random.randn(3, 1) for i in range(10000): A = np.dot(X, W) P = sigmoid(A) L = 0.5 * (y - P) ** 2 dLdP = -1.0 * (y - P) dPdA = sigmoid(A) * (1.0 - sigmoid(A)) dLdA = dLdP * dPdA dAdW = X.T dLdW = np.dot(dAdW, dLdA) W -= 0.5 * dLdW
Congratulations: this is an excellent start to understanding neural nets. Like any good tutorial, it hopefully answered some questions, but also gave you the confidence to start asking more questions. Next week, we'll dwell further on this simple example, asking:


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- created By Dr. Dos


Matthew's Grill
- Address:
213 Muddy Branch Rd, Gaithersburg, MD 20878
- Contact Number:
(301) 990-8858
- Hours:
Closes 8pm
Kuya Ja's Lechon Belly
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5268-H, Nicholson Ln, Kensington, MD 20895
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(240) 669-4383
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Closes 5pm
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- Address:
8428 Willow Oak Rd Parkville, Maryland 21234
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(443) 562-6650
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11:30 AM - 7:30 PM
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--- template: example.html title: turf-bearing description: Calculates the bearing between two point features in degrees. tags: - measurement ---
--


Class to load neural net from file, sample signals specified in that file, feed those signals into the neural net and manage the output from the neural nets.
More...
#include <DomainNetMap.hpp>
|
| virtual | ~DomainNetMap ()=default |
| |
| virtual void | sample ()=0 |
| | Samples latest signals for a specific domain and applies the resulting TensorOneD state to the neural net. More...
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| |
| virtual std::vector< std::string > | trace_names () const =0 |
| | generates the names for trace columns from the appropriate field in the neural net More...
|
| |
| virtual std::vector< double > | trace_values () const =0 |
| | Populates trace values from last_output for each index within each domain type. More...
|
| |
| virtual std::map< std::string, double > | last_output () const =0 |
| | Populates a map of trace names to the latest output from the neural net. More...
|
| |
|
| static std::unique_ptr< DomainNetMap > | make_unique (const std::string &nn_path, geopm_domain_e domain_type, int domain_index) |
| | Returns a unique_ptr to a concrete object constructed using the underlying implementation which loads neural net for a specified domain from a json file. More...
|
| |
| static std::shared_ptr< DomainNetMap > | make_shared (const std::string &nn_path, geopm_domain_e domain_type, int domain_index) |
| | Returns a shared_ptr to a concrete object constructed using the underlying implementation which loads neural net for a specified domain from a json file. More...
|
| |
Class to load neural net from file, sample signals specified in that file, feed those signals into the neural net and manage the output from the neural nets.
◆ ~DomainNetMap()
| virtual geopm::DomainNetMap::~DomainNetMap |
( |
| ) |
|
|
virtualdefault |
◆ last_output()
| virtual std::map<std::string, double> geopm::DomainNetMap::last_output |
( |
| ) |
const |
|
pure virtual |
Populates a map of trace names to the latest output from the neural net.
- Returns
- A map of string, double containing trace names and latest nn output
Implemented in geopm::DomainNetMapImp.
◆ make_shared()
| std::shared_ptr< DomainNetMap > geopm::DomainNetMap::make_shared |
( |
const std::string & |
nn_path, |
|
|
geopm_domain_e |
domain_type, |
|
|
int |
domain_index |
|
) |
| |
|
static |
Returns a shared_ptr to a concrete object constructed using the underlying implementation which loads neural net for a specified domain from a json file.
- Parameters
-
| [in] | nn_path | Path to neural net json |
| [in] | domain_type | Domain type, defined by geopm_domain_e enum |
| [in] | domain_index | Index of the domain to be measured |
- Exceptions
-
| geopm::Exception | if unable to open neural net file |
| geopm::Exception | if neural net file exceeds max size (currently 1024) |
| geopm::Exception | if neural net file does not contain a key "layers" or if "layers" does not contain an array |
| geopm::Exception | if neural net file does not contain either a non-empty array "signal_inputs" or a non-empty array "delta_inputs" |
◆ make_unique()
| std::unique_ptr< DomainNetMap > geopm::DomainNetMap::make_unique |
( |
const std::string & |
nn_path, |
|
|
geopm_domain_e |
domain_type, |
|
|
int |
domain_index |
|
) |
| |
|
static |
Returns a unique_ptr to a concrete object constructed using the underlying implementation which loads neural net for a specified domain from a json file.
- Parameters
-
| [in] | nn_path | Path to neural net json |
| [in] | domain_type | Domain type, defined by geopm_domain_e enum |
| [in] | domain_index | Index of the domain to be measured |
- Exceptions
-
| geopm::Exception | if unable to open neural net file |
| geopm::Exception | if neural net file exceeds max size (currently 1024) |
| geopm::Exception | if neural net file does not contain a key "layers" or if "layers" does not contain an array |
| geopm::Exception | if neural net file does not contain either a non-empty array "signal_inputs" or a non-empty array "delta_inputs" |
◆ sample()
| virtual void geopm::DomainNetMap::sample |
( |
| ) |
|
|
pure virtual |
◆ trace_names()
| virtual std::vector<std::string> geopm::DomainNetMap::trace_names |
( |
| ) |
const |
|
pure virtual |
generates the names for trace columns from the appropriate field in the neural net
Implemented in geopm::DomainNetMapImp.
◆ trace_values()
| virtual std::vector<double> geopm::DomainNetMap::trace_values |
( |
| ) |
const |
|
pure virtual |
Populates trace values from last_output for each index within each domain type.
- Returns
- Returns a vector of doubles containing trace values
Implemented in geopm::DomainNetMapImp.
The documentation for this class was generated from the following files:


This is a simple demo showing how to use different versions of the same library in the same project.
This projects imports three different versions of a library called "base library":
- The oldest version 1.0.0 is part of this Angular project.
- Version 2.0.0 is loaded indirectly by using another library.
- There's also version 3.0.0, loaded by just another library.
Result of the library calls:
- Version 1.0
- {{version1}}
- Version 2.0
- {{version2}}
- Version 3.0
- {{version3}}


WAKING NED DEVINE A film review by Steve Rhodes Copyright 1998 Steve Rhodes RATING (0 TO ****): ** 1/2
Will it be prison for Jackie and Michael, senior citizens living in a remote Irish village? Or will it be great riches instead?
It all depends on the success of their big scam in WAKING NED DEVINE. Honest all of their lives until a great temptation comes their way in the form of an unclaimed lottery ticket, they are now ready to prevaricate day and night.
Someone in their little hamlet has won over six million pounds in the lottery, but who? With only 52 residents they devise a scheme whereby they will wine and dine the locals in hopes of getting in the good graces of the winner. Along the way they engage in a detective game to ascertain who the lucky person is.
Veteran actors, Ian Bannen and David Kelly, play Jackie and Michael with such great gusto that it is obvious that they are having the time of their lives. Fionnula Flanagan plays Jackie's wife and co-conspirator.
Eventually Jackie and Michael locate the lucky bloke, one Ned Devine. He turns out to have suffered a fatal case of excessive luck, having died in front of his TV when the numbers were chosen.
The rest of the movie has them enlisting the entire town to collude in hiding Ned's death and convincing the lottery man from the big city that Michael is Ned.
Will the town pull it off and share in the riches or will their plan somehow go awry?
WAKING NED DEVINE suffers from an excess of good concept and casting and a deficit in plot development. The movie runs on autopilot most of the time with the townsfolk making small talk and hanging out at the local pub. Scenes of naked octogenarian males on motorcycles are certainly different, which is not the same thing as interesting.
A good-spirited film, it features lovely pictures of the Irish coastline and countryside. Although there is nothing wrong with the picture, it is rarely compelling. Without much of an ending, the film basically just stops. We are left with some nice characterizations but a series of missed opportunities in the script development. A pleasant enough movie, it could and should have been more successful.
WAKING NED DEVINE runs about 1:30. It is not rated but probably would be an R for brief scenes of full-frontal male nudity and would be fine for teenagers.
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