Table of Contents
Essence of LA - 3b1b
LA (Stanford Review)
Definitions and Intuitions
- Linear Algebra:
Linear Algebra is about two operations on a list of numbers:- Scalar Multiplication
- Vector Addition
- Vectors:
Think of each element in the vector as a scalar that scales the corresponding basis vectors.Meaning, think about how each one stretches or squishes vectors (in this case, the basis vectors \(\hat{i}, \hat{j}\))
$$\begin{bmatrix} x \\ y \end{bmatrix} = \begin{bmatrix} 1 & 0 \\ 0 & 1 \end{bmatrix} \begin{bmatrix} x \\ y \end{bmatrix} = \color{red} x \color{red} {\underbrace{\begin{bmatrix} 1 \\ 0 \end{bmatrix}}_ {\hat{i}}} + \color{red} y \color{red} {\underbrace{\begin{bmatrix} 0 \\ 1 \end{bmatrix}}_ {\hat{j}}} = \begin{bmatrix} 1\times x + 0 \times y \\ 0\times x + 1 \times y \end{bmatrix} $$
Three Perspectives on Vectors:
- Physics: an arrow pointing in space defined by the length and direction of the arrow.
- CS: vectors are Ordered Lists of numbers.
- Math: a vector is any object that can be (1) added to another vector (2) multiplied by a scalar value.
For the purposes of this treatment, think about a vector as an arrow inside a coordinate system with its’ tail at the origin:
The coordinates of a Vector:
Think about the coordinates as a list of numbers that tells you how to get from the tail of the arrow to its tip.
Each coordinate at the \(i\)th index tells you how many “steps” to take in that dimension (\(i\)th dimension).
E.g. for the \(2-\)D case, the first coordinate dictates the steps in the \(x\) dimension, and the second coordinate the steps in the \(y\) dimension.
You can completely specify a vector with its list of coordinates (uniquely).
Vector Addition:
To add the two vectors \(\overrightarrow{\mathbf{v}} + \overrightarrow{\mathbf{w}}\):- Move the tail of the second vector \(\overrightarrow{\mathbf{w}}\) to the tip of the first vector \(\overrightarrow{\mathbf{v}}\)
- Draw an arrow from the tail of \(\overrightarrow{\mathbf{v}}\) to the new tip of \(\overrightarrow{\mathbf{w}}\).
- This new arrow (vector) is the sum
-
Intuition:
Essentially, you are taking a step in the first vector direction then another step in the second vector direction. You can do so by moving the tail of the second vector to the tip of the first vector, essentially taking a step in that direction, then take a step in the direction (and length) of the second vector:
- Vector Addition Numerically:
Scalar Multiplication:
- Span:
The Span of two vectors \(\mathbf{v}, \mathbf{w}\) is the set of all linear combinations of the two vectors:$$a \mathbf{v} + b \mathbf{w} \: ; \: a,b \in \mathbb{R}$$
I.E. What are all the possible vectors you can create using the two fundamental operations on two vectors: vector addition and scalar multiplication.
As long as each vector does not point in the same direction as another vector, you can reach all possible dimensions as the number of vectors.
I.E. if you cannot create the vector using the other vectors you have, then it is pointing in a new direction. - Linearly Dependent Vectors:
If one vector is in the span of the other vectors.
Mathematically:$$a \vec{v}+b \vec{w}+c \vec{u}=\overrightarrow{0} \implies a=b=c=0$$
-
The Basis:
The Basis of a vector space is a set of linearly independent vectors that span the full space. -
Matrix as a Linear Transformation:
Note: A Transformation == Function \(\iff\) Linear Transformation == Linear Function.
With vectors as inputs and vectors as outputs.Summary:
- Each column is a transformed version of the basis vectors (e.g. \(\hat{i}, \hat{j}\))
- The result of a Matrix-Vector Product is the linear combination of the vectors with the appropriate transformed coordinate/basis vectors
i.e. Matrix-Vector Product is a way to compute what the corresponding linear transformation does to the given vector.
Matrix as a Linear Transformation:
Always think of Matrices as Transformations of Space:- A Matrix represents a specific linear transformation
- Where the columns represent the coordinates of the transformed basis vectors
- & Multiplying a matrix by a vector is EQUIVALENT to Applying the transformation to that vector
The word “transformation” suggests that you think using movement
If a transformation takes some input vector to some output vector, we imagine that input vector moving over to the output vector.
Then to understand the transformation as a whole, we might imagine watching every possible input vector move over to its corresponding output vector.
This transformation/“movement” is Linear, if it keeps all the vectors parallel and evenly spaced, and fixes the origin.
Matrices and Vectors | The Matrix-Vector Product:
Again, We think of each element in a vector as a scalar that scales the corresponding basis vectors.- Thus, if we know how the basis vectors get transformed, we can then just scale them (by multiplying with our vector elements).
Mathematically, we think of the vector:$$\mathbf{v} = \begin{bmatrix}x \\y \end{bmatrix} = x\hat{i} + y\hat{j}$$
and its transformed version:
$$\text{Transformed } \mathbf{v} = x (\text{Transformed } \hat{i}) + y (\text{Transformed } \hat{j})$$
\(\implies\) we can describe where any vector \(\mathbf{v}\) go, by describing where the basis vectors will land.
If you’re given a two-by-two matrix describing a linear transformation and some specific vector and you want to know where that linear transformation takes that vector, you can (1) take the coordinates of the vector (2) multiply them by the corresponding columns of the matrix, (3) then add together what you get.
This corresponds with the idea of adding the scaled versions of our new basis vectors.
$$ \mathbf{v} = \begin{bmatrix} x \\ y \end{bmatrix} = \begin{bmatrix} 1 & 0 \\ 0 & 1 \end{bmatrix} \begin{bmatrix} x \\ y \end{bmatrix} = \color{red} x \color{red} {\underbrace{\begin{bmatrix} 1 \\ 0 \end{bmatrix}}_ {\hat{i}}} + \color{red} y \color{red} {\underbrace{\begin{bmatrix} 0 \\ 1 \end{bmatrix}}_ {\hat{j}}} = \begin{bmatrix} 1\times x + 0 \times y \\ 0\times x + 1 \times y \end{bmatrix} = x\hat{i} + y\hat{j} $$
The Matrix-Vector Product:
Non-Square Matrices \((N \times M)\):
Map vectors from \(\mathbb{R}^M \rightarrow \mathbb{R}^N\).
They are transformations between dimensions. -
The Product of Two Matrices:
The Product of Two Matrices Corresponds to the composition of the transformations, being applied from right to left.
This is very important intuition:e.g. do matrices commute?
if you think of the matrices as transformations of space then answer quickly is no.
Equivalently, Are matrices associative? Yes, function composition is associative: \(f \circ(g \circ h) = (f \circ g) \circ h\)E.g. Applying two transformations, a Rotation and a Shear:
Instead of computing the Matrix-Vector product first, then another Matrix-Vector product on the resulting vector, we can combine/compose the two transformations/matrices into one new composition matrix that specifies the new coordinates after applying the two transformations:
where the composition matrix is the Product of the two matrices:
- Linear Transformations:
Linear Transformations are transformations that preserve the following properties:- All vectors that are parallel remain parallel
- All vectors are evenly spaced
- The origin remains fixed
Visualization:
-
Examples of NON-linear Transformations:
- The new coordinates of the basis vectors \(\hat{\imath}, \hat{\jmath}\) allow us to deduce where every vector is going to land after the transformation, by deducing the linear transformation/function on each basis vector.
So, if a vector \(\overrightarrow{\mathbf{v}}\) - defined as some linear combination of \(\hat{\imath}, \hat{\jmath}\):$$\overrightarrow{\mathbf{v}}=a \hat{\imath}+b \hat{\jmath}$$
undergoes a linear transformation, it is now defined as the exact same linear combination of the transformed coordinates of \(\hat{\imath}, \hat{\jmath}\):
$$\text { Transformed } \overrightarrow{\mathbf{v}}=a(\text { Transformed } \hat{\imath})+b(\text { Transformed } \hat{\jmath})$$
- We tend to combine the two basis vectors in a matrix side by side.
$$\begin{bmatrix} \hat{\imath} & \hat{\jmath} \end{bmatrix}$$
E.g. in 2D:
$$\begin{bmatrix} 1 & 0 0 & 1 \end{bmatrix}$$
and after a certain linear transformation, the new coordinates of the basis vectors define the new matrix:
Notes:
- You should interpret a matrix as a linear transformation of space
- Each column represents where the corresponding basis vector lands
- To Transform any vector to the new, linearly transformed space, you can multiply the matrix and the vector to get the new coordinates of the vector in that space
- A Linear Transformation corresponds to Matrix-Vector multiplication
- Each coordinate of your vector, scales the corresponding basis vector (column) in the new space
- We only need to know the coordinates of the basis vectors in the new space to specify a linear transformation
-
The Determinant:
The Determinant of a transformation is the “scaling factor” by which the transformation changed any area in the vector space.The Negative Determinant determines the orientation.
Linearity of the Determinant:
$$\text{det}(AB) = \text{det}(A) \text{det}(B)$$
-
Solving Systems of Equations:
The Equation \(A\mathbf{x} = \mathbf{b}\), finds the vector \(\mathbf{x}\) that lands on the vector \(\mathbf{b}\) when the transformation \(A\) is applied to it.Again, the intuition: is to think of a linear system of equations, geometrically, as trying to find a particular vector that once transformed/moved, lands on the output vector \(\mathbf{b}\).
- This becomes more important when you think of the different properties, of that transformation/function, encoded (now) in the matrix \(A\):
- When the \(det(A) \neq 0\) we know that space is preserved, and from the properties of linearity, we know there will always be one (unique) vector that would land on \(\mathbf{b}\) once transformed (and you can find it by “playing the transformation in reverse” i.e. the inverse matrix).
- When \(det(A) = 0\) then the space is squished down to a lower representation, resulting in information loss.
Usefullness of LA:
Linear System of Equations:
- Intuition:
- The Matrix \(A\) is a Linear Transformation
- Solving the Equation \(A \overrightarrow{\mathrm{x}}=\overrightarrow{\mathrm{v}}\) means:
We are looking for a vector \(\mathrm{x}\) which - after applying the linear transformation \(A\) - lands on \(\mathrm{v}\).
- Key Insight: you can understand the system of linear equations of \(n\) variables by thinking of transforming space and figuring out which vector lands on another!
- This becomes more important when you think of the different properties, of that transformation/function, encoded (now) in the matrix \(A\):
-
The Inverse of a matrix:
The Inverse of a matrix is the matrix such that if we “algebraically” multiply the two matrices, we get back to the original coordinates (the identity).
It is, basically, the transformation applied in reverse.The Inverse Matrix is the unique matrix/transformation such that:
$$A^{-1} A = I$$
Why inverse transformation/matrix DNE when the __det__erminant is Zero (i.e. space is squished):
To do so, is equivalent to transforming a line into a plane, which would require mapping each, individual, vector into a “whole line full of vectors” (multiple vectors); which is not something a Function can do.
Functions map single input to single output.-
Solution when the Determinant is Zero: It is still possible that a solution exists even when the Determinant is Zero.
This happens in the (lucky) case that the vector \(\mathrm{v}\) (on the RHS) “lives” on the line (squashed space):
Note: the green and red vectors are the transformed basis vectors.- This becomes more and more unlikely as the Rank of the output gets lower
e.g. Squashing a 3D space on a line (Rank=1)
- This becomes more and more unlikely as the Rank of the output gets lower
-
- The Determinants, Inverses, & Solutions to Equations:
When the \(\text{det} = 0\) the area gets squashed to \(0\), and information is lost. Thus:- The Inverse DNE
- A unique solution DNE
i.e. there is no function that can take a line onto a plane; info loss
-
The Rank:
The Rank is the dimensionality of the output of a transformation.
More precisely, it is the number of dimensions in the column space.
Viewed as a Matrix, it is the number of independent vectors (as columns) that make up the matrix. - The Column Space:
The Column Space is the set of all possible outputs of a transformation/matrix.- View each column as a basis vector; there span is then, all the possible outputs
- The Zero Vector (origin): is always in the column space (corresponds to preserving the origin)
The Column Space allows us to understand when a solution exists.
For example, even when the matrix is not full-rank (det=0) a solution might still exist; if, when \(A\) squishes space onto a line, the vector \(\mathbf{b}\) lies on that line (in the span of that line).- Formally, solution exists if \(\mathbf{b}\) is in the column space of \(A\).
-
The Null Space:
The Null Space is the set of vectors that get mapped to the origin; also known as The Kernel.The Null Space allows us to understand what the set of all possible solutions look like.
When the vector \(\mathrm{v} = \mathrm{0}\), the null space gives all the possible solutions to the equation \(A \overrightarrow{\mathrm{x}}=\overrightarrow{\mathbf{v}} = 0\)
Rank and the Zero Vector:
A Full-Rank matrix maps only the origin to itself.
A non-full rank (det=0) (rank=n-1) matrix maps a whole line to the origin, (rank=n-2) a plane to the origin, etc. -
Non-Square Matrices:{: style=”color: SteelBlue”}{: .bodyContents1 #bodyContents1144}
If the matrix has shape \(m \times n\) and \(m > n\), then it can be viewed as transforming an \(n\)-dimensional vector to \(m\)-dimensional space. (e.g. a line to a plane)When \(m < n\) then it is taking your vector from a lower dimension to a higher dimension.
Some Extra Intuition:
Nonsquare matrices are just an awkward, but space saving, notation for square matrices with zeros filling the empty slots.
Not only is every matrix “actually” square, but infinite dimensional as well. When we talk about an “n×m” matrix, that’s just a way of saying the framing the scope of whatever problem we are trying to solve, but it’s always best to try visualizing problems in the highest dimension possible even if your computation truncates the extra zeros.
This view also shows that vectors themselves are “really” just matrices in disguise, but that notion isn’t quite as enlightening although it does show how all mathematics is interconnected. Our clumsy symbolism is what makes concepts feels independent from one another.
- The Dot Product/Scalar Product:
(for vectors \(\mathbf{u}, \mathbf{v}\))$$\mathbf{u} \cdot \mathbf{v} = \mathbf{u}^T \mathbf{v}$$
- Geometrically:
- project
- Geometrically:
- Cramer’s Rule:
-
Coordinate Systems:
A Coordinate System is just a way to formalize the basis vectors and their lengths. All coordinate systems agree on where the origin is.
Coordinate Systems are a way to translate between vectors (defined w.r.t. basis vectors being scaled and added in space) and sets of numbers (the elements of the vector/list/array of numbers which we define).- Translating between Coordinate Systems
Imp: 6:47
- Translating a Matrix/Transformation between Coordinate Systems
Implicit Assumptions in a Coordinate System:
- Direction of each basis vector our vector is scaling
- The unit of distance
- Translating between Coordinate Systems
-
Eigenstuff:
Notes:
- Complex Eigenvalues, generally, correspond to some kind of rotation in the transformation/matrix (think, multiplication by \(i\) in \(\mathbb{C}\) is a \(90^{\deg}\) rotation).
- For a diagonal matrix, all the basis vectors are eigenvectors and the diagonal entries are their eigenvalues.


public class Revision extends Object implements Comparable<Revision>
Revision
which distinguishes between x and x.0, x.0.0, x.y.0, etc;
it basically keeps track of the precision of the revision string.
This is vital when referencing Gradle artifact numbers, since versions x.y.0 and version x.y are not the same.
| Modifier and Type | Class and Description |
|---|---|
static class |
Revision.Precision |
static class |
Revision.PreviewComparison
Indicates how to compare the preview field in
compareTo(Revision, PreviewComparison) |
| Modifier and Type | Field and Description |
|---|---|
static int |
IMPLICIT_MICRO_REV |
static int |
IMPLICIT_MINOR_REV |
static int |
MISSING_MAJOR_REV |
static int |
NOT_A_PREVIEW |
static Revision |
NOT_SPECIFIED |
| Constructor and Description |
|---|
Revision(int major)
Creates a new
Revision with the specified major revision and no
other revision components. |
Revision(int major,
int minor)
Creates a new
Revision with the specified major and minor
revision components and no others. |
Revision(int major,
Integer minor,
Integer micro,
Integer preview)
Creates a new
Revision with the specified components. |
Revision(int major,
int minor,
int micro)
Creates a new
Revision with the specified major, minor, and micro
revision components and no preview component. |
Revision(int major,
int minor,
int micro,
int preview)
Creates a new
Revision with the specified components. |
Revision(Revision revision)
Creates a copy of the specified
Revision . |
| Modifier and Type | Method and Description |
|---|---|
int |
compareTo(Revision rhs)
Trivial comparison of a version, e.g 17.1.2 < 18.0.0.
|
int |
compareTo(Revision rhs,
Revision.PreviewComparison comparePreview)
Trivial comparison of a version, e.g 17.1.2 < 18.0.0.
|
boolean |
equals(Object rhs)
Returns
true if this revision is equal, including in
precision to rhs . |
int |
getMajor() |
int |
getMicro() |
int |
getMinor() |
int |
getPreview() |
int |
hashCode() |
boolean |
isPreview() |
static Revision |
parseRevision(String revisionString)
Parses a string of format "major.minor.micro rcPreview" and returns a new
Revision for it. |
static Revision |
parseRevision(String revisionString,
Revision.Precision minimumPrecision)
Parses a string of format "major.minor.micro rcPreview" and returns a new
Revision for it. |
int[] |
toIntArray(boolean includePreview)
Returns an
int[] containing the Major, Minor, and Micro (and
optionally Preview) components (if specified) of this revision |
String |
toShortString()
Returns the version in a dynamic format "major.minor.micro rc#".
|
String |
toString()
Returns the version in a fixed format major.minor.micro with an optional
"rc preview#".
|
public static final int MISSING_MAJOR_REV
public static final int IMPLICIT_MINOR_REV
public static final int IMPLICIT_MICRO_REV
public static final int NOT_A_PREVIEW
public static final Revision NOT_SPECIFIED
public Revision(int major)
Revision
with the specified major revision and no
other revision components.public Revision(int major, int minor)
Revision
with the specified major and minor
revision components and no others.public Revision(Revision revision)
Revision
.public Revision(int major, int minor, int micro)
Revision
with the specified major, minor, and micro
revision components and no preview component.public Revision(int major, int minor, int micro, int preview)
Revision
with the specified components.public static Revision parseRevision(String revisionString, Revision.Precision minimumPrecision) throws NumberFormatException
Revision
for it.
All the fields except major are optional.
revisionString
- A non-null revisionString to parse.minimumPrecision
- Create a Revision
with at least the given precision,
regardless of how precise the revisionString
is.Revision
.NumberFormatException
- if the parsing failed.public static Revision parseRevision(String revisionString) throws NumberFormatException
Revision
for it.
All the fields except major are optional.
revisionString
- A non-null revisionString to parse.Revision
, with precision depending on the
precision of revisionString
.NumberFormatException
- if the parsing failed.public String toString()
public int[] toIntArray(boolean includePreview)
int[]
containing the Major, Minor, and Micro (and
optionally Preview) components (if specified) of this revisionincludePreview
- If false, the preview component of this revision will be
ignored.includePreview
is true, the
result will always be of length 4.public boolean equals(Object rhs)
true
if this revision is equal, including in
precision to rhs
. That is,
(new Revision(20)).equals(new Revision(20, 0, 0)
will return
false
.public int getMajor()
public int getMinor()
public int getMicro()
public boolean isPreview()
public int getPreview()
public String toShortString()
toString()
except it omits minor, micro or preview
versions when they are zero. For example it would return "18 rc1" instead
of "18.0.0 rc1", or "18.1 rc2" instead of "18.1.0 rc2".public int compareTo(Revision rhs)
compareTo
in interface Comparable<Revision>
rhs
- The right-hand side Revision
to compare with.public int compareTo(Revision rhs, Revision.PreviewComparison comparePreview)
rhs
- The right-hand side Revision
to compare with.comparePreview
- How to compare the preview value.Copyright © 2015–2016 SDKLite. All rights reserved.


Froghand packing up
Thank you, and Froge bless the Degeneracy.
Working on Froghand has been one of the greatest privileges of my life. I made myself an identity where people care about me. I made my thoughts known in a way that nobody else has. I earned fans and friends wide and true, and made Neocities my proud place of business. There are few other ways I would have spent the past eight months of my life working on, and given the choice, I don't believe I could think of anything else that would bring me such benefit in mind, body, and spirit.
But now I'm gone for less green pastures, and though my froggy spirit remains, it remains in a different way. I am branching out and creating brand new projects, and most importantly, I'm not doing it alone. I'm making The Degenerates now, an artist's collective whose biggest goal is to made work the world needs most - with honour, respect, and grace. Though by the time this post is published I will be the only Degenerate, I assure you... I am a patient man. Degeneracy is in the hearts of the best artists in the world. They only need to find it.
You will find my flagship project, The 10kb Art Gallery, under this banner, and where you may contact me now. And maybe if you're reading this, I'll have found a new flagship project. That's okay. That's just progress. That won't mean I'm dead though.  But remember: the more things change, the more they stay the same. The work I've done here for you? It'll always be here for you.
Be sure to check out the Degenerates and 10kB Neocities pages. Of course, mine is dead. See you!
All the Posts:
February 02:
The final posts made on Froghand: The Blog Leadup.

October 26 to Never:
The Runescape Reviews.

December 26-30:
Public domain! One artist, over 9000 hours! Anime! Sita Sings the Blues Review!
December 14-22:
Eight thousand words about a meme game: The Spongebob Squarepants: The Movie: The Game: The Review.
December 11-12:
Do you ever wonder why people watch bad things? Read Quality and the Now.
December 10:
The most fantastic thing I have ever laid eyes on: Review of an Untitled Masterpiece.
December 03-07:
The land of the unexpected feels: Shitty Vaporwave Indie Game Reviews.
November 27-29:
Hey, kids!
November 18-23:
This article got the developer to read my blog: The Beginner's Guide Review.
November 17:
There is little incentive to make good games: Dispassion in the Gaming Scene.
October 27 to November 14:
A brief epic about life, the arts, and greatness: The Froge Guide to Writing.
October 20-24:
Very good points about things I'll never buy: Nintendo Switch and Other Such Shit.
October 09-15:
Reviews cut short due to everybody getting drunk: LISA Reviews.
October 06-08:
Learn to live, not to exist: How to Live on 24 Hours a Day Review.

October 04:
It's worth it, though it is tough: Writing is Hell.

September 20-30:
Why reviewers are unqualified to review: Review of Game Reviewers.
September 19:
Venture forth into the -=VIRTUAL|PLAZA=-: My Dinner with JanusVR.
September 03-18:
Bojack Horseman episodic reviews: Horseman Review Archive.
August 18-September 01:
The Melancholy of Haruhi Suzumiya episodic reviews: Haruhi Review Archive.
August 13:
Why gaming is a bit shitty, and why it will get better: State of the Games - 2015.
August 02:
Just like one of my animes: The Cherry Tree High Duo Reviews.
June 13-14:
Watch my soul get sucked through a sippy straw: Yandere Sim Review.
June 02-07:
Learn about Valves destruction of its own industry: The Industrial Steamworks.
The Meta-Texts about the Froge:
November 16:
Welcome to the retrospective of the first six months: The First Quarterly Froge Ball.
October 17:
Some short questions about a big blog: The 10,000 Update Special.
The Web Sec Offensive:
August 03-12:
Learn where to download that good good: Scavenge from the Torrent Wasteland.
June 24-30:
Learn how to be the Ghost in the Web: OPSEC for Online Accounts.
June 19-23:
Protect your hard drive from cops: Veracrypt and the Data Cowboys.
June 15-18:
Talk about d a n k | m e m e s without the NSA snooping you: Secure Comms for the Cool Cunny.
June 10-12:
Compress your audio properly: Audio Codecs for Deff Lads.
June 08-09:
Use torrents without getting arrested: BitTorrent for Babbys.
May 31-June 1:
Make a cooler desktop, be a cooler person: Welcome to the Rice.
May 30:
Stop the feds from snooping your mail: The Great Guide to E-mail Clients.
May 29:
Stop your computer from physical pwn: Welcome to the Heist.
May 28:
Wipe your data CLEAN: Deuce the Hard Disks.
May 24-27:
Your passwords are crackable. Please change them: Master Password and the No Hack Band.
May 21-23:
Learn why closed-source software is like gambling: It's Because I'm Black, isn't it?
May 20:
Don't get arrested for porn: Don't get Caught on that CP.
Website size as of 2017-02-02: 2.7MB.
Today's page was updated on 2017-02-02 and created on 2016-05-20.
Copying is an act of love.
E-mail: "froghand@protonmail.ch"


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http://www.cmbfast.org/vacuum-cleaners/utah/salt-lake-city
-
1
References
Resources
- Photo Credit Justice image by MVit from evaluation techniques in teaching


{% extends "header.html" %} {% block body %}
Now that we've got our race car and we can drive it around, we've found that we're able to drive it right off of the screen! To stop this, we want to add some sort of boundary to our game that will stop this from happening.
Here's the new code:
import pygame pygame.init() display_width = 800 display_height = 600 black = (0,0,0) white = (255,255,255) red = (255,0,0) car_width = 73 gameDisplay = pygame.display.set_mode((display_width,display_height)) pygame.display.set_caption('A bit Racey') clock = pygame.time.Clock() carImg = pygame.image.load('racecar.png') def car(x,y): gameDisplay.blit(carImg,(x,y)) def game_loop(): x = (display_width * 0.45) y = (display_height * 0.8) x_change = 0 gameExit = False while not gameExit: for event in pygame.event.get(): if event.type == pygame.QUIT: gameExit = True if event.type == pygame.KEYDOWN: if event.key == pygame.K_LEFT: x_change = -5 if event.key == pygame.K_RIGHT: x_change = 5 if event.type == pygame.KEYUP: if event.key == pygame.K_LEFT or event.key == pygame.K_RIGHT: x_change = 0 x += x_change gameDisplay.fill(white) car(x,y) if x > display_width - car_width or x < 0: gameExit = True pygame.display.update() clock.tick(60) game_loop() pygame.quit() quit()
First, we see that we now have a new variable:
car_width = 73
This variable is used in the rest of the program to know where both edges of the car are. The car's "location" really just means the location of the top left pixel of the car. Because of this, it is helpful to also know where the right side is.
Next, we see that we've changed the "main loop" quite a bit. Now, we're calling this the game loop, and instead of the variable being crashed that exits it, it is a "gameExit" that will exit the loop. Take note of the variables that have now been moved within this loop.
Next up, we see the next major change is:
if x > display_width - car_width or x < 0: gameExit = True
This is our logic for whether or not the car has crossed over the left and right boundaries.


Vendor Name:
LOBB MANAGEMENT SERVICES
Reference Number:
6015017
Contract Date:
2012-20-04
Description of Work:
TRAINING CONSULTANTS
Contract Period:
2012-20-04 to 2013-31-03
Delivery Date:
2013-31-03
Original Contract Value:
$0.00
Current Contract Value:
$15,801.92
Comments:
Competitively sourced


Coronavirus Disease 2019
About COVID-19:
What to do:
Resources for:
Stay informed:
Subscribe to DHSS's updates by email or text:
Questions?
For non-clinical questions,
For non-clinical questions,
Case Counts and Completed Tests
How many confirmed cases are there in Alaska by region?
*Cases are assigned by date of onset, diagnosis, or report — whichever is earliest; therefore, case counts may be updated as new data become available.
-
Cumulative number of cases hospitalized to date: 09
-
Cumulative number of deaths to date: 03
-
Updated March 31, 2020;
How many cases are there in Alaska by residence* and presumed exposure source?
|
Travel-Related
|
Non-Travel
|
Close Contact
|
Under Investigation
|
Total
|
|
Municipality of
|
24
|
6
|
13
|
22
|
65
|
|
Anchorage
|
21
|
5
|
12
|
19
|
57
|
|
Eagle River/Chugiak
|
2
|
1
|
1
|
3
|
7
|
|
Girdwood
|
1
|
0
|
0
|
0
|
1
|
|
Gulf Coast
|
4
|
0
|
2
|
2
|
8
|
|
Homer
|
1
|
0
|
0
|
1
|
2
|
|
Kenai
|
1
|
0
|
0
|
0
|
1
|
|
Seward
|
1
|
0
|
0
|
0
|
1
|
|
Soldotna
|
1
|
0
|
0
|
1
|
2
|
|
Sterling
|
0
|
0
|
2
|
0
|
2
|
|
Interior
|
7
|
3
|
17
|
8
|
35
|
|
Fairbanks
|
4
|
3
|
12
|
8
|
27
|
|
North Pole
|
3
|
0
|
5
|
0
|
8
|
|
Mat-Su
|
2
|
0
|
1
|
0
|
3
|
|
Palmer
|
2
|
0
|
1
|
0
|
3
|
|
Northern
|
0
|
0
|
0
|
0
|
0
|
|
Southeast
|
3
|
2
|
15
|
2
|
22
|
|
Juneau
|
1
|
1
|
5
|
2
|
9
|
|
Ketchikan
|
2
|
1
|
10
|
0
|
13
|
|
Southwest
|
0
|
0
|
0
|
0
|
0
|
|
TOTAL
|
40
|
11
|
48
|
34
|
133
|
*Case residence may not always reflect location of exposure or testing.
-
Region:Economic regions definitions
-
Travel-related:Exposure/source of the virus was outside of Alaska.
Exposure/source of the virus was outside of Alaska.
-
Pending:Under investigation
Under investigation
-
Updated March 31, 2020;
How many tests have been completed for Alaska residents?
How many tests were completed in total?
How many tests were completed each day?
How many cases are there?
CDC Case Counts
Thank you for your patience while this page loads. Embedded content brought to you through CDC content syndication:
|
COVID-19 Case Counts: CDC Content Syndication
|
|
|
|
|
|
|


{% extends 'base.html' %} {% block main %}
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Mo was designed as an entry for the third Go Challenge with the challenge being to create a "photo-mosaic generating web application". As specified in the challenge, Mo has been written 100% in Go (aside from the HTML) using only the Go standard libraries. It currently uses Flickr as it's image source for creating the mosaic tiles.
Mo is super easy to use! Simply select a local file you'd like to turn into a photo-mosaic using the form below and click "create". The creation process can take a minute or two so please just hang out while Mo does its thing. Once the photo-mosaic is complete it will be displayed and you'll get a link that you can share with others so they can see your new work of art :)


Package SELECTive
Class Elective
- java.lang.Object
-
- SELECTive.Elective
-
public class Elective extends java.lang.Object
-
-
Nested Class Summary
Nested Classes Modifier and Type Class Description static class
Elective.ElectiveFilterType
-
Constructor Summary
Constructors Constructor Description Elective(long id, java.lang.String code, java.lang.String name, int e, SELECTive.MasterProgram prog, java.lang.String[] keys, LectureTime[] times, LectureBlock block)
Elective(java.lang.String courseCode)
Elective(java.lang.String code, java.lang.String name)
-
Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description boolean
deregisterForElective(Student stu)
boolean
edit(Admin who)
static boolean
editElective(Admin who)
static Elective[]
filterOn(Elective.ElectiveFilterType filter, java.lang.String[] argument)
Returns the electives given a certain filter and arguments.static Elective[]
getAllAvailableElectives()
LectureBlock
getBlock()
java.lang.String
getCourseCode()
LectureBlock
getElectiveBlock()
java.lang.String
getElectiveName()
long
getLecturerId()
LectureTime[]
getLectureTimes()
static java.lang.String[]
keywordsFromKeywordString(java.lang.String keywordStr)
boolean
registerForElective(Student stu)
boolean
saveElective(Admin who, boolean newElective)
-
-
-
Constructor Detail
-
Elective
public Elective(java.lang.String code, java.lang.String name)
-
Elective
public Elective(long id, java.lang.String code, java.lang.String name, int e, SELECTive.MasterProgram prog, java.lang.String[] keys, LectureTime[] times, LectureBlock block)
-
Elective
public Elective(java.lang.String courseCode)
-
-
Method Detail
-
getElectiveName
public java.lang.String getElectiveName()
-
getCourseCode
public java.lang.String getCourseCode()
-
getElectiveBlock
public LectureBlock getElectiveBlock()
-
getLecturerId
public long getLecturerId()
-
getLectureTimes
public LectureTime[] getLectureTimes()
-
getBlock
public LectureBlock getBlock()
-
getAllAvailableElectives
public static Elective[] getAllAvailableElectives()
-
filterOn
public static Elective[] filterOn(Elective.ElectiveFilterType filter, java.lang.String[] argument)
Returns the electives given a certain filter and arguments. The possible arguments for the filters are:> COURSEID args: a
String[]
of the course ids to look for > ECTS args: aString[]
of ints of ects to consider > BLOCK args: aString[]
of ints representing the blocks to search for > AVAILABILITY args: aString[]
where only the first is the file path to an ical file > KEYWORDS args: aString[]
of keyword strings- Parameters:
filter
-argument
-- Returns:
-
keywordsFromKeywordString
public static java.lang.String[] keywordsFromKeywordString(java.lang.String keywordStr)
-
saveElective
public boolean saveElective(Admin who, boolean newElective)
-
editElective
public static boolean editElective(Admin who)
-
edit
public boolean edit(Admin who)
-
registerForElective
public boolean registerForElective(Student stu)
-
deregisterForElective
public boolean deregisterForElective(Student stu)
-
-


Cranberries handed first-ever Grammy nomination for final album
by Michael StainesThe Cranberries have received their first-ever Grammy nomination.
The Limerick band were announced among the nominees for Best Rock Album this afternoon.
Their final album, ‘In the End’ was released earlier this year as a tribute to late-singer Dolores O’Riordan.
It features O’Riordan’s vocals gleaned from demos she recorded before her death in February of last year.
The first single from In the End was released on the anniversary of her passing with the album itself hitting the shelves in April of this year.
The Best Rock Album nominees are:
- Amo — Bring Me the Horizon
- Social Cues — Cage the Elephant
- In the End — The Cranberries
- Trauma — I Prevail
- Feral Roots — Rival Sons
Singer Lizzo leads the way in this year’s nominations with eight to her name. She is followed closely by Billy Eilish and Lil Nas X with six each.
The nominees will now be put to a vote among members of the Recording Academy, with winners set to be announced on January 26th.
Recording Academy CEO Deborah Dugan said: “I am in complete awe of the inspiring nominees who have poured their hearts and souls into their craft, and in turn, have provided fans worldwide a year of music that changed us, stirred our emotions and inspired us to do more.”
The awards ceremony will be hosted Alicia Keys and broadcast live from the Staples Centre in LA.


This is data01 from the Data.html page
This is a paragraph from Div01
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Taking a shower
7:00am to 7:30pm
Having a breakfast
7:30am to 8:00am
Learning JS Theory:Дэвид Флэнаган - JavaScript. Подробное руководство (6-е издание)
8:00am to 15:00pm
Rolling Scopes lecture:Javascript - scopes, hoisting and global objects
18:30pm to 20:00pm


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Clean the open street data of San Francisco Bay Area and write into MongoDB database, after which yields some visualization work.
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Applied Exploratory Data Analysis on Red Wine dataset.
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Optimization Recipes¶
All optimization APIs are designed to work with recipe files. The files encode the instructions needed for modifying the model and/or training process as a list of modifiers. Example modifiers can be anything from setting the learning rate for the optimizer to gradual magnitude pruning. The files are written in YAML and stored in YAML or markdown files using YAML front matter. The rest of the SparseML system is coded to parse the recipe files into a native format for the desired framework and apply the modifications to the model and training pipeline.
In a recipe, modifiers must be written in a list that includes “modifiers” in its name.
The easiest ways to get or create optimization recipes are by either using the pre-configured recipes in SparseZoo or using Sparsify’s autoML style creation.
However, power users may be inclined to create their own recipes by hand to enable more fine grained control or to add in custom modifiers.
A sample recipe for pruning a model generally looks like the following:
version: 0.1.0
modifiers:
- !EpochRangeModifier
start_epoch: 0.0
end_epoch: 70.0
- !LearningRateModifier
start_epoch: 0
end_epoch: -1.0
update_frequency: -1.0
init_lr: 0.005
lr_class: MultiStepLR
lr_kwargs: {'milestones': [43, 60], 'gamma': 0.1}
- !GMPruningModifier
start_epoch: 0
end_epoch: 40
update_frequency: 1.0
init_sparsity: 0.05
final_sparsity: 0.85
mask_type: unstructured
params: ['sections.0.0.conv1.weight', 'sections.0.0.conv2.weight', 'sections.0.0.conv3.weight']
Modifiers Intro¶
Recipes can contain multiple modifiers, each modifying a portion of the training process in a different way.
In general, each modifier will have a start and an end epoch for when the modifier should be active.
The modifiers will start at start_epoch
and run until end_epoch
.
Note that it does not run through end_epoch
.
Additionally, all epoch values support decimal values such that they can be started somewhere in the middle of an epoch.
For example, start_epoch: 2.5
will start in the middle of the second training epoch.
The most commonly used modifiers are enumerated as subsections below.
Training Epoch Modifiers¶
The EpochRangeModifier
controls the range of epochs for training a model.
Each supported ML framework has an implementation to enable easily retrieving this number of epochs.
Note, that this is not a hard rule and if other modifiers have a larger end_epoch
or smaller start_epoch
then those values will be used instead.
The only parameters that can be controlled for EpochRangeModifier
are the start_epoch
and end_epoch
.
Both parameters are required.
Required Parameters:
start_epoch
: The start range for the epoch (0 indexed)end_epoch
: The end range for the epoch
Example:
- !EpochRangeModifier
start_epoch: 0.0
end_epoch: 25.0
Pruning Modifiers¶
The pruning modifiers handle pruning the specified layer(s) in a given model.
ConstantPruningModifier¶
The ConstantPruningModifier
enforces the sparsity structure and level for an already pruned layer(s) in a model.
The modifier is generally used for transfer learning from an already pruned model or
to enforce sparsity while quantizing.
The weights remain trainable in this setup; however, the sparsity is unchanged.
Required Parameters:
params
: The parameters in the model to prune. This can be set to a string containing__ALL__
to prune all parameters, a list to specify the targeted parameters, or regex patterns prefixed by ‘re:’ of parameter name patterns to match. For example:['blocks.1.conv']
for PyTorch and['mnist_net/blocks/conv0/conv']
for TensorFlow. Regex can also be used to match all conv params:['re:.*conv']
for PyTorch and['re:.*/conv']
for TensorFlow.
Example:
- !ConstantPruningModifier
params: __ALL__
GMPruningModifier¶
The GMPruningModifier
prunes the parameter(s) in a model to a
target sparsity (percentage of 0’s for a layer’s param/variable)
using [gradual magnitude pruning] (https://neuralmagic.com/blog/pruning-gmp/).
This is done gradually from an initial to final sparsity (init_sparsity
, final_sparsity
)
over a range of epochs (start_epoch
, end_epoch
) and updated at a specific interval defined by the update_frequency
.
For example, using the following settings start_epoch: 0
, end_epoch: 5
, update_frequency: 1
,
init_sparsity: 0.05
, final_sparsity: 0.8
will do the following:
at epoch 0 set the sparsity for the specified param(s) to 5%
once every epoch, gradually increase the sparsity towards 80%
by the start of epoch 5, stop pruning and set the final sparsity for the specified param(s) to 80%
Required Parameters:
params
: The parameters in the model to prune. This can be set to a string containing__ALL__
to prune all parameters, a list to specify the targeted parameters, or regex patterns prefixed by ‘re:’ of parameter name patterns to match. For example:['blocks.1.conv']
for PyTorch and['mnist_net/blocks/conv0/conv']
for TensorFlow. Regex can also be used to match all conv params:['re:.*conv']
for PyTorch and['re:.*/conv']
for TensorFlow.init_sparsity
: The decimal value for the initial sparsity to start pruning with. Atstart_epoch
will set the sparsity for the param/variable to this value. Generally, this is kept at kept at 0.05 (5%).final_sparsity
: The decimal value for the final sparsity to end pruning with. By the start ofend_epoch
will set the sparsity for the param/variable to this value. Generally, this is kept in a range from 0.6 to 0.95 depending on the model and layer. Anything less than 0.4 is not useful for performance.start_epoch
: The epoch to start the pruning at (0 indexed). This supports floating-point values to enable starting pruning between epochs.end_epoch
: The epoch before which to stop pruning. This supports floating-point values to enable stopping pruning between epochs.update_frequency
: The number of epochs/fractions of an epoch between each pruning step. It supports floating-point values to enable updating inside of epochs. Generally, this is set to update once per epoch (1.0
). However, if the loss for the model recovers quickly, it should be set to a lesser value. For example: set it to0.5
for once every half epoch (twice per epoch).
Example:
- !GMPruningModifier
params: ['blocks.1.conv']
init_sparsity: 0.05
final_sparsity: 0.8
start_epoch: 5.0
end_epoch: 20.0
update_frequency: 1.0
Quantization Modifiers¶
The QuantizationModifier
sets the model to run with
quantization aware training (QAT).
QAT emulates the precision loss of int8 quantization during training so weights can be
learned to limit any accuracy loss from quantization.
Once the QuantizationModifier
is enabled, it cannot be disabled (no end_epoch
).
Quantization zero points are set to be asymmetric for activations and symmetric for weights.
Currently only available in PyTorch.
Notes:
ONNX exports of PyTorch QAT models will be QAT models themselves (emulated quantization). To convert your QAT ONNX model to a fully quantizerd model use the script
scripts/pytorch/model_quantize_qat_export.py
or the functionneuralmagicML.pytorch.quantization.quantize_qat_export
.If performing QAT on a sparse model, you must preserve sparsity during QAT by applying a
ConstantKSModifier
or have already used aGradualKSModifier
withleave_enabled
set to True.
Required Parameters:
start_epoch
: The epoch to start QAT. This supports floating-point values to enable starting pruning between epochs.
Example:
- !QuantizationModifier
start_epoch: 0.0
Learning Rate Modifiers¶
The learning rate modifiers set the learning rate (LR) for an optimizer during training. If you are using an Adam optimizer, then generally, these are not useful. If you are using a standard stochastic gradient descent optimizer, then these give a convenient way to control the LR.
SetLearningRateModifier¶
The SetLearningRateModifier
sets the learning rate (LR) for the optimizer to a specific value at a specific point
in the training process.
Required Parameters:
start_epoch
: The epoch in the training process to set thelearning_rate
value for the optimizer. This supports floating-point values to enable setting the LR between epochs.learning_rate
: The floating-point value to set as the learning rate for the optimizer atstart_epoch
.
Example:
- !SetLearningRateModifier
start_epoch: 5.0
learning_rate: 0.1
LearningRateModifier¶
The LearningRateModifier
sets schedules for controlling the learning rate for an optimizer during training.
If you are using an Adam optimizer, then generally, these are not useful.
If you are using a standard stochastic gradient descent optimizer, then these give a convenient way to control the LR.
Provided schedules to choose from are the following:
ExponentialLR
: Multiplies the learning rate by agamma
value every epoch. To use this one,lr_kwargs
should be set to a dictionary containinggamma
. For example:{'gamma': 0.9}
StepLR
: Multiplies the learning rate by agamma
value after a certain epoch period defined bystep
. To use this one,lr_kwargs
must be set to a dictionary containinggamma
andstep
. For example:{'gamma': 0.9, step: 2.0}
MultiStepLR
: Multiplies the learning rate by agamma
value at specific epoch points defined bymilestones
. To use this one,lr_kwargs
must be set to a dictionary containinggamma
andmilestones
. For example:{'gamma': 0.9, 'milestones': [2.0, 5.5, 10.0]}
Required Parameters:
start_epoch
: The epoch to start modifying the LR at (0 indexed). This supports floating-point values to enable starting pruning between epochs.end_epoch
: The epoch to stop modifying the LR before. This supports floating-point values to enable stopping pruning between epochs.lr_class
: The LR class to use, one of [ExponentialLR
,StepLR
,MultiStepLR
].lr_kwargs
: The named arguments for thelr_class
.init_lr
: [Optional] The initial LR to set atstart_epoch
and to use for creating the schedules. If not given, the optimizer’s current LR will be used at startup.
Example:
- !LearningRateModifier
start_epoch: 0.0
end_epoch: 25.0
lr_class: MultiStepLR
lr_kwargs:
gamma: 0.9
milestones: [2.0, 5.5, 10.0]
init_lr: 0.1
Params/Variables Modifiers¶
TrainableParamsModifier¶
The TrainableParamsModifier
controls the params that are marked as trainable for the current optimizer.
This is generally useful when transfer learning to easily mark which parameters should or should not be frozen/trained.
Required Parameters:
params
: The names of parameters to mark as trainable or not. This can be set to a string containing__ALL__
to mark all parameters, a list to specify the targeted parameters, or regex patterns prefixed by ‘re:’ of parameter name patterns to match. For example:['blocks.1.conv']
for PyTorch and['mnist_net/blocks/conv0/conv']
for TensorFlow. Regex can also be used to match all conv params:['re:.*conv']
for PyTorch and['re:.*/conv']
for TensorFlow.
Example:
- !TrainableParamsModifier
params: __ALL__
Optimizer Modifiers¶
SetWeightDecayModifier¶
The SetWeightDecayModifier
sets the weight decay (L2 penalty) for the optimizer to a
specific value at a specific point in the training process.
Required Parameters:
start_epoch
: The epoch in the training process to set theweight_decay
value for the optimizer. This supports floating-point values to enable setting the weight decay between epochs.weight_decay
: The floating-point value to set as the weight decay for the optimizer atstart_epoch
.
Example:
- !SetWeightDecayModifier
start_epoch: 5.0
weight_decay: 0.0


Communication Unbreakdown has paired grassroots organizing with cutting-edge data science to find conflicted voters in an evolving political landscape.
Communication Unbreakdown has paired grassroots organizing with cutting-edge data science to find conflicted voters in an evolving political landscape.


package collection
Ordering
- Alphabetic
Visibility
- Public
- All
Type Members
-
case class
CMap[A, B](f: (A) ⇒ B) extends Product with Serializable
- Annotations
- @library()
-
case class
Cons[T](h: T, t: List[T]) extends List[T] with Product with Serializable
- Annotations
- @library() @constructor( name = "List.list.Cons" )
- sealed abstract class List[T] extends AnyRef
-
case class
Nil[T]() extends List[T] with Product with Serializable
- Annotations
- @library() @constructor( name = "List.list.Nil" )
-
case class
Queue[A](front: List[A], rear: List[A]) extends Product with Serializable
- Annotations
- @library()


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 3-4
Fish fingers taste wonderful when you make them from scratch - even the kids can help! Guaranteed to impress.
Preheat the oven to 200C/180C Fan/Gas 6. Line a baking tray with non-stick parchment paper.
To make the breadcrumbs, put the bread in a food processor and blitz into fine breadcrumbs. If you do not have a food processor, you could also try grating the bread, but you would need an adult to help you with this.
Pat the fish dry using kitchen paper. Carefully cut the fish into strips, the strips should be about 2cm thick and 8cm long. Run your finger gently over the fish to make sure all the bones have been taken out, you can get an adult to double-check this for you.
Set up your assembly line. Get 3 dinner plates and one shallow bowl ready.
Put the flour on the first plate. Crack the egg into the shallow bowl and beat lightly using a fork. Put the breadcrumbs on the second plate and mix with the paprika, garlic powder and black pepper. The third plate is for the coated fish fingers.
Toss each fish finger in the flour and shake off any excess. Next, dip the fish fingers into the egg mixture and allow any extra egg liquid to drip back into the bowl. Then coat the fish fingers with the breadcrumbs. Slightly press the fish fingers into the breadcrumbs to make sure they are well coated and then put them on the third plate.
Put the fish fingers in the fridge for 10 minutes, this will help the coating stick to the fish better. Wash your hands again before moving on to the next step, it is really important to always wash your hands after handling raw fish or meat.
Arrange the fish fingers in a single layer on the prepared baking tray. Drizzle or spray some oil over the fish fingers.
Bake in the oven for 10-12 minutes, turning halfway through using cooking tongs. The fish fingers are cooked when the crumbs are golden-brown and the fish is cooked through.
These fish fingers are best served with ketchup for dipping and with sweet potato wedges and peas alongside.


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Sandra Jones and Company is passionate about our clients’ success so we customize our support and services around their unique position in the market, goals, expectations and budget. We provide turnkey support or augment our client’s internal talent. The results have made us the #1 go to consulting firm in the security industry.
Mergers & Acquisitions
Rick Gross, President & CEO of Vindicator Technologies writes: Thank you again for helping us sell Vindicator Technologies. Your industry knowledge, executive contacts, business acumen and mentoring skills when coupled with our unique value proposition, enabled us to maximize the sale of the company. You offer so much more than a traditional industry agent/broker. You are a true partner… far and away the best value. We’d use you and again and of course we highly recommend you.Due Diligence & Advisor to Investors
Richard Schneider, VP of Dailey Capital Management writes: SJandCo provided an excellent assessment of the security industry and due diligence review regarding our potential investment in the industry. Your thought provoking questions and analysis raised important issues that we needed to address with the transaction.Satisfied Clients
Jeff Kessler, Managing Director of Imperial Capital writes: I have known and worked with Sandra Jones & Company for over 15 years, and in that time I have come to rely on them for critical information and contacts in the security world. We believe there is no better consulting firm servicing the security industry.Growth Strategies
Robert Shaw, CEO of Video Insight writes: We utilized SJandCo extensively to provide strategic, marketing and operations support. Sandy’s grasp of strategic and tactical issues facing the industry allowed the business to leverage her knowledge then make more informed decision about the Company’s direction and growth plans.Distribution & Channel Strategies
Dan Moceri, CEO of Convergint Technologies writes: Sandy Jones is an icon in the security industry. Sandy knows the industry, the people, and the trends. She can assist in business development, acquisitions, and product development.Profitability Development
Bob Sommerfeld, President of G4S Integrated Systems writes: SJandCo helped refine our strategy, differentiate our offering from the many competitors and focus our scarce resources to the areas that would provide the best ROIMarketing Strategies
Martin A. Renkis, Founder & CEO of Smartvue Corporation writes: The team at SJandCo has made an important impact on how we are bringing our technologies to market. Their understanding of the security industry and their contacts have been integral in the development of the Smartvue S2 rollout program.Bringing New Products to Market
A-lan Reynolds, Senior Manager of Raytheon writes: SJandCo has been vital to our product launch strategy development. Sandra Jones’ insight has been invaluable in helping us sort through the market verticals into which to introduce a disruptive new security entrant.Voice of the Customer Research
Aisling MacRunnels, CMO of 3VR writes: SJandCo completed the critical ‘Voice of the Customer’ consulting project providing high quality results, on a timely basis, within budget.OEM Introductions
Ted Babitz, Director, Business Development of Intrado Inc. writes: SJandCo came very highly recommended, and I now know why. The firm provided us with expert market guidance, introductions to potential affiliates, and a pre-market strategy. I learned in 30 days what would have taken me months or even a year to discover on my own.- Previous
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I decided to learn programming and web development because I wanted to switch out of the semiconductor industry - where I worked for 5 years.
Previously, I also wrote patents for 2 years which included drafting patents and prosecution documents related to the automobiles, software, chemicals, etc.
Additionally, I enjoy the following things!


Stat-gram is microservice tool for Business and Channel Influencers on Instagram platform that uses AI to empower
how decisions are made and create a positive impact on Marketing and Management.
There is a gap on the market for analytics tools for Instagram business and channel influencers.
Leads generation, User experience, meaningful KPI's,
Time consuming with integration with management tools.
Using AI, that is what we do!
CUTTING EDGE TECHNOLOGY
Stat-gram delivery a cutting edge technology empower by Machine Learning Algorithms.
Our product extract profile data information from all your follows plus a addicional layer
with the followers of followers.
What we do with all this data, we process it in the back-end, save the output in the AWS database
, then we available all the information for you to play around in our dashboard tool.
PRODUCT ARCHITECTURE
Stat-gram architecture designed thinking in optimization and security.
MARKETING
Stat-gram is microservice tool for Business and Channel Influencers on Instagram platform that uses AI to empower
how decisions are made and create a positive impact on Marketing and Management.
We improve quality leads by peforming sentiment analysis in the post and coments, giving a better understand
of the audience as well as using Natural Processing Language techniques to improve strategy.
MANAGEMENT
With Stat-gram is microservice Management tool the goal is simplify the daily basis for
Marketing professionals and Managers.
This feature process the data analysis, indentify the answers for goals once set,
and delivery a project blue print for actions plus a export feature that allows
the user to connect the report with other management services such as monday.com and trello.
STAT-gram DEMO REQUEST
The only feature currently the only Machine Learning feature running is the span-detector for profiles well know as
fake Vs no fake, it runs in the backend and return to the user which one are suspicious profile.


| Path: | lib/rconfig/rconfig.rb |
| Last Update: | Wed Jan 06 17:58:24 -0600 2010 |
Copyright (c) 2009 Rahmal Conda<EMAIL_ADDRESS>The complete solution for Ruby Configuration Management. RConfig is a Ruby library that manages configuration within Ruby applications. It bridges the gap between yaml, xml, and key/value based properties files, by providing a centralized solution to handle application configuration from one location. It provides the simplicity of hash-based access, that Rubyists have come to know and love, supporting your configuration style of choice, while providing many new features, and an elegant API.
The overlay order of the config files is defined by SUFFIXES: * nil * _local * _config * _local_config * _{environment} (.i.e _development) * _{environment}_local (.i.e _development_local) * _{hostname} (.i.e _whiskey) * _{hostname}_config_local (.i.e _whiskey_config_local)
Example:
shell/console => export LANG=en demo.yml => server: address: host.domain.com port: 81 ... application.properties => debug_level = verbose ...
demo.rb =>
require 'rconfig' RConfig.config_paths = ['$HOME/config', '#{APP_ROOT}/config', '/demo/conf'] RConfig.demo[:server][:port] => 81 RConfig.demo.server.address => 'host.domain.com' RConfig[:debug_level] => 'verbose' RConfig[:lang] => 'en' ...


WITNESS LIST Corrections Committee April 4, 2005-2:00P HB 2193 For: del Llano, Ann (A.C.L.U. of Texas) Hendrick, Dana (Self) Place, Allen (Texas Criminal Defense Lawyers Association) On: Cahill, PhD, Melissa (Collin County CSCD) Cosper, Caprice (Self) Creuzot, John (Self) Donnelly, Paul David (Harris County Community Supervision and Correction's Department) Iles, Leighton (Self and Fort Bend County CSCD) Smith, Bradley (Self) Stott, Jim (Texas Probation Association) White, Bonita (TDCJ- CJAD) HB 2838 For: Hendrick, Dana (Self) Houston, Joshua (Christian Life Commission) Solomon, Emmett (Restorative Justice Ministries Network) Valentine, David (Self) On: Cosper, Caprice (Self) Creuzot, John (Self) Levin, Marc (Texas Public Policy Foundation, Center for Effective Justice) White, Bonita (TDCJ- CJAD) HB 3072 For: del Llano, Ann (A.C.L.U. of Texas) English, Patty (Austin Transitional Treatment Center) Hendrick, Dana (Self) Houston, Joshua (Christian Life Commission) Janez-Correa, Ana (National LULAC) Rayfield, Penny (Austin/Travis County Reentry Roundtable Planning Council) Solomon, Emmett (The Restorative Justice Ministries Network) Valentine, David (Self) On: Cosper, Caprice (Self) Creuzot, John (Self) Iles, Leighton (Self and Fort Bend County CSCD) Levin, Marc (Texas Public Policy Foundation, Center For Effective Justice) Smith, Bradley (Self and Texas Association of Drug Court Professionals) White, Bonita (TDCJ-CJAD)


Bases: GPyOpt.methods.bayesian_optimization.BayesianOptimization
Bases: GPyOpt.core.bo.BO
GPyOpt.interface package
GPyOpt.plotting package
Enter search terms or a module, class or function name.


This structure stores information about the tile map properties
ushort width;
The map width in pixel
ushort height;
The map height in pixel
ushort[2] mapSize;
The map size.
index 0 is width / tileWidth and index 1 is height / tileHeight
ubyte tileWidth;
The tile width in pixel (for example: 16, 32, 64)
ubyte tileHeight;
The tile height in pixel
string source;
The map filename
struct Tile;
The Tile structure contains informations about every tile on the map
ushort gid;
The gid is the Tile id.
It contains the positions of this tile on the tileset.
const uint tilesPerRow = mapWidth / tileWidth;
uint y = gid / tilesPerRow;
uint x = gid % tilesPerRow;
ushort[2] pixelCoords;
The coordinates in pixel of this tile on the map
ushort[2] tileCoords;
The coordinates of this tile on the map
string layer;
The layer of the tile, if any
abstract class TileMap: Dgame.Graphics.Transformable.Transformable, Dgame.Graphics.Drawable.Drawable;
The Tile map consist of tiles which are stored in a XML file (preferably build with tiled)
Author:
rschuett
protected abstract void _readTileMap();
The read method must be overriden by any specialized TileMap.
final this(string filename, bool compress = true);
CTor
If compress is true, only the needed Tiles are stored
(which means that are new tileset is created which contains only the needed tiles)
otherwise the whole tileset is taken.
final pure nothrow ref const(Vector2s) calculateCenter();
Calculate, store and return the center point.
final inout pure nothrow inout(ShortRect*) fetchView();
Fetch the viewport pointer so that it can modified outside.
final pure nothrow void setView(short x, short y, short w, short h);
Set a new view.
final void setView(ref const ShortRect view);
Set a new view.
final void setView(const ShortRect view);
Rvalue version.
final pure nothrow void resetView();
Reset the viewport.
final void adjustView();
Adjust the viewport.
The position is shifted about view.x * -1 and view.y - 1
so that the left upper corner of the current view is in the left upper corner of the Window.
final void load(string filename, bool compress = true);
Load a new TileMap
final const pure nothrow @property bool doCompress();
If compress is true, only the needed Tiles are stored
(which means that are new tileset is created which contains only the needed tiles)
otherwise the whole tileset is taken.
final const Vector2s convertCoords(float cx, float cy);
Convert from pixel coordinates to tile coordinates.
final const Vector2s convertCoords(ref const Vector2f vec);
Convert from pixel coordinates to tile coordinates.
final const Vector2s reconvertCoords(float cx, float cy);
Convert from tile coordinates to pixel coordinates.
final const Vector2s reconvertCoords(ref const Vector2f vec);
Convert from tile coordinates to pixel coordinates.
final const Vector2s adjustCoords(float cx, float cy);
Adjusted pixel coordinates so that they lie on valid pixel
coordinates based on tile coordinates.
final const Vector2s adjustCoords(ref const Vector2f vec);
Adjusted pixel coordinates so that they lie on valid pixel coordinates
based on tile coordinates.
final void reload(const Vector2s[] coords, const Vector2s[] newCoords);
Reload multiple tiles.
The length of coords must be equal to the length of newCoords.
See: reload for one tile
final void reload(const Vector2s[] coords, ref const Vector2s newCoord);
Replace multiple tiles with another.
final void reload(const Vector2s[] coords, const Vector2s newCoord);
Rvalue version
final void reload(ref const Vector2s coord, ref const Vector2s newCoord);
Reload one tile, which means that the tile on the coordinates coord
is replaced with the tile (and the tile surface) on the coordinates newCoord
final void reload(const Vector2s coord, const Vector2s newCoord);
Rvalue version
final void exchangeTileset(Texture tex);
Exchange the tileset
final inout pure nothrow inout(Tile[]) getTiles();
Returns all containing tiles
final const pure nothrow bool isTileAt(ref const Vector2s vec, uint* idx = null);
Check whether a tile exist on the given Coordinates.
If idx isn't null, the calculated index of the Tile at the given position is stored there.
Note:
The position must be in tile coordinates, not pixel coordinates.
final const pure nothrow bool isTileAt(short x, short y, uint* idx = null);
Check whether a tile exist on the given Coordinates.
If idx isn't null, the calculated index of the Tile at the given position is stored there.
Note:
The position must be in tile coordinates, not pixel coordinates.


MDT CAD System
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WARRANT ISSUED!
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For the 8 Queens problem, there is a valid solution regardless of where you place the first queen. Choose the position of the first queen below to simulate different valid boards.


This app calculates a hash of given text using the SHA-256 algorithm. You can always get same hash from same text. But it is very difficult to estimate original text from hash.
Hash calculation depends on jsSHA which is a JavaScript implementation of the SHA family. So, ensure that JavaScript is enabled in your web browser.
Each line in the 'TEXT' field corresponds to that of the 'Hash (HEX-encoded)' field.
GitHub - akchan/sha256-calculator
SHA-256 Calculator (2.0) Copyright (c) 2017 Satoshi Funayama


Your letters for Nov. 8
by Calgary Herald"I believe in fiscal prudence, but the predictable mix of cuts to frontline services and tax reductions for corporations goes far beyond prudence; it betrays the callous, privileged ideology of the UCP."
Alberta is a great place for green technology
Albertans should be asking why all of our oil patch companies have not gotten into green technology. There are billions of dollars to be made there. These oil companies have a glorious opportunity to make big bucks on green technology. Why are they not building electric cars? Anyway, Alberta is a perfect place for green technology. A highly trained workforce, lots of land, lots of sunshine, lots of wind. You may have noted that Warren Buffett, the genius investor as already investing in green technology in Alberta.
Ken Riley, Bassano
Why are teachers the first to go?
Re: Province puts schools in a squeeze, Nov. 6
I would like to know why the Calgary School Boards first line of defence, anytime they have budget issues, is to fire teachers. Surely they have administration staff that can be reduced in times of budget reductions rather than immediately announce that classroom teachers will be cut from staff. Their mantra seems to be they consider students their first priority. Does not fit with their actions. This really looks like a scare tactic that has worn thin.
Sylvia Madder, Calgary
Albertans are mad about hypocrisy
Re: Separation should cost Albertans, letter, Nov. 7
I am not in favour of splitting our province from the rest of Canada , but would rather see Albertans explore the Canadian Pension Plan as well as other options. However it is interesting to read that the writer from Mission City advocates for the federal government to claim rights to a corridor through Alberta, yet Alberta would be denied the same for the Trans Mountain Expansion. It is that type of hypocrisy that gets Albertans knickers in a knot.
Werner Pointmeier, Calgary
The hypocrisy of Jason Kenney
Jason Kenney’s first budget is both brazenly ideological and incredibly hypocritical. I believe in fiscal prudence, but the predictable mix of cuts to frontline services and tax reductions for corporations goes far beyond prudence; it betrays the callous, privileged ideology of the UCP. The most egregious example is de-indexing AISH. I cannot convey how repugnant and heartless this move is. People receiving AISH cannot work. In a capitalist system, premised on an individual’s ability to contribute narrowly defined economic value, these are the most vulnerable people in our society. AISH is already a pittance; the basic annual amount is $3,293 below the 2017 Canada low income measure (LIM) of poverty. This is where the hypocrisy comes in. Jason Kenney has long opposed de-indexing, or so-called “bracket creep,” and did so in the very pages of this paper. His abandonment of that position to target those most in need is disgusting.
Kevin Bruch, Calgary


This is the API
The query contains a json object (names, values) and the request returns the list of documents (regulations) where the filter matches i.e. the document contains fields with those values.
http://operando.eu/policyDB/OSP/
GET
The query filter to be matched - \"?filter=\"{json description}\"
Get a specific OSP document via the id. This will return the full OSP document in json format.
http://operando.eu/policyDB/OSP/{osp_id}/
GET
Unique identifier representing a specific osp
The query contains a json object (names, values) and the request returns the list of documents (regulations) where the filter matches i.e. the document contains fields with those values.
http://operando.eu/policyDB/Regulations/
GET
The query filter to be matched - \"?filter=\"{json description}\"
Get a specific legislation document via the id. This will return the full legislation document in json format.
http://operando.eu/policyDB/Regulations/{reg_id}/
GET
Unique identifier representing a specific user
The query contains a json object (names, values) and the request returns the list of documents (UPPs) where the filter matches i.e. each document contains fields with those values.
http://operando.eu/policyDB/UserPrivacyPolicy/
GET
The query filter to be matched - \"?filter=\"{json description}\"
Get a specific UPP document via the user's id. This will return the full user privacy policy document in json format.
http://operando.eu/policyDB/UserPrivacyPolicy/{user_id}
GET
Unique identifier representing a specific user


Inputs¶
-
delphin_6_automation.sampling.inputs.
construct_delphin_reference
(folder: str) → List[str][source]¶ Generate Delphin files for models without insulation.
-
delphin_6_automation.sampling.inputs.
construct_design_files
(folder: str) → List[str][source]¶ Generate Delphin files to cover all the design options. Options arise from separate insulation systems X variation within those e.g. thickness of insulation layer.
-
delphin_6_automation.sampling.inputs.
construction_types
() → List[str][source]¶ Gets available template strings.
Returns: list of file name strings
-
delphin_6_automation.sampling.inputs.
delphin_templates
(folder: str) → dict[source]¶ Titles on delphin files to variate (select by amount of layers).
Returns: dictionary with integer keys according to layers
-
delphin_6_automation.sampling.inputs.
implement_insulation_widths
(delphin_dict: dict, system: pandas.core.frame.DataFrame) → List[dict][source]¶ Permutate with of system applied materials
-
delphin_6_automation.sampling.inputs.
implement_system_materials
(delphin_dict: dict, system: pandas.core.frame.DataFrame) → dict[source]¶ Loop through materials of the system.
Parameters: - delphin_dict – relevant template delphin dict
- system – one insulation system’s Dataframe
Returns: new delphin dict with all system materials implemented
-
delphin_6_automation.sampling.inputs.
insulation_systems
(folder: str, rows_to_read=2, excel_file='InsulationSystems') → pandas.core.frame.DataFrame[source]¶ Reformat insulation DataFrame to different design options DataFrame
-
delphin_6_automation.sampling.inputs.
insulation_type
(folder='C:\\Users\\ocni\\PycharmProjects\\delphin_6_automation\\delphin_6_automation\\sampling/input_files') → List[List[int]][source]¶ All included material IDs in insulation systems as such.


A Warm Welcome!
I'm the co-founder of an online programmers community situated on the world wide web.
My primary skill is solid knowledge of end-to-end web development using CSS, HTML, JavaScript and PHP. I have very good hygiene and outstanding hair :)


--- layout: post title: "A heuristic for formatting code according to the AAA pattern" description: "This article describes a rule of thumb for formatting unit tests." date: 2013-06-24 19:20 UTC tags: [Unit Testing, Design Patterns] --- {% include JB/setup %}
{{ page.description }}
The Arrange Act Assert (AAA) pattern is one of the most fundamental and important patterns for writing maintainable unit tests. It states that you should separate each test into three phases (Arrange, Act, and Assert).
Like most other code, unit tests are read more than they are written, so it's important to make the tests readable. This article presents one way to make it easy for a test reader easily to distinguish the three AAA phases of a test method.
The technique is simple:
Many programmers use the AAA pattern by explicitly demarking each phase with a code comment:
[Fact] public void UseBasketPipelineOnExpensiveBasket() { // Arrange var basket = new Basket( new BasketItem("Chocolate", 50, 3), new BasketItem("Gruyère", 45.5m, 1), new BasketItem("Barolo", 250, 2)); CompositePipe<Basket> pipeline = new BasketPipeline(); // Act var actual = pipeline.Pipe(basket); // Assert var expected = new Basket( new BasketItem("Chocolate", 50, 3), new BasketItem("Gruyère", 45.5m, 1), new BasketItem("Barolo", 250, 2), new Discount(34.775m), new Vat(165.18125m), new BasketTotal(825.90625m)); Assert.Equal(expected, actual); }
Notice the use of code comments to indicate the beginning of each of the three phases.
Given an example like the above, this seems like a benign approach, but mandatory use of code comments starts to fall apart when tests are very simple.
Consider this Structural Inspection test:
[Fact] public void SutIsBasketElement() { // Arrange // Act? var sut = new Vat(); // Assert Assert.IsAssignableFrom<IBasketElement>(sut); }
Notice the question mark after the // Act
comment. It seems that the writer of the test was unsure if the act of creating an instance of the System Under Test (SUT) constitutes the Act phase.
You could just as well argue that creating the SUT is part of the Arrange phase:
[Fact] public void SutIsBasketElement() { // Arrange var sut = new Vat(); // Act // Assert Assert.IsAssignableFrom<IBasketElement>(sut); }
However, now the Act phase is empty. Clearly, using code comments to split two lines of code into three phases is not helpful to the reader.
Here's a simpler alternative:
[Fact] public void SutIsBasketElement() { var sut = new Vat(); Assert.IsAssignableFrom<IBasketElement>(sut); }
When there's only two lines of code, the test is so simple that you don't need help from code comments. If you wanted, you could even reduce that test to a single line of code, by inlining the sut
variable:
[Fact] public void SutIsBasketElement() { Assert.IsAssignableFrom<IBasketElement>(new Vat()); }
Such a test is either a degenerate case of AAA where one or more phase is empty, or else it doesn't really fit into the AAA pattern at all. In these cases, code comments are only in the way, so it's better to omit them.
Even if you have a test that you can properly divide into the three distinct AAA phases, you don't need comments or formatting if it's only three lines of code:
[Theory] [InlineData("", "", 1, 1, 1, 1, true)] [InlineData("foo", "", 1, 1, 1, 1, false)] [InlineData("", "bar", 1, 1, 1, 1, false)] [InlineData("foo", "foo", 1, 1, 1, 1, true)] [InlineData("foo", "foo", 2, 1, 1, 1, false)] [InlineData("foo", "foo", 2, 2, 1, 1, true)] [InlineData("foo", "foo", 2, 2, 2, 1, false)] [InlineData("foo", "foo", 2, 2, 2, 2, true)] public void EqualsReturnsCorrectResult( string sutName, string otherName, int sutUnitPrice, int otherUnitPrice, int sutQuantity, int otherQuantity, bool expected) { var sut = new BasketItem(sutName, sutUnitPrice, sutQuantity); var actual = sut.Equals( new BasketItem(otherName, otherUnitPrice, otherQuantity)); Assert.Equal(expected, actual); }
Three lines of code, and three phases of AAA; I think it's obvious what goes where - even if this single test method captures eight different test cases.
When you have more than three lines of code, you'll need to help the reader identify what goes where. As long as you can keep it simple, I think that you accomplish this best with simple whitespace:
[Fact] public void UseBasketPipelineOnExpensiveBasket() { var basket = new Basket( new BasketItem("Chocolate", 50, 3), new BasketItem("Gruyère", 45.5m, 1), new BasketItem("Barolo", 250, 2)); CompositePipe<Basket> pipeline = new BasketPipeline(); var actual = pipeline.Pipe(basket); var expected = new Basket( new BasketItem("Chocolate", 50, 3), new BasketItem("Gruyère", 45.5m, 1), new BasketItem("Barolo", 250, 2), new Discount(34.775m), new Vat(165.18125m), new BasketTotal(825.90625m)); Assert.Equal(expected, actual); }
This is the same test as in the motivating example, only with the comments removed. The use of whitespace makes it easy for you to identify three phases in the method, so comments are redundant.
As long as you can express each phase without using whitespace within each phase, you can omit the comments. The only whitespace in the test marks the boundaries between each phase.
If your tests grow in complexity, you may need to divide the code into various sub-phases in order to keep it readable. When this happens, you'll have to resort to using code comments to demark the phases, because use of only whitespace would be ambiguous:
[Fact] public void PipeReturnsCorrectResult() { // Arrange var r = new MockRepository(MockBehavior.Default) { DefaultValue = DefaultValue.Mock }; var v1Stub = r.Create<IBasketVisitor>(); var v2Stub = r.Create<IBasketVisitor>(); var v3Stub = r.Create<IBasketVisitor>(); var e1Stub = r.Create<IBasketElement>(); var e2Stub = r.Create<IBasketElement>(); e1Stub.Setup(e => e.Accept(v1Stub.Object)).Returns(v2Stub.Object); e2Stub.Setup(e => e.Accept(v2Stub.Object)).Returns(v3Stub.Object); var newElements = new[] { r.Create<IBasketElement>().Object, r.Create<IBasketElement>().Object, r.Create<IBasketElement>().Object, }; v3Stub .Setup(v => v.GetEnumerator()) .Returns(newElements.AsEnumerable().GetEnumerator()); var sut = new BasketVisitorPipe(v1Stub.Object); // Act var basket = new Basket(e1Stub.Object, e2Stub.Object); Basket actual = sut.Pipe(basket); // Assert Assert.True(basket.Concat(newElements).SequenceEqual(actual)); }
In this example, the Arrange phase is so complicated that I've had to divide it into various sections in order to make it just a bit more readable. Since I've had to use whitespace to indicate the various sections, I need another mechanism to indicate the three AAA phases. Code comments is an easy way to do this.
As Tim Ottinger described back in 2006, code comments are apologies for not making the code clear enough. A code comment is a code smell, because it means that the code itself isn't sufficiently self-documenting. This is also true in this case.
Whenever I need to add code comments to indicate the three AAA phases, an alarm goes off in my head. Something is wrong; the test is too complex. It would be better if I could refactor either the test or the SUT to become simpler.
When TDD’ing, I tend to accept the occasional complicated unit test method, but if I seem to be writing too many complicated unit tests, it's time to stop and think.
In Growing Object-Oriented Software, Guided by Tests, one of the most consistent pieces of advice is that you should listen to your tests.
How do you know when a test has become too complicated? If you need to added code comments to it, it probably is.
This article first appeared in the The Developer No 2/2013. It's reprinted here with kind permission.


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