--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/Prime.20Theorem.20Help.html ---
Here is the theorem I am trying to prove:
Let n > 1. If n has no positive prime factor less than or equal to sqrt(n), then n is prime.
Here is a proof from Hungerford's Abstract Algebra:
Suppose n has the given property but assume that n is not prime. Then n has at least two positive prime factors, say p1 and p2, so that n = p1p2k for some positive integer k. By hypothesis, n has no positive prime divisors less than or equal to sqrt(n). Hence p1 > sqrt(n) and p2 > sqrt(n). Hence n = p1p2k >= p1*p2 >n. Contradiction.
I am new to lean, so I am trying to translate proofs in textbooks for practice. Can anyone please tell me how to translate this proof into Lean code?
Conversation moved to here
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


Loose lips sink ships. The Navy ship returned fire after being fired upon. Being in a ship is being in a jail, with the chance of being drowned. If Marines were all that special they would have their own boats. Don’t give up the ship. The Navy, Drinkers with a Sailing problem. Keep calm and love a sailor Ain't Nobody tougher than a Sailor, except her Momma. Once a Navy Sailor, Always a Navy Sailor.
I once took a solemn oath to defend the constitution against all enemies foreign and domestic, be advised that no one has ever relieved me of my duties under this oath. Drinking is the soldier’s pleasure. A smooth sea never made a skillful sailor. Sailors have the cleanest bodies and the filthiest minds. If you can't do your job drunk you're not a Sailor. U.S. Navy, like the Army but for Smart people.


Adversarial Search
Table of contents
Minimax
The algorithm will attempt to find the best action that an agent can take, assuming that an opposing force is trying to minimize its results.
Provide the leafs nodes for the tree to see how the algorithm will solve different problems.
Alpha Beta Pruning
A slight modification to minimax, Alpha Beta Pruning will try to improve performance by ignoring nodes that cannot possibly provide a better outcome.
Provide the leafs nodes for the tree to see how the algorithm will solve different problems.


Index
All Classes|All Packages
S
- search(String, Model, String) - Method in class com.sharify.controllers.ProductController
-
Searches product from database of products.
- seller - Variable in class com.sharify.models.Product
-
Should be longer than 2 symbols, shorter than 30 and contain only latin characters.
- ServletInitializer - Class in com.sharify
-
Initializes servlet.
- ServletInitializer() - Constructor for class com.sharify.ServletInitializer
- setActive(boolean) - Method in class com.sharify.models.User
-
Set's user's "active" status.
- setAddress(String) - Method in class com.sharify.models.Product
-
Sets product's Address
- setBail(Float) - Method in class com.sharify.models.Product
-
Sets product's bail.
- setCategory(String) - Method in class com.sharify.models.Product
-
Sets product's category.
- setCity(String) - Method in class com.sharify.models.Product
-
Set's product's city.
- setEmail(String) - Method in class com.sharify.models.Product
-
Sets product's email.
- setId(Long) - Method in class com.sharify.models.Product
-
Sets product's ID.
- setId(Long) - Method in class com.sharify.models.User
-
Sets user's ID.
- setPassword(String) - Method in class com.sharify.models.User
-
Sets user's password.
- setPhoneNumber(Integer) - Method in class com.sharify.models.Product
-
Sets product's phone number.
- setPrice(Float) - Method in class com.sharify.models.Product
-
Sets product's price.
- setRoles(Set<Role>) - Method in class com.sharify.models.User
-
Sets user's roles.
- setSeller(String) - Method in class com.sharify.models.Product
-
Sets product's seller.
- setTitle(String) - Method in class com.sharify.models.Product
-
Sets product's title.
- setUsername(String) - Method in class com.sharify.models.User
-
Sets username.
- SharifyApplication - Class in com.sharify
-
Launches spring application.
- SharifyApplication() - Constructor for class com.sharify.SharifyApplication
- SharifyApplicationTests - Class in com.sharify
- SharifyApplicationTests() - Constructor for class com.sharify.SharifyApplicationTests
- SharifyController - Class in com.sharify.controllers
-
Allows user to access home page.
- SharifyController() - Constructor for class com.sharify.controllers.SharifyController
All Classes|All Packages


Man, 18, charged after live hand grenade found during house search: OPP
by The Canadian PressTYENDINAGA MOHAWK TERRITORY, Ont. -- Provincial police have charged an 18-year-old with trafficking after finding a live hand grenade while searching a home on the Tyendinaga Mohawk Territory in eastern Ontario.
Investigators say they also found illegal drugs and a handgun during the search on Thursday.
Police say the teen, whose gender was not provided by police, has been charged with two counts of trafficking drugs and one count of unauthorized possession of a firearm.
They're also charged with tampering with the serial number of a firearm and possession of explosives for an unlawful purpose.
The accused is set to appear in court early next year.


public class Matrix
extends java.lang.Object
| Constructor and Description |
|---|
Matrix(int dimension)
create a square matrix initialized to the identity
|
Matrix(int rows,
int cols)
create a matrix of any dimensions initialized to all zeroes.
|
Matrix(Matrix M)
Creates a matrix of a matrix...a copy
|
| Modifier and Type | Method and Description |
|---|---|
Matrix |
add(Matrix M2)
Adds two matrices together
|
static void |
add(Matrix r1,
double a)
Adds a constant to each element of this matrix
|
static Matrix |
add(Matrix M1,
Matrix M2)
Static method to add any two matrices
|
double |
cell(int i,
int j)
return the value in this matrix located at the ith row and jth column
|
int |
cols()
Get the number of columns in this matrix
|
static double |
det(double d1,
double d2,
double d3,
double d4)
Solves the determinate of this 2x2 matrix
|
static Matrix |
inversMat3x3(Matrix in)
Given a 3 x 3 matrix and using Determinats to solve for inverse
|
Matrix |
mult(double a)
scales a matrix, but does not destroy the content of the original
|
Matrix |
mult(Matrix M2)
Multiplies 2 matrixes together
|
static Matrix |
mult(Matrix M1,
Matrix M2)
multiplies two matrixes together
None Destructive
|
void |
multSelf(double a)
Scalar multiply in place
|
static double[] |
multVec(Matrix A,
double[] x)
Performs Ax multiplication
|
void |
print()
Prints the content of a matrix to standard out
|
void |
replace(Matrix M)
copy each cell from M to this.data
|
int |
rows()
Gets the number of rows in this matrix
|
void |
setCell(int i,
int j,
double value)
set the value of the cell at the ith row and jth column to value
|
Matrix |
subtract(Matrix M2)
Subtracts a matrix from this
|
static Matrix |
subtract(Matrix M1,
Matrix M2)
static Subtraces M2 from M1 5 = 8 - 3
|
static Matrix |
transpose(Matrix in)
Makes a transpose of the input matrix
|
public Matrix(int dimension)
dimension
- - size to make a square matrixpublic Matrix(int rows, int cols)
rows
- cols
- public Matrix(Matrix M) throws MatrixException
M
- - matrix to copyMatrixException
public void replace(Matrix M) throws MatrixException
Replaces the value of this...destructive copy
to just get a copy of a matrix
Matrix mm33 is a matrix initialized to some value
Matrix inv = Matrix.inverseMxM(mm33);
This makes a copy of mm33 into inv. mm33 is not altered in this process.
M
- - matrix to copyMatrixException
public int cols()
public int rows()
public double cell(int i, int j) throws MatrixException
Should have made this getCell, but I got too far along to make all the changes....
i
- - rowj
- - columnMatrixException
public void setCell(int i, int j, double value) throws MatrixException
i
- - rowj
- - columnvalue
- - the double to put in the cell (i,j)MatrixException
public Matrix add(Matrix M2) throws MatrixException
non-destructive
M2
- - what to add to thisMatrixException
public static void add(Matrix r1, double a) throws java.lang.Exception
r1
- - the matrix to receve the additiona
- - the vlaue to ad to each cell of the matrixjava.lang.Exception
public static Matrix add(Matrix M1, Matrix M2) throws MatrixException
M1
- M2
- MatrixException
public Matrix subtract(Matrix M2) throws MatrixException
non-destructive
M2
- MatrixException
public static Matrix subtract(Matrix M1, Matrix M2) throws MatrixException
M1
- - subtraced from matrix (the 8 in the above)M2
- - what is subtracte (the 3 in the above)MatrixException
public Matrix mult(double a) throws MatrixException
Non-destructive multiply
a
- - the scalarMatrixException
public static Matrix transpose(Matrix in) throws MatrixException
rows become columns
Row 1 is now column 1
Row 2 is now column 2
Row n is now column n
in
- - input matrixMatrixException
public static double[] multVec(Matrix A, double[] x) throws MatrixException
A
- x
- MatrixException
public void multSelf(double a)
Destructive multiply
a
- public Matrix mult(Matrix M2) throws MatrixException
3x1 * 1x3 = 3x3
None Destructive
M2
- MatrixException
public static Matrix mult(Matrix M1, Matrix M2) throws MatrixException
M1
- left hand sideM2
- right hand sideMatrixException
public void print() throws MatrixException
MatrixException
public static Matrix inversMat3x3(Matrix in) throws MatrixException
in
- - input 3x3 matrixMatrixException
public static double det(double d1, double d2, double d3, double d4)
d1
- - a11d2
- - a12d3
- - a21d4
- - a22


Windows 10 x64 (18362.388)
_KTRANSACTION_STATE
KTransactionUninitialized = 0n0
KTransactionActive = 0n1
KTransactionPreparing = 0n2
KTransactionPrepared = 0n3
KTransactionInDoubt = 0n4
KTransactionCommitted = 0n5
KTransactionAborted = 0n6
KTransactionDelegated = 0n7
KTransactionPrePreparing = 0n8
KTransactionForgotten = 0n9
KTransactionRecovering = 0n10
KTransactionPrePrepared = 0n11


Stellar’s Foundation Just Destroyed Half the Supply of Its Lumens Cryptocurrency
by Brady DaleMEXICO CITY – The Stellar Development Foundation has burned 55 billion of its XLM tokens, over half the cryptocurrency’s supply, CEO Denelle Dixon announced from the stage of the Stellar Meridian conference Monday.
Previously, there had been 105 billion XLM in existence, with 20 billion in circulation. With this burn, the supply has shrunk to 50 billion.
“We didn’t start by wanting to burn. We started by asking, ‘What do we need?'” Dixon told the room of roughly 200 attendees. “As much as we wanted to use the lumens that we held, it was very hard to get them into the market.”
The organization decided instead it was better to project how much it could actually use over a 10-year period and calibrate to that number. “To derive a plan from an arbitrary number serves no purpose,” Dixon said.
The news was greeted warmly by the crowd, many of whom likely own the token. One participant in the packed room stood up and asked everyone to give Dixon a round of applause, which they did.
In the hour following the announcement, XLM’s price jumped about 14 percent, to $0.08, according to data provider Nomics.
XLM price chart. (Screenshot via Nomics)
Dixon told CoinDesk that she couldn’t anticipate how the crypto market might react, saying:
“I don’t know. I really just don’t have a sense at all of what the market response is. From my standpoint, it’s how the ecosystem feels about it. We got a lot of positive response from the ecosystem because we are rightsizing what the foundation has and the foundation holds.”
The foundation now controls 30 billion XLM, divided into several buckets. It has 12 billion XLM in the direct development fund (formerly called “operations”), to support the organization.
In “ecosystem support” it has 2 billion XLM remaining (1 billion for currency support, and 1 billion for infrastructure grants).
Stellar has 10 billion XLM set aside to make investments (with 2 billion XLM for new products, and 8 billion XLM in its enterprise fund).
Finally, under user acquisition, the foundation has 6 billion XLM (2 billion for marketing stellar and 4 billion for in-app promotions).
The supply of XLM is fixed now because the community of token holders voted to discontinue inflation on Oct. 28.
“SDF will not burn any additional lumens,” Stellar said in a blog post.
Denelle Dixon and Jed McCaleb, Stellar’s founder, on stage at Stellar Meridian, Nov. 4, 2019, photo by Brady Dale for CoinDesk


{{Waterloo}}
This summer Waterloo iGEM worked on making the CRISPR system more flexible and expand its applications. The wet lab focused in three aspects of this. A design of the modified sgRNA have been proposed in order to add restriction sites to exchange the target using classical cloning. In addition, Waterloo iGEM attempted to mutate dCas9 to recognize different PAM sites. Furthermore, we expanded the CRISPR-Cas9 system’s applications for eukaryotic systems, using Arabodopsis thaliana as the model organism. For more details on the design visit the links below.


| (A) | ---- no text ---- | |
| 454 | (C) | On foure goiones of golde . fro þe grounde þat it beere |
| 451 | (D) | On four geauntis of gold from ground þat hit bare |
| 450 | (E) | on foure goionys of gold . to bere it fro þe grounde |
| (Ex) | ---- no text ---- | |
| 467 | (L) | On foure goions of gold þat hit fram grounde bare |
| (P) | ---- no text ---- | |
| 448 | (U) | On foure gogeons of gold / fro grounde that it bere |
| (V) | ---- no text ---- |


public class AccessibleState extends AccessibleBundle
Class AccessibleState describes a component's particular state. The actual state of the component is defined as an AccessibleStateSet, which is a composed set of AccessibleStates.
The toDisplayString method allows you to obtain the localized string for a locale independent key from a predefined ResourceBundle for the keys defined in this class.
The constants in this class present a strongly typed enumeration of common object roles. A public constructor for this class has been purposely omitted and applications should use one of the constants from this class. If the constants in this class are not sufficient to describe the role of an object, a subclass should be generated from this class and it should provide constants in a similar manner.
| Modifier and Type | Field and Description |
|---|---|
static AccessibleState |
ACTIVE
Indicates a window is currently the active window.
|
static AccessibleState |
ARMED
Indicates that the object is armed.
|
static AccessibleState |
BUSY
Indicates the current object is busy.
|
static AccessibleState |
CHECKED
Indicates this object is currently checked.
|
static AccessibleState |
COLLAPSED
Indicates this object is collapsed.
|
static AccessibleState |
EDITABLE
Indicates the user can change the contents of this object.
|
static AccessibleState |
ENABLED
Indicates this object is enabled.
|
static AccessibleState |
EXPANDABLE
Indicates this object allows progressive disclosure of its children.
|
static AccessibleState |
EXPANDED
Indicates this object is expanded.
|
static AccessibleState |
FOCUSABLE
Indicates this object can accept keyboard focus, which means all
events resulting from typing on the keyboard will normally be
passed to it when it has focus.
|
static AccessibleState |
FOCUSED
Indicates this object currently has the keyboard focus.
|
static AccessibleState |
HORIZONTAL
Indicates the orientation of this object is horizontal.
|
static AccessibleState |
ICONIFIED
Indicates this object is minimized and is represented only by an
icon.
|
static AccessibleState |
INDETERMINATE
Indicates that the object state is indeterminate.
|
static AccessibleState |
MANAGES_DESCENDANTS
Indicates this object is responsible for managing its
subcomponents.
|
static AccessibleState |
MODAL
Indicates something must be done with this object before the
user can interact with an object in a different window.
|
static AccessibleState |
MULTI_LINE
Indicates this (text) object can contain multiple lines of text
|
static AccessibleState |
MULTISELECTABLE
Indicates this object allows more than one of its children to
be selected at the same time.
|
static AccessibleState |
OPAQUE
Indicates this object paints every pixel within its
rectangular region.
|
static AccessibleState |
PRESSED
Indicates this object is currently pressed.
|
static AccessibleState |
RESIZABLE
Indicates the size of this object is not fixed.
|
static AccessibleState |
SELECTABLE
Indicates this object is the child of an object that allows its
children to be selected, and that this child is one of those
children that can be selected.
|
static AccessibleState |
SELECTED
Indicates this object is the child of an object that allows its
children to be selected, and that this child is one of those
children that has been selected.
|
static AccessibleState |
SHOWING
Indicates this object, the object's parent, the object's parent's
parent, and so on, are all visible.
|
static AccessibleState |
SINGLE_LINE
Indicates this (text) object can contain only a single line of text
|
static AccessibleState |
TRANSIENT
Indicates this object is transient.
|
static AccessibleState |
TRUNCATED
A state indicating that text is truncated by a bounding rectangle
and that some of the text is not displayed on the screen.
|
static AccessibleState |
VERTICAL
Indicates the orientation of this object is vertical.
|
static AccessibleState |
VISIBLE
Indicates this object is visible.
|
key
| Modifier | Constructor and Description |
|---|---|
protected |
AccessibleState(String key)
Creates a new AccessibleState using the given locale independent key.
|
toDisplayString, toDisplayString, toDisplayString, toString
public static final AccessibleState ACTIVE
public static final AccessibleState PRESSED
AccessibleRole.PUSH_BUTTON
public static final AccessibleState ARMED
AccessibleRole.PUSH_BUTTON
public static final AccessibleState BUSY
public static final AccessibleState CHECKED
public static final AccessibleState EDITABLE
ENABLED
public static final AccessibleState EXPANDABLE
EXPANDED
,
COLLAPSED
,
AccessibleRole.TREE
public static final AccessibleState COLLAPSED
EXPANDABLE
,
EXPANDED
,
AccessibleRole.TREE
public static final AccessibleState EXPANDED
EXPANDABLE
,
COLLAPSED
,
AccessibleRole.TREE
public static final AccessibleState ENABLED
public static final AccessibleState FOCUSABLE
FOCUSED
public static final AccessibleState FOCUSED
FOCUSABLE
public static final AccessibleState ICONIFIED
public static final AccessibleState MODAL
AccessibleRole.DIALOG
public static final AccessibleState OPAQUE
public static final AccessibleState RESIZABLE
public static final AccessibleState MULTISELECTABLE
public static final AccessibleState SELECTABLE
public static final AccessibleState SELECTED
public static final AccessibleState SHOWING
VISIBLE
public static final AccessibleState VISIBLE
SHOWING
public static final AccessibleState VERTICAL
public static final AccessibleState HORIZONTAL
public static final AccessibleState SINGLE_LINE
public static final AccessibleState MULTI_LINE
public static final AccessibleState TRANSIENT
public static final AccessibleState MANAGES_DESCENDANTS
public static final AccessibleState INDETERMINATE
public static final AccessibleState TRUNCATED
protected AccessibleState(String key)
The key String should be a locale independent key for the state. It is not intended to be used as the actual String to display to the user. To get the localized string, use toDisplayString.
key
- the locale independent name of the state.AccessibleBundle.toDisplayString(java.lang.String, java.util.Locale)
Submit a bug or feature
For further API reference and developer documentation, see Java SE Documentation. That documentation contains more detailed, developer-targeted descriptions, with conceptual overviews, definitions of terms, workarounds, and working code examples.
Copyright © 1993, 2015, Oracle and/or its affiliates. All rights reserved.


| DOSE: | 6 oz | oral | DXM | (liquid) |
| BODY WEIGHT: | 150 lb |
This summer -- the one between my Junior and Senior year -- I had taken it upon myself to expand my explorations into the world of drugs. I have tried a few substances in my short (16 year) existence: marijuana, ecstasy, nitrous oxide, salvia, pharmaceuticals (Vicodin, Zoloft, Ritalin, etc.) and ketamine, but what I have experienced this night with DXM is unlike any other drug that preceded it.
At 3:40 AM I made the whimsical decision to raid my medicine cabinet. I know not what exactly prompted this desparate foray into the realm of weak pharmaceuticals, but an I felt quite adventurous. Spotting a bottle of Vicks, I decided for the second time to attempt to 'Robo.'
I downed approximately three fourths of a bottle, all that there was, and decided that would be enough, as I was absolutely NOT going near the Tussin again. A few minutes afterwards, I sat down to watch a bad movie on television.
A half an hour of TV proved to be one of the most unrewarding experiences of my life. It was so boring I spaced out most of the time, and ended up with my thoughts wandering more than they did during a poor lecture in school. After a bit of this I could bear no more, and moved into my room somewhat disappointed with the effects of Roboing so far.
Let me be frank, the physical effects were minor. Perhaps it was the fact that I only had three fourths a bottle at my disposal, or perhaps it was that all of the drugs I have tried thus far have been very physical and barely psychedelic.
I decided to entertain myself by reading about other DXM experiences, and to see if there was anything I could draw from them. I tried listening to music, petting my animals, speaking aloud, and concentrating on thoughts, and was relatively disappointed. At this point, I would have rather taken four shots of good alcohol than four shots of Vicks.
Then, I made the best decision of the night: to look at my writing. For the next hour, I pored over my random musings from the past few years, and found it a very enjoyable and self-gratifying experience. At this point, I decided to write something, and found I had the DXM to be a fountain of inspiration.
Here is what I wrote, regardless of whether the reader likes it or not, these are my thoughts:
I constantly live in fear. I live in fear that today I will lower my bucket into my well of inspiration, and it will come up as dry as before I arrived. This is not an uncommon occurrence; all too often I find myself leaving empty-handed. And, unlike the others who work with their hands, it does not seem to get better with time.
Indeed, after years of chasing the fleeting dragon of inspiration, I find myself often leaving pages as white as I found them, and then I myself curling up into a little ball of vapid thoughts and letting my creative energies seep out of me like worthless ichor. It is so frustrating to find that I can't remedy this situation, sometimes I simply can't write.
There are those who have defeated this dragon, and who can simply create beauty from nothingness, who have found some eternal spring of inspiration in which they bask and thrive.

But, I suppose if it were so simple, everyone would be a writer and there would be nothing left to say. A paradox, but as long as inspiration safeguards my right to be original, I suppose I shall go on chasing the dragon.
Then this:
When I think of that night sky, I see a superimposed image of swirling stars. But is this a night sky I saw? Is this a night sky that has ever existed? Or is it merely a figment of my imagination, my mind feebly trying to produce an image that bears some semblance to the original?
I think that is it; I think I know not what the night sky looks like. Examining my 'recollection,' I see no noticeable constellations, nor any of the planets which so often festoon the evening sky. No, this is merely a black canvas with little white dots, something my room has drudged up in an attempt to keep me from experiencing nature.
Or perhaps I am looking at this in the wrong light. Perhaps this night sky is actually a thing of beauty, something my mind's eye has created. Maybe it outshines all other night skies, and it is a culmination of all the beauty I have experienced while laying on my back and peering into that endless expanse of blackness sprawled above me. Maybe this night sky is something no other human can or will ever experience, something uniquely mine.
Or maybe, just maybe, that's my room speaking to me, fooling myself into staying beneath this fluorescent sun for just a few more hours.
I found writing to be a most rewarding experience! All my life I have loved writing, but what a mercurial pursuit it is! I have to be in the right mood, have creative energies, be inspired, and have time to myself. So many conditions have to meet in order for my work to be acceptable, and not for me to close Document 1 without saving changes.
But DXM, the lovely mistress she is, gave me free reign to write as I please. Even now I can still find myself able to craft works, as it is but a couple hours after that one bottle.
After that, I saw the bottle in a whole new light; no longer is DXM some cough syrup, or a poor man's high, but inspiration in a bottle. It lessens my inhibitions, impedes my thoughts in no way (at least on paper, the thought of a conversation right now is most daunting). I find myself with nearly my whole vocabulary at my disposal, I can focus in on but a single thought and expand it into a whole document.
After I finished those works, I noticed it was light out, and made the decision to go for a walk around the block. Outside was beautiful, as always, and before I knew it I was a quarter of the way around the block.  I said hello to her, as if to lessen the surprise and not try to seem discreet, and she replied with 'good morning.'
Oh how right she was! It was a good morning, a beautiful morning, a morning streaked with Vicks 44 and abound with inspiration! To make matters even more odd, I ran into her on the other side of the block. This time, instead of reaffirming what a great morning it was, she simply laughed at the sight of me.
I then returned to submit this humble report. Part of the importance of it, I believe, is that it is written while I am still under the influence of DXM (which is why I chose to preface the report with that). I find it most interesting I can write uninhibited while under the influence of this substance.
I by no means profess to be a writer, nor do I plan to pursue that career (as now it seems that would require vast amounts of DXM). Instead, I merely wish to assert that I am what I am, a sixteen year old caught up in the swirling mists of his fantasies, thoughts, and fancies.
I simply cannot believe what this nectar has released in me, I knew not I had such creative energies abound. I normally can only write a tad when the circumstances are correct, but now I seem to be able to create these circumstances at my will! I sincerely hope that this is not some fleeting change that will disappear as the effects of Vicks 44 wear off, but somehow that seems all too feasable.
| Exp Year: 2001 | ID: 7494 |
| Gender: Male | |
| Added: Jun 19, 2003 | Views: 10388 |
| [ View as PDF (for printing) ] [ View as LaTeX (for geeks) ] [ Switch Colors ] |
| Erowid Experience Vault | © 1995-2008 Erowid |


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This NetLogo simulation of the 1453 Battle of Constantinople was written for a Military Science 4002 class project. This is the first NetLogo simulation I wrote from scratch. NetLogo boils down to two concepts - turtles and patches. In this case, the turtles (objects) took the form of Ottoman and Byzantine soldiers, and patches (locations) took the form of the city of Byzantine and corresponding seas. Details including the three Ottoman waves were kept true to recorded history. The Bosphorous Chain, which the Byzantines used to block out Ottoman ships from the Golden Horn, is included so as to better represent the sequence of occuring events. Ultiamtely, the Ottomans take over Constantinople nearly every time, though the means to doing so change each run; it is a simulation after all! This simulation is modeled in 2-D, but may be presented in 3-D if desired.
Estimated Building Cost: $00.00
Estimated Hours to Develop: 14


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G1 Therapeutics (GTHX) Investor Presentation - Slideshow
by SA Transcripts, https://seekingalpha.com/author/sa-transcriptsThe following slide deck was published by G1 Therapeutics, Inc. in conjunction with this event.
The following slide deck was published by G1 Therapeutics, Inc. in conjunction with this event.


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7. Class reference¶
7.1. Support Vector Machines¶
Support Vector Machine algorithms.
| svm.SVC([C, kernel, degree, gamma, coef0, ...]) | C-Support Vector Classification. |
| svm.LinearSVC([penalty, loss, dual, tol, C, ...]) | Linear Support Vector Classification. |
| svm.NuSVC([nu, kernel, degree, gamma, ...]) | Nu-Support Vector Classification. |
| svm.SVR([kernel, degree, gamma, coef0, ...]) | epsilon-Support Vector Regression. |
| svm.NuSVR([nu, C, kernel, degree, gamma, ...]) | Nu Support Vector Regression. |
| svm.OneClassSVM([kernel, degree, gamma, ...]) | Unsupervised Outliers Detection. |
| svm.l1_min_c(X, y[, loss, fit_intercept, ...]) | Return the maximum value for C that yields a model with coefficients |
7.1.1. For sparse data¶
Support Vector Machine algorithms for sparse matrices.
This module should have the same API as scikits.learn.svm, except that matrices are expected to be in some sparse format supported by scipy.sparse.
Note
Some fields, like dual_coef_ are not sparse matrices strictly speaking. However, they are converted to a sparse matrix for consistency and efficiency when multiplying to other sparse matrices.
| svm.sparse.SVC([C, kernel, degree, gamma, ...]) | SVC for sparse matrices (csr). |
| svm.sparse.NuSVC([nu, kernel, degree, ...]) | NuSVC for sparse matrices (csr). |
| svm.sparse.SVR([kernel, degree, gamma, ...]) | SVR for sparse matrices (csr) |
| svm.sparse.NuSVR([nu, C, kernel, degree, ...]) | NuSVR for sparse matrices (csr) |
| svm.sparse.OneClassSVM([kernel, degree, ...]) | NuSVR for sparse matrices (csr) |
| svm.sparse.LinearSVC([penalty, loss, dual, ...]) | Linear Support Vector Classification, Sparse Version |
7.1.2. Low-level methods¶
| svm.libsvm.fit | Train the model using libsvm (low-level method) |
| svm.libsvm.decision_function | Predict margin (libsvm name for this is predict_values) |
| svm.libsvm.predict | Predict target values of X given a model (low-level method) |
| svm.libsvm.predict_proba | Predict probabilities svm_model stores all parameters needed to predict a given value. |
| svm.libsvm.cross_validation | Binding of the cross-validation routine (low-level routine) |
7.2. Generalized Linear Models¶
scikits.learn.linear_model is a module to fit genelarized linear models. It includes Ridge regression, Bayesian Regression, Lasso and Elastic Net estimators computed with Least Angle Regression and coordinate descent.
It also implements Stochastic Gradient Descent related algorithms.
| linear_model.LinearRegression([fit_intercept]) | Ordinary least squares Linear Regression. |
| linear_model.Ridge([alpha, fit_intercept]) | Ridge regression. |
| linear_model.RidgeCV([alphas, ...]) | Ridge regression with built-in cross-validation. |
| linear_model.Lasso([alpha, fit_intercept]) | Linear Model trained with L1 prior as regularizer (aka the Lasso) |
| linear_model.LassoCV([eps, n_alphas, ...]) | Lasso linear model with iterative fitting along a regularization path |
| linear_model.ElasticNet([alpha, rho, ...]) | Linear Model trained with L1 and L2 prior as regularizer |
| linear_model.ElasticNetCV([rho, eps, ...]) | Elastic Net model with iterative fitting along a regularization path |
| linear_model.LARS([fit_intercept, verbose]) | Least Angle Regression model a.k.a. LAR |
| linear_model.LassoLARS([alpha, ...]) | Lasso model fit with Least Angle Regression a.k.a. LARS |
| linear_model.LogisticRegression([penalty, ...]) | Logistic Regression. |
| linear_model.SGDClassifier([loss, penalty, ...]) | Linear model fitted by minimizing a regularized empirical loss with SGD. |
| linear_model.SGDRegressor([loss, penalty, ...]) | Linear model fitted by minimizing a regularized empirical loss with SGD |
| linear_model.BayesianRidge([n_iter, eps, ...]) | Bayesian ridge regression |
| linear_model.ARDRegression([n_iter, eps, ...]) | Bayesian ARD regression. |
| linear_model.lasso_path(X, y, **fit_params) | Compute Lasso path with coordinate descent |
| linear_model.lars_path(X, y[, Xy, Gram, ...]) | Compute Least Angle Regression and LASSO path |
7.2.1. For sparse data¶
scikits.learn.linear_model.sparse is the sparse counterpart of scikits.learn.linear_model.
| linear_model.sparse.Lasso([alpha, fit_intercept]) | Linear Model trained with L1 prior as regularizer |
| linear_model.sparse.ElasticNet([alpha, rho, ...]) | Linear Model trained with L1 and L2 prior as regularizer |
| linear_model.sparse.SGDClassifier([loss, ...]) | Linear model fitted by minimizing a regularized empirical loss with SGD |
| linear_model.sparse.SGDRegressor([loss, ...]) | Linear model fitted by minimizing a regularized empirical loss with SGD |
7.4. Nearest Neighbors¶
Nearest Neighbor related algorithms
| neighbors.NeighborsClassifier([n_neighbors, ...]) | Classifier implementing k-Nearest Neighbor Algorithm. |
| neighbors.NeighborsRegressor([n_neighbors, ...]) | Regression based on k-Nearest Neighbor Algorithm |
| ball_tree.BallTree |
| neighbors.kneighbors_graph(X, n_neighbors[, ...]) | Computes the (weighted) graph of k-Neighbors for points in X |
7.5. Gaussian Mixture Models¶
Gaussian Mixture Models
| mixture.GMM([n_states, cvtype]) | Gaussian Mixture Model |
7.6. Hidden Markov Models¶
| hmm.GaussianHMM([n_states, cvtype, ...]) | Hidden Markov Model with Gaussian emissions |
| hmm.MultinomialHMM([n_states, startprob, ...]) | Hidden Markov Model with multinomial (discrete) emissions |
| hmm.GMMHMM([n_states, n_mix, startprob, ...]) | Hidden Markov Model with Gaussin mixture emissions |
7.7. Clustering¶
Clustering algorithms
| cluster.KMeans([k, init, n_init, max_iter, ...]) | K-Means clustering |
| cluster.MeanShift([bandwidth]) | MeanShift clustering |
| cluster.SpectralClustering([k, mode, ...]) | Apply k-means to a projection to the normalized laplacian |
| cluster.AffinityPropagation([damping, ...]) | Perform Affinity Propagation Clustering of data |
| cluster.Ward([n_clusters, memory, ...]) | Ward hierarchical clustering: constructs a tree and cuts it. |
7.8. Metrics¶
Metrics module with score functions, performance metrics and pairwise metrics or distances computation
| metrics.euclidean_distances(X, Y[, ...]) | Considering the rows of X (and Y=X) as vectors, compute the |
| metrics.confusion_matrix(y_true, y_pred[, ...]) | Compute confusion matrix to evaluate the accuracy of a classification |
| metrics.roc_curve(y_true, y_score) | compute Receiver operating characteristic (ROC) |
| metrics.auc(x, y) | Compute Area Under the Curve (AUC) using the trapezoidal rule |
| metrics.precision_score(y_true, y_pred[, ...]) | Compute the precision |
| metrics.recall_score(y_true, y_pred[, pos_label]) | Compute the recall |
| metrics.fbeta_score(y_true, y_pred, beta[, ...]) | Compute fbeta score |
| metrics.f1_score(y_true, y_pred[, pos_label]) | Compute f1 score |
| metrics.precision_recall_fscore_support(...) | Compute precisions, recalls, f-measures and support for each class |
| metrics.classification_report(y_true, y_pred) | Build a text report showing the main classification metrics |
| metrics.precision_recall_curve(y_true, ...) | Compute precision-recall pairs for different probability thresholds |
| metrics.r2_score(y_true, y_pred) | R^2 (coefficient of determination) regression score function |
| metrics.zero_one_score(y_true, y_pred) | Zero-One classification score |
| metrics.zero_one(y_true, y_pred) | Zero-One classification loss |
| metrics.mean_square_error(y_true, y_pred) | Mean square error regression loss |
7.8.1. Pairwise metrics¶
Utilities to evaluate pairwise distances or metrics between 2 sets of points.
| metrics.pairwise.euclidean_distances(X, Y[, ...]) | Considering the rows of X (and Y=X) as vectors, compute the |
| metrics.pairwise.linear_kernel(X, Y) | Compute the linear kernel between X and Y. |
| metrics.pairwise.polynomial_kernel(X, Y[, ...]) | Compute the polynomial kernel between X and Y. |
| metrics.pairwise.rbf_kernel(X, Y[, sigma]) | Compute the rbf (gaussian) kernel between X and Y. |
7.9. Covariance Estimators¶
7.9.1. Covariance estimators¶
scikits.learn.covariance is a module to fit to estimate robustly the covariance of features given a set of points. The precision matrix defined as the inverse of the covariance is also estimated. Covariance estimation is closely related to the theory of Gaussian Graphical Models.
| covariance.Covariance | |
| covariance.ShrunkCovariance([...]) | Covariance estimator with shrinkage |
| covariance.LedoitWolf([store_precision]) | LedoitWolf Estimator |
| covariance.ledoit_wolf(X[, assume_centered]) | Estimates the shrunk Ledoit-Wolf covariance matrix. |
| covariance.shrunk_covariance(emp_cov[, ...]) | Calculates a covariance matrix shrunk on the diagonal |
| covariance.oas(X[, assume_centered]) | Estimate covariance with the Oracle Approximating Shrinkage algorithm. |
7.10. Signal Decomposition¶
Matrix decomposition algorithms
| decomposition.PCA([n_components, copy, whiten]) | Principal component analysis (PCA) |
| decomposition.ProbabilisticPCA([...]) | Additional layer on top of PCA that adds a probabilistic evaluation |
| decomposition.RandomizedPCA(n_components[, ...]) | Principal component analysis (PCA) using randomized SVD |
| decomposition.KernelPCA([n_components, ...]) | Kernel Principal component analysis (KPCA) |
| decomposition.FastICA([n_components, ...]) | FastICA; a fast algorithm for Independent Component Analysis |
| decomposition.NMF([n_components, init, ...]) | Non-Negative matrix factorization by Projected Gradient (NMF) |
| decomposition.fastica(X[, n_components, ...]) | Perform Fast Independent Component Analysis. |
7.11. Linear Discriminant Analysis¶
| lda.LDA([n_components, priors]) | Linear Discriminant Analysis (LDA) |
7.12. Cross Validation¶
Utilities for cross validation and performance evaluation
| cross_val.LeaveOneOut(n[, indices]) | Leave-One-Out cross validation iterator |
| cross_val.LeavePOut(n, p[, indices]) | Leave-P-Out cross validation iterator |
| cross_val.KFold(n, k[, indices]) | K-Folds cross validation iterator |
| cross_val.StratifiedKFold(y, k[, indices]) | Stratified K-Folds cross validation iterator |
| cross_val.LeaveOneLabelOut(labels[, indices]) | Leave-One-Label_Out cross-validation iterator |
| cross_val.LeavePLabelOut(labels, p[, indices]) | Leave-P-Label_Out cross-validation iterator |
7.13. Grid Search¶
Tune the parameters of an estimator by cross-validation
| grid_search.GridSearchCV(estimator, param_grid) | Grid search on the parameters of a classifier |
7.14. Feature Selection¶
Feature slection module for python
| feature_selection.rfe.RFE([estimator, ...]) | Feature ranking with Recursive feature elimination |
| feature_selection.rfe.RFECV([estimator, ...]) | Feature ranking with Recursive feature elimination and cross validation |
7.15. Feature Extraction¶
Package for modules that deal with feature extraction from raw data
7.15.1. From images¶
Utilities to extract features from images.
| feature_extraction.image.img_to_graph(img[, ...]) | Graph of the pixel-to-pixel gradient connections |
| feature_extraction.image.grid_to_graph(n_x, n_y) | Graph of the pixel-to-pixel connections |
7.15.2. From text¶
Utilities to build dense feature vectors from text documents
| feature_extraction.text.RomanPreprocessor | Fast preprocessor suitable for roman languages .. |
| feature_extraction.text.WordNGramAnalyzer([...]) | Simple analyzer: transform a text document into a sequence of word tokens |
| feature_extraction.text.CharNGramAnalyzer([...]) | Compute character n-grams features of a text document |
| feature_extraction.text.CountVectorizer([...]) | Convert a collection of raw documents to a matrix of token counts |
| feature_extraction.text.TfidfTransformer([...]) | Transform a count matrix to a TF or TF-IDF representation |
| feature_extraction.text.Vectorizer([...]) | Convert a collection of raw documents to a matrix |
7.16. Pipeline¶
Pipeline: chain transforms and estimators to build a composite estimator.
| pipeline.Pipeline(steps) | Pipeline of transforms with a final estimator |
7.17. Partial Least Squares¶
Partial Least Square
| pls.PLSRegression([n_components, scale, ...]) | PLS regression (Also known PLS2 or PLS in case of one dimensional |
| pls.PLSCanonical([n_components, scale, ...]) | PLS canonical. PLSCanonical inherits from PLS with mode=”A” and |
| pls.CCA([n_components, scale, algorithm, ...]) | CCA Canonical Correlation Analysis. CCA inherits from PLS with |
| pls.PLSSVD([n_components, scale, copy]) | Partial Least Square SVD |


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03.08.18
Ohio University’s female leaders know firsthand that occupying high-up positions in a male-dominated field doesn’t necessarily mean equal treatment.
In a 2013 column for The Student Affairs Feature, OU Dean of Students Jenny Hall-Jones said she had never had sexist attitudes directed at her until she became a dean of students. Although she “knew it existed,” sexist discrimination was always on the outside — beyond the walls of her home and office.
Hall-Jones remembers giving a presentation to a group of incoming students during orientation. It was, as she wrote in 2013, “a disaster.”
OLIVER HAMLIN | FILE
Jenny Hall-Jones poses for a portrait in her office on April 6th, 2016.
Students tweeted about her clothing during the presentation, making fun of her red shoes and her Wizard of Oz outfit.
“Does anyone else want to bang her?” one incoming student wrote a few days later. Another inappropriate comment soon followed. When Hall-Jones tweeted back to challenge it, she was greeted with a swift and crude reply.
“F--- you,” the student tweeted.
Those types of comments and experiences, Hall-Jones said, simply didn’t happen to the former dean of students, who was a man. Five years later, the disastrous presentation is in the past, but Hall-Jones said the day-to-day microaggressions persist.
While women tend to come to her office to talk about leadership and mentoring opportunities, Hall-Jones said men are more likely to come in looking for a maternal figure. It’s an observation that Hall-Jones shared with former Dean of Students Ryan Lombardi, who one student said was like “the cool uncle” to students. Hall-Jones, meanwhile, was “like our mom.”
“It is interesting, from a research perspective,” Hall-Jones said. “It’s not my job to ‘mom’ anybody.”
For interim Executive Vice President and Provost Elizabeth Sayrs, a history of research and work in feminist theory and gender studies would present themes that came into play in her career.
“I’ve had the same experiences many women in academia have had,” Sayrs said in an email. “For example, people sometimes assume I am the administrative assistant instead of a professor or a dean, or I will have a series of meetings where I am the only woman in the room.”
Only about a quarter of college presidents in the U.S. are women, according to a 2011 report by the American Council on Education. In its 214-year history, OU has never had a woman serve as president.
And despite accounting for more than half the total workforce, many women employed at OU make less than their male counterparts. The average salary of a male tenure-track professor is about $94,000. The average female tenure-track professor makes about $7,400 less, according to a previous Post report.
Vice President for Finance and Administration Deborah Shaffer — once a first-generation student with a passion for numbers — is the first woman to hold her current office at OU. It was a similar story in her previous position at Carnegie Mellon University, where Shaffer was the school’s first female vice president for finance and chief financial officer.
“I’ve been incredibly fortunate to be a part of executive teams, and to have served under presidents, where I felt respected and where gender was not a factor,” Shaffer said in an email. “However, it would be disingenuous for me to suggest I have not encountered gender bias.”
Shaffer said she has found that gender bias and cultural expectations can present special challenges to women, especially when it comes to direct communication — a trait she said is “often admired in men.”
Sayrs credits “strong mentorship” from her OU colleagues, who she said have helped her navigate difficult issues.
“Be the best mentor you can be, especially to each other,” Sayrs said in an email. “Finding an effective and authentic way to lead takes practice and a lot of feedback, so having a close group of colleagues you can trust and share ideas and questions with is really helpful.”
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Soumen Pasari
I am a Bengaluru-based Software Developer. Worked mostly with languages like javascript, python, PHP, and related technologies for development. Have worked with the e-commerce domain and now providing cloud-related solutions in my current role. Experience in cloud platforms like Google Cloud Platform (GCP) and Microsoft Azure and roles like Fullstack Development and Data Engineering.
Apart from all these professional things as a person I really enjoy being involved with full-stack development for creating some of my personal projects and learn new things as it gives me more freedom to explore. Few other things I enjoy doing is playing competitive video games on PC like Counter strike and Valorant, and I really like to go outside see new places or visiting the same old favorite place to sit and enjoy the same old feeling of being surrounded by nature which I, at last, loves the most, it takes me to the childhood and brings back the energy.


Black Marketeer of Mammon:
If you are finished thinking, select one. Which class would you like to be?
Spellsinger.
Elemental Summoner.


--- title: Comparison of data dimensionality reduction methods! author: Yu Cao date: '2021-08-02' slug: pca-and-umap categories: - Data Mining tags: [] subtitle: '' summary: 'Comparision of the performance of Principle Component Analysis and Uniform manifold approximation and projection' authors: [] lastmod: '2021-10-07T11:40:50+01:00' featured: no image: caption: '' focal_point: '' preview_only: yes projects: [] ---
In this blog, I mainly compared two dimensionality reduction methods, i.e. PCA (Principle Component Analysis) and UMAP (Uniform manifold approximation and projection). Let’s explore which one is better, PCA, the traditional method of dimensionality reduction or, UMAP the new boy.
Let’s start with data mining. If you want to remember the codes, then you need to practice them everyday, right?
library(tidyverse)
theme_set(theme_light())
library(janitor)
The dataset used in this blog comes from TidyTuesday Dataset, 2020-05-26, about cocktail recipes.
boston_cocktails <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-26/boston_cocktails.csv")
boston_cocktails %>%
count(ingredient, sort = TRUE)
## # A tibble: 569 × 2
## ingredient n
## <chr> <int>
## 1 Gin 176
## 2 Fresh lemon juice 138
## 3 Simple Syrup 115
## 4 Vodka 114
## 5 Light Rum 113
## 6 Dry Vermouth 107
## 7 Fresh Lime Juice 107
## 8 Triple Sec 107
## 9 Powdered Sugar 90
## 10 Grenadine 85
## # … with 559 more rows
Although there are no missing values in this dataset, the ‘ingredient’ column, for example, contains many duplicate labes, such as Juice of a Lime and Lime Juice. So I did a series of data cleaning, which involved sorting out the duplicate labels in column ‘ingredient’ and, converting the categorical variables in the ‘measure’ column to numerical varables, and here is the codes:
cocktails_parsed <- boston_cocktails %>%
filter(!measure == "splash") %>%
mutate(
ingredient = str_to_lower(ingredient),
ingredient = str_replace_all(ingredient, "-", " "),
ingredient = str_remove(ingredient, " liqueur$"),
ingredient = str_remove(ingredient, " (if desired)$"),
ingredient = case_when(
str_detect(ingredient, "bitters") ~ "bitters",
str_detect(ingredient, "lemon") ~ "lemon juice",
str_detect(ingredient, "lime") ~ "lime juice",
str_detect(ingredient, "grapefruit") ~ "grapefruit juice",
str_detect(ingredient, "orange") ~ "orange juice",
TRUE ~ ingredient
),
measure = str_replace(measure, " ?1/2", ".5"),
measure = str_replace(measure, " ?3/4", ".75"),
measure = str_replace(measure, " ?1/4", ".25"),
measure = str_replace(measure, " ?For glass", "12"),
measure_number = parse_number(measure)) %>%
add_count(ingredient) %>%
filter(n > 15) %>%
select(-n) %>%
distinct(row_id, ingredient, .keep_all = TRUE) %>%
na.omit() %>%
select(-ingredient_number, -row_id, -measure)
Now it looks much better, doesn’t it?
cocktails_parsed
## # A tibble: 2,561 × 4
## name category ingredient measure_number
## <chr> <chr> <chr> <dbl>
## 1 Gauguin Cocktail Classics light rum 2
## 2 Gauguin Cocktail Classics lemon juice 1
## 3 Gauguin Cocktail Classics lime juice 1
## 4 Fort Lauderdale Cocktail Classics light rum 1.5
## 5 Fort Lauderdale Cocktail Classics sweet vermouth 0.5
## 6 Fort Lauderdale Cocktail Classics orange juice 0.25
## 7 Fort Lauderdale Cocktail Classics lime juice 0.25
## 8 Cuban Cocktail No. 1 Cocktail Classics lime juice 0.5
## 9 Cuban Cocktail No. 1 Cocktail Classics powdered sugar 0.5
## 10 Cuban Cocktail No. 1 Cocktail Classics light rum 2
## # … with 2,551 more rows
It’s time for modeling, in order to apply dimensionality reduction method, you should at least have a high dimension dataset, right? What I gonna do is make the dataset wider by using pivot_wider()
from package tidyr
.
cocktails_df <- cocktails_parsed %>%
pivot_wider(names_from = ingredient, values_from = measure_number, values_fill = 0) %>%
janitor::clean_names() %>%
na.omit()
cocktails_df
## # A tibble: 937 × 42
## name category light_rum lemon_juice lime_juice sweet_vermouth orange_juice
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Gauguin Cocktai… 2 1 1 0 0
## 2 Fort L… Cocktai… 1.5 0 0.25 0.5 0.25
## 3 Cuban … Cocktai… 2 0 0.5 0 0
## 4 Cool C… Cocktai… 0 0 0 0 1
## 5 John C… Whiskies 0 1 0 0 0
## 6 Cherry… Cocktai… 1.25 0 0 0 0
## 7 Casa B… Cocktai… 2 0 1.5 0 0
## 8 Caribb… Cocktai… 0.5 0 0 0 0
## 9 Amber … Cordial… 0 0.25 0 0 0
## 10 The Jo… Whiskies 0 0.5 0 0 0
## # … with 927 more rows, and 35 more variables: powdered_sugar <dbl>,
## # dark_rum <dbl>, cranberry_juice <dbl>, pineapple_juice <dbl>,
## # bourbon_whiskey <dbl>, simple_syrup <dbl>, cherry_flavored_brandy <dbl>,
## # light_cream <dbl>, triple_sec <dbl>, maraschino <dbl>, amaretto <dbl>,
## # grenadine <dbl>, apple_brandy <dbl>, brandy <dbl>, gin <dbl>,
## # anisette <dbl>, dry_vermouth <dbl>, apricot_flavored_brandy <dbl>,
## # bitters <dbl>, straight_rye_whiskey <dbl>, benedictine <dbl>, …
Here we go, we got a dataset with 42 columns, which is a series problem if you gonna do some classification word on this dataset. In the following, I will demonstrate the two dimensionality reduction method separately.
One of the benefits of the tidymodels
ecosystem is the flexibility and ease of trying different approaches for the same kind of task. With function recipe
, you don’t have to perform every step, you only need to write them down.
library(tidymodels)
pca_rec <- recipe(~ ., data = cocktails_df) %>%
update_role(name, category, new_role = "id") %>%
step_normalize(all_predictors()) %>%
step_pca(all_predictors(), num_comp = 2)
pca_prep <- prep(pca_rec)
pca_prep
Here, let’s focus on the variable pca_rec
, what we have done is:
- First, tell recipe()
what’s going on with our model and what data we are using.
- Second, we update the role of name
and category
columns by function update_role()
, since these are variables we want to keep around as identifiers for rows.
- Then we need to center and scale the numeric predictors.
- Finally, we use step_pca
for the actual principal component analysis. Note that actually, the PCA step is the second step. The prep
estimate the required parameters from a training set that can be later applied to other datasets for us.
Once we have our recipe done, we can explore the results by using tidy()
, which makes it possible to visualize what each components look like.
tidied_pca <- tidy(pca_prep, 2)
tidied_pca %>% filter(component %in% paste0("PC", 1:5)) %>%
mutate(component = fct_inorder(component)) %>%
ggplot(aes(value, terms, fill = terms)) +
geom_col(show.legend = FALSE) +
facet_wrap(~component, nrow = 1) +
labs(y = "")
In the plot above, we can see how much every component contributes to each category
. Clearly, the biggest difference in PC1 is powdered sugar vs. simple syrup. Let’s zoom in on the first four components, and understand which cocktail ingredients contribute in the positive and negative directions.
library(tidytext)
tidied_pca %>%
filter(component %in% paste0("PC", 1:4)) %>%
group_by(component) %>%
top_n(8, abs(value)) %>%
ungroup() %>%
mutate(terms = reorder_within(terms, abs(value), component)) %>%
ggplot(aes(abs(value), terms, fill = value > 0)) +
geom_col() +
scale_y_reordered() +
facet_wrap(~component, scales = "free_y") +
labs(fill = "positive?")
So PC1 is about powdered sugar + egg + gin drinks vs. simple syrup + lime + tequila drinks. This is the component that explains the most variation in drinks. PC2 is mostly about vermouth, both sweet and dry.
bake(pca_prep, new_data = NULL) %>%
ggplot(aes(PC1, PC2, label = name)) +
geom_point(aes(color = category), alpha = 0.7, size = 2) +
geom_text(check_overlap = TRUE) +
labs(color = NULL)
The plot above shows how the cocktails distributed in the plane of the first two components. The bake()
return the computations accroding to the recipe
. We can conclude:
- Fizzy, egg, poweder sugar drinks are to the left.
- Simple syrup, lime, tequila drinks are to the right.
- Vermouth drinks are more to the top. Although many labels are mixed together, they may be distinguishable on other components.
Thanks to the benefits of the tidymodels
ecosystem, we can switch out PCA for UMAP, simply by replace step_pca()
by step_umap
. Before that, don’t forget to import package embed
, which provide recipe steps for ways to create embeddings including UMAP.
library(embed)
umap_rec <- recipe(~ ., data = cocktails_df) %>%
update_role(name, category, new_role = "id") %>%
step_normalize(all_predictors()) %>%
step_umap(all_predictors(), num_comp = 5)
umap_prep <- prep(umap_rec)
umap_prep
Similarly, we can see how the cocktails are distributed in the plane of the first two UMAP components.
bake(umap_prep, new_data = NULL) %>%
ggplot(aes(umap_1, umap_2, label = name)) +
geom_point(aes(color = category), alpha = 0.7, size = 2) +
geom_text(check_overlap = TRUE) +
labs(color = NULL)
You can see that the distribution of the first two components of UMAP is totally different from PCA. Obviously, most of the categories didn’t mix together. That’s what I’m talking about. And I can tell, the performance of the classification algorithm trained on the first two components of UMAP will definitely better than of PCA. Let’s prove it on the kernel SVM (Support Vector Machine).
library(e1071)
svm_test <- function(num_compo, meth){
if(meth == "PCA"){
rec <- recipe(~ ., data = cocktails_df) %>%
update_role(name, category, new_role = "id") %>%
step_normalize(all_predictors()) %>%
step_pca(all_predictors(), num_comp = num_compo)
}
if(meth == "UMAP"){
rec <- recipe(~ ., data = cocktails_df) %>%
update_role(name, category, new_role = "id") %>%
step_normalize(all_predictors()) %>%
step_umap(all_predictors(), num_comp = num_compo)
}
df <- bake(prep(rec), new_data = NULL)
n <- nrow(df)
ntrain <- round(n * 0.75)
set.seed(123)
tindex <- sample(n, ntrain)
train_iris <- df[tindex, ]
test_iris <- df[-tindex, ]
svm1 <- svm(category~., data = train_iris, method = "C-classification",
kernal="radial", gamma = 0.1, cost = 10)
prediction <- predict(svm1, test_iris)
xtab <- table(test_iris$category, prediction)
result <- data.frame(test_iris$category, prediction)
accuracy <- sum(test_iris$category == prediction) / nrow(test_iris)
return(accuracy)
}
Here is a function require two variables input, the number of component and demensionality reduction method, and output the accuracy of the classification. Let’s first verify the performance of the first two components.
cat("PCA: ",svm_test(2, "PCA"), ", UMAP: ",svm_test(2,"UMAP"))
## PCA: 0.6410256 , UMAP: 0.7051282
Obviously, the first two components of UMAP contains more classification information, at least make the SVM classify more accurate.
You think that’s the end of it? We know that, in real life, we should always consider the time cost, and that is the meaningful of the dimensionality reduction method. So let add the time cost into the comparision, start by finding out how many components PCA needs to achieve the same accuracy as UMAP.
result_pca <- 1:10 %>% map_dbl(svm_test, meth = "PCA")
result_umap <- 1:10 %>% map_dbl(svm_test, meth = "UMAP")
result <- data.frame(pca = result_pca,
umap = result_umap) %>% stack()
result$index <- c(1:10,1:10)
result %>%
ggplot(aes(index, values, color = ind)) +
geom_line() +
labs(color = "method")
Seems like the accuracy converge at 75% as the number of components increase, actually after 5 components, PCA performs better than UMAP. Basically, PCA needs at least 5 components to get the height of UMAP.
library(microbenchmark)
time_UMAP <- microbenchmark(svm_test(3,"UMAP"), times = 10, unit="s")
time_PCA <- microbenchmark(svm_test(5,"PCA"), times = 10, unit="s")
cat("UMAP: ", mean(time_UMAP$time)/1e9, ", PCA: ", mean(time_PCA$time/1e9))
## UMAP: 1.903803 , PCA: 0.3264182
Here is the result, PCA undoubtedly crushes UMAP. I know, I know, you mean that this is because the dataset is so small that the advantages of low dimensionality are not fully realezed, and this is just one example and doesn’t tell us much. I totally agree, I really am. What I would like to say is, the job of a data analyst should not be to blindly pursue new technologies, but to be flexible and efficient in getting tasks done.
Some of the code for the data mining section is taken from here, I’m very grateful to Julia Silge and David Robinson for their videos.
Leave a message if you like this blog.


Review: The Solace of Open Spaces
June 23, 2013
The Solace of Open Spaces by Gretel Ehrlich
My rating: 5 of 5 stars
Exquisite short book that makes for a longer read because you want to hold on to it for as long as you can. In a series of short stories, Ehrlich narrates her experiences in Wyoming while working in a ranch. The book is beautifully written but, as a non-native english speaker, it does contain some passages/words a bit too hard.
I've always felt an inexplicable attraction for remote places, far landscapes and wild harsh nature, and this book encapsulates those three perfectly. Based on her own experiences, Ehrlich's reflects on ranchers, sheepherders, cowboys, native americans, and how they understand, repect and interact with their environment. Particularly inspiring are her views on human-animal and human-nature interactions, on which the usual either over-romantic or over-cruel view is absent. Her sober but powerful, first-person, account is probably the most appealing aspect of the book.


Checks if there's a current session for any User.
Retrieves the Auth service.
Retrieves the Database service.
Retrieve all persisted Posts ids which are the user's Favourite from Firebase.
Checks if a post is Favourite on Firebase.
Checks if there's a user logged in.
Calls the Auth service to initiate a new session for the current user.
Calls the Auth service to log in with Facebook account. If succeeds, save the user's details on Firebase.
Calls the Auth service to log in with Google account. If succeeds, save the user's details on Firebase.
Calls the Auth service to log out of the current session.
Remove a post from Favourite on Firebase.
Calls the Database service to save the user details from Registration Form on Firebase.
Calls the Database service to save the user details from OAuth(Google/Facebook) on Firebase.
Set a post as Favourite on Firebase.
Generated using TypeDoc
This class is reponsible for all operations related to the User (Registration, Authentication, Persistence).


This is a collection of lightweight Python projects that share the same policy.
The goal is to build and maintain a reliable, consistent, easy-to-use and relevant Python codebase for tech enthusiasts.
These projects cover various topics: automation, data persistence and exchange, GUI, themes, widgets, multithreading, markup, utilities, project management, et cetera.
Diogenes by Jean-Léon Gérôme - Walters Art Museum: Home page Info about artwork, Public Domain, Link
This is the random list of projects.
tool language cli library automation scripting task-runner productivity
© Jorge Royan / http://www.royan.com.ar
Backstage is a cross-platform automation tool that looks for a backstage.tasks file in the current working directory to run a specific task defined in that file on demand.
A task can be a sequence or pipeline of processes to be spawned, instructions for performing file and directory manipulation, or something more sophisticated.
Backstage exposes a command line interface that allows the user to discover available tasks, run a task with arguments, read a task documentation, use a glob-like syntax to search for a task by its name or by a keyword that is part of the documentation of the task, et cetera.
library hooks decorator tags bind
HardMediaGroup, CC BY-SA 3.0, via Wikimedia Commons
This project implements an intuitive and minimalist hooking mechanism. It exposes a decorator to tag methods and functions (targets), and so when they are called, user-defined hooks will be executed upstream or downstream.
Arguments to targets are passed to hooks which can modify them or replace the targets themselves with an arbitrary callable or None. Hooks are not directly tied to targets but to tags. Thus, hooks are loosely coupled to targets and dynamically bound to tags.
This library allows the programmer to augment a function or method. It is therefore the perfect solution to create a plugin mechanism for a project. It can also be used for debugging, benchmarking or event-driven programming, et cetera.
library theme style gui beautiful desktop interface tkinter
Cyberpunk-Theme built with TkStyle
TkStyle is a styling library for Tkinter that takes advantage of the autocomplete feature of IDEs so that you hardly need any prior Tkinter styling knowledge.
Each Tkinter widget has a set of options that allow you to define its look. For example, the tkinter.Button widget has the background and foreground options to change the background color and the text color on the button respectively.
TkStyle reproduces for each widget a class which bears the name of the widget and which has attributes representing the options to modify the appearance of the widget.
app markdown markup interactive scriptable note tkinter gui
Exn demo
Exn is a lightweight Python application for browsing a dossier of exonotes.
An exonote is plain text written with an eponymous markup language inspired by Markdown and rendered with Tkinter (the default GUI library for Python).
Interactivity can be added to an exonote by embedding GUI programs written in Python with Tkinter.
Additionally, all or part of an exonote can be arbitrarily generated using custom Python scripts.
Check out the full list of projects on GitHub.
Hi ! I'm Alex, a tech enthusiast. To get in touch with me, check out this code:
a=" dot "; b="rustic"; c="com"; d=" at "; e="alex"; f="yahoo"; print(b+e+d+f+a+c)


This is a very simple lumberjack proof of concept
The three colors refer to oak, dougless fir, and apple trees.
Each tree is generated at a different "age" meaning a different
amount of wood is harvested.
Ideally you would build something with your collected lumber.


Receive more information about our shops and stores. If you’d like to book a room for Cielito Lindo, click here.
20/20 Vision Center offers the best selection in prescription lenses, sunglasses, contact lenses, as well as vision exams. You can find everything from designer brands to affordable frames that fit your every need, all at the best prices in town.
If you need to upgrade your reading glasses, or want special sunglasses for the golf course, then visit 20/20 Vision Center and find the perfect pair for every occasion.


Stacks are a type of container designed specifically to perform in a LIFO context( last- in first- out),
where components are placed and extracted from only one end of the container.
Stack can be implemented quickly with an array or a linked list. Arrays are fast, but limited in size
and Linked Lists involves overhead to be allocated, linked, unlinked and distributed, but not limited
in size.
There are many real-life examples of a stack. Consider an example of plates stacked over one
another in the canteen. The plate which is at the top is the first one to be removed, i.e. the
plate which has been placed at the bottommost position remains in the stack for the longest period of time.


[Page 33]
DELVAL WOOD
[DÉCEMBRE 1916]
[Vendredi 29]
My motto’s while in the army is as follows
Do as you are told
Be obliging always
Smile at trouble
Never say die
Think for yourself but never speak them but just wander around and say nothing
Dont argue for it “aint worth while


This section shows either components or variables depending on what's selected under 'View Selection'. The components area has at least one example of each component to show the effect of the selected theme. Use the top tabs to control which category of components to focus on.
Under 'Variables' select 'Files' to see the files for a custom theme.


Lookfar [ae1d], port city, in Buffalo Plains [as77], in Imperia
Last updated Turn #131
Lookfar [ae1d], port city, in Buffalo Plains [as77], in Imperia
------------------------------------------------------------------------
20: Sirocco Traveller [ja8y], ship, 10 hulls, 1 rowing port, 30 sails,
20: 4% damaged, arrived from Ocean [ar77], owner:
20: Toulouse [h1q], Pen, with one worker, 32 sailors
21: Toulouse [h1q], Pen, with one worker, 32 sailors, exited Sirocco
21: Traveller [ja8y].
21: Toulouse [h1q], Pen, with one worker, 32 sailors, exited
21: Lookfar [ae1d].
22: Toulouse [h1q], Pen, with one worker, 32 sailors, arrived from Buffalo
22: Plains [as77].
22: Toulouse [h1q] bought one Manual of Construction [ag3l] from
22: Lookfar [ae1d] for 750 gold.
22: Paid a market tax of 187 gold to Lookfar [ae1d].
22: Toulouse [h1q] bought one pitch [261] from Lookfar [ae1d] for
22: 50 gold.
22: Paid a market tax of 12 gold to Lookfar [ae1d].
22: Toulouse [h1q] bought three riding horses [52] from Lookfar [ae1d] for
22: 132 gold.
22: Paid a market tax of 33 gold to Lookfar [ae1d].
22: Lookfar [ae1d] bought 50 fish [87] from Toulouse [h1q] for 100 gold.
23: Toulouse [h1q] recruited one peasant.
24: Toulouse [h1q], Pen, with one worker, 32 sailors, three riding horses,
24: one peasant, entered Sirocco Traveller [ja8y].
24: Sirocco Traveller [ja8y], ship, 10 hulls, 1 rowing port, 30 sails,
24: 4% damaged, departed for Ocean [ar77].
Routes leaving Lookfar:
North, to Ocean [ar77], The Outer Sea, 1 day
East, to Ocean [as78], The Outer Sea, 1 day
South, to Ocean [at77], The Outer Sea, 1 day
Out, plain, to Buffalo Plains [as77], 1 day
Garrison [cu0j], garrison, Imperial Empire, with ten peasants
Cities rumored to be nearby:
Cleanbones [ad7h], in Endless Plains [ao78]
Eadweard [ad9f], in Buffalo Plains [aq71]
Market report (tax of 25%):
trade who price qty wt/ea item
----- --- ----- --- ----- ----
buy ae1d 24 5 1 chimera eyes [328]
buy ae1d 17 17 2 gorgon livers [332]
buy ae1d 8 15 1 woven baskets [94]
buy ae1d 7 9 1 harpie feathers [329]
buy ae1d 2 100 2 fish [87]
sell ae1d 6 20 5 dried fish [255]
sell ae1d 44 3 300 riding horses [52]
sell ae1d 50 10 100 pitch [261]
sell ae1d 100 5 1,000 oxen [76]
sell ae1d 525 1 5 Manual of Combat [jn4c]
sell ae1d 950 1 5 old book [ih4z]


Start your own NextGen Pre-School with EuroKids! Eurokids baby steps for kids India's NextGen Pre-School invites fra
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Programming Languages
Statistic Skills
- - Structural Equation Models, Latent Curve Models, Hierarchical Linear Model and Multilevel Modeling
- - Machine Learning: Classification, Linear/Logistic Regression, Clustering/Segmentation
Research
- - Design experiments to collect human behavior data
- - Survey design, behavioral observation and coding
- - Experienced with Psychophysiological measurement, qualitative and quantitative data analysis
Data Analytics and Visualization Tools
- Experienced with Excel, Numpy, Pandas, scikit-learn, seaborn, SPSS, AMOS, Mplus, HLM and Tableau
- Survey Tool: SurveyMonkey and Qualtrics


+++ title = "Herberge der Jugend, Dresden" slug = "herberge-der-jugend-dresden" date = 2007-12-15T11:00:00+10:00 tags = ["accommodation"] locations = ["germany/dresden"] draft = false summary = """
The Herberge der Jugend, Dresden is an absolute last-resort accommodation option: I only stayed here tonight because every single other hostel in Dresden was full; don't consider staying here unless you're faced with a similarly dire predicament. It's a nice enough place: but it's more than ½ an hour from the city centre, and it's completely dead. Dresden has plenty of better hostels: do yourself a favour, book one of the others... and make your booking well in advance!
""" +++The Herberge der Jugend, Dresden is an absolute last-resort accommodation option: I only stayed here tonight because every single other hostel in Dresden was full; don't consider staying here unless you're faced with a similarly dire predicament. It's a nice enough place: but it's more than ½ an hour from the city centre, and it's completely dead. Dresden has plenty of better hostels: do yourself a favour, book one of the others... and make your booking well in advance!
Rooms: nice, and you'll get one all to yourself (for less than a crowded dorm elsewhere); although a bit cold at night.
People: huh?
Staff: only available at very limited times; although friendly and helpful when they're around.
Location: fricking miles away from anywhere; although it is, at least, (fortunately) quite close to a regular tram service, that goes straight into the city centre.
Food: none provided; but the guest kitchen is pretty god.
Internet: I think they're meant to charge you for it (they have a system where you shove tokens into a machine, in order to power the 2 PCs in the lobby), but the staff insist on giving you the tokens for free. Nice perk!
Hot water: didn't try it — probably good.


PROPOSED EXTENSIONS
In this section we sketch a number of possible extensions to the Gel
language. We have not implemented any of these.
Object Destruction
Gel's object destruction mechanism as outlined above has a number of
limitations:
- Gel does not let the programmer define a destructor
for a
class, i.e. a method which will run when an instance of the class is
destroyed.
- Gel can automatically destroy structures in which each
internal non-owning pointer points to an ancestor in the ownership
tree, but cannot automatically destroy structures containing arbitrary
non-owning pointers to internal nodes.
- Object destruction in Gel may consume a large amount of
stack space since it is inherently recursive.
To overcome these limitations, we propose adding destructors to Gel,
and propose a multi-phase object destruction mechanism. Gel
could destroy an object graph rooted at a pointer P in the following
phases.
1. Recursively descend all objects in the ownership tree below P,
running each object's destructor.
2. Recursively descend again; for each non-owning pointer in every
object, decrement the reference count of the object pointed to.
3. Recursively descend again; for each object, check that its reference
count is zero and free the object.
In many cases the compiler should be able to optimize away some or all
of the work to be performed in the first and/or second phases.
In particular, using static type analysis it will often be
possible to determine that all objects in the graph below an object of
type T will never have any destructors or non-owning pointers; in that
case the first or second phases need not run at all below such objects.
As a simple example, consider a singly linked list in which
each node contains only an integer and an owning pointer to the next
node; then the first and second phases can be optimized away
entirelyin destroying a list of this type.
Note also that in many structures each node contains only a single
owning pointer; in such structures, the compiler can save stack space
by eliminating tail recursion in generating the code to perform the
recursive descent in each phase. With these optimizations in
place, we believe that this generalized destruction algorithm might
outperform thealgorithm in the existing Gel implementation in
many cases.
User-written destructors might potentially mutate the graph of objects
being destroyed. If a destructor inserts objects into the
graph, then these objects might be destroyed in phases 2 and 3 even
though their destructors have never run. Similarly, if a
destructor removes objects whose destructors have already run, then
those objects' destructors might run againwhen they are
destroyed at a later time. This possibility does not
compromise the safety of the language, but does mean that each object's
destructor is not guaranteed to run exactly once. It might be
possible to detect destructor-induced mutations in some way and report
an error if they occur; we will not discuss this possibility further
here.
Polymorphism
The Gel type system does not support any sort of polymorphism between
owning and non-owning types. This means that it's not
possible to write a method which can receive either an owning or
non-owning pointer as an input parameter. It's also not
possible to implement a single data structure (such as a tree or hash
table) which can hold either owning or non-owning pointer.
This limitation is reflected in the Gel class library, which
contains separate ArrayList and NonOwningArrayList classes.
It would be useful to extend Gel
withgeneric types as found in Java and C#, allowing a generic
type to represent either an owning or non-owning type. As a
simple example, suppose that we'd like to implement a class
Pair<T> which holds two objects of type T. We'd
like to be able to instantiate the class using either an owning or
non-owning pointer type. Our generic type system might allow
this as follows:
class Pair<T> {
T first_, second_;
Pair(T first, T second) { first_ = first; second_ = second; }
T% First() { return first_; }
T% Second() { return second_; }
}
The system includes a type operator % which can be applied only to a
generic type, and which removes ownership from a type. In
particular, if T is Foo ^, then T% is Foo; if T is Foo, then T% is also
Foo.
A generic type system for Gel should also support covariance:
it should be possible to write a single function which can take either
a Pair<Foo> or a Pair<Foo ^> as a
parameter, and similarly it should be possible to write a single
function which can take either a Foo[] or a Foo^[] as a parameter.
We believe that it should not be difficult to design a complete
generics system for Gel including covariance, but we will not explore
the idea further in this work.
Multithreading
As described above, the Gel compiler's reference count elimination
optimization will work only in single-threaded programs; this is a
significant limitation.
It would be useful to extend Gel to be able to execute multithreaded
programs safely and efficiently. We can imagine two different
possible approaches here. First,it might be
possible to extend Gel'soptimizer to be able to infer that
many data structures are accessed only from a single thread; then
reference counts in such structures could be optimized away using the
existing algorithm. Such inference seems difficult and we are
relatively pessimistic about such an approach.
As another
approach, we could possibly extend Gel so that objects are grouped
into apartments
which may be accessed from only a single thread at
once. With this approach, the existing optimization algorithm
could be used to eliminatereference count accesses in code
which
executes inside a single apartment. It might be possible to
designsystem which guarantees that only pointers of a certain
kind may cross apartment boundaries and which allows groups of objects
to move efficiently between apartments; we leave this to further work.


UFC 244 PPV results: Stephen Thompson outstrikes Vicente Luque, takes decision
by Eddie MercadoThe UFC 244 PPV main card just watched the #9 ranked welterweight, Stephen Thompson, and the #14 ranked Vicente Luque bang it out for three rounds. Luque had strong moments in the first act, but Thompson got it together and began to hurt and outland his opposition. When the dust had settled, it was Thompson who took home the unanimous decision to get himself back into the win column.
Luque came out attacking the legs right away. Thompson was elusive as always, using his footwork to stay on the outside. Luque was finding success with throwing his punches in combinations, scoring with his left hand. Thompson was landing counters, but Luque seemed unfazed by them.
Thompson started exploding in with straight punches early in the second act. He then landed a sweet side kick that knocked over Luque. Thompson stunned Luque, causing him to cover up, but he recovered quickly. Luque returned fire, landing clean punches that backed off the advancing Wonderboy. Thompson started to put it on Luque, bloodying up his increasingly stationary opposition.
Thompson hit another side kick that knocked over Luque early in the final round. He then launched a left hand that knocked down his opponent. Thompson went in for the kill with ground strikes, but allowed Luque to stand back up. Luque kept throwing, but Thompson was so fluid with his movement, that Vicente just couldn’t land clean. Another cross dazed Luque, causing him to shell up, but he survived.


Get an electronic visa waiver to enter the UK
You can get an electronic visa waiver (EVW) instead of a visa if you have a passport from Kuwait, Oman, Qatar or the United Arab Emirates. You must pay £15 for an EVW.
An EVW lets you visit the UK for up to 6 months for tourism, business, study or medical treatment. If you’re visiting for another reason, check if you need a visa instead.
You must apply between 3 months and 48 hours before you travel.
To apply you need:
- your current passport
- an address in the UK where you will stay
- details of your journey, including departure and arrival dates and times
Before you apply
You must enter your personal details so they match your passport exactly. This includes any punctuation, eg spaces, hyphens, apostrophes or full stops.
Your travel details must also be exactly right.
You won’t be allowed to travel if your personal or travel details are wrong.
Each person travelling needs their own EVW, including children.
If someone applies on your behalf, they must enter their own contact details as well as yours.
After you apply
You’ll get an email within 24 hours with a link to download your EVW.
You must print out your EVW and show it on departure and arrival. You can’t show an electronic copy of the EVW, eg on a phone or tablet.
You can’t use your EVW to:
- work in the UK
- get married or register a civil partnership
Your EVW will only let you enter the UK once. You must get another EVW or a visa if you want to enter the UK again.
Changes to your travel plans
You must change your travel details on your EVW if you’ll:
- leave from or arrive at a different airport, port or train station than planned
- arrive in the UK more than 8 hours later than planned
You’ll need your EVW number and your date of birth to change your details.


Package mainGui
Class GuiManager
- java.lang.Object
-
- mainGui.GuiManager
-
public class GuiManager extends java.lang.Object
Control center for the core GUI functionality, primarily acting as a middle man between the main screen and content panes.- Version:
- 1.0, May 2019.
- Author:
- Jason Smit, Ariel Evangelista
-
-
Constructor Summary
Constructors Constructor Description GuiManager()
-
Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description void
closeSetupScreen(SetupScreen setupWindow)
Closes the given SetupScreen, and call the method to start the main window.void
finishGame(MainScreen mainScreen)
Close up the main screen of the game and launch a new gameCrewStatus
getCrewStatusPanel(MainScreen window)
Will create a new CrewStatus JPanelGameOver
getGameOverPanel(MainScreen window)
Will create a new GameOver JPanelInventoryDisplay
getInventoryPanel(MainScreen window)
Will create a new InventoryDisplay JPanelGameNavigation
getMainScreenPanel(MainScreen window)
Will create a new GameNavigation JPanelOutpostContent
getOutpostPanel(MainScreen window)
Will create a new OutpostContent JPanelPlanetMain
getPlanetMain(MainScreen window)
Will create a new PlanetMain JPanelShipStatus
getShipStatusPanel(MainScreen window)
Will create a new ShipStatus JPanelTravelCrewSelect
getTravelCrewPanel(MainScreen window)
Will create a new TravelCrewSelect JPanel,TravelPlanetSelect
getTravelPlanetPanel(MainScreen window)
Will create a new TravelPlanetSelect JPanelvoid
startGame()
Launches the SetupScreen which will be the entry point to the game.
-
-
-
Method Detail
-
startGame
public void startGame()
Launches the SetupScreen which will be the entry point to the game.
-
closeSetupScreen
public void closeSetupScreen(SetupScreen setupWindow)
Closes the given SetupScreen, and call the method to start the main window.- Parameters:
setupWindow
- A SetupScreen that refers to the window that needs to be closed.
-
finishGame
public void finishGame(MainScreen mainScreen)
Close up the main screen of the game and launch a new game- Parameters:
mainScreen
- The managing MainScreen class
-
getMainScreenPanel
public GameNavigation getMainScreenPanel(MainScreen window)
Will create a new GameNavigation JPanel- Parameters:
window
- The current MainScreen window who has the overall control- Returns:
- Will return a GameNavigation JPanel that will be loaded onto the main screen.
-
getTravelCrewPanel
public TravelCrewSelect getTravelCrewPanel(MainScreen window)
Will create a new TravelCrewSelect JPanel,- Parameters:
window
- The current MainScreen window who has the overall control- Returns:
- Will return a TravelCrewSelect JPanel that will then be loaded onto the main screen.
-
getTravelPlanetPanel
public TravelPlanetSelect getTravelPlanetPanel(MainScreen window)
Will create a new TravelPlanetSelect JPanel- Parameters:
window
- The current MainScreen window who has the overall control- Returns:
- Will return a TravelPlanetSelect JPanel that will then be loaded onto the main screen.
-
getPlanetMain
public PlanetMain getPlanetMain(MainScreen window)
Will create a new PlanetMain JPanel- Parameters:
window
- The current MainScreen window who has the overall control- Returns:
- Will return a PlanetMain JPanel that will then be loaded onto the main screen.
-
getInventoryPanel
public InventoryDisplay getInventoryPanel(MainScreen window)
Will create a new InventoryDisplay JPanel- Parameters:
window
- The current MainScreen window who has the overall control- Returns:
- Will return a InventoryDisplay JPanel that will then be loaded onto the main screen.
-
getOutpostPanel
public OutpostContent getOutpostPanel(MainScreen window)
Will create a new OutpostContent JPanel- Parameters:
window
- The current MainScreen window who has the overall control- Returns:
- Will return a OutpostContent JPanel that will then be loaded onto the main screen.
-
getCrewStatusPanel
public CrewStatus getCrewStatusPanel(MainScreen window)
Will create a new CrewStatus JPanel- Parameters:
window
- The current MainScreen window who has the overall control- Returns:
- Will return a CrewStatus JPanel that will then be loaded onto the main screen.
-
getShipStatusPanel
public ShipStatus getShipStatusPanel(MainScreen window)
Will create a new ShipStatus JPanel- Parameters:
window
- The current MainScreen window who has the overall control- Returns:
- Will return a ShipStatus JPanel that will then be loaded onto the main screen.
-
getGameOverPanel
public GameOver getGameOverPanel(MainScreen window)
Will create a new GameOver JPanel- Parameters:
window
- The current MainScreen window who has the overall control- Returns:
- Will return a GameOver JPanel that will then be loaded onto the main screen.
-
-


Summer, warmest season of the year, between spring and autumn. In the Northern Hemisphere, it is usually defined as the period between the summer solstice, June 21 or 22, and the autumnal equinox (day and night equal in length), September 22 or 23.
The temperature contrast between summer and the other seasons exists only in middle and high latitudes; temperatures in the equatorial regions generally vary little from month to month. A period of exceptionally hot weather, often with high humidity, during the summer is called a heat wave.
Usually summer sports include track and field, baseball, swimming, beach volleyball, and soccer.


Nanoengineering
and Biodesign Lab
Carlos Castro
Jenny Le
Josh Johnson
Christopher Lucas
Alex Marras
Randy Patton
Melika Shahhosseini
Lifeng Zhou
Yichun Zheng
Chao Min
Undergraduates
Anjelica Kucinic
Amjad Chowdhurry
Aliki Kolliopoulos
Andres Serranopaladines
+more


Captain Mouen:
Destroy the
Leto Lizardmen
troops! Then collect and bring me
10 fangs from the fallen Lizardmen
. No one will be able to dispute your victory if you bring me 10 fangs!


N.B.: The contract date represents the date that the contract is recorded in the departmental financial system.
| Vendor Name | THE 500 STAFFING INC. |
|---|---|
| Reference Number | 700274929 |
| Contract Date | 2013-10-23 |
| Description of Work | 813 - Temporary Help Services |
| Contract Period - From | 2013-10-28 |
| Contract Period - To | 2014-09-26 |
| Delivery Date | |
| Contract Value | $69,156.00 |
| Total Amended Contract Value | |
| Comments | This contract was competitively sourced.The Canada Gazette (CG) publishes 2 publications per week and ensures they are compliant with Treasury Board Common Look and Feel (CLF) guidelines and W3 standards. To do this, the HTML versions are generated by Word script and InDesign in order to assure the CLF compliancy, and the Web Master and the Intermediate Web Support need to edit and apply changes on the HTML tag in order to validate code with W3C validation tools. To help manage the workload, a temporary resource was needed to help and support employees with the maintenance of the Gazette web site and TB web standard; do corrections in HTML, XHTML, HTML5 tags; validate documents using W3C validation tools, Dreamweaver and PWGSC validation tools; edit web pages and adjust HTML5 Tags to meets the CLF2 requirements and create pages when needed to be uploaded on GC sites |


[1]
i found the other was freezing that morning.
[2]
just came back from sydney where it was single.


Recipe Search allows you to enter the recipe or ingredient of your choice and gives you the list of recipes to choose from. Click here to try it out.
You can also take advantage of the 'Search by Ingredients' function, where you can enter a list of specific ingredients and narrow down the results to recipes which include all of those ingredients. Click here to try it out.
In addition to the Recipe-Search tab, you can also try our Find-Food tab where you can find what is available nearby with just the click of a button. It reduces time and effort by eliminating the need to enter locations manually.


Gecode::SymmetryHandle Class Reference
[Branching]
A reference-counted pointer to a SymmetryObject. More...
#include <int.hh>
Public Member Functions | |
| void | increment (void) |
| Increment counter. | |
| void | decrement (void) |
| Decrement counter. | |
| SymmetryHandle (void) | |
| Default constructor. | |
| SymmetryHandle (Int::LDSB::SymmetryObject *o) | |
| Initialies with a SymmetryObject. | |
| SymmetryHandle (const SymmetryHandle &h) | |
| Copy constructor. | |
| const SymmetryHandle & | operator= (const SymmetryHandle &h) |
| Assignment operator. | |
| ~SymmetryHandle (void) | |
| Destructor. | |
Public Attributes | |
| Int::LDSB::SymmetryObject * | ref |
| Symmetry object that this handle refers to. |
Detailed Description
A reference-counted pointer to a SymmetryObject.
Definition at line 4981 of file int.hh.
Constructor & Destructor Documentation
| Gecode::SymmetryHandle::SymmetryHandle | ( | void | ) |
Default constructor.
Definition at line 37 of file sym-obj.cpp.
| Gecode::SymmetryHandle::SymmetryHandle | ( | Int::LDSB::SymmetryObject * | o | ) |
Initialies with a SymmetryObject.
Definition at line 39 of file sym-obj.cpp.
| Gecode::SymmetryHandle::SymmetryHandle | ( | const SymmetryHandle & | h | ) |
Copy constructor.
Definition at line 41 of file sym-obj.cpp.
| Gecode::SymmetryHandle::~SymmetryHandle | ( | void | ) |
Destructor.
Definition at line 57 of file sym-obj.cpp.
Member Function Documentation
| void Gecode::SymmetryHandle::increment | ( | void | ) |
Increment counter.
Definition at line 62 of file sym-obj.cpp.
| void Gecode::SymmetryHandle::decrement | ( | void | ) |
Decrement counter.
Definition at line 66 of file sym-obj.cpp.
| const SymmetryHandle & Gecode::SymmetryHandle::operator= | ( | const SymmetryHandle & | h | ) |
Assignment operator.
Definition at line 47 of file sym-obj.cpp.
Member Data Documentation
The documentation for this class was generated from the following files:
- gecode/int.hh
- gecode/int/ldsb/sym-obj.cpp


[1] much slimmer and weather app 's more important for watching the sound quality Rim needs a 3rd party browser supplied with Microsoft Calendar via Desktop Manager Browser on OS is streamlined SMS texts are grouped into conversations.
[2] much slimmer and weather app 's more important for watching the sound quality Rim needs a blackberry device it syncs easily with Microsoft Calendar via Desktop Manager Browser on OS is streamlined SMS texts are grouped into conversations.


Full Name:
Email Address:
Password:
Date of Birth:
Graduation Year:
Demographics
Gender
Male
Female
Undisclosed
Race
African American
Asian
White
American Indian
Pacific Islander
Other
Personal Preferences
Favorite Color
Attachments
Resume


Artifacts
Build artifacts to upload at the end of the build
Automatically upload your build artifacts to Amazon S3 at the end of a build, after the after_script
phase.
Please see our documentation for details.
See also
Types
Keys
If given a map, the following keys are supported:
enabled
— Enable or disable uploading artifacts (type: boolean)bucket
— The S3 bucket to upload to (type: string)endpoint
— The S3 compatible endpoint to upload to (type: string)key
— The S3 access key id (type: secure, alias:aws_access_key_id
,aws_access_key
,access_key_id
,access_key
)secret
— The S3 secret access key (type: secure, alias:aws_secret_access_key
,aws_secret_key
,secret_access_key
,secret_key
)region
— The S3 region (type: string, alias:s3_region
)paths
— Paths to the files to upload (type: sequence of string, or string)branch
— (type: string)log_format
— (type: string)target_paths
— (type: sequence of string, or string)debug
— (type: boolean)concurrency
— (type: number)max_size
— (type: number)permissions
— (type: string)working_dir
— (type: string)cache_control
— (type: string)
Examples
artifacts:
enabled: true
bucket: string
endpoint: string
artifacts: true


This file gives information about the samples provide in this directory. Just to note, none of these were created by me unless otherwise stated.


--- layout: post title: "No Title" ---
To me, “biking” is a belittling term that evokes fanny packs and hybrids, and I prefer the somewhat more dignified “cycling.I guess technically the word biking makes sense, there’s many other activities named the same way: skateboarding, trucking, roller blading, etc. Biking and biker still sounds gross to me, as if it’s the equivalent of calling walking shoeing.


Variables:
One of the most important concept in programming where you define a code and save it inside a variable as a reference which can be reused several times without having to write the code again and again.
Objects:
An object in programming can be a combination of variables, functions and data structures. It is a powerful tool and one should understand it fully if they want to master a programming language.
For loops:
Loops in programming allows you to repeat the same code until the desired condition is met. For loops are clear and precise, they are easy to understand and most commonly used in programming languages.
Debugger:
A debugger is an important tool used to find errors in the programming code. In javascript mainly the browser provided debugger are used to identify mistakes in a code and correct them.
Beautify:
Writing a maintainable code requires making it easily readable. That is why it is important to focus on writing your code neatly. Several plugins and addons are available which help you in writing a good code, for example, beautify for VS code.
Array:
It is a data structure which saves a collection of elements of the same data. For example, an array can be used to collect different variables.