October 29th 2017 - Tejpal Virdi
Convolutional Neural Networks (CNNs) are a certain type of Neural Network that excel at image recognition and classification. They are used to analyze an image through the extraction of relevant features and have special characteristics that allow it to perform better on 3D and 2D volumes of data. Through a series of layers, the CNN extracts relevant features from the input image. The four main layers are known as convolutional layers, ReLUs, pooling layers, and fully-connected layers. Just as how an artificial neural network can have multiple hidden layers, a deep CNN may repeat these layers multiple times.
If you are new to neural networks in general, we recommend that you visit our introductory blog post first.
Convolutional Layer
The Convolutional Layer is the meat of the whole process. Similar to the neural network, the convolution layer has a set of learnable weights, called filters. The filter is usually of a size smaller than 10x10 pixels and slides across the image, computing the dot product between the input pixels and filter. The resulting matrix is known as an activation map and can be stacked spatially to produce the output volume.
Here are some important terms to know: the size of the filter is often known as the kernel size, stride refers to how many units the filter moves every iteration of the convolution, and padding refers to the zeros you add around the image so the filter can be applied onto pixels nearing the edges of the image.
For image recognition, the filters learned usually represent lower to higher spatial features. For example, filters in the first convolution might represent edges or corners. Through the combination of these lower level filters, filters in later convolutions are able to take this information and look for higher level features such as faces.
ReLU
Rectified Linear Units (ReLUs) serve as activation functions, and are simply defined as:
The simplicity of this function allows for improved training speed, in both the forward and backward passes. Observe that computing the gradient of the function simply returns a 0 or 1, only dependent on sign of x. In comparison, logistic and hyperbolic activation functions, such as the sigmoid function, are prone to the vanishing gradient problem. The vanishing gradient problem is based on the gradients having an infinitesimal effect on the output. A local minimum of the loss function is found and the small changes of the gradients prevents the model from learning. However, ReLUs are not subject to this since their derivatives are simply 0 or 1.
Pooling Layer
To understand an image, the CNN must downsample it to include only relevant information. Pooling layers are used to reduce the spatial size, thus reducing the total number of parameters of the model. This lowers computation time and prevents overfitting since the output of the pooling layer is a more general representation of the input.
A simple type of pooling is max pooling, which applies a filter of size, let’s say, 2x2 across the image. For every 2x2 set of pixels on the input, it will only keep the maximum pixel value and remove the rest, thus downsampling the image by 4x in our example. Note: this filter preserves depth to keep an accurate spatial representation of the input.
Fully-Connected Layer
Fully-connected layers are used at the end of the model as a nonlinear transformation, by a simple matrix multiplication, to go back from activation maps to relevant high-dimensional data. The channels of information are flattened, and their spatial representation is lost. It is important to note that a fully-connected layer is the same thing as performing a convolutional layer with the kernel size as the size of the image.
Forward Pass
The forward pass simply involves running the input image through each of the filters, pooling layers, and ReLU’s.
Backpropagation
In the case of CNNs, backpropagation is used to make adjustments to the filters in order to get a lower loss. It is calculated similarly to the artificial neural network, however it accounts for the addition of filters. (see blog post on simple neural networks, more math explained there)
Current Architectures
Within the last few years, many new CNN architectures have come out. It’s helpful to be familiar with them since you will most likely encounter them in practice. We will briefly look at AlexNet, VGGNet, and ResNet.
AlexNet became popular after its submission to the annual ImageNet ILSVRC competition in 2012. It’s key features are its significant depth as well as its use of stacked convolutional layers without pooling after each one.
The VGGNet showcased the importance of depth in a CNN architecture. The model contained 16 convolutional and fully-connected layers with convolutions of size 3x3 and 2x2 for pooling. However, due to the depth and large number of fully-connected layers, the net ended up having over 140 million parameters, making it computationally expensive in terms of both memory and time.
The ResNet is a residual network that removes many of the drawbacks of the VGGNet, being extremely efficient through the use of skip connections, absence of fully-connected layers, and significant use of batch normalization. A skip architecture is where the network gives the output of a layer to the next layer as well as the one after it, thus skipping to the next connection. Batch normalization is a used to normalize the distribution of the input across the batch. It scales and shifts this distribution, allowing for a more stable training process. This also allows for a higher learning late without getting stuck in a local minimum of the loss function.


This is header font one
Hello there, awesome person!
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My favorate site is
Blizzard!


Porsche 911 Turbo S
Specs (reg/S version)
Price |
$115,000 / $150,000 |
Top Speed |
180mph / 186mph |
Acceleration |
|
Engine |
|
0-30mph |
1.5sec(!!) / N/A |
Type |
Twin Turbo Flat-6, 12valve SOHC / same |
0-60mph |
3.8sec / 3.6sec |
Displacement |
3.6L(3600cc) / same |
0-100 |
8.7sec / N/A |
Power |
408hp@5750rpm / 424hp@5750 |
1/4 mile |
12.3sec/114.1mph / 11.9sec/116.6mph |
Torque |
399 lbs-ft@4500rpm / 400 lbs-ft@4500rpm |
Weight |
3,323 lbs / N/A |
Handling |
|
Gas mileage |
|
Skidpad |
1.00g / .95g |
City |
13mpg / same |
600ft slalom |
69.6mph / 69.7mph |
Highway |
19mpg / same |
Powertrain |
Rear engined, All wheel drive, 6 speed manual / same |
(? = incomplete data or unverified info, N/A = info Not Available)
Beyond the specs:
It was the ultimate Porsche, the car that molded the 911 design into a supercar. This car has amazing off the line acceleration due to it traction provided by its all wheel drive system. This car gets to 60mph is a breath-takenly short 3.6 seconds! It acceleration is simply amazing, it uses its little air-cooled flat 6 engine to its very limits. That kind of acceleration combined with Porsche handling this car is a true supercar fully worth its $150,000 price tag. This car is awesome and it is fun to drive too, just beware of that easily induced oversteer (despite its chassis being set up for understeer) which even its AWD could not fully tame. Through this car can compete with V12 supercars, even turbocharged V12 supercars, the only downfall is its 186mph top speed which many other much more expensive supercars easily pass. This car will carve rubbery tracks in winding roads while digging trenches in dirt under hard acceleration, all while still being able to manage a quick direction change (just watch that throttle, ok). Porsche quality and refinement, amazing acceleration, and Porsche handling make this car a perfect match for those who lust for high performance automobiles.
~Oracle


| ff_starter_positions {ffscrapr} | R Documentation |
This function returns a tidy dataframe with positional lineup rules.
ff_starter_positions(conn, ...) ## S3 method for class 'espn_conn' ff_starter_positions(conn, ...) ## S3 method for class 'flea_conn' ff_starter_positions(conn, ...) ## S3 method for class 'mfl_conn' ff_starter_positions(conn, ...) ## S3 method for class 'sleeper_conn' ff_starter_positions(conn, ...) ## S3 method for class 'template_conn' ff_starter_positions(conn, ...)
conn |
the list object created by |
... |
other arguments (currently unused) |
A tidy dataframe of positional lineup rules, one row per position with minimum and maximum starters as well as total starter calculations.
espn_conn
: ESPN: returns min/max starters for each main player position
flea_conn
: Fleaflicker: returns minimum and maximum starters for each player position.
mfl_conn
: MFL: returns minimum and maximum starters for each player position.
sleeper_conn
: Sleeper: returns minimum and maximum starters for each player position.
template_conn
: Template: returns minimum and maximum starters for each player position.
try({ # try only shown here because sometimes CRAN checks are weird conn <- espn_connect(season = 2020, league_id = 1178049) ff_starter_positions(conn) }) # end try try({ # try only shown here because sometimes CRAN checks are weird conn <- fleaflicker_connect(season = 2020, league_id = 206154) ff_starter_positions(conn) }) # end try try({ # try only shown here because sometimes CRAN checks are weird dlfidp_conn <- mfl_connect(2020, league_id = 33158) ff_starter_positions(conn = dlfidp_conn) }) # end try try({ # try only shown here because sometimes CRAN checks are weird jml_conn <- sleeper_connect(league_id = "652718526494253056", season = 2021) ff_starter_positions(jml_conn) }) # end try template_conn <- ff_template(roster_type = "idp") ff_starter_positions(template_conn)


--- layout: post title: "The Third Sunday in Ordinary Time" date: 2021-01-24 09:00:00 categories: Sunday tags: featured image: /assets/article_images/2020-04-26-Third-Sunday-Easter/scripture.jpg image2: /assets/article_images/2020-04-26-Third-Sunday-Easter/scripture-mobile.jpg ---
Holy Mass is at the very core of our worship as a Catholic community. Nothing can ever be substituted for the celebration of the Eucharist. But in this exceptional time of difficulty for our communities, our nation, and the whole world, this order of prayer can be offered by individuals and families and engages us in our Sunday celebration of the Word of God. We can pray this prayer in our homes—the “domestic churches” which help build up the Body of Christ on earth—and thus be connected to the entire Church at prayer.
During this time of prayer, we include the opportunity to make a Spiritual Communion. Making a Spiritual Communion is a worthy practice, recommended by innumerable saints throughout the ages, which unites our whole selves to God and his Church.
Taken from the Mass and the Liturgy of the Hours
Leader: O God, ✠ come to my assistance.
Response: O Lord, make haste to help me.
Leader: Glory be to the Father, and to the Son, and to the Holy Spirit:
Response: as it was in the beginning, is now, and ever shall be, world without end. Amen.
R. I will choose Christ, I will choose love,
I choose to serve.
I give my heart, I give my life,
I give my all to you. V.
V. How many times must he call my name
and show to me that he is God?
And as a servant he calls to me,
“You must serve too.” R.
V.
And with the death of my own desires,
I’ll rise with you. R.
Leader: Let us acknowledge our sins, and seek the Lord’s pardon and peace.
Together: I confess to almighty God
and to you, my brothers and sisters,
that I have greatly sinned,
in my thoughts and in my words,
in what I have done and in what I have failed to do,
through my fault, through my fault,
through my most grievous fault;
therefore I ask blessed Mary ever-Virgin,
all the Angels and Saints,
and you, my brothers and sisters,
to pray for me to the Lord our God.
Leader: Almighty ever-living God,
direct our actions according to your good pleasure,
that in the name of your beloved Son
we may abound in good works.
Through our Lord Jesus Christ, your Son,
who lives and reigns with you in the unity of the Holy Spirit,
one God, for ever and ever.
Together: Amen.
USCCB Audio Recording of Sunday Readings
The word of the LORD came to Jonah, saying: “Set out for the great city of Nineveh, and announce to it the message that I will tell you.” So Jonah made ready and went to Nineveh, according to the LORD’S bidding. Now Nineveh was an enormously large city; it took three days to go through it. Jonah began his journey through the city, and had gone but a single day’s walk announcing, “Forty days more and Nineveh shall be destroyed,” when the people of Nineveh believed God; they proclaimed a fast and all of them, great and small, put on sackcloth.
When God saw by their actions how they turned from their evil way, he repented of the evil that he had threatened to do to them; he did not carry it out.
The Word of the Lord.
―Thanks be to God.
Reader: Teach me your ways, O Lord.
Together: Teach me your ways, O Lord.

Together: Teach me your ways, O Lord.
Reader: Remember that your compassion, O LORD,
and your love are from of old.
In your kindness remember me,
because of your goodness, O LORD.
Together: Teach me your ways, O Lord.
Reader: Good and upright is the LORD;
thus he shows sinners the way.
He guides the humble to justice
and teaches the humble his way.
Together: Teach me your ways, O Lord.
I tell you, brothers and sisters, the time is running out. From now on, let those having wives act as not having them, those weeping as not weeping, those rejoicing as not rejoicing, those buying as not owning, those using the world as not using it fully. For the world in its present form is passing away.
The word of the Lord.
―Thanks be to God.
After John had been arrested, Jesus came to Galilee proclaiming the gospel of God: “This is the time of fulfillment. The kingdom of God is at hand. Repent, and believe in the gospel.”
As he passed by the Sea of Galilee, he saw Simon and his brother Andrew casting their nets into the sea; they were fishermen. Jesus said to them, “Come after me, and I will make you fishers of men.” Then they abandoned their nets and followed him. He walked along a little farther and saw James, the son of Zebedee, and his brother John. They too were in a boat mending their nets. Then he called them. So they left their father Zebedee in the boat along with the hired men and followed him.
The Gospel of the Lord.

Response: Christ, Son of the Living God, have mercy on us.
Leader: You are seated at the right hand of the Father,
Response: have mercy on us.
Leader: Glory to the Father, and to the Son and to the Holy Spirit.
Response: Christ, Son of the Living God, have mercy on us.
Leader: Father, you sent the Holy Spirit to enlighten the hearts of men; hear us as
we pray:
Response: Enlighten your people, Lord.
Leader: Blessed are you, O God, our light, you have given us a new day resplendent
with your glory.
Response: Enlighten your people, Lord.
Leader: You enlightened the world through the resurrection of your Son, through
your Church shed this light on all men.
Response: Enlighten your people, Lord.
Leader: You gave the disciples of your only-begotten Son the Spirit’s gift of
understanding, through the same Spirit keep the Church faithful to you.
Response: Enlighten your people, Lord.
Leader: Light of nations, remember those who remain in darkness, open their eyes
and let them recognize you, the only true God.
Response: Enlighten your people, Lord.
Leader: In your mercy, continue to sanctify all who are afflicted from the present pandemic, and draw us all closer to you,
and to one another, in this time of suffering.
Response: Enlighten your people, Lord.
Leader: Let us pray together as Jesus taught us:
Together: Our Father, who art in heaven …
Leader: You have given us bread from heaven,
Response: Containing in itself all delight
Together: My Jesus,
I believe that you are present
in the Most Holy Sacrament of the Altar.
I love you above all things,
and I desire to receive you into my soul.
Since I cannot at this moment receive you sacramentally,
come at least spiritually into my heart.
I embrace you as if you were already there
and unite myself wholly to you.
Never permit me to be separated from you,
my Lord and my God!
Amen.
Canticle of Zechariah (Luke 1:68-79)
Together: ✠ Blessed be the Lord, the God of Israel;
he has come to his people and set them free.
He has raised up for us a mighty savior,
born of the house of his servant David.
Through his holy prophets he promised of old
that he would save us from our enemies,
from the hands of all who hate us.
He promised to show mercy to our fathers
and to remember his holy covenant.
This was the oath he swore to our father Abraham:
to set us free from the hands of our enemies,
free to worship him without fear,
holy and righteous in his sight
all the days of our life.
You, my child, shall be called the prophet of the Most High;
for you will go before the Lord to prepare his way,
to give his people knowledge of salvation
by the forgiveness of their sins.
In the tender compassion of our God
the dawn from on high shall break upon us,
to shine on those who dwell in darkness and the shadow of death,
and to guide our feet into the way of peace.
Glory be to the Father, and to the Son,
and to the Holy Spirit:
as it was in the beginning, is now, and ever shall be,
world without end. Amen.
Leader: ✠ May the Lord bless us, protect us from all evil,
and bring us to everlasting life.
Together: Amen.
Hymn: “I Will Choose Christ” – Text: Tom Booth, b. 1961; Music: Tom Booth; keyboard acc. by Ed Bolduc, b. 1969. Text and music © 1991, Tom Booth. Published by spiritandsong.com®, a division of OCP. All rights reserved. Printed with permission granted by OneLicense.net #A-705112.
Excerpts from The Roman Missal © 2010, ICEL; excerpts from The Liturgy of the Hours © 1994, ICEL. All rights reserved.
Lectionary for Mass for Use in the Dioceses of the United States, second typical edition, Copyright © 2001, 1998, 1997, 1986, 1970 Confraternity of Christian Doctrine; Psalm refrain © 1968, 1981, 1997, International Committee on English in the Liturgy, Inc. All rights reserved. Neither this work nor any part of it may be reproduced, distributed, performed or displayed in any medium, including electronic or digital, without permission in writing from the copyright owner.


db_checkpoint [-1v] [-h home] [-k kbytes] [-L file] [-p min]
The db_checkpoint utility is a daemon process that monitors the database log and periodically calls txn_checkpoint to checkpoint it.
The options are as follows:
db_checkpoint: ### Wed Jun 15 01:23:45 EDT 1995
This file will be removed if the db_checkpoint utility exits gracefully.
At least one of the -1, -k and -p options must be specified.
The db_checkpoint utility attaches to one or more of the Berkeley DB shared memory regions. In order to avoid region corruption, it should always be given the chance to detach and exit gracefully. To cause db_checkpoint to clean up after itself and exit, send it an interrupt signal (SIGINT).
The db_checkpoint utility does not attempt to create the Berkeley DB shared memory regions if they do not already exist. The application which creates the region should be started first, and then, once the region is created, the db_checkpoint utility should be started.
The txn_checkpoint function is the underlying function used by the db_checkpoint utility. See the db_checkpoint utility source code for an example of using txn_checkpoint in a POSIX 1003.1 environment.
The db_checkpoint utility exits 0 on success, and >0 if an error occurs.


For 50 years, WWF has been protecting the future of nature. The world's leading conservation organization, WWF works in 100 countries and is supported by 1.2 million members in the United States and close to 5 million globally.
My first paragraph.
My first paragraph
with a break in it.


VASEr functions have no state, all stateful operations are put into the class renderer
. Thus you need to pass many parameters on each VASEr call, and these parameters are stored in structures. Normally if you do not know what options to set, just put 0
at the parameter polyline_opt*
. VASEr ensures empty parameter polyline_opt* options=0
and empty structure polyline_opt opt={0};
are default options and will not cause any error.
class rendererclass renderer { public: static void init(); static void before(); static void after(); static Image get_image(); }; As stated, all stateful operations are put into the class
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struct gradientstruct gradient_stop { double t; //position char type; //GS_xx union { Color color; double weight; }; }; const char GS_none =0; const char GS_rgba =1; const char GS_rgb =2; //rgb only const char GS_alpha =3; const char GS_weight=4; struct gradient { gradient_stop* stops; //array must be sorted in ascending order of t int length; //number of stops char unit; //use_GD_XX }; const char GD_ratio =0; //default const char GD_length=1; A gradient is an array of stops that specify properties at different positions in a one dimensional space. The properties can be color or weight, and the space parameter (unit) can be the length along a curve. The default parameter is ratio, that frankly speaking t=0.5 is midway along the curve. Be aware of the union, which means each stop can only hold one type of information, which must be explicitly specified by Stops of each type must be sorted in ascending order of t, but it is okay for say stops of weight come after stops of color. If there is only one stop of a type, then it is considered constant everywhere. If there is no stop of a type, then no change will be applied. For a concrete example, look at polybezier. | ||||||||||||||||||||||||||
struct Imagestruct Image { unsigned char* buf; //must **free** buffer manually short width; short height; };the unsigned char* buf must be freed manually as in malloc/free
| ||||||||||||||||||||||||||
struct tessellator_optstruct tessellator_opt { //set the whole structure to 0 will give default options bool triangulation; char parts; //use TS_xx bool tessellate_only; void* holder; //used as (VASErin::vertex_array_holder*) if tessellate_only is true }; //for tessellator_opt.parts const char TS_core_fade =0; //default const char TS_core =1; const char TS_outer_fade=2; const char TS_inner_fade=3; a | ||||||||||||||||||||||||||
polyline()void polyline(
Vec2* P, //array of point of a polyline
Color* C, //array of color
double* weight,//array of weight
int length, //length of the array
polyline_opt* options) //extra options
All arrays must be of the same size otherwise memory error will occur.There are 3 other variants if you do not need varying color or weight.
//constant color and weight
void polyline( const Vec2*, Color, double W, int length, const polyline_opt*);
//constant weight
void polyline( const Vec2*, const Color*, double W, int length, const polyline_opt*);
//constant color
void polyline( const Vec2*, Color, const double* W, int length, const polyline_opt*);
Options
struct polyline_opt
{
//set the whole structure to 0 will give default options
const tessellator_opt* tess;
char joint; //use PLJ_xx
char cap; //use PLC_xx
bool feather;
double feathering;
bool no_feather_at_cap;
bool no_feather_at_core;
};
//for polyline_opt.joint
const char PLJ_miter =0; //default
const char PLJ_bevel =1;
const char PLJ_round =2;
//for polyline_opt.cap
const char PLC_butt =0; //default
const char PLC_round =1;
const char PLC_square=2;
const char PLC_rect =3;
const char PLC_both =0; //default
const char PLC_first =10;
const char PLC_last =20;
const char PLC_none =30;
Usage
void sample_polyline1()
{
int size_of_AP=4;
Vec2 AP[size_of_AP];
AP[0].x=200; AP[0].y=50;
AP[1].x=100; AP[1].y=150;
AP[2].x=300; AP[2].y=150;
AP[3].x=200; AP[3].y=250;
Color col={ 0.5, 0.8, 1.0, 1};
polyline( AP, col, 8.0, size_of_AP, 0);
}
void sample_polyline2()
{
int size_of_AP=4;
Vec2 AP[size_of_AP];
AP[0].x=200; AP[0].y=50;
AP[1].x=100; AP[1].y=150;
AP[2].x=300; AP[2].y=150;
AP[3].x=200; AP[3].y=250;
Color AC[size_of_AP];
{ Color col={1 , 0, 0, 0.5}; AC[0]=col;}
{ Color col={.8,.8, 0, 0.5}; AC[1]=col;}
{ Color col={ 0, 0, 1, 0.5}; AC[2]=col;}
{ Color col={1 , 0, 0, 0.5}; AC[3]=col;}
double AW[size_of_AP];
AW[0] = 1.0;
AW[1] = 15.0;
AW[2] = 15.0;
AW[3] = 1.0;
polyline_opt opt={0};
polyline( AP, AC, AW, size_of_AP, &opt);
}
example program is at samples/samples.cpp under VASEr package.
NotesVarying color is stable but will cause overdraw at degenerated cases.Varying weight is stable most of the time but will go wild when extreme case occur. This version of polyline is improved to render dense vertices for curves. Further workAfter solving the above mentioned problems, can provide the choice between color blending profiles, possibly 'hard' and 'soft'. and besides gradient, provide texture along path. | ||||||||||||||||||||||||||
segment()
void segment( Vec2, Vec2,
Color, Color,
double, double, //weight
const polyline_opt*); //same use as polyline
void segment( Vec2, Vec2, Color, double W, const polyline_opt*); //constant color and weight
void segment( Vec2, Vec2, Color, Color, double W, const polyline_opt*); //constant weight
void segment( Vec2, Vec2, Color, double W1, double W2, const polyline_opt*); //const color
segment() is merely a wrapper over polyline() . There are also three variants.
Usage
void sample_spectrum()
{
for ( int i=0; i < 20; i++)
{
Vec2 P1 = { 5+29.7*i, 187};
Vec2 P2 = { 35+29.7*i, 8};
Color C1 = { 1.0,0.0,0.5, 1.0};
Color C2 = { 0.5,0.0,1.0, 1.0};
double W1= 0.3*(i+1);
double W2= W1;
segment(P1,P2, C1,C2, W1,W2, 0);
}
}
void sample_radial_spectrum()
{
for ( double ag=0, i=0; ag < 2*vaser_pi-0.1; ag+=vaser_pi/12, i+=1)
{
double r1 = 30.0;
double r2 = 90.0;
double tx2=r2*cos(ag);
double ty2=r2*sin(ag);
double tx1=r1*cos(ag);
double ty1=r1*sin(ag);
double Ox = 120;
double Oy = 194+97;
Vec2 P1 = { Ox+tx1,Oy-ty1};
Vec2 P2 = { Ox+tx2,Oy-ty2};
Color C1 = { 1.0,0.0,0.5, 1.0};
Color C2 = { 0.5,0.0,1.0, 1.0};
double W1= 0.3*(i+1);
double W2= W1;
segment(P1,P2, C1,C2, W1,W2, 0);
}
}
example program is at samples/samples.cpp< under VASEr package. In the below spectrums, each segment is 0.3 pixel heavier than the previous segment, demonstrating sub pixel accuracy and anti aliasing quality of VASEr.
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polybezier()void polybezier( const Vec2* P, const gradient*, int length, const polybezier_opt*); void polybezier( const Vec2* P, Color, double W, int length, const polybezier_opt*);The P is an array of control points. The evaluated curve is continuous, with adjacent bezier sections sharing a common control point. so n cubic bezier sections require 3n+1 points. A pointer to gradient or constant color and width can be passed in.
Optionsstruct polybezier_opt { //set the whole structure to 0 will give default options const polyline_opt* poly; };Under the hood, bezier curves will be evaluated, and the set of points will be sent to polyline() for rendering, using const polyline_opt* poly as options if specified.
Usagevoid sample_bezier1() { int size_of_AP=4; Vec2 AP[size_of_AP]; AP[0].x=200; AP[0].y=50; AP[1].x=100; AP[1].y=150; AP[2].x=300; AP[2].y=150; AP[3].x=200; AP[3].y=250; Color col={ 0.5, 0.8, 1.0, 1}; polybezier( AP, col, 12.0, size_of_AP, 0); } void sample_bezier2() { int size_of_AP=4; Vec2 AP[size_of_AP]; AP[0].x=200; AP[0].y=50; AP[1].x=100; AP[1].y=150; AP[2].x=300; AP[2].y=150; AP[3].x=200; AP[3].y=250; Color AC[size_of_AP]; { Color col={1 , 0, 0, 0.5}; AC[0]=col;} { Color col={.8,.8, 0, 0.5}; AC[1]=col;} { Color col={ 0, 0, 1, 0.5}; AC[2]=col;} { Color col={1 , 0, 0, 0.5}; AC[3]=col;} double AW[size_of_AP]; AW[0] = 1.0; AW[1] = 15.0; AW[2] = 15.0; AW[3] = 1.0; gradient grad = {0}; gradient_stop stop[10] = {0}; grad.stops = stop; grad.length = 10; stop[0].t = 0.00; stop[0].type = GS_rgba; stop[0].color = AC[0]; stop[1].t = 0.50; stop[1].type = GS_rgba; stop[1].color = AC[2]; stop[2].t = 1.00; stop[2].type = GS_rgba; stop[2].color = AC[3]; stop[3].t = 0.00; stop[3].type = GS_weight; stop[3].weight = AW[0]; stop[4].t = 0.33; stop[4].type = GS_weight; stop[4].weight = AW[1]; stop[5].t = 0.66; stop[5].type = GS_weight; stop[5].weight = AW[2]; stop[6].t = 1.00; stop[6].type = GS_weight; stop[6].weight = AW[3]; polybezier( AP, &grad, size_of_AP, 0); }Again, example program is at samples/samples.cpp .
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Jonathan Crawford is a multi-instrumentalist, composer, sound designer and record producer based in San Francisco, CA.
As an artist, his goal is to combine his traditional, contemporary and experimental influences into music that explores exciting new territory from both a timbral and compositional point of view.
For his audience, he hopes to leave those who experience his records and live performances with a stronger sense of narrative and possiblity in their own lives.


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final class DataFrameStatFunctions extends AnyRef
Statistic functions for DataFrame
s.
- Annotations
- @Stable()
- Source
- DataFrameStatFunctions.scala
- Since
1.4.0
- Alphabetic
- By Inheritance
- DataFrameStatFunctions
- AnyRef
- Any
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Value Members
-
final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
##(): Int
- Definition Classes
- AnyRef → Any
-
final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
approxQuantile(cols: Array[String], probabilities: Array[Double], relativeError: Double): Array[Array[Double]]
Calculates the approximate quantiles of numerical columns of a DataFrame.
Calculates the approximate quantiles of numerical columns of a DataFrame.
- cols
the names of the numerical columns
- probabilities
a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the minimum, 0.5 is the median, 1 is the maximum.
- relativeError
The relative target precision to achieve (greater than or equal to 0). If set to zero, the exact quantiles are computed, which could be very expensive. Note that values greater than 1 are accepted but give the same result as 1.
- returns
the approximate quantiles at the given probabilities of each column
- Since
2.2.0
- Note
null and NaN values will be ignored in numerical columns before calculation. For columns only containing null or NaN values, an empty array is returned.
- See also
approxQuantile(col:Str* approxQuantile)
for detailed description.
-
def
approxQuantile(col: String, probabilities: Array[Double], relativeError: Double): Array[Double]
Calculates the approximate quantiles of a numerical column of a DataFrame.
Calculates the approximate quantiles of a numerical column of a DataFrame.
The result of this algorithm has the following deterministic bound: If the DataFrame has N elements and if we request the quantile at probability
p
up to errorerr
, then the algorithm will return a samplex
from the DataFrame so that the *exact* rank ofx
is close to (p * N). More precisely,floor((p - err) * N) <= rank(x) <= ceil((p + err) * N)
This method implements a variation of the Greenwald-Khanna algorithm (with some speed optimizations). The algorithm was first present in Space-efficient Online Computation of Quantile Summaries by Greenwald and Khanna.
- col
the name of the numerical column
- probabilities
a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the minimum, 0.5 is the median, 1 is the maximum.
- relativeError
The relative target precision to achieve (greater than or equal to 0). If set to zero, the exact quantiles are computed, which could be very expensive. Note that values greater than 1 are accepted but give the same result as 1.
- returns
the approximate quantiles at the given probabilities
- Since
2.0.0
- Note
null and NaN values will be removed from the numerical column before calculation. If the dataframe is empty or the column only contains null or NaN, an empty array is returned.
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
bloomFilter(col: Column, expectedNumItems: Long, numBits: Long): BloomFilter
Builds a Bloom filter over a specified column.
Builds a Bloom filter over a specified column.
- col
the column over which the filter is built
- expectedNumItems
expected number of items which will be put into the filter.
- numBits
expected number of bits of the filter.
- Since
2.0.0
-
def
bloomFilter(colName: String, expectedNumItems: Long, numBits: Long): BloomFilter
Builds a Bloom filter over a specified column.
Builds a Bloom filter over a specified column.
- colName
name of the column over which the filter is built
- expectedNumItems
expected number of items which will be put into the filter.
- numBits
expected number of bits of the filter.
- Since
2.0.0
-
def
bloomFilter(col: Column, expectedNumItems: Long, fpp: Double): BloomFilter
Builds a Bloom filter over a specified column.
Builds a Bloom filter over a specified column.
- col
the column over which the filter is built
- expectedNumItems
expected number of items which will be put into the filter.
- fpp
expected false positive probability of the filter.
- Since
2.0.0
-
def
bloomFilter(colName: String, expectedNumItems: Long, fpp: Double): BloomFilter
Builds a Bloom filter over a specified column.
Builds a Bloom filter over a specified column.
- colName
name of the column over which the filter is built
- expectedNumItems
expected number of items which will be put into the filter.
- fpp
expected false positive probability of the filter.
- Since
2.0.0
-
def
clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
-
def
corr(col1: String, col2: String): Double
Calculates the Pearson Correlation Coefficient of two columns of a DataFrame.
Calculates the Pearson Correlation Coefficient of two columns of a DataFrame.
- col1
the name of the column
- col2
the name of the column to calculate the correlation against
- returns
The Pearson Correlation Coefficient as a Double.
val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10)) .withColumn("rand2", rand(seed=27)) df.stat.corr("rand1", "rand2", "pearson") res1: Double = 0.613...
- Since
1.4.0
-
def
corr(col1: String, col2: String, method: String): Double
Calculates the correlation of two columns of a DataFrame.
Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in MLlib's Statistics.
- col1
the name of the column
- col2
the name of the column to calculate the correlation against
- returns
The Pearson Correlation Coefficient as a Double.
val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10)) .withColumn("rand2", rand(seed=27)) df.stat.corr("rand1", "rand2") res1: Double = 0.613...
- Since
1.4.0
-
def
countMinSketch(col: Column, eps: Double, confidence: Double, seed: Int): CountMinSketch
Builds a Count-min Sketch over a specified column.
Builds a Count-min Sketch over a specified column.
- col
the column over which the sketch is built
- eps
relative error of the sketch
- confidence
confidence of the sketch
- seed
random seed
- returns
a
CountMinSketch
over columncolName
- Since
2.0.0
-
def
countMinSketch(col: Column, depth: Int, width: Int, seed: Int): CountMinSketch
Builds a Count-min Sketch over a specified column.
Builds a Count-min Sketch over a specified column.
- col
the column over which the sketch is built
- depth
depth of the sketch
- width
width of the sketch
- seed
random seed
- returns
a
CountMinSketch
over columncolName
- Since
2.0.0
-
def
countMinSketch(colName: String, eps: Double, confidence: Double, seed: Int): CountMinSketch
Builds a Count-min Sketch over a specified column.
Builds a Count-min Sketch over a specified column.
- colName
name of the column over which the sketch is built
- eps
relative error of the sketch
- confidence
confidence of the sketch
- seed
random seed
- returns
a
CountMinSketch
over columncolName
- Since
2.0.0
-
def
countMinSketch(colName: String, depth: Int, width: Int, seed: Int): CountMinSketch
Builds a Count-min Sketch over a specified column.
Builds a Count-min Sketch over a specified column.
- colName
name of the column over which the sketch is built
- depth
depth of the sketch
- width
width of the sketch
- seed
random seed
- returns
a
CountMinSketch
over columncolName
- Since
2.0.0
-
def
cov(col1: String, col2: String): Double
Calculate the sample covariance of two numerical columns of a DataFrame.
Calculate the sample covariance of two numerical columns of a DataFrame.
- col1
the name of the first column
- col2
the name of the second column
- returns
the covariance of the two columns.
val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10)) .withColumn("rand2", rand(seed=27)) df.stat.cov("rand1", "rand2") res1: Double = 0.065...
- Since
1.4.0
-
def
crosstab(col1: String, col2: String): DataFrame
Computes a pair-wise frequency table of the given columns.
Computes a pair-wise frequency table of the given columns. Also known as a contingency table. The number of distinct values for each column should be less than 1e4. At most 1e6 non-zero pair frequencies will be returned. The first column of each row will be the distinct values of
col1
and the column names will be the distinct values ofcol2
. The name of the first column will becol1_col2
. Counts will be returned asLong
s. Pairs that have no occurrences will have zero as their counts. Null elements will be replaced by "null", and back ticks will be dropped from elements if they exist.- col1
The name of the first column. Distinct items will make the first item of each row.
- col2
The name of the second column. Distinct items will make the column names of the DataFrame.
- returns
A DataFrame containing for the contingency table.
val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), (3, 3))) .toDF("key", "value") val ct = df.stat.crosstab("key", "value") ct.show() +---------+---+---+---+ |key_value| 1| 2| 3| +---------+---+---+---+ | 2| 2| 0| 1| | 1| 1| 1| 0| | 3| 0| 1| 1| +---------+---+---+---+
- Since
1.4.0
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
-
def
freqItems(cols: Seq[String]): DataFrame
(Scala-specific) Finding frequent items for columns, possibly with false positives.
(Scala-specific) Finding frequent items for columns, possibly with false positives. Using the frequent element count algorithm described in here, proposed by Karp, Schenker, and Papadimitriou. Uses a
default
support of 1%.This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting
DataFrame
.- cols
the names of the columns to search frequent items in.
- returns
A Local DataFrame with the Array of frequent items for each column.
- Since
1.4.0
-
def
freqItems(cols: Seq[String], support: Double): DataFrame
(Scala-specific) Finding frequent items for columns, possibly with false positives.
(Scala-specific) Finding frequent items for columns, possibly with false positives. Using the frequent element count algorithm described in here, proposed by Karp, Schenker, and Papadimitriou.
This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting
DataFrame
.- cols
the names of the columns to search frequent items in.
- returns
A Local DataFrame with the Array of frequent items for each column.
val rows = Seq.tabulate(100) { i => if (i % 2 == 0) (1, -1.0) else (i, i * -1.0) } val df = spark.createDataFrame(rows).toDF("a", "b") // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns // "a" and "b" val freqSingles = df.stat.freqItems(Seq("a", "b"), 0.4) freqSingles.show() +-----------+-------------+ |a_freqItems| b_freqItems| +-----------+-------------+ | [1, 99]|[-1.0, -99.0]| +-----------+-------------+ // find the pair of items with a frequency greater than 0.1 in columns "a" and "b" val pairDf = df.select(struct("a", "b").as("a-b")) val freqPairs = pairDf.stat.freqItems(Seq("a-b"), 0.1) freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show() +----------+ | freq_ab| +----------+ | [1,-1.0]| | ... | +----------+
- Since
1.4.0
-
def
freqItems(cols: Array[String]): DataFrame
Finding frequent items for columns, possibly with false positives.
Finding frequent items for columns, possibly with false positives. Using the frequent element count algorithm described in here, proposed by Karp, Schenker, and Papadimitriou. Uses a
default
support of 1%.This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting
DataFrame
.- cols
the names of the columns to search frequent items in.
- returns
A Local DataFrame with the Array of frequent items for each column.
- Since
1.4.0
-
def
freqItems(cols: Array[String], support: Double): DataFrame
Finding frequent items for columns, possibly with false positives.
Finding frequent items for columns, possibly with false positives. Using the frequent element count algorithm described in here, proposed by Karp, Schenker, and Papadimitriou. The
support
should be greater than 1e-4.This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting
DataFrame
.- cols
the names of the columns to search frequent items in.
- support
The minimum frequency for an item to be considered
frequent
. Should be greater than 1e-4.- returns
A Local DataFrame with the Array of frequent items for each column.
val rows = Seq.tabulate(100) { i => if (i % 2 == 0) (1, -1.0) else (i, i * -1.0) } val df = spark.createDataFrame(rows).toDF("a", "b") // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns // "a" and "b" val freqSingles = df.stat.freqItems(Array("a", "b"), 0.4) freqSingles.show() +-----------+-------------+ |a_freqItems| b_freqItems| +-----------+-------------+ | [1, 99]|[-1.0, -99.0]| +-----------+-------------+ // find the pair of items with a frequency greater than 0.1 in columns "a" and "b" val pairDf = df.select(struct("a", "b").as("a-b")) val freqPairs = pairDf.stat.freqItems(Array("a-b"), 0.1) freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show() +----------+ | freq_ab| +----------+ | [1,-1.0]| | ... | +----------+
- Since
1.4.0
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
def
sampleBy[T](col: Column, fractions: Map[T, Double], seed: Long): DataFrame
(Java-specific) Returns a stratified sample without replacement based on the fraction given on each stratum.
(Java-specific) Returns a stratified sample without replacement based on the fraction given on each stratum.
- T
stratum type
- col
column that defines strata
- fractions
sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as zero.
- seed
random seed
- returns
a new
DataFrame
that represents the stratified sample
- Since
3.0.0
-
def
sampleBy[T](col: Column, fractions: Map[T, Double], seed: Long): DataFrame
Returns a stratified sample without replacement based on the fraction given on each stratum.
Returns a stratified sample without replacement based on the fraction given on each stratum.
- T
stratum type
- col
column that defines strata
- fractions
sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as zero.
- seed
random seed
- returns
a new
DataFrame
that represents the stratified sample The stratified sample can be performed over multiple columns:import org.apache.spark.sql.Row import org.apache.spark.sql.functions.struct val df = spark.createDataFrame(Seq(("Bob", 17), ("Alice", 10), ("Nico", 8), ("Bob", 17), ("Alice", 10))).toDF("name", "age") val fractions = Map(Row("Alice", 10) -> 0.3, Row("Nico", 8) -> 1.0) df.stat.sampleBy(struct($"name", $"age"), fractions, 36L).show() +-----+---+ | name|age| +-----+---+ | Nico| 8| |Alice| 10| +-----+---+
- Since
3.0.0
-
def
sampleBy[T](col: String, fractions: Map[T, Double], seed: Long): DataFrame
Returns a stratified sample without replacement based on the fraction given on each stratum.
Returns a stratified sample without replacement based on the fraction given on each stratum.
- T
stratum type
- col
column that defines strata
- fractions
sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as zero.
- seed
random seed
- returns
a new
DataFrame
that represents the stratified sample
- Since
1.5.0
-
def
sampleBy[T](col: String, fractions: Map[T, Double], seed: Long): DataFrame
Returns a stratified sample without replacement based on the fraction given on each stratum.
Returns a stratified sample without replacement based on the fraction given on each stratum.
- T
stratum type
- col
column that defines strata
- fractions
sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as zero.
- seed
random seed
- returns
a new
DataFrame
that represents the stratified sampleval df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), (3, 3))).toDF("key", "value") val fractions = Map(1 -> 1.0, 3 -> 0.5) df.stat.sampleBy("key", fractions, 36L).show() +---+-----+ |key|value| +---+-----+ | 1| 1| | 1| 2| | 3| 2| +---+-----+
- Since
1.5.0
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- AnyRef → Any
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()


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The class path is the path that the Java Runtime Environment (JRE) searches for classes and other resource files.
This chapter covers the following topics:
The class search path (class path) can be set using either the -classpath
option when calling a JDK tool (the preferred method) or by setting the CLASSPATH
environment variable. The -classpath
option is preferred because you can set it individually for each application without affecting other applications and without other applications modifying its value.
sdkTool -classpath classpath1:classpath2...
set CLASSPATH=classpath1;classpath2...
A command-line tool, such as java
, javac
, javadoc
, or apt
. For a listing, see JDK Tools and Utilities at
http://docs.oracle.com/javase/8/docs/technotes/tools/index.html
Class paths to the JAR, zip or class files. Each class path should end with a file name or directory depending on what you are setting the class path to, as follows:
For a JAR or zip file that contains class files, the class path ends with the name of the zip or JAR file.
For class files in an unnamed package, the class path ends with the directory that contains the class files.
For class files in a named package, the class path ends with the directory that contains the root package, which is the first package in the full package name.
Multiple path entries are separated by semicolons with no spaces around the equals sign (=) in Windows and colons in Oracle Solaris.
The default class path is the current directory. Setting the CLASSPATH
variable or using the -classpath
command-line option overrides that default, so if you want to include the current directory in the search path, then you must include a dot (.
) in the new settings.
Class path entries that are neither directories nor archives (.zip or JAR files) nor the asterisk (*
) wildcard character are ignored.
The class path tells the JDK tools and applications where to find third-party and user-defined classes that are not extensions or part of the Java platform. See The Extension Mechanism at
http://docs.oracle.com/javase/8/docs/technotes/guides/extensions/index.html
The class path needs to find any classes you have compiled with the javac
compiler. The default is the current directory to conveniently enable those classes to be found.
The JDK, the JVM and other JDK tools find classes by searching the Java platform (bootstrap) classes, any extension classes, and the class path, in that order. For details about the search strategy, see How Classes Are Found at
http://docs.oracle.com/javase/8/docs/technotes/tools/findingclasses.html
Class libraries for most applications use the extensions mechanism. You only need to set the class path when you want to load a class that is (a) not in the current directory or in any of its subdirectories, and (b) not in a location specified by the extensions mechanism.
If you upgrade from an earlier release of the JDK, then your startup settings might include CLASSPATH
settings that are no longer needed. You should remove any settings that are not application-specific, such as classes.zip
. Some third-party applications that use the Java Virtual Machine (JVM) can modify your CLASSPATH
environment variable to include the libraries they use. Such settings can remain.
You can change the class path by using the -classpath
or -cp
option of some Java commands when you call the JVM or other JDK tools or by using the CLASSPATH
environment variable. See JDK Commands Class Path Options. Using the -classpath
option is preferred over setting the CLASSPATH
environment variable because you can set it individually for each application without affecting other applications and without other applications modifying its value. See CLASSPATH Environment Variable.
Classes can be stored in directories (folders) or in archive files. The Java platform classes are stored in rt.jar. For more details about archives and information about how the class path works, see Class Path and Package Names.
Note: Some earlier releases of the JDK had a <jdk-dir>/classes
entry in the default class path. That directory exists for use by the JDK software and should not be used for application classes. Application classes should be placed in a directory outside of the JDK directory hierarchy. That way, installing a new JDK does not force you to reinstall application classes. For compatibility with earlier releases, applications that use the <jdk-dir>/classes
directory as a class library run in the current release, but there is no guarantee that they will run in future releases.
The following commands have a -classpath
option that replaces the path or paths specified by the CLASSPATH
environment variable while the tool runs: java
, jdb
, javac
, javah
and jdeps
.
The -classpath
option is the recommended option for changing class path settings, because each application can have the class path it needs without interfering with any other application.The java
command also has a -cp
option that is an abbreviation for -classpath
.
For very special cases, both the java
and javac
commands have options that let you change the path they use to find their own class libraries. Most users will never need to use those options.
As explained in JDK Commands Class Path Options, the -classpath
command-line option is preferred over the CLASSPATH
environment variable. However, if you decide to use the CLASSPATH
environment variable, this section explains how to set and clear it.
The CLASSPATH
environment variable is modified with the set command. The format is:
set CLASSPATH=path1;path2 ...
The paths should begin with the letter specifying the drive, for example, C:\. That way, the classes will still be found if you happen to switch to a different drive. If the path entries start with backslash (\) and you are on drive D:, for example, then the classes will be expected on D:, rather than C:.
If your CLASSPATH
environment variable was set to a value that is not correct, or if your startup file or script is setting an incorrect path, then you can unset CLASSPATH
with:
set CLASSPATH=
This command unsets CLASSPATH
for the current command prompt window only. You should also delete or modify your startup settings to ensure that you have the correct CLASSPATH
settings in future sessions.
If the CLASSPATH variable is set at system startup, then the place to look for it depends on your operating system:
Windows 95 and 98: Examine autoexec.bat
for the set command.
Other (Windows NT, Windows 2000, ...): The CLASSPATH environment variable can be set with the System utility in the Control Panel.
If the CLASSPATH variable is set at system startup, then the place to look for it depends on the shell you are running:
The csh
, tcsh
shells
: Examine your .cshrc
file for the setenv
command.
The sh
, ksh
shells
: Examine your .profile
file for the export
command.
Class path entries can contain the base name wildcard character (*), which is considered equivalent to specifying a list of all of the files in the directory with the extension .jar
or .JAR
. For example, the class path entry mydir/*
specifies all JAR files in the directory named mydir
. A class path entry consisting of * expands to a list of all the jar files in the current directory. Files are considered regardless of whether they are hidden (have names beginning with '.').
A class path entry that contains an asterisk (*) does not match class files. To match both classes and JAR files in a single directory mydir
, use either mydir:mydir/*
or mydir/*:mydir
. The order chosen determines whether the classes and resources in mydir
are loaded before JAR files in mydir
or vice versa.
Subdirectories are not searched recursively. For example, mydir/*
searches for JAR files only in mydir
, not in mydir/subdir1
, mydir/subdir2
, and so on.
The order in which the JAR files in a directory are enumerated in the expanded class path is not specified and may vary from platform to platform and even from moment to moment on the same machine. A well-constructed application should not depend upon any particular order. If a specific order is required, then the JAR files can be enumerated explicitly in the class path.
Expansion of wild cards is done early, before the invocation of a program's main method, rather than late, during the class-loading process. Each element of the input class path that contains a wildcard is replaced by the (possibly empty) sequence of elements generated by enumerating the JAR files in the named directory. For example, if the directory mydir
contains a.jar, b.jar, and c.jar, then the class path mydir/*
is expanded into mydir/a.jar:mydir/b.jar:mydir/c.jar
, and that string would be the value of the system property java.class.path.
The CLASSPATH
environment variable is not treated any differently from the -classpath
or -cp
options. Wild cards are honored in all of these cases. However, class path wild cards are not honored in the Class-Path jar-manifest header.
Java classes are organized into packages that are mapped to directories in the file system. But, unlike the file system, whenever you specify a package name, you specify the whole package name and never part of it. For example, the package name for java.awt.Button
is always specified as java.awt
.
For example, suppose you want the Java JRE to find a class named Cool.class in the package utility.myapp. If the path to that directory is C:\java\MyClasses\utility\myapp
, then you would set the class path so that it contains C:\java\MyClasses
. To run that application, you could use the following java
command:
java -classpath C:\java\MyClasses utility.myapp.Cool
The entire package name is specified in the command. It is not possible, for example, to set the class path so it contains C:\java\MyClasses\
utility and use the command java myapp.Cool. The class would not be found.
You might wonder what defines the package name for a class. The answer is that the package name is part of the class and cannot be modified, except by recompiling the class.
An interesting consequence of the package specification mechanism is that files that are part of the same package can exist in different directories. The package name is the same for each class, but the path to each file might start from a different directory in the class path.
When classes are stored in a directory (folder), such as c:\java\MyClasses\utility\myapp
, then the class path entry points to the directory that contains the first element of the package name (in this case, C:\java\MyClasses
, because the package name is utility.myapp).
When classes are stored in an archive file (a zi
p
or JAR
file) the class path entry is the path to and including the zip or JAR file. For example, the command to use a class library that is in a JAR
file as follows:
java -classpath C:\java\MyClasses\myclasses.jar utility.myapp.Cool
To find class files in the directory C:\java\MyClasses
and classes in C:\java\OtherClasses
, you would set the class path to the following. Note that the two paths are separated by a semicolon.
java -classpath C:\java\MyClasses;C:\java\OtherClasses ...
The order in which you specify multiple class path entries is important. The Java interpreter will look for classes in the directories in the order they appear in the class path variable. In the previous example, the Java interpreter will first look for a needed class in the directory C:\java\MyClasses
. Only when it does not find a class with the proper name in that directory will the interpreter look in the C:\java\OtherClasses
directory.


RT @davystevenson: Slides for my RubyConf talk Benchmarking Ruby are up!
✨🌟✨
https://speakerdeck.com/davystevenson/benchmarking-ruby
11/20/2014, 1:21:04 PM
Favs: 0
Retweets: 0
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RT @davystevenson: Slides for my RubyConf talk Benchmarking Ruby are up!
✨🌟✨
https://speakerdeck.com/davystevenson/benchmarking-ruby
11/20/2014, 1:21:04 PM
Favs: 0
Retweets: 0
link


Iraqi officials: 1 protester killed in renewed clashes
by QASSIM ABDUL-ZAHRABAGHDAD (AP) — Iraqi medical and security officials say one protester was killed and over 40 were wounded in renewed clashes overnight in central Baghdad.
Protesters have called for large protests to take place on Friday as Iraqi authorities continue to clamp down on the anti-government demonstrations.
The officials say the clashes happened in the Khilani area, not far from the epicenter of the protest movement in Tahrir Square, hours after demonstrators celebrated Iraq’s 2-1 World Cup qualifier win over Iran.
The officials spoke on condition of anonymity under regulations.
Amnesty International has said that security forces in Baghdad have fired tear gas grenades directly into the crowd. But Defense Minister Najah al-Shammari said Thursday an investigation has confirmed the tear gas cannisters responsible for protester deaths were not purchased by the government.
QASSIM ABDUL-ZAHRA


THE LITTLE PRINCE
“And now here is my secret, a very simple secret: It is only with the heart that one can see rightly; what is essential is invisible to the eye.”
― Antoine de Saint-Exupéry, The Little Prince
“It is much more difficult to judge oneself than to judge others. If you succeed in judging yourself rightly, then you are indeed a man of true wisdom.
― Antoine de Saint-Exupéry, The Little Prince


Financial Analysis
Sean Bird and 4 connections have given endorsements for this skill
Financial Reporting
Sean Bird and 3 connections have given endorsements for this skill
Microsoft Excel
Sean Bird and 2 connections have given endorsements for this skill
Financial Accounting
Forecasting
Management Accounting
Internal Controls
Cash Flow
Account Reconciliation
Internal Audit
Accounts Payable
Financial Statements
Tax
Accounting
Advertising
Consolidation
Microsoft Office
People Management
People Skills


00001 /********************************************************************** 00002 * GBDI Arboretum - Copyright (c) 2002-2004 GBDI-ICMC-USP 00003 * 00004 * Homepage: http://gbdi.icmc.usp.br/arboretum 00005 **********************************************************************/ 00006 /* ==================================================================== 00007 * The GBDI-ICMC-USP Software License Version 1.0 00008 * 00009 * Copyright (c) 2004 Grupo de Bases de Dados e Imagens, Instituto de 00010 * Ciências Matemáticas e de Computação, University of São Paulo - 00011 * Brazil (the Databases and Image Group - Intitute of Matematical and 00012 * Computer Sciences). All rights reserved. 00013 * 00014 * Redistribution and use in source and binary forms, with or without 00015 * modification, are permitted provided that the following conditions 00016 * are met: 00017 * 00018 * 1. Redistributions of source code must retain the above copyright 00019 * notice, this list of conditions and the following disclaimer. 00020 * 00021 * 2. Redistributions in binary form must reproduce the above copyright 00022 * notice, this list of conditions and the following disclaimer in 00023 * the documentation and/or other materials provided with the 00024 * distribution. 00025 * 00026 * 3. The end-user documentation included with the redistribution, 00027 * if any, must include the following acknowledgment: 00028 * "This product includes software developed by Grupo de Bases 00029 * de Dados e Imagens, Instituto de Ciências Matemáticas e de 00030 * Computação, University of São Paulo - Brazil (the Databases 00031 * and Image Group - Intitute of Matematical and Computer 00032 * Sciences)" 00033 * 00034 * Alternately, this acknowledgment may appear in the software itself, 00035 * if and wherever such third-party acknowledgments normally appear. 00036 * 00037 * 4. The names of the research group, institute, university, authors 00038 * and collaborators must not be used to endorse or promote products 00039 * derived from this software without prior written permission. 00040 * 00041 * 5. The names of products derived from this software may not contain 00042 * the name of research group, institute or university, without prior 00043 * written permission of the authors of this software. 00044 * 00045 * THIS SOFTWARE IS PROVIDED ``AS IS'' AND ANY EXPRESSED OR IMPLIED 00046 * WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES 00047 * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 00048 * DISCLAIMED. IN NO EVENT SHALL THE AUTHORS OF THIS SOFTWARE OR 00049 * ITS CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 00050 * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT 00051 * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF 00052 * USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND 00053 * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, 00054 * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT 00055 * OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF 00056 * SUCH DAMAGE. 00057 * 00058 * ==================================================================== 00059 * http://gbdi.icmc.usp.br/ 00060 */ 00074 00116 00127 00131 00136 00141 00149 00154 00161 00176 00198 00207 00216 00226 00242 00260 00270 00280 00290 00310 00315 00327 00338 00348 00353 00364 00369 00374 00379 00384 00391 00396 00403 00412 00418 00425 00430 00435 00461 00468 00472 00477 00482 00487 00492 00497 00511 00516 00526 00542 00560 00578 00596 00614 00642 00647 00678 00683 00688 00693 00700 00707 00712 00717 00722 00727 00733 00759 00769 00777 00787 00792 00804 00816 00827 00843 00856 00869 00883 00896 00909 00922 00930 00946 00962 00979 00996 01014 01019 01026 01051 01058 01066 01070 01075 01080 01085 01093 01100 01106 01111 01116 01121 01126 01131 01141 01147


switch
statements over enumerated types that are not exhaustive.
Example:
enum AlphaBetaGamma {
A, B, C;
void x(AlphaBetaGamma e) {
switch (e) {
}
}
}
After the quick-fix is applied:
enum AlphaBetaGamma {
A, B, C;
void x(AlphaBetaGamma e) {
switch (e) {
case A -> {}
case B -> {}
case C -> {}
}
}
}
Use the Ignore switch statements with a default branch option to ignore switch
statements that have a default
branch.


Hay Points
问题描述 :
Each employee of a bureaucracy has a job description – a few paragraphs that describe the responsibilities of the job. The employee’s job description, combined with other factors, such as seniority, is used to determine his or her salary.
The Hay Point system frees the Human Resources department from having to make an intelligent judgement as to the value of the employee; the job description is merely scanned for words and phrases that indicate responsibility. In particular, job descriptions that indicate control over a large budget or management over a large number of people yield high Hay Point scores.
The Hay Point system frees the Human Resources department from having to make an intelligent judgement as to the value of the employee; the job description is merely scanned for words and phrases that indicate responsibility. In particular, job descriptions that indicate control over a large budget or management over a large number of people yield high Hay Point scores.
You are to implement a simplified Hay Point system. You will be given a Hay Point dictionary and a number of job descriptions. For each job description you are to compute the salary associated with the job, according to the system.
输入:
The first line of input contains 2 positive integers: m <= 1000, the number of words in the Hay Point dictionary, and n <= 100, the number of job descriptions. m lines follow; each contains a word (a string of up to 16 lower-case letters) and a dollar value (a integer number between 0 and 1,000,000). Following the dictionary are the n job descriptions. Each job description consists of one or more lines of text; for your convenience the text has been converted to lower case and has no characters other than letters, numbers, and spaces. Each job description is terminated by a line containing a period.
输出:
For each job description, output the corresponding salary computed as the sum of the Hay Point values for all words that appear in the description. Words that do not appear in the dictionary have a value of 0.
样例输入:
7 2 administer 100000 spending 200000 manage 50000 responsibility 25000 expertise 100 skill 50 money 75000 the incumbent will administer the spending of kindergarden milk money and exercise responsibility for making change he or she will share responsibility for the task of managing the money with the assistant whose skill and expertise shall ensure the successful spending exercise . this individual must have the skill to perform a heart transplant and expertise in rocket science .
样例输出:
700150 150
解题代码:
import java.util.*; public class Main { public static void main(String[] args) { Scanner cin = new Scanner(System.in); String[] fLine = cin.nextLine().split(" "); int kwNum = Integer.valueOf(fLine[0]).intValue(); int descNum = Integer.valueOf(fLine[1]).intValue(); HashMap hm = new HashMap(); for(int i = 0; i < kwNum; i++) { String[] str = cin.nextLine().split(" "); hm.put(str[0], str[1]); } for(int i = 0; i < descNum; i++) { StringBuffer sb = new StringBuffer(); String job = null; int salary = 0; while(true) { job = cin.nextLine(); if(job.equals(".")) break; else sb.append(job + " "); } job = sb.toString().trim(); if(job.length() == 0) { System.out.println("0"); continue; } int eIndex = 0; String word = null; while(true) { eIndex = job.indexOf(" "); if(eIndex == -1 || eIndex == 0) { if(hm.containsKey(job)) salary += Integer.valueOf((String)hm.get(job)); break; } word = job.substring(0, eIndex); job = job.substring(eIndex + 1).trim(); if(hm.containsKey(word)) salary += Integer.valueOf((String)hm.get(word)); } System.out.println(salary); } } }
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http://beeder.me 这个网站上有很多poj和hihoCoder的题解，质量不错，欢迎转载。
第一题选错了，答案是A
这个还不错
很好。代码比书上的简练！
太好了，收藏
约瑟夫也用说这么长……很成熟的一个问题了，分治的方法解起来o(n)就可以了，有兴趣可以看看具体数学的第一章，关于约瑟夫问题推导出了一系列的结论，很漂亮
代码不错，赞一个！
想问下computeLSPArray那里，if(len!=0) len=lps[len-1]是什么意思
这个后缀数组不是常用的那个.
太变态了。我用hash居然超时了。这样break掉居然都行啊。。。
多谢支持！
This write-up presents the gentle in which we can notice the fact. this is extremely wonderful one and gives in depth info.
18题的C也是错误的


--- title: "Transaction malleability" active: false banner: "" date: 2014-02-11 ---
Many identical transactions with different transaction IDs are being relayed on the network from an unknown source. As a result, a certain number of services like Bitcoin exchanges and wallets have been found to handle these transactions improperly and are experiencing temporary issues with their internal accounting systems. Transaction malleability is a known issue and many services are mostly unaffected.
Bitcoin developers are collaborating with known affected exchanges to fix their internal systems and help resuming withdrawals as soon as possible. Some pool operators have been reported to work on blocking duplicate transactions to help mitigate the problem. Emergency fixes to the Bitcoin protocol are not required and future improvements are already being discussed.
No action is required to protect your bitcoins. Always remember to wait for one confirmation or more before spending your bitcoins. You should monitor your own exchange's website to be informed about their service status. Temporary confusion, delays and price volatility can be expected.


Uses web service provided by Faculty of Computer Science, University of Indonesia.
Try it: http://fws.cs.ui.ac.id/ StopwordRemoverSampleClient/index.jsp
This task includes case folding and remove non-alphanumeric characters.
Be warned, "tidak" (not) is also removed. Depending on what you are going to do next, removing this word may affect the result
Pak kepala desa tidak tahu bahwa 3 pencuri di rumah itu adalah teman lamanya!
pak kepala desa tahu 3 pencuri rumah teman


Bitcoin Price Analysis: Steady But Fragile Weekend – Can BTC Recover Above $7000?
by Yuval GovThe recent week was not the best for Bitcoin’s price, which lost almost 20% in just seven days.
The bad thing is that it’s still hard to see the end of those severe declines. However, if you look for a positive sign, you can find it in the fact that yesterday’s daily candle finished green, for the first time following five ugly red candles.
Looking at the following daily chart, we can see a secure area of support around $6800 – $7000. After dropping to those levels during last Friday, Bitcoin quickly corrected back above $7000, to where it’s currently trading now.
Another thing to keep in mind is the CME Futures trading periods. It appears that Bitcoin respects the price gaps created at the times when the CME is off (during weekends). Hence, there is also a high chance that the price action calmed a bit and will continue its volatility once CME goes back again on Monday morning (US time zone).
Total Market Cap: $199.3 billion
Bitcoin Market Cap: $131.6 billion
BTC Dominance Index: 66.1%
*Data by CoinGecko
Key Levels to Watch
– Support/Resistance: As mentioned above, the first level of support is $7000 – $7100. In case of a break-down, we would expect Bitcoin to reach $6700 – $6800 quickly, which was the low of Friday and the lowest price Bitcoin had seen since May 17.
Further below is $6500 (weak support), before $6200 and $6000. The last was served as legendary long-term support throughout the bearish year of 2018.
From above, Bitcoin will look to break the $7300 – $7400 resistance, before $7700 (Bitcoin actually tested the last following the Friday price drop).
A real change of momentum is possible only when breaking back above $8000, which now seems far from reality.
– The RSI Indicator: The RSI, the momentum indicator, tells the same story. As of now, the 25 crucial support level (which is the low from Friday) is still close to being tested once again.
Stochastic RSI oscillator is pointing on oversold on the 1-day chart (for the past two weeks already). This can lead to a possible positive correction in the case of a cross-over.
– Trading volume: Friday ended up with the highest volume since October 26, the day when Bitcoin saw the tremendous 42% price gains. Yesterday, as expected, didn’t carry a high amount of volume.
BTC/USD BitStamp 4-Hour Chart
BTC/USD BitStamp 1-Day Chart
Click here to start trading on BitMEX exchange and receive 10% discount on fees for 6 months.
Disclaimer: Information found on CryptoPotato is those of writers quoted. It does not represent the opinions of CryptoPotato on whether to buy, sell, or hold any investments. You are advised to conduct your own research before making any investment decisions. Use provided information at your own risk. See Disclaimer for more information.
Cryptocurrency charts by TradingView.
Technical analysis tools by Coinigy.


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Deploying Application Templates¶
A template describes a set of objects that can be parameterised and processed to produce a list of objects for creation by OpenShift Container Platform. A template can be processed to create anything you have permission to create within a project.
You can create template objects form the CLI or upload templates to a project or the global template library. Here we discuss the latter.
This is not a description of templates, it is a description of how the orchestrator allows you to to defined a set of templates for deployment to your cluster.
Templates for OKD 3.11 are well described on the RedHat site.
Orchestrator template file location¶
The orchestrator looks for template files in the templates
sub-directory of your deployment so you need to place all the
templates you wish to deploy in that directory.
Orchestrator template configuration¶
To successfully deploy templates you will need to enable the
template service broker by adding it to the okd.enable
list:
okd:
[...]
enable:
- tsb
You then need to declare the template destination project using the
template
configuration block:
okd:
[...]
template:
namespace: openshift
The namespace must be a namespace (project) that exists in your OKD
deployment. openshift
is a global/default project often used to house
application templates.
Templates are deployed by the Ansible playbook
ansible/post-okd/templates.yaml
. It inspects the selected directory,
expecting every file in it to be an OpenShift template for your cluster.


Welcome to select box paradise!
Choose String Demo John Mary Peter
How many? One Two Three Four
Are you in? Bring it on! No way man!
List options provided by backend
Map options provided by backend BOOOOM!
This is regular input field paradise!
Input your age
Are you in?
When is it going down? yyyy-MM-dd
Now let's go down to radio button hell.
Mary Jonny Boy Peter Meter
Textarea Galore!


{% extends 'base.html' %} {% block title %}Registration | {{ block.super }}{% endblock %} {% block content %} {% include 'includes/notice_box.html' with notice_message='Create new user account'%}


Easy Vote for everyone
Politico enables citizens to give their mandate to politicians running for different government offices while building trust in the process through transparency.
SignUp & VotePolitico enables citizens to give their mandate to politicians running for different government offices while building trust in the process through transparency.
SignUp & Vote


{
"name": "HackerWeb",
"short_name": "HackerWeb",
"start_url": ".",
"display": "standalone",
"background_color": "#fff",
"description": "A simply readable Hacker News app.",
"icons": [{
"src": "images/touch/homescreen48.png",
"sizes": "48x48",
"type": "image/png"
}, …],
…
}
https://developer.mozilla.org/en-US/docs/Web/Manifest
sw-precache
for the application shell.sw-toolbox
for runtime caching.angular/app-shell to show/hide certain elements in a pre-rendered component.
angular/universal to render Angular on the server.
--mobile
flag disabled with the migration to webpack
Angular Mobile Toolkit guides are outdated: 1.0.0‑beta.4
#108,
#109


KineticGold
All members who help promote
KineticGold are eligible to benefit from
KineticGold's Profit Sharing Program.
We call it:
Mining


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