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t push origin masterMerge branch 'JusethAg-jusethag-rename-inconsiste…
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t2013anurag committed Oct 18, 2017
2 parents 2cd9f20 + a412dd9 commit 5aee215
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4 changes: 4 additions & 0 deletions applet_in _java/java/README.md
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Applet Programming with Animation

When You run this code:
on mouse click car(drawn on applet) will start moving (Animation in applet)
167 changes: 167 additions & 0 deletions applet_in _java/java/car.java
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import java.applet.*;
import java.awt.*;
import java.awt.event.*;
public class car extends Applet implements Runnable,MouseListener


{
int i=0,k=0,z=0;
public void paint(Graphics g)
{
int x[]={200+i,300+i,400+i,500+i,500+i,0+i,0+i,100+i};
int y[]={200,200,300,300,400,400,300,300};
g.setColor(Color.RED);
g.fillPolygon(x,y,8);
g.setColor(Color.BLUE);
g.drawPolygon(x,y,8);
g.setColor(Color.WHITE);
g.fillOval(80+i, 350, 80, 80);
g.fillOval(320+i, 350, 80, 80);
g.setColor(Color.GREEN);
g.drawOval(80+i, 350, 80, 80);
g.drawOval(320+i, 350, 80, 80);
int x1[]={200+i,300+i,380+i,120+i};
int y1[]={220,220,300,300};

g.setColor(Color.DARK_GRAY);
g.fillPolygon(x1,y1,4);
g.setColor(Color.MAGENTA);
g.drawPolygon(x1,y1,4);

}
void update()
{

if(z%2!=0)
{
i=(i+1)%getWidth();
}
if(z%2==0)
{

if(i==0)
{
i=getWidth();
}
i=(i-1);

}

}

Thread t;
Thread t1;
public void init()
{
t=new Thread(this);
//t.setDaemon(true);
t1=new Thread(this);
//t1.setDaemon(true);
addMouseListener(this);


}
public void run()
{
if(z%2==0)
{
while(true)
{
update();
repaint();
try
{
Thread.sleep(10);
}
catch(Exception e )
{

}

}
}

if(z%2!=0)
{
while(true)
{
update();
repaint();
try
{
Thread.sleep(10);
}
catch(Exception e )
{

}
}

}

}

public void mouseClicked(MouseEvent e)
{

//if(k>0)
//{
//t.resume();

// }
System.out.println("kkk = "+e);
int x=e.getX();
int y=e.getY();

z++;
while(x>0&&x<getWidth())
{

if(z%2!=0)
{
System.out.println(x);
t.start();
}
if(z%2==0)
{
System.out.println(x);
t1.start();
}

}



}



@Override
public void mouseEntered(MouseEvent arg0) {
// TODO Auto-generated method stub

}

@Override
public void mouseExited(MouseEvent arg0) {
// TODO Auto-generated method stub

}

@Override
public void mousePressed(MouseEvent arg0)
{


}
@Override
public void mouseReleased(MouseEvent arg0)
{

}





}
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20 changes: 0 additions & 20 deletions factorial/factorial.go

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29 changes: 0 additions & 29 deletions factorial/python/factorial.py

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100 changes: 100 additions & 0 deletions gradient-escent/python/data1.csv
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32.502345269453031,31.70700584656992
53.426804033275019,68.77759598163891
61.530358025636438,62.562382297945803
47.475639634786098,71.546632233567777
59.813207869512318,87.230925133687393
55.142188413943821,78.211518270799232
52.211796692214001,79.64197304980874
39.299566694317065,59.171489321869508
48.10504169176825,75.331242297063056
52.550014442733818,71.300879886850353
45.419730144973755,55.165677145959123
54.351634881228918,82.478846757497919
44.164049496773352,62.008923245725825
58.16847071685779,75.392870425994957
56.727208057096611,81.43619215887864
48.955888566093719,60.723602440673965
44.687196231480904,82.892503731453715
60.297326851333466,97.379896862166078
45.618643772955828,48.847153317355072
38.816817537445637,56.877213186268506
66.189816606752601,83.878564664602763
65.41605174513407,118.59121730252249
47.48120860786787,57.251819462268969
41.57564261748702,51.391744079832307
51.84518690563943,75.380651665312357
59.370822011089523,74.765564032151374
57.31000343834809,95.455052922574737
63.615561251453308,95.229366017555307
46.737619407976972,79.052406169565586
50.556760148547767,83.432071421323712
52.223996085553047,63.358790317497878
35.567830047746632,41.412885303700563
42.436476944055642,76.617341280074044
58.16454011019286,96.769566426108199
57.504447615341789,74.084130116602523
45.440530725319981,66.588144414228594
61.89622268029126,77.768482417793024
33.093831736163963,50.719588912312084
36.436009511386871,62.124570818071781
37.675654860850742,60.810246649902211
44.555608383275356,52.682983366387781
43.318282631865721,58.569824717692867
50.073145632289034,82.905981485070512
43.870612645218372,61.424709804339123
62.997480747553091,115.24415280079529
32.669043763467187,45.570588823376085
40.166899008703702,54.084054796223612
53.575077531673656,87.994452758110413
33.864214971778239,52.725494375900425
64.707138666121296,93.576118692658241
38.119824026822805,80.166275447370964
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52.378446219236217,86.098291205774103
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33.644706006191782,69.89968164362763
39.557901222906828,44.862490711164398
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60.269214393997906,70.251934419771587
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31.588116998132829,50.392670135079896
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57.788223993230673,99.051414841748269
54.282328705967409,79.120646274680027
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50.282836348230731,69.510503311494389
44.211741752090113,73.687564318317285
38.005488008060688,61.366904537240131
32.940479942618296,67.170655768995118
53.691639571070056,85.668203145001542
68.76573426962166,114.85387123391394
46.230966498310252,90.123572069967423
68.319360818255362,97.919821035242848
50.030174340312143,81.536990783015028
49.239765342753763,72.111832469615663
50.039575939875988,85.232007342325673
48.149858891028863,66.224957888054632
25.128484647772304,53.454394214850524
49 changes: 49 additions & 0 deletions gradient-escent/python/gradient.py
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from numpy import *

def compute_error(b, m, points):
totalerror = 0
for i in range(0, len(points)):
x = points[i, 0]
y = points[i, 1]
totalerror += (y - (m * x + b)) ** 2
return totalerror / float(len(points))

def step_gradient(b, m, points, learning_rate):
#gradient descent
b_gradient = 0
m_gradient = 0
N = float(len(points))

for i in range(0, len(points)):
x = points[i, 0]
y = points[i, 1]
b_gradient += -(2/N) * (y - ((m * x) + b))
m_gradient += -(2 / N) * x * (y - ((m * x) + b))
new_b = b - (learning_rate * b_gradient)
new_m = m - (learning_rate * m_gradient)

return [new_b, new_m]

def gradient_descent_runner(points, initial_b, initial_m, learning_rate, num_of_iterations):
b = initial_b
m = initial_m

for i in range(num_of_iterations):
b, m = step_gradient(b, m, array(points), learning_rate)
return [b, m]

def run():
points = genfromtxt('data1.csv', delimiter=',')
learning_rate = 0.0001 #hyperparameter
#slope and yintercept y = mx + b
initial_b = 0
initial_m = 0
num_of_iterations = 1000
[b, m] = gradient_descent_runner(points, initial_b, initial_m, learning_rate, num_of_iterations)

print(b)
print(m)

if __name__ == "__main__":
run()

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