java时钟

Java GUI绘制时钟 Java GUI组件练习用Demo,主要是为了学习GUI的设计思路、组件的特性。这里的代码并不是最优雅的解决方案,千万别纠结这个……   要点: 绘图的框架应该继承自JFrame,具体组件继承自JPanel。 继承自JPanel的类覆写父类(JComponent)的paintComponent()方法,注意不是Container的paintComponents()! 这些都是不能重... 阅读详情

<html>
<body bgcolor=#3A6EA5>
<p><b>用JavaScript编程实现钟表特效</b></p>
请用查看源代码方式阅读所有程序代码。
<script language="JavaScript">
<!--
dCol='#22ff';
fCol='#e09000';
sCol='00ff00';
mCol='ff0000';
hCol='ffff00';
ClockHeight=40;
ClockWidth=40;
ClockFromMouseY=0; 
ClockFromMouseX=100;

d=new Array("星期日","星期一","星期二","星期三","星期四","星期五","星期六");
m=new Array("一月","二月","三月","四月","五月","六月","七月","八月","九月","十月","十一月","

十二月");
date=new Date();
day=date.getDate();
year=date.getYear();
if (year < 2000) year=year+1900;
TodaysDate="年 "+m[date.getMonth()]+day+"日 "+d[date.getDay()]+" "+year;
D=TodaysDate.split('');
H='...';
H=H.split('');
M='....';
M=M.split('');
S='.....';
S=S.split('');
Face='1 2 3 4 5 6 7 8 9 10 11 12';
font='Arial';
size=1;
speed=0.9;
ns=(document.layers);
ie=(document.all);
Face=Face.split(' ');
n=Face.length;
a=size*10;
ymouse=0;
xmouse=0;
scrll=0;
props="<font face="+font+" size="+size+" color="+fCol+">";
props2="<font face="+font+" size="+size+" color="+dCol+">";
Split=360/n;
Dsplit=360/D.length;
HandHeight=ClockHeight/4.5
HandWidth=ClockWidth/4.5
HandY=-7;
HandX=-2.5;
scrll=0;
step=0.06;
currStep=0;
y=new Array();x=new Array();Y=new Array();X=new Array();
for (i=0; i < n; i++){y[i]=0;x[i]=0;Y[i]=0;X[i]=0}
Dy=new Array();Dx=new Array();DY=new Array();DX=new Array();
for (i=0; i < D.length; i++){Dy[i]=0;Dx[i]=0;DY[i]=0;DX[i]=0}
if (ns){
for (i=0; i < D.length; i++)
document.write('<layer name="nsDate'+i+'" top=0 left=0 height='+a+'

width='+a+'><center>'+props2+D[i]+'</font></center></layer>');
for (i=0; i < n; i++)
document.write('<layer name="nsFace'+i+'" top=0 left=0 height='+a+'

width='+a+'><center>'+props+Face[i]+'</font></center></layer>');
for (i=0; i < S.length; i++)
document.write('<layer name=nsSeconds'+i+' top=0 left=0 width=15 height=15><font face=Arial

size=3 color='+sCol+'><center><b>'+S[i]+'</b></center></font></layer>');
for (i=0; i < M.length; i++)
document.write('<layer name=nsMinutes'+i+' top=0 left=0 width=15 height=15><font face=Arial

size=3 color='+mCol+'><center><b>'+M[i]+'</b></center></font></layer>');
for (i=0; i < H.length; i++)
document.write('<layer name=nsHours'+i+' top=0 left=0 width=15 height=15><font face=Arial

size=3 color='+hCol+'><center><b>'+H[i]+'</b></center></font></layer>');
}
if (ie) {
document.write('<div id="Od" style="position:absolute;top:0px;left:0px"><div

style="position:relative">');
for (i=0; i < D.length; i++)
document.write('<div id="ieDate"

style="position:absolute;top:0px;left:0;height:'+a+';width:'+a+';text-

align:center">'+props2+D[i]+'</font></div>');
document.write('</div></div>');
document.write('<div id="Of" style="position:absolute;top:0px;left:0px"><div

style="position:relative">');
for (i=0; i < n; i++)
document.write('<div id="ieFace"

style="position:absolute;top:0px;left:0;height:'+a+';width:'+a+';text-

align:center">'+props+Face[i]+'</font></div>');
document.write('</div></div>');
document.write('<div id="Oh" style="position:absolute;top:0px;left:0px"><div

style="position:relative">');
for (i=0; i < H.length; i++)
document.write('<div id="ieHours" style="position:absolute;width:16px;height:16px;font-

family:Arial;font-size:16px;color:'+hCol+';text-align:center;font-weight:bold">'+H[i]

+'</div>');
document.write('</div></div>');
document.write('<div id="Om" style="position:absolute;top:0px;left:0px"><div

style="position:relative">');
for (i=0; i < M.length; i++)
document.write('<div id="ieMinutes" style="position:absolute;width:16px;height:16px;font-

family:Arial;font-size:16px;color:'+mCol+';text-align:center;font-weight:bold">'+M[i]

+'</div>');
document.write('</div></div>')
document.write('<div id="Os" style="position:absolute;top:0px;left:0px"><div

style="position:relative">');
for (i=0; i < S.length; i++)
document.write('<div id="ieSeconds" style="position:absolute;width:16px;height:16px;font-

family:Arial;font-size:16px;color:'+sCol+';text-align:center;font-weight:bold">'+S[i]

+'</div>');
document.write('</div></div>')
}
(ns)?window.captureEvents(Event.MOUSEMOVE):0;
function Mouse(evnt){
ymouse = (ns)?evnt.pageY+ClockFromMouseY-(window.pageYOffset):event.y+ClockFromMouseY;
xmouse = (ns)?evnt.pageX+ClockFromMouseX:event.x+ClockFromMouseX;
}
(ns)?window.onMouseMove=Mouse:document.onmousemove=Mouse;
function ClockAndAssign(){
time = new Date ();
secs = time.getSeconds();
sec = -1.57 + Math.PI * secs/30;
mins = time.getMinutes();
min = -1.57 + Math.PI * mins/30;
hr = time.getHours();
hrs = -1.575 + Math.PI * hr/6+Math.PI*parseInt(time.getMinutes())/360;
if (ie){
Od.style.top=window.document.body.scrollTop;
Of.style.top=window.document.body.scrollTop;
Oh.style.top=window.document.body.scrollTop;
Om.style.top=window.document.body.scrollTop;
Os.style.top=window.document.body.scrollTop;
}
for (i=0; i < n; i++){
var F=(ns)?document.layers['nsFace'+i]:ieFace[i].style;
F.top=y[i] + ClockHeight*Math.sin(-1.0471 + i*Split*Math.PI/180)+scrll;
F.left=x[i] + ClockWidth*Math.cos(-1.0471 + i*Split*Math.PI/180);
}
for (i=0; i < H.length; i++){
var HL=(ns)?document.layers['nsHours'+i]:ieHours[i].style;
HL.top=y[i]+HandY+(i*HandHeight)*Math.sin(hrs)+scrll;
HL.left=x[i]+HandX+(i*HandWidth)*Math.cos(hrs);
}
for (i=0; i < M.length; i++){
var ML=(ns)?document.layers['nsMinutes'+i]:ieMinutes[i].style;
ML.top=y[i]+HandY+(i*HandHeight)*Math.sin(min)+scrll;
ML.left=x[i]+HandX+(i*HandWidth)*Math.cos(min);
}
for (i=0; i < S.length; i++){
var SL=(ns)?document.layers['nsSeconds'+i]:ieSeconds[i].style;
SL.top=y[i]+HandY+(i*HandHeight)*Math.sin(sec)+scrll;
SL.left=x[i]+HandX+(i*HandWidth)*Math.cos(sec);
}
for (i=0; i < D.length; i++){
var DL=(ns)?document.layers['nsDate'+i]:ieDate[i].style;
DL.top=Dy[i] + ClockHeight*1.5*Math.sin(currStep+i*Dsplit*Math.PI/180)+scrll;
DL.left=Dx[i] + ClockWidth*1.5*Math.cos(currStep+i*Dsplit*Math.PI/180);
}
currStep-=step;
}
function Delay(){
scrll=(ns)?window.pageYOffset:0;
Dy[0]=Math.round(DY[0]+=((ymouse)-DY[0])*speed);
Dx[0]=Math.round(DX[0]+=((xmouse)-DX[0])*speed);
for (i=1; i < D.length; i++){
Dy[i]=Math.round(DY[i]+=(Dy[i-1]-DY[i])*speed);
Dx[i]=Math.round(DX[i]+=(Dx[i-1]-DX[i])*speed);
}
y[0]=Math.round(Y[0]+=((ymouse)-Y[0])*speed);
x[0]=Math.round(X[0]+=((xmouse)-X[0])*speed);
for (i=1; i < n; i++){
y[i]=Math.round(Y[i]+=(y[i-1]-Y[i])*speed);
x[i]=Math.round(X[i]+=(x[i-1]-X[i])*speed);
}
ClockAndAssign();
setTimeout('Delay()',50);
}
if (ns||ie)window.onload=Delay;
//-->
</script>
</body>
</html>

java图形界面时钟 稍微美化了一些的界面,看起来舒服多了,获取本机时间,在表盘上显示机械钟的运动状态,不过时针跳动的有点大,因为定义的是一小时走一步,可以优化成每分钟转多大角度。 立即下载

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B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); 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