Created
March 16, 2018 19:24
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My implementation of Perceptron
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<html> | |
<head> | |
</head> | |
<body> | |
<script> | |
function usePerceptron(){ | |
perceptron(0.01); | |
perceptron(0.10); | |
perceptron(1.00); | |
} | |
function neuron(weights,args){ | |
let sum = 0.0; | |
for(let i = 0; i < args.length; i++){ | |
let single = weights[i] * args[i]; | |
sum += single; | |
} | |
return decide(sum); | |
} | |
function createZeroedVector(length){ | |
let vec=[]; | |
for(let i = 0; i < length; i++){ | |
vec.push(1); | |
} | |
return vec; | |
} | |
function decide(u){ | |
if(u >= 0) return 1; | |
else return 0; | |
} | |
function perceptron(cIntense){ //u1-u5 liniowo niezależne | |
pics=[]; | |
u1=[ | |
0.0,0.0,0.0,0.0,0.0, | |
0.0,1.0,1.0,0.0,0.0, | |
0.0,0.0,1.0,0.0,0.0, | |
0.0,0.0,1.0,0.0,0.0, | |
0.0,0.0,1.0,0.0,0.0,1.0 | |
]; | |
u2=[ | |
0.0,0.0,0.0,0.0,0.0, | |
1.0,1.0,0.0,0.0,0.0, | |
0.0,1.0,0.0,0.0,0.0, | |
0.0,1.0,0.0,0.0,0.0, | |
0.0,1.0,0.0,0.0,0.0,1.0 | |
]; | |
u3=[ | |
0.0,0.0,1.0,1.0,0.0, | |
0.0,0.0,0.0,1.0,0.0, | |
0.0,0.0,0.0,1.0,0.0, | |
0.0,0.0,0.0,0.0,0.0, | |
0.0,0.0,0.0,0.0,0.0,1.0 | |
]; | |
u4=[ | |
0.0,0.0,0.0,0.0,0.0, | |
0.0,1.0,1.0,1.0,0.0, | |
0.0,1.0,0.0,1.0,0.0, | |
0.0,1.0,1.0,1.0,0.0, | |
0.0,0.0,0.0,0.0,0.0,1.0 | |
]; | |
u5=[ | |
0.0,0.0,0.0,0.0,0.0, | |
0.0,0.0,0.0,0.0,0.0, | |
1.0,1.0,1.0,0.0,0.0, | |
1.0,0.0,1.0,0.0,0.0, | |
1.0,1.0,1.0,0.0,0.0,1.0 | |
]; | |
pics.push(u1); | |
pics.push(u2); | |
pics.push(u3); | |
pics.push(u4); | |
pics.push(u5); | |
let time = 1; | |
let good = 0; | |
let weights = createZeroedVector(26); | |
while(good < 5){ | |
let teacherResult = learnDecider(time); | |
let currentPic = pics[time % 5]; | |
let neuronResult = neuron(weights,currentPic); | |
let imageSize = 26; | |
for(let pixelPos = 0; pixelPos < imageSize; pixelPos++){ | |
weights[pixelPos] = weights[pixelPos] + cIntense * (teacherResult - neuronResult) * currentPic[pixelPos]; | |
} | |
if(learnDecider(time) == neuronResult){ | |
good+=1 | |
}else{ | |
good=0; | |
} | |
time=time+1; | |
} | |
console.log("time = ",time); | |
console.log("weights = "+weights); | |
console.log("Wynik to ",neuron(weights,pics[4])); | |
} | |
function learnDecider(time){//sygnal nauczyciela | |
if(time % 5 <= 2){ | |
return 1.0; | |
} | |
else { | |
return 0.0; | |
} | |
} | |
function addToVector(vector,number){ | |
for(let i = 0; i < vector.length; i++){ | |
vector[i] = vector[i] + number; | |
} | |
return vector; | |
} | |
function multiplyToVector(vector,number){ | |
for(let i = 0; i < vector.length; i++){ | |
vector[i] = vector[i] * number; | |
} | |
return vector; | |
} | |
usePerceptron(); | |
</script> | |
</body> | |
</head> | |
</html> |
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