| export function runNetwork(network,rows){ | |
| return rows.map(row=>{let x=network.inputMean?row.map((v,i)=>(v-network.inputMean[i])/network.inputStd[i]):row;for(let n=0;n<network.layers.length;n++){const layer=network.layers[n],y=new Float32Array(layer.bias.length);for(let j=0;j<y.length;j++){let sum=layer.bias[j],base=j*x.length;for(let k=0;k<x.length;k++)sum+=layer.weight[base+k]*x[k];y[j]=n===network.layers.length-1?sum:Math.tanh(sum);}x=y;}return Array.from(x);}); | |
| } | |
| export function decodeControl(raw){const x=Math.tanh(raw[0]),y=Math.tanh(raw[1]),m=Math.max(1,Math.hypot(x,y)),ax=Math.tanh(raw[2]),ay=Math.tanh(raw[3]),a=Math.hypot(ax,ay);return {moveX:x/m,moveY:y/m,aimX:a>.05?ax/a:1,aimY:a>.05?ay/a:0,fire:raw[4]>0};} | |
| export function validateNetwork(n){ | |
| if(n.schema!=='field-orders.neural-network.v1'||!Number.isInteger(n.inputDim)||!n.layers?.length||n.inputMean?.length!==n.inputDim||n.inputStd?.length!==n.inputDim||!n.inputStd.every(v=>Number.isFinite(v)&&v>0)||!n.inputMean.every(Number.isFinite))throw Error('Invalid neural network schema'); | |
| let width=n.inputDim; | |
| for(const layer of n.layers){if(!layer.bias?.length||layer.weight?.length!==width*layer.bias.length||!layer.bias.every(Number.isFinite)||!layer.weight.every(Number.isFinite))throw Error('Invalid neural network weights');width=layer.bias.length;} | |
| if(width!==n.outputDim)throw Error('Invalid neural network output width'); | |
| return n; | |
| } | |