class V3(var x:Double=0.0, var y:Double=0.0, var z:Double=0.0){ operator fun minus(a : V3) = V3(x - a.x, y - a.y,z - a.z) operator fun plus(a : V3) = V3(x + a.x, y + a.y,z + a.z) operator fun times(dt: Double) = V3(x*dt,y*dt,z*dt) operator fun rem(a:V3) = Math.sqrt((x-a.x)*(x-a.x) + (y-a.y)*(y-a.y) + (z-a.z)*(z-a.z)) operator fun times(a:V3) = V3(y * a.z - z * a.y, z * a.x - x * a.z, x * a.y - y * a.x) fun length2() = x*x + y*y + z*z fun length() = Math.sqrt(x*x+y*y+z*z) infix fun dot(d: V3) = x*d.x + y * d.y + z*d.z override fun toString() = "($x,$y,$z)" fun copy() = V3(x,y,z) fun addAndScaleIP(d: V3, dt: Double) { x+=d.x * dt y+=d.y * dt z+=d.z * dt } fun addIP(d: V3) { x+=d.x y+=d.y z+=d.z } fun scale(dt: Double) = V3(x*dt,y*dt,z*dt) fun isEqual(v3: V3) = x==v3.x&&y==v3.y&&z==v3.z fun normalise() = scale(1.0 / this.length()) fun isFinite() = x.isFinite() && y.isFinite() && z.isFinite() fun toPov() = "< $x,$y,$z >" fun toDoubleArray() = doubleArrayOf(x,y,z) } fun main(args:Array< String >){ val a = V3(1.2,2.5,-0.2) val b = V3(1.6,-2.5,0.2) println("a=$a") println("b=$b") val c = a + b * 2.0 // scaling val d = a dot c // infix operators val e = a * b + V3(d,0.0,0.0) // cross product println("a + b = $c") println("a + b = ${a+b}") println("e = $e") }
import tensorflow as tf import numpy as np import json import matplotlib.pyplot as plt files = [ "C:/data/tfSimple6dofH.json", "C:/data/tfSimple6dofI.json", "C:/data/tfSimple6dofJ.json", "C:/data/tfSimple6dofK.json", "C:/data/tfSimple6dofL.json", "C:/data/tfSimple6dofM.json", "C:/data/tfSimple6dofN.json", "C:/data/tfSimple6dofO.json", "C:/data/tfSimple6dofP.json" ] datasets = [json.load(open(_)) for _ in files] dataset = [] for ds in datasets: for d2 in ds: dataset.append(d2) print("length of dataset = " + str(len(dataset)) + " rows") cutoff = (len(dataset)*3)//4 train = dataset[:cutoff] test = dataset[cutoff:] x_train = np.array([_['input'] for _ in train]) y_train = np.array([_['output'] for _ in train])*0.01+0.5 x_test = np.array([_['input'] for _ in test]) y_test= np.array([_['output'] for _ in test])*0.01+0.5 model = tf.keras.models.Sequential([ tf.keras.layers.Dense(20, activation=tf.nn.sigmoid), tf.keras.layers.Dense(20, activation=tf.nn.sigmoid), tf.keras.layers.Dense(len(train[0]['output']), activation=tf.nn.sigmoid) ]) model.compile(optimizer='adam', loss='mse'#'sparse_categorical_crossentropy', #metrics=['accuracy'] ) scoreIn = [] scoreOut = [] for i in range(1000): model.fit(x_train, y_train, epochs=5, verbose=10, steps_per_epoch=5) a = model.evaluate(x_train, y_train) b = model.evaluate(x_test, y_test) print("iteration " + str(i) + " score = " + str(int(a*1e5)) + " vs " + str(int(b*1e5))+"\n") scoreIn.append(a) scoreOut.append(b) w=model.get_weights() j = [] for i in range(len(w)//2): j.append({ 'weights': w[i*2+0].tolist(), 'bias': w[i*2+1].tolist() }) json.dump({'network':j}, open("C:/tmp/weights6dofB.json",'w')) plt.plot(scoreOut[10:]);plt.plot(scoreIn[10:])This isn't really complicated at all. It may have been better, in an ideal world, to use Kotlin throughout or python throughout.
class Problem{ var nVars : Int = 0 val G = mutableListOf< DoubleArray>() val q = mutableListOf< Int>() val c = mutableListOf< Double>() val h = mutableListOf< Double>() var pEquals = 0 var mConeVars = 0 var lOrthants = 0 var sCones = 0 fun save(fn : String){ val ret = mutableListOf< String>() ret.add("(n $nVars)") ret.add("(p $pEquals)") ret.add("(m $mConeVars)") ret.add("(l $lOrthants)") ret.add("(s $sCones)") val gMatrix = newMatrix(G) val gNonZero = gMatrix.nonZeros() ret.add("(g $gNonZero)") ret.add("(end)") ret.add("") for(i in 0 until q.size){ ret.add("(q $i,${q[i]})") } for(i in 0 until c.size){ ret.add("(c $i,${c[i]})") } for(i in 0 until h.size){ ret.add("(h $i,${h[i]})") } for(i in 0 until G.size){ for(j in 0 until G[i].size){ val g2 = G[i][j] if (g2!=0.0){ ret.add("(g $i,$j,$g2)") } } } ret.save(fn) } fun addVariable(cVal : Double):Int{ this.nVars++ this.c.add(cVal) if (G.size>0) throw Exception("Add variables before adding G contributions") return this.nVars-1 } fun addOrthant(g:DoubleArray, hVal:Double){ if (sCones>0) throw Exception("Add orthants before cones") lOrthants++ mConeVars++ G.add(g) h.add(hVal) } fun addCone(g:List< DoubleArray>,hVals:List< Double>){ sCones++ q.add(g.size) mConeVars+=g.size h.addAll(hVals) G.addAll(g) } fun constrainTwice(i: Int, x: Double, tolerance:Double=0.0) { val d = DoubleArray(nVars) d[i] = 1.0 addOrthant(d, x+0.001+tolerance) val d2 = DoubleArray(nVars) d2[i] = -1.0 addOrthant(d2, -x+0.001+tolerance) } } class trajectoryInput( var pos:V3, var vel: V3, var g:Double, var dt:Double, var nSteps:Int, var verticalTime:Double, var maxThrust: Double) class ConeOptions(var justDown : Boolean, var endingFall : Boolean) fun coneTrajectory(inp:trajectoryInput, coneOptions: ConeOptions) : Problem{ /* Variables: velocities in each step abs thrust used in each step change in velocity must be less than abs thrust used. minimise total abs thrust abs thrust must be less than threshold. */ var prob = Problem() var vx = mutableListOf< Int>() var vy = mutableListOf< Int>() var vz = mutableListOf< Int>() var thrust = mutableListOf< Int>() for (i in 0 until inp.nSteps) { vx.add(prob.addVariable(0.0)) vy.add(prob.addVariable(0.0)) vz.add(prob.addVariable(0.0)) if (i < inp.nSteps - 1) { thrust.add(prob.addVariable(1.0)) // might be -1.0 } } val dai = { DoubleArray(prob.nVars) } prob.constrainTwice(vx[0], inp.vel.x) prob.constrainTwice(vy[0], inp.vel.y) prob.constrainTwice(vz[0], inp.vel.z) var driftTolerance = 0.3 prob.constrainTwice(vx[inp.nSteps - 1], 0.0, driftTolerance) prob.constrainTwice(vy[inp.nSteps - 1], 0.0, driftTolerance) prob.constrainTwice(vz[inp.nSteps - 1], 0.0, driftTolerance) if (coneOptions.endingFall) { // for the last few steps, I only want vertical movement. val verticalSteps = Math.min(inp.nSteps, (inp.verticalTime / inp.dt).toInt()) val acceleration = inp.maxThrust + inp.g // g negative, so this'll be about 20m/s normally. for (i in inp.nSteps - verticalSteps until inp.nSteps - 1) { prob.constrainTwice(vx[i], 0.0, driftTolerance) prob.constrainTwice(vz[i], 0.0, driftTolerance) if (i == inp.nSteps - verticalSteps) { val timeToEnd = (inp.nSteps - i) * inp.dt val height = -0.5 + 0.5 * timeToEnd * timeToEnd * acceleration val dMinH = dai() dMinH[vy[i]] = -0.5 for (j in i until inp.nSteps - 1) { dMinH[vy[j]] = -1.0 } // dMinH is now the height at this timestep prob.addOrthant(dMinH * -1.0, height * -1.0) } } } // The sum of the velocities must be equal to the negative of initial position, so that it ends up at 0. val dx = dai() val dy = dai() val dz = dai() dx[vx[0]] = inp.dt * 0.5 dy[vy[0]] = inp.dt * 0.5 dz[vz[0]] = inp.dt * 0.5 for (i in 1 until inp.nSteps - 1) { dx[vx[i]] = inp.dt dy[vy[i]] = inp.dt dz[vz[i]] = inp.dt } dx[vx[inp.nSteps - 1]] = inp.dt * 0.5 dy[vy[inp.nSteps - 1]] = inp.dt * 0.5 dz[vz[inp.nSteps - 1]] = inp.dt * 0.5 var posTolerance = 2.0 prob.addOrthant(dx, -inp.pos.x + posTolerance) prob.addOrthant(dy, -inp.pos.y+ posTolerance) prob.addOrthant(dz, -inp.pos.z+ posTolerance) prob.addOrthant(dx * -1.0, inp.pos.x+ posTolerance) prob.addOrthant(dy * -1.0, inp.pos.y+ posTolerance) prob.addOrthant(dz * -1.0, inp.pos.z+ posTolerance) for (i in 0 until inp.nSteps - 1) { val d = dai() d[thrust[i]] = 1.0 prob.addOrthant(d, inp.maxThrust) prob.addOrthant(d*-1.0, 0.0) // this constrains it to be positive, shouldn't be necessary. if (coneOptions.justDown) { // The thrust has to be upward: no boosting downwards. val d2 = dai() d2[vy[i]] = 1.0 d2[vy[i + 1]] = -1.0 prob.addOrthant(d2, -inp.g * inp.dt * 1) } } for (i in 0 until inp.nSteps - 1) { val drad = dai() drad[thrust[i]] = -inp.dt val dx = dai() dx[vx[i]] = 1.0 dx[vx[i + 1]] = -1.0 val dy = dai() dy[vy[i]] = 1.0 dy[vy[i + 1]] = -1.0 val dz = dai() dz[vz[i]] = 1.0 dz[vz[i + 1]] = -1.0 prob.addCone(listOf(drad, dx, dy, dz), listOf(0.0, 0.0, -inp.g*inp.dt, 0.0)) } return prob } class TrajectoryOutput(val pos : List< V3>, val vel : List< V3>, val thrust : List< V3>) fun getTrajUnknownN(inp:trajectoryInput, coneOptions: ConeOptions) : TrajectoryOutput{ var min = 2 var max = 250 var mid = 75 for(i in 0 until 10){ inp.nSteps = mid //println("trying ${inp.nSteps} steps" ) val ret = getTraj(inp, coneOptions) if (ret == null){ min = mid mid = (max+min)/2 if (mid == min) mid++ } else{ max = mid mid = (max + min)/2 if (mid==min || mid==max) return ret } } return getTraj(inp, coneOptions)!! }
class ConicControlOutput(val thrustDir : V3, val pointDir:V3, val expectedPosition : List< V3>){ fun getControl(s : Dynamics.State, d : Dynamics) : DoubleArray{ var pd = pointDir.copy().normalise() var minCosTheta = 0.7 if (pd.y < minCosTheta){ pd.y = 0.0 pd = pd.normalise() * Math.sqrt(1 - minCosTheta*minCosTheta) pd.y=minCosTheta } var target = pd *-1.0 var targetW = (s.rot.fo * target) + s.w*0.3 val ret = DoubleArray(d.thrusters.size) for(i in 0 until d.thrusters.size){ val thruster = d.thrusters[i] val dw = s.rot.objToAbs(thruster.dir * thruster.pos) dot targetW var result = 0.0 if (dw > 0) result = Math.min(dw*0.03,1.0) ret[i] = result } var mainThrust = thrustDir dot (s.rot.objToAbs(d.thrusters[0].dir)) mainThrust *= 1.0 / (d.thrusters[0].force / s.getMass()) if (mainThrust < 0) mainThrust=0.0 if (mainThrust > 1) mainThrust = 1.0 ret[0] = mainThrust return ret } }
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