diff --git a/crocoddyl/actuation.py b/crocoddyl/actuation.py
index 446ee7498e4830326db75f583b4b38c1f42f32ca..deace92e157adca34b690211b8b2662ec881eae3 100644
--- a/crocoddyl/actuation.py
+++ b/crocoddyl/actuation.py
@@ -13,8 +13,7 @@ class ActuationModelDoublePendulum:
def calc(self, data, x, u):
S = np.zeros([self.nv,self.nu])
- #S[1] = 1
- S[0] = 1
+ S[1] = 1
data.a = np.dot(S,u)
return data.a
@@ -34,8 +33,7 @@ class ActuationDataDoublePendulum:
self.A = np.zeros([nv, ndx + nu]) # result of calcDiff
self.Ax = self.A[:, :ndx]
self.Au = self.A[:, ndx:]
- # self.Au[1,0] = 1
- self.Au[0,0] = 1
+ self.Au[1,0] = 1
class ActuationModelUAM:
'''
diff --git a/crocoddyl/cost.py b/crocoddyl/cost.py
index 9a5b5bc7f3e638b5845bd6661e7b624d466d7f62..b2cc49845c2d803f62a30bcaa7109283e35b2048 100644
--- a/crocoddyl/cost.py
+++ b/crocoddyl/cost.py
@@ -711,12 +711,13 @@ class CostModelDoublePendulum(CostModelPinocchio):
J[:2,:2] = np.diag([c1, c2])
J[2:4,:2] = np.diag([s1, s2])
J[4:6,2:4] = np.diag([1,1])
- data.Lx[:] = np.dot(np.transpose(J), Ax)
+ data.Lx[:] = np.dot(J.T, Ax)
H = np.zeros([6, 4])
H[:2,:2] = np.diag([c1**2-s1**2, c2**2-s2**2])
H[2:4,:2] = np.diag([s1**2+(1-c1)*c1, s2**2+(1-c2)*c2])
- J[4:6,2:4] = np.diag([1,1])
- data.Lxx[:, :] = np.dot(np.transpose(H), Axx)
+ H[4:6,2:4] = np.diag([1,1])
+ Lxx = np.dot(H.T, Axx)
+ data.Lxx[:, :] = np.diag([Lxx[0,0], Lxx[1,0], Lxx[2,0], Lxx[3,0]])
return data.cost
diff --git a/examples/notebooks/borinot_arm.ipynb b/examples/notebooks/borinot_arm.ipynb
index 33b03b1df2bcf050d624b592317e3f44b8506721..401568e46397d79232d854a1a24179cdf7544a01 100644
--- a/examples/notebooks/borinot_arm.ipynb
+++ b/examples/notebooks/borinot_arm.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -13,7 +13,7 @@
"robot = loadBorinotArm()\n",
"robot.initViewer(loadModel=True)\n",
"\n",
- "q0 = [1, 0]\n",
+ "q0 = [3.14, 0]\n",
"robot.q0.flat = q0\n",
"robot.framesForwardKinematics(robot.q0)\n",
"robot.display(robot.q0)\n",
@@ -46,4083 +46,42 @@
"cell_type": "code",
"execution_count": 2,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- " [-108.10281188]]\n",
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- "[[ 84.93209721]\n",
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- " [1.18435695]]\n",
- "[[2.56525585]\n",
- " [1.18659515]]\n",
- "[[2.45434848]\n",
- " [1.17975368]]\n",
- "[[2.33448862]\n",
- " [1.16267219]]\n",
- "[[2.20679507]\n",
- " [1.13444267]]\n",
- "[[2.07229358]\n",
- " [1.09445556]]\n",
- "[[1.93189544]\n",
- " [1.0424342 ]]\n",
- "[[1.78638261]\n",
- " [0.97845605]]\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import time \n",
- "dt = 0.01\n",
- "t = np.arange(0,20,dt)\n",
- "q0 = np.array([[1, 0]]).T\n",
+ "dt = 0.05\n",
+ "t = np.arange(0,3,dt)\n",
+ "q0 = np.array([3.14, 0])\n",
"\n",
"q = q0\n",
- "q_d = np.zeros([2,1])\n",
- "q_dd = np.zeros([2,1])\n",
- "\n",
+ "q_d = np.zeros([2])\n",
+ "q_dd = np.zeros([2])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
"for i in range(len(t)):\n",
- " pin.computeAllTerms(robot.model, robot.data, q, q_d)\n",
+ " pin.computeAllTerms(robot.model, robot.data, a2m(q), a2m(q_d))\n",
" M = robot.data.M\n",
" Minv = np.linalg.inv(M)\n",
- " nle = robot.data.nle\n",
- " q_dd = np.dot(Minv, m2a(-nle)).T\n",
+ " r = np.zeros(2)\n",
+ " tau = np.zeros([2])\n",
+ " r = tau - m2a(robot.data.nle) \n",
+ " q_dd = np.dot(Minv, r)\n",
+ " q = q + q_d*dt + q_dd*dt**2\n",
" q_d = q_d + q_dd*dt\n",
- " q = q + q_d*dt + 0.5*q_dd*dt**2\n",
- " print q_dd\n",
- " robot.framesForwardKinematics(q)\n",
- " robot.display(q)\n",
+ " pin.forwardKinematics(robot.model, robot.data, a2m(q), a2m(q_d))\n",
+ " robot.display(a2m(q))\n",
" time.sleep(0.01)"
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
@@ -4134,7 +93,7 @@
" frame=state, \n",
" ref=state.zero, \n",
" nu=1,\n",
- " activation=ActivationModelWeightedQuad(np.array([1]*4+[0.001]*2))) \n",
+ " activation=ActivationModelWeightedQuad(np.array([10,1,10,1]+[0.1]*2))) \n",
"\n",
"runningCostModel = CostModelSum(robot.model, nu=1)\n",
"terminalCostModel = CostModelSum(robot.model, nu=1)\n",
@@ -4142,13 +101,33 @@
"runningCostModel.addCost(name=\"regx\", weight=1e-6, cost=xRegCost)\n",
"runningCostModel.addCost(name=\"regu\", weight=1e-6, cost=uRegCost)\n",
"runningCostModel.addCost(name=\"pend\", weight=1, cost=xPendCost)\n",
- "terminalCostModel.addCost(name=\"ori2\", weight=100, cost=xPendCost)"
+ "terminalCostModel.addCost(name=\"ori2\", weight=50, cost=xPendCost)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([1, 1, 1, 1, 1, 1])"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.array([1,1,1,1,1,1])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
"outputs": [],
"source": [
"actModel = ActuationModelDoublePendulum(robot.model)\n",
@@ -4168,16 +147,16 @@
"\n",
"# For this optimal control problem, we define 250 knots (or running action\n",
"# models) plus a terminal knot\n",
- "T = 50\n",
+ "T = 60\n",
"x0 = np.array([3.14, 0, 0., 0. ])\n",
"problem = ShootingProblem(x0, [runningModel] * T, terminalModel)"
]
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 21,
"metadata": {
- "scrolled": true
+ "scrolled": false
},
"outputs": [
{
@@ -4185,233 +164,261 @@
"output_type": "stream",
"text": [
"iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 0 2.83982e+01 9.27808e+00 -3.85416e+05 1.00000e+01 1.00000e+01 0.0312 0\n",
- " 1 6.95634e+01 1.20638e+06 -4.07604e+05 1.00000e+01 1.00000e+01 0.0156 0\n",
- " 2 1.59378e+02 1.68478e+06 -4.41648e+05 1.00000e+01 1.00000e+01 0.0156 0\n",
- " 3 1.81440e+02 2.44201e+06 -5.21164e+05 1.00000e+01 1.00000e+01 0.0078 0\n",
- " 4 2.20343e+02 2.25083e+06 -5.73673e+05 1.00000e+01 1.00000e+01 0.0156 0\n",
- " 5 2.26017e+02 2.08305e+06 -8.98806e+05 1.00000e+01 1.00000e+01 0.0039 0\n",
- " 6 2.26691e+02 2.05176e+06 -1.02668e+06 1.00000e+01 1.00000e+01 0.0039 0\n",
- " 7 2.19081e+02 2.11255e+06 -1.19586e+06 1.00000e+01 1.00000e+01 0.0039 0\n",
- " 8 2.14579e+02 2.53114e+06 -1.45523e+06 1.00000e+02 1.00000e+02 0.0020 0\n",
- " 9 2.07156e+02 2.36532e+06 -1.59605e+07 1.00000e+02 1.00000e+02 0.0039 0\n",
+ " 0 3.69322e+01 4.00077e-01 -2.42118e+05 1.00000e-09 1.00000e-09 0.0156 0\n",
+ " 1 1.06958e+02 1.09817e+04 -1.53840e+05 1.00000e-09 1.00000e-09 0.0156 0\n",
+ " 2 1.63942e+02 1.20163e+04 -6.61968e+04 1.00000e-09 1.00000e-09 0.0156 0\n",
+ " 3 2.15054e+02 2.44469e+04 -2.73148e+04 1.00000e-09 1.00000e-09 0.0156 0\n",
+ " 4 2.81349e+02 7.90349e+04 -1.02097e+04 1.00000e-09 1.00000e-09 0.0312 0\n",
+ " 5 2.99798e+02 2.11671e+05 -1.16727e+03 1.00000e-09 1.00000e-09 0.0312 0\n",
+ " 6 2.97934e+02 2.62602e+05 -2.79618e+02 1.00000e-09 1.00000e-09 0.0625 0\n",
+ " 7 3.86648e+02 2.79628e+05 -9.03739e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 8 3.56496e+02 3.05065e+05 2.10063e+02 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 9 3.34571e+02 3.18361e+05 1.98154e+02 1.00000e-09 1.00000e-09 0.0625 0\n",
"iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 10 2.17055e+02 1.66913e+06 -2.25723e+07 1.00000e+03 1.00000e+03 0.0020 0\n",
- " 11 2.45549e+02 6.16016e+06 -2.59431e+08 1.00000e+03 1.00000e+03 0.0039 0\n",
- " 12 2.48770e+02 8.06116e+06 -3.10413e+08 1.00000e+04 1.00000e+04 0.0020 0\n",
- " 13 2.52721e+02 4.32358e+08 -2.95084e+09 1.00000e+05 1.00000e+05 0.0020 0\n",
- " 14 2.56272e+02 3.85908e+10 -2.67943e+10 1.00000e+06 1.00000e+06 0.0020 0\n",
- " 15 2.59190e+02 3.87690e+12 -2.36756e+11 1.00000e+07 1.00000e+07 0.0020 0\n",
- " 16 2.74200e+02 4.08787e+14 -2.05662e+12 1.00000e+07 1.00000e+07 0.0039 0\n",
- " 17 2.82143e+02 1.50162e+15 -1.55727e+12 1.00000e+07 1.00000e+07 0.0039 0\n",
- " 18 2.85922e+02 2.40069e+15 -1.18457e+12 1.00000e+07 1.00000e+07 0.0039 0\n",
- " 19 2.88445e+02 2.69560e+15 -9.11614e+11 1.00000e+07 1.00000e+07 0.0078 0\n",
+ " 10 3.17185e+02 2.94918e+05 1.85317e+02 1.00000e-09 1.00000e-09 0.0625 0\n",
+ " 11 3.03955e+02 2.69587e+05 1.70976e+02 1.00000e-09 1.00000e-09 0.0625 0\n",
+ " 12 2.97158e+02 2.44718e+05 1.68433e+02 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 13 2.95010e+02 2.23988e+05 1.55772e+02 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 14 2.92127e+02 1.77842e+05 1.34269e+02 1.00000e-09 1.00000e-09 0.0625 0\n",
+ " 15 2.90237e+02 1.58263e+05 1.20819e+02 1.00000e-09 1.00000e-09 0.0625 0\n",
+ " 16 2.89086e+02 1.40933e+05 1.07796e+02 1.00000e-09 1.00000e-09 0.0625 0\n",
+ " 17 2.91530e+02 1.25615e+05 9.83780e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 18 2.93471e+02 1.01389e+05 7.82353e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 19 3.20074e+02 8.45151e+04 5.00839e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
"iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 20 2.91453e+02 2.42330e+15 -5.79848e+11 1.00000e+07 1.00000e+07 0.0078 0\n",
- " 21 2.94692e+02 2.50455e+15 -4.00647e+11 1.00000e+07 1.00000e+07 0.0078 0\n",
- " 22 2.99026e+02 3.30433e+15 -2.95408e+11 1.00000e+07 1.00000e+07 0.0078 0\n",
- " 23 3.02757e+02 1.24877e+16 -2.28527e+11 1.00000e+07 1.00000e+07 0.0078 0\n",
- " 24 3.04889e+02 4.43691e+15 -1.83230e+11 1.00000e+07 1.00000e+07 0.0078 0\n",
- " 25 3.05726e+02 4.33095e+15 -1.50974e+11 1.00000e+07 1.00000e+07 0.0078 0\n",
- " 26 3.07955e+02 3.23622e+15 -1.27075e+11 1.00000e+07 1.00000e+07 0.0078 0\n",
- " 27 3.10313e+02 2.53741e+15 -1.08786e+11 1.00000e+07 1.00000e+07 0.0078 0\n",
- " 28 3.12410e+02 2.25850e+15 -9.44176e+10 1.00000e+07 1.00000e+07 0.0078 0\n",
- " 29 3.15447e+02 2.04771e+15 -8.15636e+10 1.00000e+07 1.00000e+07 0.0156 0\n",
+ " 20 3.27004e+02 1.15773e+05 6.12089e+01 1.00000e-09 1.00000e-09 0.0625 0\n",
+ " 21 3.37051e+02 1.14404e+05 5.21526e+01 1.00000e-09 1.00000e-09 0.0625 0\n",
+ " 22 3.47284e+02 1.16724e+05 4.12767e+01 1.00000e-09 1.00000e-09 0.0625 0\n",
+ " 23 3.61630e+02 1.15360e+05 2.05498e+01 1.00000e-09 1.00000e-09 0.0625 0\n",
+ " 24 3.69814e+02 1.55834e+05 -1.82432e+01 1.00000e-09 1.00000e-09 0.0312 0\n",
+ " 25 3.83622e+02 2.11950e+05 -5.73130e+01 1.00000e-09 1.00000e-09 0.0312 0\n",
+ " 26 4.04999e+02 3.78516e+05 -1.27011e+02 1.00000e-09 1.00000e-09 0.0312 0\n",
+ " 27 3.04226e+03 8.89445e+05 -2.16174e+02 1.00000e-09 1.00000e-09 0.0625 0\n",
+ " 28 2.96260e+03 1.94558e+07 4.74121e+03 1.00000e-09 1.00000e-09 0.0156 0\n",
+ " 29 2.89227e+03 3.20377e+07 4.56920e+03 1.00000e-09 1.00000e-09 0.0156 0\n",
"iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 30 3.20767e+02 1.71853e+15 -6.42512e+10 1.00000e+07 1.00000e+07 0.0156 0\n",
- " 31 3.24924e+02 1.73009e+15 -5.21326e+10 1.00000e+07 1.00000e+07 0.0156 0\n",
- " 32 3.28408e+02 1.85535e+15 -4.32293e+10 1.00000e+07 1.00000e+07 0.0156 0\n",
- " 33 3.30399e+02 2.18508e+15 -3.64437e+10 1.00000e+07 1.00000e+07 0.0156 0\n",
- " 34 3.30158e+02 3.01798e+15 -3.11217e+10 1.00000e+07 1.00000e+07 0.0156 0\n",
- " 35 3.31341e+02 5.44947e+15 -2.72829e+10 1.00000e+07 1.00000e+07 0.0078 0\n",
- " 36 3.32504e+02 9.06935e+15 -2.54378e+10 1.00000e+07 1.00000e+07 0.0078 0\n",
- " 37 3.34891e+02 2.05998e+16 -2.37600e+10 1.00000e+07 1.00000e+07 0.0078 0\n",
- " 38 3.32239e+02 9.83463e+16 -2.22233e+10 1.00000e+07 1.00000e+07 0.0078 0\n",
- " 39 3.32678e+02 1.60644e+17 -2.08275e+10 1.00000e+07 1.00000e+07 0.0078 0\n",
+ " 30 2.84216e+03 5.46099e+07 4.43829e+03 1.00000e-09 1.00000e-09 0.0312 0\n",
+ " 31 2.79386e+03 2.15807e+07 4.04823e+03 1.00000e-09 1.00000e-09 0.0156 0\n",
+ " 32 2.66402e+03 7.69171e+07 3.10104e+03 1.00000e-09 1.00000e-09 0.0312 0\n",
+ " 33 2.57381e+03 3.82400e+07 1.67203e+03 1.00000e-09 1.00000e-09 0.0156 0\n",
+ " 34 2.54173e+03 2.12230e+07 3.15291e+03 1.00000e-09 1.00000e-09 0.0078 0\n",
+ " 35 2.50917e+03 2.28277e+07 2.84282e+03 1.00000e-09 1.00000e-09 0.0078 0\n",
+ " 36 2.42059e+03 1.83943e+07 2.80926e+03 1.00000e-09 1.00000e-09 0.0156 0\n",
+ " 37 2.35571e+03 4.53314e+06 2.88468e+03 1.00000e-09 1.00000e-09 0.0156 0\n",
+ " 38 2.29450e+03 2.94511e+06 3.01538e+03 1.00000e-09 1.00000e-09 0.0156 0\n",
+ " 39 2.23646e+03 2.95397e+06 3.04682e+03 1.00000e-09 1.00000e-09 0.0156 0\n",
"iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 40 3.33193e+02 6.71628e+16 -1.95422e+10 1.00000e+07 1.00000e+07 0.0078 0\n",
- " 41 3.34101e+02 3.48634e+16 -1.83590e+10 1.00000e+07 1.00000e+07 0.0078 0\n",
- " 42 3.36775e+02 1.64924e+16 -1.69933e+10 1.00000e+07 1.00000e+07 0.0156 0\n",
- " 43 3.39209e+02 3.27752e+15 -1.50687e+10 1.00000e+07 1.00000e+07 0.0156 0\n",
- " 44 3.41330e+02 1.47937e+15 -1.34126e+10 1.00000e+07 1.00000e+07 0.0156 0\n",
- " 45 1.96754e+06 9.58405e+14 -1.16229e+10 1.00000e+07 1.00000e+07 0.0312 0\n",
- " 46 1.96755e+06 1.55207e+15 -9.35168e+09 1.00000e+07 1.00000e+07 0.0312 0\n",
- " 47 1.96755e+06 1.41239e+15 -7.07277e+09 1.00000e+07 1.00000e+07 0.0625 0\n",
- " 48 1.96755e+06 1.19413e+15 -4.71613e+09 1.00000e+07 1.00000e+07 0.0625 0\n",
- " 49 1.96755e+06 1.05834e+15 -3.22724e+09 1.00000e+07 1.00000e+07 0.0625 0\n",
+ " 40 2.18184e+03 3.20187e+06 2.98093e+03 1.00000e-09 1.00000e-09 0.0156 0\n",
+ " 41 2.06283e+03 3.58034e+06 2.76530e+03 1.00000e-09 1.00000e-09 0.0312 0\n",
+ " 42 1.96765e+03 3.53539e+06 1.37594e+03 1.00000e-09 1.00000e-09 0.0312 0\n",
+ " 43 1.86980e+03 4.01495e+06 1.83770e+02 1.00000e-09 1.00000e-09 0.0312 0\n",
+ " 44 1.82959e+03 3.00959e+06 1.84260e+03 1.00000e-09 1.00000e-09 0.0156 0\n",
+ " 45 1.67903e+03 2.92441e+06 1.63419e+03 1.00000e-09 1.00000e-09 0.0312 0\n",
+ " 46 1.56821e+03 2.94444e+06 1.61861e+03 1.00000e-09 1.00000e-09 0.0312 0\n",
+ " 47 1.51188e+03 2.16622e+06 1.62038e+03 1.00000e-09 1.00000e-09 0.0312 0\n",
+ " 48 1.47221e+03 1.78256e+06 1.60973e+03 1.00000e-09 1.00000e-09 0.0312 0\n",
+ " 49 1.42969e+03 1.73753e+06 1.59498e+03 1.00000e-09 1.00000e-09 0.0312 0\n",
"iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 50 1.96755e+06 9.38407e+14 -1.93985e+09 1.00000e+07 1.00000e+07 0.1250 0\n",
- " 51 1.96756e+06 7.32415e+14 -1.01845e+09 1.00000e+07 1.00000e+07 0.1250 0\n",
- " 52 1.96756e+06 5.61397e+14 -6.11271e+08 1.00000e+07 1.00000e+07 0.1250 0\n",
- " 53 1.96755e+06 4.23222e+14 -2.76927e+08 1.00000e+07 1.00000e+07 0.2500 0\n",
- " 54 1.96755e+06 2.44829e+14 -1.61554e+08 1.00000e+07 1.00000e+07 0.2500 0\n",
- " 55 1.96755e+06 1.41363e+14 -1.02799e+08 1.00000e+07 1.00000e+07 0.2500 0\n",
- " 56 1.96755e+06 7.97816e+13 -6.45227e+07 1.00000e+07 1.00000e+07 0.2500 0\n",
- " 57 1.96755e+06 4.40816e+13 4.54441e+04 1.00000e+07 1.00000e+07 0.5000 0\n",
- " 58 1.96755e+06 1.12670e+13 -2.69749e+02 1.00000e+07 1.00000e+07 0.5000 0\n",
- " 59 1.96755e+06 2.74090e+12 5.89423e+06 1.00000e+06 1.00000e+06 1.0000 1\n",
+ " 50 1.35578e+03 1.65609e+06 1.48932e+03 1.00000e-09 1.00000e-09 0.0625 0\n",
+ " 51 1.23650e+03 1.58521e+06 1.22446e+03 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 52 1.18605e+03 1.13222e+06 9.81387e+02 1.00000e-09 1.00000e-09 0.0625 0\n",
+ " 53 1.13860e+03 9.95712e+05 9.37734e+02 1.00000e-09 1.00000e-09 0.0625 0\n",
+ " 54 1.09941e+03 8.30033e+05 8.24965e+02 1.00000e-09 1.00000e-09 0.0625 0\n",
+ " 55 1.06417e+03 7.39428e+05 7.23574e+02 1.00000e-09 1.00000e-09 0.0625 0\n",
+ " 56 1.00827e+03 6.76196e+05 6.49559e+02 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 57 9.60038e+02 5.72434e+05 5.40036e+02 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 58 9.32590e+02 6.01817e+05 4.14413e+02 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 59 9.08566e+02 4.06614e+05 3.24408e+02 1.00000e-09 1.00000e-09 0.5000 0\n",
"iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 60 1.96755e+06 5.33750e+08 3.89237e-01 1.00000e+05 1.00000e+05 1.0000 1\n",
- " 61 1.96754e+06 5.33723e+08 3.89244e+00 1.00000e+04 1.00000e+04 1.0000 1\n",
- " 62 1.96751e+06 5.33447e+08 3.89311e+01 1.00000e+03 1.00000e+03 1.0000 1\n",
- " 63 1.96712e+06 5.30469e+08 3.89989e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 64 1.96673e+06 5.29894e+08 3.89982e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 65 1.96634e+06 5.29334e+08 3.89978e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 66 1.96595e+06 5.28788e+08 3.89975e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 67 1.96556e+06 5.28257e+08 3.89973e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 68 1.96517e+06 5.27740e+08 3.89973e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 69 1.96478e+06 5.27238e+08 3.89975e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 60 8.94121e+02 4.21678e+05 2.86102e+02 1.00000e-09 1.00000e-09 0.0625 0\n",
+ " 61 8.64166e+02 3.40199e+05 2.70807e+02 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 62 8.48218e+02 3.51569e+05 2.89312e+02 1.00000e-09 1.00000e-09 0.0625 0\n",
+ " 63 8.31981e+02 3.73936e+05 3.18707e+02 1.00000e-09 1.00000e-09 0.0625 0\n",
+ " 64 8.24092e+02 3.88829e+05 4.01007e+02 1.00000e-09 1.00000e-09 0.0312 0\n",
+ " 65 8.17135e+02 5.39409e+05 5.46952e+02 1.00000e-09 1.00000e-09 0.0156 0\n",
+ " 66 8.09434e+02 7.14737e+05 5.76387e+02 1.00000e-09 1.00000e-09 0.0156 0\n",
+ " 67 7.98049e+02 8.88812e+05 5.64268e+02 1.00000e-09 1.00000e-09 0.0312 0\n",
+ " 68 7.68908e+02 1.03626e+06 5.11594e+02 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 69 7.34227e+02 4.66997e+05 3.96413e+02 1.00000e-09 1.00000e-09 0.2500 0\n",
"iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 70 1.96439e+06 5.26749e+08 3.89977e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 71 1.96400e+06 5.26274e+08 3.89981e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 72 1.96361e+06 5.25812e+08 3.89986e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 73 1.96322e+06 5.25364e+08 3.89992e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 74 1.96283e+06 5.24929e+08 3.89999e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 75 1.96244e+06 5.24506e+08 3.90006e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 76 1.96205e+06 5.24096e+08 3.90015e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 77 1.96166e+06 5.23698e+08 3.90024e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 78 1.96127e+06 5.23312e+08 3.90034e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 79 1.96088e+06 5.22937e+08 3.90044e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 70 7.09251e+02 2.53414e+05 2.98731e+02 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 71 6.81394e+02 2.02792e+05 2.37017e+02 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 72 6.56273e+02 2.20441e+05 1.66143e+02 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 73 6.41038e+02 1.70077e+05 1.13641e+02 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 74 6.33920e+02 1.06675e+05 9.28492e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 75 6.29017e+02 4.73885e+04 8.20266e+01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 76 6.23077e+02 2.47788e+05 8.36601e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 77 6.19159e+02 1.06836e+05 7.27925e+01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 78 6.14156e+02 4.79003e+04 7.55618e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 79 6.11053e+02 3.49728e+04 6.42295e+01 1.00000e-09 1.00000e-09 0.2500 0\n",
"iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 80 1.96049e+06 5.22574e+08 3.90055e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 81 1.96010e+06 5.22222e+08 3.90065e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 82 1.95971e+06 5.21880e+08 3.90077e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 83 1.95932e+06 5.21548e+08 3.90088e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 84 1.95893e+06 5.21227e+08 3.90100e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 85 1.95854e+06 5.20915e+08 3.90111e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 86 1.95815e+06 5.20612e+08 3.90122e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 87 1.95775e+06 5.20318e+08 3.90133e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 88 1.95736e+06 5.20033e+08 3.90144e+02 1.00000e+02 1.00000e+02 1.0000 1\n"
+ " 80 6.08647e+02 3.40752e+04 7.15732e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 81 6.04176e+02 2.96021e+04 6.26665e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 82 6.02491e+02 2.55608e+04 5.16847e+01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 83 5.99168e+02 3.05765e+04 5.52358e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 84 5.96067e+02 2.89171e+04 4.75835e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 85 5.93180e+02 2.77018e+04 4.31490e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 86 5.90499e+02 2.68026e+04 3.94451e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 87 5.87904e+02 2.55754e+04 3.61755e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 88 5.86719e+02 2.37913e+04 3.28942e+01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 89 5.83564e+02 2.77219e+04 3.32681e+01 1.00000e-09 1.00000e-09 0.2500 0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- " 89 1.95697e+06 5.19755e+08 3.90155e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
"iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 90 1.95658e+06 5.19486e+08 3.90165e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 91 1.95619e+06 5.19224e+08 3.90174e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 92 1.95580e+06 5.18969e+08 3.90183e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 93 1.95541e+06 5.18721e+08 3.90191e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 94 1.95502e+06 5.18479e+08 3.90198e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 95 1.95463e+06 5.18243e+08 3.90205e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 96 1.95424e+06 5.18013e+08 3.90210e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 97 1.95385e+06 5.17789e+08 3.90214e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 98 1.95346e+06 5.17569e+08 3.90217e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
- " 99 1.95307e+06 5.17354e+08 3.90219e+02 1.00000e+02 1.00000e+02 1.0000 1\n"
+ " 90 5.79916e+02 2.62083e+04 2.93626e+01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 91 5.76686e+02 2.13074e+04 2.46840e+01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 92 5.75554e+02 1.69815e+04 2.22984e+01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 93 5.73421e+02 1.80388e+04 2.96336e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 94 5.71645e+02 1.27997e+04 1.82280e+01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 95 5.70015e+02 1.20544e+04 2.08043e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 96 5.68817e+02 9.56750e+03 1.52030e+01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 97 5.67363e+02 1.04158e+04 1.87783e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 98 5.66095e+02 9.08136e+03 1.50942e+01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 99 5.64302e+02 8.77571e+03 1.74565e+01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
+ " 100 5.62333e+02 1.01427e+04 2.34368e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 101 5.60839e+02 5.87835e+03 1.44288e+01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 102 5.59963e+02 1.07652e+04 2.58961e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 103 5.58818e+02 6.10980e+03 1.55720e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 104 5.57771e+02 3.25974e+03 8.12973e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 105 5.56969e+02 4.08881e+03 1.02860e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 106 5.56174e+02 2.57280e+03 5.99404e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 107 5.55899e+02 3.36096e+03 7.84363e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 108 5.55424e+02 8.66043e+03 1.87587e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 109 5.54496e+02 5.92336e+03 1.23806e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
+ " 110 5.53788e+02 4.65201e+03 7.88604e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 111 5.53271e+02 5.51584e+03 1.13144e+01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 112 5.51919e+02 8.99521e+03 2.15248e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 113 5.51089e+02 4.69214e+03 1.22457e+01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 114 5.50523e+02 1.12072e+04 2.69121e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 115 5.49556e+02 6.12199e+03 1.48897e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 116 5.48741e+02 2.77994e+03 6.09736e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 117 5.48164e+02 3.52929e+03 8.29119e+00 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 118 5.47633e+02 1.90495e+03 3.97957e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 119 5.47228e+02 2.50094e+03 5.74839e+00 1.00000e-09 1.00000e-09 0.1250 0\n",
+ "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
+ " 120 5.46850e+02 1.49792e+03 2.83630e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 121 5.46718e+02 2.09436e+03 4.44970e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 122 5.46270e+02 6.08477e+03 1.28130e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 123 5.45740e+02 3.87948e+03 7.25354e+00 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 124 5.45208e+02 3.01168e+03 4.00495e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 125 5.44863e+02 3.96339e+03 6.98522e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 126 5.44035e+02 7.22921e+03 1.64124e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 127 5.43624e+02 3.62263e+03 9.02543e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 128 5.43171e+02 9.29248e+03 2.22470e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 129 5.42506e+02 4.97860e+03 1.16623e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
+ " 130 5.42037e+02 2.14840e+03 4.18279e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 131 5.41651e+02 2.96496e+03 6.61381e+00 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 132 5.41373e+02 1.46496e+03 2.68288e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 133 5.41112e+02 2.04903e+03 4.59226e+00 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 134 5.40949e+02 1.03497e+03 1.81702e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 135 5.40761e+02 1.53258e+03 3.45276e+00 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 136 5.40663e+02 7.75189e+02 1.30613e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 137 5.40516e+02 1.22861e+03 2.75293e+00 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 138 5.40451e+02 6.28609e+02 1.00461e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 139 5.40329e+02 1.05500e+03 2.30815e+00 1.00000e-09 1.00000e-09 0.1250 0\n",
+ "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
+ " 140 5.40276e+02 5.65302e+02 8.36913e-01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 141 5.40165e+02 9.80151e+02 2.03334e+00 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 142 5.40103e+02 5.82776e+02 7.73748e-01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 143 5.39987e+02 1.01551e+03 1.90695e+00 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 144 5.39888e+02 7.20574e+02 8.35814e-01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 145 5.39812e+02 1.24345e+03 1.99141e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 146 5.39495e+02 3.55859e+03 6.60769e+00 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 147 5.39274e+02 2.52704e+03 3.76222e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 148 5.38735e+02 5.86814e+03 1.12954e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 149 5.38452e+02 3.32883e+03 6.47367e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
+ " 150 5.37977e+02 7.16389e+03 1.69660e+01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 151 5.37451e+02 3.66210e+03 8.80655e+00 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 152 5.37077e+02 1.55251e+03 3.32318e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 153 5.36772e+02 2.26651e+03 5.39779e+00 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 154 5.36578e+02 1.03661e+03 2.09859e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 155 5.36376e+02 1.61962e+03 3.85476e+00 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 156 5.36281e+02 7.41355e+02 1.42010e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 157 5.36137e+02 1.24529e+03 2.97774e+00 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 158 5.36096e+02 5.53504e+02 1.02242e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 159 5.35986e+02 1.00686e+03 2.42855e+00 1.00000e-09 1.00000e-09 0.1250 0\n",
+ "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
+ " 160 5.35935e+02 4.28672e+02 7.78496e-01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 161 5.35887e+02 2.96620e+02 4.39498e-01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 162 5.35848e+02 4.05258e+02 8.23160e-01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 163 5.35833e+02 2.34852e+02 3.89308e-01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 164 5.35796e+02 4.62685e+02 1.02659e+00 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 165 5.35772e+02 2.28785e+02 4.28415e-01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 166 5.35757e+02 1.34619e+02 1.89806e-01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 167 5.35737e+02 2.04990e+02 4.20938e-01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 168 5.35731e+02 1.10469e+02 1.63303e-01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 169 5.35711e+02 1.97158e+02 4.30760e-01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
+ " 170 5.35702e+02 9.75119e+01 1.51440e-01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 171 5.35693e+02 6.90480e+01 8.13791e-02 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 172 5.35684e+02 9.76316e+01 1.68727e-01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 173 5.35679e+02 6.24982e+01 8.00210e-02 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 174 5.35670e+02 1.06579e+02 2.02417e-01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 175 5.35667e+02 6.17581e+01 8.68814e-02 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 176 5.35657e+02 1.24280e+02 2.54752e-01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 177 5.35651e+02 6.57412e+01 1.01696e-01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 178 5.35646e+02 4.68822e+01 5.02552e-02 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 179 5.35639e+02 6.79594e+01 1.11637e-01 1.00000e-09 1.00000e-09 0.1250 0\n"
]
},
{
- "data": {
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- "([array([3.14, 0. , 0. , 0. ]),\n",
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- " array([ 4.86750785, -2.63430672, 25.12047811, -36.32513002]),\n",
- " array([ 4.90181205, -3.30834939, 0.68608406, -13.48085354]),\n",
- " array([ 5.13704949, -3.85732504, 4.70474872, -10.97951293]),\n",
- " array([ 5.27053136, -4.21491356, 2.66963739, -7.15177042]),\n",
- " array([ 5.10096685, -4.23035985, -3.39129009, -0.30892575]),\n",
- " array([ 4.61449776, -3.91774769, -9.72938187, 6.2522432 ]),\n",
- " array([ 3.71607371, -3.41562815, -17.96848093, 10.04239083]),\n",
- " array([ 2.91858252, -3.05025842, -15.9498238 , 7.30739446]),\n",
- " array([ 2.44622551, -2.75284526, -9.4471403 , 5.94826334]),\n",
- " array([ 2.13954992, -2.50056558, -6.13351181, 5.0455936 ]),\n",
- " array([ 2.11334426, -2.38573774, -0.52411312, 2.29655681]),\n",
- " array([ 2.22425181, -2.38281567, 2.21815101, 0.05844139]),\n",
- " array([ 2.3233763 , -2.39666355, 1.98248986, -0.27695765]),\n",
- " array([ 2.38620184, -2.40215391, 1.25651065, -0.1098073 ]),\n",
- " array([ 2.42900412, -2.39942577, 0.85604564, 0.0545628 ]),\n",
- " array([ 2.37866904, -2.34781154, -1.00670151, 1.03228463]),\n",
- " array([ 2.26436366, -2.25832833, -2.28610765, 1.78966431]),\n",
- " array([ 2.11972426, -2.14415614, -2.89278792, 2.28344383]),\n",
- " array([ 1.96119986, -2.01311828, -3.17048809, 2.62075712]),\n",
- " array([ 1.84825004, -1.90595888, -2.25899634, 2.14318799]),\n",
- " array([ 1.85827716, -1.89356191, 0.20054242, 0.24793935]),\n",
- " array([ 1.82877292, -1.85715271, -0.59008488, 0.72818404]),\n",
- " array([ 1.8585163 , -1.87218511, 0.59486764, -0.30064799]),\n",
- " array([ 1.82941436, -1.84306738, -0.58203871, 0.58235458]),\n",
- " array([ 1.86926473, -1.87112395, 0.79700736, -0.56113129]),\n",
- " array([ 1.80030384, -1.81326971, -1.37921793, 1.15708472]),\n",
- " array([ 1.87926223, -1.87614995, 1.5791678 , -1.2576048 ]),\n",
- " array([ 1.76868939, -1.78648045, -2.21145665, 1.79339002]),\n",
- " array([ 1.87000929, -1.86976616, 2.02639791, -1.66571413]),\n",
- " array([ 1.73768245, -1.76277058, -2.64653685, 2.13991161]),\n",
- " array([ 1.87357673, -1.87822875, 2.71788567, -2.30916353]),\n",
- " array([ 1.79202356, -1.81253846, -1.63106336, 1.31380585]),\n",
- " array([ 1.90071838, -1.90254136, 2.17389643, -1.80005797]),\n",
- " array([ 1.79957819, -1.82509714, -2.02280395, 1.54888435]),\n",
- " array([ 1.95285971, -1.95230914, 3.0656304 , -2.54424008]),\n",
- " array([ 1.82076425, -1.85616943, -2.64190911, 1.92279435]),\n",
- " array([ 2.02598825, -2.02155899, 4.10448005, -3.30779138]),\n",
- " array([ 1.76238029, -1.83968601, -5.27215919, 3.63745973]),\n",
- " array([ 2.2236518 , -2.20567403, 9.22543009, -7.31976045]),\n",
- " array([ 1.80618935, -1.94705447, -8.34924896, 5.17239129]),\n",
- " array([ 2.7170008 , -2.56374923, 18.21622898, -12.33389531]),\n",
- " array([ 1.37875731, -1.9564153 , -26.76486981, 12.14667859]),\n",
- " array([ 3.12718889, -2.45942524, 34.96863156, -10.06019881]),\n",
- " array([ 1.07026085, 0.19958046, -41.13856071, 53.18011399]),\n",
- " array([ 3.97007227, -6.11704092, 57.99622842, -126.33242759]),\n",
- " array([ 2.85021855, -6.67195725, -22.39707437, -11.09832649]),\n",
- " array([ -1.04880503, -2.09256952, -77.98047166, 91.58775454]),\n",
- " array([ 3.24654603, 6.99270482, 85.90702108, 181.70548676]),\n",
- " array([ 175.09740165, -253.96886277, 3437.01711257, -5219.23135168])],\n",
- " [array([0.30365791]),\n",
- " array([1.3918582]),\n",
- " array([-1.27998786]),\n",
- " array([0.21838972]),\n",
- " array([0.03972067]),\n",
- " array([-0.22198985]),\n",
- " array([-0.17553317]),\n",
- " array([-0.06914778]),\n",
- " array([0.38233783]),\n",
- " array([0.11677162]),\n",
- " array([-0.13311587]),\n",
- " array([-0.04804288]),\n",
- " array([-0.15372965]),\n",
- " array([-0.31327122]),\n",
- " array([-0.33192044]),\n",
- " array([-0.30621529]),\n",
- " array([-0.38117842]),\n",
- " array([-0.37032727]),\n",
- " array([-0.36732096]),\n",
- " array([-0.38241408]),\n",
- " array([-0.3259617]),\n",
- " array([-0.21611061]),\n",
- " array([-0.46039707]),\n",
- " array([-0.31273251]),\n",
- " array([-0.49299025]),\n",
- " array([-0.29818413]),\n",
- " array([-0.57005997]),\n",
- " array([-0.17666418]),\n",
- " array([-0.69450901]),\n",
- " array([-0.07713001]),\n",
- " array([-0.76533476]),\n",
- " array([0.0121343]),\n",
- " array([-0.74074475]),\n",
- " array([-0.11056255]),\n",
- " array([-0.72108403]),\n",
- " array([-0.01045953]),\n",
- " array([-0.82382048]),\n",
- " array([0.11599702]),\n",
- " array([-1.06947555]),\n",
- " array([0.71538597]),\n",
- " array([-1.55552232]),\n",
- " array([1.56631427]),\n",
- " array([-2.62722941]),\n",
- " array([4.28966026]),\n",
- " array([-4.23256431]),\n",
- " array([2.33773888]),\n",
- " array([-6.53296773]),\n",
- " array([-0.30557005]),\n",
- " array([5.78055657]),\n",
- " array([12.65581929])],\n",
- " False)"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
+ " 180 5.35635e+02 4.69500e+01 5.18769e-02 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 181 5.35628e+02 7.27644e+01 1.24725e-01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 182 5.35624e+02 4.89416e+01 5.54115e-02 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 183 5.35617e+02 8.00134e+01 1.40884e-01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 184 5.35613e+02 5.29710e+01 6.08276e-02 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 185 5.35604e+02 9.00227e+01 1.60363e-01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 186 5.35600e+02 5.95614e+01 6.84421e-02 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 187 5.35590e+02 1.03630e+02 1.83770e-01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 188 5.35585e+02 6.98350e+01 7.90273e-02 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 189 5.35574e+02 1.22536e+02 2.12322e-01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
+ " 190 5.35566e+02 8.60258e+01 9.41792e-02 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 191 5.35552e+02 1.50191e+02 2.48511e-01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 192 5.35540e+02 1.12718e+02 1.17257e-01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 193 5.35521e+02 1.93996e+02 2.97831e-01 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 194 5.35501e+02 1.59965e+02 1.55842e-01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 195 5.35488e+02 2.71126e+02 3.73549e-01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 196 5.35430e+02 6.75075e+02 1.21961e+00 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 197 5.35388e+02 4.87055e+02 6.12627e-01 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 198 5.35282e+02 1.17158e+03 1.92026e+00 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 199 5.35156e+02 9.72225e+02 1.12517e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
+ " 200 5.34945e+02 2.14756e+03 3.24721e+00 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 201 5.34652e+02 1.81303e+03 2.16978e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 202 5.34309e+02 3.06248e+03 5.28960e+00 1.00000e-09 1.00000e-09 0.1250 0\n",
+ " 203 5.33943e+02 1.97489e+03 3.30196e+00 1.00000e-09 1.00000e-09 0.2500 0\n"
+ ]
+ },
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-21-87701ea220e3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;31m#ddp.solve(init_xs=xs0,init_us=us0,maxiter=600)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mddp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaxiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m600\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0;31m# Solving it with the DDP algorithm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;31m#ddp.solve()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/home/jmarti/robotics/toolboxes/crocoddyl/crocoddyl/fddp.pyc\u001b[0m in \u001b[0;36msolve\u001b[0;34m(self, maxiter, init_xs, init_us, isFeasible, regInit)\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 144\u001b[0m \u001b[0;31m# t = time.time()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomputeDirection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecalc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrecalc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 146\u001b[0m \u001b[0;31m# print \"TIME, Solving: Compute direction. Iteration \" + str(i) + \": \" + str(time.time()-t)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 147\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mArithmeticError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/home/jmarti/robotics/toolboxes/crocoddyl/crocoddyl/fddp.pyc\u001b[0m in \u001b[0;36mcomputeDirection\u001b[0;34m(self, recalc)\u001b[0m\n\u001b[1;32m 71\u001b[0m \"\"\"\n\u001b[1;32m 72\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrecalc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 74\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackwardPass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mVx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/home/jmarti/robotics/toolboxes/crocoddyl/crocoddyl/fddp.pyc\u001b[0m in \u001b[0;36mcalc\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 52\u001b[0m \"\"\" Compute the tangent (LQR) model.\n\u001b[1;32m 53\u001b[0m \"\"\"\n\u001b[0;32m---> 54\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalcDiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 55\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misFeasible\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0;31m# Gap store the state defect from the guess to feasible (rollout) trajectory, i.e.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/home/jmarti/robotics/toolboxes/crocoddyl/crocoddyl/shooting.pyc\u001b[0m in \u001b[0;36mcalcDiff\u001b[0;34m(self, xs, us)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mus\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrunningModels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrunningDatas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalcDiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 34\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mterminalModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalcDiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mterminalData\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrunningDatas\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mterminalData\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/home/jmarti/robotics/toolboxes/crocoddyl/crocoddyl/integrated_action.pyc\u001b[0m in \u001b[0;36mcalcDiff\u001b[0;34m(self, data, x, u, recalc)\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0mnv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeStep\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrecalc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 41\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdifferential\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalcDiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdifferential\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecalc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0mdxnext_dx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdxnext_ddx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mState\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mJintegrate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/home/jmarti/robotics/toolboxes/crocoddyl/crocoddyl/integrated_action.pyc\u001b[0m in \u001b[0;36mcalc\u001b[0;34m(self, data, x, u)\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcalc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0mnq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeStep\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0macc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcost\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdifferential\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdifferential\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 26\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwithCostResiduals\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcostResiduals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdifferential\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcostResiduals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/home/jmarti/robotics/toolboxes/crocoddyl/crocoddyl/flying.pyc\u001b[0m in \u001b[0;36mcalc\u001b[0;34m(self, data, x, u)\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtauq\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactuation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactuation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m \u001b[0mpinocchio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomputeAllTerms\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpinocchio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpinocchio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 32\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mM\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpinocchio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mM\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMinv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
}
],
"source": [
@@ -4420,8 +427,14 @@
"ddp.callback = [CallbackDDPVerbose()]\n",
"ddp.callback.append(CallbackDDPLogger())\n",
"\n",
+ "us0 = np.zeros([60,1])\n",
+ "us0 = us0 + 4\n",
+ "xs0 = [problem.initialState]*len(ddp.models())\n",
+ "\n",
+ "#ddp.solve(init_xs=xs0,init_us=us0,maxiter=600)\n",
+ "ddp.solve(maxiter=600)\n",
"# Solving it with the DDP algorithm\n",
- "ddp.solve()"
+ "#ddp.solve()"
]
},
{
@@ -4441,7 +454,7 @@
{
"data": {
"text/plain": [
- "4.95"
+ "<matplotlib.legend.Legend at 0x7fa10020a710>"
]
},
"execution_count": 12,
@@ -4450,9 +463,21 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fa1402ab910>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
"text/plain": [
- "<Figure size 1080x720 with 2 Axes>"
+ "<matplotlib.figure.Figure at 0x7fa10029ec90>"
]
},
"metadata": {
@@ -4474,10 +499,48 @@
"axs[0].set_title('Moments')\n",
"axs[0].legend(['M1','M2'])\n",
"\n",
- "t_state = t\n",
- "t_state[-1]"
+ "t_state = np.append(t, t[-1]+dt)\n",
+ "state = np.vstack(ddp.xs)\n",
+ "fig, axs = plt.subplots(1,2, figsize=(15,10))\n",
+ "fig.suptitle('States')\n",
+ "axs[0].plot(t_state,state[:,0], t_state, state[:,1])\n",
+ "axs[0].set_title('Position')\n",
+ "axs[0].legend(['Link 1','Link2'])\n",
+ "axs[1].plot(t_state,state[:,2], t_state, state[:,3])\n",
+ "axs[1].set_title('Vels')\n",
+ "axs[1].legend(['Link 1','Link2'])"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "H = np.zeros([6, 4])\n",
+ "c1 = 1\n",
+ "c2 = 1\n",
+ "s1 = 0\n",
+ "s2 = 0\n",
+ "H[:2,:2] = np.diag([c1**2-s1**2, c2**2-s2**2])\n",
+ "H[2:4,:2] = np.diag([s1**2+(1-c1)*c1, s2**2+(1-c2)*c2])\n",
+ "H[4:6,2:4] = np.diag([1,1])\n",
+ "weights = np.array([10]*4 + [0.1]*2)\n",
+ "Axx = weights[:, None]\n",
+ "Lxx = np.dot(H.T, Axx)\n",
+ "Lxx\n",
+ "L = np.zeros([4,4])\n",
+ "L[:,:] = np.diag([Lxx[0,0], Lxx[1,0], Lxx[2,0], Lxx[3,0]])\n",
+ "L"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -4502,7 +565,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.16"
+ "version": "2.7.12"
}
},
"nbformat": 4,
diff --git a/examples/notebooks/kinton_flying_ee.ipynb b/examples/notebooks/kinton_flying_ee.ipynb
index 23d6c63a4ee2a57ccc27f65ec48bb9d768d6a80b..2b178b5473fadce6ab482ef73e7011e67c501258 100644
--- a/examples/notebooks/kinton_flying_ee.ipynb
+++ b/examples/notebooks/kinton_flying_ee.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -14,7 +14,7 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -28,6 +28,27 @@
"robot.framesForwardKinematics(q0)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "AttributeError",
+ "evalue": "'Model' object has no attribute 'defaultState'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-9-0edc85ca2fe1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefaultState\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mAttributeError\u001b[0m: 'Model' object has no attribute 'defaultState'"
+ ]
+ }
+ ],
+ "source": [
+ "rmodel.defaultState\n"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 35,