main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\min f(x)=\\max _{1 \\leq i \\leq 6} f_{i}(x)$\n",
    "\n",
    "\n",
    "$\\begin{aligned} f_{1}(x)=& x_{1}^{2}+x_{2}^{2}+x_{1} x_{2}-14 x_{1}-16 x_{2}+\\left(x_{3}-10\\right)^{2} \\\\ &+4\\left(x_{4}-5\\right)^{2}+\\left(x_{5}-3\\right)^{2}+2\\left(x_{6}-1\\right)^{2}+5 x_{7}^{2} \\\\ &+7\\left(x_{8}-11\\right)^{2}+2\\left(x_{9}-10\\right)^{2}+\\left(x_{10}-7\\right)^{2}+45, \\\\ f_{2}(x)=& f_{1}(x)+10\\left(3\\left(x_{1}-2\\right)^{2} \\ldots\\right.\\\\ &\\left.+4\\left(x_{2}-3\\right)^{2}+2 x_{3}^{2}-7 x_{4}-120\\right), \\\\ f_{3}(x)=& f_{1}(x)+10\\left(5 x_{1}^{2}+8 x_{2}+\\left(x_{3}-6\\right)^{2}-2 x_{4}-40\\right), \\\\ f_{4}(x)=& f_{1}(x)+10\\left(0.5\\left(x_{1}-8\\right)^{2} \\cdots\\right.\\\\ &\\left.+2\\left(x_{2}-4\\right)^{2}+3 x_{5}^{2}-x_{6}-30\\right), \\\\ f_{5}(x)=& f_{1}(x)+10\\left(x_{1}^{2}+2\\left(x_{2}-2\\right)^{2}\\right.\\\\ &\\left.-2 x_{1} x_{2}+14 x_{5}-6 x_{6}\\right), \\\\ f_{6}(x)=& f_{1}(x)+10\\left(-3 x_{1}+6 x_{2}+12\\left(x_{9}-8\\right)^{2}-7 x_{10}\\right), \\\\ g_{1}(x)=& 4 x_{1}+5 x_{2}-3 x_{7}+9 x_{8}-105, \\\\ g_{2}(x)=& 10 x_{1}-8 x_{2}-17 x_{7}+2 x_{8}, \\\\ g_{3}(x)=&-8 x_{1}+2 x_{2}+5 x_{9}-2 x_{10}-12 . \\end{aligned}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "from IPython.display import clear_output\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.color_palette(\"bright\")\n",
    "import matplotlib as mpl\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "import torch\n",
    "from torch import Tensor\n",
    "from torch import nn\n",
    "from torch.nn  import functional as F \n",
    "from torch.autograd import Variable\n",
    "from numpy.linalg import inv\n",
    "\n",
    "class ODEF(nn.Module):\n",
    "    def forward_with_grad(self, z, t, grad_outputs):\n",
    "        \"\"\"Compute f and a df/dz, a df/dp, a df/dt\"\"\"\n",
    "        batch_size = z.shape[0]\n",
    "\n",
    "        out = self.forward(z, t)\n",
    "\n",
    "        a = grad_outputs\n",
    "\n",
    "        adfdz, adfdt, *adfdp = torch.autograd.grad(\n",
    "            (out,), (z, t) + tuple(self.parameters()), grad_outputs=(a),\n",
    "            allow_unused=True, retain_graph=True\n",
    "        )\n",
    "        \n",
    "        # grad method automatically sums gradients for batch items, we have to expand them back \n",
    "        if adfdp is not None:\n",
    "            adfdp = torch.cat([p_grad.flatten() for p_grad in adfdp]).unsqueeze(0)\n",
    "            adfdp = adfdp.expand(batch_size, -1) / batch_size\n",
    "        if adfdt is not None:\n",
    "            adfdt = adfdt.expand(batch_size, 1) / batch_size\n",
    "        \n",
    "        return out, adfdz, adfdt, adfdp\n",
    "\n",
    "    def flatten_parameters(self):\n",
    "        p_shapes = []\n",
    "        flat_parameters = []\n",
    "        for p in self.parameters():\n",
    "            p_shapes.append(p.size())\n",
    "            flat_parameters.append(p.flatten())\n",
    "        return torch.cat(flat_parameters)\n",
    "\n",
    "class weightedcustom(ODEF):\n",
    "    def __init__(self,A1,A2,A3,A4,A5,A6):\n",
    "        super(weightedcustom,self).__init__()\n",
    "        self.A1 = nn.Parameter(A1)\n",
    "        self.A2 = nn.Parameter(A2)\n",
    "        self.A3 = nn.Parameter(A3)\n",
    "        self.A4 = nn.Parameter(A4)\n",
    "        self.A5 = nn.Parameter(A5)\n",
    "        self.A6 = nn.Parameter(A6)\n",
    "    def f1(self,x):\n",
    "        return x[0]**2 + x[1]**2 + x[0]*x[1] - 14*x[0] - 16*x[1] + (x[2]-10)**2 + 4*(x[3]-5)**2 + (x[4]-3)**2 + 2*(x[5]-1)**2 + 5*x[6]**2 + 7*(x[7]-11)**2 + 2*(x[8]-10)**2 + (x[9]-7)**2 + 45\n",
    "    def f2(self,x):\n",
    "        return self.f1(x) + 10*(3*(x[0]-2)**2 + 4*(x[1]-3)**3 + 2*x[2]**2 - 7*x[3]*2 -120)\n",
    "    def f3(self,x):\n",
    "        return self.f1(x) + 10*(5*x[0]**2 + 8*x[1] + (x[2]-6)**2 - 2*x[3] - 40 )\n",
    "    def f4(self,x):\n",
    "        return self.f1(x) + 10*(0.5*(x[0]-8)**2 + 2*(x[1]-4)**2 + 3*x[4]**2 - x[5] - 30)\n",
    "    def f5(self,x):\n",
    "        return self.f1(x) + 10*(x[0]**2 + 2*(x[1]-2)**2 - 2*x[0]*x[1] + 14*x[4] - 6*x[5])\n",
    "    def f6(self,x):\n",
    "        return self.f1(x)  + 10*(-3*x[0] + 6*x[1] + 12*(x[8]-8)**2 - 7*x[9])\n",
    "    def g1(self,x):\n",
    "        return 4*x[0] + 5*x[1] - 3*x[6] + 9*x[7] - 105\n",
    "    def g2(self,x):\n",
    "        return 10*x[0] - 8*x[1] - 17*x[6] + 2*x[7] \n",
    "    def g3(self,x):\n",
    "        return (-8)*x[0] + 2*x[1] + 5*x[8] - 2*x[9] - 12\n",
    "    def sign(self,g):\n",
    "        if g>0:\n",
    "            return 1\n",
    "        elif g<0:\n",
    "            return 0\n",
    "        else:\n",
    "            p = np.random.rand()\n",
    "            return p\n",
    "    def forward(self, x, t):\n",
    "        C1 = Tensor([[4,5,0,0,0,0,-3,9,0,0]])\n",
    "        C2 = Tensor([[10,-8,0,0,0,0,-17,2,0,0]])\n",
    "        C3 = Tensor([[-8,2,0,0,0,0,0,0,5,-2]])\n",
    "        B1 = Tensor([[14,16,20,40,6,4,0,154,40,14]])\n",
    "        B2 = B1 + Tensor([[120,240,0,70,0,0,0,0,0,0]])\n",
    "        B3 = B1 + Tensor([[0,-80,120,20,0,0,0,0,0,0]])\n",
    "        B4 = B1 + Tensor([[80,160,0,0,0,10,0,0,0,0]])\n",
    "        B5 = B1 + Tensor([[0,80,0,0,-140,60,0,0,0,0]])\n",
    "        B6 = B1 + Tensor([[30,-60,0,0,0,0,0,0,1920,70]])\n",
    "        p = 1.0\n",
    "        index = []\n",
    "        index.append(self.f1(x[0]).item())\n",
    "        index.append(self.f2(x[0]).item())\n",
    "        index.append(self.f3(x[0]).item())\n",
    "        index.append(self.f4(x[0]).item())\n",
    "        index.append(self.f5(x[0]).item())\n",
    "        index.append(self.f6(x[0]).item())\n",
    "        i = np.argmax(np.array(index))\n",
    "        if i==0:\n",
    "            ex = (1-self.sign(self.g1(x[0])))*(1-self.sign(self.g2(x[0])))*(torch.mm(x,(self.A1 ).T) - B1)\n",
    "        elif i==1:\n",
    "            ex = (1-self.sign(self.g1(x[0])))*(1-self.sign(self.g2(x[0])))*(torch.mm(x,(self.A2).T) - B2)\n",
    "        elif i==2:\n",
    "            ex = (1-self.sign(self.g1(x[0])))*(1-self.sign(self.g2(x[0])))*(torch.mm(x,(self.A3 ).T) - B3)\n",
    "        elif i==3:\n",
    "            ex = (1-self.sign(self.g1(x[0])))*(1-self.sign(self.g2(x[0])))*(torch.mm(x,(self.A4 ).T) - B4)\n",
    "        elif i==4:\n",
    "            ex = (1-self.sign(self.g1(x[0])))*(1-self.sign(self.g2(x[0])))*(torch.mm(x,(self.A5 ).T) - B5)\n",
    "        elif i==5:\n",
    "            ex = (1-self.sign(self.g1(x[0])))*(1-self.sign(self.g2(x[0])))*(torch.mm(x,(self.A6 ).T) - B6)\n",
    "\n",
    "        ex += self.sign(self.g1(x[0]))*C1\n",
    "        ex += self.sign(self.g2(x[0]))*C2\n",
    "        ex += self.sign(self.g3(x[0]))*C3\n",
    "        return -ex\n",
    "\n",
    "\n",
    "class SpiralFunctionTest(weightedcustom):\n",
    "    def __init__(self):\n",
    "        A1 = Tensor([[2,1,0,0,0,0,0,0,0,0],\n",
    "            [1,2,0,0,0,0,0,0,0,0],\n",
    "            [0,0,2,0,0,0,0,0,0,0],\n",
    "            [0,0,0,8,0,0,0,0,0,0],\n",
    "            [0,0,0,0,2,0,0,0,0,0],\n",
    "            [0,0,0,0,0,4,0,0,0,0],\n",
    "            [0,0,0,0,0,0,10,0,0,0],\n",
    "            [0,0,0,0,0,0,0,14,0,0],\n",
    "            [0,0,0,0,0,0,0,0,4,0],\n",
    "            [0,0,0,0,0,0,0,0,0,2]])\n",
    "        A2 = Tensor([[60,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,80,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,40,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0]])\n",
    "        A3 = Tensor([[100,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,20,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0]])\n",
    "        A4 = Tensor([[20,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,40,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,60,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0]])\n",
    "        A5 = Tensor([[20,-20,0,0,0,0,0,0,0,0],\n",
    "                        [-20,40,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0]])\n",
    "        A6 = Tensor([[0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0],\n",
    "                        [0,0,0,0,0,0,0,0,240,0],\n",
    "                        [0,0,0,0,0,0,0,0,0,0]])\n",
    "\n",
    "        super(SpiralFunctionTest, self).__init__(A1,A2,A3,A4,A5,A6)\n",
    "def ode_solve(z0, t0, t1, f):\n",
    "    \"\"\"\n",
    "    Simplest Euler ODE initial value solver\n",
    "    \"\"\"\n",
    "    h_max = 0.001\n",
    "    n_steps = math.ceil((abs(t1 - t0)/h_max).max().item())\n",
    "\n",
    "    h = (t1 - t0)/n_steps\n",
    "    t = t0\n",
    "    z = z0\n",
    "    \n",
    "    for i_step in range(n_steps):\n",
    "        z = z + h * f(z, t)\n",
    "        t = t + h\n",
    "    return z\n",
    "def ode_solve1(z0, t0, t1, f):\n",
    "    \"\"\"\n",
    "    Simplest RK4 ODE initial value solver\n",
    "    \"\"\"\n",
    "    h_max = 0.001\n",
    "    n_steps = math.ceil((abs(t1 - t0)/h_max).max().item())\n",
    "\n",
    "    h = (t1 - t0)/n_steps\n",
    "    t = t0\n",
    "    z = z0\n",
    "    # print(h)\n",
    "    # h=Tensor([[0.001]])\n",
    "    for i_step in range(n_steps):\n",
    "        k1 = h*f(z, t)\n",
    "        k2 = h * (f((z+h/2), (z+k1/2)))\n",
    "        k3 = h * (f((z+h/2), (z+k2/2)))\n",
    "        k4 = h * (f((z+h), (z+k3)))\n",
    "        k = (k1+2*k2+2*k3+k4)/6\n",
    "        z = z + k\n",
    "        t = t + h\n",
    "    return z\n",
    "class NeuralODE(nn.Module):\n",
    "    def __init__(self, func):\n",
    "        super(NeuralODE, self).__init__()\n",
    "        assert isinstance(func, ODEF)\n",
    "        self.func = func\n",
    "\n",
    "    def forward(self, z0, t=Tensor([0., 1.]), return_whole_sequence=False):\n",
    "        # print(\"ccccccheck\",z0)\n",
    "        # print(self.func)\n",
    "        t = t.to(z0)\n",
    "        z = ODEAdjoint.apply(z0, t, self.func.flatten_parameters(), self.func)\n",
    "        \n",
    "        if return_whole_sequence:\n",
    "            return z\n",
    "        else:\n",
    "            return z[-1]\n",
    "class ODEAdjoint(torch.autograd.Function):\n",
    "    @staticmethod\n",
    "    def forward(ctx, z0, t, flat_parameters, func):\n",
    "        assert isinstance(func, ODEF)\n",
    "        bs, *z_shape = z0.size()\n",
    "        time_len = t.size(0)\n",
    "\n",
    "        with torch.no_grad():\n",
    "            z = torch.zeros(time_len, bs, *z_shape).to(z0)\n",
    "            #print(time_len)\n",
    "            z[0] = z0\n",
    "            for i_t in range(time_len - 1):\n",
    "                #print(z[0],t[i_t],t[i_t+1], func)\n",
    "                #z0 = ode_solve(z0, t[i_t], t[i_t+1], func)\n",
    "                z0 = ode_solve1(z0, t[i_t], t[i_t+1], func)\n",
    "                z[i_t+1] = z0\n",
    "\n",
    "        ctx.func = func\n",
    "        ctx.save_for_backward(t, z.clone(), flat_parameters)\n",
    "        return z\n",
    "\n",
    "def conduct_experiment(ode_true):\n",
    "    time=[]\n",
    "    # Create data\n",
    "    #z0 = Variable(torch.Tensor([[0.6, 0.3]]))\n",
    "    #z0 = Variable(torch.Tensor([[4.2,3.5,6.3,5.4,0.5,8.6,1.7,7.8,7.9,5.5]]))\n",
    "    \n",
    "    z0 = Variable(torch.Tensor([[5,6,7,8,9,10,11,12,13,14]]))\n",
    "    #z0 = Variable(torch.Tensor([[2,3,5,5,1,2,7,3,6,10]]))\n",
    "    t_max = 6.29*5\n",
    "    n_points = 100\n",
    "\n",
    "    index_np = np.arange(0, n_points, 1, dtype=np.int)\n",
    "    index_np = np.hstack([index_np[:, None]])\n",
    "    times_np = np.linspace(0, t_max, num=n_points)\n",
    "    times_np = np.hstack([times_np[:, None]])\n",
    "\n",
    "    times = torch.from_numpy(times_np[:, :, None]).to(z0)\n",
    "    obs = ode_true(z0, times, return_whole_sequence=True).detach()\n",
    "    time.append(times)\n",
    "    return obs,time\n",
    "ode_true = NeuralODE(SpiralFunctionTest())\n",
    "def train(ode_true,z_init):\n",
    "    time=[]\n",
    "    time_arrs=[]\n",
    "    sols = []\n",
    "    # Create data    \n",
    "    # z_init = [Variable(torch.randn(1, 3)) for i in range(num)]\n",
    "    for z_0 in z_init:\n",
    "        t_max = 6.29*5\n",
    "        n_points = 100\n",
    "\n",
    "        index_np = np.arange(0, n_points, 1, dtype=np.int)\n",
    "        index_np = np.hstack([index_np[:, None]])\n",
    "        times_np = np.linspace(0, t_max, num=n_points)\n",
    "        times_np = np.hstack([times_np[:, None]])\n",
    "\n",
    "        times = torch.from_numpy(times_np[:, :, None]).to(z_0)\n",
    "        obs = ode_true(z_0, times, return_whole_sequence=True).detach()\n",
    "        time.append(times)\n",
    "\n",
    "        sol_arr = obs.cpu().detach().numpy()\n",
    "        time_arr = time[0].cpu().detach().numpy()\n",
    "        Z=[]\n",
    "        for i in range(10):\n",
    "            z=sol_arr[:,:,i].reshape(-1).tolist()\n",
    "            Z.append(z)\n",
    "        sols.append(Z)\n",
    "        time_arrs.append(time_arr)\n",
    "    t=time_arrs[0][:,:,0].reshape(-1).tolist()\n",
    "    plt.figure(figsize=(20,15))\n",
    "    plt.rcParams.update({'font.size': 16})\n",
    "    for i in range(len(z_init)):\n",
    "        #if i==0:\n",
    "        plt.plot(t, sols[i][0][:],color='red',label=r'$x_{1}(t)$',linewidth=1)\n",
    "        plt.plot(t, sols[i][1][:],color='green',label=r'$x_{2}(t)$',linewidth=1)\n",
    "        plt.plot(t, sols[i][2][:],color='blue',label=r'$x_{3}(t)$',linewidth=1)\n",
    "        plt.plot(t, sols[i][3][:],color='orange',label=r'$x_{4}(t)$',linewidth=1)\n",
    "        plt.plot(t, sols[i][4][:],color='purple',label=r'$x_{5}(t)$',linewidth=1)\n",
    "        plt.plot(t, sols[i][5][:],color='brown',label=r'$x_{6}(t)$',linewidth=1)\n",
    "        plt.plot(t, sols[i][6][:],color='pink',label=r'$x_{7}(t)$',linewidth=1)\n",
    "        plt.plot(t, sols[i][7][:],color='gray',label=r'$x_{8}(t)$',linewidth=1)\n",
    "        plt.plot(t, sols[i][8][:],color='olive',label=r'$x_{9}(t)$',linewidth=1)\n",
    "        plt.plot(t, sols[i][9][:],color='cyan',label=r'$x_{10}(t)$',linewidth=1)\n",
    "\n",
    "        # else:\n",
    "        #     plt.plot(t, sols[i][0][:],color='r',linewidth=4)\n",
    "        #     plt.plot(t, sols[i][1][:],color='g',linewidth=4)\n",
    "        #     plt.plot(t, sols[i][2][:],color='b',linewidth=4)\n",
    "    plt.xlabel('time / depth')\n",
    "    plt.ylabel('x(t)')\n",
    "    plt.legend([r'$x_{1}(t)$',r'$x_{2}(t)$',r'$x_{3}(t)$',r'$x_{4}(t)$',r'$x_{5}(t)$',r'$x_{6}(t)$',r'$x_{7}(t)$',r'$x_{8}(t)$',r'$x_{9}(t)$',r'$x_{10}(t)$'])\n",
    "    plt.savefig(\"output/result1.png\")\n",
    "    clear_output(wait=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "count=5\n",
    "z_init = [Variable(torch.randn(1, 10)) for i in range(count)]\n",
    "train(ode_true,z_init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'time / depth')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 4320x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sol,time = conduct_experiment(ode_true)\n",
    "sol_arr = sol.cpu().detach().numpy()\n",
    "time_arr = time[0].cpu().detach().numpy()\n",
    "Z=[]\n",
    "for i in range(10):\n",
    "    z=sol_arr[:,:,i].reshape(-1).tolist()\n",
    "    Z.append(z)\n",
    "t=time_arr[:,:,0].reshape(-1).tolist()\n",
    "fig = plt.figure(figsize=(60,10))\n",
    "ax = fig.add_subplot(121)\n",
    "# for i in range(10):\n",
    "#     ax.plot(t, Z[i][:],label=f\"x_{i}\", lw=1)\n",
    "#     #ax.plot(t, Z[i][:],label=r'$x_{i}$', lw=1)\n",
    "ax.plot(t, Z[0][:],label=r'$x_{1}(t)$', lw=1)\n",
    "ax.plot(t, Z[1][:],label=r'$x_{2}(t)$', lw=1)\n",
    "ax.plot(t, Z[2][:],label=r'$x_{3}(t)$', lw=1)\n",
    "ax.plot(t, Z[3][:],label=r'$x_{4}(t)$', lw=1)\n",
    "ax.plot(t, Z[4][:],label=r'$x_{5}(t)$', lw=1)\n",
    "ax.plot(t, Z[5][:],label=r'$x_{6}(t)$', lw=1)\n",
    "ax.plot(t, Z[6][:],label=r'$x_{7}(t)$', lw=1)\n",
    "ax.plot(t, Z[7][:],label=r'$x_{8}(t)$', lw=1)\n",
    "ax.plot(t, Z[8][:],label=r'$x_{9}(t)$', lw=1)\n",
    "ax.plot(t, Z[9][:],label=r'$x_{10}(t)$', lw=1)\n",
    "ax.legend()\n",
    "ax.set_ylabel('x(t)')\n",
    "ax.set_xlabel('time / depth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\begin{array}{ll}\n",
    "\\min & f(x)=\\frac{x^{T} Q x+a^{T} x+a_{0}}{c^{T} x+c_{0}} \\\\\n",
    "\\text { s.t. } & x_{1}+x_{2}-x_{3}=3 \\\\\n",
    "& x_{1}-2 x_{2}+x_{4}=0 \\\\\n",
    "& x_{1}^{2}+2 x_{2}^{4}-x_{3} \\leq 10 \\\\\n",
    "& -x_{1}-x_{2}-x_{3}+x_{4} \\leq 5 \\\\\n",
    "& x_{2}^{2}-2 x_{1} x_{2}+x_{3}+x_{4}^{2} \\leq 10 \\\\\n",
    "& x_{1}^{2}+x_{2}^{2}+x_{3}^{2} \\leq 5\n",
    "\\end{array}\n",
    "$$\n",
    "where\n",
    "$$\n",
    "\\begin{aligned}\n",
    "&Q=\\left(\\begin{array}{cccc}\n",
    "-1 & 0.5 & 1 & 0 \\\\\n",
    "0.5 & 5.5 & -1 & -0.5 \\\\\n",
    "1 & -1 & 1 & 0 \\\\\n",
    "0 & -0.5 & 0 & 0\n",
    "\\end{array}\\right), \\quad a=\\left(\\begin{array}{c}\n",
    "1 \\\\\n",
    "-1 \\\\\n",
    "-1 \\\\\n",
    "1\n",
    "\\end{array}\\right) \\\\\n",
    "&a_{0}=-2, \\\\\n",
    "&c=(1,1,1,-1)^{T}, \\quad c_{0}=6\n",
    "\\end{aligned}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "from IPython.display import clear_output\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.color_palette(\"bright\")\n",
    "import matplotlib as mpl\n",
    "import matplotlib.cm as cm\n",
    "from tqdm import tqdm\n",
    "import torch\n",
    "from torch import Tensor\n",
    "from torch import nn\n",
    "from torch.nn  import functional as F \n",
    "from torch.autograd import Variable\n",
    "from numpy.linalg import inv\n",
    "\n",
    "class ODEF(nn.Module):\n",
    "    def forward_with_grad(self, z, t, grad_outputs):\n",
    "        \"\"\"Compute f and a df/dz, a df/dp, a df/dt\"\"\"\n",
    "        batch_size = z.shape[0]\n",
    "\n",
    "        out = self.forward(z, t)\n",
    "\n",
    "        a = grad_outputs\n",
    "\n",
    "        adfdz, adfdt, *adfdp = torch.autograd.grad(\n",
    "            (out,), (z, t) + tuple(self.parameters()), grad_outputs=(a),\n",
    "            allow_unused=True, retain_graph=True\n",
    "        )\n",
    "        \n",
    "        # grad method automatically sums gradients for batch items, we have to expand them back \n",
    "        if adfdp is not None:\n",
    "            adfdp = torch.cat([p_grad.flatten() for p_grad in adfdp]).unsqueeze(0)\n",
    "            adfdp = adfdp.expand(batch_size, -1) / batch_size\n",
    "        if adfdt is not None:\n",
    "            adfdt = adfdt.expand(batch_size, 1) / batch_size\n",
    "        \n",
    "        return out, adfdz, adfdt, adfdp\n",
    "\n",
    "    def flatten_parameters(self):\n",
    "        p_shapes = []\n",
    "        flat_parameters = []\n",
    "        for p in self.parameters():\n",
    "            p_shapes.append(p.size())\n",
    "            flat_parameters.append(p.flatten())\n",
    "        return torch.cat(flat_parameters)\n",
    "\n",
    "class weightedcustom(ODEF):\n",
    "    def __init__(self,theta=123):\n",
    "        super(weightedcustom,self).__init__()\n",
    "        self.theta = theta\n",
    "    def h1(self,x):\n",
    "        return x[0]+x[1]-x[2]-3\n",
    "    def h2(self,x):\n",
    "        return x[0]-2*x[1]+x[3]\n",
    "    def g1(self,x):\n",
    "        return x[0]**2 + 2*(x[1]**4) - x[2] -10\n",
    "    def g2(self,x):\n",
    "        return -x[0] - x[1] - x[2] + x[3] -5\n",
    "    def g3(self,x):\n",
    "        return x[1]**2 - 2*x[0]*x[1] + x[2] + x[3]**2 - 10\n",
    "    def g4(self,x):\n",
    "        return x[0]**2 + x[1]**2 + x[2]**2  -5\n",
    "    def sign(self,g):\n",
    "        if g>0:\n",
    "            return 1\n",
    "        elif g<0:\n",
    "            return -1\n",
    "        else:\n",
    "            p = np.random.rand()\n",
    "            return p\n",
    "    def forward(self, x, t):\n",
    "        Q = Tensor([[-1,0.5,1,0],\n",
    "                    [0.5,5.5,-1,-0.5],\n",
    "                    [1,-1,1,0],\n",
    "                    [0,-0.5,0,0]])\n",
    "        a = Tensor([[1,-1,-1,1]])\n",
    "        a0 = -2\n",
    "        c = Tensor([[1,1,1,-1]])\n",
    "        c0 = 6\n",
    "        ex = ((a+torch.mm(x,Q)*2)*(torch.mm(x,c.T)+c0) - (a0+torch.mm(x,a.T)+torch.mm((torch.mm(x,Q)),x.T)) *c)/((torch.mm(x,c.T)+c0)**2)\n",
    "        index = []\n",
    "        if torch.abs(torch.abs(self.h1(x[0]))-torch.abs(self.h2(x[0])))<1e-2:\n",
    "            if x[0][0]+x[0][1]-x[0][2]>=0 and x[0][0]-2*x[0][1]+x[0][3]>=0:\n",
    "                A = [1,-1/2,-1/2,1/2]\n",
    "                ex += self.theta*Tensor([A])\n",
    "            elif x[0][0]+x[0][1]-x[0][2]>=0 and x[0][0]-2*x[0][1]+x[0][3]<0:\n",
    "                A = [0,3/2,-1/2,-1/2]\n",
    "                ex += self.theta*Tensor([A])\n",
    "            elif x[0][0]+x[0][1]-x[0][2]<0 and x[0][0]-2*x[0][1]+x[0][3]>=0:\n",
    "                A = [0,-3/2,1/2,1/2]\n",
    "                ex += self.theta*Tensor([A])\n",
    "        else:\n",
    "            index.append(torch.abs(self.h1(x[0])).item())\n",
    "            index.append(torch.abs(self.h2(x[0])).item())\n",
    "            i = np.argmax(np.array(index))\n",
    "            if i==0:\n",
    "                A = []\n",
    "                if x[0][0]+x[0][1]-x[0][2]-3>=0:\n",
    "                    A = [1,1,-1,0]\n",
    "                else:\n",
    "                    A = [-1,-1,1,0]\n",
    "                ex += self.theta*Tensor([A])\n",
    "            elif i==1:\n",
    "                A = []\n",
    "                if x[0][0]-2*x[0][1]+x[0][3]>=0:\n",
    "                    A = [1,-2,0,1]\n",
    "                else:\n",
    "                    A = [-1,2,0,-1]\n",
    "                ex += self.theta*Tensor([A])\n",
    "        index1 = []\n",
    "        index1.append(self.g1(x[0]).item())\n",
    "        index1.append(self.g2(x[0]).item())\n",
    "        index1.append(self.g3(x[0]).item())\n",
    "        index1.append(self.g4(x[0]).item())\n",
    "        j = np.argmax(np.array(index1))\n",
    "\n",
    "        if j==0:\n",
    "            if self.g1(x[0])>0:\n",
    "                B = [2*(x[0][0].item()),8*((x[0][1]**3).item()),-1,0]\n",
    "                ex += self.theta*Tensor([B])\n",
    "        elif j==1:\n",
    "            if self.g2(x[0])>0:\n",
    "                B = [-1,-1,-1,1]\n",
    "                ex += self.theta*Tensor([B])\n",
    "        elif j==2:\n",
    "            if self.g3(x[0])>0:\n",
    "                B = [-2*(x[0][1].item()),2*(x[0][1].item())-2*(x[0][0].item()),1,2*(x[0][3].item())]\n",
    "                ex += self.theta*Tensor([B])\n",
    "        elif j==3:\n",
    "            if self.g3(x[0])>0:\n",
    "                B = [2*(x[0][0].item()),2*(x[0][1].item()),2*(x[0][2].item()),0]\n",
    "                ex += self.theta*Tensor([B])\n",
    "        return -ex\n",
    "\n",
    "\n",
    "class SpiralFunctionTest(weightedcustom):\n",
    "    def __init__(self,theta):\n",
    "        super(SpiralFunctionTest, self).__init__(theta)\n",
    "def ode_solve(z0, t0, t1, f):\n",
    "    \"\"\"\n",
    "    Simplest Euler ODE initial value solver\n",
    "    \"\"\"\n",
    "    h_max = 0.001\n",
    "    n_steps = math.ceil((abs(t1 - t0)/h_max).max().item())\n",
    "\n",
    "    h = (t1 - t0)/n_steps\n",
    "    t = t0\n",
    "    z = z0\n",
    "    h=Tensor([[0.0001]])\n",
    "    for i_step in range(n_steps):\n",
    "\n",
    "        z = z + h * f(z, t)\n",
    "        #print(z)\n",
    "        t = t + h\n",
    "    return z\n",
    "def ode_solve1(z0, t0, t1, f):\n",
    "    \"\"\"\n",
    "    Simplest RK4 ODE initial value solver\n",
    "    \"\"\"\n",
    "    h_max = 0.001\n",
    "    n_steps = math.ceil((abs(t1 - t0)/h_max).max().item())\n",
    "\n",
    "    h = (t1 - t0)/n_steps\n",
    "    t = t0\n",
    "    z = z0\n",
    "    h=Tensor([[0.0001]])\n",
    "    for i_step in range(n_steps):\n",
    "        k1 = h*f(z, t)\n",
    "        k2 = h * (f((z+h/2), (z+k1/2)))\n",
    "        k3 = h * (f((z+h/2), (z+k2/2)))\n",
    "        k4 = h * (f((z+h), (z+k3)))\n",
    "        k = (k1+2*k2+2*k3+k4)/6\n",
    "        z = z + k\n",
    "        \n",
    "        t = t + h\n",
    "    return z\n",
    "class NeuralODE(nn.Module):\n",
    "    def __init__(self, func):\n",
    "        super(NeuralODE, self).__init__()\n",
    "        assert isinstance(func, ODEF)\n",
    "        self.func = func\n",
    "\n",
    "    def forward(self, z0, t=Tensor([0., 1.]), return_whole_sequence=False):\n",
    "        t = t.to(z0)\n",
    "        z = ODEAdjoint.apply(z0, t, self.func)\n",
    "        \n",
    "        if return_whole_sequence:\n",
    "            return z\n",
    "        else:\n",
    "            return z[-1]\n",
    "def RMSELoss(yhat,y):\n",
    "    return torch.sqrt(torch.mean((yhat-y)**2))\n",
    "class ODEAdjoint(torch.autograd.Function):\n",
    "    @staticmethod\n",
    "    def forward(ctx, z0, t, func):\n",
    "        assert isinstance(func, ODEF)\n",
    "        bs, *z_shape = z0.size()\n",
    "        time_len = t.size(0)\n",
    "\n",
    "        with torch.no_grad():\n",
    "            z = torch.zeros(time_len, bs, *z_shape).to(z0)\n",
    "            z[0] = z0\n",
    "            for i_t in range(time_len - 1):\n",
    "                #z0 = ode_solve(z0, t[i_t], t[i_t+1], func)\n",
    "                z0 = ode_solve1(z0, t[i_t], t[i_t+1], func)\n",
    "                z[i_t+1] = z0\n",
    "                if RMSELoss(z0,z[i_t])<1e-4:\n",
    "                    print(i_t)\n",
    "                    break\n",
    "        ctx.func = func\n",
    "        return z\n",
    "def conduct_experiment(ode_true):\n",
    "    time=[]\n",
    "    # Create data\n",
    "    #z0 = Variable(torch.Tensor([[0.6, 0.3]]))\n",
    "    #z0 = Variable(torch.Tensor([[4.2,3.5,6.3,5.4,0.5,8.6,1.7,7.8,7.9,5.5]]))\n",
    "    \n",
    "    z0 = Variable(torch.Tensor([[1,1,-1,1]]))\n",
    "    #z0 = Variable(torch.Tensor([[2.0598, -0.4858, -1.4260, -3.0313]]))\n",
    "    #z0 = torch.tensor([[4.,1.,-3.,1.]], requires_grad=True)\n",
    "    t_max = 200\n",
    "    n_points = 100\n",
    "\n",
    "    index_np = np.arange(0, n_points, 1, dtype=np.int)\n",
    "    index_np = np.hstack([index_np[:, None]])\n",
    "    times_np = np.linspace(0, t_max, num=n_points)\n",
    "    times_np = np.hstack([times_np[:, None]])\n",
    "\n",
    "    times = torch.from_numpy(times_np[:, :, None]).to(z0)\n",
    "    obs = ode_true(z0, times, return_whole_sequence=True).detach()\n",
    "    time.append(times)\n",
    "    return obs,time\n",
    "def train(ode_true,count,z_init):\n",
    "    time=[]\n",
    "    time_arrs=[]\n",
    "    sols = []\n",
    "    # Create data    \n",
    "    # z_init = [Variable(torch.randn(1, 3)) for i in range(num)]\n",
    "    for z_0 in z_init:\n",
    "        t_max = 20\n",
    "        n_points = 100\n",
    "\n",
    "        index_np = np.arange(0, n_points, 1, dtype=np.int)\n",
    "        index_np = np.hstack([index_np[:, None]])\n",
    "        times_np = np.linspace(0, t_max, num=n_points)\n",
    "        times_np = np.hstack([times_np[:, None]])\n",
    "\n",
    "        times = torch.from_numpy(times_np[:, :, None]).to(z_0)\n",
    "        obs = ode_true(z_0, times, return_whole_sequence=True).detach()\n",
    "        time.append(times)\n",
    "\n",
    "        sol_arr = obs.cpu().detach().numpy()\n",
    "        time_arr = time[0].cpu().detach().numpy()\n",
    "        Z=[]\n",
    "        for i in range(3):\n",
    "            z=sol_arr[:,:,i].reshape(-1).tolist()\n",
    "            Z.append(z)\n",
    "        sols.append(Z)\n",
    "        time_arrs.append(time_arr)\n",
    "    t=time_arrs[0][:,:,0].reshape(-1).tolist()\n",
    "    plt.figure(figsize=(20,15))\n",
    "    plt.rcParams.update({'font.size': 16})\n",
    "    for i in range(len(z_init)):\n",
    "        #if i==0:\n",
    "        plt.plot(t, sols[i][0][:],color='r',label=r'$x_{1}(t)$',linewidth=1)\n",
    "        plt.plot(t, sols[i][1][:],color='g',label=r'$x_{2}(t)$',linewidth=1)\n",
    "        plt.plot(t, sols[i][2][:],color='b',label=r'$x_{3}(t)$',linewidth=1)\n",
    "        # else:\n",
    "        #     plt.plot(t, sols[i][0][:],color='r',linewidth=4)\n",
    "        #     plt.plot(t, sols[i][1][:],color='g',linewidth=4)\n",
    "        #     plt.plot(t, sols[i][2][:],color='b',linewidth=4)\n",
    "    plt.xlabel('time / depth')\n",
    "    plt.ylabel('x(t)')\n",
    "    plt.legend([r'$x_{1}(t)$',r'$x_{2}(t)$',r'$x_{3}(t)$'])\n",
    "    plt.savefig(\"output/theta_\"+str(count)+\".png\")\n",
    "    clear_output(wait=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "theta_s =  range(25,201,25)\n",
    "ode_trues = []\n",
    "\n",
    "for i in theta_s:\n",
    "    \n",
    "    ode_trues.append(NeuralODE(SpiralFunctionTest(theta=i)))\n",
    "count=5\n",
    "z_init = [Variable(torch.randn(1, 3)) for i in range(count)]\n",
    "for func,num in tqdm(zip(ode_trues,theta_s)):\n",
    "    conduct_experiment(func,num,z_init)\n",
    "    print(\"Done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ode_true = NeuralODE(SpiralFunctionTest(theta=158))\n",
    "sol,time = conduct_experiment(ode_true) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sol_arr = sol.cpu().detach().numpy()\n",
    "time_arr = time[0].cpu().detach().numpy()\n",
    "Z=[]\n",
    "for i in range(4):\n",
    "    z=sol_arr[:,:,i].reshape(-1).tolist()\n",
    "    Z.append(z)\n",
    "t=time_arr[:,:,0].reshape(-1).tolist()\n",
    "fig = plt.figure(figsize=(60,10))\n",
    "ax = fig.add_subplot(121)\n",
    "ax.plot(t, Z[0][:],label=r'$x_{1}(t)$', lw=1)\n",
    "ax.plot(t, Z[1][:],label=r'$x_{2}(t)$', lw=1)\n",
    "ax.plot(t, Z[2][:],label=r'$x_{3}(t)$', lw=1)\n",
    "ax.plot(t, Z[3][:],label=r'$x_{4}(t)$', lw=1)\n",
    "ax.legend()\n",
    "ax.set_ylabel('x(t)')\n",
    "ax.set_xlabel('time / depth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\min f(x) = \\dfrac{x^TQx+ax+a_0}{cx+c_0}$\n",
    " $$st: \\begin{cases} Ax \\le b\\\\ x \\in \\Omega\\end{cases}$$\n",
    "where\n",
    "$$ Q = \\begin{bmatrix} 5 & -1 & 2 & 0 \\\\ -1 & 5 & -1 & 3 \\\\ 2 & -1 & 3 & 0 \\\\ 0 & 3 & 0 & 5    \\end{bmatrix}, a = \\begin{bmatrix} 1\\\\ -2 \\\\ -2 \\\\ 1 \\end{bmatrix}, c = \\begin{bmatrix} 2\\\\ 1 \\\\ 1 \\\\ 0 \\end{bmatrix},a_0=-2,c_0=4, A = \\begin{bmatrix} 1 & -1 & 0 & 0 \\\\  0 & 0 & 1 & -1    \\end{bmatrix}, b = \\begin{bmatrix} 1\\\\2   \\end{bmatrix} $$\n",
    "and\n",
    "$\\Omega = \\{x\\in \\mathbf{R^4} : 5\\le x_i \\le 10, i=1,2,3,4\\}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "from IPython.display import clear_output\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.color_palette(\"bright\")\n",
    "import matplotlib as mpl\n",
    "import matplotlib.cm as cm\n",
    "import torch\n",
    "from torch import Tensor\n",
    "from torch import nn\n",
    "from torch.nn  import functional as F \n",
    "from torch.autograd import Variable\n",
    "from numpy.linalg import inv\n",
    "use_cuda = torch.cuda.is_available()\n",
    "class weightedcustom(nn.Module):\n",
    "    def __init__(self,theta=123):\n",
    "        super(weightedcustom,self).__init__()\n",
    "        self.theta = theta\n",
    "    def h1(self,x):\n",
    "        return x[0]-x[1]-1\n",
    "    def h2(self,x):\n",
    "        return x[2]-x[3]-2\n",
    "    def g1(self,x):\n",
    "        return x[0]-10\n",
    "    def g2(self,x):\n",
    "        return x[1]-10\n",
    "    def g3(self,x):\n",
    "        return x[2]-10\n",
    "    def g4(self,x):\n",
    "        return x[3]-10\n",
    "    def g5(self,x):\n",
    "        return 5-x[0]\n",
    "    def g6(self,x):\n",
    "        return 5-x[1]\n",
    "    def g7(self,x):\n",
    "        return 5-x[2]\n",
    "    def g8(self,x):\n",
    "        return 5-x[3]\n",
    "    def forward(self, x, t):\n",
    "        Q = Tensor([[5,-1,2,0],\n",
    "                    [-1,5,-1,3],\n",
    "                    [2,-1,3,0],\n",
    "                    [0,3,0,5]])\n",
    "        a = Tensor([[1,-2,-2,1]])\n",
    "        a0 = -2\n",
    "        c = Tensor([[2,1,1,0]])\n",
    "        c0 = 4\n",
    "        \n",
    "        with torch.set_grad_enabled(True):\n",
    "            x.requires_grad=True\n",
    "            #x = x.to('cuda')\n",
    "            x.retain_grad()\n",
    "            #print(x)\n",
    "            f = (torch.mm((torch.mm(x,Q)),x.T)+a0+torch.mm(x,a.T))/(torch.mm(x,c.T)+c0)\n",
    "            index = []\n",
    "            index.append(torch.abs(self.h1(x[0])).item())\n",
    "            index.append(torch.abs(self.h2(x[0])).item())\n",
    "            i = np.argmax(np.array(index))\n",
    "            if i==0:\n",
    "                A = []\n",
    "                if x[0][0]-x[0][1]-1>=0:\n",
    "                    A = [1,-1,0,0]\n",
    "                else:\n",
    "                    A = [-1,1,0,0]\n",
    "                f += self.theta*torch.mm(x,(Tensor([A])).T)\n",
    "            elif i==1:\n",
    "                A = []\n",
    "                if x[0][2]-x[0][3]-2>=0:\n",
    "                    A = [0,0,1,-1]\n",
    "                else:\n",
    "                    A = [0,0,-1,1]\n",
    "                f += self.theta*torch.mm(x,(Tensor([A])).T)\n",
    "            \n",
    "            index1 = [0]\n",
    "            index1.append(self.g1(x[0]).item())\n",
    "            index1.append(self.g2(x[0]).item())\n",
    "            index1.append(self.g3(x[0]).item())\n",
    "            index1.append(self.g4(x[0]).item())\n",
    "            index1.append(self.g5(x[0]).item())\n",
    "            index1.append(self.g6(x[0]).item())\n",
    "            index1.append(self.g7(x[0]).item())\n",
    "            index1.append(self.g8(x[0]).item())\n",
    "            j = np.argmax(np.array(index1))\n",
    "\n",
    "            if j!=0:\n",
    "                if j==1:\n",
    "                    B = [1,0,0,0]\n",
    "                    f += self.theta*torch.mm(x,(Tensor([B])).T)\n",
    "                elif j==2:\n",
    "                    B = [0,1,0,0]\n",
    "                    f += self.theta*torch.mm(x,(Tensor([B])).T)\n",
    "                elif j==3:\n",
    "                    B = [0,0,1,0]\n",
    "                    f += self.theta*torch.mm(x,(Tensor([B])).T)\n",
    "                elif j==4:\n",
    "                    B = [0,0,0,1]\n",
    "                    f += self.theta*torch.mm(x,(Tensor([B])).T)\n",
    "                if j==5:\n",
    "                    B = [-1,0,0,0]\n",
    "                    f += self.theta*torch.mm(x,(Tensor([B])).T)\n",
    "                elif j==6:\n",
    "                    B = [0,-1,0,0]\n",
    "                    f += self.theta*torch.mm(x,(Tensor([B])).T)\n",
    "                elif j==7:\n",
    "                    B = [0,0,-1,0]\n",
    "                    f += self.theta*torch.mm(x,(Tensor([B])).T)\n",
    "                elif j==8:\n",
    "                    B = [0,0,0,-1]\n",
    "                    f += self.theta*torch.mm(x,(Tensor([B])).T)\n",
    "            f.backward(retain_graph=True)\n",
    "            return -x.grad\n",
    "\n",
    "def ode_solve(z0, t0, t1, f):\n",
    "    \"\"\"\n",
    "    Simplest Euler ODE initial value solver\n",
    "    \"\"\"\n",
    "    h_max = 0.001\n",
    "    n_steps = math.ceil((abs(t1 - t0)/h_max).max().item())\n",
    "\n",
    "    h = (t1 - t0)/n_steps\n",
    "    t = t0\n",
    "    z = z0\n",
    "    h=Tensor([[0.0001]]).to('cuda')\n",
    "    for i_step in range(n_steps):\n",
    "\n",
    "        z = z + h * f(z, t)\n",
    "        #print(z)\n",
    "        t = t + h\n",
    "    return z\n",
    "def ode_solve1(z0, t0, t1, f):\n",
    "    \"\"\"\n",
    "    Simplest RK4 ODE initial value solver\n",
    "    \"\"\"\n",
    "    h_max = 0.001\n",
    "    n_steps = math.ceil((abs(t1 - t0)/h_max).max().item())\n",
    "\n",
    "    h = (t1 - t0)/n_steps\n",
    "    t = t0\n",
    "    z = z0\n",
    "\n",
    "    h=Tensor([[0.0001]])\n",
    "    #print(n_steps)\n",
    "    for i_step in range(n_steps):\n",
    "        k1 = h*f(z, t)\n",
    "        k2 = h * (f((z+h/2), (z+k1/2)))\n",
    "        k3 = h * (f((z+h/2), (z+k2/2)))\n",
    "        k4 = h * (f((z+h), (z+k3)))\n",
    "        k = (1/6)*(k1+2*k2+2*k3+k4)\n",
    "        z = z + k\n",
    "        \n",
    "        t = t + h\n",
    "    return z\n",
    "class NeuralODE(nn.Module):\n",
    "    def __init__(self, func):\n",
    "        super(NeuralODE, self).__init__()\n",
    "        self.func = func\n",
    "\n",
    "    def forward(self, z0, t=Tensor([0., 1.]), return_whole_sequence=False):\n",
    "        t = t.to(z0)\n",
    "        z = ODEAdjoint.apply(z0, t, self.func)\n",
    "        \n",
    "        if return_whole_sequence:\n",
    "            return z\n",
    "        else:\n",
    "            return z[-1]\n",
    "def RMSELoss(yhat,y):\n",
    "    return torch.sqrt(torch.mean((yhat-y)**2))\n",
    "class ODEAdjoint(torch.autograd.Function):\n",
    "    @staticmethod\n",
    "    def forward(ctx, z0, t, func):\n",
    "        bs, *z_shape = z0.size()\n",
    "        time_len = t.size(0)\n",
    "        z = torch.zeros(time_len, bs, *z_shape).to(z0)\n",
    "        z[0] = z0\n",
    "        for i_t in range(time_len - 1):\n",
    "            #z0 = ode_solve(z0, t[i_t], t[i_t+1], func)\n",
    "            z0 = ode_solve1(z0, t[i_t], t[i_t+1], func)\n",
    "            #print(RMSELoss(z0,z[i_t]))\n",
    "            z[i_t+1] = z0\n",
    "            if RMSELoss(z0,z[i_t])<1e-4:\n",
    "                print(i_t)\n",
    "                break\n",
    "\n",
    "        ctx.func = func\n",
    "        return z\n",
    "def conduct_experiment(ode_true):\n",
    "    time=[]\n",
    "    z0 = torch.ones(1, 4, requires_grad=True)\n",
    "    t_max = 50\n",
    "    n_points = 200\n",
    "\n",
    "    index_np = np.arange(0, n_points, 1, dtype=np.int)\n",
    "    index_np = np.hstack([index_np[:, None]])\n",
    "    times_np = np.linspace(0, t_max, num=n_points)\n",
    "    times_np = np.hstack([times_np[:, None]])\n",
    "\n",
    "    times = torch.from_numpy(times_np[:, :, None]).to(z0)\n",
    "    obs = ode_true(z0, times, return_whole_sequence=True).detach()\n",
    "    time.append(times)\n",
    "    return obs,time\n",
    "def train(ode_true,count,z_init):\n",
    "    time=[]\n",
    "    time_arrs=[]\n",
    "    sols = []\n",
    "    # Create data    \n",
    "    # z_init = [Variable(torch.randn(1, 3)) for i in range(num)]\n",
    "    for z_0 in z_init:\n",
    "        t_max = 6.29*5\n",
    "        n_points = 100\n",
    "\n",
    "        index_np = np.arange(0, n_points, 1, dtype=np.int)\n",
    "        index_np = np.hstack([index_np[:, None]])\n",
    "        times_np = np.linspace(0, t_max, num=n_points)\n",
    "        times_np = np.hstack([times_np[:, None]])\n",
    "\n",
    "        times = torch.from_numpy(times_np[:, :, None]).to(z_0)\n",
    "        obs = ode_true(z_0, times, return_whole_sequence=True).detach()\n",
    "        time.append(times)\n",
    "\n",
    "        sol_arr = obs.cpu().detach().numpy()\n",
    "        time_arr = time[0].cpu().detach().numpy()\n",
    "        Z=[]\n",
    "        for i in range(3):\n",
    "            z=sol_arr[:,:,i].reshape(-1).tolist()\n",
    "            Z.append(z)\n",
    "        sols.append(Z)\n",
    "        time_arrs.append(time_arr)\n",
    "    t=time_arrs[0][:,:,0].reshape(-1).tolist()\n",
    "    plt.figure(figsize=(20,15))\n",
    "    plt.rcParams.update({'font.size': 16})\n",
    "    for i in range(len(z_init)):\n",
    "        plt.plot(t, sols[i][0][:],color='r',label=r'$x_{1}(t)$',linewidth=1)\n",
    "        plt.plot(t, sols[i][1][:],color='g',label=r'$x_{2}(t)$',linewidth=1)\n",
    "        plt.plot(t, sols[i][2][:],color='b',label=r'$x_{3}(t)$',linewidth=1)\n",
    "    plt.xlabel('time / depth')\n",
    "    plt.ylabel('x(t)')\n",
    "    plt.legend([r'$x_{1}(t)$',r'$x_{2}(t)$',r'$x_{3}(t)$'])\n",
    "    plt.savefig(\"output/theta_\"+str(count)+\".png\")\n",
    "    clear_output(wait=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "theta_s =  range(25,201,25)\n",
    "ode_trues = []\n",
    "\n",
    "for i in theta_s:\n",
    "    \n",
    "    ode_trues.append(NeuralODE(SpiralFunctionTest(theta=i)))\n",
    "count=5\n",
    "z_init = [Variable(torch.randn(1, 3)) for i in range(count)]\n",
    "for func,num in tqdm(zip(ode_trues,theta_s)):\n",
    "    conduct_experiment(func,num,z_init)\n",
    "    print(\"Done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ode_true = NeuralODE(weightedcustom(theta=100))\n",
    "sol,time = conduct_experiment(ode_true) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sol_arr = sol.cpu().detach().numpy()\n",
    "time_arr = time[0].cpu().detach().numpy()\n",
    "Z=[]\n",
    "for i in range(4):\n",
    "    z=sol_arr[:,:,i].reshape(-1).tolist()\n",
    "    Z.append(z)\n",
    "t=time_arr[:,:,0].reshape(-1).tolist()\n",
    "fig = plt.figure(figsize=(60,10))\n",
    "ax = fig.add_subplot(121)\n",
    "ax.plot(t, Z[0][:],label=r'$x_{1}(t)$', lw=1)\n",
    "ax.plot(t, Z[1][:],label=r'$x_{2}(t)$', lw=1)\n",
    "ax.plot(t, Z[2][:],label=r'$x_{3}(t)$', lw=1)\n",
    "ax.plot(t, Z[3][:],label=r'$x_{4}(t)$', lw=1)\n",
    "ax.legend()\n",
    "ax.set_ylabel('x(t)')\n",
    "ax.set_xlabel('time / depth')"
   ]
  }
 ],
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