Spatial_Advection_Diffusion_WeakLib_KRR-DEV2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
      "Populating the interactive namespace from numpy and matplotlib\n",
      "Sklearn's version: 1.2.2\n",
      "mrmr is not installed in the env you are using. This may cause an error in future if you try to use the (missing) lib.\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import numpy as np\n",
    "from parametric_pde_find import *\n",
    "from robust_pde_diff import DLrSR, RobustPCA\n",
    "from scipy.integrate import odeint\n",
    "from scipy.signal import savgol_filter\n",
    "from numpy.fft import fft, ifft, fftfreq\n",
    "\n",
    "from best_subset import *\n",
    "from solvel0 import solvel0\n",
    "from UBIC import fit_blr, UBIC, decreasing_values_indices\n",
    "\n",
    "from sklearn.linear_model import BayesianRidge, ARDRegression, Ridge, Lasso\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import train_test_split, KFold\n",
    "\n",
    "from findiff import FinDiff\n",
    "\n",
    "from tqdm import tqdm\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "def coeff_error(est, ground): return 100*np.linalg.norm(est-ground, 1)/np.linalg.norm(ground, 1)\n",
    "\n",
    "def ssr2llf(ssr, nobs):\n",
    "    nobs2 = nobs / 2.0\n",
    "    llf = -nobs2 * np.log(2 * np.pi) - nobs2 * np.log(ssr / nobs) - nobs2\n",
    "    return llf\n",
    "\n",
    "def log_like_value(prediction, ground):                                                                                                               \n",
    "    nobs = float(ground.shape[0])\n",
    "    ssr = np.sum(np.abs(ground - prediction)**2)\n",
    "    return ssr2llf(ssr, nobs)\n",
    "\n",
    "def BIC_AIC(prediction, ground, nparams, reg_func = lambda x: x):\n",
    "    nparams = reg_func(nparams)\n",
    "    llf = log_like_value(prediction, ground)\n",
    "    return -2*llf + np.log(ground.shape[0])*nparams, -2*llf + 2*nparams\n",
    "\n",
    "def rss2bic(rss, nparams, nobs, epsilon=1e-5):\n",
    "    return nobs*np.log(2*np.pi*rss/nobs+epsilon) + np.log(nobs)*nparams\n",
    "\n",
    "def AIC_Loss(As,bs,x,epsilon=1e-5):\n",
    "    # D: Number of candidates | m: either len(t) or len(x) (temporal or spatial group)\n",
    "    D,m = x.shape\n",
    "    # n: Number of horizon\n",
    "    n,_ = As[0].shape\n",
    "    N = n*m\n",
    "    # Complexity\n",
    "    k = np.count_nonzero(x)/m\n",
    "    # AIC\n",
    "    rss = [np.linalg.norm(bs[j] - As[j].dot(x[:,j].reshape(D,1)))**2 for j in range(m)]\n",
    "    rss = np.sum(rss)\n",
    "    return N*np.log(rss/N+epsilon) + 2*k + (2*k**2+2*k)/(N-k-1)\n",
    "\n",
    "# Sensitivity to epsilon was never discussed!\n",
    "def BIC_Loss(As,bs,x,epsilon=1e-5):\n",
    "    # D: Number of candidates | m: either len(t) or len(x) (temporal or spatial group)\n",
    "    D,m = x.shape\n",
    "    # n: Number of horizon\n",
    "    n,_ = As[0].shape\n",
    "    N = n*m\n",
    "    # Complexity\n",
    "    k = np.count_nonzero(x)/m\n",
    "    # BIC\n",
    "    res = np.vstack([bs[j] - As[j]@x[:, j:j+1] for j in range(m)])\n",
    "    assert len(res) == n*m\n",
    "    rss = np.linalg.norm(res, ord=2)**2 \n",
    "    # llf = ssr2llf(rss, N)\n",
    "    # -2*llf + np.log(N)*k # AIC: -2*llf + 2*k\n",
    "    # N*np.log(2*np.pi*rss/N) + np.log(N)*k\n",
    "    return N*np.log(2*np.pi*rss/N+epsilon) + np.log(N)*k\n",
    "\n",
    "np.random.seed(1234)\n",
    "fontsize = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def advection_rhs(u, t, params):\n",
    "    k, L, x = params    \n",
    "    kappa = -1.5 + 1.0*np.cos(2*x*np.pi/L)\n",
    "    deriv = ifft(1j*k*fft(kappa*u)) + 0.1*ifft(-k**2*fft(u))\n",
    "    return real(deriv)\n",
    "\n",
    "def spatial_temporal_group(Theta, Ut, domain_shape, dependent=\"temporal\"):\n",
    "    n, m = domain_shape\n",
    "    assert n*m == Theta.shape[0], Ut.shape[0]\n",
    "    if dependent == \"temporal\":\n",
    "        Theta_grouped = [(Theta[j*n:(j+1)*n,:]).real for j in range(m)]\n",
    "        Ut_grouped = [(Ut[j*n:(j+1)*n]).real for j in range(m)]\n",
    "    elif dependent == \"spatial\":\n",
    "        Theta_grouped = [(Theta[n*np.arange(m)+j,:]).real for j in range(n)]\n",
    "        Ut_grouped = [(Ut[n*np.arange(m)+j]).real for j in range(n)]\n",
    "    else: return\n",
    "    return Theta_grouped, Ut_grouped\n",
    "\n",
    "def normalize_groups(As, bs, normalize=2):\n",
    "    m = len(As)\n",
    "    n,D = As[0].shape\n",
    "    \n",
    "    # get norm of each column\n",
    "    candidate_norms = np.zeros(D)\n",
    "    for i in range(D):\n",
    "        candidate_norms[i] = Norm(np.vstack([A[:,i] for A in As]), normalize)\n",
    "\n",
    "    norm_bs = [m*Norm(b, normalize) for b in bs]\n",
    "\n",
    "    # normalize \n",
    "    for i in range(m):\n",
    "        As[i] = As[i].dot(np.diag(candidate_norms**-1))\n",
    "        bs[i] = bs[i]/norm_bs[i]\n",
    "        \n",
    "    return As, bs\n",
    "\n",
    "def remove_f(uu, percent):\n",
    "    if percent <= 0: return uu\n",
    "    PSD = (uu*np.conj(uu))/np.prod(uu.shape)\n",
    "    PSD = PSD.real\n",
    "    mask = (PSD>np.percentile(PSD, percent)).astype(np.float32)\n",
    "    return uu*mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set size of grid\n",
    "n = 256\n",
    "m = 256\n",
    "L = 5\n",
    "\n",
    "# Set up grid\n",
    "x = np.linspace(-L,L,n+1)[:-1];   dx = x[1]-x[0]\n",
    "t = np.linspace(0,5,m);         dt = t[1]-t[0]\n",
    "k = 2*np.pi*fftfreq(n, d = dx)\n",
    "\n",
    "# Initial condition\n",
    "np.random.seed(0)\n",
    "u0 = np.cos(2*x*np.pi/L)\n",
    "u01 = np.exp(-x**2)\n",
    "\n",
    "# Solve with time dependent u_x term\n",
    "params = (k,L,x)\n",
    "u = odeint(advection_rhs, u0, t, args=(params,)).T\n",
    "u1 = odeint(advection_rhs, u01, t, args=(params,)).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "u_x_true = -1.5 + 1.0*np.cos(2*x*np.pi/L) \n",
    "u_true = -2*np.pi/L*np.sin(2*x*np.pi/L)\n",
    "u_xx_true = 0.1*np.ones(n)\n",
    "\n",
    "fig = figure(figsize=(16,4))\n",
    "X, T = np.meshgrid(x, t)\n",
    "\n",
    "subplot(1,2,1)\n",
    "pcolor(X, T, u.T, cmap=cm.coolwarm)\n",
    "xlabel('x', fontsize = fontsize)\n",
    "ylabel('t', fontsize = fontsize)\n",
    "xticks(fontsize = fontsize)\n",
    "yticks(fontsize = fontsize)\n",
    "xlim([x[0],x[-1]])\n",
    "\n",
    "subplot(1,2,2)\n",
    "plot(x,u_true, label='$u$')\n",
    "plot(x,u_x_true, label='$u_{x}$')\n",
    "plot(x,u_xx_true, label='$u_{xx}$')\n",
    "xlim([x[0],x[-1]])\n",
    "ylim([-2.75, 1.5])\n",
    "\n",
    "xticks(fontsize = fontsize)\n",
    "yticks(fontsize = fontsize-2)\n",
    "xlabel('x', fontsize = fontsize)\n",
    "legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize = fontsize+2)\n",
    "show()\n",
    "# fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_lv = 2\n",
    "noise_lv = abs(noise_lv)\n",
    "u_clean = u.copy()\n",
    "u = u + 0.01*noise_lv*std(u)*np.random.randn(*u.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "is_denoising = True\n",
    "if is_denoising:\n",
    "    window_size = 15\n",
    "    poly_order = 2\n",
    "    # equivalent to np.hstack([savgol_filter(u[:, i], window_size, poly_order).reshape(-1, 1) for i in range(u.shape[-1])])\n",
    "    u = savgol_filter(u, window_size, poly_order, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ut, Theta, rhs_des = build_linear_system(u, dt, dx, D=4, P=3, time_diff = 'FD', space_diff = 'Fourier')\n",
    "# Theta_grouped = [real(Theta[n*np.arange(m)+j,:]) for j in range(n)]\n",
    "# Ut_grouped = [real(Ut[n*np.arange(m)+j]) for j in range(n)]\n",
    "\n",
    "# physics is such fundamental that it can be discovered from in low-freq data (validation set)\n",
    "fft_percent = 90\n",
    "acc = 8\n",
    "d_dx = FinDiff(0, dx, 1, acc=acc); u_x = d_dx(u)\n",
    "d_dxx = FinDiff(0, dx, 2, acc=acc); u_xx = d_dxx(u)\n",
    "d_dxxx = FinDiff(0, dx, 3, acc=acc); u_xxx = d_dxxx(u)\n",
    "d_dt = FinDiff(1, dx, 1, acc=acc); u_t = d_dt(u)\n",
    "    \n",
    "phi1 = [u**i for i in range(0, 4)]; phi1_names = [\"\", \"u\", \"u^{2}\", \"u^{3}\"]\n",
    "phi2 = [u_x, u_xx, u_xxx]; phi2_names = [\"u_{x}\", \"u_{xx}\", \"u_{xxx}\"]\n",
    "phi = phi1.copy(); phi_names = phi1_names.copy()\n",
    "phi.extend([p1*p2 for p1 in phi1 for p2 in phi2])\n",
    "phi = np.array(phi)\n",
    "phi_names.extend([p1+p2 for p1 in phi1_names for p2 in phi2_names])\n",
    "fft_phi = [remove_f(np.fft.fft(_, axis=-1), fft_percent) for _ in phi]\n",
    "fft_u_t = remove_f(np.fft.fft(u_t, axis=-1), fft_percent)\n",
    "\n",
    "Theta_grouped = np.moveaxis(phi, 0, -1)\n",
    "Ut_grouped = np.expand_dims(u_t, -1)\n",
    "rhs_des = phi_names\n",
    "fft_Theta_grouped = np.moveaxis(fft_phi, 0, -1)\n",
    "fft_Ut_grouped = np.expand_dims(fft_u_t, -1)\n",
    "\n",
    "del phi1, phi2, phi1_names, phi2_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 u\n",
      "4 u_{x}\n",
      "5 u_{xx}\n",
      "6 u_{xxx}\n",
      "8 uu_{xx}\n",
      "11 u^{2}u_{xx}\n",
      "14 u^{3}u_{xx}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solve and plot\n",
    "Xi,Tol,Losses = TrainSGTRidge(Theta_grouped, Ut_grouped)\n",
    "xi = Xi[np.argmin(Losses)]\n",
    "\n",
    "pylab.rcParams['figure.figsize'] = (12, 8)\n",
    "for j in range(xi.shape[0]): \n",
    "    if np.linalg.norm(xi[j,:]) > 10**-10:\n",
    "        print(j, rhs_des[j])\n",
    "        plot(x, xi[j,:], label = rhs_des[j])\n",
    "                \n",
    "title('Convection Diffusion Equation Coefficients', fontsize = 16)\n",
    "xlabel('x', fontsize = 16)\n",
    "ylabel('Coefficient Value', fontsize = 16)\n",
    "legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize = 14)\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# percent = 90\n",
    "# est_xi = []\n",
    "# for i in range(fu.shape[0]):\n",
    "#     # phi_g = np.vstack((u[i:i+1, :], u_x[i:i+1], u_xx[i:i+1])).T\n",
    "#     # u_t_g = u_t[i:i+1, :].reshape(-1, 1)\n",
    "#     f1, f2, f3 = fu, fu_x, fu_xx\n",
    "#     f1, f2, f3 = remove_f(f1, percent), remove_f(f2, percent), remove_f(f3, percent)\n",
    "#     phi_g = np.vstack((f1[i:i+1, :], f2[i:i+1], f3[i:i+1])).T\n",
    "#     u_t_g = fu_t[i:i+1, :].reshape(-1, 1)\n",
    "#     est_xi.append(np.linalg.lstsq(phi_g, u_t_g, rcond=None)[0])\n",
    "# est_xi = np.hstack(est_xi)\n",
    "\n",
    "# est_xi  = linear_fit_grouped_data((Theta_grouped, Ut_grouped), (1, 4, 5))\n",
    "# plt.plot(est_xi[0])\n",
    "# plt.plot(est_xi[1])\n",
    "# plt.plot(est_xi[2]); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                                                                                       | 0/256 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2024-06-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [01:31<00:00,  2.81it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{(1, 3, 4, 6, 10, 12, 13, 15),\n",
       " (1, 3, 4, 5, 9, 11, 13, 14, 15),\n",
       " (1, 3, 4, 7, 8, 11, 12, 13, 14),\n",
       " (1, 4, 5, 6, 8, 9, 10, 14, 15),\n",
       " (0, 1, 2, 3, 5, 6, 10, 12, 14, 15),\n",
       " (1, 2, 4, 6, 8, 14),\n",
       " (0, 1, 2, 4, 6, 8, 9, 10, 14, 15),\n",
       " (0, 1, 3, 4, 5, 8, 9, 12, 15),\n",
       " (1, 3, 4, 5, 6, 14, 15),\n",
       " (1, 3, 4, 5, 6, 10, 11, 13, 14, 15),\n",
       " (1, 2, 3, 4, 5, 8, 10, 11, 13, 14),\n",
       " (0, 1, 2, 4, 5),\n",
       " (0, 1, 2, 3, 4, 5, 6, 7, 11, 14),\n",
       " (1, 4, 5, 7, 13),\n",
       " (1, 3, 4, 5, 10, 11, 12),\n",
       " (0, 2, 4, 6),\n",
       " (1, 2, 3, 4, 5, 11),\n",
       " (2, 4, 5),\n",
       " (0, 1, 2, 3, 4, 5, 6, 10, 11, 12),\n",
       " (1, 3, 4, 12, 14),\n",
       " (1, 3, 4, 6, 7, 9, 15),\n",
       " (4, 5, 6, 11, 14, 15),\n",
       " (0, 2, 3, 4, 6, 10, 12, 14, 15),\n",
       " (0, 1, 2, 3, 4, 6, 7, 13),\n",
       " (0, 1, 2, 4, 8, 15),\n",
       " (0, 1, 3, 4, 5, 6, 8, 11),\n",
       " (3, 15),\n",
       " (1, 4, 8, 9, 10, 11, 12, 14, 15),\n",
       " (1, 4, 6, 12, 13, 14, 15),\n",
       " (1, 2, 4, 5, 7, 8, 9, 10, 11, 12),\n",
       " (0, 1, 2, 4, 5, 6, 7, 9, 11, 12, 14, 15),\n",
       " (0, 1, 2, 4, 5, 6, 7, 10, 11, 14),\n",
       " (0, 1, 4, 5, 9, 12),\n",
       " (1, 2, 3, 4, 6, 7, 12, 15),\n",
       " (3, 4, 5, 10, 11, 13, 15),\n",
       " (0, 1, 2, 3, 4, 5, 10, 11),\n",
       " (1, 4, 13, 14),\n",
       " (0, 1, 2, 3, 4, 7, 9, 10, 15),\n",
       " (2, 3, 4, 5, 8, 10, 11, 14),\n",
       " (1, 2, 4, 10, 12, 13, 15),\n",
       " (1, 3, 4, 6, 9, 15),\n",
       " (1, 4, 6, 9, 12),\n",
       " (0, 1, 3, 4, 6),\n",
       " (1, 2, 3, 4, 7, 8, 11, 12, 14, 15),\n",
       " (1, 3, 4, 5, 10, 11),\n",
       " (3, 4, 6, 8, 9, 11, 12, 13, 14, 15),\n",
       " (1, 2, 4, 10, 11),\n",
       " (1, 4, 10, 12, 15),\n",
       " (1, 3, 4, 7),\n",
       " (1, 4, 6, 9, 12, 14, 15),\n",
       " (1, 4, 5, 11, 15),\n",
       " (1, 3, 4, 5, 9, 10, 11, 12, 15),\n",
       " (4, 6, 9, 14, 15),\n",
       " (0, 1, 2, 3, 4, 5, 7, 11, 12, 14),\n",
       " (0, 1, 3, 4, 5, 8, 12, 13, 14),\n",
       " (1, 2, 3, 4, 6, 7),\n",
       " (4, 6, 7, 8, 11, 12, 14, 15),\n",
       " (1, 4, 6, 7, 10, 12, 15),\n",
       " (1, 3, 4, 6, 7, 9, 10, 14, 15),\n",
       " (0, 1, 3, 4, 6, 9, 10, 11, 15),\n",
       " (0, 3, 4, 6, 7, 9, 11, 12, 13, 14),\n",
       " (0, 1, 2, 4, 5, 6, 9, 11, 12, 14, 15),\n",
       " (1, 2, 4, 12),\n",
       " (0, 1, 4, 5, 7, 8, 11, 12, 15),\n",
       " (3, 4, 5, 6, 7, 10, 12, 13, 15),\n",
       " (1, 2, 3, 4, 6, 8, 12, 14, 15),\n",
       " (0, 1, 3, 4, 5, 6, 7, 10, 13),\n",
       " (0, 1, 3, 4, 5, 6, 7, 9, 12, 13),\n",
       " (2, 4, 5, 6, 8, 11, 13, 14),\n",
       " (4, 9, 12, 15),\n",
       " (4, 6, 10, 13, 14),\n",
       " (1, 4, 5, 10),\n",
       " (2, 4, 5, 6),\n",
       " (1, 2, 3, 4, 5, 7, 8, 10, 14),\n",
       " (1, 4, 5, 7, 8, 11, 12, 13, 14, 15),\n",
       " (1, 4, 6, 12, 13, 14),\n",
       " (4, 11, 14),\n",
       " (1, 4, 6, 9, 10, 12, 13, 15),\n",
       " (1, 4, 7, 8, 14),\n",
       " (0, 2, 3, 4, 5, 6, 11, 12, 14, 15),\n",
       " (1, 2, 3, 4, 6, 7, 12),\n",
       " (4, 7, 13, 14, 15),\n",
       " (0, 1, 2, 4, 5, 7, 9, 12, 13, 15),\n",
       " (0, 1, 3, 4, 7, 9, 12),\n",
       " (0, 1, 3, 4, 6, 7, 8, 11, 12, 14),\n",
       " (4, 7, 11, 12, 14, 15),\n",
       " (1, 4, 6, 7, 9, 12, 13, 15),\n",
       " (4, 5, 10, 11, 13, 14),\n",
       " (3, 4, 8, 11, 14, 15),\n",
       " (1, 4, 7, 8, 9, 10, 11, 13, 14, 15),\n",
       " (1, 4, 5, 10, 12, 15),\n",
       " (0, 1, 3, 4, 5, 7, 11, 12, 14, 15),\n",
       " (1, 3, 4, 5, 8, 10, 11, 12, 14),\n",
       " (0, 1, 4, 5, 6, 9, 12),\n",
       " (2, 4, 5, 7, 11, 13, 14),\n",
       " (1, 3, 4, 5, 6, 8, 9, 14, 15),\n",
       " (1, 4, 7, 9, 10, 11, 13, 14, 15),\n",
       " (0, 1, 2, 3, 4, 6, 9, 13, 15),\n",
       " (3, 4, 11, 14),\n",
       " (3, 4, 6, 11, 15),\n",
       " (2, 4, 5, 6, 8, 11, 13, 14, 15),\n",
       " (0, 2, 3, 4, 10, 13),\n",
       " (1, 2, 4, 5, 6, 7, 8, 9, 14, 15),\n",
       " (1, 4, 10, 12, 14, 15),\n",
       " (1, 4, 5, 6, 8, 9, 12, 14, 15),\n",
       " (1, 4, 5, 7, 11, 12, 14, 15),\n",
       " (1, 4, 5, 6, 7, 9, 12, 15),\n",
       " (2, 4, 5, 6, 9, 12, 15),\n",
       " (0, 1, 3, 4, 5, 6, 9, 11, 12, 14),\n",
       " (1, 3, 4, 15),\n",
       " (1, 4, 5, 7, 12, 13),\n",
       " (0, 1, 2, 4, 5, 12, 15),\n",
       " (1, 4, 5, 6, 9, 11, 12, 14, 15),\n",
       " (1, 4, 9, 11),\n",
       " (2, 4, 8, 11, 12, 13, 14),\n",
       " (3, 4, 5, 6, 9, 11, 12, 15),\n",
       " (0, 1, 4, 5, 6, 7, 8, 9, 12, 14),\n",
       " (0, 1, 4, 5, 6, 7, 8, 10, 11),\n",
       " (1, 4, 5, 6, 11),\n",
       " (1, 3, 4, 5, 6, 8),\n",
       " (0, 1, 2, 3, 4, 9, 13, 14, 15),\n",
       " (0, 1, 2, 4, 9, 10, 13, 15),\n",
       " (1, 4, 5, 6, 9, 12, 15),\n",
       " (4, 7, 8, 11, 12, 14, 15),\n",
       " (1, 4, 5, 6, 9, 12),\n",
       " (4, 5, 6, 9, 11, 12, 14, 15),\n",
       " (0, 1, 3, 4, 5, 6, 7, 11, 12, 14),\n",
       " (4, 6, 13, 14),\n",
       " (0, 1, 2, 4, 6, 7, 9, 12),\n",
       " (0, 1, 2, 4, 7, 13),\n",
       " (0, 3, 4, 5, 6, 10, 11, 13, 15),\n",
       " (1, 4, 6, 8, 9, 10, 11, 14, 15),\n",
       " (3, 4, 6, 14, 15),\n",
       " (1, 2, 4, 6, 7, 8, 9, 14, 15),\n",
       " (3, 4, 5, 9, 11, 12, 13, 14, 15),\n",
       " (4, 6, 14, 15),\n",
       " (0, 1, 2, 4, 6, 8, 9, 15),\n",
       " (1, 4, 5, 7, 8, 11, 13),\n",
       " (1, 3, 4, 10),\n",
       " (1, 2, 3, 4, 6, 7, 9, 11, 12, 15),\n",
       " (0, 1, 4, 6, 7, 10, 11, 12, 13, 15),\n",
       " (1, 3, 4, 6, 8, 9, 10, 11, 14, 15),\n",
       " (1, 4, 5, 6, 9, 13, 15),\n",
       " (1, 2, 4, 5, 6, 9, 10, 13, 15),\n",
       " (0, 2, 3, 4, 10, 14, 15),\n",
       " (1, 4, 6, 9, 12, 15),\n",
       " (1, 2, 3, 4, 6, 8, 9, 13, 14, 15),\n",
       " (1, 2, 3, 4, 5, 7, 8, 13, 14),\n",
       " (0, 1, 3, 4, 5, 6, 7, 8, 10, 13),\n",
       " (1, 4, 7, 8, 9, 10, 11, 12, 13, 15),\n",
       " (0, 1, 2, 3, 4, 9, 10, 12, 13, 15),\n",
       " (1, 2, 4, 10, 12, 13),\n",
       " (1, 2, 4, 5, 7, 8, 9, 11, 12),\n",
       " (0, 1, 2, 4, 7, 8),\n",
       " (2, 3, 4, 9, 11, 12, 14),\n",
       " (1, 4, 7, 12, 15),\n",
       " (1, 4, 5, 9, 10, 11, 12, 15),\n",
       " (1, 4, 9, 10, 12, 15),\n",
       " (1, 4, 6, 8, 14),\n",
       " (3, 4, 8, 11, 13, 14, 15),\n",
       " (1, 3, 4, 5),\n",
       " (1, 4, 6, 7, 9, 12, 13, 14, 15),\n",
       " (0, 1, 3, 4, 7, 8, 14),\n",
       " (0, 1, 2, 4, 11, 14),\n",
       " (1, 4, 5, 6, 7, 8, 9, 12, 13, 14),\n",
       " (1, 3, 4, 6, 7, 9, 12, 15),\n",
       " (1, 2, 3, 4, 5, 7, 10, 11),\n",
       " (2, 4, 5, 7, 8, 11, 13, 14),\n",
       " (1, 4, 6, 7, 9, 13, 15),\n",
       " (1, 4, 5, 6, 7, 8, 9, 12, 14, 15),\n",
       " (1, 2, 4, 5, 6, 11, 12, 13, 14, 15),\n",
       " (2, 4, 7, 8, 9, 10, 11, 15),\n",
       " (2, 4, 11, 12, 14),\n",
       " (1, 4, 8, 10, 13),\n",
       " (1, 2, 4, 5, 6, 7, 11, 12, 14, 15),\n",
       " (0, 1, 2, 4, 5, 8, 11, 15),\n",
       " (1, 3, 4, 6, 7, 11),\n",
       " (1, 4, 6, 7, 8),\n",
       " (0, 1, 4, 5, 6, 7, 12, 15),\n",
       " (0, 1, 4, 7, 8, 9, 13, 14, 15),\n",
       " (1, 4, 5, 8),\n",
       " (1, 4, 5, 10, 12),\n",
       " (1, 3, 4, 5, 6, 7, 10, 11, 13),\n",
       " (1, 4, 5, 6, 8, 15),\n",
       " (0, 1, 2, 4, 6, 7, 8, 15),\n",
       " (1, 4, 6, 8, 9, 10, 14, 15),\n",
       " (0, 2, 4, 5, 10, 11, 12, 13, 14, 15),\n",
       " (2, 3, 4, 9, 10, 15),\n",
       " (2, 4, 5, 6, 11, 12, 15),\n",
       " (0, 1, 2, 4, 5, 6, 11, 12, 15),\n",
       " (1, 4, 6, 10, 11, 14),\n",
       " (0, 2, 3, 4, 7, 10, 12, 13, 15),\n",
       " (1, 4, 5, 6, 14),\n",
       " (0, 1, 2, 4, 6, 15),\n",
       " (0, 1, 2, 4, 6, 7, 9, 10, 12, 13),\n",
       " (1, 4, 5, 6, 7, 11, 12, 14),\n",
       " (1, 4, 7, 11, 12, 15),\n",
       " (0, 1, 2, 4, 6, 8, 9, 14, 15),\n",
       " (1, 2, 3, 4, 5, 7, 11, 13, 14, 15),\n",
       " (1, 4, 9, 11, 15),\n",
       " (0, 1, 4, 5, 6, 7, 11, 12, 14, 15),\n",
       " (1, 4, 8, 10, 12, 14, 15),\n",
       " (0, 1, 3, 4, 5, 6, 7, 12, 15),\n",
       " (1, 4, 6, 10, 14),\n",
       " (3, 4, 9, 10, 11, 12, 13, 14, 15),\n",
       " (0, 1, 3, 4, 5, 6, 12, 14, 15),\n",
       " (1, 4, 5, 6, 9, 10, 12, 13),\n",
       " (4, 5, 13),\n",
       " (1, 4, 6, 8, 9, 10, 13, 14, 15),\n",
       " (1, 4, 7, 12),\n",
       " (3, 4, 5),\n",
       " (1, 4, 12),\n",
       " (3, 4, 8, 14, 15),\n",
       " (1, 3, 4, 6, 8, 9, 15),\n",
       " (3, 4, 5, 6, 8, 10, 12, 13, 15),\n",
       " (0, 1, 3, 4, 6, 7, 10, 12, 15),\n",
       " (0, 1, 3, 4),\n",
       " (3, 4, 5, 6, 8, 9, 11, 14, 15),\n",
       " (3, 4, 5, 8, 9, 11, 12, 13, 14, 15),\n",
       " (0, 1, 2, 4, 5, 7, 8, 13),\n",
       " (1, 2, 4, 5, 6, 13, 15),\n",
       " (2, 4, 6, 8, 11, 13, 14, 15),\n",
       " (1, 2, 3, 4),\n",
       " (1, 4, 7, 9, 11, 12, 14),\n",
       " (3, 5, 10, 11, 12, 13, 14, 15),\n",
       " (0, 1, 2, 4, 6, 8, 9, 10, 12, 14),\n",
       " (4, 6, 13),\n",
       " (4, 5, 11, 12, 14, 15),\n",
       " (1, 4, 5, 6, 9, 12, 13),\n",
       " (0, 1, 2, 4, 7, 8, 9, 15),\n",
       " (0, 1, 4, 5, 6, 11, 14),\n",
       " (2, 4, 5, 6, 7, 8, 9, 10, 13),\n",
       " (1, 2, 4, 8, 15),\n",
       " (1, 3, 4, 5, 7, 9, 11, 12, 14, 15),\n",
       " (0, 1, 4, 7, 8, 10, 13),\n",
       " (1, 3, 4, 7, 9, 15),\n",
       " (1, 4, 6, 13, 14),\n",
       " (1, 4, 7, 10, 11, 12, 13, 14, 15),\n",
       " (10, 13, 14),\n",
       " (4, 5, 6, 11, 12, 14),\n",
       " (1, 2, 3, 4, 6, 7, 8, 13, 14, 15),\n",
       " (4, 5, 6, 9, 12, 15),\n",
       " (1, 2, 3, 4, 9, 12, 15),\n",
       " (1, 3, 4, 6, 9, 14, 15),\n",
       " (0, 1, 3, 4, 5, 7, 10),\n",
       " (1, 4, 7, 10, 12),\n",
       " (1, 4, 5, 8, 10, 11, 13, 14),\n",
       " (1, 4, 6, 14, 15),\n",
       " (1, 4, 7),\n",
       " (1, 2, 3, 4, 6, 7, 12, 13, 15),\n",
       " (1, 3, 4, 5, 6, 8, 9, 12, 13, 14),\n",
       " (1, 4, 8, 9, 11, 12),\n",
       " (0, 1, 4, 5, 7, 8, 9, 13, 14, 15),\n",
       " (2, 3, 4, 7, 8, 11, 12, 14, 15),\n",
       " (0, 1, 4, 6, 7),\n",
       " (3, 4, 5, 6, 10, 11, 12, 13, 14, 15),\n",
       " (1, 4, 5, 6, 8, 9, 10, 11, 13, 14),\n",
       " (4, 6, 9, 12, 15),\n",
       " (0, 1, 4, 5, 7, 8, 9, 12),\n",
       " (1, 2, 3, 4, 5, 6, 7, 10, 11, 13),\n",
       " (0, 1, 2, 4, 6, 7, 8, 9, 12),\n",
       " (4, 9, 12, 14, 15),\n",
       " (1, 4, 6, 8, 10, 11, 12, 13, 14, 15),\n",
       " (1, 3, 4, 6, 7, 9, 10, 11, 13),\n",
       " (1, 2, 3, 4, 7, 12),\n",
       " (4, 5, 11, 13, 14),\n",
       " (4, 15),\n",
       " (1, 4, 7, 8, 11, 12, 13, 15),\n",
       " (4, 10, 13, 15),\n",
       " (0, 1, 4, 6, 7, 10, 12, 13, 14, 15),\n",
       " (3, 4, 13),\n",
       " (3, 4, 5, 7, 11),\n",
       " (1, 3, 4, 5, 6, 9, 11, 12, 13, 15),\n",
       " (0, 1, 4, 7, 8, 9, 10, 11, 12),\n",
       " (0, 1, 2, 3, 4, 8, 9, 13, 14, 15),\n",
       " (2, 3, 4, 10, 12, 14),\n",
       " (1, 4, 8, 9, 15),\n",
       " (0, 1, 2, 4, 5, 7, 9),\n",
       " (3, 4, 5, 6, 11, 12, 14, 15),\n",
       " (2, 4, 5, 6, 12, 15),\n",
       " (1, 2, 4, 5, 9, 10, 14),\n",
       " (1, 4, 6, 15),\n",
       " (0, 1, 2, 4, 7, 8, 11, 14),\n",
       " (1, 2, 4, 5, 13, 15),\n",
       " (3, 4, 6, 10, 13, 14, 15),\n",
       " (4, 10),\n",
       " (1, 3, 4, 13, 14),\n",
       " (0, 1, 2, 3, 4, 5, 7, 11, 14, 15),\n",
       " (2, 3, 4, 5, 6, 8, 11, 13, 14),\n",
       " (1, 4, 6, 7, 9, 12, 13),\n",
       " (0, 2, 3, 4, 6, 8, 10, 11, 14, 15),\n",
       " (1, 4),\n",
       " (1, 2, 4, 5, 7, 8, 9, 14, 15),\n",
       " (2, 4, 6, 7, 10, 12, 15),\n",
       " (1, 4, 7, 15),\n",
       " (0, 1, 3, 4, 6, 7, 9, 10, 15),\n",
       " (1, 2, 4, 5, 6, 12),\n",
       " (1, 4, 5, 7, 8, 11, 13, 14),\n",
       " (2, 4, 5, 6, 7, 11, 12, 15),\n",
       " (0, 1, 2, 4, 5, 6, 8, 12, 15),\n",
       " (1, 4, 9, 11, 12, 14, 15),\n",
       " (0, 1, 4, 6, 7, 8, 9, 10, 13, 15),\n",
       " (4, 8, 11, 12, 14, 15),\n",
       " (3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15),\n",
       " (0, 1, 4, 9, 10, 15),\n",
       " (1, 4, 6, 9, 11),\n",
       " (1, 4, 6, 10),\n",
       " (1, 3, 4, 9, 12, 14, 15),\n",
       " (4, 8, 13, 14, 15),\n",
       " (0, 1, 4, 5, 6, 9, 10, 11, 14, 15),\n",
       " (1, 4, 6, 10, 12),\n",
       " (4, 12, 14),\n",
       " (0, 1, 4, 6, 12, 13, 14, 15),\n",
       " (0, 1, 2, 4, 8, 9, 14, 15),\n",
       " (1, 4, 5, 6, 8, 9, 10, 13, 14, 15),\n",
       " (0, 2, 3, 4, 5, 6, 9, 10, 11, 13, 15),\n",
       " (4, 5),\n",
       " (3, 4, 7, 14, 15),\n",
       " (0, 1, 3, 4, 5, 6, 10, 11, 14),\n",
       " (1, 2, 4, 9, 10, 12, 13, 15),\n",
       " (0, 1, 2, 4, 5, 6, 7, 13, 14),\n",
       " (1, 4, 8, 9, 11, 12, 14, 15),\n",
       " (4, 5, 11),\n",
       " (1, 4, 7, 10),\n",
       " (0, 1, 3, 4, 5, 6, 7, 12, 14, 15),\n",
       " (4, 11, 12, 14),\n",
       " (1, 3, 4, 5, 8, 10, 11, 13, 14),\n",
       " (1, 4, 5, 12, 14),\n",
       " (1, 4, 9, 10, 13),\n",
       " (3, 4, 5, 6, 9, 12, 15),\n",
       " (4, 6, 12, 15),\n",
       " (0, 1, 4, 7, 9, 12, 15),\n",
       " (1, 2, 4, 6, 7, 11, 12, 14, 15),\n",
       " (1, 3, 4, 6, 9, 13, 14, 15),\n",
       " (1, 4, 5, 6, 9, 11, 15),\n",
       " (1, 3, 4, 5, 8, 11, 13, 14, 15),\n",
       " (1, 2, 4, 6, 11, 12, 13, 14, 15),\n",
       " (0, 2, 4, 13),\n",
       " (1, 3, 4, 7, 11, 12, 15),\n",
       " (1, 2, 4, 5, 6, 8, 9, 14, 15),\n",
       " (0, 1, 3, 4, 5, 6, 7),\n",
       " (1, 4, 6, 7, 8, 9, 11, 13, 14, 15),\n",
       " (1, 2, 3, 4, 13, 14, 15),\n",
       " (1, 4, 6, 8, 11, 12, 14),\n",
       " (1, 3, 4, 5, 6, 9, 12, 15),\n",
       " (1, 4, 5, 6, 8, 9, 14, 15),\n",
       " (1, 4, 9, 15),\n",
       " (0, 1, 3, 4, 6, 7, 10, 11, 13),\n",
       " (0, 1, 2, 3, 4, 5, 11),\n",
       " (1, 4, 5, 6, 8, 9, 11, 12, 14),\n",
       " (0, 1, 2, 4, 5, 6, 9, 12, 13, 15),\n",
       " (0, 1, 3, 4, 5, 8, 9, 12),\n",
       " (1, 2, 4, 5, 6, 9, 10, 11, 13, 15),\n",
       " (0, 1, 4, 5, 6, 9, 11, 14, 15),\n",
       " (4, 8, 10, 11, 12, 14, 15),\n",
       " (1, 2, 4, 5, 8, 11),\n",
       " (1, 2, 3, 4, 6, 9, 13, 15),\n",
       " (0, 1, 4, 9, 10),\n",
       " (0, 1, 2, 4, 5, 7),\n",
       " (0, 1, 2, 3, 4, 5, 7, 8, 10, 14),\n",
       " (0, 1, 2, 4, 7),\n",
       " (0, 1, 4, 5, 8, 14),\n",
       " (0, 1, 2, 3, 4, 6, 10, 12, 13, 15),\n",
       " (4, 5, 6, 9, 13),\n",
       " (1, 3, 4, 14),\n",
       " (1, 4, 5, 7, 8, 9, 13, 14, 15),\n",
       " (0, 1, 3, 4, 5, 7),\n",
       " (1, 4, 5, 8, 14, 15),\n",
       " (1, 4, 9, 10),\n",
       " (0, 1, 2, 4, 5, 6, 7, 9, 15),\n",
       " (0, 1, 3, 4, 5, 6, 11, 12, 14, 15),\n",
       " (1, 4, 5, 7, 8, 10, 11, 14),\n",
       " (0, 1, 2, 3, 4, 5, 6),\n",
       " (0, 1, 3, 4, 6, 8),\n",
       " (1, 3, 4),\n",
       " (0, 1, 2, 3, 4, 5, 6, 7, 10, 11),\n",
       " (1, 4, 12, 13, 14),\n",
       " (3, 4, 5, 6, 8, 10, 11, 13, 14, 15),\n",
       " (0, 1, 3, 4, 5, 9, 12),\n",
       " (1, 4, 7, 8, 9, 12),\n",
       " (0, 1, 3, 4, 5, 6, 7, 8),\n",
       " (3, 4, 5, 8, 9, 11, 14, 15),\n",
       " (1, 2, 3, 4, 6, 12, 13),\n",
       " (1, 3, 4, 6, 8, 11, 13, 14),\n",
       " (1, 4, 6, 13),\n",
       " (0, 1, 4, 5, 8, 9),\n",
       " (3, 4, 5, 8, 9, 10, 11, 12, 14, 15),\n",
       " (1, 4, 5, 7, 10),\n",
       " (1, 2, 3, 4, 6, 9, 10, 12, 13, 15),\n",
       " (1, 3, 4, 9),\n",
       " (10, 15),\n",
       " (1, 2, 3, 4, 6, 7, 8, 11, 12, 13),\n",
       " (0, 1, 2, 4, 5, 8, 10, 11, 15),\n",
       " (1, 2, 3, 4, 7, 9, 10, 12, 15),\n",
       " (3, 4, 6, 9, 11, 12, 14, 15),\n",
       " (1, 4, 5, 7, 8, 12, 13),\n",
       " (2, 4, 5, 7, 11, 14),\n",
       " (1, 4, 5, 10, 12, 13, 15),\n",
       " (1, 4, 5, 6, 9, 14, 15),\n",
       " (1, 3, 4, 6, 8, 9, 14, 15),\n",
       " (1, 2, 3, 4, 8, 13, 14, 15),\n",
       " (1, 4, 5, 6, 8, 10, 14),\n",
       " (0, 1, 2, 4, 6, 7, 8, 9, 10, 12),\n",
       " (1, 4, 6, 9, 13, 15),\n",
       " (0, 1, 2, 4, 9, 11, 12, 14, 15),\n",
       " (1, 2, 4, 6, 7, 9, 14),\n",
       " (0, 1, 2, 4, 5, 6, 7, 12, 14, 15),\n",
       " (0, 1, 3, 4, 5, 6, 7, 9, 12, 15),\n",
       " (0, 1, 2, 3, 4, 6, 9, 10, 12, 15),\n",
       " (1, 3, 4, 8, 9, 14, 15),\n",
       " (0, 2, 3, 4, 10, 12, 13, 15),\n",
       " (1, 4, 5, 12),\n",
       " (3, 4, 5, 6, 9, 12, 13, 14, 15),\n",
       " (4, 10, 11, 13, 15),\n",
       " (1, 4, 5, 11, 12),\n",
       " (2, 3, 4, 10, 13),\n",
       " (1, 2, 3, 4, 6, 10, 12, 13, 15),\n",
       " (3, 4, 6, 8, 9, 11, 12, 14, 15),\n",
       " (0, 2, 4, 11, 12, 14, 15),\n",
       " (1, 3, 4, 9, 10, 12, 15),\n",
       " (1, 4, 5, 9, 11, 12, 14, 15),\n",
       " (1, 3, 4, 13, 14, 15),\n",
       " (4, 8, 11, 13, 14, 15),\n",
       " (14,),\n",
       " (0, 1, 3, 4, 5, 6, 11, 14),\n",
       " (1, 2, 3, 4, 6),\n",
       " (1, 4, 6, 7, 9, 14, 15),\n",
       " (1, 2, 4, 5, 7, 8, 9, 10, 14, 15),\n",
       " (1, 3, 4, 9, 13, 14, 15),\n",
       " (0, 1, 2, 4, 5, 6, 11, 12, 14, 15),\n",
       " (1, 4, 6, 7, 8, 11, 14),\n",
       " (1, 2, 4, 7, 9, 11, 12, 13, 15),\n",
       " (1, 4, 5, 8, 9, 10, 14, 15),\n",
       " (0, 1, 2, 3, 4, 5, 7, 9, 11, 12),\n",
       " (1, 2, 4, 5, 8, 11, 13, 14),\n",
       " (1, 4, 5, 7),\n",
       " (1, 4, 5, 12, 13, 15),\n",
       " (1, 2, 3, 4, 5, 7, 8, 10, 11),\n",
       " (0, 1, 4, 6, 9, 10, 12),\n",
       " (1, 4, 6, 8, 10, 11, 13, 14),\n",
       " (1, 4, 5, 7, 12, 15),\n",
       " (0, 1, 3, 4, 7, 8, 11, 12, 14),\n",
       " (0, 1, 4, 5, 6, 7, 8, 9, 11, 15),\n",
       " (1, 4, 5, 6, 8, 14),\n",
       " (1, 4, 6, 12, 14),\n",
       " (0, 1, 4, 5, 6, 7, 8, 9, 12),\n",
       " (0, 2, 3, 4, 5, 6, 9, 11, 12, 14, 15),\n",
       " (1, 4, 9, 13),\n",
       " (0, 1, 4, 5, 6, 8, 9, 14, 15),\n",
       " (1, 4, 5, 6, 8, 9, 11, 13, 14, 15),\n",
       " (1, 2, 4, 5, 8, 9, 11, 15),\n",
       " (1, 3, 4, 7, 8, 10, 11, 12, 13),\n",
       " (0, 1, 2, 3, 4, 5, 6, 7, 10, 14),\n",
       " (0, 1, 2, 4, 5, 6, 7, 8, 9, 11, 12, 15),\n",
       " (1, 4, 5, 6, 13),\n",
       " (4, 7, 13),\n",
       " (1, 2, 4, 5, 7, 11, 12, 14, 15),\n",
       " (3, 4, 5, 6, 8, 9, 14, 15),\n",
       " (1, 3, 4, 6, 8, 9, 10, 14, 15),\n",
       " (2, 3, 4, 9, 12, 13, 14, 15),\n",
       " (0, 1, 2, 3, 4),\n",
       " (0, 1, 2, 4, 5, 6, 7, 8, 13),\n",
       " (0, 1, 3, 4, 5, 6, 7, 11),\n",
       " (0, 1, 3, 4, 5, 6, 7, 10, 11, 14),\n",
       " (1, 4, 5, 6, 7, 9, 12),\n",
       " (3, 4, 10, 15),\n",
       " (1, 4, 9, 12, 15),\n",
       " (3, 4, 6, 8, 10, 11, 14, 15),\n",
       " (1, 3, 4, 6, 7, 9, 11, 14, 15),\n",
       " (1, 4, 7, 9, 10, 13, 14, 15),\n",
       " (0, 1, 2, 4, 5, 6, 8, 9, 12, 15),\n",
       " (1, 4, 7, 9, 10, 15),\n",
       " (1, 4, 9, 12, 13, 15),\n",
       " (0, 1, 2, 3, 4, 6, 7, 9, 10, 15),\n",
       " (1, 3, 4, 6, 10, 12, 13),\n",
       " (1, 4, 5, 11, 12, 14),\n",
       " (0, 1, 2, 4, 6, 9, 10, 12, 14, 15),\n",
       " (3, 4, 5, 13, 14),\n",
       " (4,),\n",
       " (2, 4, 6, 7, 10, 12, 13, 15),\n",
       " (1, 3, 4, 5, 8, 9, 14),\n",
       " (1, 2, 3, 4, 5, 9, 11, 12),\n",
       " (1, 2, 4, 5, 7, 9),\n",
       " (1, 2, 3, 4, 9, 12, 13, 14, 15),\n",
       " (3, 4, 5, 6, 8, 9, 11, 12, 14, 15),\n",
       " (0, 1, 3, 4, 5, 8, 11, 15),\n",
       " (1, 4, 5, 7, 8, 9, 11, 12, 15),\n",
       " (1, 4, 5, 6, 8),\n",
       " (0, 1, 4, 5, 6, 7, 9, 12),\n",
       " (0, 1, 4, 5, 6, 8, 9, 11, 12, 14),\n",
       " (1, 4, 5, 6, 12, 15),\n",
       " (0, 1, 2, 4, 6, 7, 12, 15),\n",
       " (2, 4, 12, 15),\n",
       " (1, 3, 4, 10, 13),\n",
       " (1, 3, 4, 5, 6, 12, 13),\n",
       " (1, 2, 3, 4, 5, 6, 7, 8, 15),\n",
       " (1, 4, 10, 11, 14),\n",
       " (1, 3, 4, 5, 6, 9, 11, 12, 14),\n",
       " (1, 4, 6, 9, 12, 13, 14),\n",
       " (1, 3, 4, 6, 8, 11, 12, 14, 15),\n",
       " (1, 2, 4, 5, 6, 11, 13, 14),\n",
       " (1, 3, 4, 11, 14),\n",
       " (3, 4, 13, 15),\n",
       " (1, 2, 3, 4, 5, 8, 9, 13, 14, 15),\n",
       " (1, 4, 5, 8, 9, 11, 13, 14),\n",
       " (1, 4, 5, 15),\n",
       " (1, 2, 4, 7, 10, 11, 12, 13, 14, 15),\n",
       " (0, 1, 3, 4, 7, 10),\n",
       " (1, 4, 5, 7, 8, 10, 14),\n",
       " (1, 4, 7, 9, 13, 15),\n",
       " (1, 4, 5, 9, 11, 13, 14),\n",
       " (1, 4, 6, 8, 9, 12, 14, 15),\n",
       " (3, 4, 5, 8, 9, 10, 11, 14, 15),\n",
       " (4, 5, 6, 11, 13, 14),\n",
       " (0, 1, 3, 4, 5, 7, 8, 13, 14),\n",
       " (0, 1, 3, 4, 5, 6, 7, 9, 12),\n",
       " (1, 4, 8, 10, 11, 12, 15),\n",
       " (0, 1, 4, 6, 7, 8, 9, 10, 11, 14),\n",
       " (1, 4, 11, 12),\n",
       " (1, 4, 6, 8, 14, 15),\n",
       " (1, 3, 4, 10, 12, 15),\n",
       " (1, 4, 5, 7, 11, 12, 14),\n",
       " (0, 1, 3, 4, 5, 6, 11, 12, 15),\n",
       " (1, 3, 4, 5, 11, 15),\n",
       " (1, 4, 5, 6, 11, 14),\n",
       " (1, 2, 3, 4, 9, 13, 14, 15),\n",
       " (0, 2, 3, 4, 5, 6, 10, 11, 13, 15),\n",
       " (0, 1, 2, 4, 5, 7, 13),\n",
       " (1, 4, 6, 7, 8, 10, 11, 12, 14, 15),\n",
       " (1, 4, 12, 13),\n",
       " (0, 1, 2, 4, 5, 6, 8, 12, 14, 15),\n",
       " (3, 4, 6, 8, 9, 12, 14, 15),\n",
       " (1, 3, 4, 5, 7, 8, 11, 13),\n",
       " (1, 2, 3, 4, 7, 9, 11, 12, 15),\n",
       " (1, 2, 4, 6, 10, 13),\n",
       " (4, 10, 13),\n",
       " (0, 1, 2, 4, 11, 12, 14, 15),\n",
       " (3, 10, 14, 15),\n",
       " (1, 2, 3, 4, 6, 7, 9, 12, 13, 15),\n",
       " (2, 4),\n",
       " (1, 4, 6, 9, 11, 12, 13, 15),\n",
       " (0, 1, 3, 4, 5, 6, 10, 11),\n",
       " (1, 4, 8, 11, 12, 13),\n",
       " (4, 5, 12, 15),\n",
       " (0, 2, 3, 4, 7, 10, 13),\n",
       " (0, 1, 3, 4, 5, 7, 9, 14),\n",
       " (4, 10, 14, 15),\n",
       " (1, 2, 3, 4, 6, 7, 10, 12, 13, 15),\n",
       " (1, 3, 4, 6, 9, 10, 11),\n",
       " (0, 1, 3, 4, 6, 7, 8),\n",
       " (0, 1, 2, 4, 6, 7, 9, 10, 15),\n",
       " (0, 1, 3, 4, 5, 6, 7, 8, 11),\n",
       " (0, 1, 4, 5, 7, 8, 9, 11, 15),\n",
       " (3, 4, 5, 8, 11, 14, 15),\n",
       " (0, 1, 3, 4, 5, 6, 7, 9, 11, 12),\n",
       " (0, 1, 4, 5, 8, 11, 14),\n",
       " (0, 1, 2, 4, 6, 8, 15),\n",
       " (1, 3, 4, 5, 6, 9, 10, 15),\n",
       " (0, 1, 2, 4, 5, 11, 14),\n",
       " (1, 4, 5, 6, 7, 11),\n",
       " (1, 3, 4, 5, 6, 8, 9, 11, 12, 14),\n",
       " (1, 2, 4, 5, 14, 15),\n",
       " (4, 11, 13, 14),\n",
       " (1, 4, 14),\n",
       " (0, 1, 2, 4, 6, 7, 8, 12),\n",
       " (1, 2, 3, 4, 6, 7, 9, 12, 15),\n",
       " (0, 1, 4, 5, 6, 7, 8, 14),\n",
       " (1, 3, 4, 5, 7, 8, 11, 12, 13, 14),\n",
       " (2, 4, 5, 6, 8, 9, 10, 13),\n",
       " (0, 1, 2, 4, 5, 6, 8, 12, 14),\n",
       " (0, 1, 2, 3, 4, 5, 6, 7, 8, 15),\n",
       " (1, 3, 4, 6, 10),\n",
       " (0, 1, 2, 4, 5, 6, 8, 11, 12, 15),\n",
       " (1, 3, 4, 6, 7, 10, 13),\n",
       " (1, 4, 8, 15),\n",
       " (0, 1, 3, 4, 7, 8, 11, 14),\n",
       " (1, 4, 7, 8, 9, 14, 15),\n",
       " (0, 1, 2, 4, 5, 6, 7, 9, 12, 13, 15),\n",
       " (1, 4, 5, 7, 8, 13, 14),\n",
       " (0, 2, 3, 4, 5, 8, 11, 13, 14, 15),\n",
       " (0, 1, 2, 4, 6, 12),\n",
       " (3, 4, 5, 6, 7, 10, 12, 13, 14, 15),\n",
       " (1, 3, 4, 7, 11),\n",
       " (0, 1, 2, 3, 4, 5, 7, 10, 11, 12),\n",
       " (1, 2, 4, 5, 6, 8, 9, 11, 14, 15),\n",
       " (3, 4, 5, 6, 13, 14, 15),\n",
       " (4, 6, 15),\n",
       " (1, 2, 4, 6, 8, 9, 14, 15),\n",
       " (1, 2, 3, 4, 5, 6, 11, 14),\n",
       " (1, 4, 5, 6, 12, 13, 15),\n",
       " (0, 1, 3, 4, 5, 6, 11),\n",
       " (1, 3, 4, 8, 9, 13, 14, 15),\n",
       " (2, 3, 4, 8, 9, 13, 14, 15),\n",
       " (1, 2, 4, 6, 7, 12, 14, 15),\n",
       " (4, 13, 14),\n",
       " (3, 4, 5, 8, 9, 10, 11, 13, 14, 15),\n",
       " (0, 1, 4, 5, 7, 14),\n",
       " (1, 4, 5, 7, 9, 10, 11, 13, 14, 15),\n",
       " (4, 12, 13, 15),\n",
       " (1, 4, 7, 9),\n",
       " (1, 4, 9),\n",
       " (0, 1, 4, 5, 6, 7, 10, 11, 14),\n",
       " (0, 1, 3, 4, 5, 7, 11, 14),\n",
       " (0, 1, 4, 5, 6, 7, 8, 9, 10, 12),\n",
       " (2, 4, 6, 9, 10, 12, 13, 15),\n",
       " (1, 2, 3, 4, 5, 6, 11, 12, 14),\n",
       " (1, 2, 3, 4, 5, 7, 11, 14, 15),\n",
       " (0, 1, 3, 4, 5, 7, 12, 15),\n",
       " (1, 4, 11, 15),\n",
       " (0, 3, 4, 5, 6, 11, 12, 14),\n",
       " (3, 5, 9, 10, 11, 12, 13, 14, 15),\n",
       " (4, 12, 13, 14, 15),\n",
       " (4, 6, 12, 14),\n",
       " (3, 4, 6, 7, 10, 12, 13, 15),\n",
       " (1, 3, 4, 10, 11),\n",
       " (0, 1, 3, 4, 6, 7, 9, 10, 12, 15),\n",
       " (0, 1, 3, 4, 6, 13, 14),\n",
       " (3, 4, 6, 12, 13),\n",
       " (2, 3, 4, 12, 13, 14),\n",
       " (0, 1, 2, 3, 4, 7, 9, 13, 14, 15),\n",
       " (0, 1, 2, 4, 6, 7, 9, 13, 15),\n",
       " (1, 4, 5, 6, 11, 12, 13, 14, 15),\n",
       " (1, 3, 4, 7, 8, 10, 11, 12, 13, 15),\n",
       " (0, 1, 2, 4, 5, 6, 7, 10, 15),\n",
       " (3, 4, 6, 13, 14),\n",
       " (0, 2, 4, 11, 12, 15),\n",
       " (0, 1, 4, 5, 7, 8, 9, 14, 15),\n",
       " (1, 4, 6, 9, 11, 15),\n",
       " (1, 4, 5, 6, 7, 10, 11, 12, 14, 15),\n",
       " (1, 3, 4, 6, 14, 15),\n",
       " (1, 4, 9, 10, 11, 14, 15),\n",
       " (3, 4, 15),\n",
       " (1, 3, 4, 10, 11, 12),\n",
       " (4, 5, 6, 7, 9, 10, 13),\n",
       " (1, 4, 5, 6, 7, 12, 15),\n",
       " (0, 1, 2, 3, 4, 5, 7, 12),\n",
       " (0, 1, 4, 6, 7, 10, 11, 12, 15),\n",
       " (1, 2, 4, 5, 11, 13, 14, 15),\n",
       " (1, 2, 4, 6, 8, 15),\n",
       " (1, 3, 4, 10, 12, 13),\n",
       " (0, 1, 2, 4, 5, 6),\n",
       " (3, 4, 9, 13, 15),\n",
       " (1, 3, 4, 5, 6, 8, 12, 13, 14, 15),\n",
       " (1, 2, 3, 4, 5, 7, 8, 11, 13, 14),\n",
       " (3, 4, 12, 13),\n",
       " (0, 2, 4, 7),\n",
       " (1, 2, 3, 4, 6, 9, 12, 13, 14, 15),\n",
       " (1, 3, 4, 5, 9, 10, 11, 12, 14, 15),\n",
       " (1, 2, 3, 4, 5, 6, 9, 11, 12),\n",
       " (1, 2, 3, 4, 6, 7, 9, 11, 14, 15),\n",
       " (4, 5, 6, 8, 11, 13, 14),\n",
       " (4, 12),\n",
       " (0, 1, 2, 4, 5, 6, 8, 9, 12, 14, 15),\n",
       " (0, 1, 4, 5, 7, 8, 11, 12, 14, 15),\n",
       " (1, 4, 6, 7, 9, 12, 15),\n",
       " (1, 4, 5, 6, 8, 11, 14),\n",
       " (0, 1, 3, 4, 6, 7),\n",
       " (1, 3, 4, 5, 6, 9, 10, 11, 15),\n",
       " (1, 3, 4, 8, 11, 14),\n",
       " (4, 8, 11, 14),\n",
       " (1, 4, 13, 15),\n",
       " (3, 4, 5, 11, 14),\n",
       " (1, 4, 6, 9, 13),\n",
       " (2, 4, 6, 10, 13),\n",
       " (1, 4, 6, 12),\n",
       " (1, 3, 4, 9, 10, 13, 15),\n",
       " (4, 7),\n",
       " (1, 3, 4, 6, 7, 9, 14, 15),\n",
       " (1, 2, 4, 5, 11, 13, 14),\n",
       " (2, 4, 5, 7, 11),\n",
       " (0, 1, 2, 4, 5, 6, 8, 14),\n",
       " (3, 4, 5, 6, 8, 9, 12, 15),\n",
       " (0, 2, 4, 6, 11, 12, 14, 15),\n",
       " (0, 1, 2, 4, 6, 7, 8),\n",
       " (1, 4, 5, 8, 9, 11, 12, 13, 14, 15),\n",
       " (0, 1, 2, 4, 5, 6, 9, 11, 12, 14),\n",
       " (0, 1, 2, 3, 4, 10, 12, 13, 15),\n",
       " (0, 1, 2, 4, 5, 6, 9, 15),\n",
       " (1, 4, 6, 9, 14, 15),\n",
       " (1, 2, 4, 5, 6, 9, 12, 13, 15),\n",
       " (1, 4, 9, 10, 15),\n",
       " (0, 1, 2, 4, 5, 6, 7, 8, 9, 12),\n",
       " (1, 4, 10, 11, 12, 13, 14, 15),\n",
       " (1, 4, 6, 7),\n",
       " (0, 1, 2, 3, 4, 6, 9, 10, 13, 15),\n",
       " (0, 1, 2, 4, 7, 8, 11, 12, 14),\n",
       " (1, 2, 4, 5, 6, 9, 10, 13, 14, 15),\n",
       " (3, 4, 5, 6, 11, 12, 14),\n",
       " (2, 4, 14),\n",
       " (1, 4, 6, 7, 8, 11, 12, 14, 15),\n",
       " (1, 2, 3, 4, 5, 6, 7, 8, 10, 11),\n",
       " (1, 4, 5, 9, 10, 12, 15),\n",
       " (4, 5, 6, 8, 11, 12, 13, 14, 15),\n",
       " (0, 1, 3, 4, 5, 14),\n",
       " (0, 1, 4, 6, 7, 8, 9, 12, 15),\n",
       " (4, 8, 11, 12, 14),\n",
       " (1, 4, 6, 10, 12, 13),\n",
       " (1, 3, 4, 5, 7, 11, 15),\n",
       " (2, 3, 4, 5, 7, 8, 13, 14),\n",
       " (1, 4, 6, 10, 13, 14),\n",
       " (1, 3, 4, 5, 6, 14),\n",
       " (1, 4, 8, 11, 13, 14),\n",
       " (1, 4, 5, 8, 9, 10, 11, 12, 14, 15),\n",
       " (0, 1, 4, 5, 8, 9, 11, 14),\n",
       " (0, 1, 3, 4, 5, 8, 10, 11, 12, 14),\n",
       " (5, 10, 11, 13, 14),\n",
       " (3, 4, 11, 15),\n",
       " (0, 1, 3, 4, 7, 10, 13),\n",
       " (1, 4, 9, 13, 15),\n",
       " (0, 1, 2, 4, 5, 9),\n",
       " (0, 1, 5, 6, 10, 11, 12, 13, 14, 15),\n",
       " (0, 1, 4, 5, 8, 9, 11, 12, 14),\n",
       " (0, 1, 2, 4, 9),\n",
       " (0, 1, 4, 6, 7, 8, 9, 11, 12, 15),\n",
       " (0, 1, 3, 4, 5, 6, 9, 11, 12, 15),\n",
       " (1, 4, 5, 8, 9, 14, 15),\n",
       " (2, 3, 4, 5, 6, 10, 11, 13, 15),\n",
       " (1, 4, 6, 7, 8, 11, 12, 13, 14, 15),\n",
       " (0, 1, 3, 4, 5, 7, 8, 11, 14),\n",
       " (1, 4, 8, 10, 11, 14),\n",
       " (4, 14, 15),\n",
       " (1, 4, 6, 12, 13),\n",
       " (1, 2, 3, 4, 7, 9, 12, 15),\n",
       " (1, 4, 9, 12),\n",
       " (2, 3, 4, 8, 9, 11, 13, 14, 15),\n",
       " (4, 5, 6, 11, 12, 14, 15),\n",
       " (1, 2, 3, 4, 5, 7, 8, 13, 14, 15),\n",
       " (0, 1, 3, 4, 5, 7, 10, 11),\n",
       " (1, 4, 10, 13),\n",
       " (0, 1, 3, 4, 5, 6, 7, 11, 12, 15),\n",
       " (0, 1, 4, 5, 7, 8, 11),\n",
       " (0, 1, 2, 3, 4, 8, 13, 14, 15),\n",
       " (0, 1, 3, 4, 5, 6, 7, 10),\n",
       " (0, 1, 3, 4, 5, 7, 11, 14, 15),\n",
       " (1, 2, 3, 4, 6, 12, 15),\n",
       " (0, 1, 3, 4, 5, 6, 7, 10, 11, 13),\n",
       " (0, 1, 2, 3, 4, 5, 7, 8, 10, 11),\n",
       " (0, 1, 3, 4, 10),\n",
       " (4, 10, 11, 14),\n",
       " (1, 4, 6, 7, 9, 11, 12, 13, 14),\n",
       " (1, 3, 4, 5, 6, 8, 9, 10, 11, 15),\n",
       " (1, 4, 5, 7, 12),\n",
       " (0, 1, 2, 4, 5, 6, 12, 15),\n",
       " (0, 1, 2, 4, 5, 6, 8, 9, 12, 14),\n",
       " (0, 1, 4, 5, 6, 11, 14, 15),\n",
       " (1, 4, 5, 7, 8, 11, 14),\n",
       " (1, 3, 4, 11),\n",
       " (3, 4, 7, 9, 10, 11, 13, 15),\n",
       " (0, 1, 2, 4, 7, 8, 9, 12, 14, 15),\n",
       " (0, 1, 2, 4, 5, 6, 9, 10, 12, 13),\n",
       " (0, 1, 2, 4, 5, 6, 7, 9, 12),\n",
       " (1, 3, 4, 5, 8, 9, 11, 12, 14, 15),\n",
       " (4, 5, 7, 11, 13, 14),\n",
       " (1, 4, 9, 12, 14, 15),\n",
       " (2, 3, 4, 9, 13, 14, 15),\n",
       " (3, 14),\n",
       " (0, 1, 2, 4, 5, 9, 14, 15),\n",
       " (1, 4, 5, 11, 13, 14),\n",
       " (4, 10, 12, 14),\n",
       " (1, 2, 3, 4, 6, 7, 12, 14),\n",
       " (1, 3, 4, 5, 6, 11, 12, 14, 15),\n",
       " (3, 4, 6, 10, 11, 12, 13, 14, 15),\n",
       " (3, 4, 12, 14, 15),\n",
       " (0, 1, 4, 7, 12, 15),\n",
       " (0, 1, 2, 4, 5, 6, 7, 8, 9, 15),\n",
       " (1, 3, 4, 5, 6, 7, 8, 9, 10, 11),\n",
       " (3, 4, 8, 9, 10, 11, 13, 14, 15),\n",
       " (1, 3, 4, 5, 6, 7, 10, 13),\n",
       " (0, 1, 3, 4, 5),\n",
       " (3, 4, 8, 10, 13, 14, 15),\n",
       " (1, 3, 4, 11, 13),\n",
       " (3, 4, 8, 9, 12, 14, 15),\n",
       " (0, 1, 2, 4, 7, 8, 9, 11, 12, 14),\n",
       " (0, 1, 2, 4, 9, 14, 15),\n",
       " (1, 2, 4, 5, 7, 8, 10, 14),\n",
       " (0, 1, 4, 5, 6, 8, 9, 11, 12),\n",
       " (4, 7, 9, 13, 15),\n",
       " (1, 3, 4, 6),\n",
       " (1, 3, 4, 5, 6, 8, 9, 10, 14, 15),\n",
       " (1, 4, 5, 11, 14),\n",
       " (2, 3, 4, 10, 15),\n",
       " (1, 4, 5, 9, 12, 15),\n",
       " (3, 4, 14, 15),\n",
       " (2, 4, 6, 7, 9, 14, 15),\n",
       " (1, 3, 4, 5, 6, 9, 15),\n",
       " (2, 4, 6, 7, 9, 10, 12, 13, 15),\n",
       " (0, 1, 2, 4, 5, 6, 8, 12),\n",
       " (0, 1, 2, 4, 5, 7, 9, 11, 14, 15),\n",
       " (1, 4, 7, 8, 9, 10, 13, 14, 15),\n",
       " (0, 1, 5, 6, 8, 10, 12, 14, 15),\n",
       " (4, 5, 6, 12, 15),\n",
       " (1, 3, 4, 5, 8, 13, 14),\n",
       " (1, 4, 6, 7, 9),\n",
       " (1, 3, 4, 6, 8, 9, 12, 14, 15),\n",
       " (1, 4, 7, 8, 11, 14),\n",
       " (1, 3, 4, 6, 8, 14, 15),\n",
       " (1, 3, 4, 6, 9, 10, 11, 12),\n",
       " (2, 3, 4, 5, 13, 14),\n",
       " (3, 4, 5, 10, 11, 13, 14, 15),\n",
       " (3, 5, 7, 9, 10, 11, 12, 13, 14, 15),\n",
       " (1, 4, 8, 10, 11, 13, 14),\n",
       " (1, 4, 5, 8, 10, 14),\n",
       " (0, 1, 4, 5, 7, 8, 10, 11),\n",
       " (3, 4, 6, 7, 14, 15),\n",
       " (1, 4, 5, 9),\n",
       " (1, 4, 5, 6, 9, 10, 13, 15),\n",
       " (1, 4, 6, 7, 8, 9, 12, 13, 14, 15),\n",
       " (1, 3, 4, 8, 11, 13, 14, 15),\n",
       " (2, 4, 6, 12, 15),\n",
       " (1, 4, 6, 9, 11, 12, 13, 14),\n",
       " (0, 1, 2, 3, 4, 5, 7, 9, 12, 15),\n",
       " (1, 4, 5, 8, 9, 12, 14, 15),\n",
       " (1, 4, 6, 7, 12, 13),\n",
       " (3, 4),\n",
       " (0, 1, 4, 6, 7, 9, 14, 15),\n",
       " (0, 1, 2, 4, 5, 7, 9, 15),\n",
       " (11,),\n",
       " (1, 2, 3, 4, 5, 6, 10, 12, 13, 15),\n",
       " (0, 1, 3, 4, 6, 7, 9, 12, 15),\n",
       " (1, 4, 6, 7, 9, 12, 13, 14),\n",
       " (0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 15),\n",
       " (0, 1, 2, 4, 5, 6, 7, 9, 12, 14, 15),\n",
       " (1, 4, 6, 8, 11, 12, 13, 15),\n",
       " (2, 3, 4, 13, 14, 15),\n",
       " (1, 2, 4, 13, 15),\n",
       " (0, 1, 3, 4, 5, 7, 10, 11, 14),\n",
       " (0, 1, 4, 7, 14),\n",
       " (0, 1, 4, 7, 8, 10, 11, 13, 14),\n",
       " (1, 4, 5, 6, 15),\n",
       " (3, 4, 6, 10, 13),\n",
       " (1, 2, 3, 4, 6, 7, 8, 10, 11, 14),\n",
       " (0, 1, 2, 3, 4, 5, 6, 9, 10, 11, 12, 13, 15),\n",
       " (4, 6, 10, 13),\n",
       " (0, 1, 4, 5, 7, 8, 14),\n",
       " (1, 2, 3, 4, 6, 7, 9, 10, 12, 15),\n",
       " (4, 5, 6, 12),\n",
       " (3, 4, 6, 11, 12, 13, 14, 15),\n",
       " (0, 1, 2, 4, 5, 6, 7, 8, 15),\n",
       " (1, 4, 6, 7, 8, 10, 11, 13, 14),\n",
       " (0, 1, 4, 10),\n",
       " (0, 2, 4, 5, 6, 11, 12, 14, 15),\n",
       " (1, 3, 4, 6, 11, 13),\n",
       " (1, 2, 4),\n",
       " (0, 1, 3, 4, 6, 7, 9, 10, 11, 15),\n",
       " (1, 4, 5, 6, 12, 13, 14, 15),\n",
       " (0, 2, 4, 5, 10, 11),\n",
       " (0, 1, 2, 4, 6, 9, 15),\n",
       " (1, 2, 3, 4, 12, 13, 14, 15),\n",
       " (2, 3, 4, 8, 10, 12, 14),\n",
       " (0, 1, 3, 4, 7, 10, 11, 13),\n",
       " (2, 3, 4, 5, 6, 9, 11, 12, 14, 15),\n",
       " (0, 1, 4, 5, 6, 7, 8, 9, 11, 12),\n",
       " (1, 4, 5, 6, 8, 11),\n",
       " (0, 1, 2, 3, 4, 6, 7, 8, 12, 14),\n",
       " (0, 1, 4, 6, 9, 12, 13, 14, 15),\n",
       " (1, 2, 3, 4, 5, 9, 11, 14),\n",
       " (1, 4, 8, 14),\n",
       " (2, 3, 4, 8, 9, 11, 12, 14),\n",
       " (1, 2, 3, 4, 7, 8, 13),\n",
       " (1, 2, 3, 4, 8, 9, 13, 14, 15),\n",
       " (1, 4, 5, 6, 10),\n",
       " (1, 4, 8, 9, 12, 13, 15),\n",
       " (1, 3, 4, 5, 6, 7, 9, 15),\n",
       " (4, 6, 14),\n",
       " (1, 3, 4, 5, 6, 12, 15),\n",
       " (1, 4, 6, 7, 8, 10, 13, 14),\n",
       " (1, 4, 5, 8, 9, 11, 12, 15),\n",
       " (2, 3, 4, 6, 10, 13, 14, 15),\n",
       " (0, 1, 4, 6, 7, 11, 12, 15),\n",
       " (0, 1, 3, 4, 5, 7, 9, 10, 11, 12),\n",
       " (1, 4, 6, 7, 13, 14, 15),\n",
       " (3, 4, 5, 6, 8, 14, 15),\n",
       " (2, 3, 4, 5, 8, 9, 11, 13, 14, 15),\n",
       " (0, 1, 4, 5, 7, 8, 10, 11, 13),\n",
       " (3, 4, 5, 6, 8, 9, 10, 12, 13, 15),\n",
       " (1, 4, 8, 14, 15),\n",
       " (0, 1, 2, 4, 6, 12, 15),\n",
       " (1, 4, 8),\n",
       " (1, 2, 4, 5, 6, 9, 10, 13, 14),\n",
       " (1, 4, 5, 6, 9, 11, 12, 14),\n",
       " (0, 1, 3, 4, 5, 8, 10, 11),\n",
       " (1, 3, 4, 12, 14, 15),\n",
       " (1, 2, 3, 4, 9, 10, 12, 13, 15),\n",
       " (4, 7, 8, 11, 12, 13, 14, 15),\n",
       " (1, 4, 5, 8, 13, 14),\n",
       " (4, 12, 14, 15),\n",
       " (2, 4, 6, 12, 13, 15),\n",
       " (0, 1, 2, 4, 5, 6, 9, 12, 13),\n",
       " (4, 9, 15),\n",
       " (1, 4, 5, 7, 8, 9, 14, 15),\n",
       " (1, 4, 11, 14),\n",
       " (1, 2, 4, 5, 6, 10, 11, 12, 14, 15),\n",
       " (1, 4, 6, 7, 9, 10, 12, 13, 15),\n",
       " (3, 4, 6, 8, 10, 11, 12, 13, 14, 15),\n",
       " (1, 2, 3, 4, 5, 6, 11, 13, 14),\n",
       " (0, 2, 4, 11, 14),\n",
       " (4, 6, 9),\n",
       " (0, 1, 2, 4, 5, 7, 15),\n",
       " (0, 1, 4, 5, 6, 7, 8, 12, 14),\n",
       " (0, 1, 2, 4, 5, 7, 12, 15),\n",
       " (1, 4, 12, 15),\n",
       " (0, 2, 3, 4, 6, 9, 10, 13, 14, 15),\n",
       " (0, 1, 2, 4, 6, 7, 9, 12, 13, 15),\n",
       " (1, 3, 4, 7, 9, 10, 12, 15),\n",
       " (1, 4, 5, 7, 8, 14),\n",
       " (1, 4, 5, 9, 15),\n",
       " (0, 1, 3, 4, 5, 6, 9, 12),\n",
       " (1, 2, 3, 4, 7, 13),\n",
       " (0, 1, 3, 4, 5, 6, 9, 10, 12),\n",
       " (3, 4, 5, 8, 11, 12, 14, 15),\n",
       " (1, 2, 4, 6, 7, 9, 10, 12, 13, 15),\n",
       " (0, 1, 2, 4, 5, 6, 7, 10, 14),\n",
       " (0, 1, 3, 4, 5, 8, 11, 14, 15),\n",
       " (0, 1, 4, 5, 7, 8),\n",
       " (1, 4, 5, 6, 7, 8, 10, 11, 13),\n",
       " (0, 1, 3, 4, 5, 10, 11),\n",
       " (3, 4, 14),\n",
       " (1, 4, 7, 8, 11, 13, 14),\n",
       " (1, 2, 3, 4, 6, 8, 10, 14),\n",
       " (1, 4, 6, 8, 9, 14, 15),\n",
       " (0, 1, 3, 4, 6, 7, 10),\n",
       " (0, 1, 2, 4, 8, 11, 13, 14),\n",
       " (1, 4, 5, 6, 7, 9, 10, 15),\n",
       " (1, 2, 3, 4, 5, 7, 9, 10, 12, 15),\n",
       " (2, 3, 4, 5, 6, 10, 11, 13, 14, 15),\n",
       " (0, 1, 2, 4, 5, 7, 10),\n",
       " (3, 4, 6, 9, 11, 12, 13, 14, 15),\n",
       " (0, 1, 2, 3, 4, 5, 6, 8, 10, 11, 12, 15),\n",
       " (1, 3, 4, 5, 6, 7, 12, 13, 14, 15),\n",
       " (3, 4, 5, 11, 12, 14, 15),\n",
       " (3, 4, 6, 10, 12, 13),\n",
       " (1, 4, 5, 7, 11, 14),\n",
       " (4, 11),\n",
       " (1, 3, 4, 13, 15),\n",
       " (1, 3, 4, 5, 7, 8, 10, 11, 13),\n",
       " (1, 4, 5, 6, 8, 9, 11, 12, 13, 15),\n",
       " (0, 1, 2, 4, 5, 6, 7, 10),\n",
       " (1, 3, 4, 8, 11, 13),\n",
       " (4, 6, 7, 10, 12),\n",
       " (0, 1, 3, 4, 5, 6, 8, 12, 13, 14),\n",
       " (0, 1, 3, 4, 5, 6, 7, 11, 14),\n",
       " (1, 4, 5, 6, 8, 12, 14),\n",
       " (1, 4, 5, 11, 12, 14, 15),\n",
       " (0, 1, 4, 5, 6, 10, 12, 15),\n",
       " (1, 4, 5, 6, 7, 8, 10, 11, 13, 14),\n",
       " (1, 4, 14, 15),\n",
       " (0, 1, 2, 4, 6, 8, 12),\n",
       " (4, 12, 15),\n",
       " (4, 6),\n",
       " (1, 3, 4, 5, 7, 8, 13, 14),\n",
       " (1, 4, 5, 6, 11, 12, 15),\n",
       " (1, 4, 5, 6, 9, 11, 12, 13, 14, 15),\n",
       " (0, 2, 3, 4, 6, 10, 13, 14, 15),\n",
       " (1, 3, 4, 5, 6),\n",
       " (1, 2, 4, 5, 7, 8, 9, 11, 12, 15),\n",
       " (4, 5, 12),\n",
       " (0, 1, 3, 4, 5, 7, 14),\n",
       " (0, 1, 2, 4, 6, 9, 10, 12, 13),\n",
       " (1, 4, 11),\n",
       " (1, 2, 4, 5, 8, 13, 14, 15),\n",
       " (1, 4, 6, 8, 10, 14),\n",
       " (1, 3, 4, 6, 8, 9, 10, 12, 14),\n",
       " (0, 1, 4, 5, 6, 7, 8, 11),\n",
       " (4, 11, 12, 15),\n",
       " (0, 2, 4, 6, 9, 12, 13, 15),\n",
       " (3, 4, 6, 8, 11, 12, 13, 14, 15),\n",
       " (4, 5, 7, 11, 14),\n",
       " (1, 4, 5, 12, 15),\n",
       " (0, 1, 4, 5, 7, 8, 13, 14),\n",
       " (0, 1, 3, 4, 5, 8, 10, 11, 13, 14),\n",
       " (1, 3, 4, 5, 8, 9, 13, 14, 15),\n",
       " (1, 4, 7, 9, 10, 13, 15),\n",
       " (1, 4, 6, 11, 12, 14),\n",
       " (1, 2, 4, 6, 7, 10, 13),\n",
       " (1, 4, 6, 7, 8, 9, 10, 11, 14),\n",
       " (1, 2, 3, 4, 6, 8, 9, 14, 15),\n",
       " (0, 1, 3, 4, 5, 10, 11, 12),\n",
       " (1, 4, 6, 7, 10, 11, 12, 13, 14, 15),\n",
       " (2, 4, 13),\n",
       " (0, 1, 2, 3, 4, 5, 7, 10, 11),\n",
       " (1, 2, 3, 4, 6, 8, 14, 15),\n",
       " (1, 4, 7, 8, 11, 12, 13, 14, 15),\n",
       " (0, 1, 2, 4, 5, 6, 7, 9, 10, 15),\n",
       " (0, 1, 3, 4, 5, 7, 9),\n",
       " (1, 3, 4, 5, 7, 11, 14),\n",
       " (1, 4, 6),\n",
       " (1, 2, 3, 4, 7, 8, 11, 13, 14),\n",
       " (0, 1, 2, 3, 4, 6, 7, 10, 13),\n",
       " (1, 3, 4, 5, 6, 10, 11, 12, 14),\n",
       " (1, 4, 7, 9, 15),\n",
       " (1, 3, 4, 6, 10, 11, 12),\n",
       " (1, 5, 6, 10, 12, 15),\n",
       " (1, 2, 3, 4, 6, 8, 10, 12, 14, 15),\n",
       " (1, 3, 4, 6, 7, 9, 10, 11),\n",
       " (2, 4, 10, 13),\n",
       " (1, 2, 4, 9, 14, 15),\n",
       " (1, 2, 4, 5, 7, 9, 12, 15),\n",
       " (1, 3, 4, 5, 6, 11, 13, 14),\n",
       " (0, 1, 2, 4, 5, 8),\n",
       " (0, 1, 2, 4, 8),\n",
       " ...}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidates = set()\n",
    "# max_complexity=X_weak.shape[-1]\n",
    "max_complexity = 10\n",
    "for i in trange(len(Theta_grouped)):    \n",
    "    candidates = candidates.union(set(solvel0(Theta_grouped[i], Ut_grouped[i], \n",
    "                                              max_complexity=max_complexity, \n",
    "                                              miosr=True, refine=True, verbose=False)))\n",
    "candidates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59.6726680587608\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 1384/1384 [02:56<00:00,  7.84it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 15),\n",
       " (0, 1, 2, 3, 4, 5, 6, 7, 8, 15),\n",
       " (0, 1, 2, 3, 4, 5, 6, 9, 10, 11, 13, 15),\n",
       " (0, 1, 2, 3, 4, 5, 7),\n",
       " (0, 1, 2, 4, 5, 6, 7, 8, 9, 11, 15),\n",
       " (0, 1, 2, 4, 5, 6, 7, 8, 13),\n",
       " (0, 1, 3, 4, 5, 6, 7, 8),\n",
       " (0, 1, 4, 5, 7, 8),\n",
       " (1, 4, 5),\n",
       " (1, 4, 5, 6, 7),\n",
       " (1, 4, 5, 7),\n",
       " (4,),\n",
       " (4, 5)}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lam = 0\n",
    "for j in range(len(Ut_grouped)):\n",
    "    est_xi = Ridge(alpha=0, fit_intercept=False).fit(Theta_grouped[j], Ut_grouped[j]).coef_.reshape(-1, 1)\n",
    "    difference = Ut_grouped[j] - Theta_grouped[j].dot(est_xi)\n",
    "    lam += (np.linalg.norm(difference, 2)**2)/(np.linalg.norm(est_xi, 2)**2)\n",
    "lam /= len(Ut_grouped)\n",
    "print(lam)\n",
    "\n",
    "track = {}\n",
    "for indices in tqdm(candidates):\n",
    "    com = len(indices)\n",
    "    rss = 0\n",
    "    for j in range(len(Ut_grouped)):\n",
    "        est_xi = Ridge(alpha=lam, fit_intercept=False).fit(Theta_grouped[j][:, indices], Ut_grouped[j]).coef_.reshape(-1, 1)\n",
    "        difference = fft_Ut_grouped[j] - fft_Theta_grouped[j][:, indices].dot(est_xi)\n",
    "        rss += np.linalg.norm(difference, 2)**2 + lam*np.linalg.norm(est_xi, 2)**2\n",
    "    if com not in track or track[com][1] > rss:\n",
    "        track[com] = indices, rss\n",
    "candidates = set([e[0] for e in list(track.values())])\n",
    "candidates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ic_scores = {}\n",
    "for indices in candidates:\n",
    "    est_xi = np.zeros((Theta_grouped[0].shape[-1], len(Ut_grouped)))\n",
    "    for j in range(len(Theta_grouped)):\n",
    "        est_xi[indices, j:j+1] = np.linalg.lstsq(Theta_grouped[j][:, indices], Ut_grouped[j], rcond=None)[0]\n",
    "    aic = PDE_FIND_Loss(Theta_grouped, Ut_grouped, est_xi)\n",
    "    bic = BIC_Loss(Theta_grouped, Ut_grouped, est_xi)\n",
    "    if len(indices) not in ic_scores: ic_scores[len(indices)] = (aic, bic, indices)\n",
    "    elif aic < ic_scores[len(indices)][0]: ic_scores[len(indices)] = (aic, bic, indices)\n",
    "\n",
    "com_ics = sorted([(len(e[2]), e[0], e[1]) for e in list(ic_scores.values())])\n",
    "complexities = [e[0] for e in com_ics]\n",
    "aics = [e[1] for e in com_ics]\n",
    "bics = [e[2] for e in com_ics]\n",
    "print(complexities[np.argmin(aics)])\n",
    "plt.plot(complexities, aics, '-o'); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(complexities[np.argmin(bics)])\n",
    "plt.plot(complexities, bics, '-o'); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:00<00:00, 1036.90it/s]\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:00<00:00, 1118.23it/s]\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:00<00:00, 1141.85it/s]\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:00<00:00, 1247.83it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:00<00:00, 956.01it/s]\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:00<00:00, 1211.23it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:00<00:00, 856.19it/s]\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:00<00:00, 1494.07it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:00<00:00, 728.71it/s]\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:00<00:00, 1334.46it/s]\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:00<00:00, 1362.57it/s]\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:00<00:00, 1011.23it/s]\n",
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:00<00:00, 1287.44it/s]\n"
     ]
    }
   ],
   "source": [
    "best_subsets = {}\n",
    "for indices in candidates:\n",
    "    est_xi = []\n",
    "    uncert = 0.0\n",
    "    rss = 0.0\n",
    "    for j in trange(0, len(Theta_grouped), 1):        \n",
    "        # model = Ridge(alpha=lam, fit_intercept=False)\n",
    "        model = BayesianRidge(fit_intercept=False)\n",
    "        model.fit(Theta_grouped[j][:, indices], Ut_grouped[j])\n",
    "        post_prediction = (fft_Theta_grouped[j][:, indices]@model.coef_).reshape(-1, 1)\n",
    "        rss = rss + np.linalg.norm(fft_Ut_grouped[j] - post_prediction)**2\n",
    "        \n",
    "        coeff = model.coef_.reshape(-1, 1)\n",
    "        uncert = uncert + np.sqrt(np.diag(model.sigma_)).sum()/abs(model.coef_).sum()\n",
    "        est_xi.append(coeff)\n",
    "        \n",
    "    ### For Ridge ###\n",
    "    # uncert = np.hstack(est_xi)\n",
    "    # uncert = uncert.std(axis=1).sum()/np.abs(uncert.mean(axis=1)).sum()\n",
    "    # uncert = np.mean(uncert.std(axis=1)/np.abs(uncert.mean(axis=1)))\n",
    "    # uncert = np.mean(uncert.std(axis=1))\n",
    "        \n",
    "    if len(indices) not in best_subsets: best_subsets[len(indices)] = (rss, uncert, indices)\n",
    "    elif rss < best_subsets[len(indices)][0]: best_subsets[len(indices)] = (rss, uncert, indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epsilon 246.74675421449035\n",
      "3\n",
      "(1, 4, 5)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nobs = Theta_grouped[0].shape[0]*len(Ut_grouped)\n",
    "min_uncert = np.min([best_subsets[com][1] for com in best_subsets])\n",
    "epsilon = 1\n",
    "epsilon = epsilon*max(1e-5, np.max([2*np.pi*best_subsets[com][0]/nobs for com in best_subsets]))\n",
    "print(\"epsilon\", epsilon)\n",
    "complexities = sorted(best_subsets.keys())\n",
    "bics = []\n",
    "uncertainties = []\n",
    "for com in complexities:\n",
    "    rss, uncert, indices = best_subsets[com]\n",
    "    uncert = uncert/min_uncert\n",
    "    uncertainties.append(uncert)\n",
    "    bics.append(rss2bic(rss, com+uncert, nobs, epsilon))\n",
    "uncertainties = np.array(uncertainties)\n",
    "best_indices = best_subsets[complexities[np.argmin(bics)]][-1]\n",
    "print(complexities[np.argmin(bics)])\n",
    "print(best_indices)\n",
    "plt.plot(complexities, bics, '-o'); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 4, 5) selected\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(8.0,\n",
       " array([406433.23035836, 404806.82374803, 402802.83340399, 402964.36951272,\n",
       "        403061.1084607 , 403179.04366499, 403122.99865728, 403415.12847212,\n",
       "        403452.38083231, 403781.43115008, 404286.90662538, 404334.20444335,\n",
       "        405966.60839392]),\n",
       " 2)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "verbose = True\n",
    "tau = 3\n",
    "b_bics = []\n",
    "lower_bounds = []\n",
    "for com in complexities:\n",
    "    rss, _, indices = best_subsets[com]\n",
    "    assert com == len(indices)\n",
    "    # lower_bound = 2*np.abs(ssr2llf(rss, nobs))/np.log(nobs)-com\n",
    "    lower_bound = 2*ssr2llf(rss, nobs)/np.log(nobs)-com\n",
    "    lower_bounds.append(lower_bound)\n",
    "    b_bics.append(rss2bic(rss, com, nobs, epsilon))\n",
    "    \n",
    "last_lam = np.log10(max(max(lower_bounds/uncertainties), 1e-8))\n",
    "delta = last_lam/tau\n",
    "now_lam = last_lam-delta\n",
    "last_ubic = UBIC(b_bics, uncertainties, nobs, hyp=10**last_lam)\n",
    "last_bc = np.argmin(last_ubic)\n",
    "\n",
    "thres = 1e-4\n",
    "percent = 75\n",
    "b_bics = np.array(b_bics)\n",
    "complexities = np.array(complexities)\n",
    "d_complexities = complexities[decreasing_values_indices(b_bics)]\n",
    "d_bics = b_bics[decreasing_values_indices(b_bics)]\n",
    "thres = max(thres, np.percentile(np.abs(np.diff(d_bics)/(np.diff(d_complexities)*d_bics[:-1])), percent))\n",
    "\n",
    "while now_lam >= 0:\n",
    "    now_ubic = UBIC(b_bics, uncertainties, nobs, hyp=10**now_lam)\n",
    "    now_bc = np.argmin(now_ubic)\n",
    "\n",
    "    diff_com = now_bc-last_bc\n",
    "    diff_bic = b_bics[now_bc]-b_bics[last_bc]\n",
    "\n",
    "    imp = np.nan\n",
    "    if np.abs(diff_com) > 0:\n",
    "        imp = abs(diff_bic/(b_bics[last_bc]*diff_com))\n",
    "\n",
    "    if verbose:\n",
    "        print(last_bc, '--->', now_bc, np.nan_to_num(imp, nan=np.inf))\n",
    "\n",
    "    if (diff_com > 0 and (diff_bic > 0 or imp < thres)) or \\\n",
    "        (diff_com < 0 and diff_bic > 0 and imp > thres):\n",
    "        print(\"break\")\n",
    "        break\n",
    "\n",
    "    last_lam = now_lam\n",
    "    now_lam = last_lam-delta\n",
    "    last_ubic = now_ubic\n",
    "    last_bc = now_bc\n",
    "    now_lam = round(now_lam, 8)\n",
    "    \n",
    "last_lam = abs(round(last_lam, 8))\n",
    "best_indices = best_subsets[complexities[last_bc]][-1]; print(best_indices, \"selected\")\n",
    "last_lam, last_ubic, last_bc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(uncertainties); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(last_ubic); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:pysr]",
   "language": "python",
   "name": "conda-env-pysr-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}